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Sample records for acoustic signal classification

  1. Detection and Classification of Whale Acoustic Signals

    NASA Astrophysics Data System (ADS)

    Xian, Yin

    This dissertation focuses on two vital challenges in relation to whale acoustic signals: detection and classification. In detection, we evaluated the influence of the uncertain ocean environment on the spectrogram-based detector, and derived the likelihood ratio of the proposed Short Time Fourier Transform detector. Experimental results showed that the proposed detector outperforms detectors based on the spectrogram. The proposed detector is more sensitive to environmental changes because it includes phase information. In classification, our focus is on finding a robust and sparse representation of whale vocalizations. Because whale vocalizations can be modeled as polynomial phase signals, we can represent the whale calls by their polynomial phase coefficients. In this dissertation, we used the Weyl transform to capture chirp rate information, and used a two dimensional feature set to represent whale vocalizations globally. Experimental results showed that our Weyl feature set outperforms chirplet coefficients and MFCC (Mel Frequency Cepstral Coefficients) when applied to our collected data. Since whale vocalizations can be represented by polynomial phase coefficients, it is plausible that the signals lie on a manifold parameterized by these coefficients. We also studied the intrinsic structure of high dimensional whale data by exploiting its geometry. Experimental results showed that nonlinear mappings such as Laplacian Eigenmap and ISOMAP outperform linear mappings such as PCA and MDS, suggesting that the whale acoustic data is nonlinear. We also explored deep learning algorithms on whale acoustic data. We built each layer as convolutions with either a PCA filter bank (PCANet) or a DCT filter bank (DCTNet). With the DCT filter bank, each layer has different a time-frequency scale representation, and from this, one can extract different physical information. Experimental results showed that our PCANet and DCTNet achieve high classification rate on the whale

  2. Perceptually-driven signal analysis for acoustic event classification

    NASA Astrophysics Data System (ADS)

    Philips, Scott M.

    In many acoustic signal processing applications human listeners are able to outperform automated processing techniques, particularly in the identification and classification of acoustic events. The research discussed in this paper develops a framework for employing perceptual information from human listening experiments to improve automatic event classification. We focus on the identification of new signal attributes, or features, that are able to predict the human performance observed in formal listening experiments. Using this framework, our newly identified features have the ability to elevate automatic classification performance closer to the level of human listeners. We develop several new methods for learning a perceptual feature transform from human similarity measures. In addition to providing a more fundamental basis for uncovering perceptual features than previous approaches, these methods also lead to a greater insight into how humans perceive sounds in a dataset. We also develop a new approach for learning a perceptual distance metric. This metric is shown to be applicable to modern kernel-based techniques used in machine learning and provides a connection between the fields of psychoacoustics and machine learning. Our research demonstrates these new methods in the area of active sonar signal processing. There is anecdotal evidence within the sonar community that human operators are adept in the task of discriminating between active sonar target and clutter echoes. We confirm this ability in a series of formal listening experiments. With the results of these experiments, we then identify perceptual features and distance metrics using our novel methods. The results show better agreement with human performance than previous approaches. While this work demonstrates these methods using perceptual similarity measures from active sonar data, they are applicable to any similarity measure between signals.

  3. Method of detection, classification, and identification of objects employing acoustic signal analysis

    NASA Astrophysics Data System (ADS)

    Orzanowski, Tomasz; Madura, Henryk; Sosnowski, Tomasz; Chmielewski, Krzysztof

    2008-10-01

    The methods of detection and identification of objects based on acoustic signal analysis are used in many applications, e.g., alarm systems, military battlefield reconnaissance systems, intelligent ammunition, and others. The construction of technical objects such as vehicle or helicopter gives some possibilities to identify them on the basis of acoustic signals generated by those objects. In this paper a method of automatic detection, classification and identification of military vehicles and helicopters using a digital analysis of acoustic signals is presented. The method offers a relatively high probability of object detection in attendance of other disturbing acoustic signals. Moreover, it provides low probability of false classification and identification of object. The application of this method to acoustic sensor for the anti-helicopter mine is also presented.

  4. Signal processing for passive detection and classification of underwater acoustic signals

    NASA Astrophysics Data System (ADS)

    Chung, Kil Woo

    2011-12-01

    This dissertation examines signal processing for passive detection, classification and tracking of underwater acoustic signals for improving port security and the security of coastal and offshore operations. First, we consider the problem of passive acoustic detection of a diver in a shallow water environment. A frequency-domain multi-band matched-filter approach to swimmer detection is presented. The idea is to break the frequency contents of the hydrophone signals into multiple narrow frequency bands, followed by time averaged (about half of a second) energy calculation over each band. Then, spectra composed of such energy samples over the chosen frequency bands are correlated to form a decision variable. The frequency bands with highest Signal/Noise ratio are used for detection. The performance of the proposed approach is demonstrated for experimental data collected for a diver in the Hudson River. We also propose a new referenceless frequency-domain multi-band detector which, unlike other reference-based detectors, does not require a diver specific signature. Instead, our detector matches to a general feature of the diver spectrum in the high frequency range: the spectrum is roughly periodic in time and approximately flat when the diver exhales. The performance of the proposed approach is demonstrated by using experimental data collected from the Hudson River. Moreover, we present detection, classification and tracking of small vessel signals. Hydroacoustic sensors can be applied for the detection of noise generated by vessels, and this noise can be used for vessel detection, classification and tracking. This dissertation presents recent improvements aimed at the measurement and separation of ship DEMON (Detection of Envelope Modulation on Noise) acoustic signatures in busy harbor conditions. Ship signature measurements were conducted in the Hudson River and NY Harbor. The DEMON spectra demonstrated much better temporal stability compared with the full ship

  5. Graph-based sensor fusion for classification of transient acoustic signals.

    PubMed

    Srinivas, Umamahesh; Nasrabadi, Nasser M; Monga, Vishal

    2015-03-01

    Advances in acoustic sensing have enabled the simultaneous acquisition of multiple measurements of the same physical event via co-located acoustic sensors. We exploit the inherent correlation among such multiple measurements for acoustic signal classification, to identify the launch/impact of munition (i.e., rockets, mortars). Specifically, we propose a probabilistic graphical model framework that can explicitly learn the class conditional correlations between the cepstral features extracted from these different measurements. Additionally, we employ symbolic dynamic filtering-based features, which offer improvements over the traditional cepstral features in terms of robustness to signal distortions. Experiments on real acoustic data sets show that our proposed algorithm outperforms conventional classifiers as well as the recently proposed joint sparsity models for multisensor acoustic classification. Additionally our proposed algorithm is less sensitive to insufficiency in training samples compared to competing approaches. PMID:25014986

  6. Acoustic emission signal classification for gearbox failure detection

    NASA Astrophysics Data System (ADS)

    Shishino, Jun

    The purpose of this research is to develop a methodology and technique to determine the optimal number of clusters in acoustic emission (AE) data obtained from a ground test stand of a rotating H-60 helicopter tail gearbox by using mathematical algorithms and visual inspection. Signs of fatigue crack growth were observed from the AE signals acquired from the result of the optimal number of clusters in a data set. Previous researches have determined the number of clusters by visually inspecting the AE plots from number of iterations. This research is focused on finding the optimal number of clusters in the data set by using mathematical algorithms then using visual verification to confirm it. The AE data were acquired from the ground test stand that simulates the tail end of an H-60 Seahawk at Naval Air Station in Patuxant River, Maryland. The data acquired were filtered to eliminate durations that were greater than 100,000 is and 0 energy hit data to investigate the failure mechanisms occurring on the output bevel gear. From the filtered data, different AE signal parameters were chosen to perform iterations to see which clustering algorithms and number of outputs is the best. The clustering algorithms utilized are the Kohonen Self-organizing Map (SOM), k-mean and Gaussian Mixture Model (GMM). From the clustering iterations, the three cluster criterion algorithms were performed to observe the suggested optimal number of cluster by the criterions. The three criterion algorithms utilized are the Davies-Bouldin, Silhouette and Tou Criterions. After the criterions had suggested the optimal number of cluster for each data set, visual verification by observing the AE plots and statistical analysis of each cluster were performed. By observing the AE plots and the statistical analysis, the optimal number of cluster in the data set and effective clustering algorithms were determined. Along with the optimal number of clusters and effective clustering algorithm, the mechanisms

  7. Extruded Bread Classification on the Basis of Acoustic Emission Signal With Application of Artificial Neural Networks

    NASA Astrophysics Data System (ADS)

    Świetlicka, Izabela; Muszyński, Siemowit; Marzec, Agata

    2015-04-01

    The presented work covers the problem of developing a method of extruded bread classification with the application of artificial neural networks. Extruded flat graham, corn, and rye breads differening in water activity were used. The breads were subjected to the compression test with simultaneous registration of acoustic signal. The amplitude-time records were analyzed both in time and frequency domains. Acoustic emission signal parameters: single energy, counts, amplitude, and duration acoustic emission were determined for the breads in four water activities: initial (0.362 for rye, 0.377 for corn, and 0.371 for graham bread), 0.432, 0.529, and 0.648. For classification and the clustering process, radial basis function, and self-organizing maps (Kohonen network) were used. Artificial neural networks were examined with respect to their ability to classify or to cluster samples according to the bread type, water activity value, and both of them. The best examination results were achieved by the radial basis function network in classification according to water activity (88%), while the self-organizing maps network yielded 81% during bread type clustering.

  8. Multiple target tracking and classification improvement using data fusion at node level using acoustic signals

    NASA Astrophysics Data System (ADS)

    Damarla, T. R.; Whipps, Gene

    2005-05-01

    Target tracking and classification using passive acoustic signals is difficult at best as the signals are contaminated by wind noise, multi-path effects, road conditions, and are generally not deterministic. In addition, microphone characteristics, such as sensitivity, vary with the weather conditions. The problem is further compounded if there are multiple targets, especially if some are measured with higher signal-to-noise ratios (SNRs) than the others and they share spectral information. At the U. S. Army Research Laboratory we have conducted several field experiments with a convoy of two, three, four and five vehicles traveling on different road surfaces, namely gravel, asphalt, and dirt roads. The largest convoy is comprised of two tracked vehicles and three wheeled vehicles. Two of the wheeled vehicles are heavy trucks and one is a light vehicle. We used a super-resolution direction-of-arrival estimator, specifically the minimum variance distortionless response, to compute the bearings of the targets. In order to classify the targets, we modeled the acoustic signals emanated from the targets as a set of coupled harmonics, which are related to the engine-firing rate, and subsequently used a multivariate Gaussian classifier. Independent of the classifier, we find tracking of wheeled vehicles to be intermittent as the signals from vehicles with high SNR dominate the much quieter wheeled vehicles. We used several fusion techniques to combine tracking and classification results to improve final tracking and classification estimates. We will present the improvements (or losses) made in tracking and classification of all targets. Although improvements in the estimates for tracked vehicles are not noteworthy, significant improvements are seen in the case of wheeled vehicles. We will present the fusion algorithm used.

  9. Classification of acoustic emission signals using wavelets and Random Forests : Application to localized corrosion

    NASA Astrophysics Data System (ADS)

    Morizet, N.; Godin, N.; Tang, J.; Maillet, E.; Fregonese, M.; Normand, B.

    2016-03-01

    This paper aims to propose a novel approach to classify acoustic emission (AE) signals deriving from corrosion experiments, even if embedded into a noisy environment. To validate this new methodology, synthetic data are first used throughout an in-depth analysis, comparing Random Forests (RF) to the k-Nearest Neighbor (k-NN) algorithm. Moreover, a new evaluation tool called the alter-class matrix (ACM) is introduced to simulate different degrees of uncertainty on labeled data for supervised classification. Then, tests on real cases involving noise and crevice corrosion are conducted, by preprocessing the waveforms including wavelet denoising and extracting a rich set of features as input of the RF algorithm. To this end, a software called RF-CAM has been developed. Results show that this approach is very efficient on ground truth data and is also very promising on real data, especially for its reliability, performance and speed, which are serious criteria for the chemical industry.

  10. Detection of geodesic acoustic mode oscillations, using multiple signal classification analysis of Doppler backscattering signal on Tore Supra

    NASA Astrophysics Data System (ADS)

    Vermare, L.; Hennequin, P.; Gürcan, Ö. D.; the Tore Supra Team

    2012-06-01

    This paper presents the first observation of geodesic acoustic modes (GAMs) on Tore Supra plasmas. Using the Doppler backscattering system, the oscillations of the plasma flow velocity, localized between r/a = 0.85 and r/a = 0.95, and with a frequency, typically around 10 kHz, have been observed at the plasma edge in numerous discharges. When the additional heating power is varied, the frequency is found to scale with Cs/R. The MUltiple SIgnal Classification (MUSIC) algorithm is employed to access the temporal evolution of the perpendicular velocity of density fluctuations. The method is presented in some detail, and is validated and compared against standard methods, such as the conventional fast Fourier transform method, using a synthetic signal. It stands out as a powerful data analysis method to follow the Doppler frequency with a high temporal resolution, which is important in order to extract the dynamics of GAMs.

  11. Traceability of Acoustic Emission measurements for a proposed calibration method - Classification of characteristics and identification using signal analysis

    NASA Astrophysics Data System (ADS)

    Griffin, James

    2015-01-01

    When using Acoustic Emission (AE) technologies, tensile, compressive and shear stress/strain tests can provide a detector for material deformation and dislocations. In this paper improvements are made to standardise calibration techniques for AE against known metrics such as force. AE signatures were evaluated from various calibration energy sources based on the energy from the first harmonic (dominant energy band) [1,2]. The effects of AE against its calibration identity are investigated: where signals are correlated to the average energy and distance of the detected phenomena. In addition, extra tests are investigated in terms of the tensile tests and single grit tests characterising different materials. Necessary translations to the time-frequency domain were necessary when segregating salient features between different material properties. Continuing this work the obtained AE is summarised and evaluated by a Neural Network (NN) regression classification technique which identifies how far the malformation has progressed (in terms of energy/force) during material transformation. Both genetic-fuzzy clustering and tree rule based classifier techniques were used as the second and third classification techniques respectively to verify the NN output giving a weighted three classifier system. The work discussed in this paper looks at both distance and force relationships for various prolonged Acoustic Emission stresses. Later such analysis was realised with different classifier models and finally implemented into the Simulink simulations. Further investigations were made into classifier models for different material interactions in terms of force and distance which add further dimension to this work with different materials based simulation realisations. Within the statistical analysis section there are two varying prolonged stress tests which together offer the mechanical calibration system (automated solenoid and pencil break calibration system). Taking such a

  12. Ocean acoustic hurricane classification.

    PubMed

    Wilson, Joshua D; Makris, Nicholas C

    2006-01-01

    Theoretical and empirical evidence are combined to show that underwater acoustic sensing techniques may be valuable for measuring the wind speed and determining the destructive power of a hurricane. This is done by first developing a model for the acoustic intensity and mutual intensity in an ocean waveguide due to a hurricane and then determining the relationship between local wind speed and underwater acoustic intensity. From this it is shown that it should be feasible to accurately measure the local wind speed and classify the destructive power of a hurricane if its eye wall passes directly over a single underwater acoustic sensor. The potential advantages and disadvantages of the proposed acoustic method are weighed against those of currently employed techniques. PMID:16454274

  13. Acoustic emission and signal analysis

    NASA Astrophysics Data System (ADS)

    Rao, A. K.

    1990-01-01

    A review is given of the acoustic emission (AE) phenomenon and its applications in NDE and geological rock mechanics. Typical instrumentation used in AE signal detection, data acquisition, processing, and analysis is discussed. The parameters used in AE signal analysis are outlined, and current methods of AE signal analysis procedures are discussed. A literature review is presented on the pattern classification of AE signals. A discussion then follows on the application of AE in aircraft component monitoring, with an experiment described which focuses on in-flight AE monitoring during fatigue crack growth in an aero engine mount. A pattern recognition approach is detailed for the classification of the experimental data. The approach subjects each of the data files to a cluster analysis by the threshold-k-means scheme. The technique is shown to classify the data successfully.

  14. Acoustic firearm discharge detection and classification in an enclosed environment.

    PubMed

    Luzi, Lorenzo; Gonzalez, Eric; Bruillard, Paul; Prowant, Matthew; Skorpik, James; Hughes, Michael; Child, Scott; Kist, Duane; McCarthy, John E

    2016-05-01

    Two different signal processing algorithms are described for detection and classification of acoustic signals generated by firearm discharges in small enclosed spaces. The first is based on the logarithm of the signal energy. The second is a joint entropy. The current study indicates that a system using both signal energy and joint entropy would be able to both detect weapon discharges and classify weapon type, in small spaces, with high statistical certainty. PMID:27250165

  15. Acoustic network event classification using swarm optimization

    NASA Astrophysics Data System (ADS)

    Burman, Jerry

    2013-05-01

    Classifying acoustic signals detected by distributed sensor networks is a difficult problem due to the wide variations that can occur in the transmission of terrestrial, subterranean, seismic and aerial events. An acoustic event classifier was developed that uses particle swarm optimization to perform a flexible time correlation of a sensed acoustic signature to reference data. In order to mitigate the effects from interference such as multipath, the classifier fuses signatures from multiple sensors to form a composite sensed acoustic signature and then automatically matches the composite signature with reference data. The approach can classify all types of acoustic events but is particularly well suited to explosive events such as gun shots, mortar blasts and improvised explosive devices that produce an acoustic signature having a shock wave component that is aperiodic and non-linear. The classifier was applied to field data and yielded excellent results in terms of reconstructing degraded acoustic signatures from multiple sensors and in classifying disparate acoustic events.

  16. Acoustically-Induced Electrical Signals

    NASA Astrophysics Data System (ADS)

    Brown, S. R.

    2014-12-01

    We have observed electrical signals excited by and moving along with an acoustic pulse propagating in a sandstone sample. Using resonance we are now studying the characteristics of this acousto-electric signal and determining its origin and the controlling physical parameters. Four rock samples with a range of porosities, permeabilities, and mineralogies were chosen: Berea, Boise, and Colton sandstones and Austin Chalk. Pore water salinity was varied from deionized water to sea water. Ag-AgCl electrodes were attached to the sample and were interfaced to a 4-wire electrical resistivity system. Under computer control, the acoustic signals were excited and the electrical response was recorded. We see strong acoustically-induced electrical signals in all samples, with the magnitude of the effect for each rock getting stronger as we move from the 1st to the 3rd harmonics in resonance. Given a particular fluid salinity, each rock has its own distinct sensitivity in the induced electrical effect. For example at the 2nd harmonic, Berea Sandstone produces the largest electrical signal per acoustic power input even though Austin Chalk and Boise Sandstone tend to resonate with much larger amplitudes at the same harmonic. Two effects are potentially responsible for this acoustically-induced electrical response: one the co-seismic seismo-electric effect and the other a strain-induced resistivity change known as the acousto-electric effect. We have designed experimental tests to separate these mechanisms. The tests show that the seismo-electric effect is dominant in our studies. We note that these experiments are in a fluid viscosity dominated seismo-electric regime, leading to a simple interpretation of the signals where the electric potential developed is proportional to the local acceleration of the rock. Toward a test of this theory we have measured the local time-varying acoustic strain in our samples using a laser vibrometer.

  17. Acoustic Localization with Infrasonic Signals

    NASA Astrophysics Data System (ADS)

    Threatt, Arnesha; Elbing, Brian

    2015-11-01

    Numerous geophysical and anthropogenic events emit infrasonic frequencies (<20 Hz), including volcanoes, hurricanes, wind turbines and tornadoes. These sounds, which cannot be heard by the human ear, can be detected from large distances (in excess of 100 miles) due to low frequency acoustic signals having a very low decay rate in the atmosphere. Thus infrasound could be used for long-range, passive monitoring and detection of these events. An array of microphones separated by known distances can be used to locate a given source, which is known as acoustic localization. However, acoustic localization with infrasound is particularly challenging due to contamination from other signals, sensitivity to wind noise and producing a trusted source for system development. The objective of the current work is to create an infrasonic source using a propane torch wand or a subwoofer and locate the source using multiple infrasonic microphones. This presentation will present preliminary results from various microphone configurations used to locate the source.

  18. Empirical mode decomposition for analyzing acoustical signals

    NASA Technical Reports Server (NTRS)

    Huang, Norden E. (Inventor)

    2005-01-01

    The present invention discloses a computer implemented signal analysis method through the Hilbert-Huang Transformation (HHT) for analyzing acoustical signals, which are assumed to be nonlinear and nonstationary. The Empirical Decomposition Method (EMD) and the Hilbert Spectral Analysis (HSA) are used to obtain the HHT. Essentially, the acoustical signal will be decomposed into the Intrinsic Mode Function Components (IMFs). Once the invention decomposes the acoustic signal into its constituting components, all operations such as analyzing, identifying, and removing unwanted signals can be performed on these components. Upon transforming the IMFs into Hilbert spectrum, the acoustical signal may be compared with other acoustical signals.

  19. Artillery/mortar type classification based on detected acoustic transients

    NASA Astrophysics Data System (ADS)

    Morcos, Amir; Grasing, David; Desai, Sachi

    2008-04-01

    Feature extraction methods based on the statistical analysis of the change in event pressure levels over a period and the level of ambient pressure excitation facilitate the development of a robust classification algorithm. The features reliably discriminates mortar and artillery variants via acoustic signals produced during the launch events. Utilizing acoustic sensors to exploit the sound waveform generated from the blast for the identification of mortar and artillery variants as type A, etcetera through analysis of the waveform. Distinct characteristics arise within the different mortar/artillery variants because varying HE mortar payloads and related charges emphasize varying size events at launch. The waveform holds various harmonic properties distinct to a given mortar/artillery variant that through advanced signal processing and data mining techniques can employed to classify a given type. The skewness and other statistical processing techniques are used to extract the predominant components from the acoustic signatures at ranges exceeding 3000m. Exploiting these techniques will help develop a feature set highly independent of range, providing discrimination based on acoustic elements of the blast wave. Highly reliable discrimination will be achieved with a feed-forward neural network classifier trained on a feature space derived from the distribution of statistical coefficients, frequency spectrum, and higher frequency details found within different energy bands. The processes that are described herein extend current technologies, which emphasis acoustic sensor systems to provide such situational awareness.

  20. Lake bed classification using acoustic data

    USGS Publications Warehouse

    Yin, Karen K.; Li, Xing; Bonde, John; Richards, Carl; Cholwek, Gary

    1998-01-01

    As part of our effort to identify the lake bed surficial substrates using remote sensing data, this work designs pattern classifiers by multivariate statistical methods. Probability distribution of the preprocessed acoustic signal is analyzed first. A confidence region approach is then adopted to improve the design of the existing classifier. A technique for further isolation is proposed which minimizes the expected loss from misclassification. The devices constructed are applicable for real-time lake bed categorization. A mimimax approach is suggested to treat more general cases where the a priori probability distribution of the substrate types is unknown. Comparison of the suggested methods with the traditional likelihood ratio tests is discussed.

  1. Signal classification using global dynamical models, Part I: Theory

    SciTech Connect

    Kadtke, J.; Kremliovsky, M.

    1996-06-01

    Detection and classification of signals is one of the principal areas of signal processing, and the utilization of nonlinear information has long been considered as a way of improving performance beyond standard linear (e.g. spectral) techniques. Here, we develop a method for using global models of chaotic dynamical systems theory to define a signal classification processing chain, which is sensitive to nonlinear correlations in the data. We use it to demonstrate classification in high noise regimes (negative SNR), and argue that classification probabilities can be directly computed from ensemble statistics in the model coefficient space. We also develop a modification for non-stationary signals (i.e. transients) using non-autonomous ODEs. In Part II of this paper, we demonstrate the analysis on actual open ocean acoustic data from marine biologics. {copyright} {ital 1996 American Institute of Physics.}

  2. Ultrasonic acoustic health monitoring of ball bearings using neural network pattern classification of power spectral density

    NASA Astrophysics Data System (ADS)

    Kirchner, William; Southward, Steve; Ahmadian, Mehdi

    2010-03-01

    This paper presents a generic passive non-contact based approach using ultrasonic acoustic emissions (UAE) to facilitate the neural network classification of bearing health, and more specifically the bearing operating condition. The acoustic emission signals used in this study are in the ultrasonic range (20-120 kHz). A direct benefit of microphones capable of measurements in this frequency range is their inherent directionality. Using selected bands from the UAE power spectrum signature, it is possible to pose the health monitoring problem as a multi-class classification problem, and make use of a single neural network to classify the ultrasonic acoustic emission signatures. Artificial training data, based on statistical properties of a significantly smaller experimental data set is used to train the neural network. This specific approach is generic enough to suggest that it is applicable to a variety of systems and components where periodic acoustic emissions exist.

  3. Digital signal processing in acoustics. I

    NASA Astrophysics Data System (ADS)

    Davies, H.; McNeil, D. J.

    1985-11-01

    Digital signal processing techniques have gained steadily in importance over the past few years in many areas of science and engineering and have transformed the character of instrumentation used in laboratory and plant. This is particularly marked in acoustics, which has both benefited from the developments in signal processing and provided significant stimulus for these developments. As a result acoustical techniques are now used in a very wide range of applications and acoustics is one area in which digital signal processing is exploited to its limits. For example, the development of fast algorithms for computing Fourier transforms and the associated developments in hardware have led to remarkable advances in the use of spectral analysis as a means of investigating the nature and characteristics of acoustic sources. Speech research has benefited considerably in this respect, and, in a rather more technological application, spectral analysis of machinery noise provides information about changes in machine condition which may indicate imminent failure. More recently the observation that human and animal muscles emit low intensity noise suggests that spectral analysis of this noise may yield information about muscle structure and performance.

  4. Classification of munition fill using laser acoustics

    SciTech Connect

    Rodriguez, J.G.; Blackwood, L.G.

    1997-08-01

    Identification of a munition fill is easier if one can determine if there is fill material present (empty versus full), and if so, the phase (solid or liquid) of the fill. Previous munition inspection efforts by the Idaho National Engineering and Environmental Laboratory (INEEL) determined that resonance information could determine the fill. A portable, noncontacting laser-acoustic system was developed by INEEL that uses a low-power laser system to measure the container`s vibration characteristics in response to an acoustic excitation. These vibration characteristics were shown to be functions of the fill material and munition geometry. The laser acoustic system was used to characterize the fill of over one hundred 155-mm munitions. Additional research and development using this system is being performed for the Mobile Munitions Assessment System.

  5. Acoustic signal processing toolbox for array processing

    NASA Astrophysics Data System (ADS)

    Pham, Tien; Whipps, Gene T.

    2003-08-01

    The US Army Research Laboratory (ARL) has developed an acoustic signal processing toolbox (ASPT) for acoustic sensor array processing. The intent of this document is to describe the toolbox and its uses. The ASPT is a GUI-based software that is developed and runs under MATLAB. The current version, ASPT 3.0, requires MATLAB 6.0 and above. ASPT contains a variety of narrowband (NB) and incoherent and coherent wideband (WB) direction-of-arrival (DOA) estimation and beamforming algorithms that have been researched and developed at ARL. Currently, ASPT contains 16 DOA and beamforming algorithms. It contains several different NB and WB versions of the MVDR, MUSIC and ESPRIT algorithms. In addition, there are a variety of pre-processing, simulation and analysis tools available in the toolbox. The user can perform simulation or real data analysis for all algorithms with user-defined signal model parameters and array geometries.

  6. Ice breakup: Observations of the acoustic signal

    NASA Astrophysics Data System (ADS)

    Waddell, S. R.; Farmer, D. M.

    1988-03-01

    We describe observations of ambient sound beneath landfast ice in the Canadian Arctic Archipelago and interpret its evolution over the period June-August in terms of ice cracking and disintegration. The data were recorded on six bands between 50 and 14,500 Hz for the period April 2 to August 7, 1986, in Dolphin and Union Strait. The frequency dependence of the attenuation of sound in water allows separation of distant and local noise sources. In conjunction with satellite imagery and meteorological data, it is shown that strong signals in the acoustic time series are associated with major breakup events. The acoustic signal can provide predictive information about ice conditions and the approach of breakup.

  7. Acoustic signal propagation characterization of conduit networks

    NASA Astrophysics Data System (ADS)

    Khan, Muhammad Safeer

    Analysis of acoustic signal propagation in conduit networks has been an important area of research in acoustics. One major aspect of analyzing conduit networks as acoustic channels is that a propagating signal suffers frequency dependent attenuation due to thermo-viscous boundary layer effects and the presence of impedance mismatches such as side branches. The signal attenuation due to side branches is strongly influenced by their numbers and dimensions such as diameter and length. Newly developed applications for condition based monitoring of underground conduit networks involve measurement of acoustic signal attenuation through tests in the field. In many cases the exact installation layout of the field measurement location may not be accessible or actual installation may differ from the documented layout. The lack of exact knowledge of numbers and lengths of side branches, therefore, introduces uncertainty in the measurements of attenuation and contributes to the random variable error between measured results and those predicted from theoretical models. There are other random processes in and around conduit networks in the field that also affect the propagation of an acoustic signal. These random processes include but are not limited to the presence of strong temperature and humidity gradients within the conduits, blockages of variable sizes and types, effects of aging such as cracks, bends, sags and holes, ambient noise variations and presence of variable layer of water. It is reasonable to consider that the random processes contributing to the error in the measured attenuation are independent and arbitrarily distributed. The error, contributed by a large number of independent sources of arbitrary probability distributions, is best described by an approximately normal probability distribution in accordance with the central limit theorem. Using an analytical approach to model the attenuating effect of each of the random variable sources can be very complex and

  8. Acoustic target detection and classification using neural networks

    NASA Technical Reports Server (NTRS)

    Robertson, James A.; Conlon, Mark

    1993-01-01

    A neural network approach to the classification of acoustic emissions of ground vehicles and helicopters is demonstrated. Data collected during the Joint Acoustic Propagation Experiment conducted in July of l991 at White Sands Missile Range, New Mexico was used to train a classifier to distinguish between the spectrums of a UH-1, M60, M1 and M114. An output node was also included that would recognize background (i.e. no target) data. Analysis revealed specific hidden nodes responding to the features input into the classifier. Initial results using the neural network were encouraging with high correct identification rates accompanied by high levels of confidence.

  9. Identifying Potential Noise Sources within Acoustic Signals

    NASA Astrophysics Data System (ADS)

    Holcomb, Victoria; Lewalle, Jacques

    2013-11-01

    We test a new algorithm for its ability to detect sources of noise within random background. The goal of these tests is to better understand how to identify sources within acoustic signals while simultaneously determining the strengths and weaknesses of the algorithm in question. Unlike previously published algorithms, the antenna method does not pinpoint events by looking for the most energetic portions of a signal. The algorithm searches for the ideal lag combinations between three signals by taking excerpts of possible events. The excerpt with the lowest calculated minimum distance between possible events is how the algorithm identifies sources. At the minimum distance, the events are close in time and frequency. This method can be compared to the cross correlation and denoising methods to better understand its effectiveness. This work is supported in part by Spectral Energies LLC, under an SBIR grant from AFRL, as well as the Syracuse University MAE department.

  10. Segmentation and classification of shallow subbottom acoustic data, using image processing and neural networks

    NASA Astrophysics Data System (ADS)

    Yegireddi, Satyanarayana; Thomas, Nitheesh

    2014-06-01

    Subbottom acoustic profiler provides acoustic imaging of the subbottom structure constituting the upper sediment layers of the seabed, which is essential for geological and offshore geo-engineering studies. Delineation of the subbottom structure from a noisy acoustic data and classification of the sediment strata is a challenging task with the conventional signal processing techniques. Image processing techniques utilise the spatial variability of the image characteristics, known for their potential in medical imaging and pattern recognition applications. In the present study, they are found to be good in demarcating the boundaries of the sediment layers associated with weak acoustic reflectivity, masked by noisy background. The study deals with application of image processing techniques, like segmentation in identification of subbottom features and extraction of textural feature vectors using grey level co-occurrence matrix statistics. And also attempted classification using Self Organised Map, an unsupervised neural network model utilising these feature vectors. The methodology was successfully demonstrated in demarcating the different sediment layers from the subbottom images and established the sediments constituting the inferred four subsurface sediment layers differ from each other. The network model was also tested for its consistency, with repeated runs of different configuration of the network. Also the ability of simulated network was tested using a few untrained test images representing the similar environment and the classification results show a good agreement with the anticipated.

  11. Bird population density estimated from acoustic signals

    USGS Publications Warehouse

    Dawson, D.K.; Efford, M.G.

    2009-01-01

    Many animal species are detected primarily by sound. Although songs, calls and other sounds are often used for population assessment, as in bird point counts and hydrophone surveys of cetaceans, there are few rigorous methods for estimating population density from acoustic data. 2. The problem has several parts - distinguishing individuals, adjusting for individuals that are missed, and adjusting for the area sampled. Spatially explicit capture-recapture (SECR) is a statistical methodology that addresses jointly the second and third parts of the problem. We have extended SECR to use uncalibrated information from acoustic signals on the distance to each source. 3. We applied this extension of SECR to data from an acoustic survey of ovenbird Seiurus aurocapilla density in an eastern US deciduous forest with multiple four-microphone arrays. We modelled average power from spectrograms of ovenbird songs measured within a window of 0??7 s duration and frequencies between 4200 and 5200 Hz. 4. The resulting estimates of the density of singing males (0??19 ha -1 SE 0??03 ha-1) were consistent with estimates of the adult male population density from mist-netting (0??36 ha-1 SE 0??12 ha-1). The fitted model predicts sound attenuation of 0??11 dB m-1 (SE 0??01 dB m-1) in excess of losses from spherical spreading. 5.Synthesis and applications. Our method for estimating animal population density from acoustic signals fills a gap in the census methods available for visually cryptic but vocal taxa, including many species of bird and cetacean. The necessary equipment is simple and readily available; as few as two microphones may provide adequate estimates, given spatial replication. The method requires that individuals detected at the same place are acoustically distinguishable and all individuals vocalize during the recording interval, or that the per capita rate of vocalization is known. We believe these requirements can be met, with suitable field methods, for a significant

  12. An improved multivariate analytical method to assess the accuracy of acoustic sediment classification maps.

    NASA Astrophysics Data System (ADS)

    Biondo, M.; Bartholomä, A.

    2014-12-01

    High resolution hydro acoustic methods have been successfully employed for the detailed classification of sedimentary habitats. The fine-scale mapping of very heterogeneous, patchy sedimentary facies, and the compound effect of multiple non-linear physical processes on the acoustic signal, cause the classification of backscatter images to be subject to a great level of uncertainty. Standard procedures for assessing the accuracy of acoustic classification maps are not yet established. This study applies different statistical techniques to automated classified acoustic images with the aim of i) quantifying the ability of backscatter to resolve grain size distributions ii) understanding complex patterns influenced by factors other than grain size variations iii) designing innovative repeatable statistical procedures to spatially assess classification uncertainties. A high-frequency (450 kHz) sidescan sonar survey, carried out in the year 2012 in the shallow upper-mesotidal inlet the Jade Bay (German North Sea), allowed to map 100 km2 of surficial sediment with a resolution and coverage never acquired before in the area. The backscatter mosaic was ground-truthed using a large dataset of sediment grab sample information (2009-2011). Multivariate procedures were employed for modelling the relationship between acoustic descriptors and granulometric variables in order to evaluate the correctness of acoustic classes allocation and sediment group separation. Complex patterns in the acoustic signal appeared to be controlled by the combined effect of surface roughness, sorting and mean grain size variations. The area is dominated by silt and fine sand in very mixed compositions; in this fine grained matrix, percentages of gravel resulted to be the prevailing factor affecting backscatter variability. In the absence of coarse material, sorting mostly affected the ability to detect gradual but significant changes in seabed types. Misclassification due to temporal discrepancies

  13. A probablistic neural network classification system for signal and image processing

    SciTech Connect

    Bowman, B.

    1994-11-15

    The Acoustical Heart Valve Analysis Package is a system for signal and image processing and classification. It is being developed in both Matlab and C, to provide an interactive, interpreted environment, and has been optimized for large scale matrix operations. It has been used successfully to classify acoustic signals from implanted prosthetic heart valves in human patients, and will be integrated into a commercial Heart Valve Screening Center. The system uses several standard signal processing algorithms, as well as supervised learning techniques using the probabilistic neural network (PNN). Although currently used for the acoustic heart valve application, the algorithms and modular design allow it to be used for other applications, as well. We will describe the signal classification system, and show results from a set of test valves.

  14. Acoustic signals generated in inclined granular flows

    NASA Astrophysics Data System (ADS)

    Tan, Danielle S.; Jenkins, James T.; Keast, Stephen C.; Sachse, Wolfgang H.

    2015-10-01

    Spontaneous avalanching in specific deserts produces a low-frequency sound known as "booming." This creates a puzzle, because avalanches down the face of a dune result in collisions between sand grains that occur at much higher frequencies. Reproducing this phenomenon in the laboratory permits a better understanding of the underlying mechanisms for the generation of such lower frequency acoustic emissions, which may also be relevant to other dry granular flows. Here we report measurements of low-frequency acoustical signals, produced by dried "sounding" sand (sand capable of booming in the desert) flowing down an inclined chute. The amplitude of the signal diminishes over time but reappears upon drying of the sand. We show that the presence of this sound in the experiments may provide supporting evidence for a previously published "waveguide" explanation for booming. Also, we propose a model based on kinetic theory for a sheared inclined flow in which the flowing layer exhibits "breathing" modes superimposed on steady shearing. The predicted oscillation frequency is of a similar order of magnitude as the measurements, indicating that small perturbations can sustain oscillations of a low frequency. However, the frequency is underestimated, which indicates that the stiffness has been underestimated. Also, the model predicts a discrete spectrum of frequencies, instead of the broadband spectrum measured experimentally.

  15. Study of acoustic emission sources and signals

    NASA Astrophysics Data System (ADS)

    Pumarega, M. I. López; Armeite, M.; Oliveto, M. E.; Piotrkowski, R.; Ruzzante, J. E.

    2002-05-01

    Methods of acoustic emission (AE) signal analysis give information about material conditions, since AE generated in stressed solids can be used to indicate cracks and defect positions so as their damaging potential. We present a review of results of laboratory AE tests on metallic materials. Rings of seamless steel tubes, with and without oxide layers, were cut and then deformed by opening their ends. Seamless Zry-4 tubes were submitted to hydraulic stress tests until rupture with a purposely-constructed hydraulic system. In burst type signals, their parameters, Amplitude (A), Duration (D) and Risetime (R), were statistically studied. Amplitudes were found to follow the Log-normal distribution. This led to infer that the detected AE signal, is the complex consequence of a great number of random independent sources, which individual effects are linked. We could show, using cluster analysis for A, D and R mean values, with 5 clusters, coincidence between the clusters and the test types. A slight linear correlation was obtained for the parameters A and D. The arrival time of the AE signals was also studied, which conducted to discussing Poisson and Polya processes. The digitized signals were studied as (1/f)β noises. The general results are coherent if we consider the AE phenomena in the frame of Self Organized Criticality theory.

  16. Diagnostics of DC and Induction Motors Based on the Analysis of Acoustic Signals

    NASA Astrophysics Data System (ADS)

    Glowacz, A.

    2014-10-01

    In this paper, a non-invasive method of early fault diagnostics of electric motors was proposed. This method uses acoustic signals generated by electric motors. Essential features were extracted from acoustic signals of motors. A plan of study of acoustic signals of electric motors was proposed. Researches were carried out for faultless induction motor, induction motor with one faulty rotor bar, induction motor with two faulty rotor bars and flawless Direct Current, and Direct Current motor with shorted rotor coils. Researches were carried out for methods of signal processing: log area ratio coefficients, Multiple signal classification, Nearest Neighbor classifier and the Bayes classifier. A pattern creation process was carried out using 40 samples of sound. In the identification process 130 five-second test samples were used. The proposed approach will also reduce the costs of maintenance and the number of faulty motors in the industry.

  17. Wavelet packet transform for detection of single events in acoustic emission signals

    NASA Astrophysics Data System (ADS)

    Bianchi, Davide; Mayrhofer, Erwin; Gröschl, Martin; Betz, Gerhard; Vernes, András

    2015-12-01

    Acoustic emission signals in tribology can be used for monitoring the state of bodies in contact and relative motion. The recorded signal includes information which can be associated with different events, such as the formation and propagation of cracks, appearance of scratches and so on. One of the major challenges in analyzing these acoustic emission signals is to identify parts of the signal which belong to such an event and discern it from noise. In this contribution, a wavelet packet decomposition within the framework of multiresolution analysis theory is considered to analyze acoustic emission signals to investigate the failure of tribological systems. By applying the wavelet packet transform a method for the extraction of single events in rail contact fatigue test is proposed. The extraction of such events at several stages of the test permits a classification and the analysis of the evolution of cracks in the rail.

  18. Spatial acoustic signal processing for immersive communication

    NASA Astrophysics Data System (ADS)

    Atkins, Joshua

    Computing is rapidly becoming ubiquitous as users expect devices that can augment and interact naturally with the world around them. In these systems it is necessary to have an acoustic front-end that is able to capture and reproduce natural human communication. Whether the end point is a speech recognizer or another human listener, the reduction of noise, reverberation, and acoustic echoes are all necessary and complex challenges. The focus of this dissertation is to provide a general method for approaching these problems using spherical microphone and loudspeaker arrays.. In this work, a theory of capturing and reproducing three-dimensional acoustic fields is introduced from a signal processing perspective. In particular, the decomposition of the spatial part of the acoustic field into an orthogonal basis of spherical harmonics provides not only a general framework for analysis, but also many processing advantages. The spatial sampling error limits the upper frequency range with which a sound field can be accurately captured or reproduced. In broadband arrays, the cost and complexity of using multiple transducers is an issue. This work provides a flexible optimization method for determining the location of array elements to minimize the spatial aliasing error. The low frequency array processing ability is also limited by the SNR, mismatch, and placement error of transducers. To address this, a robust processing method is introduced and used to design a reproduction system for rendering over arbitrary loudspeaker arrays or binaurally over headphones. In addition to the beamforming problem, the multichannel acoustic echo cancellation (MCAEC) issue is also addressed. A MCAEC must adaptively estimate and track the constantly changing loudspeaker-room-microphone response to remove the sound field presented over the loudspeakers from that captured by the microphones. In the multichannel case, the system is overdetermined and many adaptive schemes fail to converge to

  19. Classification of spontaneous EEG signals in migraine

    NASA Astrophysics Data System (ADS)

    Bellotti, R.; De Carlo, F.; de Tommaso, M.; Lucente, M.

    2007-08-01

    We set up a classification system able to detect patients affected by migraine without aura, through the analysis of their spontaneous EEG patterns. First, the signals are characterized by means of wavelet-based features, than a supervised neural network is used to classify the multichannel data. For the feature extraction, scale-dependent and scale-independent methods are considered with a variety of wavelet functions. Both the approaches provide very high and almost comparable classification performances. A complete separation of the two groups is obtained when the data are plotted in the plane spanned by two suitable neural outputs.

  20. Applications of pattern classification to time-domain signals

    NASA Astrophysics Data System (ADS)

    Bertoncini, Crystal Ann

    Many different kinds of physics are used in sensors that produce time-domain signals, such as ultrasonics, acoustics, seismology, and electromagnetics. The waveforms generated by these sensors are used to measure events or detect flaws in applications ranging from industrial to medical and defense-related domains. Interpreting the signals is challenging because of the complicated physics of the interaction of the fields with the materials and structures under study. Often the method of interpreting the signal varies by the application, but automatic detection of events in signals is always useful in order to attain results quickly with less human error. One method of automatic interpretation of data is pattern classification, which is a statistical method that assigns predicted labels to raw data associated with known categories. In this work, we use pattern classification techniques to aid automatic detection of events in signals using features extracted by a particular application of the wavelet transform, the Dynamic Wavelet Fingerprint (DWFP), as well as features selected through physical interpretation of the individual applications. The wavelet feature extraction method is general for any time-domain signal, and the classification results can be improved by features drawn for the particular domain. The success of this technique is demonstrated through four applications: the development of an ultrasonographic periodontal probe, the identification of flaw type in Lamb wave tomographic scans of an aluminum pipe, prediction of roof falls in a limestone mine, and automatic identification of individual Radio Frequency Identification (RFID) tags regardless of its programmed code. The method has been shown to achieve high accuracy, sometimes as high as 98%.

  1. Acoustic signal detection of manatee calls

    NASA Astrophysics Data System (ADS)

    Niezrecki, Christopher; Phillips, Richard; Meyer, Michael; Beusse, Diedrich O.

    2003-04-01

    The West Indian manatee (trichechus manatus latirostris) has become endangered partly because of a growing number of collisions with boats. A system to warn boaters of the presence of manatees, that can signal to boaters that manatees are present in the immediate vicinity, could potentially reduce these boat collisions. In order to identify the presence of manatees, acoustic methods are employed. Within this paper, three different detection algorithms are used to detect the calls of the West Indian manatee. The detection systems are tested in the laboratory using simulated manatee vocalizations from an audio compact disc. The detection method that provides the best overall performance is able to correctly identify ~=96% of the manatee vocalizations. However the system also results in a false positive rate of ~=16%. The results of this work may ultimately lead to the development of a manatee warning system that can warn boaters of the presence of manatees.

  2. Surface Electromyography Signal Processing and Classification Techniques

    PubMed Central

    Chowdhury, Rubana H.; Reaz, Mamun B. I.; Ali, Mohd Alauddin Bin Mohd; Bakar, Ashrif A. A.; Chellappan, Kalaivani; Chang, Tae. G.

    2013-01-01

    Electromyography (EMG) signals are becoming increasingly important in many applications, including clinical/biomedical, prosthesis or rehabilitation devices, human machine interactions, and more. However, noisy EMG signals are the major hurdles to be overcome in order to achieve improved performance in the above applications. Detection, processing and classification analysis in electromyography (EMG) is very desirable because it allows a more standardized and precise evaluation of the neurophysiological, rehabitational and assistive technological findings. This paper reviews two prominent areas; first: the pre-processing method for eliminating possible artifacts via appropriate preparation at the time of recording EMG signals, and second: a brief explanation of the different methods for processing and classifying EMG signals. This study then compares the numerous methods of analyzing EMG signals, in terms of their performance. The crux of this paper is to review the most recent developments and research studies related to the issues mentioned above. PMID:24048337

  3. Classification of stop place in consonant-vowel contexts using feature extrapolation of acoustic-phonetic features in telephone speech.

    PubMed

    Lee, Jung-Won; Choi, Jeung-Yoon; Kang, Hong-Goo

    2012-02-01

    Knowledge-based speech recognition systems extract acoustic cues from the signal to identify speech characteristics. For channel-deteriorated telephone speech, acoustic cues, especially those for stop consonant place, are expected to be degraded or absent. To investigate the use of knowledge-based methods in degraded environments, feature extrapolation of acoustic-phonetic features based on Gaussian mixture models is examined. This process is applied to a stop place detection module that uses burst release and vowel onset cues for consonant-vowel tokens of English. Results show that classification performance is enhanced in telephone channel-degraded speech, with extrapolated acoustic-phonetic features reaching or exceeding performance using estimated Mel-frequency cepstral coefficients (MFCCs). Results also show acoustic-phonetic features may be combined with MFCCs for best performance, suggesting these features provide information complementary to MFCCs. PMID:22352523

  4. Acoustic signalling reflects personality in a social mammal

    PubMed Central

    Friel, Mary; Kunc, Hansjoerg P.; Griffin, Kym; Asher, Lucy; Collins, Lisa M.

    2016-01-01

    Social interactions among individuals are often mediated through acoustic signals. If acoustic signals are consistent and related to an individual's personality, these consistent individual differences in signalling may be an important driver in social interactions. However, few studies in non-human mammals have investigated the relationship between acoustic signalling and personality. Here we show that acoustic signalling rate is repeatable and strongly related to personality in a highly social mammal, the domestic pig (Sus scrofa domestica). Furthermore, acoustic signalling varied between environments of differing quality, with males from a poor-quality environment having a reduced vocalization rate compared with females and males from an enriched environment. Such differences may be mediated by personality with pigs from a poor-quality environment having more reactive and more extreme personality scores compared with pigs from an enriched environment. Our results add to the evidence that acoustic signalling reflects personality in a non-human mammal. Signals reflecting personalities may have far reaching consequences in shaping the evolution of social behaviours as acoustic communication forms an integral part of animal societies. PMID:27429775

  5. Acoustic signalling reflects personality in a social mammal.

    PubMed

    Friel, Mary; Kunc, Hansjoerg P; Griffin, Kym; Asher, Lucy; Collins, Lisa M

    2016-06-01

    Social interactions among individuals are often mediated through acoustic signals. If acoustic signals are consistent and related to an individual's personality, these consistent individual differences in signalling may be an important driver in social interactions. However, few studies in non-human mammals have investigated the relationship between acoustic signalling and personality. Here we show that acoustic signalling rate is repeatable and strongly related to personality in a highly social mammal, the domestic pig (Sus scrofa domestica). Furthermore, acoustic signalling varied between environments of differing quality, with males from a poor-quality environment having a reduced vocalization rate compared with females and males from an enriched environment. Such differences may be mediated by personality with pigs from a poor-quality environment having more reactive and more extreme personality scores compared with pigs from an enriched environment. Our results add to the evidence that acoustic signalling reflects personality in a non-human mammal. Signals reflecting personalities may have far reaching consequences in shaping the evolution of social behaviours as acoustic communication forms an integral part of animal societies. PMID:27429775

  6. High-performance air acoustic detection and classification sensor

    NASA Astrophysics Data System (ADS)

    Porter, Richard; Raines, Robert; Jones, Barry

    2009-05-01

    Acoustic signals are a principal detection modality for unattended sensor systems. However, the performance of these systems is frequently suboptimal due to insufficient dynamic range in small systems or excess power consumption in larger systems. This paper discusses an approach to developing an unattended ground sensor (UGS) system that has the best features of both worlds. This system, developed by McQ Inc., has exceptional dynamic range (> 100 dB) while operating at power levels of 1.5-5 watts. The system also has a user definable signal parameter library and automated detection methodology that will be described.

  7. Acoustic emission classification for failure prediction due to mechanical fatigue

    NASA Astrophysics Data System (ADS)

    Emamian, Vahid; Kaveh, Mostafa; Tewfik, Ahmed H.

    2000-06-01

    Acoustic Emission signals (AE), generated by the formation and growth of micro-cracks in metal components, have the potential for use in mechanical fault detection in monitoring complex- shaped components in machinery including helicopters and aircraft. A major challenge for an AE-based fault detection algorithm is to distinguish crack-related AE signals from other interfering transient signals, such as fretting-related AE signals and electromagnetic transients. Although under a controlled laboratory environment we have fewer interference sources, there are other undesired sources which have to be considered. In this paper, we present some methods, which make their decision based on the features extracted from time-delay and joint time-frequency components by means of a Self- Organizing Map (SOM) neural network using experimental data collected in a laboratory by colleagues at the Georgia Institute of Technology.

  8. Interpretation of acoustic signals from fluidzed beds

    SciTech Connect

    Halow, J.S.; Daw, C.S.; Finney, C.E.A.; Nguyen, K.

    1996-12-31

    Rhythmic {open_quotes}whooshing{close_quotes} sounds associated with rising bubbles are a characteristic feature of many fluidized beds. Although clearly distinguishable to the ear, these sounds are rather complicated in detail and seem to contain a large background of apparently irrelevant stochastic noise. While it is clear that these sounds contain some information about bed dynamics, it is not obvious how this information can be interpreted in a meaningful way. In this presentation we describe a technique for processing bed sounds that appears to work well for beds with large particles operating in a slugging or near-slugging mode. We find that our processing algorithm allows us to determine important bubble/slug features from sound measurements alone, including slug location at any point in time, the average bubble frequency and frequency variation, and corresponding dynamic pressure drops at different bed locations. We also have been able to correlate a portion of the acoustic signal with particle impacts on surfaces and particle motions near the grid. We conclude from our observations that relatively simple sound measurements can provide much diagnostic information and could be potentially used for bed control. 5 refs., 4 figs.

  9. Thirty years of underwater acoustic signal processing in China

    NASA Astrophysics Data System (ADS)

    Li, Qihu

    2012-11-01

    Advances in technology and theory in 30 years of underwater acoustic signal processing and its applications in China are presented in this paper. The topics include research work in the field of underwater acoustic signal modeling, acoustic field matching, ocean waveguide and internal wave, the extraction and processing technique for acoustic vector signal information, the space/time correlation characteristics of low frequency acoustic channels, the invariant features of underwater target radiated noise, the transmission technology of underwater voice/image data and its anti-interference technique. Some frontier technologies in sonar design are also discussed, including large aperture towed line array sonar, high resolution synthetic aperture sonar, deep sea siren and deep sea manned subsea vehicle, diver detection sonar and demonstration projector of national ocean monitoring system in China, etc.

  10. Classification of heart valve condition using acoustic measurements

    SciTech Connect

    Clark, G.

    1994-11-15

    Prosthetic heart valves and the many great strides in valve design have been responsible for extending the life spans of many people with serious heart conditions. Even though the prosthetic valves are extremely reliable, they are eventually susceptible to long-term fatigue and structural failure effects expected from mechanical devices operating over long periods of time. The purpose of our work is to classify the condition of in vivo Bjork-Shiley Convexo-Concave (BSCC) heart valves by processing acoustic measurements of heart valve sounds. The structural failures of interest for Bscc valves is called single leg separation (SLS). SLS can occur if the outlet strut cracks and separates from the main structure of the valve. We measure acoustic opening and closing sounds (waveforms) using high sensitivity contact microphones on the patient`s thorax. For our analysis, we focus our processing and classification efforts on the opening sounds because they yield direct information about outlet strut condition with minimal distortion caused by energy radiated from the valve disc.

  11. Classification of Hazelnut Kernels by Using Impact Acoustic Time-Frequency Patterns

    NASA Astrophysics Data System (ADS)

    Kalkan, Habil; Ince, Nuri Firat; Tewfik, Ahmed H.; Yardimci, Yasemin; Pearson, Tom

    2007-12-01

    Hazelnuts with damaged or cracked shells are more prone to infection with aflatoxin producing molds ( Aspergillus flavus). These molds can cause cancer. In this study, we introduce a new approach that separates damaged/cracked hazelnut kernels from good ones by using time-frequency features obtained from impact acoustic signals. The proposed technique requires no prior knowledge of the relevant time and frequency locations. In an offline step, the algorithm adaptively segments impact signals from a training data set in time using local cosine packet analysis and a Kullback-Leibler criterion to assess the discrimination power of different segmentations. In each resulting time segment, the signal is further decomposed into subbands using an undecimated wavelet transform. The most discriminative subbands are selected according to the Euclidean distance between the cumulative probability distributions of the corresponding subband coefficients. The most discriminative subbands are fed into a linear discriminant analysis classifier. In the online classification step, the algorithm simply computes the learned features from the observed signal and feeds them to the linear discriminant analysis (LDA) classifier. The algorithm achieved a throughput rate of 45 nuts/s and a classification accuracy of 96% with the 30 most discriminative features, a higher rate than those provided with prior methods.

  12. Pulse analysis of acoustic emission signals

    NASA Technical Reports Server (NTRS)

    Houghton, J. R.; Packman, P. F.

    1977-01-01

    A method for the signature analysis of pulses in the frequency domain and the time domain is presented. Fourier spectrum, Fourier transfer function, shock spectrum and shock spectrum ratio were examined in the frequency domain analysis, and pulse shape deconvolution was developed for use in the time domain analysis. Comparisons of the relative performance of each analysis technique are made for the characterization of acoustic emission pulses recorded by a measuring system. To demonstrate the relative sensitivity of each of the methods to small changes in the pulse shape, signatures of computer modeled systems with analytical pulses are presented. Optimization techniques are developed and used to indicate the best design parameters values for deconvolution of the pulse shape. Several experiments are presented that test the pulse signature analysis methods on different acoustic emission sources. These include acoustic emissions associated with: (1) crack propagation, (2) ball dropping on a plate, (3) spark discharge and (4) defective and good ball bearings. Deconvolution of the first few micro-seconds of the pulse train are shown to be the region in which the significant signatures of the acoustic emission event are to be found.

  13. Pulse analysis of acoustic emission signals

    NASA Technical Reports Server (NTRS)

    Houghton, J. R.; Packman, P. F.

    1977-01-01

    A method for the signature analysis of pulses in the frequency domain and the time domain is presented. Fourier spectrum, Fourier transfer function, shock spectrum and shock spectrum ratio were examined in the frequency domain analysis and pulse shape deconvolution was developed for use in the time domain analysis. Comparisons of the relative performance of each analysis technique are made for the characterization of acoustic emission pulses recorded by a measuring system. To demonstrate the relative sensitivity of each of the methods to small changes in the pulse shape, signatures of computer modeled systems with analytical pulses are presented. Optimization techniques are developed and used to indicate the best design parameter values for deconvolution of the pulse shape. Several experiments are presented that test the pulse signature analysis methods on different acoustic emission sources. These include acoustic emission associated with (a) crack propagation, (b) ball dropping on a plate, (c) spark discharge, and (d) defective and good ball bearings. Deconvolution of the first few micro-seconds of the pulse train is shown to be the region in which the significant signatures of the acoustic emission event are to be found.

  14. Speaker verification using combined acoustic and EM sensor signal processing

    SciTech Connect

    Ng, L C; Gable, T J; Holzrichter, J F

    2000-11-10

    Low Power EM radar-like sensors have made it possible to measure properties of the human speech production system in real-time, without acoustic interference. This greatly enhances the quality and quantity of information for many speech related applications. See Holzrichter, Burnett, Ng, and Lea, J. Acoustic. SOC. Am . 103 ( 1) 622 (1998). By combining the Glottal-EM-Sensor (GEMS) with the Acoustic-signals, we've demonstrated an almost 10 fold reduction in error rates from a speaker verification system experiment under a moderate noisy environment (-10dB).

  15. Low Bandwidth Vocoding using EM Sensor and Acoustic Signal Processing

    SciTech Connect

    Ng, L C; Holzrichter, J F; Larson, P E

    2001-10-25

    Low-power EM radar-like sensors have made it possible to measure properties of the human speech production system in real-time, without acoustic interference [1]. By combining these data with the corresponding acoustic signal, we've demonstrated an almost 10-fold bandwidth reduction in speech compression, compared to a standard 2.4 kbps LPC10 protocol used in the STU-III (Secure Terminal Unit, third generation) telephone. This paper describes a potential EM sensor/acoustic based vocoder implementation.

  16. Acoustic Aspects of Photoacoustic Signal Generation and Detection in Gases

    NASA Astrophysics Data System (ADS)

    Miklós, A.

    2015-09-01

    In this paper photoacoustic signal generation and detection in gases is investigated and discussed from the standpoint of acoustics. Four topics are considered: the effect of the absorption-desorption process of modulated and pulsed light on the heat power density released in the gas; the generation of the primary sound by the released heat in an unbounded medium; the excitation of an acoustic resonator by the primary sound; and finally, the generation of the measurable PA signal by a microphone. When light is absorbed by a molecule and the excess energy is relaxed by collisions with the surrounding molecules, the average kinetic energy, thus also the temperature of an ensemble of molecules (called "particle" in acoustics) will increase. In other words heat energy is added to the energy of the particle. The rate of the energy transfer is characterized by the heat power density. A simple two-level model of absorption-desorption is applied for describing the heat power generation process for modulated and pulsed illumination. Sound generation by a laser beam in an unbounded medium is discussed by means of the Green's function technique. It is shown that the duration of the generated sound pulse depends mostly on beam geometry. A photoacoustic signal is mostly detected in a photoacoustic cell composed of acoustic resonators, buffers, filters, etc. It is not easy to interpret the measured PA signal in such a complicated acoustic system. The acoustic response of a PA detector to different kinds of excitations (modulated cw, pulsed, periodic pulse train) is discussed. It is shown that acoustic resonators respond very differently to modulated cw excitation and to excitation by a pulse train. The microphone for detecting the PA signal is also a part of the acoustic system; its properties have to be taken into account by the design of a PA detector. The moving membrane of the microphone absorbs acoustic energy; thus, it may influence the resonance frequency and

  17. Wavelet-based ground vehicle recognition using acoustic signals

    NASA Astrophysics Data System (ADS)

    Choe, Howard C.; Karlsen, Robert E.; Gerhart, Grant R.; Meitzler, Thomas J.

    1996-03-01

    We present, in this paper, a wavelet-based acoustic signal analysis to remotely recognize military vehicles using their sound intercepted by acoustic sensors. Since expedited signal recognition is imperative in many military and industrial situations, we developed an algorithm that provides an automated, fast signal recognition once implemented in a real-time hardware system. This algorithm consists of wavelet preprocessing, feature extraction and compact signal representation, and a simple but effective statistical pattern matching. The current status of the algorithm does not require any training. The training is replaced by human selection of reference signals (e.g., squeak or engine exhaust sound) distinctive to each individual vehicle based on human perception. This allows a fast archiving of any new vehicle type in the database once the signal is collected. The wavelet preprocessing provides time-frequency multiresolution analysis using discrete wavelet transform (DWT). Within each resolution level, feature vectors are generated from statistical parameters and energy content of the wavelet coefficients. After applying our algorithm on the intercepted acoustic signals, the resultant feature vectors are compared with the reference vehicle feature vectors in the database using statistical pattern matching to determine the type of vehicle from where the signal originated. Certainly, statistical pattern matching can be replaced by an artificial neural network (ANN); however, the ANN would require training data sets and time to train the net. Unfortunately, this is not always possible for many real world situations, especially collecting data sets from unfriendly ground vehicles to train the ANN. Our methodology using wavelet preprocessing and statistical pattern matching provides robust acoustic signal recognition. We also present an example of vehicle recognition using acoustic signals collected from two different military ground vehicles. In this paper, we will

  18. Underwater Signal Modeling for Subsurface Classification Using Computational Intelligence.

    NASA Astrophysics Data System (ADS)

    Setayeshi, Saeed

    In the thesis a method for underwater layered media (UWLM) modeling is proposed, and a simple nonlinear structure for implementation of this model based on the behaviour of its characteristics and the propagation of the acoustic signal in the media accounting for attenuation effects is designed. The model that responds to the acoustic input is employed to test the artificial intelligence classifiers ability. Neural network models, the basic principles of the back-propagation algorithm, and the Hopfield model of associative memories are reviewed, and they are employed to use min-max amplitude ranges of a reflected signal of UWLM based on attenuation effects, to define the classes of the synthetic data, detect its peak features and estimate parameters of the media. It has been found that there is a correlation between the number of layers in the media and the optimum number of nodes in the hidden layer of the neural networks. The integration of the result of the neural networks that classify and detect underwater layered media acoustic signals based on attenuation effects to prove the correspondence between the peak points and decay values has introduced a powerful tool for UWLM identification. The methods appear to have applications in replacing original system, for parameter estimation and output prediction in system identification by the proposed networks. The results of computerized simulation of the UWLM modeling in conjunction with the proposed neural networks training process are given. Fuzzy sets is an idea that allows representing and manipulating inexact concepts, fuzzy min-max pattern classification method, and the learning and recalling algorithms for fuzzy neural networks implementation is explained in this thesis. A fuzzy neural network that uses peak amplitude ranges to define classes is proposed and evaluated for UWLM pattern recognition. It is demonstrated to be able to classify the layered media data sets, and can distinguish between the peak points

  19. Acoustic signals of baby black caimans.

    PubMed

    Vergne, Amélie L; Aubin, Thierry; Taylor, Peter; Mathevon, Nicolas

    2011-12-01

    In spite of the importance of crocodilian vocalizations for the understanding of the evolution of sound communication in Archosauria and due to the small number of experimental investigations, information concerning the vocal world of crocodilians is limited. By studying black caimans Melanosuchus niger in their natural habitat, here we supply the experimental evidence that juvenile crocodilians can use a graded sound system in order to elicit adapted behavioral responses from their mother and siblings. By analyzing the acoustic structure of calls emitted in two different situations ('undisturbed context', during which spontaneous calls of juvenile caimans were recorded without perturbing the group, and a simulated 'predator attack', during which calls were recorded while shaking juveniles) and by testing their biological relevance through playback experiments, we reveal the existence of two functionally different types of juvenile calls that produce a different response from the mother and other siblings. Young black caimans can thus modulate the structure of their vocalizations along an acoustic continuum as a function of the emission context. Playback experiments show that both mother and juveniles discriminate between these 'distress' and 'contact' calls. Acoustic communication is thus an important component mediating relationships within family groups in caimans as it is in birds, their archosaurian relatives. Although probably limited, the vocal repertoire of young crocodilians is capable of transmitting the information necessary for allowing siblings and mother to modulate their behavior. PMID:21978842

  20. Acoustic analysis and mood classification of pain-relieving music.

    PubMed

    Knox, Don; Beveridge, Scott; Mitchell, Laura A; MacDonald, Raymond A R

    2011-09-01

    Listening to preferred music (that which is chosen by the participant) has been shown to be effective in mitigating the effects of pain when compared to silence and a variety of distraction techniques. The wide range of genre, tempo, and structure in music chosen by participants in studies utilizing experimentally induced pain has led to the assertion that structure does not play a significant role, rather listening to preferred music renders the music "functionally equivalent" as regards its effect upon pain perception. This study addresses this assumption and performs detailed analysis of a selection of music chosen from three pain studies. Music analysis showed significant correlation between timbral and tonal aspects of music and measurements of pain tolerance and perceived pain intensity. Mood classification was performed using a hierarchical Gaussian Mixture Model, which indicated the majority of the chosen music expressed contentment. The results suggest that in addition to personal preference, associations with music and the listening context, emotion expressed by music, as defined by its acoustical content, is important to enhancing emotional engagement with music and therefore enhances the level of pain reduction and tolerance. PMID:21895104

  1. The correlation dimension: A robust chaotic feature for classifying acoustic emission signals generated in construction materials

    NASA Astrophysics Data System (ADS)

    Kacimi, S.; Laurens, S.

    2009-07-01

    In the field of acoustic emission (AE) source recognition, this paper presents a classification feature based on the paradigm of nonlinear dynamical systems, often referred to as chaos theory. The approach considers signals as time series expressing an underlying dynamical phenomenon and enclosing all the information regarding the dynamics. The scientific knowledge on nonlinear dynamical systems has considerably improved for the past 40 years. The dynamical behavior is analyzed in the phase space, which is the space generated by the state variables of the system. The time evolution of a system is expressed in the phase space by trajectories, and the asymptotic behavior of trajectories defines a space area which is referred to as a system attractor. Dynamical systems may be characterized by the topological properties of attractors, such as the correlation dimension, which is a fractal dimension. According to Takens theorem, even if the system is not clearly defined, it is possible to infer topological information about the attractor from experimental observations. Such a method, which is called phase space reconstruction, was successfully applied for the classification of acoustic emission waveforms propagating in more or less complex materials such as granite and concrete. Laboratory tests were carried out in order to collect numerous AE waveforms from various controlled acoustic sources. Then, each signal was processed to extract a reconstructed attractor from which the correlation dimension was computed. The first results of this research show that the correlation dimension assessed after phase space reconstruction is very relevant and robust for classifying AE signals. These promising results may be explained by the fact that the totality of the signal is used to achieve classifying information. Moreover, due to the self-similar nature of attractors, the correlation dimension, and thus a correlation dimension-based classification approach, is theoretically

  2. Leak detection in gas pipeline by acoustic and signal processing - A review

    NASA Astrophysics Data System (ADS)

    Adnan, N. F.; Ghazali, M. F.; Amin, M. M.; Hamat, A. M. A.

    2015-12-01

    The pipeline system is the most important part in media transport in order to deliver fluid to another station. The weak maintenance and poor safety will contribute to financial losses in term of fluid waste and environmental impacts. There are many classifications of techniques to make it easier to show their specific method and application. This paper's discussion about gas leak detection in pipeline system using acoustic method will be presented in this paper. The wave propagation in the pipeline is a key parameter in acoustic method when the leak occurs and the pressure balance of the pipe will generated by the friction between wall in the pipe. The signal processing is used to decompose the raw signal and show in time- frequency. Findings based on the acoustic method can be used for comparative study in the future. Acoustic signal and HHT is the best method to detect leak in gas pipelines. More experiments and simulation need to be carried out to get the fast result of leaking and estimation of their location.

  3. Perceptual and Acoustic Reliability Estimates for the Speech Disorders Classification System (SDCS)

    ERIC Educational Resources Information Center

    Shriberg, Lawrence D.; Fourakis, Marios; Hall, Sheryl D.; Karlsson, Heather B.; Lohmeier, Heather L.; McSweeny, Jane L.; Potter, Nancy L.; Scheer-Cohen, Alison R.; Strand, Edythe A.; Tilkens, Christie M.; Wilson, David L.

    2010-01-01

    A companion paper describes three extensions to a classification system for paediatric speech sound disorders termed the Speech Disorders Classification System (SDCS). The SDCS uses perceptual and acoustic data reduction methods to obtain information on a speaker's speech, prosody, and voice. The present paper provides reliability estimates for…

  4. Analysis of acoustic signals on welding and cutting

    SciTech Connect

    Morita, Takao; Ogawa, Yoji; Sumitomo, Takashi

    1995-12-31

    The sounds emitted during the welding and cutting processes are closely related to the processing phenomena, and sometimes they provide useful information for evaluation of their processing conditions. The analyses of acoustic signals from arc welding, plasma arc cutting, oxy-flame cutting, and water jet cutting are carried out in details in order to develop effective signal processing algorithm. The sound from TIG arc welding has the typical line spectrum which principal frequency, is almost the same as that of supplied electricity. The disturbance of welding process is clearly appeared oil the acoustic emission. The sound exposure level for CO{sub 2} or MIG welding is higher than that for TIG welding, and the relative intensity of the typical line spectrum caused by supplied electricity becomes low. But the sudden transition of welding condition oil produces an apparent change of sound exposure level. On the contrary, the acoustics from cutting processes are much louder than those of arc welding and show more chaotic behavior because the supplied fluid velocity and temperature of arc for cutting processes are much higher than those for welding processes. Therefore, it requires a special technique to extract the well meaning signals from the loud acoustic sounds. Further point of view, the reduction of acoustic exposure level becomes an important research theme with the growth of application fields of cutting processes.

  5. Atmospheric influence on volcano-acoustic signals

    NASA Astrophysics Data System (ADS)

    Matoza, Robin; de Groot-Hedlin, Catherine; Hedlin, Michael; Fee, David; Garcés, Milton; Le Pichon, Alexis

    2010-05-01

    Volcanoes are natural sources of infrasound, useful for studying infrasonic propagation in the atmosphere. Large, explosive volcanic eruptions typically produce signals that can be recorded at ranges of hundreds of kilometers propagating in atmospheric waveguides. In addition, sustained volcanic eruptions can produce smaller-amplitude repetitive signals recordable at >10 km range. These include repetitive impulsive signals and continuous tremor signals. The source functions of these signals can remain relatively invariant over timescales of weeks to months. Observed signal fluctuations from such persistent sources at an infrasound recording station may therefore be attributed to dynamic atmospheric propagation effects. We present examples of repetitive and sustained volcano infrasound sources at Mount St. Helens, Washington and Kilauea Volcano, Hawaii, USA. The data recorded at >10 km range show evidence of propagation effects induced by tropospheric variability at the mesoscale and microscale. Ray tracing and finite-difference simulations of the infrasound propagation produce qualitatively consistent results. However, the finite-difference simulations indicate that low-frequency effects such as diffraction, and scattering from topography may be important factors for infrasonic propagation at this scale.

  6. Weapon identification using hierarchical classification of acoustic signatures

    NASA Astrophysics Data System (ADS)

    Khan, Saad; Divakaran, Ajay; Sawhney, Harpreet S.

    2009-05-01

    We apply a unique hierarchical audio classification technique to weapon identification using gunshot analysis. The Audio Classification classifies each audio segment as one of ten weapon classes (e.g., 9mm, 22, shotgun etc.) using lowcomplexity Gaussian Mixture Models (GMM). The first level of hierarchy consists of classification into broad weapons categories such as Rifle, Hand-Gun etc. and the second consists of classification into specific weapons such as 9mm, 357 etc. Our experiments have yielded over 90% classification accuracy at the coarse (rifle-handgun) level of the classification hierarchy and over 85% accuracy at the finer level (weapon category such as 9mm).

  7. Mathematical model for classification of EEG signals

    NASA Astrophysics Data System (ADS)

    Ortiz, Victor H.; Tapia, Juan J.

    2015-09-01

    A mathematical model to filter and classify brain signals from a brain machine interface is developed. The mathematical model classifies the signals from the different lobes of the brain to differentiate the signals: alpha, beta, gamma and theta, besides the signals from vision, speech, and orientation. The model to develop further eliminates noise signals that occur in the process of signal acquisition. This mathematical model can be used on different platforms interfaces for rehabilitation of physically handicapped persons.

  8. A survey on acoustic signature recognition and classification techniques for persistent surveillance systems

    NASA Astrophysics Data System (ADS)

    Shirkhodaie, Amir; Alkilani, Amjad

    2012-06-01

    Application of acoustic sensors in Persistent Surveillance Systems (PSS) has received considerable attention over the last two decades because they can be rapidly deployed and have low cost. Conventional utilization of acoustic sensors in PSS spans a wide range of applications including: vehicle classification, target tracking, activity understanding, speech recognition, shooter detection, etc. This paper presents a current survey of physics-based acoustic signature classification techniques for outdoor sounds recognition and understanding. Particularly, this paper focuses on taxonomy and ontology of acoustic signatures resulted from group activities. The taxonomy and supportive ontology considered include: humanvehicle, human-objects, and human-human interactions. This paper, in particular, exploits applicability of several spectral analysis techniques as a means to maximize likelihood of correct acoustic source detection, recognition, and discrimination. Spectral analysis techniques based on Fast Fourier Transform, Discrete Wavelet Transform, and Short Time Fourier Transform are considered for extraction of features from acoustic sources. In addition, comprehensive overviews of most current research activities related to scope of this work are presented with their applications. Furthermore, future potential direction of research in this area is discussed for improvement of acoustic signature recognition and classification technology suitable for PSS applications.

  9. Modulation of Radio Frequency Signals by Nonlinearly Generated Acoustic Fields

    NASA Astrophysics Data System (ADS)

    Johnson, Spencer Joseph

    Acousto-electromagnetic scattering is a process in which an acoustic excitation is utilized to induce modulation on an electromagnetic (EM) wave. This phenomenon can be exploited in remote sensing and detection schemes whereby target objects are mechanically excited by high powered acoustic waves resulting in unique object characterizations when interrogated with EM signals. Implementation of acousto-EM sensing schemes, however, are limited by a lack of fundamental understanding of the nonlinear interaction between acoustic and EM waves and inefficient simulation methods in the determination of the radiation patterns of higher order scattered acoustic fields. To address the insufficient simulation issue, a computationally efficient mathematical model describing higher order scattered sound fields, particularly of third-order in which a 40x increase in computation speed is achieved, is derived using a multi-Gaussian beam (MGB) expansion that expresses the sound field of any arbitrary axially symmetric beam as a series of Gaussian base functions. The third-order intermodulation (IM3) frequency components are produced by considering the cascaded nonlinear second-order effects when analyzing the interaction between the first- and second-order frequency components during the nonlinear scattering of sound by sound from two noncollinear ultrasonic baffled piston sources. The theory is extended to the modeling of the sound beams generated by parametric transducer arrays, showing that the MGB model can be efficiently used to calculate both the second- and third-order sound fields of the array. Additionally, a near-to-far-field (NTFF) transformation method is developed to model the far-field characteristics of scattered sound fields, extending Kirchhoff's theorem, typically applied to EM waves, determining the far-field patterns of an acoustic source from amplitude and phase measurements made in the near-field by including the higher order sound fields generated by the

  10. Gearbox fault diagnosis based on deep random forest fusion of acoustic and vibratory signals

    NASA Astrophysics Data System (ADS)

    Li, Chuan; Sanchez, René-Vinicio; Zurita, Grover; Cerrada, Mariela; Cabrera, Diego; Vásquez, Rafael E.

    2016-08-01

    Fault diagnosis is an effective tool to guarantee safe operations in gearboxes. Acoustic and vibratory measurements in such mechanical devices are all sensitive to the existence of faults. This work addresses the use of a deep random forest fusion (DRFF) technique to improve fault diagnosis performance for gearboxes by using measurements of an acoustic emission (AE) sensor and an accelerometer that are used for monitoring the gearbox condition simultaneously. The statistical parameters of the wavelet packet transform (WPT) are first produced from the AE signal and the vibratory signal, respectively. Two deep Boltzmann machines (DBMs) are then developed for deep representations of the WPT statistical parameters. A random forest is finally suggested to fuse the outputs of the two DBMs as the integrated DRFF model. The proposed DRFF technique is evaluated using gearbox fault diagnosis experiments under different operational conditions, and achieves 97.68% of the classification rate for 11 different condition patterns. Compared to other peer algorithms, the addressed method exhibits the best performance. The results indicate that the deep learning fusion of acoustic and vibratory signals may improve fault diagnosis capabilities for gearboxes.

  11. Wideband link-budget analysis for undersea acoustic signaling

    NASA Astrophysics Data System (ADS)

    Rice, Joseph A.; Hansen, Joseph T.

    2002-11-01

    Link-budget analysis is commonly applied to satellite and wireless communications for estimating the signal-to-noise ratio (SNR) at the receiver. Link-budget analysis considers transmitter power, transmitter antenna gain, channel losses, channel noise, and receiver antenna gain. For underwater signaling, the terms of the sonar equation readily translate to a formulation of the link budget. However, the strong frequency dependence of underwater acoustic propagation requires special consideration, and is represented as an intermediate result called the channel SNR. The channel SNR includes ambient-noise and transmission-loss components. Several acoustic communication and navigation problems are addressed through wideband link-budget analyses. [Work sponsored by ONR 321.

  12. Signal processing methodologies for an acoustic fetal heart rate monitor

    NASA Technical Reports Server (NTRS)

    Pretlow, Robert A., III; Stoughton, John W.

    1992-01-01

    Research and development is presented of real time signal processing methodologies for the detection of fetal heart tones within a noise-contaminated signal from a passive acoustic sensor. A linear predictor algorithm is utilized for detection of the heart tone event and additional processing derives heart rate. The linear predictor is adaptively 'trained' in a least mean square error sense on generic fetal heart tones recorded from patients. A real time monitor system is described which outputs to a strip chart recorder for plotting the time history of the fetal heart rate. The system is validated in the context of the fetal nonstress test. Comparisons are made with ultrasonic nonstress tests on a series of patients. Comparative data provides favorable indications of the feasibility of the acoustic monitor for clinical use.

  13. A matched filter algorithm for acoustic signal detection

    NASA Astrophysics Data System (ADS)

    Jordan, D. W.

    1985-06-01

    This thesis is a presentation of several alternative acoustic filter designs which allow Space Shuttle payload experiment initiation prior to launch. This initiation is accomplished independently of any spacecraft services by means of a matched band-pass filter tuned to the acoustic signal characteristic of the Auxiliary Power Unit (APU) which is brought up to operating RPM's approximately five minutes prior to launch. These alternative designs include an analog filter built around operational amplifiers, a digital IIR design implemented with an INTEL 2920 Signal Processor, and an Adaptive FIR Weiner design. Working prototypes of the first two filters are developed and a discussion of the advantage of the 2920 digital design is presented.

  14. Modeling of acoustic emission signal propagation in waveguides.

    PubMed

    Zelenyak, Andreea-Manuela; Hamstad, Marvin A; Sause, Markus G R

    2015-01-01

    Acoustic emission (AE) testing is a widely used nondestructive testing (NDT) method to investigate material failure. When environmental conditions are harmful for the operation of the sensors, waveguides are typically mounted in between the inspected structure and the sensor. Such waveguides can be built from different materials or have different designs in accordance with the experimental needs. All these variations can cause changes in the acoustic emission signals in terms of modal conversion, additional attenuation or shift in frequency content. A finite element method (FEM) was used to model acoustic emission signal propagation in an aluminum plate with an attached waveguide and was validated against experimental data. The geometry of the waveguide is systematically changed by varying the radius and height to investigate the influence on the detected signals. Different waveguide materials were implemented and change of material properties as function of temperature were taken into account. Development of the option of modeling different waveguide options replaces the time consuming and expensive trial and error alternative of experiments. Thus, the aim of this research has important implications for those who use waveguides for AE testing. PMID:26007731

  15. Modeling of Acoustic Emission Signal Propagation in Waveguides

    PubMed Central

    Zelenyak, Andreea-Manuela; Hamstad, Marvin A.; Sause, Markus G. R.

    2015-01-01

    Acoustic emission (AE) testing is a widely used nondestructive testing (NDT) method to investigate material failure. When environmental conditions are harmful for the operation of the sensors, waveguides are typically mounted in between the inspected structure and the sensor. Such waveguides can be built from different materials or have different designs in accordance with the experimental needs. All these variations can cause changes in the acoustic emission signals in terms of modal conversion, additional attenuation or shift in frequency content. A finite element method (FEM) was used to model acoustic emission signal propagation in an aluminum plate with an attached waveguide and was validated against experimental data. The geometry of the waveguide is systematically changed by varying the radius and height to investigate the influence on the detected signals. Different waveguide materials were implemented and change of material properties as function of temperature were taken into account. Development of the option of modeling different waveguide options replaces the time consuming and expensive trial and error alternative of experiments. Thus, the aim of this research has important implications for those who use waveguides for AE testing. PMID:26007731

  16. Prediction of acoustic feature parameters using myoelectric signals.

    PubMed

    Lee, Ki-Seung

    2010-07-01

    It is well-known that a clear relationship exists between human voices and myoelectric signals (MESs) from the area of the speaker's mouth. In this study, we utilized this information to implement a speech synthesis scheme in which MES alone was used to predict the parameters characterizing the vocal-tract transfer function of specific speech signals. Several feature parameters derived from MES were investigated to find the optimal feature for maximization of the mutual information between the acoustic and the MES features. After the optimal feature was determined, an estimation rule for the acoustic parameters was proposed, based on a minimum mean square error (MMSE) criterion. In a preliminary study, 60 isolated words were used for both objective and subjective evaluations. The results showed that the average Euclidean distance between the original and predicted acoustic parameters was reduced by about 30% compared with the average Euclidean distance of the original parameters. The intelligibility of the synthesized speech signals using the predicted features was also evaluated. A word-level identification ratio of 65.5% and a syllable-level identification ratio of 73% were obtained through a listening test. PMID:20172775

  17. INSTRUMENTATION FOR SURVEYING ACOUSTIC SIGNALS IN NATURAL GAS TRANSMISSION LINES

    SciTech Connect

    John L. Loth; Gary J. Morris; George M. Palmer; Richard Guiler; Deepak Mehra

    2003-09-01

    In the U.S. natural gas is distributed through more than one million miles of high-pressure transmission pipelines. If all leaks and infringements could be detected quickly, it would enhance safety and U.S. energy security. Only low frequency acoustic waves appear to be detectable over distances up to 60 km where pipeline shut-off valves provide access to the inside of the pipeline. This paper describes a Portable Acoustic Monitoring Package (PAMP) developed to record and identify acoustic signals characteristic of: leaks, pump noise, valve and flow metering noise, third party infringement, manual pipeline water and gas blow-off, etc. This PAMP consists of a stainless steel 1/2 inch NPT plumbing tree rated for use on 1000 psi pipelines. Its instrumentation is designed to measure acoustic waves over the entire frequency range from zero to 16,000 Hz by means of four instruments: (1) microphone, (2) 3-inch water full range differential pressure transducer with 0.1% of range sensitivity, (3) a novel 3 inch to 100 inch water range amplifier, using an accumulator with needle valve and (4) a line-pressure transducer. The weight of the PAMP complete with all accessories is 36 pounds. This includes a remote control battery/switch box assembly on a 25-foot extension chord, a laptop data acquisition computer on a field table and a sun shield.

  18. Quadratic Time-Frequency Analysis of Hydroacoustic Signals as Applied to Acoustic Emissions of Large Whales

    NASA Astrophysics Data System (ADS)

    Le Bras, Ronan; Victor, Sucic; Damir, Malnar; Götz, Bokelmann

    2014-05-01

    In order to enrich the set of attributes in setting up a large database of whale signals, as envisioned in the Baleakanta project, we investigate methods of time-frequency analysis. The purpose of establishing the database is to increase and refine knowledge of the emitted signal and of its propagation characteristics, leading to a better understanding of the animal migrations in a non-invasive manner and to characterize acoustic propagation in oceanic media. The higher resolution for signal extraction and a better separation from other signals and noise will be used for various purposes, including improved signal detection and individual animal identification. The quadratic class of time-frequency distributions (TFDs) is the most popular set of time-frequency tools for analysis and processing of non-stationary signals. Two best known and most studied members of this class are the spectrogram and the Wigner-Ville distribution. However, to be used efficiently, i.e. to have highly concentrated signal components while significantly suppressing interference and noise simultaneously, TFDs need to be optimized first. The optimization method used in this paper is based on the Cross-Wigner-Ville distribution, and unlike similar approaches it does not require prior information on the analysed signal. The method is applied to whale signals, which, just like the majority of other real-life signals, can generally be classified as multicomponent non-stationary signals, and hence time-frequency techniques are a natural choice for their representation, analysis, and processing. We present processed data from a set containing hundreds of individual calls. The TFD optimization method results into a high resolution time-frequency representation of the signals. It allows for a simple extraction of signal components from the TFD's dominant ridges. The local peaks of those ridges can then be used for the signal components instantaneous frequency estimation, which in turn can be used as

  19. Laser-induced thermal acoustics (LITA) signals from finite beams

    NASA Astrophysics Data System (ADS)

    Cummings, E. B.; Leyva, I. A.; Hornung, H. G.

    1995-06-01

    Laser-induced thermal acoustics (LITA) is a four-wave mixing technique that may be employed to measure sound speeds, transport properties, velocities, and susceptibilities of fluids. It is particularly effective in high-pressure gases ( greater than 1 bar). An analytical expression for LITA signals is derived by the use of linearized equations of hydrodynamics and light scattering. This analysis, which includes full finite-beam-size effects and the optoacoustic effects of thermalization and electrostriction, predicts the amplitude and the time history of narrow-band time-resolved LITA and broadband spectrally resolved (mulitplex) LITA signals. The time behavior of the detected LITA signal depends significantly on the detection solid angle, with implications for the measurement of diffusivities by the use of LITA and the proper physical picture of LITA scattering. This and other elements of the physics of LITA that emerge from the analysis are discussed. Theoretical signals are compared with experimental LITA data.

  20. Terrain-Moisture Classification Using GPS Surface-Reflected Signals

    NASA Technical Reports Server (NTRS)

    Grant, Michael S.; Acton, Scott T.; Katzberg, Stephen J.

    2006-01-01

    In this study we present a novel method of land surface classification using surface-reflected GPS signals in combination with digital imagery. Two GPS-derived classification features are merged with visible image data to create terrain-moisture (TM) classes, defined here as visibly identifiable terrain or landcover classes containing a surface/soil moisture component. As compared to using surface imagery alone, classification accuracy is significantly improved for a number of visible classes when adding the GPS-based signal features. Since the strength of the reflected GPS signal is proportional to the amount of moisture in the surface, use of these GPS features provides information about the surface that is not obtainable using visible wavelengths alone. Application areas include hydrology, precision agriculture, and wetlands mapping.

  1. Classification of underwater targets from autonomous underwater vehicle sampled bistatic acoustic scattered fields.

    PubMed

    Fischell, Erin M; Schmidt, Henrik

    2015-12-01

    One of the long term goals of autonomous underwater vehicle (AUV) minehunting is to have multiple inexpensive AUVs in a harbor autonomously classify hazards. Existing acoustic methods for target classification using AUV-based sensing, such as sidescan and synthetic aperture sonar, require an expensive payload on each outfitted vehicle and post-processing and/or image interpretation. A vehicle payload and machine learning classification methodology using bistatic angle dependence of target scattering amplitudes between a fixed acoustic source and target has been developed for onboard, fully autonomous classification with lower cost-per-vehicle. To achieve the high-quality, densely sampled three-dimensional (3D) bistatic scattering data required by this research, vehicle sampling behaviors and an acoustic payload for precision timed data acquisition with a 16 element nose array were demonstrated. 3D bistatic scattered field data were collected by an AUV around spherical and cylindrical targets insonified by a 7-9 kHz fixed source. The collected data were compared to simulated scattering models. Classification and confidence estimation were shown for the sphere versus cylinder case on the resulting real and simulated bistatic amplitude data. The final models were used for classification of simulated targets in real time in the LAMSS MOOS-IvP simulation package [M. Benjamin, H. Schmidt, P. Newman, and J. Leonard, J. Field Rob. 27, 834-875 (2010)]. PMID:26723332

  2. Acoustic bottom detection and seabed classification in the German Bight, southern North Sea

    NASA Astrophysics Data System (ADS)

    Bartholomä, Alexander

    2006-09-01

    To investigate the hydrodynamic activity of the seabed in the German Bight, underwater remote sensing was carried out over an area of 32 km2 located 20 km northeast of Helgoland island in the southern North Sea in January, May and August 2001. On the basis of acoustic seabed classification, six seabed types have been identified by the combined evaluation of side-scan sonar records, wave-shape analysis of echo-sounder data, and 100 grab samples. In five seabed types, the acoustic classes can be distinguished on the basis of sediment characteristics, comprising size components ranging from coarse pebbles to fine sand. The sixth seabed type corresponds to large pebbles and cobbles which are completely overgrown with brown algae. Statistically, the complex spatial patchiness of the six classes varied significantly in the course of the study period. During the winter period (January 2001), the study site was dominated by coarse material, except for a small area of finer sediment in the centre. With the onset of more moderate weather conditions in spring (May 2001), a general fining trend in sediment composition was observed, especially in the deeper western parts of the study area. In summer (August 2001), finer sediments still dominated but a slight increase in signal roughness suggests an overprint by coarser lag deposits and/or denser coverage by benthic organisms (e.g. Lanice conchilega) which then were found more frequently in grab samples, in association with finer sand. These findings demonstrate that the distribution of seafloor sediments and their benthic fauna in the deeper part of the German Bight region are controlled largely by seasonal changes in hydrodynamic conditions. These changes are reflected in correspondingly high variability in the complex patchiness of sediment distribution patterns, which would not have been adequately resolved by any standard sampling procedure.

  3. Low-Frequency Acoustic Signals Propagation in Buried Pipelines

    NASA Astrophysics Data System (ADS)

    Ovchinnikov, A. L.; Lapshin, B. M.

    2016-01-01

    The article deals with the issues concerning acoustic signals propagation in the large-diameter oil pipelines caused by mechanical action on the pipe body. Various mechanisms of signals attenuation are discussed. It is shown that the calculation of the attenuation caused only by internal energy loss, i.e, the presence of viscosity, thermal conductivity and liquid pipeline wall friction lead to low results. The results of experimental studies, carried out using the existing pipeline with a diameter of 1200 mm. are shown. It is experimentally proved that the main mechanism of signal attenuation is the energy emission into the environment. The numerical values of attenuation coefficients that are 0,14- 0.18 dB/m for the pipeline of 1200 mm in diameter, in the frequency range from 50 Hz to 500 Hz, are determined.

  4. Subsurface event detection and classification using Wireless Signal Networks.

    PubMed

    Yoon, Suk-Un; Ghazanfari, Ehsan; Cheng, Liang; Pamukcu, Sibel; Suleiman, Muhannad T

    2012-01-01

    Subsurface environment sensing and monitoring applications such as detection of water intrusion or a landslide, which could significantly change the physical properties of the host soil, can be accomplished using a novel concept, Wireless Signal Networks (WSiNs). The wireless signal networks take advantage of the variations of radio signal strength on the distributed underground sensor nodes of WSiNs to monitor and characterize the sensed area. To characterize subsurface environments for event detection and classification, this paper provides a detailed list and experimental data of soil properties on how radio propagation is affected by soil properties in subsurface communication environments. Experiments demonstrated that calibrated wireless signal strength variations can be used as indicators to sense changes in the subsurface environment. The concept of WSiNs for the subsurface event detection is evaluated with applications such as detection of water intrusion, relative density change, and relative motion using actual underground sensor nodes. To classify geo-events using the measured signal strength as a main indicator of geo-events, we propose a window-based minimum distance classifier based on Bayesian decision theory. The window-based classifier for wireless signal networks has two steps: event detection and event classification. With the event detection, the window-based classifier classifies geo-events on the event occurring regions that are called a classification window. The proposed window-based classification method is evaluated with a water leakage experiment in which the data has been measured in laboratory experiments. In these experiments, the proposed detection and classification method based on wireless signal network can detect and classify subsurface events. PMID:23202191

  5. Subsurface Event Detection and Classification Using Wireless Signal Networks

    PubMed Central

    Yoon, Suk-Un; Ghazanfari, Ehsan; Cheng, Liang; Pamukcu, Sibel; Suleiman, Muhannad T.

    2012-01-01

    Subsurface environment sensing and monitoring applications such as detection of water intrusion or a landslide, which could significantly change the physical properties of the host soil, can be accomplished using a novel concept, Wireless Signal Networks (WSiNs). The wireless signal networks take advantage of the variations of radio signal strength on the distributed underground sensor nodes of WSiNs to monitor and characterize the sensed area. To characterize subsurface environments for event detection and classification, this paper provides a detailed list and experimental data of soil properties on how radio propagation is affected by soil properties in subsurface communication environments. Experiments demonstrated that calibrated wireless signal strength variations can be used as indicators to sense changes in the subsurface environment. The concept of WSiNs for the subsurface event detection is evaluated with applications such as detection of water intrusion, relative density change, and relative motion using actual underground sensor nodes. To classify geo-events using the measured signal strength as a main indicator of geo-events, we propose a window-based minimum distance classifier based on Bayesian decision theory. The window-based classifier for wireless signal networks has two steps: event detection and event classification. With the event detection, the window-based classifier classifies geo-events on the event occurring regions that are called a classification window. The proposed window-based classification method is evaluated with a water leakage experiment in which the data has been measured in laboratory experiments. In these experiments, the proposed detection and classification method based on wireless signal network can detect and classify subsurface events. PMID:23202191

  6. Identifying fatigue crack geometric features from acoustic emission signals

    NASA Astrophysics Data System (ADS)

    Bao, Jingjing; Poddar, Banibrata; Giurgiutiu, Victor

    2016-04-01

    Acoustic emission (AE) caused by the growth of fatigue crack were well studied by researchers. Conventional approaches predominantly are based on statistical analysis. In this study we focus on identifying geometric features of the crack from the AE signals using physics based approach. One of the main challenges of this approach is to develop a physics of materials based understanding of the generation and propagation of acoustic emissions due to the growth of a fatigue crack. As the geometry changes due to the crack growth, so does the local vibration modes around the crack. Our aim is to understand these changing local vibration modes and find possible relation between the AE signal features and the geometric features of the crack. Finite element (FE) analysis was used to model AE events due to fatigue crack growth. This was done using dipole excitation at the crack tips. Harmonic analysis was also performed on these FE models to understand the local vibration modes. Experimental study was carried out to verify these results. Piezoelectric wafer active sensors (PWAS) were used to excite cracked specimen and the local vibration modes were captured using laser Doppler vibrometry. The preliminary results show that the AE signals do carry the information related to the crack geometry.

  7. Primary acoustic signal structure during free falling drop collision with a water surface

    NASA Astrophysics Data System (ADS)

    Chashechkin, Yu. D.; Prokhorov, V. E.

    2016-04-01

    Consistent optical and acoustic techniques have been used to study the structure of hydrodynamic disturbances and acoustic signals generated as a free falling drop penetrates water. The relationship between the structures of hydrodynamic and acoustic perturbations arising as a result of a falling drop contacting with the water surface and subsequent immersion into water is traced. The primary acoustic signal is characterized, in addition to stably reproduced features (steep leading edge followed by long decay with local pressure maxima), by irregular high-frequency packets, which are studied for the first time. Reproducible experimental data are used to recognize constant and variable components of the primary acoustic signal.

  8. Cardiac arrhythmia classification using multi-modal signal analysis.

    PubMed

    Kalidas, V; Tamil, L S

    2016-08-01

    In this paper, as a contribution to the Physionet/Computing in Cardiology 2015 Challenge, we present individual algorithms to accurately classify five different life threatening arrhythmias with the goal of suppressing false alarm generation in intensive care units. Information obtained by analysing electrocardiogram, photoplethysmogram and arterial blood pressure signals was utilized to develop the classification models. Prior to classification, the signals were subject to a signal pre-processing stage for quality analysis. Classification was performed using a combination of support vector machine based machine learning approach and logical analysis techniques. The predicted result for a certain arrhythmia classification model was verified by logical analysis to aid in reduction of false alarms. Separate feature vectors were formed for predicting the presence or absence of each arrhythmia, using both spectral and time-domain information. The training and test data were obtained from the Physionet/CinC Challenge 2015 database. Classification algorithms were written for two different categories of data, namely real-time and retrospective, whose data lengths were 10 s and an additional 30 s, respectively. For the real-time test dataset, sensitivity of 94% and specificity of 82% were obtained. Similarly, for the retrospective test dataset, sensitivity of 94% and specificity of 86% were obtained. PMID:27454417

  9. Signal classification using global dynamical models, Part II: SONAR data analysis

    NASA Astrophysics Data System (ADS)

    Kremliovsky, Michael; Kadtke, James

    1996-06-01

    In Part I of this paper, we described a numerical method for nonlinear signal detection and classification which made use of techniques borrowed from dynamical systems theory. Here in Part II of the paper, we will describe an example of data analysis using this method, for data consisting of open ocean acoustic (SONAR) recordings of marine mammal transients, supplied from NUWC sources. The purpose here is two-fold: first to give a more operational description of the technique and provide rules-of-thumb for parameter choices; and second to discuss some new issues raised by the analysis of non-ideal (real-world) data sets. The particular data set considered here is quite non-stationary, relatively noisy, is not clearly localized in the background, and as such provides a difficult challenge for most detection/classification schemes.

  10. Signal classification using global dynamical models, Part II: SONAR data analysis

    SciTech Connect

    Kremliovsky, M.; Kadtke, J.

    1996-06-01

    In Part I of this paper, we described a numerical method for nonlinear signal detection and classification which made use of techniques borrowed from dynamical systems theory. Here in Part II of the paper, we will describe an example of data analysis using this method, for data consisting of open ocean acoustic (SONAR) recordings of marine mammal transients, supplied from NUWC sources. The purpose here is two-fold: first to give a more operational description of the technique and provide rules-of-thumb for parameter choices; and second to discuss some new issues raised by the analysis of non-ideal (real-world) data sets. The particular data set considered here is quite non-stationary, relatively noisy, is not clearly localized in the background, and as such provides a difficult challenge for most detection/classification schemes. {copyright} {ital 1996 American Institute of Physics.}

  11. Precursory acoustic signals and ground deformation in volcanic explosions

    NASA Astrophysics Data System (ADS)

    Bowman, D. C.; Kim, K.; Anderson, J.; Lees, J. M.; Taddeucci, J.; Graettinger, A. H.; Sonder, I.; Valentine, G.

    2013-12-01

    We investigate precursory acoustic signals that appear prior to volcanic explosions in real and experimental settings. Acoustic records of a series of experimental blasts designed to mimic maar explosions show precursory energy 0.02 to 0.05 seconds before the high amplitude overpressure arrival. These blasts consisted of 1 to 1/3 lb charges detonated in unconsolidated granular material at depths between 0.5 and 1 m, and were performed during the Buffalo Man Made Maars experiment in Springville, New York, USA. The preliminary acoustic arrival is 1 to 2 orders of magnitude lower in amplitude compared to the main blast wave. The waveforms vary from blast to blast, perhaps reflecting the different explosive yields and burial depths of each shot. Similar arrivals are present in some infrasound records at Santiaguito volcano, Guatemala, where they precede the main blast signal by about 2 seconds and are about 1 order of magnitude weaker. Precursory infrasound has also been described at Sakurajima volcano, Japan (Yokoo et al, 2013; Bull. Volc. Soc. Japan, 58, 163-181) and Suwanosejima volcano, Japan (Yokoo and Iguchi, 2010; JVGR, 196, 287-294), where it is attributed to rapid deformation of the vent region. Vent deformation has not been directly observed at these volcanoes because of the difficulty of visually observing the crater floor. However, particle image velocimetry of video records at Santiaguito has revealed rapid and widespread ground motion just prior to eruptions (Johnson et al, 2008; Nature, 456, 377-381) and may be the cause of much of the infrasound recorded at that volcano (Johnson and Lees, 2010; GRL, 37, L22305). High speed video records of the blasts during the Man Made Maars experiment also show rapid deformation of the ground immediately before the explosion plume breaches the surface. We examine the connection between source yield, burial depths, ground deformation, and the production of initial acoustic phases for each simulated maar explosion. We

  12. Computational principles underlying the recognition of acoustic signals in insects.

    PubMed

    Clemens, Jan; Hennig, R Matthias

    2013-08-01

    Many animals produce pulse-like signals during acoustic communication. These signals exhibit structure on two time scales: they consist of trains of pulses that are often broadcast in packets-so called chirps. Temporal parameters of the pulse and of the chirp are decisive for female preference. Despite these signals being produced by animals from many different taxa (e.g. frogs, grasshoppers, crickets, bushcrickets, flies), a general framework for their evaluation is still lacking. We propose such a framework, based on a simple and physiologically plausible model. The model consists of feature detectors, whose time-varying output is averaged over the signal and then linearly combined to yield the behavioral preference. We fitted this model to large data sets collected in two species of crickets and found that Gabor filters--known from visual and auditory physiology--explain the preference functions in these two species very well. We further explored the properties of Gabor filters and found a systematic relationship between parameters of the filters and the shape of preference functions. Although these Gabor filters were relatively short, they were also able to explain aspects of the preference for signal parameters on the longer time scale due to the integration step in our model. Our framework explains a wide range of phenomena associated with female preference for a widespread class of signals in an intuitive and physiologically plausible fashion. This approach thus constitutes a valuable tool to understand the functioning and evolution of communication systems in many species. PMID:23417450

  13. Classification of transient signals using sparse representations over adaptive dictionaries

    NASA Astrophysics Data System (ADS)

    Moody, Daniela I.; Brumby, Steven P.; Myers, Kary L.; Pawley, Norma H.

    2011-06-01

    Automatic classification of broadband transient radio frequency (RF) signals is of particular interest in persistent surveillance applications. Because such transients are often acquired in noisy, cluttered environments, and are characterized by complex or unknown analytical models, feature extraction and classification can be difficult. We propose a fast, adaptive classification approach based on non-analytical dictionaries learned from data. Conventional representations using fixed (or analytical) orthogonal dictionaries, e.g., Short Time Fourier and Wavelet Transforms, can be suboptimal for classification of transients, as they provide a rigid tiling of the time-frequency space, and are not specifically designed for a particular signal class. They do not usually lead to sparse decompositions, and require separate feature selection algorithms, creating additional computational overhead. Pursuit-type decompositions over analytical, redundant dictionaries yield sparse representations by design, and work well for target signals in the same function class as the dictionary atoms. The pursuit search however has a high computational cost, and the method can perform poorly in the presence of realistic noise and clutter. Our approach builds on the image analysis work of Mairal et al. (2008) to learn a discriminative dictionary for RF transients directly from data without relying on analytical constraints or additional knowledge about the signal characteristics. We then use a pursuit search over this dictionary to generate sparse classification features. We demonstrate that our learned dictionary is robust to unexpected changes in background content and noise levels. The target classification decision is obtained in almost real-time via a parallel, vectorized implementation.

  14. Modern Techniques in Acoustical Signal and Image Processing

    SciTech Connect

    Candy, J V

    2002-04-04

    Acoustical signal processing problems can lead to some complex and intricate techniques to extract the desired information from noisy, sometimes inadequate, measurements. The challenge is to formulate a meaningful strategy that is aimed at performing the processing required even in the face of uncertainties. This strategy can be as simple as a transformation of the measured data to another domain for analysis or as complex as embedding a full-scale propagation model into the processor. The aims of both approaches are the same--to extract the desired information and reject the extraneous, that is, develop a signal processing scheme to achieve this goal. In this paper, we briefly discuss this underlying philosophy from a ''bottom-up'' approach enabling the problem to dictate the solution rather than visa-versa.

  15. Signal Restoration of Non-stationary Acoustic Signals in the Time Domain

    NASA Technical Reports Server (NTRS)

    Babkin, Alexander S.

    1988-01-01

    Signal restoration is a method of transforming a nonstationary signal acquired by a ground based microphone to an equivalent stationary signal. The benefit of the signal restoration is a simplification of the flight test requirements because it could dispense with the need to acquire acoustic data with another aircraft flying in concert with the rotorcraft. The data quality is also generally improved because the contamination of the signal by the propeller and wind noise is not present. The restoration methodology can also be combined with other data acquisition methods, such as a multiple linear microphone array for further improvement of the test results. The methodology and software are presented for performing the signal restoration in the time domain. The method has no restrictions on flight path geometry or flight regimes. Only requirement is that the aircraft spatial position be known relative to the microphone location and synchronized with the acoustic data. The restoration process assumes that the moving source radiates a stationary signal, which is then transformed into a nonstationary signal by various modulation processes. The restoration contains only the modulation due to the source motion.

  16. Techniques of EMG signal analysis: detection, processing, classification and applications

    PubMed Central

    Hussain, M.S.; Mohd-Yasin, F.

    2006-01-01

    Electromyography (EMG) signals can be used for clinical/biomedical applications, Evolvable Hardware Chip (EHW) development, and modern human computer interaction. EMG signals acquired from muscles require advanced methods for detection, decomposition, processing, and classification. The purpose of this paper is to illustrate the various methodologies and algorithms for EMG signal analysis to provide efficient and effective ways of understanding the signal and its nature. We further point up some of the hardware implementations using EMG focusing on applications related to prosthetic hand control, grasp recognition, and human computer interaction. A comparison study is also given to show performance of various EMG signal analysis methods. This paper provides researchers a good understanding of EMG signal and its analysis procedures. This knowledge will help them develop more powerful, flexible, and efficient applications. PMID:16799694

  17. Study on demodulated signal distribution and acoustic pressure phase sensitivity of a self-interfered distributed acoustic sensing system

    NASA Astrophysics Data System (ADS)

    Shang, Ying; Yang, Yuan-Hong; Wang, Chen; Liu, Xiao-Hui; Wang, Chang; Peng, Gang-Ding

    2016-06-01

    We propose a demodulated signal distribution theory for a self-interfered distributed acoustic sensing system. The distribution region of Rayleigh backscattering including the acoustic sensing signal in the sensing fiber is investigated theoretically under different combinations of both the path difference and pulse width Additionally we determine the optimal solution between the path difference and pulse width to obtain the maximum phase change per unit length. We experimentally test this theory and realize a good acoustic pressure phase sensitivity of  ‑150 dB re rad/(μPa·m) of fiber in the frequency range from 200 Hz to 1 kHz.

  18. A comparative evaluation of piezoelectric sensors for acoustic emission-based impact location estimation and damage classification in composite structures

    NASA Astrophysics Data System (ADS)

    Uprety, Bibhisha; Kim, Sungwon; Mathews, V. John; Adams, Daniel O.

    2015-03-01

    Acoustic Emission (AE) based Structural Health Monitoring (SHM) is of great interest for detecting impact damage in composite structures. Within the aerospace industry the need to detect and locate these events, even when no visible damage is present, is important both from the maintenance and design perspectives. In this investigation, four commercially available piezoelectric sensors were evaluated for usage in an AE-based SHM system. Of particular interest was comparing the acoustic response of the candidate piezoelectric sensors for impact location estimations as well as damage classification resulting from the impact in fiber-reinforced composite structures. Sensor assessment was performed based on response signal characterization and performance for active testing at 300 kHz and steel-ball drop testing using both aluminum and carbon/epoxy composite plates. Wave mode velocities calculated from the measured arrival times were found to be in good agreement with predictions obtained using both the Disperse code and finite element analysis. Differences in the relative strength of the received wave modes, the overall signal strengths and signal-to-noise ratios were observed through the use of both active testing as well as passive steel-ball drop testing. Further comparative is focusing on assessing AE sensor performance for use in impact location estimation algorithms as well as detecting and classifying damage produced in composite structures due to impact events.

  19. Floc Growth and Changes in ADV Acoustic Backscatter Signal

    NASA Astrophysics Data System (ADS)

    Rouhnia, M.; Keyvani, A.; Strom, K.

    2013-12-01

    A series of experiments were conducted to examine the effect of mud floc growth on the acoustic back-scatter signal recorded by a Nortek Vector acoustic Doppler velocimeter (ADV). Several studies have shown that calibration equations can be developed to link the backscatter strength with average suspended sediment concentration (SSC) when the sediment particle size distribution remains constant. However, when mud is present, the process of flocculation can alter the suspended particle size distribution. Past studies have shown that it is still unclear as to the degree of dependence of the calibration equation on changes in floc size. Part of the ambiguity lies in the fact that flocs can be porous and rather loosely packed and therefore might not scatter to the same extent as a grain of sand. In addition, direct, detailed measurements of floc size have not accompanied experiments examining the dependence of ADV backscatter and suspended sediment concentration. In this research, a set of laboratory experiments is used to test how floc growth affects the backscatter strength. The laboratory data is examined in light of an analytic model that was developed based on scatter theory to account for changes in both SSC and the floc properties of size and density. For the experiments, a turbulent suspension was created in a tank with a rotating paddle. Fixed concentrations of a mixture of kaolinite and montmorillonite were added to the tank in a step-wise manner. For each step, the flocs were allowed to grow to their equilibrium size before breaking the flocs with high turbulent mixing, adding more sediment, and then returning the mixing rate to a range suitable for the re-growth of flocs. During each floc growth phase, data was simultaneously collected at the same elevation in the tank using a floc camera to capture the changes in floc size, a Nortek Vector ADV for the acoustic backscatter, and a Campbell Scientific OBS 3+ for optical backscatter. Physical samples of the

  20. Unsupervised classification of operator workload from brain signals

    NASA Astrophysics Data System (ADS)

    Schultze-Kraft, Matthias; Dähne, Sven; Gugler, Manfred; Curio, Gabriel; Blankertz, Benjamin

    2016-06-01

    Objective. In this study we aimed for the classification of operator workload as it is expected in many real-life workplace environments. We explored brain-signal based workload predictors that differ with respect to the level of label information required for training, including entirely unsupervised approaches. Approach. Subjects executed a task on a touch screen that required continuous effort of visual and motor processing with alternating difficulty. We first employed classical approaches for workload state classification that operate on the sensor space of EEG and compared those to the performance of three state-of-the-art spatial filtering methods: common spatial patterns (CSPs) analysis, which requires binary label information; source power co-modulation (SPoC) analysis, which uses the subjects’ error rate as a target function; and canonical SPoC (cSPoC) analysis, which solely makes use of cross-frequency power correlations induced by different states of workload and thus represents an unsupervised approach. Finally, we investigated the effects of fusing brain signals and peripheral physiological measures (PPMs) and examined the added value for improving classification performance. Main results. Mean classification accuracies of 94%, 92% and 82% were achieved with CSP, SPoC, cSPoC, respectively. These methods outperformed the approaches that did not use spatial filtering and they extracted physiologically plausible components. The performance of the unsupervised cSPoC is significantly increased by augmenting it with PPM features. Significance. Our analyses ensured that the signal sources used for classification were of cortical origin and not contaminated with artifacts. Our findings show that workload states can be successfully differentiated from brain signals, even when less and less information from the experimental paradigm is used, thus paving the way for real-world applications in which label information may be noisy or entirely unavailable.

  1. Adaptive Plasticity in Wild Field Cricket’s Acoustic Signaling

    PubMed Central

    Bertram, Susan M.; Harrison, Sarah J.; Thomson, Ian R.; Fitzsimmons, Lauren P.

    2013-01-01

    Phenotypic plasticity can be adaptive when phenotypes are closely matched to changes in the environment. In crickets, rhythmic fluctuations in the biotic and abiotic environment regularly result in diel rhythms in density of sexually active individuals. Given that density strongly influences the intensity of sexual selection, we asked whether crickets exhibit plasticity in signaling behavior that aligns with these rhythmic fluctuations in the socio-sexual environment. We quantified the acoustic mate signaling behavior of wild-caught males of two cricket species, Gryllus veletis and G. pennsylvanicus. Crickets exhibited phenotypically plastic mate signaling behavior, with most males signaling more often and more attractively during the times of day when mating activity is highest in the wild. Most male G. pennsylvanicus chirped more often and louder, with shorter interpulse durations, pulse periods, chirp durations, and interchirp durations, and at slightly higher carrier frequencies during the time of the day that mating activity is highest in the wild. Similarly, most male G. veletis chirped more often, with more pulses per chirp, longer interpulse durations, pulse periods, and chirp durations, shorter interchirp durations, and at lower carrier frequencies during the time of peak mating activity in the wild. Among-male variation in signaling plasticity was high, with some males signaling in an apparently maladaptive manner. Body size explained some of the among-male variation in G. pennsylvanicus plasticity but not G. veletis plasticity. Overall, our findings suggest that crickets exhibit phenotypically plastic mate attraction signals that closely match the fluctuating socio-sexual context they experience. PMID:23935965

  2. Optical observations of meteors generating infrasound-I: Acoustic signal identification and phenomenology

    NASA Astrophysics Data System (ADS)

    Silber, Elizabeth A.; Brown, Peter G.

    2014-11-01

    We analyse infrasound signals from 71 bright meteors/fireballs simultaneously detected by video to investigate the phenomenology and characteristics of meteor-generated near-field infrasound (<300 km) and shock production. A taxonomy for meteor generated infrasound signal classification has been developed using the time-pressure signal of the infrasound arrivals. Based on the location along the meteor trail where the infrasound signal originates, we find most signals are associated with cylindrical shocks, with about a quarter of events evidencing spherical shocks associated with fragmentation episodes and optical flares. The video data indicate that all events with ray launch angles >117° from the trajectory heading are most likely generated by a spherical shock, while infrasound produced by the meteors with ray launch angles ≤117° can be attributed to both a cylindrical line source and a spherical shock. We find that meteors preferentially produce infrasound toward the end of their trails with a smaller number showing a preference for mid-trail production. Meteors producing multiple infrasound arrivals show a strong infrasound source height skewness to the end of trails and are much more likely to be associated with optical flares. We find that about 1% of all our optically-recorded meteors have associated detected infrasound and estimate that regional meteor infrasound events should occur on the order of once per week and dominate in numbers over infrasound associated with more energetic (but rarer) bolides. While a significant fraction of our meteors generating infrasound (~1/4 of single arrivals) are produced by fragmentation events, we find no instances where acoustic radiation is detectable more than about 60° beyond the ballistic regime at our meteoroid sizes (grams to tens of kilograms) emphasizing the strong anisotropy in acoustic radiation for meteors which are dominated by cylindrical line source geometry, even in the presence of fragmentation.

  3. Extended amplification of acoustic signals by amphibian burrows.

    PubMed

    Muñoz, Matías I; Penna, Mario

    2016-07-01

    Animals relying on acoustic signals for communication must cope with the constraints imposed by the environment for sound propagation. A resource to improve signal broadcast is the use of structures that favor the emission or the reception of sounds. We conducted playback experiments to assess the effect of the burrows occupied by the frogs Eupsophus emiliopugini and E. calcaratus on the amplitude of outgoing vocalizations. In addition, we evaluated the influence of these cavities on the reception of externally generated sounds potentially interfering with conspecific communication, namely, the vocalizations emitted by four syntopic species of anurans (E. emiliopugini, E. calcaratus, Batrachyla antartandica, and Pleurodema thaul) and the nocturnal owls Strix rufipes and Glaucidium nanum. Eupsophus advertisement calls emitted from within the burrows experienced average amplitude gains of 3-6 dB at 100 cm from the burrow openings. Likewise, the incoming vocalizations of amphibians and birds were amplified on average above 6 dB inside the cavities. The amplification of internally broadcast Eupsophus vocalizations favors signal detection by nearby conspecifics. Reciprocally, the amplification of incoming conspecific and heterospecific signals facilitates the detection of neighboring males and the monitoring of the levels of potentially interfering biotic noise by resident frogs, respectively. PMID:27209276

  4. Discriminative likelihood score weighting based on acoustic-phonetic classification for speaker identification

    NASA Astrophysics Data System (ADS)

    Suh, Youngjoo; Kim, Hoirin

    2014-12-01

    In this paper, a new discriminative likelihood score weighting technique is proposed for speaker identification. The proposed method employs a discriminative weighting of frame-level log-likelihood scores with acoustic-phonetic classification in the Gaussian mixture model (GMM)-based speaker identification. Experiments performed on the Aurora noise-corrupted TIMIT database showed that the proposed approach provides meaningful performance improvement with an overall relative error reduction of 15.8% over the maximum likelihood-based baseline GMM approach.

  5. Classifying multi-frequency fisheries acoustic data using a robust probabilistic classification technique.

    PubMed

    Anderson, C I H; Horne, J K; Boyle, J

    2007-06-01

    A robust probabilistic classification technique, using expectation maximization of finite mixture models, is used to analyze multi-frequency fisheries acoustic data. The number of clusters is chosen using the Bayesian Information Criterion. Probabilities of membership to clusters are used to classify each sample. The utility of the technique is demonstrated using two examples: the Gulf of Alaska representing a low-diversity, well-known system; and the Mid-Atlantic Ridge, a species-rich, relatively unknown system. PMID:17552574

  6. A framework for the damage evaluation of acoustic emission signals through Hilbert-Huang transform

    NASA Astrophysics Data System (ADS)

    Siracusano, Giulio; Lamonaca, Francesco; Tomasello, Riccardo; Garescì, Francesca; Corte, Aurelio La; Carnì, Domenico Luca; Carpentieri, Mario; Grimaldi, Domenico; Finocchio, Giovanni

    2016-06-01

    The acoustic emission (AE) is a powerful and potential nondestructive testing method for structural monitoring in civil engineering. Here, we show how systematic investigation of crack phenomena based on AE data can be significantly improved by the use of advanced signal processing techniques. Such data are a fundamental source of information that can be used as the basis for evaluating the status of the material, thereby paving the way for a new frontier of innovation made by data-enabled analytics. In this article, we propose a framework based on the Hilbert-Huang Transform for the evaluation of material damages that (i) facilitates the systematic employment of both established and promising analysis criteria, and (ii) provides unsupervised tools to achieve an accurate classification of the fracture type, the discrimination between longitudinal (P-) and traversal (S-) waves related to an AE event. The experimental validation shows promising results for a reliable assessment of the health status through the monitoring of civil infrastructures.

  7. Paediatric heart sound signal analysis towards classification using multifractal spectra.

    PubMed

    Gavrovska, Ana; Zajić, Goran; Bogdanović, Vesna; Reljin, Irini; Reljin, Branimir

    2016-09-01

    Healthy versus unhealthy heart sound computer-aided classification tools are very popular for supporting clinical decisions. In this paper a new method is proposed for the classification of heart sound recordings from a statistical standpoint without detection and localization of fundamental heart sounds (S1, S2). This study analyzes the possibility of detecting healthy heart sound signal from a large set of measurements, corresponding to different pathologies, such as aortic regurgitation, mitral regurgitation, aortic stenosis and ventricular septal defects. The proposed method employs singularity spectra analysis and long-term dependency of irregular structures. Healthy signals are firstly separated from the rest of the recordings. In the second step, the signals with a click syndrome, used here as a reference, are detected in the unhealthy group. Innocent murmurs have not been considered in this paper. Each auscultatory recording is classified into one of the following classes: healthy; click syndrome; and other heart dysfunctions. The results of the proposed method provided high recall and precision values for each of the three classes. Since the presence of additive noise may affect the classification, we also analyzed the possibility of classifying signals in such circumstances. The method was tested, verified and showed high accuracy. PMID:27510224

  8. Acoustic Emission Signals in Thin Plates Produced by Impact Damage

    NASA Technical Reports Server (NTRS)

    Prosser, William H.; Gorman, Michael R.; Humes, Donald H.

    1999-01-01

    Acoustic emission (AE) signals created by impact sources in thin aluminum and graphite/epoxy composite plates were analyzed. Two different impact velocity regimes were studied. Low-velocity (less than 0.21 km/s) impacts were created with an airgun firing spherical steel projectiles (4.5 mm diameter). High-velocity (1.8 to 7 km/s) impacts were generated with a two-stage light-gas gun firing small cylindrical nylon projectiles (1.5 mm diameter). Both the impact velocity and impact angle were varied. The impacts did not penetrate the aluminum plates at either low or high velocities. For high-velocity impacts in composites, there were both impacts that fully penetrated the plate as well as impacts that did not. All impacts generated very large amplitude AE signals (1-5 V at the sensor), which propagated as plate (extensional and/or flexural) modes. In the low-velocity impact studies, the signal was dominated by a large flexural mode with only a small extensional mode component detected. As the impact velocity was increased within the low velocity regime, the overall amplitudes of both the extensional and flexural modes increased. In addition, a relative increase in the amplitude of high-frequency components of the flexural mode was also observed. Signals caused by high-velocity impacts that did not penetrate the plate contained both a large extensional and flexural mode component of comparable amplitudes. The signals also contained components of much higher frequency and were easily differentiated from those caused by low-velocity impacts. An interesting phenomenon was observed in that the large flexural mode component, seen in every other case, was absent from the signal when the impact particle fully penetrated through the composite plates.

  9. Frequency invariant classification of ultrasonic weld inspection signals.

    PubMed

    Polikar, R; Udpa, L; Udpa, S S; Taylor, T

    1998-01-01

    Automated signal classification systems are finding increasing use in many applications for the analysis and interpretation of large volumes of signals. Such systems show consistency of response and help reduce the effect of variabilities associated with human interpretation. This paper deals with the analysis of ultrasonic NDE signals obtained during weld inspection of piping in boiling water reactors. The overall approach consists of three major steps, namely, frequency invariance, multiresolution analysis, and neural network classification. The data are first preprocessed whereby signals obtained using different transducer center frequencies are transformed to an equivalent reference frequency signal. Discriminatory features are then extracted using a multiresolution analysis technique, namely, the discrete wavelet transform (DWT). The compact feature vector obtained using wavelet analysis is classified using a multilayer perceptron neural network. Two different databases containing weld inspection signals have been used to test the performance of the neural network. Initial results obtained using this approach demonstrate the effectiveness of the frequency invariance processing technique and the DWT analysis method employed for feature extraction. PMID:18244213

  10. Acoustic Resonance Spectroscopy (ARS) Munition Classification System enhancements. Final report

    SciTech Connect

    Vela, O.A.; Huggard, J.C.

    1997-09-18

    Acoustic Resonance Spectroscopy (ARS) is a non-destructive evaluation technology developed at the Los Alamos National Laboratory (LANL). This technology has resulted in three generations of instrumentation, funded by the Defense Special Weapons Agency (DSWA), specifically designed for field identification of chemical weapon (CW) munitions. Each generation of ARS instrumentation was developed with a specific user in mind. The ARS1OO was built for use by the U.N. Inspection Teams going into Iraq immediately after the Persian Gulf War. The ARS200 was built for use in the US-Russia Bilateral Chemical Weapons Treaty (the primary users for this system are the US Onsite Inspection Agency (OSIA) and their Russian counterparts). The ARS300 was built with the requirements of the Organization for the Prohibition of Chemical Weapons (OPCW) in mind. Each successive system is an improved version of the previous system based on learning the weaknesses of each and, coincidentally, on the fact that more time was available to do a requirements analysis and the necessary engineering development. The ARS300 is at a level of development that warrants transferring the technology to a commercial vendor. Since LANL will supply the computer software to the selected vendor, it is possible for LANL to continue to improve the decision algorithms, add features where necessary, and adjust the user interface before the final transfer occurs. This paper describes the current system, ARS system enhancements, and software enhancements. Appendices contain the Operations Manual (software Version 3.01), and two earlier reports on enhancements.

  11. A discriminant bispectrum feature for surface electromyogram signal classification.

    PubMed

    Chen, Xinpu; Zhu, Xiangyang; Zhang, Dingguo

    2010-03-01

    This paper presents a discriminant bispectrum (DBS) feature extraction approach to surface electromyogram (sEMG) signal classification for prosthetic control. The proposed feature extraction method involves two steps: (1) the bispectrum matrix integration, and (2) the Fisher linear discriminant (FLD) projection. We compare DBS with other conventional features, such as autoregressive coefficients, root mean square, power spectral distribution and time domain statistics. First, the separability of the features is investigated by the visualization of feature distribution in the FLD subspace and quantitative measurement (Davies-Boulder clustering index). Then four linear and non-linear classifiers are used to evaluate the discriminative powers of the features in terms of classification accuracy (CA). The experimental results show that DBS has better performance than other features for identifying the motion patterns of sEMG signals, and the best CA result of DBS is 99.4%. PMID:19955011

  12. Vibration signal classification by wavelet packet energy flow manifold learning

    NASA Astrophysics Data System (ADS)

    He, Qingbo

    2013-04-01

    This paper proposes a new study to explore the wavelet packet energy (WPE) flow characteristics of vibration signals by using the manifold learning technique. This study intends to discover the nonlinear manifold information from the WPE flow map of vibration signals to characterize and discriminate different classes. A new feature, called WPE manifold feature, is achieved by three main steps: first, the wavelet packet transform (WPT) is conducted to decompose multi-class signals into a library of time-frequency subspaces; second, the WPE is calculated in each subspace to produce a feature vector for each signal; and finally, low-dimensional manifold features carrying class information are extracted from the WPE library for either training or testing samples by using the manifold learning algorithm. The new feature reveals the nonlinear WPE flow structure among various redundant time-frequency subspaces. It combines the benefits of time-frequency characteristics and nonlinear information, and hence exhibits valuable properties for vibration signal classification. The effectiveness and the merits of the proposed method are confirmed by case studies on vibration analysis-based machine fault classification.

  13. Use of acoustic classification of sidescan sonar data for mapping benthic habitat in the Northern Channel Islands, California

    USGS Publications Warehouse

    Cochrane, G.R.; Lafferty, K.D.

    2002-01-01

    Highly reflective seafloor features imaged by sidescan sonar in nearshore waters off the Northern Channel Islands (California, USA) have been observed in subsequent submersible dives to be areas of thin sand coverihg bedrock. Adjacent areas of rocky seafloor, suitable as habitat for endangered species of abalone and rockfish, and encrusting organisms, cannot be differentiated from the areas of thin sand on the basis of acoustic backscatter (i.e. grey level) alone. We found second-order textural analysis of sidescan sonar data useful to differentiate the bottom types where data is not degraded by near-range distortion (caused by slant-range and ground-range corrections), and where data is not degraded by far-range signal attenuation. Hand editing based on submersible observations is necessary to completely convert the sidescan sonar image to a bottom character classification map suitable for habitat mapping. ?? 2002 Elsevier Science Ltd. All rights reserved.

  14. Use of acoustic classification of sidescan sonar data for mapping benthic habitat in the Northern Channel Islands, California

    NASA Astrophysics Data System (ADS)

    Cochrane, Guy R.; Lafferty, Kevin D.

    2002-03-01

    Highly reflective seafloor features imaged by sidescan sonar in nearshore waters off the Northern Channel Islands (California, USA) have been observed in subsequent submersible dives to be areas of thin sand covering bedrock. Adjacent areas of rocky seafloor, suitable as habitat for endangered species of abalone and rockfish, and encrusting organisms, cannot be differentiated from the areas of thin sand on the basis of acoustic backscatter (i.e. grey level) alone. We found second-order textural analysis of sidescan sonar data useful to differentiate the bottom types where data is not degraded by near-range distortion (caused by slant-range and ground-range corrections), and where data is not degraded by far-range signal attenuation. Hand editing based on submersible observations is necessary to completely convert the sidescan sonar image to a bottom character classification map suitable for habitat mapping.

  15. Ocean acoustic signal processing: A model-based approach

    SciTech Connect

    Candy, J.V. ); Sullivan, E.J. )

    1992-12-01

    A model-based approach is proposed to solve the ocean acoustic signal processing problem that is based on a state-space representation of the normal-mode propagation model. It is shown that this representation can be utilized to spatially propagate both modal (depth) and range functions given the basic parameters (wave numbers, etc.) developed from the solution of the associated boundary value problem. This model is then generalized to the stochastic case where an approximate Gauss--Markov model evolves. The Gauss--Markov representation, in principle, allows the inclusion of stochastic phenomena such as noise and modeling errors in a consistent manner. Based on this framework, investigations are made of model-based solutions to the signal enhancement, detection and related parameter estimation problems. In particular, a modal/pressure field processor is designed that allows {ital in} {ital situ} recursive estimation of the sound velocity profile. Finally, it is shown that the associated residual or so-called innovation sequence that ensues from the recursive nature of this formulation can be employed to monitor the model's fit to the data and also form the basis of a sequential detector.

  16. Acoustic emission source localization based on distance domain signal representation

    NASA Astrophysics Data System (ADS)

    Gawronski, M.; Grabowski, K.; Russek, P.; Staszewski, W. J.; Uhl, T.; Packo, P.

    2016-04-01

    Acoustic emission is a vital non-destructive testing technique and is widely used in industry for damage detection, localisation and characterization. The latter two aspects are particularly challenging, as AE data are typically noisy. What is more, elastic waves generated by an AE event, propagate through a structural path and are significantly distorted. This effect is particularly prominent for thin elastic plates. In these media the dispersion phenomenon results in severe localisation and characterization issues. Traditional Time Difference of Arrival methods for localisation techniques typically fail when signals are highly dispersive. Hence, algorithms capable of dispersion compensation are sought. This paper presents a method based on the Time - Distance Domain Transform for an accurate AE event localisation. The source localisation is found through a minimization problem. The proposed technique focuses on transforming the time signal to the distance domain response, which would be recorded at the source. Only, basic elastic material properties and plate thickness are used in the approach, avoiding arbitrary parameters tuning.

  17. Semi-real-time monitoring of cracking on couplings by neural network analysis of acoustic emission signals

    NASA Astrophysics Data System (ADS)

    Godinez-Azcuaga, Valery F.; Shu, Fong; Finlayson, Richard D.; O'Donnell, Bruce W.

    2004-07-01

    This paper presents the results obtained during the development of a semi-real-time monitoring methodology based on Neural Network Pattern Recognition of Acoustic Emission (AE) signals for early detection of cracks in couplings used in aircraft and engine drive systems. AE signals were collected in order to establish a baseline of a gear-testing fixture background noise and its variations due to rotational speed and torque. Also, simulated cracking signals immersed in background noise were collected. EDM notches were machined in the driving gear and the load on the gearbox was increased until damaged was induced. Using these data, a Neural Network Signal Classifier (NNSC) was implemented and tested. The testing showed that the NNSC was capable of correctly identifying six different classes of AE signals corresponding to different gearbox operation conditions. Also, a semi-real-time classification software was implemented. This software includes functions that allow the user to view and classify AE data from a dynamic process as they are recorded at programmable time intervals. The software is capable of monitoring periodic statistics of AE data, which can be used as an indicator of damage presence and severity in a dynamic system. The semi-real-time classification software was successfully tested in situations where a delay of 10 seconds between data acquisition and classification was achieved with a hit rate of 50 hits/second per channel on eight active AE channels.

  18. A Fibre Bragg Grating Sensor as a Receiver for Acoustic Communications Signals

    PubMed Central

    Wild, Graham; Hinckley, Steven

    2011-01-01

    A Fibre Bragg Grating (FBG) acoustic sensor is used as a receiver for acoustic communications signals. Acoustic transmissions were generated in aluminium and Carbon Fibre Composite (CFC) panels. The FBG receiver was coupled to the bottom surface opposite a piezoelectric transmitter. For the CFC, a second FBG was embedded within the layup for comparison. We show the transfer function, frequency response, and transient response of the acoustic communications channels. In addition, the FBG receiver was used to detect Phase Shift Keying (PSK) communications signals, which was shown to be the most robust method in a highly resonant communications channel. PMID:22346585

  19. Multiple signal classification for self-mixing flowmetry.

    PubMed

    Nikolić, Milan; Lim, Yah Leng; Bertling, Karl; Taimre, Thomas; Rakić, Aleksandar D

    2015-03-20

    For the first time to our knowledge, we apply the multiple signal classification (MUSIC) algorithm to signals obtained from a self-mixing flow sensor. We find that MUSIC accurately extracts the fluid velocity and exhibits a markedly better signal-to-noise ratio (SNR) than the commonly used fast Fourier transform (FFT) method. We compare the performance of the MUSIC and FFT methods for three decades of scatterer concentration and fluid velocities from 0.5 to 50 mm/s. MUSIC provided better linearity than the FFT and was able to accurately function over a wider range of algorithm parameters. MUSIC exhibited excellent linearity and SNR even at low scatterer concentration, at which the FFT's SNR decreased to impractical levels. This makes MUSIC a particularly attractive method for flow measurement systems with a low density of scatterers such as microfluidic and nanofluidic systems and blood flow in capillaries. PMID:25968500

  20. Hybrid Feature Selection for Myoelectric Signal Classification Using MICA

    NASA Astrophysics Data System (ADS)

    Naik, Ganesh R.; Kumar, Dinesh K.

    2010-03-01

    This paper presents a novel method to enhance the performance of Independent Component Analysis (ICA) of myoelectric signal by decomposing the signal into components originating from different muscles. First, we use Multi run ICA (MICA) algorithm to separate the muscle activities. Pattern classification of the separated signal is performed in the second step with a back propagation neural network. The focus of this work is to establish a simple, yet robust system that can be used to identify subtle complex hand actions and gestures for control of prosthesis and other computer assisted devices. Testing was conducted using several single shot experiments conducted with five subjects. The results indicate that the system is able to classify four different wrist actions with near 100% accuracy.

  1. Detection of Cracking Levels in Brittle Rocks by Parametric Analysis of the Acoustic Emission Signals

    NASA Astrophysics Data System (ADS)

    Moradian, Zabihallah; Einstein, Herbert H.; Ballivy, Gerard

    2016-03-01

    Determination of the cracking levels during the crack propagation is one of the key challenges in the field of fracture mechanics of rocks. Acoustic emission (AE) is a technique that has been used to detect cracks as they occur across the specimen. Parametric analysis of AE signals and correlating these parameters (e.g., hits and energy) to stress-strain plots of rocks let us detect cracking levels properly. The number of AE hits is related to the number of cracks, and the AE energy is related to magnitude of the cracking event. For a full understanding of the fracture process in brittle rocks, prismatic specimens of granite containing pre-existing flaws have been tested in uniaxial compression tests, and their cracking process was monitored with both AE and high-speed video imaging. In this paper, the characteristics of the AE parameters and the evolution of cracking sequences are analyzed for every cracking level. Based on micro- and macro-crack damage, a classification of cracking levels is introduced. This classification contains eight stages (1) crack closure, (2) linear elastic deformation, (3) micro-crack initiation (white patch initiation), (4) micro-crack growth (stable crack growth), (5) micro-crack coalescence (macro-crack initiation), (6) macro-crack growth (unstable crack growth), (7) macro-crack coalescence and (8) failure.

  2. Joint deconvolution and classification with applications to passive acoustic underwater multipath.

    PubMed

    Anderson, Hyrum S; Gupta, Maya R

    2008-11-01

    This paper addresses the problem of classifying signals that have been corrupted by noise and unknown linear time-invariant (LTI) filtering such as multipath, given labeled uncorrupted training signals. A maximum a posteriori approach to the deconvolution and classification is considered, which produces estimates of the desired signal, the unknown channel, and the class label. For cases in which only a class label is needed, the classification accuracy can be improved by not committing to an estimate of the channel or signal. A variant of the quadratic discriminant analysis (QDA) classifier is proposed that probabilistically accounts for the unknown LTI filtering, and which avoids deconvolution. The proposed QDA classifier can work either directly on the signal or on features whose transformation by LTI filtering can be analyzed; as an example a classifier for subband-power features is derived. Results on simulated data and real Bowhead whale vocalizations show that jointly considering deconvolution with classification can dramatically improve classification performance over traditional methods over a range of signal-to-noise ratios. PMID:19045785

  3. Filtering of Acoustic Signals within the Hearing Organ

    PubMed Central

    Ramamoorthy, Sripriya; Chen, Fangyi; Jacques, Steven L.; Wang, Ruikang; Choudhury, Niloy; Fridberger, Anders

    2014-01-01

    The detection of sound by the mammalian hearing organ involves a complex mechanical interplay among different cell types. The inner hair cells, which are the primary sensory receptors, are stimulated by the structural vibrations of the entire organ of Corti. The outer hair cells are thought to modulate these sound-evoked vibrations to enhance hearing sensitivity and frequency resolution, but it remains unclear whether other structures also contribute to frequency tuning. In the current study, sound-evoked vibrations were measured at the stereociliary side of inner and outer hair cells and their surrounding supporting cells, using optical coherence tomography interferometry in living anesthetized guinea pigs. Our measurements demonstrate the presence of multiple vibration modes as well as significant differences in frequency tuning and response phase among different cell types. In particular, the frequency tuning at the inner hair cells differs from other cell types, causing the locus of maximum inner hair cell activation to be shifted toward the apex of the cochlea compared with the outer hair cells. These observations show that additional processing and filtering of acoustic signals occur within the organ of Corti before inner hair cell excitation, representing a departure from established theories. PMID:24990925

  4. MASS-DEPENDENT BARYON ACOUSTIC OSCILLATION SIGNAL AND HALO BIAS

    SciTech Connect

    Wang Qiao; Zhan Hu

    2013-05-10

    We characterize the baryon acoustic oscillations (BAO) feature in halo two-point statistics using N-body simulations. We find that nonlinear damping of the BAO signal is less severe for halos in the mass range we investigate than for dark matter. The amount of damping depends weakly on the halo mass. The correlation functions show a mass-dependent drop of the halo clustering bias below roughly 90 h {sup -1} Mpc, which coincides with the scale of the BAO trough. The drop of bias is 4% for halos with mass M > 10{sup 14} h {sup -1} M{sub Sun} and reduces to roughly 2% for halos with mass M > 10{sup 13} h {sup -1} M{sub Sun }. In contrast, halo biases in simulations without BAO change more smoothly around 90 h {sup -1} Mpc. In Fourier space, the bias of M > 10{sup 14} h {sup -1} M{sub Sun} halos decreases smoothly by 11% from wavenumber k = 0.012 h Mpc{sup -1} to 0.2 h Mpc{sup -1}, whereas that of M > 10{sup 13} h {sup -1} M{sub Sun} halos decreases by less than 4% over the same range. By comparing the halo biases in pairs of otherwise identical simulations, one with and the other without BAO, we also observe a modulation of the halo bias. These results suggest that precise calibrations of the mass-dependent BAO signal and scale-dependent bias on large scales would be needed for interpreting precise measurements of the two-point statistics of clusters or massive galaxies in the future.

  5. Classification of heart valve single leg separations from acoustic clinical measurements

    SciTech Connect

    Clark, G.A.; Bowman, B.C.; Boruta, N.; Thomas, G.H.; Jones, H.E.; Buhl, M.R.

    1994-05-01

    Our system classifies the condition (intact or single leg separated) of in vivo Bjork-Shiley Convexo-Concave (BSCC) heart valves by processing acoustic measurements of clinical heart valve opening sounds. We use spectral features as inputs to a two-stage classifier, which first classifies individual heart beats, then classifies valves. Performance is measured by probability of detection and probability of false alarm, and by confidence intervals on the probability of correct classification. The novelty of the work lies in the application of advanced techniques to real heart valve data, and extensions of published algorithms that enhance their applicability. We show that even when given a very small number of training samples, the classifier can achieve a probability of correct classification of 100%.

  6. Comparative study of PCA in classification of multichannel EMG signals.

    PubMed

    Geethanjali, P

    2015-06-01

    Electromyographic (EMG) signals are abundantly used in the field of rehabilitation engineering in controlling the prosthetic device and significantly essential to find fast and accurate EMG pattern recognition system, to avoid intrusive delay. The main objective of this paper is to study the influence of Principal component analysis (PCA), a transformation technique, in pattern recognition of six hand movements using four channel surface EMG signals from ten healthy subjects. For this reason, time domain (TD) statistical as well as auto regression (AR) coefficients are extracted from the four channel EMG signals. The extracted statistical features as well as AR coefficients are transformed using PCA to 25, 50 and 75 % of corresponding original feature vector space. The classification accuracy of PCA transformed and non-PCA transformed TD statistical features as well as AR coefficients are studied with simple logistic regression (SLR), decision tree (DT) with J48 algorithm, logistic model tree (LMT), k nearest neighbor (kNN) and neural network (NN) classifiers in the identification of six different movements. The Kruskal-Wallis (KW) statistical test shows that there is a significant reduction (P < 0.05) in classification accuracy with PCA transformed features compared to non-PCA transformed features. SLR with non-PCA transformed time domain (TD) statistical features performs better in accuracy and computational power compared to other features considered in this study. In addition, the motion control of three drives for six movements of the hand is implemented with SLR using TD statistical features in off-line with TMSLF2407 digital signal controller (DSC). PMID:25860845

  7. A comparative study of the svm and k-nn machine learning algorithms for the diagnosis of respiratory pathologies using pulmonary acoustic signals

    PubMed Central

    2014-01-01

    Background Pulmonary acoustic parameters extracted from recorded respiratory sounds provide valuable information for the detection of respiratory pathologies. The automated analysis of pulmonary acoustic signals can serve as a differential diagnosis tool for medical professionals, a learning tool for medical students, and a self-management tool for patients. In this context, we intend to evaluate and compare the performance of the support vector machine (SVM) and K-nearest neighbour (K-nn) classifiers in diagnosis respiratory pathologies using respiratory sounds from R.A.L.E database. Results The pulmonary acoustic signals used in this study were obtained from the R.A.L.E lung sound database. The pulmonary acoustic signals were manually categorised into three different groups, namely normal, airway obstruction pathology, and parenchymal pathology. The mel-frequency cepstral coefficient (MFCC) features were extracted from the pre-processed pulmonary acoustic signals. The MFCC features were analysed by one-way ANOVA and then fed separately into the SVM and K-nn classifiers. The performances of the classifiers were analysed using the confusion matrix technique. The statistical analysis of the MFCC features using one-way ANOVA showed that the extracted MFCC features are significantly different (p < 0.001). The classification accuracies of the SVM and K-nn classifiers were found to be 92.19% and 98.26%, respectively. Conclusion Although the data used to train and test the classifiers are limited, the classification accuracies found are satisfactory. The K-nn classifier was better than the SVM classifier for the discrimination of pulmonary acoustic signals from pathological and normal subjects obtained from the RALE database. PMID:24970564

  8. Classification of communication signals of the little brown bat

    NASA Astrophysics Data System (ADS)

    Melendez, Karla V.; Jones, Douglas L.; Feng, Albert S.

    2005-09-01

    Little brown bats, Myotis lucifugus, are known for their ability to echolocate and utilize their echolocation system to navigate, locate, and identify prey. Their echolocation signals have been characterized in detail, but their communication signals are poorly understood despite their widespread use during the social interactions. The goal of this study was to characterize the communication signals of little brown bats. Sound recordings were made overnight on five individual bats (housed separately from a large group of captive bats) for 7 nights, using a Pettersson ultrasound detector D240x bat detector and Nagra ARES-BB digital recorder. The spectral and temporal characteristics of recorded sounds were first analyzed using BATSOUND software from Pettersson. Sounds were first classified by visual observation of calls' temporal pattern and spectral composition, and later using an automatic classification scheme based on multivariate statistical parameters in MATLAB. Human- and machine-based analysis revealed five discrete classes of bat's communication signals: downward frequency-modulated calls, constant frequency calls, broadband noise bursts, broadband chirps, and broadband click trains. Future studies will focus on analysis of calls' spectrotemporal modulations to discriminate any subclasses that may exist. [Research supported by Grant R01-DC-04998 from the National Institute for Deafness and Communication Disorders.

  9. Neural network approach to classification of infrasound signals

    NASA Astrophysics Data System (ADS)

    Lee, Dong-Chang

    >92% classification rates) using eight independent datasets; each dataset consists of three-element (each element being a characterization parameter) feature vectors. The validated networks are tested against an expert, Prof. Charles R. Wilson, who has been studying those signals for decades. From the graphical comparisons, we conclude that such networks are excellent candidate for substituting the expert. Advantages to such networks include robustness and resistance to errors and the bias of a human operator.

  10. Study of acoustic emission signals during fracture shear deformation

    NASA Astrophysics Data System (ADS)

    Ostapchuk, A. A.; Pavlov, D. V.; Markov, V. K.; Krasheninnikov, A. V.

    2016-07-01

    We study acoustic manifestations of different regimes of shear deformation of a fracture filled with a thin layer of granular material. It is established that the observed acoustic portrait is determined by the structure of the fracture at the mesolevel. Joint analysis of the activity of acoustic pulses and their spectral characteristics makes it possible to construct the pattern of internal evolutionary processes occurring in the thin layer of the interblock contact and consider the fracture deformation process as the evolution of a self-organizing system.

  11. Synergy of seismic, acoustic, and video signals in blast analysis

    SciTech Connect

    Anderson, D.P.; Stump, B.W.; Weigand, J.

    1997-09-01

    The range of mining applications from hard rock quarrying to coal exposure to mineral recovery leads to a great variety of blasting practices. A common characteristic of many of the sources is that they are detonated at or near the earth`s surface and thus can be recorded by camera or video. Although the primary interest is in the seismic waveforms that these blasts generate, the visual observations of the blasts provide important constraints that can be applied to the physical interpretation of the seismic source function. In particular, high speed images can provide information on detonation times of individuals charges, the timing and amount of mass movement during the blasting process and, in some instances, evidence of wave propagation away from the source. All of these characteristics can be valuable in interpreting the equivalent seismic source function for a set of mine explosions and quantifying the relative importance of the different processes. This paper documents work done at the Los Alamos National Laboratory and Southern Methodist University to take standard Hi-8 video of mine blasts, recover digital images from them, and combine them with ground motion records for interpretation. The steps in the data acquisition, processing, display, and interpretation are outlined. The authors conclude that the combination of video with seismic and acoustic signals can be a powerful diagnostic tool for the study of blasting techniques and seismology. A low cost system for generating similar diagnostics using consumer-grade video camera and direct-to-disk video hardware is proposed. Application is to verification of the Comprehensive Test Ban Treaty.

  12. Kernel-based machine learning techniques for infrasound signal classification

    NASA Astrophysics Data System (ADS)

    Tuma, Matthias; Igel, Christian; Mialle, Pierrick

    2014-05-01

    Infrasound monitoring is one of four remote sensing technologies continuously employed by the CTBTO Preparatory Commission. The CTBTO's infrasound network is designed to monitor the Earth for potential evidence of atmospheric or shallow underground nuclear explosions. Upon completion, it will comprise 60 infrasound array stations distributed around the globe, of which 47 were certified in January 2014. Three stages can be identified in CTBTO infrasound data processing: automated processing at the level of single array stations, automated processing at the level of the overall global network, and interactive review by human analysts. At station level, the cross correlation-based PMCC algorithm is used for initial detection of coherent wavefronts. It produces estimates for trace velocity and azimuth of incoming wavefronts, as well as other descriptive features characterizing a signal. Detected arrivals are then categorized into potentially treaty-relevant versus noise-type signals by a rule-based expert system. This corresponds to a binary classification task at the level of station processing. In addition, incoming signals may be grouped according to their travel path in the atmosphere. The present work investigates automatic classification of infrasound arrivals by kernel-based pattern recognition methods. It aims to explore the potential of state-of-the-art machine learning methods vis-a-vis the current rule-based and task-tailored expert system. To this purpose, we first address the compilation of a representative, labeled reference benchmark dataset as a prerequisite for both classifier training and evaluation. Data representation is based on features extracted by the CTBTO's PMCC algorithm. As classifiers, we employ support vector machines (SVMs) in a supervised learning setting. Different SVM kernel functions are used and adapted through different hyperparameter optimization routines. The resulting performance is compared to several baseline classifiers. All

  13. Estimation of the Tool Condition by Applying the Wavelet Transform to Acoustic Emission Signals

    SciTech Connect

    Gomez, M. P.; Piotrkowski, R.; Ruzzante, J. E.; D'Attellis, C. E.

    2007-03-21

    This work follows the search of parameters to evaluate the tool condition in machining processes. The selected sensing technique is acoustic emission and it is applied to a turning process of steel samples. The obtained signals are studied using the wavelet transformation. The tool wear level is quantified as a percentage of the final wear specified by the Standard ISO 3685. The amplitude and relevant scale obtained of acoustic emission signals could be related with the wear level.

  14. Investigation of Interference Phenomena of Broadband Acoustic Vector Signals in Shallow Water

    NASA Astrophysics Data System (ADS)

    Piao, Shengchun; Ren, Qunyan

    2010-09-01

    Although the ocean environment in shallow water is very complex, there still exists stable interference pattern for broadband low frequency sound propagation. The waveguide invariant concept is introduced to describe the broadband interference structure of the acoustic pressure field in a waveguide and now it is widely used in underwater acoustic signal processing. Acoustic vector sensor can measure the particle velocity in the ocean and provides more information for the underwater sound field. In this paper, the interference phenomena of broadband vector acoustic signals in shallow water are investigated by numerical simulation. Energy spatial-frequency distributions are shown for energy flux density vector obtained by combination of pressure and particle velocity signals and they are analyzed according to normal mode theory. Comparisons of the interference structure between the scale acoustic field and vector acoustic field also have been made. The waveguide invariant concept is extended to describe the interference structure of vector acoustic field in shallow water. A method for extraction of the waveguide invariant from interference patterns in vector acoustic field spectrograms is presented, which can be used in matched-field processing and geoacoustic inversion. It is shown that this method may have more advantages than the traditional methods which calculate the waveguide invariant using measured sound pressure in the ocean.

  15. Denoising of human speech using combined acoustic and em sensor signal processing

    SciTech Connect

    Ng, L C; Burnett, G C; Holzrichter, J F; Gable, T J

    1999-11-29

    Low Power EM radar-like sensors have made it possible to measure properties of the human speech production system in real-time, without acoustic interference. This greatly enhances the quality and quantify of information for many speech related applications. See Holzrichter, Burnett, Ng, and Lea, J. Acoustic. Soc. Am. 103 (1) 622 (1998). By using combined Glottal-EM- Sensor- and Acoustic-signals, segments of voiced, unvoiced, and no-speech can be reliably defined. Real-time Denoising filters can be constructed to remove noise from the user's corresponding speech signal.

  16. Bio-inspired UAV routing, source localization, and acoustic signature classification for persistent surveillance

    NASA Astrophysics Data System (ADS)

    Burman, Jerry; Hespanha, Joao; Madhow, Upamanyu; Pham, Tien

    2011-06-01

    A team consisting of Teledyne Scientific Company, the University of California at Santa Barbara and the Army Research Laboratory* is developing technologies in support of automated data exfiltration from heterogeneous battlefield sensor networks to enhance situational awareness for dismounts and command echelons. Unmanned aerial vehicles (UAV) provide an effective means to autonomously collect data from a sparse network of unattended ground sensors (UGSs) that cannot communicate with each other. UAVs are used to reduce the system reaction time by generating autonomous collection routes that are data-driven. Bio-inspired techniques for search provide a novel strategy to detect, capture and fuse data. A fast and accurate method has been developed to localize an event by fusing data from a sparse number of UGSs. This technique uses a bio-inspired algorithm based on chemotaxis or the motion of bacteria seeking nutrients in their environment. A unique acoustic event classification algorithm was also developed based on using swarm optimization. Additional studies addressed the problem of routing multiple UAVs, optimally placing sensors in the field and locating the source of gunfire at helicopters. A field test was conducted in November of 2009 at Camp Roberts, CA. The field test results showed that a system controlled by bio-inspired software algorithms can autonomously detect and locate the source of an acoustic event with very high accuracy and visually verify the event. In nine independent test runs of a UAV, the system autonomously located the position of an explosion nine times with an average accuracy of 3 meters. The time required to perform source localization using the UAV was on the order of a few minutes based on UAV flight times. In June 2011, additional field tests of the system will be performed and will include multiple acoustic events, optimal sensor placement based on acoustic phenomenology and the use of the International Technology Alliance (ITA

  17. System And Method For Characterizing Voiced Excitations Of Speech And Acoustic Signals, Removing Acoustic Noise From Speech, And Synthesizi

    DOEpatents

    Burnett, Greg C.; Holzrichter, John F.; Ng, Lawrence C.

    2006-04-25

    The present invention is a system and method for characterizing human (or animate) speech voiced excitation functions and acoustic signals, for removing unwanted acoustic noise which often occurs when a speaker uses a microphone in common environments, and for synthesizing personalized or modified human (or other animate) speech upon command from a controller. A low power EM sensor is used to detect the motions of windpipe tissues in the glottal region of the human speech system before, during, and after voiced speech is produced by a user. From these tissue motion measurements, a voiced excitation function can be derived. Further, the excitation function provides speech production information to enhance noise removal from human speech and it enables accurate transfer functions of speech to be obtained. Previously stored excitation and transfer functions can be used for synthesizing personalized or modified human speech. Configurations of EM sensor and acoustic microphone systems are described to enhance noise cancellation and to enable multiple articulator measurements.

  18. System and method for characterizing voiced excitations of speech and acoustic signals, removing acoustic noise from speech, and synthesizing speech

    DOEpatents

    Burnett, Greg C.; Holzrichter, John F.; Ng, Lawrence C.

    2004-03-23

    The present invention is a system and method for characterizing human (or animate) speech voiced excitation functions and acoustic signals, for removing unwanted acoustic noise which often occurs when a speaker uses a microphone in common environments, and for synthesizing personalized or modified human (or other animate) speech upon command from a controller. A low power EM sensor is used to detect the motions of windpipe tissues in the glottal region of the human speech system before, during, and after voiced speech is produced by a user. From these tissue motion measurements, a voiced excitation function can be derived. Further, the excitation function provides speech production information to enhance noise removal from human speech and it enables accurate transfer functions of speech to be obtained. Previously stored excitation and transfer functions can be used for synthesizing personalized or modified human speech. Configurations of EM sensor and acoustic microphone systems are described to enhance noise cancellation and to enable multiple articulator measurements.

  19. System and method for characterizing voiced excitations of speech and acoustic signals, removing acoustic noise from speech, and synthesizing speech

    DOEpatents

    Burnett, Greg C.; Holzrichter, John F.; Ng, Lawrence C.

    2006-02-14

    The present invention is a system and method for characterizing human (or animate) speech voiced excitation functions and acoustic signals, for removing unwanted acoustic noise which often occurs when a speaker uses a microphone in common environments, and for synthesizing personalized or modified human (or other animate) speech upon command from a controller. A low power EM sensor is used to detect the motions of windpipe tissues in the glottal region of the human speech system before, during, and after voiced speech is produced by a user. From these tissue motion measurements, a voiced excitation function can be derived. Further, the excitation function provides speech production information to enhance noise removal from human speech and it enables accurate transfer functions of speech to be obtained. Previously stored excitation and transfer functions can be used for synthesizing personalized or modified human speech. Configurations of EM sensor and acoustic microphone systems are described to enhance noise cancellation and to enable multiple articulator measurements.

  20. System and method for characterizing voiced excitations of speech and acoustic signals, removing acoustic noise from speech, and synthesizing speech

    DOEpatents

    Burnett, Greg C.; Holzrichter, John F.; Ng, Lawrence C.

    2006-08-08

    The present invention is a system and method for characterizing human (or animate) speech voiced excitation functions and acoustic signals, for removing unwanted acoustic noise which often occurs when a speaker uses a microphone in common environments, and for synthesizing personalized or modified human (or other animate) speech upon command from a controller. A low power EM sensor is used to detect the motions of windpipe tissues in the glottal region of the human speech system before, during, and after voiced speech is produced by a user. From these tissue motion measurements, a voiced excitation function can be derived. Further, the excitation function provides speech production information to enhance noise removal from human speech and it enables accurate transfer functions of speech to be obtained. Previously stored excitation and transfer functions can be used for synthesizing personalized or modified human speech. Configurations of EM sensor and acoustic microphone systems are described to enhance noise cancellation and to enable multiple articulator measurements.

  1. Limited condition dependence of male acoustic signals in the grasshopper Chorthippus biguttulus

    PubMed Central

    Franzke, Alexandra; Reinhold, Klaus

    2012-01-01

    In many animal species, male acoustic signals serve to attract a mate and therefore often play a major role for male mating success. Male body condition is likely to be correlated with male acoustic signal traits, which signal male quality and provide choosy females indirect benefits. Environmental factors such as food quantity or quality can influence male body condition and therefore possibly lead to condition-dependent changes in the attractiveness of acoustic signals. Here, we test whether stressing food plants influences acoustic signal traits of males via condition-dependent expression of these traits. We examined four male song characteristics, which are vital for mate choice in females of the grasshopper Chorthippus biguttulus. Only one of the examined acoustic traits, loudness, was significantly altered by changing body condition because of drought- and moisture-related stress of food plants. No condition dependence could be observed for syllable to pause ratio, gap duration within syllables, and onset accentuation. We suggest that food plant stress and therefore food plant quality led to shifts in loudness of male grasshopper songs via body condition changes. The other three examined acoustic traits of males do not reflect male body condition induced by food plant quality. PMID:22957192

  2. Call Transmission Efficiency in Native and Invasive Anurans: Competing Hypotheses of Divergence in Acoustic Signals

    PubMed Central

    Llusia, Diego; Gómez, Miguel; Penna, Mario; Márquez, Rafael

    2013-01-01

    Invasive species are a leading cause of the current biodiversity decline, and hence examining the major traits favouring invasion is a key and long-standing goal of invasion biology. Despite the prominent role of the advertisement calls in sexual selection and reproduction, very little attention has been paid to the features of acoustic communication of invasive species in nonindigenous habitats and their potential impacts on native species. Here we compare for the first time the transmission efficiency of the advertisement calls of native and invasive species, searching for competitive advantages for acoustic communication and reproduction of introduced taxa, and providing insights into competing hypotheses in evolutionary divergence of acoustic signals: acoustic adaptation vs. morphological constraints. Using sound propagation experiments, we measured the attenuation rates of pure tones (0.2–5 kHz) and playback calls (Lithobates catesbeianus and Pelophylax perezi) across four distances (1, 2, 4, and 8 m) and over two substrates (water and soil) in seven Iberian localities. All factors considered (signal type, distance, substrate, and locality) affected transmission efficiency of acoustic signals, which was maximized with lower frequency sounds, shorter distances, and over water surface. Despite being broadcast in nonindigenous habitats, the advertisement calls of invasive L. catesbeianus were propagated more efficiently than those of the native species, in both aquatic and terrestrial substrates, and in most of the study sites. This implies absence of optimal relationship between native environments and propagation of acoustic signals in anurans, in contrast to what predicted by the acoustic adaptation hypothesis, and it might render these vertebrates particularly vulnerable to intrusion of invasive species producing low frequency signals, such as L. catesbeianus. Our findings suggest that mechanisms optimizing sound transmission in native habitat can play a

  3. Call transmission efficiency in native and invasive anurans: competing hypotheses of divergence in acoustic signals.

    PubMed

    Llusia, Diego; Gómez, Miguel; Penna, Mario; Márquez, Rafael

    2013-01-01

    Invasive species are a leading cause of the current biodiversity decline, and hence examining the major traits favouring invasion is a key and long-standing goal of invasion biology. Despite the prominent role of the advertisement calls in sexual selection and reproduction, very little attention has been paid to the features of acoustic communication of invasive species in nonindigenous habitats and their potential impacts on native species. Here we compare for the first time the transmission efficiency of the advertisement calls of native and invasive species, searching for competitive advantages for acoustic communication and reproduction of introduced taxa, and providing insights into competing hypotheses in evolutionary divergence of acoustic signals: acoustic adaptation vs. morphological constraints. Using sound propagation experiments, we measured the attenuation rates of pure tones (0.2-5 kHz) and playback calls (Lithobates catesbeianus and Pelophylax perezi) across four distances (1, 2, 4, and 8 m) and over two substrates (water and soil) in seven Iberian localities. All factors considered (signal type, distance, substrate, and locality) affected transmission efficiency of acoustic signals, which was maximized with lower frequency sounds, shorter distances, and over water surface. Despite being broadcast in nonindigenous habitats, the advertisement calls of invasive L. catesbeianus were propagated more efficiently than those of the native species, in both aquatic and terrestrial substrates, and in most of the study sites. This implies absence of optimal relationship between native environments and propagation of acoustic signals in anurans, in contrast to what predicted by the acoustic adaptation hypothesis, and it might render these vertebrates particularly vulnerable to intrusion of invasive species producing low frequency signals, such as L. catesbeianus. Our findings suggest that mechanisms optimizing sound transmission in native habitat can play a less

  4. A unique method to study acoustic transmission through ducts using signal synthesis and averaging of acoustic pulses

    NASA Technical Reports Server (NTRS)

    Salikuddin, M.; Ramakrishnan, R.; Ahuja, K. K.; Brown, W. H.

    1981-01-01

    An acoustic impulse technique using a loudspeaker driver is developed to measure the acoustic properties of a duct/nozzle system. A signal synthesis method is used to generate a desired single pulse with a flat spectrum. The convolution of the desired signal and the inverse Fourier transform of the reciprocal of the driver's response are then fed to the driver. A signal averaging process eliminates the jet mixing noise from the mixture of jet noise and the internal noise, thereby allowing very low intensity signals to be measured accurately, even for high velocity jets. A theoretical analysis is carried out to predict the incident sound field; this is used to help determine the number and locations of the induct measurement points to account for the contributions due to higher order modes present in the incident tube method. The impulse technique is validated by comparing experimentally determined acoustic characteristics of a duct-nozzle system with similar results obtained by the impedance tube method. Absolute agreement in the comparisons was poor, but the overall shapes of the time histories and spectral distributions were much alike.

  5. Signal recovery technique based on a physical method of underwater acoustics

    NASA Astrophysics Data System (ADS)

    Guo, Xinyi; Wu, Guoqing; Ma, Li

    2010-09-01

    In the underwater sound channel we often use an array to receive signals from distant sources. The received signals are often mixed with environmental interference. In the complex acoustic environment, received signals are distorted greatly and elongated in time. In many practical applications such as sound communications, sound remote sensing and active sonar signals, we hope to obtain the original signal's waveform. In general theory, the received signals are the convolution of emission signals and Green's function of environment. In unknown Green's function of environment, simply relying on the array to record the information to determine the sound source signal wave propagation features and the environment is not enough. However, in certain circumstances, based on a physics method of underwater acoustics, the spread of recovery technology is successful.

  6. Dolphin's echolocation signals in a complicated acoustic environment

    NASA Astrophysics Data System (ADS)

    Ivanov, M. P.

    2004-07-01

    Echolocation abilities of a dolphin ( Tursiops truncatus ponticus) were investigated in laboratory conditions. The experiment was carried out in an open cage using an acoustic control over the behavior of the animal detecting underwater objects in a complicated acoustic environment. Targets of different strength were used as test objects. The dolphin was found to be able to detect objects at distances exceeding 650 m. For the target location, the dolphin used both single-pulse and multipulse echolocation modes. Time characteristics of echolocation pulses and time sequences of pulses as functions of the distance to the target were obtained.

  7. Classification of Motor Imagery EEG Signals with Support Vector Machines and Particle Swarm Optimization.

    PubMed

    Ma, Yuliang; Ding, Xiaohui; She, Qingshan; Luo, Zhizeng; Potter, Thomas; Zhang, Yingchun

    2016-01-01

    Support vector machines are powerful tools used to solve the small sample and nonlinear classification problems, but their ultimate classification performance depends heavily upon the selection of appropriate kernel and penalty parameters. In this study, we propose using a particle swarm optimization algorithm to optimize the selection of both the kernel and penalty parameters in order to improve the classification performance of support vector machines. The performance of the optimized classifier was evaluated with motor imagery EEG signals in terms of both classification and prediction. Results show that the optimized classifier can significantly improve the classification accuracy of motor imagery EEG signals. PMID:27313656

  8. Classification of Motor Imagery EEG Signals with Support Vector Machines and Particle Swarm Optimization

    PubMed Central

    Ma, Yuliang; Ding, Xiaohui; She, Qingshan; Luo, Zhizeng; Potter, Thomas; Zhang, Yingchun

    2016-01-01

    Support vector machines are powerful tools used to solve the small sample and nonlinear classification problems, but their ultimate classification performance depends heavily upon the selection of appropriate kernel and penalty parameters. In this study, we propose using a particle swarm optimization algorithm to optimize the selection of both the kernel and penalty parameters in order to improve the classification performance of support vector machines. The performance of the optimized classifier was evaluated with motor imagery EEG signals in terms of both classification and prediction. Results show that the optimized classifier can significantly improve the classification accuracy of motor imagery EEG signals. PMID:27313656

  9. Contribution to classification of buried objects based on acoustic impedance matching.

    PubMed

    Stepanić, J; Wüstenberg, H; Krstelj, V; Mrasek, H

    2003-03-01

    Determination of material the buried objects are made of could contribute significantly to their recognition, or classification. This is important in detecting buried antipersonnel landmines within the context of humanitarian demining, as well as in a variety of other applications. In this article the concept has been formulated of the approach to buried object's material determination starting with ultrasonic impulse propagation analysis in a particular testing set configuration. The impulse propagates through a characterized transfer material in such a way that a part of it, a reflected wave, carries the information about the buried object's surface material acoustic impedance. The limit of resolution capability is theoretically analyzed and experimentally evaluated and the influencing factors described. Among these, the contact between clean surfaces of the transfer material and buried object is emphasized. PMID:12565075

  10. Netted sensors-based vehicle acoustic classification at Tier 1 nodes

    NASA Astrophysics Data System (ADS)

    Jacyna, Garry M.; Christou, Carol T.; George, Bryan; Necioglu, Burhan F.

    2005-05-01

    The MITRE Corporation has embarked on a three-year internally-funded research program in netted sensors with applications to border monitoring, situational awareness in support of combat identification, and urban warfare. The first-year effort emphasized a border monitoring application for dismounted personnel and vehicle surveillance. This paper will focus primarily on the Tier 1 acoustic-based vehicle classification component. We discuss the development and implementation of a robust linear-weighted classifier on a Mica2 Crossbow mote using feature extraction algorithms specifically developed by MITRE for mote-based processing applications. These include a block floating point Fast Fourier Transform (FFT) algorithm and an 8-band proportional bandwidth filter bank. Results of in-field testing are compared and contrasted with theoretically-derived performance bounds.

  11. An information processing method for acoustic emission signal inspired from musical staff

    NASA Astrophysics Data System (ADS)

    Zheng, Wei; Wu, Chunxian

    2016-01-01

    This study proposes a musical-staff-inspired signal processing method for standard description expressions for discrete signals and describing the integrated characteristics of acoustic emission (AE) signals. The method maps various AE signals with complex environments into the normalized musical space. Four new indexes are proposed to comprehensively describe the signal. Several key features, such as contour, amplitude, and signal changing rate, are quantitatively expressed in a normalized musical space. The processed information requires only a small storage space to maintain high fidelity. The method is illustrated by using experiments on sandstones and computed tomography (CT) scanning to determine its validity for AE signal processing.

  12. System and method for investigating sub-surface features of a rock formation with acoustic sources generating coded signals

    SciTech Connect

    Vu, Cung Khac; Nihei, Kurt; Johnson, Paul A; Guyer, Robert; Ten Cate, James A; Le Bas, Pierre-Yves; Larmat, Carene S

    2014-12-30

    A system and a method for investigating rock formations includes generating, by a first acoustic source, a first acoustic signal comprising a first plurality of pulses, each pulse including a first modulated signal at a central frequency; and generating, by a second acoustic source, a second acoustic signal comprising a second plurality of pulses. A receiver arranged within the borehole receives a detected signal including a signal being generated by a non-linear mixing process from the first-and-second acoustic signal in a non-linear mixing zone within the intersection volume. The method also includes-processing the received signal to extract the signal generated by the non-linear mixing process over noise or over signals generated by a linear interaction process, or both.

  13. Corruption of ant acoustical signals by mimetic social parasites

    PubMed Central

    Schönrogge, Karsten; Bonelli, Simona; Barbero, Francesca; Balletto, Emilio

    2010-01-01

    Recent recordings of the stridulations of Myrmica ants revealed that their queens made distinctive sounds from their workers, although the acoustics of queens and workers, respectively, were the same in different species of Myrmica. Queen recordings induced enhanced protective behavior when played to workers in the one species tested. Larvae and pupae of the butterfly genus Maculinea inhabit Myrmica colonies as social parasites, and both stages generate sounds that mimic those of a Myrmica queen, inducing similar superior treatments from workers as their model. We discuss how initial penetration and acceptance as a colony member is achieved by Maculinea through mimicking the species-specific semio-chemicals of their hosts, and how acoustical mimicry is then employed to elevate the parasite’s membership of that society towards the highest attainable level in their host’s hierarchy. We postulate that, if acoustics is as well developed a means of communication in certain ants as these studies suggest, then others among an estimated 10,000 species of ant social parasite may supplement their well-known use of chemical and tactile mimicry to trick host ants with mimicry of host acoustical systems. PMID:20585513

  14. Evidence for cultural dialects in vocal emotion expression: acoustic classification within and across five nations.

    PubMed

    Laukka, Petri; Neiberg, Daniel; Elfenbein, Hillary Anger

    2014-06-01

    The possibility of cultural differences in the fundamental acoustic patterns used to express emotion through the voice is an unanswered question central to the larger debate about the universality versus cultural specificity of emotion. This study used emotionally inflected standard-content speech segments expressing 11 emotions produced by 100 professional actors from 5 English-speaking cultures. Machine learning simulations were employed to classify expressions based on their acoustic features, using conditions where training and testing were conducted on stimuli coming from either the same or different cultures. A wide range of emotions were classified with above-chance accuracy in cross-cultural conditions, suggesting vocal expressions share important characteristics across cultures. However, classification showed an in-group advantage with higher accuracy in within- versus cross-cultural conditions. This finding demonstrates cultural differences in expressive vocal style, and supports the dialect theory of emotions according to which greater recognition of expressions from in-group members results from greater familiarity with culturally specific expressive styles. PMID:24749633

  15. Copula filtration of spoken language signals on the background of acoustic noise

    NASA Astrophysics Data System (ADS)

    Kolchenko, Lilia V.; Sinitsyn, Rustem B.

    2010-09-01

    This paper is devoted to the filtration of acoustic signals on the background of acoustic noise. Signal filtering is done with the help of a nonlinear analogue of a correlation function - a copula. The copula is estimated with the help of kernel estimates of the cumulative distribution function. At the second stage we suggest a new procedure of adaptive filtering. The silence and sound intervals are detected before the filtration with the help of nonparametric algorithm. The results are confirmed by experimental processing of spoken language signals.

  16. Forward model of thermally-induced acoustic signal specific to intralumenal detection geometry

    NASA Astrophysics Data System (ADS)

    Mukherjee, Sovanlal; Bunting, Charles F.; Piao, Daqing

    2011-03-01

    This work investigates a forward model associated with intra-lumenal detection of acoustic signal originated from transient thermal-expansion of the tissue. The work is specific to intra-lumenal thermo-acoustic tomography (TAT) which detects the contrast of tissue dielectric properties with ultrasonic resolution, but it is also extendable to intralumenal photo-acoustic tomography (PAT) which detects the contrast of light absorption properties of tissue with ultrasound resolution. Exact closed-form frequency-domain or time-domain forward model of thermally-induced acoustic signal have been studied rigorously for planar geometry and two other geometries, including cylindrical and spherical geometries both of which is specific to external-imaging, i.e. breast or brain imaging using an externally-deployed applicator. This work extends the existing studies to the specific geometry of internal or intra-lumenal imaging, i.e., prostate imaging by an endo-rectally deployed applicator. In this intra-lumenal imaging geometry, both the source that excites the transient thermal-expansion of the tissue and the acoustic transducer that acquires the thermally-induced acoustic signal are assumed enclosed by the tissue and on the surface of a long cylindrical applicator. The Green's function of the frequency-domain thermo-acoustic equation in spherical coordinates is expanded to cylindrical coordinates associated with intra-lumenal geometry. Inverse Fourier transform is then applied to obtain a time-domain solution of the thermo-acoustic pressure wave for intra-lumenal geometry. Further employment of the boundary condition to the "convex" applicator-tissue interface would render an exact forward solution toward accurate reconstruction for intra-lumenal thermally-induced acoustic imaging.

  17. Characterizing, synthesizing, and/or canceling out acoustic signals from sound sources

    DOEpatents

    Holzrichter, John F.; Ng, Lawrence C.

    2007-03-13

    A system for characterizing, synthesizing, and/or canceling out acoustic signals from inanimate and animate sound sources. Electromagnetic sensors monitor excitation sources in sound producing systems, such as animate sound sources such as the human voice, or from machines, musical instruments, and various other structures. Acoustical output from these sound producing systems is also monitored. From such information, a transfer function characterizing the sound producing system is generated. From the transfer function, acoustical output from the sound producing system may be synthesized or canceled. The systems disclosed enable accurate calculation of transfer functions relating specific excitations to specific acoustical outputs. Knowledge of such signals and functions can be used to effect various sound replication, sound source identification, and sound cancellation applications.

  18. Sources and Radiation Patterns of Volcano-Acoustic Signals Investigated with Field-Scale Chemical Explosions

    NASA Astrophysics Data System (ADS)

    Bowman, D. C.; Lees, J. M.; Taddeucci, J.; Graettinger, A. H.; Sonder, I.; Valentine, G.

    2014-12-01

    We investigate the processes that give rise to complex acoustic signals during volcanic blasts by monitoring buried chemical explosions with infrasound and audio range microphones, strong motion sensors, and high speed imagery. Acoustic waveforms vary with scaled depth of burial (SDOB, units in meters per cube root of joules), ranging from high amplitude, impulsive, gas expansion dominated signals at low SDOB to low amplitude, longer duration, ground motion dominated signals at high SDOB. Typically, the sudden upward acceleration of the substrate above the blast produces the first acoustic arrival, followed by a second pulse due to the eruption of pressurized gas at the surface. Occasionally, a third overpressure occurs when displaced material decelerates upon impact with the ground. The transition between ground motion dominated and gas release dominated acoustics ranges between 0.0038-0.0018 SDOB, respectively. For example, one explosion registering an SDOB=0.0031 produced two overpressure pulses of approximately equal amplitude, one due to ground motion, the other to gas release. Recorded volcano infrasound has also identified distinct ground motion and gas release components during explosions at Sakurajima, Santiaguito, and Karymsky volcanoes. Our results indicate that infrasound records may provide a proxy for the depth and energy of these explosions. Furthermore, while magma fragmentation models indicate the possibility of several explosions during a single vulcanian eruption (Alidibirov, Bull Volc., 1994), our results suggest that a single explosion can also produce complex acoustic signals. Thus acoustic records alone cannot be used to distinguish between single explosions and multiple closely-spaced blasts at volcanoes. Results from a series of lateral blasts during the 2014 field experiment further indicates whether vent geometry can produce directional acoustic radiation patterns like those observed at Tungarahua volcano (Kim et al., GJI, 2012). Beside

  19. Depth classification of underwater targets based on complex acoustic intensity of normal modes

    NASA Astrophysics Data System (ADS)

    Yang, Guang; Yin, Jingwei; Yu, Yun; Shi, Zhenhua

    2016-04-01

    In order to solve the problem of depth classification of the underwater target in a very low frequency acoustic field, the active component of cross spectra of particle pressure and horizontal velocity (ACCSPPHV) is adopted to distinguish the surface vessel and the underwater target. According to the effective depth of a Pekeris waveguide, the placing depth forecasting equations of passive vertical double vector hydrophones are proposed. Numerical examples show that when the sum of depths of two hydrophones is the effective depth, the sign distribution of ACCSPPHV has nothing to do with horizontal distance; in addition, the sum of the first critical surface and the second critical surface is equal to the effective depth. By setting the first critical surface less than the difference between the effective water depth and the actual water depth, that is, the second critical surface is greater than the actual depth, the three positive and negative regions of the whole ocean volume are equivalent to two positive and negative regions and therefore the depth classification of the underwater target is obtained. Besides, when the 20 m water depth is taken as the first critical surface in the simulation of underwater targets (40 Hz, 50 Hz, and 60 Hz respectively), the effectiveness of the algorithm and the correctness of relevant conclusions are verified, and the analysis of the corresponding forecasting performance is conducted.

  20. Pulse analysis of acoustic emission signals. Ph.D. Thesis

    NASA Technical Reports Server (NTRS)

    Houghton, J. R.

    1976-01-01

    A method for the signature analysis of pulses in the frequency domain and the time domain is presented. Fourier spectrum, Fourier transfer function, shock spectrum and shock spectrum ratio are examined in the frequency domain analysis, and pulse shape deconvolution is developed for use in the time domain analysis. To demonstrate the relative sensitivity of each of the methods to small changes in the pulse shape, signatures of computer modeled systems with analytical pulses are presented. Optimization techniques are developed and used to indicate the best design parameters values for deconvolution of the pulse shape. Several experiments are presented that test the pulse signature analysis methods on different acoustic emission sources. These include acoustic emissions associated with: (1) crack propagation, (2) ball dropping on a plate, (3) spark discharge and (4) defective and good ball bearings.

  1. Acoustic diagnosis of pulmonary hypertension: automated speech- recognition-inspired classification algorithm outperforms physicians.

    PubMed

    Kaddoura, Tarek; Vadlamudi, Karunakar; Kumar, Shine; Bobhate, Prashant; Guo, Long; Jain, Shreepal; Elgendi, Mohamed; Coe, James Y; Kim, Daniel; Taylor, Dylan; Tymchak, Wayne; Schuurmans, Dale; Zemp, Roger J; Adatia, Ian

    2016-01-01

    We hypothesized that an automated speech- recognition-inspired classification algorithm could differentiate between the heart sounds in subjects with and without pulmonary hypertension (PH) and outperform physicians. Heart sounds, electrocardiograms, and mean pulmonary artery pressures (mPAp) were recorded simultaneously. Heart sound recordings were digitized to train and test speech-recognition-inspired classification algorithms. We used mel-frequency cepstral coefficients to extract features from the heart sounds. Gaussian-mixture models classified the features as PH (mPAp ≥ 25 mmHg) or normal (mPAp < 25 mmHg). Physicians blinded to patient data listened to the same heart sound recordings and attempted a diagnosis. We studied 164 subjects: 86 with mPAp ≥ 25 mmHg (mPAp 41 ± 12 mmHg) and 78 with mPAp < 25 mmHg (mPAp 17 ± 5 mmHg) (p  < 0.005). The correct diagnostic rate of the automated speech-recognition-inspired algorithm was 74% compared to 56% by physicians (p = 0.005). The false positive rate for the algorithm was 34% versus 50% (p = 0.04) for clinicians. The false negative rate for the algorithm was 23% and 68% (p = 0.0002) for physicians. We developed an automated speech-recognition-inspired classification algorithm for the acoustic diagnosis of PH that outperforms physicians that could be used to screen for PH and encourage earlier specialist referral. PMID:27609672

  2. Acoustic diagnosis of pulmonary hypertension: automated speech- recognition-inspired classification algorithm outperforms physicians

    PubMed Central

    Kaddoura, Tarek; Vadlamudi, Karunakar; Kumar, Shine; Bobhate, Prashant; Guo, Long; Jain, Shreepal; Elgendi, Mohamed; Coe, James Y; Kim, Daniel; Taylor, Dylan; Tymchak, Wayne; Schuurmans, Dale; Zemp, Roger J.; Adatia, Ian

    2016-01-01

    We hypothesized that an automated speech- recognition-inspired classification algorithm could differentiate between the heart sounds in subjects with and without pulmonary hypertension (PH) and outperform physicians. Heart sounds, electrocardiograms, and mean pulmonary artery pressures (mPAp) were recorded simultaneously. Heart sound recordings were digitized to train and test speech-recognition-inspired classification algorithms. We used mel-frequency cepstral coefficients to extract features from the heart sounds. Gaussian-mixture models classified the features as PH (mPAp ≥ 25 mmHg) or normal (mPAp < 25 mmHg). Physicians blinded to patient data listened to the same heart sound recordings and attempted a diagnosis. We studied 164 subjects: 86 with mPAp ≥ 25 mmHg (mPAp 41 ± 12 mmHg) and 78 with mPAp < 25 mmHg (mPAp 17 ± 5 mmHg) (p  < 0.005). The correct diagnostic rate of the automated speech-recognition-inspired algorithm was 74% compared to 56% by physicians (p = 0.005). The false positive rate for the algorithm was 34% versus 50% (p = 0.04) for clinicians. The false negative rate for the algorithm was 23% and 68% (p = 0.0002) for physicians. We developed an automated speech-recognition-inspired classification algorithm for the acoustic diagnosis of PH that outperforms physicians that could be used to screen for PH and encourage earlier specialist referral. PMID:27609672

  3. Acoustic and ultrasonic signals as diagnostic tools for check valves

    SciTech Connect

    Auyang, M.K. )

    1993-05-01

    A typical nuclear plant has between 60 and 115 safety-related check valves ranging from 2 to 30 in. The majority of these valves control water flow. Recent studies done by the Institute of Nuclear Power Operations (INPO), Electric Power Research Institute (EPRI) and the US Nuclear Regulatory Commission (NRC) found that many of these safety-related valves were not functioning properly. Typical problems found in these valves included disk flutter, backstop tapping, flow leakage, disk pin and hinge pin wear, or even missing disks. These findings led to INPO's Significant Operating Experience Report (SOER, 1986), and finally, NRC generic letter 8904, which requires that all safety-related check valves in a nuclear plant be regularly monitored. In response to this need, the industry has developed various diagnostic equipment to monitor and test check valves, using technologies ranging from acoustics and ultrasonics to magnetic - even radiography has been considered. Of these, systems that depend on a combination of acoustic and ultrasonic techniques are among the most promising for two reasons: these two technologies supplement each other, making diagnosis of the check valves much more certain than any single technology, and this approach can be made nonintrusive. The nonintrusive feature allows the check valves to be monitored and diagnosed without being disassembled or removed from the piping system. This paper shows that by carefully studying the acoustic and ultrasonic signatures acquired from a check value, either individually or in combination, an individual with the proper training and experience in acoustic and ultrasonic signature analyses can deduce the structural integrity of the check valve with good confidence. Most of the conclusions are derived from controlled experiments in the laboratory where the diagnosis can be verified. Other conclusions were based on test data obtained in the field.

  4. On the recognition of compromise in sensing systems: rewired acoustic arrays and distorted route estimation and classification

    NASA Astrophysics Data System (ADS)

    Thornley, David J.; Damarla, Thyagaraju; Srivastava, Mani B.; Mylaraswami, Dinkar

    2009-09-01

    A group of acoustic arrays that provide direction of approach estimates also support classification of vehicles using the beams formed during that estimation. Successful simultaneous tracking and classification has demonstrated the value of such a sensing resource as a UGS installation. We now consider potential attacks on the integrity of such an installation, describing the effect of compromised acoustic arrays in the data analysis and tracking and classification results. We indicate how these can be automatically recognized, and note that calibration methods intended for deployment time can be used for recovery during operation, which opens the door to methods for recovery from the compromise without re-configuring the equipment, using abductive reasoning to discover the necessary re-processing structure. By rotating an acoustic array, the tracking stability and implied path of a tracked entity can be distorted while leaving the data and analysis from individual arrays self-consistent. Less structured modifications, such as unstructured re-ordering of microphone connections, impact the basic data analysis. We examine the effect of these classes of attack on the integrity of a set of unattended acoustic arrays, and consider the steps necessary for detection, diagnosis, and recovering an effective sensing system. Understaning these steps plays an important part in reasoning in support of balance of investment, planning, operation and post-hoc analysis.

  5. Surface Roughness Evaluation Based on Acoustic Emission Signals in Robot Assisted Polishing

    PubMed Central

    de Agustina, Beatriz; Marín, Marta María; Teti, Roberto; Rubio, Eva María

    2014-01-01

    The polishing process is the most common technology used in applications where a high level of surface quality is demanded. The automation of polishing processes is especially difficult due to the high level of skill and dexterity that is required. Much of this difficulty arises because of the lack of reliable data on the effect of the polishing parameters on the resulting surface roughness. An experimental study was developed to evaluate the surface roughness obtained during Robot Assisted Polishing processes by the analysis of acoustic emission signals in the frequency domain. The aim is to find out a trend of a feature or features calculated from the acoustic emission signals detected along the process. Such an evaluation was made with the objective of collecting valuable information for the establishment of the end point detection of polishing process. As a main conclusion, it can be affirmed that acoustic emission (AE) signals can be considered useful to monitor the polishing process state. PMID:25405509

  6. The Effect of Habitat Acoustics on Common Marmoset Vocal Signal Transmission

    PubMed Central

    MORRILL, RYAN J.; THOMAS, A. WREN; SCHIEL, NICOLA; SOUTO, ANTONIO; MILLER, CORY T.

    2013-01-01

    Noisy acoustic environments present several challenges for the evolution of acoustic communication systems. Among the most significant is the need to limit degradation of spectro-temporal signal structure in order to maintain communicative efficacy. This can be achieved by selecting for several potentially complementary processes. Selection can act on behavioral mechanisms permitting signalers to control the timing and occurrence of signal production to avoid acoustic interference. Likewise, the signal itself may be the target of selection, biasing the evolution of its structure to comprise acoustic features that avoid interference from ambient noise or degrade minimally in the habitat. Here, we address the latter topic for common marmoset (Callithrix jacchus) long-distance contact vocalizations, known as phee calls. Our aim was to test whether this vocalization is specifically adapted for transmission in a species-typical forest habitat, the Atlantic forests of northeastern Brazil. We combined seasonal analyses of ambient habitat acoustics with experiments in which pure tones, clicks, and vocalizations were broadcast and rerecorded at different distances to characterize signal degradation in the habitat. Ambient sound was analyzed from intervals throughout the day and over rainy and dry seasons, showing temporal regularities across varied timescales. Broadcast experiment results indicated that the tone and click stimuli showed the typically inverse relationship between frequency and signaling efficacy. Although marmoset phee calls degraded over distance with marked predictability compared with artificial sounds, they did not otherwise appear to be specially designed for increased transmission efficacy or minimal interference in this habitat. We discuss these data in the context of other similar studies and evidence of potential behavioral mechanisms for avoiding acoustic interference in order to maintain effective vocal communication in common marmosets. PMID

  7. Computer-aided methods of the LPI radar signal detection and classification

    NASA Astrophysics Data System (ADS)

    Grishin, Yury; Janczak, Dariusz

    2008-01-01

    The paper describes a possible structure of the LPI radar signal classification algorithm based on using a computer system with elements of the artificial intelligence (AI). Such an algorithm uses a combination of different signal processing tools such as the Wigner-Ville Distribution, the Wavelet Transform and the Cyclostationary Signal Analysis. The efficiency of these transformations with respect to different kinds of digital LPI radar signal modulation is considered. For a final classification and parameters extraction on the base of time-frequency or bifrequency representation the artificial intelligence methods can be used. One of the possible approaches to solving the radar signal classification problem is to use a proposed in the paper algorithm which consists of several steps: time-frequency or bifrequency transformations, a noise reduction procedure with using a two-dimensional filter, the RBF artificial neural network (NN) probability density function estimator which extracts the feature vector used for the final radar signal classification without an operator.

  8. System and method for investigating sub-surface features of a rock formation with acoustic sources generating conical broadcast signals

    DOEpatents

    Vu, Cung Khac; Skelt, Christopher; Nihei, Kurt; Johnson, Paul A.; Guyer, Robert; Ten Cate, James A.; Le Bas, Pierre -Yves; Larmat, Carene S.

    2015-08-18

    A method of interrogating a formation includes generating a conical acoustic signal, at a first frequency--a second conical acoustic signal at a second frequency each in the between approximately 500 Hz and 500 kHz such that the signals intersect in a desired intersection volume outside the borehole. The method further includes receiving, a difference signal returning to the borehole resulting from a non-linear mixing of the signals in a mixing zone within the intersection volume.

  9. Beeping and piping: characterization of two mechano-acoustic signals used by honey bees in swarming.

    PubMed

    Schlegel, Thomas; Visscher, P Kirk; Seeley, Thomas D

    2012-12-01

    Of the many signals used by honey bees during the process of swarming, two of them--the stop signal and the worker piping signal--are not easily distinguished for both are mechano-acoustic signals produced by scout bees who press their bodies against other bees while vibrating their wing muscles. To clarify the acoustic differences between these two signals, we recorded both signals from the same swarm and at the same time, and compared them in terms of signal duration, fundamental frequency, and frequency modulation. Stop signals and worker piping signals differ in all three variables: duration, 174 ± 64 vs. 602 ± 377 ms; fundamental frequency, 407 vs. 451 Hz; and frequency modulation, absent vs. present. While it remains unclear which differences the bees use to distinguish the two signals, it is clear that they do so for the signals have opposite effects. Stop signals cause inhibition of actively dancing scout bees whereas piping signals cause excitation of quietly resting non-scout bees. PMID:23149930

  10. Frequency Characteristics of Acoustic Emission Signals from Cementitious Waste-forms with Encapsulated Al

    SciTech Connect

    Spasova, Lyubka M.; Ojovan, Michael I.

    2007-07-01

    Acoustic emission (AE) signals were continuously recorded and their intrinsic frequency characteristics examined in order to evaluate the mechanical performance of cementitious wasteform samples with encapsulated Al waste. The primary frequency in the power spectrum and its range of intensity for the detected acoustic waves were potentially related with appearance of different micro-mechanical events caused by Al corrosion within the encapsulating cement system. In addition the process of cement matrix hardening has been shown as a source of AE signals characterized with essentially higher primary frequency (above 2 MHz) compared with those due to Al corrosion development (below 40 kHz) and cement cracking (above 100 kHz). (authors)

  11. Search for acoustic signals from high energy cascades

    NASA Technical Reports Server (NTRS)

    Bell, R.; Bowen, T.

    1985-01-01

    High energy cosmic ray secondaries can be detected by means of the cascades they produce when they pass through matter. When the charged particles of these cascades ionize the matter they are traveling through, the heat produced and resulting thermal expansion causes a thermoacoustic wave. These sound waves travel at about one hundred-thousandth the speed of light, and should allow an array of acoustic transducers to resolve structure in the cascade to about 1 cm without high speed electronics or segmentation of the detector.

  12. Research on power-law acoustic transient signal detection based on wavelet transform

    NASA Astrophysics Data System (ADS)

    Han, Jian-hui; Yang, Ri-jie; Wang, Wei

    2007-11-01

    Aiming at the characteristics of acoustic transient signal emitted from antisubmarine weapon which is being dropped into water (torpedo, aerial sonobuoy and rocket assisted depth charge etc.), such as short duration, low SNR, abruptness and instability, based on traditional power-law detector, a new method to detect acoustic transient signal is proposed. Firstly wavelet transform is used to de-noise signal, removes random spectrum components and improves SNR. Then Power- Law detector is adopted to detect transient signal. The simulation results show the method can effectively extract envelop characteristic of transient signal on the condition of low SNR. The performance of WT-Power-Law markedly outgoes that of traditional Power-Law detection method.

  13. Usage Autocorrelation Function in the Capacity of Indicator Shape of the Signal in Acoustic Emission Testing of Intricate Castings

    NASA Astrophysics Data System (ADS)

    Popkov, Artem

    2016-01-01

    The article contains information about acoustic emission signals analysing using autocorrelation function. Operation factors were analysed, such as shape of signal, the origins time and carrier frequency. The purpose of work is estimating the validity of correlations methods analysing signals. Acoustic emission signal consist of different types of waves, which propagate on different trajectories in object of control. Acoustic emission signal is amplitude-, phase- and frequency-modeling signal. It was described by carrier frequency at a given point of time. Period of signal make up 12.5 microseconds and carrier frequency make up 80 kHz for analysing signal. Usage autocorrelation function like indicator the origin time of acoustic emission signal raises validity localization of emitters.

  14. Beeping and piping: characterization of two mechano-acoustic signals used by honey bees in swarming

    NASA Astrophysics Data System (ADS)

    Schlegel, Thomas; Visscher, P. Kirk; Seeley, Thomas D.

    2012-12-01

    Of the many signals used by honey bees during the process of swarming, two of them—the stop signal and the worker piping signal—are not easily distinguished for both are mechano-acoustic signals produced by scout bees who press their bodies against other bees while vibrating their wing muscles. To clarify the acoustic differences between these two signals, we recorded both signals from the same swarm and at the same time, and compared them in terms of signal duration, fundamental frequency, and frequency modulation. Stop signals and worker piping signals differ in all three variables: duration, 174 ± 64 vs. 602 ± 377 ms; fundamental frequency, 407 vs. 451 Hz; and frequency modulation, absent vs. present. While it remains unclear which differences the bees use to distinguish the two signals, it is clear that they do so for the signals have opposite effects. Stop signals cause inhibition of actively dancing scout bees whereas piping signals cause excitation of quietly resting non-scout bees.

  15. Wayside acoustic diagnosis of defective train bearings based on signal resampling and information enhancement

    NASA Astrophysics Data System (ADS)

    He, Qingbo; Wang, Jun; Hu, Fei; Kong, Fanrang

    2013-10-01

    The diagnosis of train bearing defects plays a significant role to maintain the safety of railway transport. Among various defect detection techniques, acoustic diagnosis is capable of detecting incipient defects of a train bearing as well as being suitable for wayside monitoring. However, the wayside acoustic signal will be corrupted by the Doppler effect and surrounding heavy noise. This paper proposes a solution to overcome these two difficulties in wayside acoustic diagnosis. In the solution, a dynamically resampling method is firstly presented to reduce the Doppler effect, and then an adaptive stochastic resonance (ASR) method is proposed to enhance the defective characteristic frequency automatically by the aid of noise. The resampling method is based on a frequency variation curve extracted from the time-frequency distribution (TFD) of an acoustic signal by dynamically minimizing the local cost functions. For the ASR method, the genetic algorithm is introduced to adaptively select the optimal parameter of the multiscale noise tuning (MST)-based stochastic resonance (SR) method. The proposed wayside acoustic diagnostic scheme combines signal resampling and information enhancement, and thus is expected to be effective in wayside defective bearing detection. The experimental study verifies the effectiveness of the proposed solution.

  16. Development of an Acoustic Signal Analysis Tool “Auto-F” Based on the Temperament Scale

    NASA Astrophysics Data System (ADS)

    Modegi, Toshio

    The MIDI interface is originally designed for electronic musical instruments but we consider this music-note based coding concept can be extended for general acoustic signal description. We proposed applying the MIDI technology to coding of bio-medical auscultation sound signals such as heart sounds for retrieving medical records and performing telemedicine. Then we have tried to extend our encoding targets including vocal sounds, natural sounds and electronic bio-signals such as ECG, using Generalized Harmonic Analysis method. Currently, we are trying to separate vocal sounds included in popular songs and encode both vocal sounds and background instrumental sounds into separate MIDI channels. And also, we are trying to extract articulation parameters such as MIDI pitch-bend parameters in order to reproduce natural acoustic sounds using a GM-standard MIDI tone generator. In this paper, we present an overall algorithm of our developed acoustic signal analysis tool, based on those research works, which can analyze given time-based signals on the musical temperament scale. The prominent feature of this tool is producing high-precision MIDI codes, which reproduce the similar signals as the given source signal using a GM-standard MIDI tone generator, and also providing analyzed texts in the XML format.

  17. Acoustic Scattering by an Heterogeneous River Bed: Relationship to Bathymetry and Implications for Sediment Classification using Multibeam Echosounder Data

    NASA Astrophysics Data System (ADS)

    Buscombe, D.; Grams, P. E.; Kaplinski, M. A.

    2013-12-01

    Bed sediment classification using backscatter intensities from multibeam echosounder (MBES) systems in rivers is attractive due to its high coverage and resolution, limited costs compared to conventional sampling, and the potential combination of bathymetric and bottom sediment mapping in one instrument. Sediment classification by means of hydro-acoustic remote sensing is becoming an established discipline in oceanography. A number of techniques have been proposed, none of which has become the preferred method. In rivers, however, the field is relatively new and faces challenges not typically encountered in deep ocean settings. For example, river beds tend to have larger mean and maximum slopes than typical seabeds. Shallow water depths not only make MBES deployments more difficult, but also make the size of the beam footprint on the bed small which can lead to relatively noisy backscatter data. In particular, sediments can more heterogeneous in terms of: 1) range of particle sizes (both in a given area and over an entire mapped reach); 2) range of grain size over proximal bedform fields; 3) superimposed bedforms; and 4) abrupt sedimentological transitions over small scales. This sediment heterogeneity means grain-size usually changes along swath, which has a number of implications for existing sediment classification methods which use the distribution of backscatter intensities over all acoustic beams. We discuss these implications with reference to MBES data collected from the Colorado River in Grand Canyon, Arizona. We analyze the scale-dependence of probability density functions (PDF) of measured elevations in different sedimentological settings, which reveals the appropriate spatial scale at which to apply acoustic scattering theories. We also discuss the joint PDF of elevation and backscatter over different scales as a means by which to create an adaptive gridding scheme in which each grid is scaled appropriately, in situations with rapidly changing

  18. Mode tomography using signals from the Long Range Ocean Acoustic Propagation EXperiment (LOAPEX)

    NASA Astrophysics Data System (ADS)

    Chandrayadula, Tarun K.

    Ocean acoustic tomography uses acoustic signals to infer the environmental properties of the ocean. The procedure for tomography consists of low frequency acoustic transmissions at mid-water depths to receivers located at hundreds of kilometer ranges. The arrival times of the signal at the receiver are then inverted for the sound speed of the background environment. Using this principle, experiments such as the 2004 Long Range Ocean Acoustic Propagation EXperiment have used acoustic signals recorded across Vertical Line Arrays (VLAs) to infer the Sound Speed Profile (SSP) across depth. The acoustic signals across the VLAs can be represented in terms of orthonormal basis functions called modes. The lower modes of the basis set concentrated around mid-water propagate longer distances and can be inverted for mesoscale effects such as currents and eddies. In spite of these advantages, mode tomography has received less attention. One of the important reasons for this is that internal waves in the ocean cause significant amplitude and travel time fluctuations in the modes. The amplitude and travel time fluctuations cause errors in travel time estimates. The absence of a statistical model and the lack of signal processing techniques for internal wave effects have precluded the modes from being used in tomographic inversions. This thesis estimates a statistical model for modes affected by internal waves and then uses the estimated model to design appropriate signal processing methods to obtain tomographic observables for the low modes. In order to estimate a statistical model, this thesis uses both the LOAPEX signals and also numerical simulations. The statistical model describes the amplitude and phase coherence across different frequencies for modes at different ranges. The model suggests that Matched Subspace Detectors (MSDs) based on the amplitude statistics of the modes are the optimum detectors to make travel time estimates for modes up to 250 km. The mean of the

  19. Acoustic Signal Processing for Pipe Condition Assessment (WaterRF Report 4360)

    EPA Science Inventory

    Unique to prestressed concrete cylinder pipe (PCCP), individual wire breaks create an excitation in the pipe wall that may vary in response to the remaining compression of the pipe core. This project was designed to improve acoustic signal processing for pipe condition assessment...

  20. Acoustic emission from single point machining: Part 2, Signal changes with tool wear. Revised

    SciTech Connect

    Heiple, C.R.; Carpenter, S.H.; Armentrout, D.L.; McManigle, A.P.

    1989-12-31

    Changes in acoustic emission signal characteristics with tool wear were monitored during single point machining of 4340 steel and Ti-6Al-4V heat treated to several strength levels, 606l-T6 aluminum, 304 stainless steel, 17-4PH stainless steel, 410 stainless steel, lead, and teflon. No signal characteristic changed in the same way with tool wear for all materials tested. A single change in a particular AE signal characteristic with tool wear valid for all materials probably does not exist. Nevertheless, changes in various signal characteristic with wear for a given material may be sufficient to be used to monitor tool wear.

  1. Acoustic emission from single point machining: Part 2, Signal changes with tool wear

    SciTech Connect

    Heiple, C.R.; Carpenter, S.H.; Armentrout, D.L.; McManigle, A.P.

    1989-01-01

    Changes in acoustic emission signal characteristics with tool wear were monitored during single point machining of 4340 steel and Ti-6Al-4V heat treated to several strength levels, 606l-T6 aluminum, 304 stainless steel, 17-4PH stainless steel, 410 stainless steel, lead, and teflon. No signal characteristic changed in the same way with tool wear for all materials tested. A single change in a particular AE signal characteristic with tool wear valid for all materials probably does not exist. Nevertheless, changes in various signal characteristic with wear for a given material may be sufficient to be used to monitor tool wear.

  2. A method for reducing the level of spurious signals in surface acoustic wave filters

    NASA Astrophysics Data System (ADS)

    Borodii, Iu. N.; Grankin, I. M.; Zapunnyi, A. P.; Kolomeiko, A. V.

    1986-03-01

    A method for reducing spurious signals in surface acoustic wave (SAW) filters is proposed whereby both bulk and reflected wave signals are attenuated by electrodes of special configuration providing synphase addition of the useful signal and nonsynphase addition of spurious signal components. The electrodes of the input and output converters are made with a common focus point and equal angular apertures. The shape of the electrodes of the focusing converters on anisotropic crystal surfaces is determined by the corresponding SAW group velocity curve. An implementation of the method proposed here is examined together with some test results.

  3. Infrasonic and seismic signals from earthquakes and explosions observed with Plostina seismo-acoustic array

    NASA Astrophysics Data System (ADS)

    Ghica, D.; Ionescu, C.

    2012-04-01

    Plostina seismo-acoustic array has been recently deployed by the National Institute for Earth Physics in the central part of Romania, near the Vrancea epicentral area. The array has a 2.5 km aperture and consists of 7 seismic sites (PLOR) and 7 collocated infrasound instruments (IPLOR). The array is being used to assess the importance of collocated seismic and acoustic sensors for the purposes of (1) seismic monitoring of the local and regional events, and (2) acoustic measurement, consisting of detection of the infrasound events (explosions, mine and quarry blasts, earthquakes, aircraft etc.). This paper focuses on characterization of infrasonic and seismic signals from the earthquakes and explosions (accidental and mining type). Two Vrancea earthquakes with magnitude above 5.0 were selected to this study: one occurred on 1st of May 2011 (MD = 5.3, h = 146 km), and the other one, on 4th October 2011 (MD = 5.2, h = 142 km). The infrasonic signals from the earthquakes have the appearance of the vertical component of seismic signals. Because the mechanism of the infrasonic wave formation is the coupling of seismic waves with the atmosphere, trace velocity values for such signals are compatible with the characteristics of the various seismic phases observed with PLOR array. The study evaluates and characterizes, as well, infrasound and seismic data recorded from the explosion caused by the military accident produced at Evangelos Florakis Naval Base, in Cyprus, on 11th July 2011. Additionally, seismo-acoustic signals presumed to be related to strong mine and quarry blasts were investigated. Ground truth of mine observations provides validation of this interpretation. The combined seismo-acoustic analysis uses two types of detectors for signal identification: one is the automatic detector DFX-PMCC, applied for infrasound detection and characterization, while the other one, which is used for seismic data, is based on array processing techniques (beamforming and frequency

  4. Antifade sonar employs acoustic field diversity to recover signals from multipath fading

    SciTech Connect

    Lubman, D.

    1996-04-01

    Co-located pressure and particle motion (PM) hydrophones together with four-channel diversity combiners may be used to recover signals from multipath fading. Multipath fading is important in both shallow and deep water propagation and can be an important source of signal loss. The acoustic field diversity concept arises from the notion of conservation of signal energy and the observation that in rooms at least, the total acoustic energy density is the sum of potential energy (scalar field-sound pressure) and kinetic energy (vector field-sound PM) portions. One pressure hydrophone determines acoustic potential energy density at a point. In principle, three PM sensors (displacement, velocity, or acceleration) directed along orthogonal axes describe the kinetic energy density at a point. For a single plane wave, the time-averaged potential and kinetic field energies are identical everywhere. In multipath interference, however, potential and kinetic field energies at a point are partitioned unequally, depending mainly on relative signal phases. Thus, when pressure signals are in deep fade, abundant kinetic field signal energy may be available at that location. Performance benefits require a degree of uncorrelated fading between channels. The expectation of nearly uncorrelated fading is motivated from room theory. Performance benefits for sonar limited by independent Rayleigh fading are suggested by analogy to antifade radio. Average SNR can be improved by several decibels, holding time on target is multiplied manifold, and the bit error rate for data communication is reduced substantially. {copyright} {ital 1996 American Institute of Physics.}

  5. Antifade sonar employs acoustic field diversity to recover signals from multipath fading

    NASA Astrophysics Data System (ADS)

    Lubman, David

    1996-04-01

    Co-located pressure and particle motion (PM) hydrophones together with four-channel diversity combiners may be used to recover signals from multipath fading. Multipath fading is important in both shallow and deep water propagation and can be an important source of signal loss. The acoustic field diversity concept arises from the notion of conservation of signal energy and the observation that in rooms at least, the total acoustic energy density is the sum of potential energy (scalar field-sound pressure) and kinetic energy (vector field-sound PM) portions. One pressure hydrophone determines acoustic potential energy density at a point. In principle, three PM sensors (displacement, velocity, or acceleration) directed along orthogonal axes describe the kinetic energy density at a point. For a single plane wave, the time-averaged potential and kinetic field energies are identical everywhere. In multipath interference, however, potential and kinetic field energies at a point are partitioned unequally, depending mainly on relative signal phases. Thus, when pressure signals are in deep fade, abundant kinetic field signal energy may be available at that location. Performance benefits require a degree of uncorrelated fading between channels. The expectation of nearly uncorrelated fading is motivated from room theory. Performance benefits for sonar limited by independent Rayleigh fading are suggested by analogy to antifade radio. Average SNR can be improved by several decibels, holding time on target is multiplied manifold, and the bit error rate for data communication is reduced substantially.

  6. Classifying acoustic signals into phoneme categories: average and dyslexic readers make use of complex dynamical patterns and multifractal scaling properties of the speech signal

    PubMed Central

    2015-01-01

    Several competing aetiologies of developmental dyslexia suggest that the problems with acquiring literacy skills are causally entailed by low-level auditory and/or speech perception processes. The purpose of this study is to evaluate the diverging claims about the specific deficient peceptual processes under conditions of strong inference. Theoretically relevant acoustic features were extracted from a set of artificial speech stimuli that lie on a /bAk/-/dAk/ continuum. The features were tested on their ability to enable a simple classifier (Quadratic Discriminant Analysis) to reproduce the observed classification performance of average and dyslexic readers in a speech perception experiment. The ‘classical’ features examined were based on component process accounts of developmental dyslexia such as the supposed deficit in Envelope Rise Time detection and the deficit in the detection of rapid changes in the distribution of energy in the frequency spectrum (formant transitions). Studies examining these temporal processing deficit hypotheses do not employ measures that quantify the temporal dynamics of stimuli. It is shown that measures based on quantification of the dynamics of complex, interaction-dominant systems (Recurrence Quantification Analysis and the multifractal spectrum) enable QDA to classify the stimuli almost identically as observed in dyslexic and average reading participants. It seems unlikely that participants used any of the features that are traditionally associated with accounts of (impaired) speech perception. The nature of the variables quantifying the temporal dynamics of the speech stimuli imply that the classification of speech stimuli cannot be regarded as a linear aggregate of component processes that each parse the acoustic signal independent of one another, as is assumed by the ‘classical’ aetiologies of developmental dyslexia. It is suggested that the results imply that the differences in speech perception performance between

  7. Classifying acoustic signals into phoneme categories: average and dyslexic readers make use of complex dynamical patterns and multifractal scaling properties of the speech signal.

    PubMed

    Hasselman, Fred

    2015-01-01

    Several competing aetiologies of developmental dyslexia suggest that the problems with acquiring literacy skills are causally entailed by low-level auditory and/or speech perception processes. The purpose of this study is to evaluate the diverging claims about the specific deficient peceptual processes under conditions of strong inference. Theoretically relevant acoustic features were extracted from a set of artificial speech stimuli that lie on a /bAk/-/dAk/ continuum. The features were tested on their ability to enable a simple classifier (Quadratic Discriminant Analysis) to reproduce the observed classification performance of average and dyslexic readers in a speech perception experiment. The 'classical' features examined were based on component process accounts of developmental dyslexia such as the supposed deficit in Envelope Rise Time detection and the deficit in the detection of rapid changes in the distribution of energy in the frequency spectrum (formant transitions). Studies examining these temporal processing deficit hypotheses do not employ measures that quantify the temporal dynamics of stimuli. It is shown that measures based on quantification of the dynamics of complex, interaction-dominant systems (Recurrence Quantification Analysis and the multifractal spectrum) enable QDA to classify the stimuli almost identically as observed in dyslexic and average reading participants. It seems unlikely that participants used any of the features that are traditionally associated with accounts of (impaired) speech perception. The nature of the variables quantifying the temporal dynamics of the speech stimuli imply that the classification of speech stimuli cannot be regarded as a linear aggregate of component processes that each parse the acoustic signal independent of one another, as is assumed by the 'classical' aetiologies of developmental dyslexia. It is suggested that the results imply that the differences in speech perception performance between average and

  8. Seismic and Acoustic Array Monitoring of Signal from Tungurahua Volcano, Ecuador

    NASA Astrophysics Data System (ADS)

    Terbush, B. R.; Anthony, R. E.; Johnson, J. B.; Ruiz, M. C.

    2012-12-01

    Tungurahua Volcano is an active stratovolcano located in Ecuador's eastern Cordillera. Since its most recent cycle of eruptive activity, beginning in 1999, it has produced both strombolian-to-vulcanian eruptions, and regular vapor emissions. Tungurahua is located above the city of Baños, so volcanic activity is well-monitored by Ecuador's Instituto Geofisico Nacional with a seismic and infrasound network, and other surveillance tools. Toward better understanding of the complex seismic and acoustic signals associated with low-level Tungurahua activity, and which are often low in signal-to-noise, we deployed temporary seismo-acoustic arrays between June 9th and 20th in 2012. This deployment was part of a Field Volcano Geophysics class, a collaboration between New Mexico Institute of Mining and Technology and the Escuela Politecnica Nacional's Instituto Geofísico in Ecuador. Two six-element arrays were deployed on the flank of the volcano. A seismo-acoustic array, which consisted of combined broadband seismic and infrasound sensors, possessed 100-meter spacing, and was deployed five kilometers north of the vent in an open field at 2700 m. The second array had only acoustic sensors with 30-meter spacing, and was deployed approximately six kilometers northwest of the vent, on an old pyroclastic flow deposit. The arrays picked up signals from four distinct explosion events, a number of diverse tremor signals, local volcano tectonic and long period earthquakes, and a regional tectonic event of magnitude 4.9. Coherency of both seismic and acoustic array data was quantified using Fisher Statistics, which was effective for identifying myriad signals. For most signals Fisher Statistics were particularly high in low frequency bands, between 0.5 and 2 Hz. Array analyses helped to filter out noise induced by cultural sources and livestock signals, which were particularly pronounced in the deployment site. Volcan Tungurahua sources were considered plane wave signals and could

  9. Acoustic tweezers for studying intracellular calcium signaling in SKBR-3 human breast cancer cells

    PubMed Central

    Hwang, Jae Youn; Yoon, Chi Woo; Lim, Hae Gyun; Park, Jin Man; Yoon, Sangpil; Lee, Jungwoo; Shung, K. Kirk

    2016-01-01

    Extracellular matrix proteins such as fibronectin (FNT) play crucial roles in cell proliferation, adhesion, and migration. For better understanding of these associated cellular activities, various microscopic manipulation tools have been used to study their intracellular signaling pathways. Recently, it has appeared that acoustic tweezers may possess similar capabilities in the study. Therefore, we here demonstrate that our newly developed acoustic tweezers with a high-frequency lithium niobate ultrasonic transducer have potentials to study intracellular calcium signaling by FNT-binding to human breast cancer cells (SKBR-3). It is found that intracellular calcium elevations in SKBR-3 cells, initially occurring on the microbead-contacted spot and then eventually spreading over the entire cell, are elicited by attaching an acoustically trapped FNT-coated microbead. Interestingly, they are suppressed by either extracellular calcium elimination or phospholipase C (PLC) inhibition. Hence, this suggests that our acoustic tweezers may serve as an alternative tool in the study of intracellular signaling by FNT-binding activities. PMID:26150401

  10. Data quality enhancement and knowledge discovery from relevant signals in acoustic emission

    NASA Astrophysics Data System (ADS)

    Mejia, Felipe; Shyu, Mei-Ling; Nanni, Antonio

    2015-10-01

    The increasing popularity of structural health monitoring has brought with it a growing need for automated data management and data analysis tools. Of great importance are filters that can systematically detect unwanted signals in acoustic emission datasets. This study presents a semi-supervised data mining scheme that detects data belonging to unfamiliar distributions. This type of outlier detection scheme is useful detecting the presence of new acoustic emission sources, given a training dataset of unwanted signals. In addition to classifying new observations (herein referred to as "outliers") within a dataset, the scheme generates a decision tree that classifies sub-clusters within the outlier context set. The obtained tree can be interpreted as a series of characterization rules for newly-observed data, and they can potentially describe the basic structure of different modes within the outlier distribution. The data mining scheme is first validated on a synthetic dataset, and an attempt is made to confirm the algorithms' ability to discriminate outlier acoustic emission sources from a controlled pencil-lead-break experiment. Finally, the scheme is applied to data from two fatigue crack-growth steel specimens, where it is shown that extracted rules can adequately describe crack-growth related acoustic emission sources while filtering out background "noise." Results show promising performance in filter generation, thereby allowing analysts to extract, characterize, and focus only on meaningful signals.

  11. Elevated stress hormone diminishes the strength of female preferences for acoustic signals in the green treefrog.

    PubMed

    Davis, A Gabriell; Leary, Christopher J

    2015-03-01

    Mate selection can be stressful; time spent searching for mates can increase predation risk and/or decrease food consumption, resulting in elevated stress hormone levels. Both high predation risk and low food availability are often associated with increased variation in mate choice by females, but it is not clear whether stress hormone levels contribute to such variation in female behavior. We examined how the stress hormone corticosterone (CORT) affects female preferences for acoustic signals in the green treefrog, Hyla cinerea. Specifically, we assessed whether CORT administration affects female preferences for call rate - an acoustic feature that is typically under directional selection via mate choice by females in most anurans and other species that communicate using acoustic signals. Using a dual speaker playback paradigm, we show that females that were administered higher doses of CORT were less likely to choose male advertisement calls broadcast at high rates. Neither CORT dose nor level was related to the latency of female phonotactic responses, suggesting that elevated CORT does not influence the motivation to mate. Results were also not related to circulating sex steroids (i.e., progesterone, androgens or estradiol) that have traditionally been the focus of studies examining the hormonal basis for variation in female mate choice. Our results thus indicate that elevated CORT levels decrease the strength of female preferences for acoustic signals. PMID:25644312

  12. Multi-scale morphology analysis of acoustic emission signal and quantitative diagnosis for bearing fault

    NASA Astrophysics Data System (ADS)

    Wang, Wen-Jing; Cui, Ling-Li; Chen, Dao-Yun

    2016-04-01

    Monitoring of potential bearing faults in operation is of critical importance to safe operation of high speed trains. One of the major challenges is how to differentiate relevant signals to operational conditions of bearings from noises emitted from the surrounding environment. In this work, we report a procedure for analyzing acoustic emission signals collected from rolling bearings for diagnosis of bearing health conditions by examining their morphological pattern spectrum (MPS) through a multi-scale morphology analysis procedure. The results show that acoustic emission signals resulted from a given type of bearing faults share rather similar MPS curves. Further examinations in terms of sample entropy and Lempel-Ziv complexity of MPS curves suggest that these two parameters can be utilized to determine damage modes.

  13. Temperature and Pressure Dependence of Signal Amplitudes for Electrostriction Laser-Induced Thermal Acoustics

    NASA Technical Reports Server (NTRS)

    Herring, Gregory C.

    2015-01-01

    The relative signal strength of electrostriction-only (no thermal grating) laser-induced thermal acoustics (LITA) in gas-phase air is reported as a function of temperature T and pressure P. Measurements were made in the free stream of a variable Mach number supersonic wind tunnel, where T and P are varied simultaneously as Mach number is varied. Using optical heterodyning, the measured signal amplitude (related to the optical reflectivity of the acoustic grating) was averaged for each of 11 flow conditions and compared to the expected theoretical dependence of a pure-electrostriction LITA process, where the signal is proportional to the square root of [P*P /( T*T*T)].

  14. A study of the connection between tidal velocities, soliton packets and acoustic signal losses

    NASA Astrophysics Data System (ADS)

    Chin-Bing, Stanley A.; Warn-Varnas, Alex C.; King, David B.; Lamb, Kevin G.; Hawkins, James A.; Teixeira, Marvi

    2002-11-01

    Coupled ocean model and acoustic model simulations of soliton packets in the Yellow Sea have indicated that the environmental conditions necessary for anomalous signal losses can occur several times in a 24 h period. These conditions and the subsequent signal losses were observed in simulations made over an 80 h space-time evolution of soliton packets that were generated by a 0.7 m/s tidal velocity [Chin-Bing et al., J. Acoust. Soc. Am. 111, 2459 (2002)]. This particular tidal velocity was used to initiate the Lamb soliton model because the soliton packets that were generated compared favorably with SAR measurements of soliton packets in the Yellow Sea. The tidal velocities in this region can range from 0.3 m/s to 1.2 m/s. In this work we extend our simulations and analyses to include soliton packets generated by other tidal velocities in the 0.3-1.2 m/s band. Anomalous signal losses are again observed. Examples will be shown that illustrate the connections between the tidal velocities, the soliton packets that are generated by these tidal velocities, and the signal losses that can occur when acoustic signals are propagated through these soliton packets. [Work supported by ONR/NRL and by a High Performance Computing DoD grant.

  15. Assessing the accuracy of acoustic seabed classification for mapping coral reef environments in South Florida (Broward County, USA).

    PubMed

    Moyer, Ryan P; Riegl, Bernhard; Banks, Kenneth; Dodge, Richard E

    2005-05-01

    The Atlantic coast of Broward County, Florida (USA) is paralleled by a series of progressively deeper, shore-parallel coral reef communities. Two of these reef systems are drowned early Holocene coral reefs of 5 ky and 7 ky uncorrected radiocarbon age. Despite the case of access to these reefs, and their major contribution to the local economy, accurate benthic habitat maps of the area are not available. Ecological studies have shown that different benthic communities (i.e. communities composed of different biological taxa) exist along several spatial gradients on all reefs. Since these studies are limited by time and spatial extent, acoustic surveys with the QTCView V bottom classification system based on a 50 kHz transducer were used as an alternative method of producing habitat maps. From the acoustic data of a 3.1 km(2) survey area, spatial prediction maps were created for the area. These were compared with habitat maps interpreted from in situ data and Laser Airborne Depth Sounder (LADS) bathymetry, in order to ground-truth the remotely sensed data. An error matrix was used to quantitatively determine the accuracy of the acoustically derived spatial prediction model against the maps derived from the in situ and LADS data sets. Confusion analysis of 100 random points showed that the system was able to distinguish areas of reef from areas of rubble and sand with an overall accuracy of 61%. When asked to detect more subtle spatial differences, for example, those between distinct reef communities, the classification was only about 40% accurate. We discuss to what degree a synthesis of acoustic and in situ techniques can provide accurate habitat maps in coral reef environments, and conclude that acoustic methods were able to reflect the spatial extent and composition of at least three different biological communities. PMID:17465157

  16. Listening to the Deep: live monitoring of ocean noise and cetacean acoustic signals.

    PubMed

    André, M; van der Schaar, M; Zaugg, S; Houégnigan, L; Sánchez, A M; Castell, J V

    2011-01-01

    The development and broad use of passive acoustic monitoring techniques have the potential to help assessing the large-scale influence of artificial noise on marine organisms and ecosystems. Deep-sea observatories have the potential to play a key role in understanding these recent acoustic changes. LIDO (Listening to the Deep Ocean Environment) is an international project that is allowing the real-time long-term monitoring of marine ambient noise as well as marine mammal sounds at cabled and standalone observatories. Here, we present the overall development of the project and the use of passive acoustic monitoring (PAM) techniques to provide the scientific community with real-time data at large spatial and temporal scales. Special attention is given to the extraction and identification of high frequency cetacean echolocation signals given the relevance of detecting target species, e.g. beaked whales, in mitigation processes, e.g. during military exercises. PMID:21665016

  17. Fatigue level estimation of monetary bills based on frequency band acoustic signals with feature selection by supervised SOM

    NASA Astrophysics Data System (ADS)

    Teranishi, Masaru; Omatu, Sigeru; Kosaka, Toshihisa

    Fatigued monetary bills adversely affect the daily operation of automated teller machines (ATMs). In order to make the classification of fatigued bills more efficient, the development of an automatic fatigued monetary bill classification method is desirable. We propose a new method by which to estimate the fatigue level of monetary bills from the feature-selected frequency band acoustic energy pattern of banking machines. By using a supervised self-organizing map (SOM), we effectively estimate the fatigue level using only the feature-selected frequency band acoustic energy pattern. Furthermore, the feature-selected frequency band acoustic energy pattern improves the estimation accuracy of the fatigue level of monetary bills by adding frequency domain information to the acoustic energy pattern. The experimental results with real monetary bill samples reveal the effectiveness of the proposed method.

  18. Ductile Deformation of Dehydrating Serpentinite Evidenced by Acoustic Signal Monitoring

    NASA Astrophysics Data System (ADS)

    Gasc, J.; Hilairet, N.; Wang, Y.; Schubnel, A. J.

    2012-12-01

    Serpentinite dehydration is believed to be responsible for triggering earthquakes at intermediate depths (i.e., 60-300 km) in subduction zones. Based on experimental results, some authors have proposed mechanisms that explain how brittle deformation can occur despite high pressure and temperature conditions [1]. However, reproducing microseismicity in the laboratory associated with the deformation of dehydrating serpentinite remains challenging. A recent study showed that, even for fast dehydration kinetics, ductile deformation could take place rather than brittle faulting in the sample [2]. This latter study was conducted in a multi-anvil apparatus without the ability to control differential stress during dehydration. We have since conducted controlled deformation experiments in the deformation-DIA (D-DIA) on natural serpentinite samples at sector 13 (GSECARS) of the APS. Monochromatic radiation was used with both a 2D MAR-CCD detector and a CCD camera to determine the stress and the strain of the sample during the deformation process [3]. In addition, an Acoustic Emission (AE) recording setup was used to monitor the microseismicity from the sample, using piezo-ceramic transducers glued on the basal truncation of the anvils. The use of six independent transducers allows locating the AEs and calculating the corresponding focal mechanisms. The samples were deformed at strain rates of 10-5-10-4 s-1 under confining pressures of 3-5 GPa. Dehydration was triggered during the deformation by heating the samples at rates ranging from 5 to 60 K/min. Before the onset of the dehydration, X-ray diffraction data showed that the serpentinite sustained ~1 GPa of stress which plummeted when dehydration occurred. Although AEs were recorded during the compression and decompression stages, no AEs ever accompanied this stress drop, suggesting ductile deformation of the samples. Hence, unlike many previous studies, no evidence for fluid embrittlement and anticrack generation was found

  19. Acoustics

    NASA Astrophysics Data System (ADS)

    The acoustics research activities of the DLR fluid-mechanics department (Forschungsbereich Stroemungsmechanik) during 1988 are surveyed and illustrated with extensive diagrams, drawings, graphs, and photographs. Particular attention is given to studies of helicopter rotor noise (high-speed impulsive noise, blade/vortex interaction noise, and main/tail-rotor interaction noise), propeller noise (temperature, angle-of-attack, and nonuniform-flow effects), noise certification, and industrial acoustics (road-vehicle flow noise and airport noise-control installations).

  20. Acoustic cardiac signals analysis: a Kalman filter–based approach

    PubMed Central

    Salleh, Sheik Hussain; Hussain, Hadrina Sheik; Swee, Tan Tian; Ting, Chee-Ming; Noor, Alias Mohd; Pipatsart, Surasak; Ali, Jalil; Yupapin, Preecha P

    2012-01-01

    Auscultation of the heart is accompanied by both electrical activity and sound. Heart auscultation provides clues to diagnose many cardiac abnormalities. Unfortunately, detection of relevant symptoms and diagnosis based on heart sound through a stethoscope is difficult. The reason GPs find this difficult is that the heart sounds are of short duration and separated from one another by less than 30 ms. In addition, the cost of false positives constitutes wasted time and emotional anxiety for both patient and GP. Many heart diseases cause changes in heart sound, waveform, and additional murmurs before other signs and symptoms appear. Heart-sound auscultation is the primary test conducted by GPs. These sounds are generated primarily by turbulent flow of blood in the heart. Analysis of heart sounds requires a quiet environment with minimum ambient noise. In order to address such issues, the technique of denoising and estimating the biomedical heart signal is proposed in this investigation. Normally, the performance of the filter naturally depends on prior information related to the statistical properties of the signal and the background noise. This paper proposes Kalman filtering for denoising statistical heart sound. The cycles of heart sounds are certain to follow first-order Gauss–Markov process. These cycles are observed with additional noise for the given measurement. The model is formulated into state-space form to enable use of a Kalman filter to estimate the clean cycles of heart sounds. The estimates obtained by Kalman filtering are optimal in mean squared sense. PMID:22745550

  1. Recognition of forearm muscle activity by continuous classification of multi-site mechanomyogram signals.

    PubMed

    Alves, Natasha; Chau, Tom

    2010-01-01

    Recent studies on identifying multiple activation states from mechanomyogram (MMG) signals for the purpose of controlling switching interfaces have employed pattern recognition methods where MMG signal features from multiple muscle sites are extracted and classified. The purpose of this study is to determine if MMG signal features retain enough discriminatory information to allow reliable continuous classification, and to determine if there is a decline in classification accuracy over short time periods. MMG signals were recorded from two accelerometers attached to the flexor carpi radialis and extensor carpi radialis muscles of 12 able-bodied participants as participants performed three classes of forearm muscle activity. The data were collected over five recording sessions, with a ten-minute interval between each session. The data were spliced into 256 ms epochs, and a comprehensive set of signal features was extracted. A pattern classifier, trained with continuously acquired signal features from the first recording session, was tested with signals recorded from the other sessions. The average classification accuracy over the five sessions was 89 ± 2%. There was no obvious declining trend in classification accuracy with time. These results show that MMG signals recorded at the forearm retain enough discriminatory information to allow continuous recognition of hand motion across multiple (>90) repetitions, and the MMG-classifier does not show short-term degradation. These results indicate the potential of MMG as a multifunction control signal for muscle-machine interfaces. PMID:21097038

  2. Extraction of fault component from abnormal sound in diesel engines using acoustic signals

    NASA Astrophysics Data System (ADS)

    Dayong, Ning; Changle, Sun; Yongjun, Gong; Zengmeng, Zhang; Jiaoyi, Hou

    2016-06-01

    In this paper a method for extracting fault components from abnormal acoustic signals and automatically diagnosing diesel engine faults is presented. The method named dislocation superimposed method (DSM) is based on the improved random decrement technique (IRDT), differential function (DF) and correlation analysis (CA). The aim of DSM is to linearly superpose multiple segments of abnormal acoustic signals because of the waveform similarity of faulty components. The method uses sample points at the beginning of time when abnormal sound appears as the starting position for each segment. In this study, the abnormal sound belonged to shocking faulty type; thus, the starting position searching method based on gradient variance was adopted. The coefficient of similar degree between two same sized signals is presented. By comparing with a similar degree, the extracted fault component could be judged automatically. The results show that this method is capable of accurately extracting the fault component from abnormal acoustic signals induced by faulty shocking type and the extracted component can be used to identify the fault type.

  3. Signal characteristics of an underwater explosive acoustic telemetry system

    SciTech Connect

    Calloway, T.M.

    1984-07-01

    Pressure pulses from small (<1 gm) explosive charges detonated between depths of 150 and 1200 m and detected by hydrophones submerged at a depth of 45 m are analyzed. Experimental data on peak pressures, time constants, and shock-wave/bubble-pulse intervals are summarized. The mass of each explosive is converted to its TNT energy-equivalent mass, which is used in fitting semiempirical scaling laws to the data. Equations are obtained for predicting the characteristics of the signal, given the range and depth of the explosive together with its TNT energy-equivalent mass. The parameter values that provide the best fit of the scaling laws to the experimental data are compared with those values applicable to larger explosives (>50 gm) detonated within 150 m of the surface. 20 references, 7 figures, 8 tables.

  4. Near- Source, Seismo-Acoustic Signals Accompanying a NASCAR Race at the Texas Motor Speedway

    NASA Astrophysics Data System (ADS)

    Stump, B. W.; Hayward, C.; Underwood, R.; Howard, J. E.; MacPhail, M. D.; Golden, P.; Endress, A.

    2014-12-01

    Near-source, seismo-acoustic observations provide a unique opportunity to characterize urban sources, remotely sense human activities including vehicular traffic and monitor large engineering structures. Energy separately coupled into the solid earth and atmosphere provides constraints on not only the location of these sources but also the physics of the generating process. Conditions and distances at which these observations can be made are dependent upon not only local geological conditions but also atmospheric conditions at the time of the observations. In order to address this range of topics, an empirical, seismo-acoustic study was undertaken in and around the Texas Motor Speedway in the Dallas-Ft. Worth area during the first week of April 2014 at which time a range of activities associated with a series of NASCAR races occurred. Nine, seismic sensors were deployed around the 1.5-mile track for purposes of documenting the direct-coupled seismic energy from the passage of the cars and other vehicles on the track. Six infrasound sensors were deployed on a rooftop in a rectangular array configuration designed to provide high frequency beam forming for acoustic signals. Finally, a five-element infrasound array was deployed outside the track in order to characterize how the signals propagate away from the sources in the near-source region. Signals recovered from within the track were able to track and characterize the motion of a variety of vehicles during the race weekend including individual racecars. Seismic data sampled at 1000 sps documented strong Doppler effects as the cars approached and moved away from individual sensors. There were faint seismic signals that arrived at seismic velocity but local acoustic to seismic coupling as supported by the acoustic observations generated the majority of seismic signals. Actual seismic ground motions were small as demonstrated by the dominance of regional seismic signals from a magnitude 4.0 earthquake that arrived at

  5. Multivariate classification of animal communication signals: a simulation-based comparison of alternative signal processing procedures using electric fishes.

    PubMed

    Crampton, William G R; Davis, Justin K; Lovejoy, Nathan R; Pensky, Marianna

    2008-01-01

    Evolutionary studies of communication can benefit from classification procedures that allow individual animals to be assigned to groups (e.g. species) on the basis of high-dimension data representing their signals. Prior to classification, signals are usually transformed by a signal processing procedure into structural features. Applications of these signal processing procedures to animal communication have been largely restricted to the manual or semi-automated identification of landmark features from graphical representations of signals. Nonetheless, theory predicts that automated time-frequency-based digital signal processing (DSP) procedures can represent signals more efficiently (using fewer features) than can landmark procedures or frequency-based DSP - allowing more accurate classification. Moreover, DSP procedures are objective in that they require little previous knowledge of signal diversity, and are relatively free from potentially ungrounded assumptions of cross-taxon homology. Using a model data set of electric organ discharge waveforms from five sympatric species of the electric fish Gymnotus, we adopted an exhaustive simulation approach to investigate the classificatory performance of different signal processing procedures. We considered a landmark procedure, a frequency-based DSP procedure (the fast Fourier transform), and two kinds of time-frequency-based DSP procedures (a short-time Fourier transform, and several implementations of the discrete wavelet transform -DWT). The features derived from each of these signal processing procedures were then subjected to dimension reduction procedures to separate those features which permit the most effective discrimination among groups of signalers. We considered four alternative dimension reduction methods. Finally, each combination of reduced data was submitted to classification by linear discriminant analysis. Our results support theoretical predictions that time-frequency DSP procedures (especially DWT

  6. Detection/classification/quantification of chemical agents using an array of surface acoustic wave (SAW) devices

    NASA Astrophysics Data System (ADS)

    Milner, G. Martin

    2005-05-01

    ChemSentry is a portable system used to detect, identify, and quantify chemical warfare (CW) agents. Electro chemical (EC) cell sensor technology is used for blood agents and an array of surface acoustic wave (SAW) sensors is used for nerve and blister agents. The combination of the EC cell and the SAW array provides sufficient sensor information to detect, classify and quantify all CW agents of concern using smaller, lighter, lower cost units. Initial development of the SAW array and processing was a key challenge for ChemSentry requiring several years of fundamental testing of polymers and coating methods to finalize the sensor array design in 2001. Following the finalization of the SAW array, nearly three (3) years of intensive testing in both laboratory and field environments were required in order to gather sufficient data to fully understand the response characteristics. Virtually unbounded permutations of agent characteristics and environmental characteristics must be considered in order to operate against all agents and all environments of interest to the U.S. military and other potential users of ChemSentry. The resulting signal processing design matched to this extensive body of measured data (over 8,000 agent challenges and 10,000 hours of ambient data) is considered to be a significant advance in state-of-the-art for CW agent detection.

  7. Seismo-acoustic signals associated with degassing explosions recorded at Shishaldin Volcano, Alaska, 2003 2004

    NASA Astrophysics Data System (ADS)

    Petersen, Tanja; McNutt, Stephen R.

    2007-03-01

    In summer 2003, a Chaparral Model 2 microphone was deployed at Shishaldin Volcano, Aleutian Islands, Alaska. The pressure sensor was co-located with a short-period seismometer on the volcano’s north flank at a distance of 6.62 km from the active summit vent. The seismo-acoustic data exhibit a correlation between impulsive acoustic signals (1 2 Pa) and long-period (LP, 1 2 Hz) earthquakes. Since it last erupted in 1999, Shishaldin has been characterized by sustained seismicity consisting of many hundreds to two thousand LP events per day. The activity is accompanied by up to ˜200 m high discrete gas puffs exiting the small summit vent, but no significant eruptive activity has been confirmed. The acoustic waveforms possess similarity throughout the data set (July 2003 November 2004) indicating a repetitive source mechanism. The simplicity of the acoustic waveforms, the impulsive onsets with relatively short (˜10 20 s) gradually decaying codas and the waveform similarities suggest that the acoustic pulses are generated at the fluid air interface within an open-vent system. SO2 measurements have revealed a low SO2 flux, suggesting a hydrothermal system with magmatic gases leaking through. This hypothesis is supported by the steady-state nature of Shishaldin’s volcanic system since 1999. Time delays between the seismic LP and infrasound onsets were acquired from a representative day of seismo-acoustic data. A simple model was used to estimate source depths. The short seismo-acoustic delay times have revealed that the seismic and acoustic sources are co-located at a depth of 240±200 m below the crater rim. This shallow depth is confirmed by resonance of the upper portion of the open conduit, which produces standing waves with f=0.3 Hz in the acoustic waveform codas. The infrasound data has allowed us to relate Shishaldin’s LP earthquakes to degassing explosions, created by gas volume ruptures from a fluid air interface.

  8. Space-based RF signal classification using adaptive wavelet features

    SciTech Connect

    Caffrey, M.; Briles, S.

    1995-04-01

    RF signals are dispersed in frequency as they propagate through the ionosphere. For wide-band signals, this results in nonlinearly- chirped-frequency, transient signals in the VHF portion of the spectrum. This ionospheric dispersion provide a means of discriminating wide-band transients from other signals (e.g., continuous-wave carriers, burst communications, chirped-radar signals, etc.). The transient nature of these dispersed signals makes them candidates for wavelet feature selection. Rather than choosing a wavelet ad hoc, we adaptively compute an optimal mother wavelet via a neural network. Gaussian weighted, linear frequency modulate (GLFM) wavelets are linearly combined by the network to generate our application specific mother wavelet, which is optimized for its capacity to select features that discriminate between the dispersed signals and clutter (e.g., multiple continuous-wave carriers), not for its ability to represent the dispersed signal. The resulting mother wavelet is then used to extract features for a neutral network classifier. The performance of the adaptive wavelet classifier is the compared to an FFT based neural network classifier.

  9. Punch stretching process monitoring using acoustic emission signal analysis. II - Application of frequency domain deconvolution

    NASA Technical Reports Server (NTRS)

    Liang, Steven Y.; Dornfeld, David A.; Nickerson, Jackson A.

    1987-01-01

    The coloring effect on the acoustic emission signal due to the frequency response of the data acquisition/processing instrumentation may bias the interpretation of AE signal characteristics. In this paper, a frequency domain deconvolution technique, which involves the identification of the instrumentation transfer functions and multiplication of the AE signal spectrum by the inverse of these system functions, has been carried out. In this way, the change in AE signal characteristics can be better interpreted as the result of the change in only the states of the process. Punch stretching process was used as an example to demonstrate the application of the technique. Results showed that, through the deconvolution, the frequency characteristics of AE signals generated during the stretching became more distinctive and can be more effectively used as tools for process monitoring.

  10. Problems Associated with Statistical Pattern Recognition of Acoustic Emission Signals in a Compact Tension Fatigue Specimen

    NASA Technical Reports Server (NTRS)

    Hinton, Yolanda L.

    1999-01-01

    Acoustic emission (AE) data were acquired during fatigue testing of an aluminum 2024-T4 compact tension specimen using a commercially available AE system. AE signals from crack extension were identified and separated from noise spikes, signals that reflected from the specimen edges, and signals that saturated the instrumentation. A commercially available software package was used to train a statistical pattern recognition system to classify the signals. The software trained a network to recognize signals with a 91-percent accuracy when compared with the researcher's interpretation of the data. Reasons for the discrepancies are examined and it is postulated that additional preprocessing of the AE data to focus on the extensional wave mode and eliminate other effects before training the pattern recognition system will result in increased accuracy.

  11. Non-invasive estimation of static and pulsatile intracranial pressure from transcranial acoustic signals.

    PubMed

    Levinsky, Alexandra; Papyan, Surik; Weinberg, Guy; Stadheim, Trond; Eide, Per Kristian

    2016-05-01

    The aim of the present study was to examine whether a method for estimation of non-invasive ICP (nICP) from transcranial acoustic (TCA) signals mixed with head-generated sounds estimate the static and pulsatile invasive ICP (iICP). For that purpose, simultaneous iICP and mixed TCA signals were obtained from patients undergoing continuous iICP monitoring as part of clinical management. The ear probe placed in the right outer ear channel sent a TCA signal with fixed frequency (621 Hz) that was picked up by the left ear probe along with acoustic signals generated by the intracranial compartment. Based on a mathematical model of the association between mixed TCA and iICP, the static and pulsatile nICP values were determined. Total 39 patients were included in the study; the total number of observations for prediction of static and pulsatile iICP were 5789 and 6791, respectively. The results demonstrated a good agreement between iICP/nICP observations, with mean difference of 0.39 mmHg and 0.53 mmHg for static and pulsatile ICP, respectively. In summary, in this cohort of patients, mixed TCA signals estimated the static and pulsatile iICP with rather good accuracy. Further studies are required to validate whether mixed TCA signals may become useful for measurement of nICP. PMID:26997563

  12. Dual fiber Bragg gratings configuration-based fiber acoustic sensor for low-frequency signal detection

    NASA Astrophysics Data System (ADS)

    Yang, Dong; Wang, Shun; Lu, Ping; Liu, Deming

    2014-11-01

    We propose and fabricate a new type fiber acoustic sensor based on dual fiber Bragg gratings (FBGs) configuration. The acoustic sensor head is constructed by putting the sensing cells enclosed in an aluminum cylinder space built by two Cband FBGs and a titanium diaphragm of 50 um thickness. One end of each FBG is longitudinally adhered to the diaphragm by UV glue. Both of the two FBGs are employed for reflecting light. The dual FBGs play roles not only as signal transmission system but also as sensing component, and they demodulate each other's optical signal mutually during the measurement. Both of the two FBGs are pre-strained and the output optical power experiences fluctuation in a linear relationship along with a variation of axial strain and surrounding acoustic interference. So a precise approach to measure the frequency and sound pressure of the acoustic disturbance is achieved. Experiments are performed and results show that a relatively flat frequency response in a range from 200 Hz to 1 kHz with the average signal-to-noise ratio (SNR) above 21 dB is obtained. The maximum sound pressure sensitivity of 11.35mV/Pa is achieved with the Rsquared value of 0.99131 when the sound pressure in the range of 87.7-106.6dB. It has potential applications in low frequency signal detection. Owing to its direct self-demodulation method, the sensing system reveals the advantages of easy to demodulate, good temperature stability and measurement reliability. Besides, performance of the proposed sensor could be improved by optimizing the parameters of the sensor, especially the diaphragm.

  13. Circuit for echo and noise suppression of acoustic signals transmitted through a drill string

    DOEpatents

    Drumheller, D.S.; Scott, D.D.

    1993-12-28

    An electronic circuit for digitally processing analog electrical signals produced by at least one acoustic transducer is presented. In a preferred embodiment of the present invention, a novel digital time delay circuit is utilized which employs an array of First-in-First-out (FiFo) microchips. Also, a bandpass filter is used at the input to this circuit for isolating drill string noise and eliminating high frequency output. 20 figures.

  14. The Acoustic Structure and Information Content of Female Koala Vocal Signals

    PubMed Central

    Charlton, Benjamin D.

    2015-01-01

    Determining the information content of animal vocalisations can give valuable insights into the potential functions of vocal signals. The source-filter theory of vocal production allows researchers to examine the information content of mammal vocalisations by linking variation in acoustic features with variation in relevant physical characteristics of the caller. Here I used a source-filter theory approach to classify female koala vocalisations into different call-types, and determine which acoustic features have the potential to convey important information about the caller to other conspecifics. A two-step cluster analysis classified female calls into bellows, snarls and tonal rejection calls. Additional results revealed that female koala vocalisations differed in their potential to provide information about a given caller’s phenotype that may be of importance to receivers. Female snarls did not contain reliable acoustic cues to the caller’s identity and age. In contrast, female bellows and tonal rejection calls were individually distinctive, and the tonal rejection calls of older female koalas had consistently lower mean, minimum and maximum fundamental frequency. In addition, female bellows were significantly shorter in duration and had higher fundamental frequency, formant frequencies, and formant frequency spacing than male bellows. These results indicate that female koala vocalisations have the potential to signal the caller’s identity, age and sex. I go on to discuss the anatomical basis for these findings, and consider the possible functional relevance of signalling this type of information in the koala’s natural habitat. PMID:26465340

  15. The Acoustic Structure and Information Content of Female Koala Vocal Signals.

    PubMed

    Charlton, Benjamin D

    2015-01-01

    Determining the information content of animal vocalisations can give valuable insights into the potential functions of vocal signals. The source-filter theory of vocal production allows researchers to examine the information content of mammal vocalisations by linking variation in acoustic features with variation in relevant physical characteristics of the caller. Here I used a source-filter theory approach to classify female koala vocalisations into different call-types, and determine which acoustic features have the potential to convey important information about the caller to other conspecifics. A two-step cluster analysis classified female calls into bellows, snarls and tonal rejection calls. Additional results revealed that female koala vocalisations differed in their potential to provide information about a given caller's phenotype that may be of importance to receivers. Female snarls did not contain reliable acoustic cues to the caller's identity and age. In contrast, female bellows and tonal rejection calls were individually distinctive, and the tonal rejection calls of older female koalas had consistently lower mean, minimum and maximum fundamental frequency. In addition, female bellows were significantly shorter in duration and had higher fundamental frequency, formant frequencies, and formant frequency spacing than male bellows. These results indicate that female koala vocalisations have the potential to signal the caller's identity, age and sex. I go on to discuss the anatomical basis for these findings, and consider the possible functional relevance of signalling this type of information in the koala's natural habitat. PMID:26465340

  16. Moisture estimation in power transformer oil using acoustic signals and spectral kurtosis

    NASA Astrophysics Data System (ADS)

    Leite, Valéria C. M. N.; Veloso, Giscard F. C.; Borges da Silva, Luiz Eduardo; Lambert-Torres, Germano; Borges da Silva, Jonas G.; Onofre Pereira Pinto, João

    2016-03-01

    The aim of this paper is to present a new technique for estimating the contamination by moisture in power transformer insulating oil based on the spectral kurtosis analysis of the acoustic signals of partial discharges (PDs). Basically, in this approach, the spectral kurtosis of the PD acoustic signal is calculated and the correlation between its maximum value and the moisture percentage is explored to find a function that calculates the moisture percentage. The function can be easily implemented in DSP, FPGA, or any other type of embedded system for online moisture monitoring. To evaluate the proposed approach, an experiment is assembled with a piezoelectric sensor attached to a tank, which is filled with insulating oil samples contaminated by different levels of moisture. A device generating electrical discharges is submerged into the oil to simulate the occurrence of PDs. Detected acoustic signals are processed using fast kurtogram algorithm to extract spectral kurtosis values. The obtained data are used to find the fitting function that relates the water contamination to the maximum value of the spectral kurtosis. Experimental results show that the proposed method is suitable for online monitoring system of power transformers.

  17. Classification of convulsive psychogenic non-epileptic seizures using muscle transforms obtained from accelerometry signal.

    PubMed

    Kusmakar, Shitanshu; Gubbi, Jayavardhana; Yan, Bernard; O'Brien, Terence J; Palaniswami, Marimuthu

    2015-08-01

    Convulsive psychogenic non-epileptic seizure (PNES) can be characterized as events which mimics epileptic seizures but do not show any characteristic changes on electroencephalogram (EEG). Correct diagnosis requires video-electroencephalography monitoring (VEM) as the diagnosis of PNES is extremely difficult in primary health care. Recent work has demonstrated the usefulness of accelerometry signal taken during a seizure in classification of PNES. In this work, a new direction has been explored to understand the role of different muscles in PNES. This is achieved by modeling the muscle activity of ten different upper limb muscles as a resultant function of accelerometer signal. Using these models, the accelerometer signals recorded from convulsive epileptic patients were transformed into individual muscle components. Based on this, an automated algorithm for classification of convulsive PNES is proposed. The algorithm calculates four wavelet domain features based on signal power, approximate entropy, kurtosis and signal skewness. These features were then used to build a classification model using support vector machines (SVM) classifier. It was found that the transforms corresponding to anterior deltoid and brachioradialis results in good PNES classification accuracy. The algorithm showed a high sensitivity of 93.33% and an overall PNES classification accuracy of 89% with the transform corresponding to anterior deltoid. PMID:26736329

  18. Acoustic effects of the ATOC signal (75 Hz, 195 dB) on dolphins and whales

    SciTech Connect

    Au, W.W.; Nachtigall, P.E.; Pawloski, J.L.

    1997-05-01

    The Acoustic Thermometry of Ocean Climate (ATOC) program of Scripps Institution of Oceanography and the Applied Physics Laboratory, University of Washington, will broadcast a low-frequency 75-Hz phase modulated acoustic signal over ocean basins in order to study ocean temperatures on a global scale and examine the effects of global warming. One of the major concerns is the possible effect of the ATOC signal on marine life, especially on dolphins and whales. In order to address this issue, the hearing sensitivity of a false killer whale ({ital Pseudorca crassidens}) and a Risso{close_quote}s dolphin ({ital Grampus griseus}) to the ATOC sound was measured behaviorally. A staircase procedure with the signal levels being changed in 1-dB steps was used to measure the animals{close_quote} threshold to the actual ATOC coded signal. The results indicate that small odontocetes such as the {ital Pseudorca} and {ital Grampus} swimming directly above the ATOC source will not hear the signal unless they dive to a depth of approximately 400 m. A sound propagation analysis suggests that the sound-pressure level at ranges greater than 0.5 km will be less than 130 dB for depths down to about 500 m. Several species of baleen whales produce sounds much greater than 170{endash}180 dB. With the ATOC source on the axis of the deep sound channel (greater than 800 m), the ATOC signal will probably have minimal physical and physiological effects on cetaceans. {copyright} {ital 1997 Acoustical Society of America.}

  19. Classification of subsurface objects using singular values derived from signal frames

    SciTech Connect

    Chambers, David H; Paglieroni, David W

    2014-05-06

    The classification system represents a detected object with a feature vector derived from the return signals acquired by an array of N transceivers operating in multistatic mode. The classification system generates the feature vector by transforming the real-valued return signals into complex-valued spectra, using, for example, a Fast Fourier Transform. The classification system then generates a feature vector of singular values for each user-designated spectral sub-band by applying a singular value decomposition (SVD) to the N.times.N square complex-valued matrix formed from sub-band samples associated with all possible transmitter-receiver pairs. The resulting feature vector of singular values may be transformed into a feature vector of singular value likelihoods and then subjected to a multi-category linear or neural network classifier for object classification.

  20. Signal Decompostion and Diagnostic Classification of the Electromyogram Using a Novel Neural Network Technique

    PubMed Central

    Spitzer, A.R.; Hassoun, M.; Wang, C.; Bearden, F.

    1990-01-01

    Interpretation of physiologic signals to assist medical diagnosis requires human expertise. Success in automating this process has been limited. We present a three-step method for automated interpretation of the EMG. Signal decomposition and classification steps, which have not been automated using traditional computer methods, utilize neural networks. To deal with poorly described signals, a novel decomposition method, pseudoun-supervised learning, has been developed. The resulting method is considerably more robust than prior methods.

  1. Response of acoustic signals generated in water by energetic xenon ions

    NASA Astrophysics Data System (ADS)

    Miyachi, T.; Nakamura, Y.; Kuraza, G.; Fujii, M.; Nagashima, A.; Hasebe, N.; Kobayashi, M. N.; Kobayashi, S.; Miyajima, M.; Okudaira, O.; Yamashita, N.; Shibata, H.; Murakami, T.; Uchihori, Y.; Okada, N.; Tou, T.

    2006-05-01

    The acoustic signals generated by bombarding 400 MeV/n xenon ions in water were studied using an array of piezoelectric lead-zirconate-titanate elements. The observed signal was reduced to a bipolar form through Fourier analysis. The output voltage corresponded to the amount of energy deposit in water, and it tailed off beyond the range of 400 MeV/n xenon in water. This magnitude was explained qualitatively as cumulative processes. Its behavior was consistent with the calculations based on the Bethe-Bloch formula. Possible applications of this detector to radiology and heavily doped radiation detectors are described.

  2. Automated Authorship Attribution Using Advanced Signal Classification Techniques

    PubMed Central

    Ebrahimpour, Maryam; Putniņš, Tālis J.; Berryman, Matthew J.; Allison, Andrew; Ng, Brian W.-H.; Abbott, Derek

    2013-01-01

    In this paper, we develop two automated authorship attribution schemes, one based on Multiple Discriminant Analysis (MDA) and the other based on a Support Vector Machine (SVM). The classification features we exploit are based on word frequencies in the text. We adopt an approach of preprocessing each text by stripping it of all characters except a-z and space. This is in order to increase the portability of the software to different types of texts. We test the methodology on a corpus of undisputed English texts, and use leave-one-out cross validation to demonstrate classification accuracies in excess of 90%. We further test our methods on the Federalist Papers, which have a partly disputed authorship and a fair degree of scholarly consensus. And finally, we apply our methodology to the question of the authorship of the Letter to the Hebrews by comparing it against a number of original Greek texts of known authorship. These tests identify where some of the limitations lie, motivating a number of open questions for future work. An open source implementation of our methodology is freely available for use at https://github.com/matthewberryman/author-detection. PMID:23437047

  3. Automated authorship attribution using advanced signal classification techniques.

    PubMed

    Ebrahimpour, Maryam; Putniņš, Tālis J; Berryman, Matthew J; Allison, Andrew; Ng, Brian W-H; Abbott, Derek

    2013-01-01

    In this paper, we develop two automated authorship attribution schemes, one based on Multiple Discriminant Analysis (MDA) and the other based on a Support Vector Machine (SVM). The classification features we exploit are based on word frequencies in the text. We adopt an approach of preprocessing each text by stripping it of all characters except a-z and space. This is in order to increase the portability of the software to different types of texts. We test the methodology on a corpus of undisputed English texts, and use leave-one-out cross validation to demonstrate classification accuracies in excess of 90%. We further test our methods on the Federalist Papers, which have a partly disputed authorship and a fair degree of scholarly consensus. And finally, we apply our methodology to the question of the authorship of the Letter to the Hebrews by comparing it against a number of original Greek texts of known authorship. These tests identify where some of the limitations lie, motivating a number of open questions for future work. An open source implementation of our methodology is freely available for use at https://github.com/matthewberryman/author-detection. PMID:23437047

  4. Acoustics

    NASA Technical Reports Server (NTRS)

    Goodman, Jerry R.; Grosveld, Ferdinand

    2007-01-01

    The acoustics environment in space operations is important to maintain at manageable levels so that the crewperson can remain safe, functional, effective, and reasonably comfortable. High acoustic levels can produce temporary or permanent hearing loss, or cause other physiological symptoms such as auditory pain, headaches, discomfort, strain in the vocal cords, or fatigue. Noise is defined as undesirable sound. Excessive noise may result in psychological effects such as irritability, inability to concentrate, decrease in productivity, annoyance, errors in judgment, and distraction. A noisy environment can also result in the inability to sleep, or sleep well. Elevated noise levels can affect the ability to communicate, understand what is being said, hear what is going on in the environment, degrade crew performance and operations, and create habitability concerns. Superfluous noise emissions can also create the inability to hear alarms or other important auditory cues such as an equipment malfunctioning. Recent space flight experience, evaluations of the requirements in crew habitable areas, and lessons learned (Goodman 2003; Allen and Goodman 2003; Pilkinton 2003; Grosveld et al. 2003) show the importance of maintaining an acceptable acoustics environment. This is best accomplished by having a high-quality set of limits/requirements early in the program, the "designing in" of acoustics in the development of hardware and systems, and by monitoring, testing and verifying the levels to ensure that they are acceptable.

  5. Behavioral assessment of acoustic parameters relevant to signal recognition and preference in a vocal fish.

    PubMed

    McKibben, J R; Bass, A H

    1998-12-01

    Acoustic signal recognition depends on the receiver's processing of the physical attributes of a sound. This study takes advantage of the simple communication sounds produced by plainfin midshipman fish to examine effects of signal variation on call recognition and preference. Nesting male midshipman generate both long duration (> 1 min) sinusoidal-like "hums" and short duration "grunts." The hums of neighboring males often overlap, creating beat waveforms. Presentation of humlike, single tone stimuli, but not grunts or noise, elicited robust attraction (phonotaxis) by gravid females. In two-choice tests, females differentiated and chose between acoustic signals that differed in duration, frequency, amplitude, and fine temporal content. Frequency preferences were temperature dependent, in accord with the known temperature dependence of hum fundamental frequency. Concurrent hums were simulated with two-tone beat stimuli, either presented from a single speaker or produced more naturally by interference between adjacent sources. Whereas certain single-source beats reduced stimulus attractiveness, beats which resolved into unmodulated tones at their sources did not affect preference. These results demonstrate that phonotactic assessment of stimulus relevance can be applied in a teleost fish, and that multiple signal parameters can affect receiver response in a vertebrate with relatively simple communication signals. PMID:9857511

  6. Seismo-acoustic Signals Recorded at KSIAR, the Infrasound Array Installed at PS31

    NASA Astrophysics Data System (ADS)

    Kim, T. S.; Che, I. Y.; Jeon, J. S.; Chi, H. C.; Kang, I. B.

    2014-12-01

    One of International Monitoring System (IMS)'s primary seismic stations, PS31, called Korea Seismic Research Station (KSRS), was installed around Wonju, Korea in 1970s. It has been operated by US Air Force Technical Applications Center (AFTAC) for more than 40 years. KSRS is composed of 26 seismic sensors including 19 short period, 6 long period and 1 broad band seismometers. The 19 short period sensors were used to build an array with a 10-km aperture while the 6 long period sensors were used for a relatively long period array with a 40-km aperture. After KSRS was certified as an IMS station in 2006 by Comprehensive Nuclear Test Ban Treaty Organization (CTBTO), Korea Institute of Geoscience and Mineral Resources (KIGAM) which is the Korea National Data Center started to take over responsibilities on the operation and maintenance of KSRS from AFTAC. In April of 2014, KIGAM installed an infrasound array, KSIAR, on the existing four short period seismic stations of KSRS, the sites KS05, KS06, KS07 and KS16. The collocated KSIAR changed KSRS from a seismic array into a seismo-acoustic array. The aperture of KSIAR is 3.3 km. KSIAR also has a 100-m small aperture infrasound array at KS07. The infrasound data from KSIAR except that from the site KS06 is being transmitted in real time to KIGAM with VPN and internet line. An initial analysis on seismo-acoustic signals originated from local and regional distance ranges has been performed since May 2014. The analysis with the utilization of an array process called Progressive Multi-Channel Correlation (PMCC) detected seismo-acoustic signals caused by various sources including small explosions in relation to constructing local tunnels and roads. Some of them were not found in the list of automatic bulletin of KIGAM. The seismo-acoustic signals recorded by KSIAR are supplying a useful information for discriminating local and regional man-made events from natural events.

  7. Towards Automatic Classification of Exoplanet-Transit-Like Signals: A Case Study on Kepler Mission Data

    NASA Astrophysics Data System (ADS)

    Valizadegan, Hamed; Martin, Rodney; McCauliff, Sean D.; Jenkins, Jon Michael; Catanzarite, Joseph; Oza, Nikunj C.

    2015-08-01

    Building new catalogues of planetary candidates, astrophysical false alarms, and non-transiting phenomena is a challenging task that currently requires a reviewing team of astrophysicists and astronomers. These scientists need to examine more than 100 diagnostic metrics and associated graphics for each candidate exoplanet-transit-like signal to classify it into one of the three classes. Considering that the NASA Explorer Program's TESS mission and ESA's PLATO mission survey even a larger area of space, the classification of their transit-like signals is more time-consuming for human agents and a bottleneck to successfully construct the new catalogues in a timely manner. This encourages building automatic classification tools that can quickly and reliably classify the new signal data from these missions. The standard tool for building automatic classification systems is the supervised machine learning that requires a large set of highly accurate labeled examples in order to build an effective classifier. This requirement cannot be easily met for classifying transit-like signals because not only are existing labeled signals very limited, but also the current labels may not be reliable (because the labeling process is a subjective task). Our experiments with using different supervised classifiers to categorize transit-like signals verifies that the labeled signals are not rich enough to provide the classifier with enough power to generalize well beyond the observed cases (e.g. to unseen or test signals). That motivated us to utilize a new category of learning techniques, so-called semi-supervised learning, that combines the label information from the costly labeled signals, and distribution information from the cheaply available unlabeled signals in order to construct more effective classifiers. Our study on the Kepler Mission data shows that semi-supervised learning can significantly improve the result of multiple base classifiers (e.g. Support Vector Machines, Ada

  8. Acoustic alarm signalling facilitates predator protection of treehoppers by mutualist ant bodyguards

    PubMed Central

    Morales, Manuel A; Barone, Jennifer L; Henry, Charles S

    2008-01-01

    Mutualism is a net positive interaction that includes varying degrees of both costs and benefits. Because tension between the costs and benefits of mutualism can lead to evolutionary instability, identifying mechanisms that regulate investment between partners is critical to understanding the evolution and maintenance of mutualism. Recently, studies have highlighted the importance of interspecific signalling as one mechanism for regulating investment between mutualist partners. Here, we provide evidence for interspecific alarm signalling in an insect protection mutualism and we demonstrate a functional link between this acoustic signalling and efficacy of protection. The treehopper Publilia concava Say (Hemiptera: Membracidae) is an insect that provides ants with a carbohydrate-rich excretion called honeydew in return for protection from predators. Adults of this species produce distinct vibrational signals in the context of predator encounters. In laboratory trials, putative alarm signal production significantly increased following initial contact with ladybeetle predators (primarily Harmonia axyridis Pallas, Coleoptera: Coccinellidae), but not following initial contact with ants. In field trials, playback of a recorded treehopper alarm signal resulted in a significant increase in both ant activity and the probability of ladybeetle discovery by ants relative to both silence and treehopper courtship signal controls. Our results show that P. concava treehoppers produce alarm signals in response to predator threat and that this signalling can increase effectiveness of predator protection by ants. PMID:18480015

  9. Temporal patterns in the acoustic signals of beaked whales at Cross Seamount.

    PubMed

    Johnston, D W; McDonald, M; Polovina, J; Domokos, R; Wiggins, S; Hildebrand, J

    2008-04-23

    Seamounts may influence the distribution of marine mammals through a combination of increased ocean mixing, enhanced local productivity and greater prey availability. To study the effects of seamounts on the presence and acoustic behaviour of cetaceans, we deployed a high-frequency acoustic recording package on the summit of Cross Seamount during April through October 2005. The most frequently detected cetacean vocalizations were echolocation sounds similar to those produced by ziphiid and mesoplodont beaked whales together with buzz-type signals consistent with prey-capture attempts. Beaked whale signals occurred almost entirely at night throughout the six-month deployment. Measurements of prey presence with a Simrad EK-60 fisheries acoustics echo sounder indicate that Cross Seamount may enhance local productivity in near-surface waters. Concentrations of micronekton were aggregated over the seamount in near-surface waters at night, and dense concentrations of nekton were detected across the surface of the summit. Our results suggest that seamounts may provide enhanced foraging opportunities for beaked whales during the night through a combination of increased productivity, vertical migrations by micronekton and local retention of prey. Furthermore, the summit of the seamount may act as a barrier against which whales concentrate prey. PMID:18252660

  10. Incident signal power comparison for localization of concurrent multiple acoustic sources.

    PubMed

    Salvati, Daniele; Canazza, Sergio

    2014-01-01

    In this paper, a method to solve the localization of concurrent multiple acoustic sources in large open spaces is presented. The problem of the multisource localization in far-field conditions is to correctly associate the direction of arrival (DOA) estimated by a network array system to the same source. The use of systems implementing a Bayesian filter is a traditional approach to address the problem of localization in multisource acoustic scenario. However, in a real noisy open space the acoustic sources are often discontinuous with numerous short-duration events and thus the filtering methods may have difficulty to track the multiple sources. Incident signal power comparison (ISPC) is proposed to compute DOAs association. ISPC is based on identifying the incident signal power (ISP) of the sources on a microphone array using beamforming methods and comparing the ISP between different arrays using spectral distance (SD) measurement techniques. This method solves the ambiguities, due to the presence of simultaneous sources, by identifying sounds through a minimization of an error criterion on SD measures of DOA combinations. The experimental results were conducted in an outdoor real noisy environment and the ISPC performance is reported using different beamforming techniques and SD functions. PMID:24701179

  11. Incident Signal Power Comparison for Localization of Concurrent Multiple Acoustic Sources

    PubMed Central

    2014-01-01

    In this paper, a method to solve the localization of concurrent multiple acoustic sources in large open spaces is presented. The problem of the multisource localization in far-field conditions is to correctly associate the direction of arrival (DOA) estimated by a network array system to the same source. The use of systems implementing a Bayesian filter is a traditional approach to address the problem of localization in multisource acoustic scenario. However, in a real noisy open space the acoustic sources are often discontinuous with numerous short-duration events and thus the filtering methods may have difficulty to track the multiple sources. Incident signal power comparison (ISPC) is proposed to compute DOAs association. ISPC is based on identifying the incident signal power (ISP) of the sources on a microphone array using beamforming methods and comparing the ISP between different arrays using spectral distance (SD) measurement techniques. This method solves the ambiguities, due to the presence of simultaneous sources, by identifying sounds through a minimization of an error criterion on SD measures of DOA combinations. The experimental results were conducted in an outdoor real noisy environment and the ISPC performance is reported using different beamforming techniques and SD functions. PMID:24701179

  12. [Epileptic EEG signal classification based on wavelet packet transform and multivariate multiscale entropy].

    PubMed

    Xu, Yonghong; Li, Xingxing; Zhao, Yong

    2013-10-01

    In this paper, a new method combining wavelet packet transform and multivariate multiscale entropy for the classification of epilepsy EEG signals is introduced. Firstly, the original EEG signals are decomposed at multi-scales with the wavelet packet transform, and the wavelet packet coefficients of the required frequency bands are extracted. Secondly, the wavelet packet coefficients are processed with multivariate multiscale entropy algorithm. Finally, the EEG data are classified by support vector machines (SVM). The experimental results on the international public Bonn epilepsy EEG dataset show that the proposed method can efficiently extract epileptic features and the accuracy of classification result is satisfactory. PMID:24459973

  13. Effects of temporal envelope modulation on acoustic signal recognition in a vocal fish, the plainfin midshipman.

    PubMed

    McKibben, J R; Bass, A H

    2001-06-01

    Amplitude modulation is an important parameter defining vertebrate acoustic communication signals. Nesting male plainfin midshipman fish, Porichthys notatus, emit simple, long duration hums in which modulation is strikingly absent. Envelope modulation is, however, introduced when the hums of adjacent males overlap to produce acoustic beats. Hums attract gravid females and can be mimicked with continuous tones at the fundamental frequency. While individual hums have flat envelopes, other midshipman signals are amplitude modulated. This study used one-choice playback tests with gravid females to examine the role of envelope modulation in hum recognition. Various pulse train and two-tone beat stimuli resembling natural communication signals were presented individually, and the responses compared to those for continuous pure tones. The effectiveness of pulse trains was graded and depended upon both pulse duration and the ratio of pulse to gap length. Midshipman were sensitive to beat modulations from 0.5 to 10 Hz, with fewer fish approaching the beat than the pure tone. Reducing the degree of modulation increased the effectiveness of beat stimuli. Hence, the lack of modulation in the midshipman's advertisement call corresponds to the importance of envelope modulation for the categorization of communication signals even in this relatively simple system. PMID:11425135

  14. A hardware model of the auditory periphery to transduce acoustic signals into neural activity

    PubMed Central

    Tateno, Takashi; Nishikawa, Jun; Tsuchioka, Nobuyoshi; Shintaku, Hirofumi; Kawano, Satoyuki

    2013-01-01

    To improve the performance of cochlear implants, we have integrated a microdevice into a model of the auditory periphery with the goal of creating a microprocessor. We constructed an artificial peripheral auditory system using a hybrid model in which polyvinylidene difluoride was used as a piezoelectric sensor to convert mechanical stimuli into electric signals. To produce frequency selectivity, the slit on a stainless steel base plate was designed such that the local resonance frequency of the membrane over the slit reflected the transfer function. In the acoustic sensor, electric signals were generated based on the piezoelectric effect from local stress in the membrane. The electrodes on the resonating plate produced relatively large electric output signals. The signals were fed into a computer model that mimicked some functions of inner hair cells, inner hair cell–auditory nerve synapses, and auditory nerve fibers. In general, the responses of the model to pure-tone burst and complex stimuli accurately represented the discharge rates of high-spontaneous-rate auditory nerve fibers across a range of frequencies greater than 1 kHz and middle to high sound pressure levels. Thus, the model provides a tool to understand information processing in the peripheral auditory system and a basic design for connecting artificial acoustic sensors to the peripheral auditory nervous system. Finally, we discuss the need for stimulus control with an appropriate model of the auditory periphery based on auditory brainstem responses that were electrically evoked by different temporal pulse patterns with the same pulse number. PMID:24324432

  15. The effect of artificial rain on backscattered acoustic signal: first measurements

    NASA Astrophysics Data System (ADS)

    Titchenko, Yuriy; Karaev, Vladimir; Meshkov, Evgeny; Goldblat, Vladimir

    The problem of rain influencing on a characteristics of backscattered ultrasonic and microwave signal by water surface is considered. The rain influence on backscattering process of electromagnetic waves was investigated in laboratory and field experiments, for example [1-3]. Raindrops have a significant impact on backscattering of microwave and influence on wave spectrum measurement accuracy by string wave gauge. This occurs due to presence of raindrops in atmosphere and modification of the water surface. For measurements of water surface characteristics during precipitation we propose to use an acoustic system. This allows us obtaining of the water surface parameters independently on precipitation in atmosphere. The measurements of significant wave height of water surface using underwater acoustical systems are well known [4, 5]. Moreover, the variance of orbital velocity can be measure using these systems. However, these methods cannot be used for measurements of slope variance and the other second statistical moments of water surface that required for analyzing the radar backscatter signal. An original design Doppler underwater acoustic wave gauge allows directly measuring the surface roughness characteristics that affect on electromagnetic waves backscattering of the same wavelength [6]. Acoustic wave gauge is Doppler ultrasonic sonar which is fixed near the bottom on the floating disk. Measurements are carried out at vertically orientation of sonar antennas towards water surface. The first experiments were conducted with the first model of an acoustic wave gauge. The acoustic wave gauge (8 mm wavelength) is equipped with a transceiving antenna with a wide symmetrical antenna pattern. The gauge allows us to measure Doppler spectrum and cross section of backscattered signal. Variance of orbital velocity vertical component can be retrieved from Doppler spectrum with high accuracy. The result of laboratory and field experiments during artificial rain is presented

  16. Wavelet Transform Of Acoustic Signal From A Ranque- Hilsch Vortex Tube

    NASA Astrophysics Data System (ADS)

    Istihat, Y.; Wisnoe, W.

    2015-09-01

    This paper presents the frequency analysis of flow in a Ranque-Hilsch Vortex Tube (RHVT) obtained from acoustic signal using microphones in an isolated formation setup. Data Acquisition System (DAS) that incorporates Analog to Digital Converter (ADC) with laptop computer has been used to acquire the wave data. Different inlet pressures (20, 30, 40, 50 and 60 psi) are supplied and temperature differences are recorded. Frequencies produced from a RHVT are experimentally measured and analyzed by means of Wavelet Transform (WT). Morlet Wavelet is used and relation between Pressure variation, Temperature and Frequency are studied. Acoustic data has been analyzed using Matlab® and time-frequency analysis (Scalogram) is presented. Results show that the Pressure is proportional with the Frequency inside the RHVT whereby two distinct working frequencies is pronounced in between 4-8 kHz.

  17. Adaptive frequency estimation by MUSIC (Multiple Signal Classification) method

    NASA Astrophysics Data System (ADS)

    Karhunen, Juha; Nieminen, Esko; Joutsensalo, Jyrki

    During the last years, the eigenvector-based method called MUSIC has become very popular in estimating the frequencies of sinusoids in additive white noise. Adaptive realizations of the MUSIC method are studied using simulated data. Several of the adaptive realizations seem to give in practice equally good results as the nonadaptive standard realization. The only exceptions are instantaneous gradient type algorithms that need considerably more samples to achieve a comparable performance. A new method is proposed for constructing initial estimates to the signal subspace. The method improves often dramatically the performance of instantaneous gradient type algorithms. The new signal subspace estimate can also be used to define a frequency estimator directly or to simplify eigenvector computation.

  18. Beamforming in an acoustic shadow

    NASA Technical Reports Server (NTRS)

    Havelock, David; Stinson, Michael; Daigle, Gilles

    1993-01-01

    The sound field deep within an acoustic shadow region is less well understood than that outside the shadow region. Signal levels are substantially lower within the shadow, but beamforming difficulties arise for other reasons such as loss of spatial coherence. Based on analysis of JAPE-91 data, and other data, three types of characteristic signals within acoustic shadow regions are identified. These signal types may correspond to different, intermittent signal propagation conditions. Detection and classification algorithms might take advantage of the signal characteristics. Frequency coherence is also discussed. The extent of coherence across frequencies is shown to be limited, causing difficulties for source classification based on harmonic amplitude relationships. Discussions emphasize short-term characteristics on the order of one second. A video presentation on frequency coherence shows the similarity, in the presence of atmospheric turbulence, between the received signal from a stable set of harmonics generated by a loudspeaker and that received from a helicopter hovering behind a hill.

  19. A machine learning approach to multi-level ECG signal quality classification.

    PubMed

    Li, Qiao; Rajagopalan, Cadathur; Clifford, Gari D

    2014-12-01

    Current electrocardiogram (ECG) signal quality assessment studies have aimed to provide a two-level classification: clean or noisy. However, clinical usage demands more specific noise level classification for varying applications. This work outlines a five-level ECG signal quality classification algorithm. A total of 13 signal quality metrics were derived from segments of ECG waveforms, which were labeled by experts. A support vector machine (SVM) was trained to perform the classification and tested on a simulated dataset and was validated using data from the MIT-BIH arrhythmia database (MITDB). The simulated training and test datasets were created by selecting clean segments of the ECG in the 2011 PhysioNet/Computing in Cardiology Challenge database, and adding three types of real ECG noise at different signal-to-noise ratio (SNR) levels from the MIT-BIH Noise Stress Test Database (NSTDB). The MITDB was re-annotated for five levels of signal quality. Different combinations of the 13 metrics were trained and tested on the simulated datasets and the best combination that produced the highest classification accuracy was selected and validated on the MITDB. Performance was assessed using classification accuracy (Ac), and a single class overlap accuracy (OAc), which assumes that an individual type classified into an adjacent class is acceptable. An Ac of 80.26% and an OAc of 98.60% on the test set were obtained by selecting 10 metrics while 57.26% (Ac) and 94.23% (OAc) were the numbers for the unseen MITDB validation data without retraining. By performing the fivefold cross validation, an Ac of 88.07±0.32% and OAc of 99.34±0.07% were gained on the validation fold of MITDB. PMID:25306242

  20. A hybrid classifier fusion approach for motor unit potential classification during EMG signal decomposition.

    PubMed

    Rasheed, Sarbast; Stashuk, Daniel W; Kamel, Mohamed S

    2007-09-01

    In this paper, we propose a hybrid classifier fusion scheme for motor unit potential classification during electromyographic (EMG) signal decomposition. The scheme uses an aggregator module consisting of two stages of classifier fusion: the first at the abstract level using class labels and the second at the measurement level using confidence values. Performance of the developed system was evaluated using one set of real signals and two sets of simulated signals and was compared with the performance of the constituent base classifiers and the performance of a one-stage classifier fusion approach. Across the EMG signal data sets used and relative to the performance of base classifiers, the hybrid approach had better average classification performance overall. For the set of simulated signals of varying intensity, the hybrid classifier fusion system had on average an improved correct classification rate (CCr) (6.1%) and reduced error rate (Er) (0.4%). For the set of simulated signals of varying amounts of shape and/or firing pattern variability, the hybrid classifier fusion system had on average an improved CCr (6.2%) and reduced Er (0.9%). For real signals, the hybrid classifier fusion system had on average an improved CCr (7.5%) and reduced Er (1.7%). PMID:17867366

  1. Phylogenetic signal in the acoustic parameters of the advertisement calls of four clades of anurans

    PubMed Central

    2013-01-01

    Background Anuran vocalizations, especially their advertisement calls, are largely species-specific and can be used to identify taxonomic affiliations. Because anurans are not vocal learners, their vocalizations are generally assumed to have a strong genetic component. This suggests that the degree of similarity between advertisement calls may be related to large-scale phylogenetic relationships. To test this hypothesis, advertisement calls from 90 species belonging to four large clades (Bufo, Hylinae, Leptodactylus, and Rana) were analyzed. Phylogenetic distances were estimated based on the DNA sequences of the 12S mitochondrial ribosomal RNA gene, and, for a subset of 49 species, on the rhodopsin gene. Mean values for five acoustic parameters (coefficient of variation of root-mean-square amplitude, dominant frequency, spectral flux, spectral irregularity, and spectral flatness) were computed for each species. We then tested for phylogenetic signal on the body-size-corrected residuals of these five parameters, using three statistical tests (Moran’s I, Mantel, and Blomberg’s K) and three models of genetic distance (pairwise distances, Abouheif’s proximities, and the variance-covariance matrix derived from the phylogenetic tree). Results A significant phylogenetic signal was detected for most acoustic parameters on the 12S dataset, across statistical tests and genetic distance models, both for the entire sample of 90 species and within clades in several cases. A further analysis on a subset of 49 species using genetic distances derived from rhodopsin and from 12S broadly confirmed the results obtained on the larger sample, indicating that the phylogenetic signals observed in these acoustic parameters can be detected using a variety of genetic distance models derived either from a variable mitochondrial sequence or from a conserved nuclear gene. Conclusions We found a robust relationship, in a large number of species, between anuran phylogenetic relatedness and

  2. Influence of attenuation on acoustic emission signals in carbon fiber reinforced polymer panels.

    PubMed

    Asamene, Kassahun; Hudson, Larry; Sundaresan, Mannur

    2015-05-01

    Influence of attenuation on acoustic emission (AE) signals in Carbon Fiber Reinforced Polymer (CFRP) crossply and quasi-isotropic panels is examined in this paper. Attenuation coefficients of the fundamental antisymmetric (A0) and symmetric (S0) wave modes were determined experimentally along different directions for the two types of CFRP panels. In the frequency range from 100 kHz to 500 kHz, the A0 mode undergoes significantly greater changes due to material related attenuation compared to the S0 mode. Moderate to strong changes in the attenuation levels were noted with propagation directions. Such mode and frequency dependent attenuation introduces major changes in the characteristics of AE signals depending on the position of the AE sensor relative to the source. Results from finite element simulations of a microscopic damage event in the composite laminates are used to illustrate attenuation related changes in modal and frequency components of AE signals. PMID:25682294

  3. Statistical Analysis of Noisy Signals Using Classification Tools

    SciTech Connect

    Thompson, Sandra E.; Heredia-Langner, Alejandro; Johnson, Timothy J.; Foster, Nancy S.; Valentine, Nancy B.; Amonette, James E.

    2005-06-04

    The potential use of chemicals, biotoxins and biological pathogens are a threat to military and police forces as well as the general public. Rapid identification of these agents is made difficult due to the noisy nature of the signal that can be obtained from portable, in-field sensors. In previously published articles, we created a flowchart that illustrated a method for triaging bacterial identification by combining standard statistical techniques for discrimination and identification with mid-infrared spectroscopic data. The present work documents the process of characterizing and eliminating the sources of the noise and outlines how multidisciplinary teams are necessary to accomplish that goal.

  4. Estimates of the prevalence of anomalous signal losses in the Yellow Sea derived from acoustic and oceanographic computer model simulations

    NASA Astrophysics Data System (ADS)

    Chin-Bing, Stanley A.; King, David B.; Warn-Varnas, Alex C.; Lamb, Kevin G.; Hawkins, James A.; Teixeira, Marvi

    2002-05-01

    The results from collocated oceanographic and acoustic simulations in a region of the Yellow Sea near the Shandong peninsula have been presented [Chin-Bing et al., J. Acoust. Soc. Am. 108, 2577 (2000)]. In that work, the tidal flow near the peninsula was used to initialize a 2.5-dimensional ocean model [K. G. Lamb, J. Geophys. Res. 99, 843-864 (1994)] that subsequently generated internal solitary waves (solitons). The validity of these soliton simulations was established by matching satellite imagery taken over the region. Acoustic propagation simulations through this soliton field produced results similar to the anomalous signal loss measured by Zhou, Zhang, and Rogers [J. Acoust. Soc. Am. 90, 2042-2054 (1991)]. Analysis of the acoustic interactions with the solitons also confirmed the hypothesis that the loss mechanism involved acoustic mode coupling. Recently we have attempted to estimate the prevalence of these anomalous signal losses in this region. These estimates were made from simulating acoustic effects over an 80 hour space-time evolution of soliton packets. Examples will be presented that suggest the conditions necessary for anomalous signal loss may be more prevalent than previously thought. [Work supported by ONR/NRL and by a High Performance Computing DoD grant.

  5. Noise affects the shape of female preference functions for acoustic signals.

    PubMed

    Reichert, Michael S; Ronacher, Bernhard

    2015-02-01

    The shape of female mate preference functions influences the speed and direction of sexual signal evolution. However, the expression of female preferences is modulated by interactions between environmental conditions and the female's sensory processing system. Noise is an especially relevant environmental condition because it interferes directly with the neural processing of signals. Although noise is therefore likely a significant force in the evolution of communication systems, little is known about its effects on preference function shape. In the grasshopper Chorthippus biguttulus, female preferences for male calling song characteristics are likely to be affected by noise because its auditory system is sensitive to fine temporal details of songs. We measured female preference functions for variation in male song characteristics in several levels of masking noise and found strong effects of noise on preference function shape. The overall responsiveness to signals in noise generally decreased. Preference strength increased for some signal characteristics and decreased for others, largely corresponding to expectations based on neurophysiological studies of acoustic signal processing. These results suggest that different signal characteristics will be favored under different noise conditions, and thus that signal evolution may proceed differently depending on the extent and temporal patterning of environmental noise. PMID:25546134

  6. Predictions of acoustic signals from explosions above and below the ocean surface: source region calculations

    SciTech Connect

    Clarke, D.B.; Piacsek, A.; White, J.W.

    1996-12-01

    In support of the Comprehensive Test Ban, research is underway on the long range propagation of signals from nuclear explosions in the deep underwater sound (SOFAR) channel. This first phase of our work at LLNL on signals in the source regions considered explosions in or above the deep (5000 m) ocean. We studied the variation of wave properties and source region energy coupling as a function of height or depth of burst. Initial calculations on CALE, a two-dimensional hydrodynamics code developed at LLNL by Robert Tipton, were linked at a few hundred milliseconds to a version of NRL`s weak shock code, NPE, which solves the nonlinear progressive wave equation. The wave propagation simulation was performed down to 5000 m depth and out to 10,000 m range. We have developed a procedure to convert the acoustic signals at 10 km range into `starter fields` for calculations on a linear acoustics code which will extend the propagation to ocean basin distances. Recently we have completed calculations to evaluate environmental effects (shallow water, bottom interactions) on signal propagation. We compared results at 25 km range from three calculations of the same I kiloton burst (50 m height-of-burst) in three different environments, namely, deep water, shallow water, and a case with shallow water sloping to deep water. Several results from this last `sloping bottom` case will be 2016 discussed below. In this shallow water study, we found that propagation through shallow water complicates and attenuates the signal; the changes made to the signal may impact detection and discrimination for bursts in some locations.

  7. Hand posture classification using electrocorticography signals in the gamma band over human sensorimotor brain areas

    PubMed Central

    Chestek, Cynthia A.; Gilja, Vikash; Blabe, Christine H.; Foster, Brett L.; Shenoy, Krishna V.; Parvizi, Josef; Henderson, Jaimie M.

    2013-01-01

    Objective Brain machine interface systems translate recorded neural signals into command signals for assistive technology. In individuals with upper limb amputation or cervical spinal cord injury, the restoration of a useful hand grasp could significantly improve daily function. We sought to determine if electrocorticographic (ECoG) signals contain sufficient information to select among multiple hand postures for a prosthetic hand, orthotic, or functional electrical stimulation system. Approach We recorded ECoG signals from subdural macro- and microelectrodes implanted in motor areas of three participants who were undergoing inpatient monitoring for diagnosis and treatment of intractable epilepsy. Participants performed 5 distinct isometric hand postures, as well as 4 distinct finger movements. Several control experiments were attempted in order to remove sensory information from the classification results. Online experiments were performed with 2 participants. Main Results Classification rates were 68%, 84%, and 81% for correct identification of 5 isometric hand postures offline. Using 3 potential controls for removing sensory signals, error rates were approximately doubled on average (2.1×). A similar increase in errors (2.6×) was noted when the participant was asked to make simultaneous wrist movements along with the hand postures. In online experiments, fist versus rest was successfully classified on 97% of trials; the classification output drove a prosthetic hand. Online classification performance for a larger number of hand postures remained above chance, but substantially below offline performance. In addition, the long integration windows used would preclude the use of decoded signals for control of a BCI system. Significance These results suggest that ECoG is a plausible source of command signals for prosthetic grasp selection. Overall, avenues remain for improvement through better electrode designs and placement, better participant training, and

  8. Hand posture classification using electrocorticography signals in the gamma band over human sensorimotor brain areas

    NASA Astrophysics Data System (ADS)

    Chestek, Cynthia A.; Gilja, Vikash; Blabe, Christine H.; Foster, Brett L.; Shenoy, Krishna V.; Parvizi, Josef; Henderson, Jaimie M.

    2013-04-01

    Objective. Brain-machine interface systems translate recorded neural signals into command signals for assistive technology. In individuals with upper limb amputation or cervical spinal cord injury, the restoration of a useful hand grasp could significantly improve daily function. We sought to determine if electrocorticographic (ECoG) signals contain sufficient information to select among multiple hand postures for a prosthetic hand, orthotic, or functional electrical stimulation system.Approach. We recorded ECoG signals from subdural macro- and microelectrodes implanted in motor areas of three participants who were undergoing inpatient monitoring for diagnosis and treatment of intractable epilepsy. Participants performed five distinct isometric hand postures, as well as four distinct finger movements. Several control experiments were attempted in order to remove sensory information from the classification results. Online experiments were performed with two participants. Main results. Classification rates were 68%, 84% and 81% for correct identification of 5 isometric hand postures offline. Using 3 potential controls for removing sensory signals, error rates were approximately doubled on average (2.1×). A similar increase in errors (2.6×) was noted when the participant was asked to make simultaneous wrist movements along with the hand postures. In online experiments, fist versus rest was successfully classified on 97% of trials; the classification output drove a prosthetic hand. Online classification performance for a larger number of hand postures remained above chance, but substantially below offline performance. In addition, the long integration windows used would preclude the use of decoded signals for control of a BCI system. Significance. These results suggest that ECoG is a plausible source of command signals for prosthetic grasp selection. Overall, avenues remain for improvement through better electrode designs and placement, better participant training

  9. Signal diversification in Oecanthus tree crickets is shaped by energetic, morphometric, and acoustic trade-offs.

    PubMed

    Symes, L B; Ayres, M P; Cowdery, C P; Costello, R A

    2015-06-01

    Physiology, physics, and ecological interactions can generate trade-offs within species, but may also shape divergence among species. We tested whether signal divergence in Oecanthus tree crickets is shaped by acoustic, energetic, and behavioral trade-offs. We found that species with faster pulse rates, produced by opening and closing wings up to twice as many times per second, did not have higher metabolic costs of calling. The relatively constant energetic cost across species is explained by trade-offs between the duration and repetition rate of acoustic signals-species with fewer stridulatory teeth closed their wings more frequently such that the number of teeth struck per second of calling and the resulting duty cycle were relatively constant across species. Further trade-offs were evident in relationships between signals and body size. Calling was relatively inexpensive for small males, permitting them to call for much of the night, but at low amplitude. Large males produced much louder calls, reaching up to four times more area, but the energetic costs increased substantially with increasing size and the time spent calling dropped to only 20% of the night. These trade-offs indicate that the trait combinations that arise in these species represent a limited subset of conceivable trait combinations. PMID:25903317

  10. The potential influence of morphology on the evolutionary divergence of an acoustic signal

    PubMed Central

    Pitchers, W. R.; Klingenberg, C.P.; Tregenza, Tom; Hunt, J.; Dworkin, I.

    2014-01-01

    The evolution of acoustic behaviour and that of the morphological traits mediating its production are often coupled. Lack of variation in the underlying morphology of signalling traits has the potential to constrain signal evolution. This relationship is particularly likely in field crickets, where males produce acoustic advertisement signals to attract females by stridulating with specialized structures on their forewings. In this study, we characterise the size and geometric shape of the forewings of males from six allopatric populations of the black field cricket (Teleogryllus commodus) known to have divergent advertisement calls. We sample from each of these populations using both wild-caught and common-garden reared cohorts, allowing us to test for multivariate relationships between wing morphology and call structure. We show that the allometry of shape has diverged across populations. However, there was a surprisingly small amount of covariation between wing shape and call structure within populations. Given the importance of male size for sexual selection in crickets, the divergence we observe among populations has the potential to influence the evolution of advertisement calls in this species. PMID:25223712

  11. The potential influence of morphology on the evolutionary divergence of an acoustic signal.

    PubMed

    Pitchers, W R; Klingenberg, C P; Tregenza, T; Hunt, J; Dworkin, I

    2014-10-01

    The evolution of acoustic behaviour and that of the morphological traits mediating its production are often coupled. Lack of variation in the underlying morphology of signalling traits has the potential to constrain signal evolution. This relationship is particularly likely in field crickets, where males produce acoustic advertisement signals to attract females by stridulating with specialized structures on their forewings. In this study, we characterize the size and geometric shape of the forewings of males from six allopatric populations of the black field cricket (Teleogryllus commodus) known to have divergent advertisement calls. We sample from each of these populations using both wild-caught and common-garden-reared cohorts, allowing us to test for multivariate relationships between wing morphology and call structure. We show that the allometry of shape has diverged across populations. However, there was a surprisingly small amount of covariation between wing shape and call structure within populations. Given the importance of male size for sexual selection in crickets, the divergence we observe among populations has the potential to influence the evolution of advertisement calls in this species. PMID:25223712

  12. Long Recording Sequences: How to Track the Intra-Individual Variability of Acoustic Signals

    PubMed Central

    Lengagne, Thierry; Gomez, Doris; Josserand, Rémy; Voituron, Yann

    2015-01-01

    Recently developed acoustic technologies - like automatic recording units - allow the recording of long sequences in natural environments. These devices are used for biodiversity survey but they could also help researchers to estimate global signal variability at various (individual, population, species) scales. While sexually-selected signals are expected to show a low intra-individual variability at relatively short time scale, this variability has never been estimated so far. Yet, measuring signal variability in controlled conditions should prove useful to understand sexual selection processes and should help design acoustic sampling schedules and to analyse long call recordings. We here use the overall call production of 36 male treefrogs (Hyla arborea) during one night to evaluate within-individual variability in call dominant frequency and to test the efficiency of different sampling methods at capturing such variability. Our results confirm that using low number of calls underestimates call dominant frequency variation of about 35% in the tree frog and suggest that the assessment of this variability is better by using 2 or 3 short and well-distributed records than by using samples made of consecutive calls. Hence, 3 well-distributed 2-minutes records (beginning, middle and end of the calling period) are sufficient to capture on average all the nightly variability, whereas a sample of 10 000 consecutive calls captures only 86% of it. From a biological point of view, the call dominant frequency variability observed in H. arborea (116Hz on average but up to 470 Hz of variability during the course of the night for one male) challenge about its reliability in mate quality assessment. Automatic acoustic recording units will provide long call sequences in the near future and it will be then possible to confirm such results on large samples recorded in more complex field conditions. PMID:25970183

  13. Heterodyne signal-to-noise ratios in acoustic mode scattering experiments

    NASA Technical Reports Server (NTRS)

    Cochran, W. R.

    1980-01-01

    The relation between the signal to noise ratio (SNR) obtained in heterodyne detection of radiation scattered from acoustic modes in crystalline solids and the scattered spectral density function is studied. It is shown that in addition to the information provided by the measured frequency shifts and line widths, measurement of the SNR provides a determination of the absolute elasto-optical (Pockel's) constants. Examples are given for cubic crystals, and acceptable SNR values are obtained for scattering from thermally excited phonons at 10.6 microns, with no external perturbation of the sample necessary. The results indicate the special advantages of the method for the study of semiconductors.

  14. Mate preference in the painted goby: the influence of visual and acoustic courtship signals.

    PubMed

    Amorim, M Clara P; da Ponte, Ana Nunes; Caiano, Manuel; Pedroso, Silvia S; Pereira, Ricardo; Fonseca, Paulo J

    2013-11-01

    We tested the hypothesis that females of a small vocal marine fish with exclusive paternal care, the painted goby, prefer high parental-quality mates such as large or high-condition males. We tested the effect of male body size and male visual and acoustic courtship behaviour (playback experiments) on female mating preferences by measuring time spent near one of a two-choice stimuli. Females did not show preference for male size but preferred males that showed higher levels of courtship, a trait known to advertise condition (fat reserves). Also, time spent near the preferred male depended on male courtship effort. Playback experiments showed that when sound was combined with visual stimuli (a male confined in a small aquarium placed near each speaker), females spent more time near the male associated with courtship sound than with the control male (associated with white noise or silence). Although male visual courtship effort also affected female preference in the pre-playback period, this effect decreased during playback and disappeared in the post-playback period. Courtship sound stimuli alone did not elicit female preference in relation to a control. Taken together, the results suggest that visual and mainly acoustic courtship displays are subject to mate preference and may advertise parental quality in this species. Our results indicate that visual and acoustic signals interplay in a complex fashion and highlight the need to examine how different sensory modalities affect mating preferences in fish and other vertebrates. PMID:23948469

  15. Multimodal integration of micro-Doppler sonar and auditory signals for behavior classification with convolutional networks.

    PubMed

    Dura-Bernal, Salvador; Garreau, Guillaume; Georgiou, Julius; Andreou, Andreas G; Denham, Susan L; Wennekers, Thomas

    2013-10-01

    The ability to recognize the behavior of individuals is of great interest in the general field of safety (e.g. building security, crowd control, transport analysis, independent living for the elderly). Here we report a new real-time acoustic system for human action and behavior recognition that integrates passive audio and active micro-Doppler sonar signatures over multiple time scales. The system architecture is based on a six-layer convolutional neural network, trained and evaluated using a dataset of 10 subjects performing seven different behaviors. Probabilistic combination of system output through time for each modality separately yields 94% (passive audio) and 91% (micro-Doppler sonar) correct behavior classification; probabilistic multimodal integration increases classification performance to 98%. This study supports the efficacy of micro-Doppler sonar systems in characterizing human actions, which can then be efficiently classified using ConvNets. It also demonstrates that the integration of multiple sources of acoustic information can significantly improve the system's performance. PMID:23924412

  16. Neural Mechanisms for Acoustic Signal Detection under Strong Masking in an Insect.

    PubMed

    Kostarakos, Konstantinos; Römer, Heiner

    2015-07-22

    Communication is fundamental for our understanding of behavior. In the acoustic modality, natural scenes for communication in humans and animals are often very noisy, decreasing the chances for signal detection and discrimination. We investigated the mechanisms enabling selective hearing under natural noisy conditions for auditory receptors and interneurons of an insect. In the studied katydid Mecopoda elongata species-specific calling songs (chirps) are strongly masked by signals of another species, both communicating in sympatry. The spectral properties of the two signals are similar and differ only in a small frequency band at 2 kHz present in the chirping species. Receptors sharply tuned to 2 kHz are completely unaffected by the masking signal of the other species, whereas receptors tuned to higher audio and ultrasonic frequencies show complete masking. Intracellular recordings of identified interneurons revealed two mechanisms providing response selectivity to the chirp. (1) Response selectivity is when several identified interneurons exhibit remarkably selective responses to the chirps, even at signal-to-noise ratios of -21 dB, since they are sharply tuned to 2 kHz. Their dendritic arborizations indicate selective connectivity with low-frequency receptors tuned to 2 kHz. (2) Novelty detection is when a second group of interneurons is broadly tuned but, because of strong stimulus-specific adaptation to the masker spectrum and "novelty detection" to the 2 kHz band present only in the conspecific signal, these interneurons start to respond selectively to the chirp shortly after the onset of the continuous masker. Both mechanisms provide the sensory basis for hearing at unfavorable signal-to-noise ratios. Significance statement: Animal and human acoustic communication may suffer from the same "cocktail party problem," when communication happens in noisy social groups. We address solutions for this problem in a model system of two katydids, where one species

  17. Neural Mechanisms for Acoustic Signal Detection under Strong Masking in an Insect

    PubMed Central

    Römer, Heiner

    2015-01-01

    Communication is fundamental for our understanding of behavior. In the acoustic modality, natural scenes for communication in humans and animals are often very noisy, decreasing the chances for signal detection and discrimination. We investigated the mechanisms enabling selective hearing under natural noisy conditions for auditory receptors and interneurons of an insect. In the studied katydid Mecopoda elongata species-specific calling songs (chirps) are strongly masked by signals of another species, both communicating in sympatry. The spectral properties of the two signals are similar and differ only in a small frequency band at 2 kHz present in the chirping species. Receptors sharply tuned to 2 kHz are completely unaffected by the masking signal of the other species, whereas receptors tuned to higher audio and ultrasonic frequencies show complete masking. Intracellular recordings of identified interneurons revealed two mechanisms providing response selectivity to the chirp. (1) Response selectivity is when several identified interneurons exhibit remarkably selective responses to the chirps, even at signal-to-noise ratios of −21 dB, since they are sharply tuned to 2 kHz. Their dendritic arborizations indicate selective connectivity with low-frequency receptors tuned to 2 kHz. (2) Novelty detection is when a second group of interneurons is broadly tuned but, because of strong stimulus-specific adaptation to the masker spectrum and “novelty detection” to the 2 kHz band present only in the conspecific signal, these interneurons start to respond selectively to the chirp shortly after the onset of the continuous masker. Both mechanisms provide the sensory basis for hearing at unfavorable signal-to-noise ratios. SIGNIFICANCE STATEMENT Animal and human acoustic communication may suffer from the same “cocktail party problem,” when communication happens in noisy social groups. We address solutions for this problem in a model system of two katydids, where one

  18. Continuous perception and graded categorization: Electrophysiological evidence for a linear relationship between the acoustic signal and perceptual encoding of speech

    PubMed Central

    Toscano, Joseph C.; McMurray, Bob; Dennhardt, Joel; Luck, Steven. J.

    2012-01-01

    Speech sounds are highly variable, yet listeners readily extract information from them and transform continuous acoustic signals into meaningful categories during language comprehension. A central question is whether perceptual encoding captures continuous acoustic detail in a one-to-one fashion or whether it is affected by categories. We addressed this in an event-related potential (ERP) experiment in which listeners categorized spoken words that varied along a continuous acoustic dimension (voice onset time; VOT) in an auditory oddball task. We found that VOT effects were present through a late stage of perceptual processing (N1 component, ca. 100 ms poststimulus) and were independent of categories. In addition, effects of within-category differences in VOT were present at a post-perceptual categorization stage (P3 component, ca. 450 ms poststimulus). Thus, at perceptual levels, acoustic information is encoded continuously, independent of phonological information. Further, at phonological levels, fine-grained acoustic differences are preserved along with category information. PMID:20935168

  19. Clustering reveals cavitation-related acoustic emission signals from dehydrating branches.

    PubMed

    Vergeynst, Lidewei L; Sause, Markus G R; De Baerdemaeker, Niels J F; De Roo, Linus; Steppe, Kathy

    2016-06-01

    The formation of air emboli in the xylem during drought is one of the key processes leading to plant mortality due to loss in hydraulic conductivity, and strongly fuels the interest in quantifying vulnerability to cavitation. The acoustic emission (AE) technique can be used to measure hydraulic conductivity losses and construct vulnerability curves. For years, it has been believed that all the AE signals are produced by the formation of gas emboli in the xylem sap under tension. More recent experiments, however, demonstrate that gas emboli formation cannot explain all the signals detected during drought, suggesting that different sources of AE exist. This complicates the use of the AE technique to measure emboli formation in plants. We therefore analysed AE waveforms measured on branches of grapevine (Vitis vinifera L. 'Chardonnay') during bench dehydration with broadband sensors, and applied an automated clustering algorithm in order to find natural clusters of AE signals. We used AE features and AE activity patterns during consecutive dehydration phases to identify the different AE sources. Based on the frequency spectrum of the signals, we distinguished three different types of AE signals, of which the frequency cluster with high 100-200 kHz frequency content was strongly correlated with cavitation. Our results indicate that cavitation-related AE signals can be filtered from other AE sources, which presents a promising avenue into quantifying xylem embolism in plants in laboratory and field conditions. PMID:27095256

  20. Natural vs human-induced changes at the Tauranga Harbour area (New Zealand): a time -series acoustic seabed classification comparison

    NASA Astrophysics Data System (ADS)

    Capperucci, Ruggero Maria; Bartholomä, Alexander; Renken, Sabrina; De Lange, Willem

    2013-04-01

    The Tauranga Harbour Bay (New Zealand) is a mesotidal estuary system, enclosed by the Matakana barrier island. It hosts the leading export port in New Zealand and the second largest import port by value. Coastal changes are well documented over the last decades, mainly at the southern entrance of the area, between Matakana Island and Mt. Maunganui. It is an extremely dynamic environment, where natural processes are strongly influenced by human activities. In particular, the understanding of the recent evolution of the system is crucial for policymakers. In fact, the cumulative impact due to the maintenance of the port (mainly dredging activities, shipping, facilities construction, but also increasing tourism) and its already approved expansion clashes with the claim of the local Maori communities, which recently leaded to a court action. A hydroacoustic multiple-device survey (Side-scan Sonar SSS, Multibeam Echo-sounder MBES and Single Beam Echo-sounder) coupled with sediment sampling was carried out in March 2011 over an area of 0.8 km2, southern Matakana Island, along the Western Channel. The area is not directly impacted by dredging activities, resulting in an optimal testing site for assessing indirect effects of human disturbance on coastal dynamics. The main goals were: 1. To test the response of different acoustic systems in such a highly dynamic environment; 2. To study the influence of dredging activities on sediment dynamics and habitat changes, by means of comparing the current data with existing ones, in order to distinguish between natural and human induced changes Results demonstrate a good agreement between acoustic classifications from different systems. They seem to be mainly driven by the sediment distribution, with a distinctive fingerprint given by shells and shell fragments. Nevertheless, the presence of relevant topographic features (i.e. large bedform fields) influences swath-looking systems (SSS and MBES). SSS and MBES classifications tend

  1. Degradation of Coherence of Acoustic Signals Resulting from Inhomogeneities in the Sea.

    NASA Astrophysics Data System (ADS)

    Dobbins, Peter F.

    Available from UMI in association with The British Library. This thesis attempts to answer the question 'Is there an ultimate limit to the resolution of a sonar transducer due to sea water inhomogeneity?' The problem has been separated into three components: the sea water medium, acoustic propagation through this random medium, and the effects of the resulting phase and amplitude fluctuations on the performance of transducer arrays. The model for the medium is based on a spatial spectrum of the fluctuations in temperature and refractive index, and is divided into wavenumber ranges where phenomena such as turbulence of viscous dissipation dominate. The model for acoustic propagation uses the Rytov method, assuming that multiple scattering is not significant. The effects of fluctuations of array directivity were investigated using both numerical simulations and theory based on the plane wave spectrum. Laboratory experiments were conducted to confirm aspects of the propagation theory. Experiments at sea were carried out to validate the model of the medium; profiles of temperature, salinity, sound speed and current velocity were measured, and good agreement with the model was obtained in all cases. These data were used with the propagation theory to determine spatial correlation functions of phase and amplitude fluctuations in acoustic signals at a number of frequencies. Finally, these results were used to estimate the changes in beampattern for arrays of various sizes. It was found that there is a limit to the angular resolution of a linear array, determined by the width of the plane wave spectrum, and this limit is reached when the length of the array approaches the correlation scale of the acoustic fluctuations.

  2. System and method for characterizing, synthesizing, and/or canceling out acoustic signals from inanimate sound sources

    DOEpatents

    Holzrichter, John F.; Burnett, Greg C.; Ng, Lawrence C.

    2007-10-16

    A system and method for characterizing, synthesizing, and/or canceling out acoustic signals from inanimate sound sources is disclosed. Propagating wave electromagnetic sensors monitor excitation sources in sound producing systems, such as machines, musical instruments, and various other structures. Acoustical output from these sound producing systems is also monitored. From such information, a transfer function characterizing the sound producing system is generated. From the transfer function, acoustical output from the sound producing system may be synthesized or canceled. The methods disclosed enable accurate calculation of matched transfer functions relating specific excitations to specific acoustical outputs. Knowledge of such signals and functions can be used to effect various sound replication, sound source identification, and sound cancellation applications.

  3. System and method for characterizing synthesizing and/or canceling out acoustic signals from inanimate sound sources

    DOEpatents

    Holzrichter, John F.; Burnett, Greg C.; Ng, Lawrence C.

    2003-01-01

    A system and method for characterizing, synthesizing, and/or canceling out acoustic signals from inanimate sound sources is disclosed. Propagating wave electromagnetic sensors monitor excitation sources in sound producing systems, such as machines, musical instruments, and various other structures. Acoustical output from these sound producing systems is also monitored. From such information, a transfer function characterizing the sound producing system is generated. From the transfer function, acoustical output from the sound producing system may be synthesized or canceled. The methods disclosed enable accurate calculation of matched transfer functions relating specific excitations to specific acoustical outputs. Knowledge of such signals and functions can be used to effect various sound replication, sound source identification, and sound cancellation applications.

  4. System and method for characterizing, synthesizing, and/or canceling out acoustic signals from inanimate sound sources

    DOEpatents

    Holzrichter, John F; Burnett, Greg C; Ng, Lawrence C

    2013-05-21

    A system and method for characterizing, synthesizing, and/or canceling out acoustic signals from inanimate sound sources is disclosed. Propagating wave electromagnetic sensors monitor excitation sources in sound producing systems, such as machines, musical instruments, and various other structures. Acoustical output from these sound producing systems is also monitored. From such information, a transfer function characterizing the sound producing system is generated. From the transfer function, acoustical output from the sound producing system may be synthesized or canceled. The methods disclosed enable accurate calculation of matched transfer functions relating specific excitations to specific acoustical outputs. Knowledge of such signals and functions can be used to effect various sound replication, sound source identification, and sound cancellation applications.

  5. Classification of change detection and change blindness from near-infrared spectroscopy signals

    NASA Astrophysics Data System (ADS)

    Tanaka, Hirokazu; Katura, Takusige

    2011-08-01

    Using a machine-learning classification algorithm applied to near-infrared spectroscopy (NIRS) signals, we classify a success (change detection) or a failure (change blindness) in detecting visual changes for a change-detection task. Five subjects perform a change-detection task, and their brain activities are continuously monitored. A support-vector-machine algorithm is applied to classify the change-detection and change-blindness trials, and correct classification probability of 70-90% is obtained for four subjects. Two types of temporal shapes in classification probabilities are found: one exhibiting a maximum value after the task is completed (postdictive type), and another exhibiting a maximum value during the task (predictive type). As for the postdictive type, the classification probability begins to increase immediately after the task completion and reaches its maximum in about the time scale of neuronal hemodynamic response, reflecting a subjective report of change detection. As for the predictive type, the classification probability shows an increase at the task initiation and is maximal while subjects are performing the task, predicting the task performance in detecting a change. We conclude that decoding change detection and change blindness from NIRS signal is possible and argue some future applications toward brain-machine interfaces.

  6. Spatio-spectral filters for low-density surface electromyographic signal classification.

    PubMed

    Huang, Gan; Zhang, Zhiguo; Zhang, Dingguo; Zhu, Xiangyang

    2013-05-01

    In this paper, we proposed to utilize a novel spatio-spectral filter, common spatio-spectral pattern (CSSP), to improve the classification accuracy in identifying intended motions based on low-density surface electromyography (EMG). Five able-bodied subjects and a transradial amputee participated in an experiment of eight-task wrist and hand motion recognition. Low-density (six channels) surface EMG signals were collected on forearms. Since surface EMG signals are contaminated by large amount of noises from various sources, the performance of the conventional time-domain feature extraction method is limited. The CSSP method is a classification-oriented optimal spatio-spectral filter, which is capable of separating discriminative information from noise and, thus, leads to better classification accuracy. The substantially improved classification accuracy of the CSSP method over the time-domain and other methods is observed in all five able-bodied subjects and verified via the cross-validation. The CSSP method can also achieve better classification accuracy in the amputee, which shows its potential use for functional prosthetic control. PMID:23385330

  7. A new feature extraction method for signal classification applied to cord dorsum potential detection.

    PubMed

    Vidaurre, D; Rodríguez, E E; Bielza, C; Larrañaga, P; Rudomin, P

    2012-10-01

    In the spinal cord of the anesthetized cat, spontaneous cord dorsum potentials (CDPs) appear synchronously along the lumbo-sacral segments. These CDPs have different shapes and magnitudes. Previous work has indicated that some CDPs appear to be specially associated with the activation of spinal pathways that lead to primary afferent depolarization and presynaptic inhibition. Visual detection and classification of these CDPs provides relevant information on the functional organization of the neural networks involved in the control of sensory information and allows the characterization of the changes produced by acute nerve and spinal lesions. We now present a novel feature extraction approach for signal classification, applied to CDP detection. The method is based on an intuitive procedure. We first remove by convolution the noise from the CDPs recorded in each given spinal segment. Then, we assign a coefficient for each main local maximum of the signal using its amplitude and distance to the most important maximum of the signal. These coefficients will be the input for the subsequent classification algorithm. In particular, we employ gradient boosting classification trees. This combination of approaches allows a faster and more accurate discrimination of CDPs than is obtained by other methods. PMID:22929924

  8. A new feature extraction method for signal classification applied to cord dorsum potentials detection

    PubMed Central

    Vidaurre, D.; Rodríguez, E. E.; Bielza, C.; Larrañaga, P.; Rudomin, P.

    2012-01-01

    In the spinal cord of the anesthetized cat, spontaneous cord dorsum potentials (CDPs) appear synchronously along the lumbo-sacral segments. These CDPs have different shapes and magnitudes. Previous work has indicated that some CDPs appear to be specially associated with the activation of spinal pathways that lead to primary afferent depolarization and presynaptic inhibition. Visual detection and classification of these CDPs provides relevant information on the functional organization of the neural networks involved in the control of sensory information and allows the characterization of the changes produced by acute nerve and spinal lesions. We now present a novel feature extraction approach for signal classification, applied to CDP detection. The method is based on an intuitive procedure. We first remove by convolution the noise from the CDPs recorded in each given spinal segment. Then, we assign a coefficient for each main local maximum of the signal using its amplitude and distance to the most important maximum of the signal. These coefficients will be the input for the subsequent classification algorithm. In particular, we employ gradient boosting classification trees. This combination of approaches allows a faster and more accurate discrimination of CDPs than is obtained by other methods. PMID:22929924

  9. An Enhanced Data Visualization Method for Diesel Engine Malfunction Classification Using Multi-Sensor Signals

    PubMed Central

    Li, Yiqing; Wang, Yu; Zi, Yanyang; Zhang, Mingquan

    2015-01-01

    The various multi-sensor signal features from a diesel engine constitute a complex high-dimensional dataset. The non-linear dimensionality reduction method, t-distributed stochastic neighbor embedding (t-SNE), provides an effective way to implement data visualization for complex high-dimensional data. However, irrelevant features can deteriorate the performance of data visualization, and thus, should be eliminated a priori. This paper proposes a feature subset score based t-SNE (FSS-t-SNE) data visualization method to deal with the high-dimensional data that are collected from multi-sensor signals. In this method, the optimal feature subset is constructed by a feature subset score criterion. Then the high-dimensional data are visualized in 2-dimension space. According to the UCI dataset test, FSS-t-SNE can effectively improve the classification accuracy. An experiment was performed with a large power marine diesel engine to validate the proposed method for diesel engine malfunction classification. Multi-sensor signals were collected by a cylinder vibration sensor and a cylinder pressure sensor. Compared with other conventional data visualization methods, the proposed method shows good visualization performance and high classification accuracy in multi-malfunction classification of a diesel engine. PMID:26506347

  10. A new feature extraction method for signal classification applied to cord dorsum potential detection

    NASA Astrophysics Data System (ADS)

    Vidaurre, D.; Rodríguez, E. E.; Bielza, C.; Larrañaga, P.; Rudomin, P.

    2012-10-01

    In the spinal cord of the anesthetized cat, spontaneous cord dorsum potentials (CDPs) appear synchronously along the lumbo-sacral segments. These CDPs have different shapes and magnitudes. Previous work has indicated that some CDPs appear to be specially associated with the activation of spinal pathways that lead to primary afferent depolarization and presynaptic inhibition. Visual detection and classification of these CDPs provides relevant information on the functional organization of the neural networks involved in the control of sensory information and allows the characterization of the changes produced by acute nerve and spinal lesions. We now present a novel feature extraction approach for signal classification, applied to CDP detection. The method is based on an intuitive procedure. We first remove by convolution the noise from the CDPs recorded in each given spinal segment. Then, we assign a coefficient for each main local maximum of the signal using its amplitude and distance to the most important maximum of the signal. These coefficients will be the input for the subsequent classification algorithm. In particular, we employ gradient boosting classification trees. This combination of approaches allows a faster and more accurate discrimination of CDPs than is obtained by other methods.

  11. Adaptive neuro-fuzzy inference system for classification of ECG signals using Lyapunov exponents.

    PubMed

    Ubeyli, Elif Derya

    2009-03-01

    This paper describes the application of adaptive neuro-fuzzy inference system (ANFIS) model for classification of electrocardiogram (ECG) signals. Decision making was performed in two stages: feature extraction by computation of Lyapunov exponents and classification by the ANFIS trained with the backpropagation gradient descent method in combination with the least squares method. Four types of ECG beats (normal beat, congestive heart failure beat, ventricular tachyarrhythmia beat, and atrial fibrillation beat) obtained from the PhysioBank database were classified by four ANFIS classifiers. To improve diagnostic accuracy, the fifth ANFIS classifier (combining ANFIS) was trained using the outputs of the four ANFIS classifiers as input data. The proposed ANFIS model combined the neural network adaptive capabilities and the fuzzy logic qualitative approach. Some conclusions concerning the saliency of features on classification of the ECG signals were obtained through analysis of the ANFIS. The performance of the ANFIS model was evaluated in terms of training performance and classification accuracies and the results confirmed that the proposed ANFIS model has potential in classifying the ECG signals. PMID:19084286

  12. An enhanced data visualization method for diesel engine malfunction classification using multi-sensor signals.

    PubMed

    Li, Yiqing; Wang, Yu; Zi, Yanyang; Zhang, Mingquan

    2015-01-01

    The various multi-sensor signal features from a diesel engine constitute a complex high-dimensional dataset. The non-linear dimensionality reduction method, t-distributed stochastic neighbor embedding (t-SNE), provides an effective way to implement data visualization for complex high-dimensional data. However, irrelevant features can deteriorate the performance of data visualization, and thus, should be eliminated a priori. This paper proposes a feature subset score based t-SNE (FSS-t-SNE) data visualization method to deal with the high-dimensional data that are collected from multi-sensor signals. In this method, the optimal feature subset is constructed by a feature subset score criterion. Then the high-dimensional data are visualized in 2-dimension space. According to the UCI dataset test, FSS-t-SNE can effectively improve the classification accuracy. An experiment was performed with a large power marine diesel engine to validate the proposed method for diesel engine malfunction classification. Multi-sensor signals were collected by a cylinder vibration sensor and a cylinder pressure sensor. Compared with other conventional data visualization methods, the proposed method shows good visualization performance and high classification accuracy in multi-malfunction classification of a diesel engine. PMID:26506347

  13. Signal-to-noise ratio for acoustic detection in the deep ocean

    NASA Technical Reports Server (NTRS)

    Bowen, T.

    1979-01-01

    A simple method is presented for studying the thermoacoustic wave generated by a heat pulse. The signal-to-noise ratio (S/N) is then calculated for a typical hadronic-electromagnetic cascade in the deep ocean where low frequencies are masked by surface noise. It is found that a maximum useful range of about 16 km is found for typical conditions at 5 km depth. It is shown that in order to obtain useful signals with S/N greater than 100 at distances of 1 to 16 km, the cascade energy must be 10 to the 16th to 10 to the 18th eV. Finally, attention is given to further refinements of the theory of acoustic detection which remain to be investigated.

  14. Detection of Delamination in Concrete Bridge Decks Using Mfcc of Acoustic Impact Signals

    NASA Astrophysics Data System (ADS)

    Zhang, G.; Harichandran, R. S.; Ramuhalli, P.

    2010-02-01

    Delamination of the concrete cover is a commonly observed damage in concrete bridge decks. The delamination is typically initiated by corrosion of the upper reinforcing bars and promoted by freeze-thaw cycling and traffic loading. The detection of delamination is important for bridge maintenance and acoustic non-destructive evaluation (NDE) is widely used due to its low cost, speed, and easy implementation. In traditional acoustic approaches, the inspector sounds the surface of the deck by impacting it with a hammer or bar, or by dragging a chain, and assesses delamination by the "hollowness" of the sound. The detection of the delamination is subjective and requires extensive training. To improve performance, this paper proposes an objective method for delamination detection. In this method, mel-frequency cepstral coefficients (MFCC) of the signal are extracted. Some MFCC are then selected as features for detection purposes using a mutual information criterion. Finally, the selected features are used to train a classifier which is subsequently used for detection. In this work, a simple quadratic Bayesian classifier is used. Different numbers of features are used to compare the performance of the detection method. The results show that the performance first increases with the number of features, but then decreases after an optimal value. The optimal number of features based on the recorded signals is four, and the mean error rate is only 3.3% when four features are used. Therefore, the proposed algorithm has sufficient accuracy to be used in field detection.

  15. Quaternion-Based Signal Analysis for Motor Imagery Classification from Electroencephalographic Signals

    PubMed Central

    Batres-Mendoza, Patricia; Montoro-Sanjose, Carlos R.; Guerra-Hernandez, Erick I.; Almanza-Ojeda, Dora L.; Rostro-Gonzalez, Horacio; Romero-Troncoso, Rene J.; Ibarra-Manzano, Mario A.

    2016-01-01

    Quaternions can be used as an alternative to model the fundamental patterns of electroencephalographic (EEG) signals in the time domain. Thus, this article presents a new quaternion-based technique known as quaternion-based signal analysis (QSA) to represent EEG signals obtained using a brain-computer interface (BCI) device to detect and interpret cognitive activity. This quaternion-based signal analysis technique can extract features to represent brain activity related to motor imagery accurately in various mental states. Experimental tests in which users where shown visual graphical cues related to left and right movements were used to collect BCI-recorded signals. These signals were then classified using decision trees (DT), support vector machine (SVM) and k-nearest neighbor (KNN) techniques. The quantitative analysis of the classifiers demonstrates that this technique can be used as an alternative in the EEG-signal modeling phase to identify mental states. PMID:26959029

  16. Quaternion-Based Signal Analysis for Motor Imagery Classification from Electroencephalographic Signals.

    PubMed

    Batres-Mendoza, Patricia; Montoro-Sanjose, Carlos R; Guerra-Hernandez, Erick I; Almanza-Ojeda, Dora L; Rostro-Gonzalez, Horacio; Romero-Troncoso, Rene J; Ibarra-Manzano, Mario A

    2016-01-01

    Quaternions can be used as an alternative to model the fundamental patterns of electroencephalographic (EEG) signals in the time domain. Thus, this article presents a new quaternion-based technique known as quaternion-based signal analysis (QSA) to represent EEG signals obtained using a brain-computer interface (BCI) device to detect and interpret cognitive activity. This quaternion-based signal analysis technique can extract features to represent brain activity related to motor imagery accurately in various mental states. Experimental tests in which users where shown visual graphical cues related to left and right movements were used to collect BCI-recorded signals. These signals were then classified using decision trees (DT), support vector machine (SVM) and k-nearest neighbor (KNN) techniques. The quantitative analysis of the classifiers demonstrates that this technique can be used as an alternative in the EEG-signal modeling phase to identify mental states. PMID:26959029

  17. Silent katydid females are at higher risk of bat predation than acoustically signalling katydid males

    PubMed Central

    Raghuram, Hanumanthan; Deb, Rittik; Nandi, Diptarup; Balakrishnan, Rohini

    2015-01-01

    Males that produce conspicuous mate attraction signals are often at high risk of predation from eavesdropping predators. Females of such species typically search for signalling males and their higher motility may also place them at risk. The relative predation risk faced by males and females in the context of mate-finding using long-distance signals has rarely been investigated. In this study, we show, using a combination of diet analysis and behavioural experiments, that katydid females, who do not produce acoustic signals, are at higher risk of predation from a major bat predator, Megaderma spasma, than calling males. Female katydids were represented in much higher numbers than males in the culled remains beneath roosts of M. spasma. Playback experiments using katydid calls revealed that male calls were approached in only about one-third of the trials overall, whereas tethered, flying katydids were always approached and attacked. Our results question the idea that necessary costs of mate-finding, including risk of predation, are higher in signalling males than in searching females. PMID:25429019

  18. Processing of simple and complex acoustic signals in a tonotopically organized ear

    PubMed Central

    Hummel, Jennifer; Wolf, Konstantin; Kössl, Manfred; Nowotny, Manuela

    2014-01-01

    Processing of complex signals in the hearing organ remains poorly understood. This paper aims to contribute to this topic by presenting investigations on the mechanical and neuronal response of the hearing organ of the tropical bushcricket species Mecopoda elongata to simple pure tone signals as well as to the conspecific song as a complex acoustic signal. The high-frequency hearing organ of bushcrickets, the crista acustica (CA), is tonotopically tuned to frequencies between about 4 and 70 kHz. Laser Doppler vibrometer measurements revealed a strong and dominant low-frequency-induced motion of the CA when stimulated with either pure tone or complex stimuli. Consequently, the high-frequency distal area of the CA is more strongly deflected by low-frequency-induced waves than by high-frequency-induced waves. This low-frequency dominance will have strong effects on the processing of complex signals. Therefore, we additionally studied the neuronal response of the CA to native and frequency-manipulated chirps. Again, we found a dominant influence of low-frequency components within the conspecific song, indicating that the mechanical vibration pattern highly determines the neuronal response of the sensory cells. Thus, we conclude that the encoding of communication signals is modulated by ear mechanics. PMID:25339727

  19. Silent katydid females are at higher risk of bat predation than acoustically signalling katydid males.

    PubMed

    Raghuram, Hanumanthan; Deb, Rittik; Nandi, Diptarup; Balakrishnan, Rohini

    2015-01-01

    Males that produce conspicuous mate attraction signals are often at high risk of predation from eavesdropping predators. Females of such species typically search for signalling males and their higher motility may also place them at risk. The relative predation risk faced by males and females in the context of mate-finding using long-distance signals has rarely been investigated. In this study, we show, using a combination of diet analysis and behavioural experiments, that katydid females, who do not produce acoustic signals, are at higher risk of predation from a major bat predator, Megaderma spasma, than calling males. Female katydids were represented in much higher numbers than males in the culled remains beneath roosts of M. spasma. Playback experiments using katydid calls revealed that male calls were approached in only about one-third of the trials overall, whereas tethered, flying katydids were always approached and attacked. Our results question the idea that necessary costs of mate-finding, including risk of predation, are higher in signalling males than in searching females. PMID:25429019

  20. Processing of simple and complex acoustic signals in a tonotopically organized ear.

    PubMed

    Hummel, Jennifer; Wolf, Konstantin; Kössl, Manfred; Nowotny, Manuela

    2014-12-01

    Processing of complex signals in the hearing organ remains poorly understood. This paper aims to contribute to this topic by presenting investigations on the mechanical and neuronal response of the hearing organ of the tropical bushcricket species Mecopoda elongata to simple pure tone signals as well as to the conspecific song as a complex acoustic signal. The high-frequency hearing organ of bushcrickets, the crista acustica (CA), is tonotopically tuned to frequencies between about 4 and 70 kHz. Laser Doppler vibrometer measurements revealed a strong and dominant low-frequency-induced motion of the CA when stimulated with either pure tone or complex stimuli. Consequently, the high-frequency distal area of the CA is more strongly deflected by low-frequency-induced waves than by high-frequency-induced waves. This low-frequency dominance will have strong effects on the processing of complex signals. Therefore, we additionally studied the neuronal response of the CA to native and frequency-manipulated chirps. Again, we found a dominant influence of low-frequency components within the conspecific song, indicating that the mechanical vibration pattern highly determines the neuronal response of the sensory cells. Thus, we conclude that the encoding of communication signals is modulated by ear mechanics. PMID:25339727

  1. When males whistle at females: complex FM acoustic signals in cockroaches

    NASA Astrophysics Data System (ADS)

    Sueur, Jérôme; Aubin, Thierry

    2006-10-01

    Male cockroaches of the species Elliptorhina chopardi expel air through a pair of modified abdominal spiracles during courtship. This air expulsion simultaneously produces air and substrate-borne vibrations. We described and compared in details these two types of vibrations. Our analysis of the air-borne signals shows that males can produce three categories of signals with distinct temporal and frequency parameters. “Pure whistles” consist of two independent harmonic series fast frequency modulated with independent harmonics that can cross each other. “Noisy whistles” also possess two independent voices but include a noisy broad-band frequency part in the middle. Hiss sounds are more noise-like, being made of a broad-band frequency spectrum. All three call types are unusually high in dominant frequency (>5 kHz) for cockroaches. The substrate-borne signals are categorised similarly. Some harmonics of the substrate-borne signals were filtered out, however, making the acoustic energy centered on fewer frequency bands. Our analysis shows that cockroach signals are complex, with fast frequency modulations and two distinct voices. These results also readdress the question of what system could potentially receive and decode the information contained within such complex sounds.

  2. Monitoring Rock Failure Processes Using the Hilbert-Huang Transform of Acoustic Emission Signals

    NASA Astrophysics Data System (ADS)

    Zhang, Ji; Peng, Weihong; Liu, Fengyu; Zhang, Haixiang; Li, Zhijian

    2016-02-01

    Rock fracturing generates acoustic emission (AE) signals that have statistical parameters referred to as AE signal parameters (AESP). Identification of rock fracturing or the failure process stage using such data raises several challenges. This study proposes a Hilbert-Huang transform-based AE processing approach to capture the time-frequency characteristics of both AE signals and AESP during rock failure processes. The damage occurring in tested rock specimens can be illustrated through analysis using this method. In this study, the specimens were 25 × 60 × 150 mm3 in size and were compressed at a displacement rate of 0.05 mm/min until failure. The recorded data included force and displacement, AE signals, and AESP. The AESP in the last third of the strain range period and 14 typical moments of strong AE signals were selected for further investigation. These results show that AE signals and AESP can be jointly used for identification of deformation stages. The transition between linear and nonlinear deformation stages was found to last for a short period in this process. The instantaneous frequency of the AE effective energy rate increased linearly from 0.5 to 1.5 Hz. Attenuation of elastic waves spreading in rock samples developed with deformation, as illustrated in the Hilbert spectra of AE signals. This attenuation is frequency dependent. Furthermore, AE signals in the softening process showed a complex frequency distribution attributed to the mechanical properties of the tested specimen. The results indicate that rock failure is predictable. The novel technology applied in this study is feasible for analysis of the entire deformation process, including softening and failure processes.

  3. Predict or classify: The deceptive role of time-locking in brain signal classification

    PubMed Central

    Rusconi, Marco; Valleriani, Angelo

    2016-01-01

    Several experimental studies claim to be able to predict the outcome of simple decisions from brain signals measured before subjects are aware of their decision. Often, these studies use multivariate pattern recognition methods with the underlying assumption that the ability to classify the brain signal is equivalent to predict the decision itself. Here we show instead that it is possible to correctly classify a signal even if it does not contain any predictive information about the decision. We first define a simple stochastic model that mimics the random decision process between two equivalent alternatives, and generate a large number of independent trials that contain no choice-predictive information. The trials are first time-locked to the time point of the final event and then classified using standard machine-learning techniques. The resulting classification accuracy is above chance level long before the time point of time-locking. We then analyze the same trials using information theory. We demonstrate that the high classification accuracy is a consequence of time-locking and that its time behavior is simply related to the large relaxation time of the process. We conclude that when time-locking is a crucial step in the analysis of neural activity patterns, both the emergence and the timing of the classification accuracy are affected by structural properties of the network that generates the signal. PMID:27320688

  4. Predict or classify: The deceptive role of time-locking in brain signal classification.

    PubMed

    Rusconi, Marco; Valleriani, Angelo

    2016-01-01

    Several experimental studies claim to be able to predict the outcome of simple decisions from brain signals measured before subjects are aware of their decision. Often, these studies use multivariate pattern recognition methods with the underlying assumption that the ability to classify the brain signal is equivalent to predict the decision itself. Here we show instead that it is possible to correctly classify a signal even if it does not contain any predictive information about the decision. We first define a simple stochastic model that mimics the random decision process between two equivalent alternatives, and generate a large number of independent trials that contain no choice-predictive information. The trials are first time-locked to the time point of the final event and then classified using standard machine-learning techniques. The resulting classification accuracy is above chance level long before the time point of time-locking. We then analyze the same trials using information theory. We demonstrate that the high classification accuracy is a consequence of time-locking and that its time behavior is simply related to the large relaxation time of the process. We conclude that when time-locking is a crucial step in the analysis of neural activity patterns, both the emergence and the timing of the classification accuracy are affected by structural properties of the network that generates the signal. PMID:27320688

  5. Predict or classify: The deceptive role of time-locking in brain signal classification

    NASA Astrophysics Data System (ADS)

    Rusconi, Marco; Valleriani, Angelo

    2016-06-01

    Several experimental studies claim to be able to predict the outcome of simple decisions from brain signals measured before subjects are aware of their decision. Often, these studies use multivariate pattern recognition methods with the underlying assumption that the ability to classify the brain signal is equivalent to predict the decision itself. Here we show instead that it is possible to correctly classify a signal even if it does not contain any predictive information about the decision. We first define a simple stochastic model that mimics the random decision process between two equivalent alternatives, and generate a large number of independent trials that contain no choice-predictive information. The trials are first time-locked to the time point of the final event and then classified using standard machine-learning techniques. The resulting classification accuracy is above chance level long before the time point of time-locking. We then analyze the same trials using information theory. We demonstrate that the high classification accuracy is a consequence of time-locking and that its time behavior is simply related to the large relaxation time of the process. We conclude that when time-locking is a crucial step in the analysis of neural activity patterns, both the emergence and the timing of the classification accuracy are affected by structural properties of the network that generates the signal.

  6. Classification of Internal Carotid Artery Doppler Signals Using Hidden Markov Model and Wavelet Transform with Entropy

    NASA Astrophysics Data System (ADS)

    Uğuz, Harun; Kodaz, Halife

    Doppler ultrasound has been usually preferred for investigation of the artery conditions in the last two decade, since it is a non-invasive method which is not risky. In this study, a biomedical system based on Discrete Hidden Markov Model (DHMM) has been developed in order to classify the internal carotid artery Doppler signals recorded from 191 subjects (136 of them had suffered from internal carotid artery stenosis and rest of them had been healthy subjects). Developed system comprises of three stages. In the first stage, for feature extraction, obtained Doppler signals were separated to its sub-bands using Discrete Wavelet Transform (DWT). In the second stage, entropy of each sub-band was calculated using Shannon entropy algorithm to reduce the dimensionality of the feature vectors via DWT. In the third stage, the reduced features of carotid artery Doppler signals were used as input patterns of the DHMM classifier. Our proposed method reached 97.38% classification accuracy with 5 fold cross validation (CV) technique. The classification results showed that purposed method is effective for classification of internal carotid artery Doppler signals.

  7. A novel feature extracting method of QRS complex classification for mobile ECG signals

    NASA Astrophysics Data System (ADS)

    Zhu, Lingyun; Wang, Dong; Huang, Xianying; Wang, Yue

    2007-12-01

    The conventional classification parameters of QRS complex suffer from larger activity rang of patients and lower signal to noise ratio in mobile cardiac telemonitoring system and can not meet the identification needs of ECG signal. Based on individual sinus heart rhythm template built with mobile ECG signals in time window, we present semblance index to extract the classification features of QRS complex precisely and expeditiously. Relative approximation r2 and absolute error r3 are used as estimating parameters of semblance between testing QRS complex and template. The evaluate parameters corresponding to QRS width and types are demonstrated to choose the proper index. The results show that 99.99 percent of the QRS complex for sinus and superventricular ECG signals can be distinguished through r2 but its average accurate ratio is only 46.16%. More than 97.84 percent of QRS complexes are identified using r3 but its accurate ratio to the sinus and superventricular is not better than r2. By the feature parameter of width, only 42.65 percent of QRS complexes are classified correctly, but its accurate ratio to the ventricular is superior to r2. To combine the respective superiority of three parameters, a nonlinear weighing computation of QRS width, r2 and r3 is introduced and the total classification accuracy up to 99.48% by combing indexes.

  8. Cognitive sensor networks for structure defect monitoring and classification using guided wave signals

    NASA Astrophysics Data System (ADS)

    Jin, Yuanwei; O'Donoughue, Nicholas; Moura, José M. F.; Harley, Joel; Garrett, James H.; Oppenheim, Irving J.; Soibelman, Lucio; Ying, Yujie; He, Lin

    2010-04-01

    This paper develops a framework of a cognitive sensor networks system for structure defect monitoring and classification using guided wave signals. Guided ultrasonic waves that can propagate long distances along civil structures have been widely studied for inspection and detection of structure damage. Smart ultrasonic sensors arranged as a spatially distributed cognitive sensor networks system can transmit and receive ultrasonic guided waves to interrogate structure defects such as cracks and corrosion. A distinguishing characteristic of the cognitive sensor networks system is that it adaptively probes and learns about the environment, which enables constant optimization in response to its changing understanding of the defect response. In this paper, we develop a sequential multiple hypothesis testing scheme combined with adaptive waveform transmission for defect monitoring and classification. The performance is verified using numerical simulations of guided elastic wave propagation on a pipe model and by Monte Carlo simulations for computing the probability of correct classification.

  9. Single-trial classification of NIRS signals during emotional induction tasks: towards a corporeal machine interface

    PubMed Central

    2009-01-01

    Background Corporeal machine interfaces (CMIs) are one of a few available options for restoring communication and environmental control to those with severe motor impairments. Cognitive processes detectable solely with functional imaging technologies such as near-infrared spectroscopy (NIRS) can potentially provide interfaces requiring less user training than conventional electroencephalography-based CMIs. We hypothesized that visually-cued emotional induction tasks can elicit forehead hemodynamic activity that can be harnessed for a CMI. Methods Data were collected from ten able-bodied participants as they performed trials of positively and negatively-emotional induction tasks. A genetic algorithm was employed to select the optimal signal features, classifier, task valence (positive or negative emotional value of the stimulus), recording site, and signal analysis interval length for each participant. We compared the performance of Linear Discriminant Analysis and Support Vector Machine classifiers. The latency of the NIRS hemodynamic response was estimated as the time required for classification accuracy to stabilize. Results Baseline and activation sequences were classified offline with accuracies upwards of 75.0%. Feature selection identified common time-domain discriminatory features across participants. Classification performance varied with the length of the input signal, and optimal signal length was found to be feature-dependent. Statistically significant increases in classification accuracy from baseline rates were observed as early as 2.5 s from initial stimulus presentation. Conclusion NIRS signals during affective states were shown to be distinguishable from baseline states with classification accuracies significantly above chance levels. Further research with NIRS for corporeal machine interfaces is warranted. PMID:19900285

  10. Fault diagnosis of reciprocating compressor valve with the method integrating acoustic emission signal and simulated valve motion

    NASA Astrophysics Data System (ADS)

    Wang, Yuefei; Xue, Chuang; Jia, Xiaohan; Peng, Xueyuan

    2015-05-01

    This paper proposes a method of diagnosing faults in reciprocating compressor valves using the acoustic emission signal coupled with the simulated valve motion. The actual working condition of a valve can be obtained by analyzing the acoustic emission signal in the crank angle domain and the valve movement can be predicted by simulating the valve motion. The exact opening and closing locations of a normal valve, provided by the simulated valve motion, can be used as references for the valve fault diagnosis. The typical valve faults are diagnosed to validate the feasibility and accuracy of the proposed method. The experimental results indicate that this method can easily distinguish the normal valve, valve flutter and valve delayed closing conditions. The characteristic locations of the opening and closing of the suction and discharge valves can be clearly identified in the waveform of the acoustic emission signal and the simulated valve motion.

  11. A methodology to condition distorted acoustic emission signals to identify fracture timing from human cadaver spine impact tests.

    PubMed

    Arun, Mike W J; Yoganandan, Narayan; Stemper, Brian D; Pintar, Frank A

    2014-12-01

    While studies have used acoustic sensors to determine fracture initiation time in biomechanical studies, a systematic procedure is not established to process acoustic signals. The objective of the study was to develop a methodology to condition distorted acoustic emission data using signal processing techniques to identify fracture initiation time. The methodology was developed from testing a human cadaver lumbar spine column. Acoustic sensors were glued to all vertebrae, high-rate impact loading was applied, load-time histories were recorded (load cell), and fracture was documented using CT. Compression fracture occurred to L1 while other vertebrae were intact. FFT of raw voltage-time traces were used to determine an optimum frequency range associated with high decibel levels. Signals were bandpass filtered in this range. Bursting pattern was found in the fractured vertebra while signals from other vertebrae were silent. Bursting time was associated with time of fracture initiation. Force at fracture was determined using this time and force-time data. The methodology is independent of selecting parameters a priori such as fixing a voltage level(s), bandpass frequency and/or using force-time signal, and allows determination of force based on time identified during signal processing. The methodology can be used for different body regions in cadaver experiments. PMID:25241279

  12. Multi-bearing defect detection with trackside acoustic signal based on a pseudo time-frequency analysis and Dopplerlet filter

    NASA Astrophysics Data System (ADS)

    Zhang, Haibin; Lu, Siliang; He, Qingbo; Kong, Fanrang

    2016-03-01

    The diagnosis of train bearing defects based on the acoustic signal acquired by a trackside microphone plays a significant role in the transport system. However, the wayside acoustic signal suffers from the Doppler distortion due to the high moving speed and also contains the multi-source signals from different train bearings. This paper proposes a novel solution to overcome the two difficulties in trackside acoustic diagnosis. In the method a pseudo time-frequency analysis (PTFA) based on an improved Dopplerlet transform (IDT) is presented to acquire the time centers for different bearings. With the time centers, we design a series of Dopplerlet filters (DF) in time-frequency domain to work on the signal's time-frequency distribution (TFD) gained by the short time Fourier transform (STFT). Then an inverse STFT (ISTFT) is utilized to get the separated signals for each sound source which means bearing here. Later the resampling method based on certain motion parameters eliminates the Doppler Effect and finally the diagnosis can be made effectively according to the envelope spectrum of each separated signal. With the effectiveness of the technique validated by both simulated and experimental cases, the proposed wayside acoustic diagnostic scheme is expected to be available in wayside defective bearing detection.

  13. Classification of signaling proteins based on molecular star graph descriptors using Machine Learning models.

    PubMed

    Fernandez-Lozano, Carlos; Cuiñas, Rubén F; Seoane, José A; Fernández-Blanco, Enrique; Dorado, Julian; Munteanu, Cristian R

    2015-11-01

    Signaling proteins are an important topic in drug development due to the increased importance of finding fast, accurate and cheap methods to evaluate new molecular targets involved in specific diseases. The complexity of the protein structure hinders the direct association of the signaling activity with the molecular structure. Therefore, the proposed solution involves the use of protein star graphs for the peptide sequence information encoding into specific topological indices calculated with S2SNet tool. The Quantitative Structure-Activity Relationship classification model obtained with Machine Learning techniques is able to predict new signaling peptides. The best classification model is the first signaling prediction model, which is based on eleven descriptors and it was obtained using the Support Vector Machines-Recursive Feature Elimination (SVM-RFE) technique with the Laplacian kernel (RFE-LAP) and an AUROC of 0.961. Testing a set of 3114 proteins of unknown function from the PDB database assessed the prediction performance of the model. Important signaling pathways are presented for three UniprotIDs (34 PDBs) with a signaling prediction greater than 98.0%. PMID:26297890

  14. Deciphering acoustic emission signals in drought stressed branches: the missing link between source and sensor.

    PubMed

    Vergeynst, Lidewei L; Sause, Markus G R; Hamstad, Marvin A; Steppe, Kathy

    2015-01-01

    When drought occurs in plants, acoustic emission (AE) signals can be detected, but the actual causes of these signals are still unknown. By analyzing the waveforms of the measured signals, it should, however, be possible to trace the characteristics of the AE source and get information about the underlying physiological processes. A problem encountered during this analysis is that the waveform changes significantly from source to sensor and lack of knowledge on wave propagation impedes research progress made in this field. We used finite element modeling and the well-known pencil lead break source to investigate wave propagation in a branch. A cylindrical rod of polyvinyl chloride was first used to identify the theoretical propagation modes. Two wave propagation modes could be distinguished and we used the finite element model to interpret their behavior in terms of source position for both the PVC rod and a wooden rod. Both wave propagation modes were also identified in drying-induced signals from woody branches, and we used the obtained insights to provide recommendations for further AE research in plant science. PMID:26191070

  15. Acoustic emission from single point machining: Source mechanisms and signal changes with tool wear

    SciTech Connect

    Heiple, C.R.; Carpenter, S.H.; Armentrout, D.L.; McManigle, A.P.

    1994-05-01

    Acoustic emission (AE) was monitored during single point, continuous machining of 4340 steel and Ti-6Al-4V as a function of heat treatment. Heat treatments that increase the strength of 4340 steel substantially increase the amount of AE produced during deformation, while heat treatments that increase the strength of Ti-6Al-4V dramatically decrease the amount of AE produced during deformation. There was little change in root-mean-square (rms) AE level during machining for either alloy as a function of prior heat treatment, demonstrating that chip deformation is not a major source of AE in single point machining. Additional data from a variety of materials suggest that sliding friction between the nose and/or flank of the tool and the newly machined surface is the primary source of AE. Changes in AE signal characteristics with tool wear were also monitored during single point machining. No signal characteristic changed in the same way with tool wear for all materials tested. A single change in a particular AE signal characteristic with tool wear valid for all materials probably does not exist. Nevertheless, changes in various signal characteristics with wear for a given material may be sufficient to be used to monitor tool wear.

  16. Deciphering acoustic emission signals in drought stressed branches: the missing link between source and sensor

    PubMed Central

    Vergeynst, Lidewei L.; Sause, Markus G. R.; Hamstad, Marvin A.; Steppe, Kathy

    2015-01-01

    When drought occurs in plants, acoustic emission (AE) signals can be detected, but the actual causes of these signals are still unknown. By analyzing the waveforms of the measured signals, it should, however, be possible to trace the characteristics of the AE source and get information about the underlying physiological processes. A problem encountered during this analysis is that the waveform changes significantly from source to sensor and lack of knowledge on wave propagation impedes research progress made in this field. We used finite element modeling and the well-known pencil lead break source to investigate wave propagation in a branch. A cylindrical rod of polyvinyl chloride was first used to identify the theoretical propagation modes. Two wave propagation modes could be distinguished and we used the finite element model to interpret their behavior in terms of source position for both the PVC rod and a wooden rod. Both wave propagation modes were also identified in drying-induced signals from woody branches, and we used the obtained insights to provide recommendations for further AE research in plant science. PMID:26191070

  17. Analysis and classification of delay-sensitive cortical neurons based on response to temporal parameters in echolocation signals.

    PubMed

    Chittajallu, S K; Palakal, M J; Wong, D

    1995-04-01

    Echolocating bats generate an acoustic image of their target by processing target-reflected echoes of their emitted biosonar pulses. Efforts in building computational models of auditory processing in the bat auditory system, using extensive neurophysiological data from cortical studies are challenged by the intrinsic complexity and the significant variability in neural response to stimuli. In this paper, we use a computerized method for the analysis and classification of delay-sensitive neurons to classify neurons from the auditory cortex of Myotis lucifugus, a species that echolocates with FM signals. The coefficients of the bi-linear fit to the best delay response surfaces (mean R2 = 0.01) were used in classifying the neurons. Six classes were derived that corresponded to the four previously characterized neurophysiologically. The first class corresponded to delay-tuned neurons which exhibited a constant best delay at different pulse repetition rates and pulse durations. Three other classes corresponded to the different subtypes of tracking neurons which changed their best delay to one or both of these stimulus temporal parameters. Two additional classes were differentiated although their best-delay response were similar to either the delay-tuned or the duration and pulse-repetition rate sensitive class. Artificial delay-sensitive neurons built from the parameters of the centroid of each class, will serve a key role in the FM bat auditory system model that we are building. PMID:7642448

  18. Divergence of acoustic signals in a widely distributed frog: relevance of inter-male interactions.

    PubMed

    Velásquez, Nelson A; Opazo, Daniel; Díaz, Javier; Penna, Mario

    2014-01-01

    Divergence of acoustic signals in a geographic scale results from diverse evolutionary forces acting in parallel and affecting directly inter-male vocal interactions among disjunct populations. Pleurodema thaul is a frog having an extensive latitudinal distribution in Chile along which males' advertisement calls exhibit an important variation. Using the playback paradigm we studied the evoked vocal responses of males of three populations of P. thaul in Chile, from northern, central and southern distribution. In each population, males were stimulated with standard synthetic calls having the acoustic structure of local and foreign populations. Males of both northern and central populations displayed strong vocal responses when were confronted with the synthetic call of their own populations, giving weaker responses to the call of the southern population. The southern population gave stronger responses to calls of the northern population than to the local call. Furthermore, males in all populations were stimulated with synthetic calls for which the dominant frequency, pulse rate and modulation depth were varied parametrically. Individuals from the northern and central populations gave lower responses to a synthetic call devoid of amplitude modulation relative to stimuli containing modulation depths between 30-100%, whereas the southern population responded similarly to all stimuli in this series. Geographic variation in the evoked vocal responses of males of P. thaul underlines the importance of inter-male interactions in driving the divergence of the acoustic traits and contributes evidence for a role of intra-sexual selection in the evolution of the sound communication system of this anuran. PMID:24489957

  19. Classification

    ERIC Educational Resources Information Center

    Clary, Renee; Wandersee, James

    2013-01-01

    In this article, Renee Clary and James Wandersee describe the beginnings of "Classification," which lies at the very heart of science and depends upon pattern recognition. Clary and Wandersee approach patterns by first telling the story of the "Linnaean classification system," introduced by Carl Linnacus (1707-1778), who is…

  20. Detection and processing of electromagnetic and near-field acoustic signals in elasmobranch fishes.

    PubMed Central

    Kalmijn, A D

    2000-01-01

    The acoustic near field of quietly moving underwater objects and the bio-electric field of aquatic animals exhibit great similarity, as both are predominantly governed by Laplace's equation. The acoustic and electrical sensory modalities thus may, in directing fishes to their prey, employ analogous processing algorithms, suggesting a common evolutionary design, founded on the salient physical features shared by the respective stimulus fields. Sharks and rays are capable of orientating to the earth's magnetic field and, hence, have a magnetic sense. The electromagnetic theory of orientation offers strong arguments for the animals using the electric fields induced by ocean currents and by their own motions in the earth's magnetic field. In the animal's frame of reference, in which the sense organs are at rest, the classical concept of motional electricity must be interpreted in relativistic terms. In the ampullae of Lorenzini, weak electric fields cause the ciliated apical receptor-cell membranes to produce graded, negative receptor currents opposite in direction to the fields applied. The observed currents form part of a positive-feedback mechanism, supporting the generation of receptor potentials much larger than the input signal. Acting across the basal cell membranes, the receptor potentials control the process of synaptic transmission. PMID:11079385

  1. Statistical Modeling of Large-Scale Signal Path Loss in Underwater Acoustic Networks

    PubMed Central

    Llor, Jesús; Malumbres, Manuel Perez

    2013-01-01

    In an underwater acoustic channel, the propagation conditions are known to vary in time, causing the deviation of the received signal strength from the nominal value predicted by a deterministic propagation model. To facilitate a large-scale system design in such conditions (e.g., power allocation), we have developed a statistical propagation model in which the transmission loss is treated as a random variable. By applying repetitive computation to the acoustic field, using ray tracing for a set of varying environmental conditions (surface height, wave activity, small node displacements around nominal locations, etc.), an ensemble of transmission losses is compiled and later used to infer the statistical model parameters. A reasonable agreement is found with log-normal distribution, whose mean obeys a log-distance increases, and whose variance appears to be constant for a certain range of inter-node distances in a given deployment location. The statistical model is deemed useful for higher-level system planning, where simulation is needed to assess the performance of candidate network protocols under various resource allocation policies, i.e., to determine the transmit power and bandwidth allocation necessary to achieve a desired level of performance (connectivity, throughput, reliability, etc.). PMID:23396190

  2. Amplitude-Frequency Analysis of Signals of Acoustic Emission from Granite Fractured at Elevated Temperatures

    NASA Astrophysics Data System (ADS)

    Shcherbakov, I. P.; Chmel‧, A. E.

    2015-05-01

    The problem of stability of underground structures serving to store radioactive waste, to gasify carbon, and to utilize geothermal energy is associated with the action of elevated temperatures and pressures. The acoustic-emission method makes it possible to monitor the accumulation of microcracks arising in stress fields of both thermal and mechanical origin. In this report, the authors give results of a laboratory investigation into the acoustic emission from granite subjected to impact fracture at temperatures of up to 600°C. An amplitude-frequency analysis of acousticemission signals has enabled the authors to evaluate the dimension of the arising microcracks and to determine their character (intergranular or intragranular). It has been shown that intergranular faults on the boundaries between identical minerals predominate at room temperature (purely mechanical action); at a temperature of 300°C (impact plus thermoelastic stresses), there also appear cracks on the quartz-feldspar boundaries; finally, at temperatures of 500-600°C, it is intragranular faults that predominate in feldspar. The dimensions of the above three types of microcracks are approximately 2, 0.8, and 0.3 mm respectively.

  3. Can acoustic emissions patterns signal imminence of avalanche events in a growing sand pile?

    NASA Astrophysics Data System (ADS)

    Vögtli, Melanie; Lehmann, Peter; Breitenstein, Daniel; Or, Dani

    2014-05-01

    Gravity driven mass release is often triggered abruptly with limited precursory cues to indicate imminent failure and thus limiting early warning. Evidence suggests that with increased mechanical loading of a slope, numerous local damage events marking friction between rearranged particles or breakage of roots release strain energy as elastic waves measurable as acoustic emissions. We examined the potential predictability of mass release events from preceding acoustic emission (AE) signatures in a well-known and simple model system of a growing sand pile. We installed four AE-sensors within the core of a 30 cm (diameter) sand pile fed by a constant input of grains and mounted on a balance. Subsequent to the convergence of the slope to dynamic angle of repose, sand avalanche across the bottom boundary were monitored by abrupt mass change and by the amplitudes and number of AE events (recorded at high frequency and averaged to 0.2 s). We detected a systematic change of AE-patterns characterized by systematically decreasing AE standard deviation prior to each mass release. Although the lead time following minimum AE standard deviation was relatively short (10s of seconds), the AE signature already started to change minutes before the mass release. Accordingly the information embedded in AE signal dynamics could potentially offer larger lead times for systems of practical interest.

  4. Statistical modeling of large-scale signal path loss in underwater acoustic networks.

    PubMed

    Llor, Jesús; Malumbres, Manuel Perez

    2013-01-01

    In an underwater acoustic channel, the propagation conditions are known to vary in time, causing the deviation of the received signal strength from the nominal value predicted by a deterministic propagation model. To facilitate a large-scale system design in such conditions (e.g., power allocation), we have developed a statistical propagation model in which the transmission loss is treated as a random variable. By applying repetitive computation to the acoustic field, using ray tracing for a set of varying environmental conditions (surface height, wave activity, small node displacements around nominal locations, etc.), an ensemble of transmission losses is compiled and later used to infer the statistical model parameters. A reasonable agreement is found with log-normal distribution, whose mean obeys a log-distance increases, and whose variance appears to be constant for a certain range of inter-node distances in a given deployment location. The statistical model is deemed useful for higher-level system planning, where simulation is needed to assess the performance of candidate network protocols under various resource allocation policies, i.e., to determine the transmit power and bandwidth allocation necessary to achieve a desired level of performance (connectivity, throughput, reliability, etc.). PMID:23396190

  5. Signal/background classification in a cosmic ray space experiment by a modular neural system

    NASA Astrophysics Data System (ADS)

    Bellotti, Roberto; Castellano, Marcello; De Marzo, Carlo N.; Satalino, Giuseppe

    1995-04-01

    In the cosmic ray space experiments, the separation of the signal from background is a hard task. Due to the well-known critical conditions that characterize this class of experiments, some changes of the detector performances can be observed during the data taking. As a consequence, differences between the test and real data are found as systematic errors in the classification phase. In this paper, a modular classification system based on neural networks is proposed for the signal/background discrimination task in cosmic ray space experiments, without a priori knowledge of the discriminating feature distributions. The system is composed by two neural modules. The first one is a self organizing map (SOM) that both clusters the real data space in suitable classes of similarity and builds a prototype for each of them; a skilled inspection of the prototypes defines the signal and background. The second one, a multi layer perceptron (MLP) with a single hidden layer, adapts the classification model based on training/test data to the real experimental conditions. The MLP synaptic weights adaptive formation takes into account the labelled real data set as defined in the first system-phase. The modular neural system has been applied in the context of TRAMP-Si experiment, performed on the NASA Balloon-Borne Magnet Facility, for the positron/proton discrimination.

  6. Acoustic-Seismic Coupling of Broadband Signals - Analysis of Potential Disturbances during CTBT On-Site Inspection Measurements

    NASA Astrophysics Data System (ADS)

    Liebsch, Mattes; Altmann, Jürgen

    2015-04-01

    For the verification of the Comprehensive Nuclear Test Ban Treaty (CTBT) the precise localisation of possible underground nuclear explosion sites is important. During an on-site inspection (OSI) sensitive seismic measurements of aftershocks can be performed, which, however, can be disturbed by other signals. To improve the quality and effectiveness of these measurements it is essential to understand those disturbances so that they can be reduced or prevented. In our work we focus on disturbing signals caused by airborne sources: When the sound of aircraft (as often used by the inspectors themselves) hits the ground, it propagates through pores in the soil. Its energy is transferred to the ground and soil vibrations are created which can mask weak aftershock signals. The understanding of the coupling of acoustic waves to the ground is still incomplete. However, it is necessary to improve the performance of an OSI, e.g. to address potential consequences for the sensor placement, the helicopter trajectories etc. We present our recent advances in this field. We performed several measurements to record sound pressure and soil velocity produced by various sources, e.g. broadband excitation by jet aircraft passing overhead and signals artificially produced by a speaker. For our experimental set-up microphones were placed close to the ground and geophones were buried in different depths in the soil. Several sensors were shielded from the directly incident acoustic signals by a box coated with acoustic damping material. While sound pressure under the box was strongly reduced, the soil velocity measured under the box was just slightly smaller than outside of it. Thus these soil vibrations were mostly created outside the box and travelled through the soil to the sensors. This information is used to estimate characteristic propagation lengths of the acoustically induced signals in the soil. In the seismic data we observed interference patterns which are likely caused by the

  7. Feature extraction and classification for EEG signals using wavelet transform and machine learning techniques.

    PubMed

    Amin, Hafeez Ullah; Malik, Aamir Saeed; Ahmad, Rana Fayyaz; Badruddin, Nasreen; Kamel, Nidal; Hussain, Muhammad; Chooi, Weng-Tink

    2015-03-01

    This paper describes a discrete wavelet transform-based feature extraction scheme for the classification of EEG signals. In this scheme, the discrete wavelet transform is applied on EEG signals and the relative wavelet energy is calculated in terms of detailed coefficients and the approximation coefficients of the last decomposition level. The extracted relative wavelet energy features are passed to classifiers for the classification purpose. The EEG dataset employed for the validation of the proposed method consisted of two classes: (1) the EEG signals recorded during the complex cognitive task--Raven's advance progressive metric test and (2) the EEG signals recorded in rest condition--eyes open. The performance of four different classifiers was evaluated with four performance measures, i.e., accuracy, sensitivity, specificity and precision values. The accuracy was achieved above 98 % by the support vector machine, multi-layer perceptron and the K-nearest neighbor classifiers with approximation (A4) and detailed coefficients (D4), which represent the frequency range of 0.53-3.06 and 3.06-6.12 Hz, respectively. The findings of this study demonstrated that the proposed feature extraction approach has the potential to classify the EEG signals recorded during a complex cognitive task by achieving a high accuracy rate. PMID:25649845

  8. On the application of optimal wavelet filter banks for ECG signal classification

    NASA Astrophysics Data System (ADS)

    Hadjiloucas, S.; Jannah, N.; Hwang, F.; Galvão, R. K. H.

    2014-03-01

    This paper discusses ECG signal classification after parametrizing the ECG waveforms in the wavelet domain. Signal decomposition using perfect reconstruction quadrature mirror filter banks can provide a very parsimonious representation of ECG signals. In the current work, the filter parameters are adjusted by a numerical optimization algorithm in order to minimize a cost function associated to the filter cut-off sharpness. The goal consists of achieving a better compromise between frequency selectivity and time resolution at each decomposition level than standard orthogonal filter banks such as those of the Daubechies and Coiflet families. Our aim is to optimally decompose the signals in the wavelet domain so that they can be subsequently used as inputs for training to a neural network classifier.

  9. Method for simultaneously making a plurality of acoustic signal sensor elements

    NASA Technical Reports Server (NTRS)

    Bryant, Timothy D. (Inventor); Wynkoop, Mark W. (Inventor); Holloway, Nancy M. H. (Inventor); Zuckerwar, Allan J. (Inventor)

    2005-01-01

    A fetal heart monitoring system preferably comprising a backing plate having a generally concave front surface and a generally convex back surface, and at least one sensor element attached to the concave front surface for acquiring acoustic fetal heart signals produced by a fetus within a body. The sensor element has a shape that conforms to the generally concave back surface of the backing plate. In one embodiment, the at least one sensor element comprises an inner sensor, and a plurality of outer sensors surrounding the inner sensor. The fetal heart monitoring system can further comprise a web belt, and a web belt guide movably attached to the web belt. The web belt guide being is to the convex back surface of the backing plate.

  10. Acoustic signals generated in piezoelectric lead zirconate titanate elements by direct bombardment with xenon ions

    NASA Astrophysics Data System (ADS)

    Miyachi, T.; Nakamura, Y.; Kuraza, G.; Fujii, M.; Nagashima, A.; Hasebe, N.; Kobayashi, M. N.; Kobayashi, S.; Miyajima, M.; Mori, K.; Okudaira, O.; Yamashita, N.; Shibata, H.; Murakami, T.; Uchihori, Y.; Okada, N.

    2006-12-01

    Acoustic signals were observed with a lead-zirconate-titanate (PZT) element that was directly irradiated with a 368 MeV/n xenon beam. Using an array comprising PZT elements, the energy loss in the PZT was studied. These elements are sensitive to an energy deposit of 100 nJ. A series of values of output voltage vs. integrated thickness of PZT was represented along a line similar to the ionization loss calculated by the Bethe-Bloch formula. The induced voltage was attributed to several processes—ionization, thermal, elastic, and piezoelectric processes. This study describes the possible applications of the PZT element as an active medium for calorimeters and a monitor for hypervelocity impact of space dust.

  11. Multiplex transmission system for gate drive signals of inverter circuit using surface acoustic wave filters

    NASA Astrophysics Data System (ADS)

    Suzuki, Akifumi; Ueda, Kensuke; Goka, Shigeyoshi; Wada, Keiji; Kakio, Shoji

    2016-07-01

    We propose and fabricate a multiplexed transmission system based on frequency-division multiple access (FDMA) with surface acoustic wave (SAW) filters. SAW filters are suitable for use in wide-gap switching devices and multilevel inverters because of their capability to operate at high temperatures, good electrical isolation, low cost, and high reliability. Our proposed system reduces the number of electrical signal wires needed to control each switching device and eliminates the need for isolation circuits, simplifying the transmission system and gate drive circuits. We successfully controlled two switching devices with a single coaxial line and confirmed the operation of a single-phase half-bridge inverter at a supply voltage of 100 V, and the total delay time to control the switching devices was less than 2.5 µs. Our experimental results validated our proposed system.

  12. Evolution of the electron acoustic signal as function of doping level in III-V semiconductors

    SciTech Connect

    Bresse, J.F.; Papadopoulo, A.C.

    1988-07-01

    The evolution of the electron acoustic signal has been measured for Be- and Si-doped GaAs and Ga/sub 0.28/Al/sub 0.19/In/sub 0.53/As layers with doping levels from10/sup 17/ to 10/sup 20/ at. cm/sup -3/. The samples have also been analyzed by cathodoluminescence spectroscopy for near-band-edge transition and deep level emission. The results are explained by the reduction of the mean free path of phonons, giving rise to a lattice thermal conductivity decrease. Meanwhile, the electronic part of the thermal conductivity of these compounds is found to be nearly negligible.

  13. Efficient blind dereverberation and echo cancellation based on independent component analysis for actual acoustic signals.

    PubMed

    Takeda, Ryu; Nakadai, Kazuhiro; Takahashi, Toru; Komatani, Kazunori; Ogata, Tetsuya; Okuno, Hiroshi G

    2012-01-01

    This letter presents a new algorithm for blind dereverberation and echo cancellation based on independent component analysis (ICA) for actual acoustic signals. We focus on frequency domain ICA (FD-ICA) because its computational cost and speed of learning convergence are sufficiently reasonable for practical applications such as hands-free speech recognition. In applying conventional FD-ICA as a preprocessing of automatic speech recognition in noisy environments, one of the most critical problems is how to cope with reverberations. To extract a clean signal from the reverberant observation, we model the separation process in the short-time Fourier transform domain and apply the multiple input/output inverse-filtering theorem (MINT) to the FD-ICA separation model. A naive implementation of this method is computationally expensive, because its time complexity is the second order of reverberation time. Therefore, the main issue in dereverberation is to reduce the high computational cost of ICA. In this letter, we reduce the computational complexity to the linear order of the reverberation time by using two techniques: (1) a separation model based on the independence of delayed observed signals with MINT and (2) spatial sphering for preprocessing. Experiments show that the computational cost grows in proportion to the linear order of the reverberation time and that our method improves the word correctness of automatic speech recognition by 10 to 20 points in a RT₂₀= 670 ms reverberant environment. PMID:22023192

  14. Implementing wavelet packet transform for valve failure detection using vibration and acoustic emission signals

    NASA Astrophysics Data System (ADS)

    Sim, H. Y.; Ramli, R.; Abdullah, M. A. K.

    2012-05-01

    The efficiency of reciprocating compressors relies heavily on the health condition of its moving components, most importantly its valves. Previous studies showed good correlation between the dynamic response and the physical condition of the valves. These can be achieved by employing vibration technique which is capable of monitoring the response of the valve, and acoustic emission technique which is capable of detecting the valves' material deformation. However, the relationship/comparison between the two techniques is rarely investigated. In this paper, the two techniques were examined using time-frequency analysis. Wavelet packet transform (WPT) was chosen as the multi-resolution analysis technique over continuous wavelet transform (CWT), and discrete wavelet transform (DWT). This is because WPT could overcome the high computational time and high redundancy problem in CWT and could provide detailed analysis of the high frequency components compared to DWT. The features of both signals can be extracted by evaluating the normalised WPT coefficients for different time window under different valve conditions. By comparing the normalised coefficients over a certain time frame and frequency range, the feature vectors revealing the condition of valves can be constructed. One way analysis of variance was employed on these feature vectors to test the significance of data under different valve conditions. It is believed that AE signals can give a better representation of the valve condition as it can detect both the fluid motion and material deformation of valves as compared to the vibration signals.

  15. The Acoustic Signal of a Helicopter can be Used to Track it With Seismic Arrays

    NASA Astrophysics Data System (ADS)

    Eibl, Eva P. S.; Lokmer, Ivan; Bean, Christopher J.; Akerlie, Eggert

    2016-04-01

    We apply traditional frequency domain methods usually applied to volcanic tremor on seismic recordings of a helicopter. On a volcano the source can be repeating, closely spaced earthquakes whereas for a helicopter the source are repeating pressure pulses from the rotor blades that are converted through acoustic-to-seismic coupling. In both cases the seismic signal is referred to as tremor. As frequency gliding is in this case merely caused by the Doppler effect, not a change in the source, we can use its shape to deduce properties of the helicopter. We show in this analysis that the amount of rotor blades, rotor revolutions per minute (RPM), flight direction, height and location can be deduced. The signal was recorded by a seven station broadband array with an aperture of 1.6 km. Our spacing is close enough to record the signal at all stations and far enough to observe traveltime differences. We perform a detailed spectral and location analysis of the signal, and compare our results with the known information on the helicopter's speed, location, height, the frequency of the blades rotation and the amount of blades. This analysis is based on the characteristic shape of the curve i.e. speed of the gliding, minimum and maximum fundamental frequency, amplitudes at the inflection points at different stations and traveltimes deduced from the inflection points at different stations. The helicopter GPS track gives us a robust way of testing the method. This observation has an educative value, because the same principles can be applied to signals in different disciplines.

  16. Precursory Acoustic Signals Detection in Rockfall Events by Means of Optical Fiber Sensors

    NASA Astrophysics Data System (ADS)

    Schenato, L.; Marcato, G.; Gruca, G.; Iannuzzi, D.; Palmieri, L.; Galtarossa, A.; Pasuto, A.

    2012-12-01

    Rockfalls represent a major source of hazard in mountain areas: they occur at the apex of a process of stress accumulation in the unstable slope, during which part of the accumulated energy is released in small internal cracks. These cracks and the related acoustic emissions (AE) can, therefore, be used as precursory signals, through which the unstable rock could be monitored. In particular, according to previous scientific literature AE can be monitored in the range 20÷100 kHz. With respect to traditional AE sensors, such as accelerometers and piezoelectric transducers, fiber optic sensors (FOSs) may provide a reliable solution, potentially offering more robustness to electromagnetic interference, smaller form factor, multiplexing ability and increased distance range and higher sensitivity. To explore this possibility, in this work we have experimentally analyzed two interferometric fiber optical sensors for AE detection in rock masses. In particular, the first sensor is made of 100 m of G.657 optical fiber, tightly wound on an aluminum flanged hollow mandrel (inner diameter 30 mm, height 42 mm) that is isolated from the environment with acoustic absorbing material. A 4-cm-long M10 screw, which acts also as the main mean of acoustic coupling between the rock and the sensor, is used to fasten the sensor to the rock. This fiber coil sensor (FCS) is inserted in the sensing arm of a fiber Mach-Zehnder interferometer. The second sensor consists in a micro cantilever carved on the top of a cylindrical silica ferrule, with a marked mechanical resonance at about 12.5 kHz (Q-factor of about 400). A standard single mode fiber is housed in the same ferrule and the gap between the cantilever and the fiber end face acts as a vibration-sensitive Fabry-Perot cavity, interrogated with a low-coherence laser, tuned at the quadrature point of the cavity. The sensor is housed in a 2-cm-long M10 bored bolt. Performance have been compared with those from a standard piezo

  17. Intelligent feature selection techniques for pattern classification of Lamb wave signals

    SciTech Connect

    Hinders, Mark K.; Miller, Corey A.

    2014-02-18

    Lamb wave interaction with flaws is a complex, three-dimensional phenomenon, which often frustrates signal interpretation schemes based on mode arrival time shifts predicted by dispersion curves. As the flaw severity increases, scattering and mode conversion effects will often dominate the time-domain signals, obscuring available information about flaws because multiple modes may arrive on top of each other. Even for idealized flaw geometries the scattering and mode conversion behavior of Lamb waves is very complex. Here, multi-mode Lamb waves in a metal plate are propagated across a rectangular flat-bottom hole in a sequence of pitch-catch measurements corresponding to the double crosshole tomography geometry. The flaw is sequentially deepened, with the Lamb wave measurements repeated at each flaw depth. Lamb wave tomography reconstructions are used to identify which waveforms have interacted with the flaw and thereby carry information about its depth. Multiple features are extracted from each of the Lamb wave signals using wavelets, which are then fed to statistical pattern classification algorithms that identify flaw severity. In order to achieve the highest classification accuracy, an optimal feature space is required but it’s never known a priori which features are going to be best. For structural health monitoring we make use of the fact that physical flaws, such as corrosion, will only increase over time. This allows us to identify feature vectors which are topologically well-behaved by requiring that sequential classes “line up” in feature vector space. An intelligent feature selection routine is illustrated that identifies favorable class distributions in multi-dimensional feature spaces using computational homology theory. Betti numbers and formal classification accuracies are calculated for each feature space subset to establish a correlation between the topology of the class distribution and the corresponding classification accuracy.

  18. Intelligent feature selection techniques for pattern classification of Lamb wave signals

    NASA Astrophysics Data System (ADS)

    Hinders, Mark K.; Miller, Corey A.

    2014-02-01

    Lamb wave interaction with flaws is a complex, three-dimensional phenomenon, which often frustrates signal interpretation schemes based on mode arrival time shifts predicted by dispersion curves. As the flaw severity increases, scattering and mode conversion effects will often dominate the time-domain signals, obscuring available information about flaws because multiple modes may arrive on top of each other. Even for idealized flaw geometries the scattering and mode conversion behavior of Lamb waves is very complex. Here, multi-mode Lamb waves in a metal plate are propagated across a rectangular flat-bottom hole in a sequence of pitch-catch measurements corresponding to the double crosshole tomography geometry. The flaw is sequentially deepened, with the Lamb wave measurements repeated at each flaw depth. Lamb wave tomography reconstructions are used to identify which waveforms have interacted with the flaw and thereby carry information about its depth. Multiple features are extracted from each of the Lamb wave signals using wavelets, which are then fed to statistical pattern classification algorithms that identify flaw severity. In order to achieve the highest classification accuracy, an optimal feature space is required but it's never known a priori which features are going to be best. For structural health monitoring we make use of the fact that physical flaws, such as corrosion, will only increase over time. This allows us to identify feature vectors which are topologically well-behaved by requiring that sequential classes "line up" in feature vector space. An intelligent feature selection routine is illustrated that identifies favorable class distributions in multi-dimensional feature spaces using computational homology theory. Betti numbers and formal classification accuracies are calculated for each feature space subset to establish a correlation between the topology of the class distribution and the corresponding classification accuracy.

  19. Estimation of glottal source features from the spectral envelope of the acoustic speech signal

    NASA Astrophysics Data System (ADS)

    Torres, Juan Felix

    Speech communication encompasses diverse types of information, including phonetics, affective state, voice quality, and speaker identity. From a speech production standpoint, the acoustic speech signal can be mainly divided into glottal source and vocal tract components, which play distinct roles in rendering the various types of information it contains. Most deployed speech analysis systems, however, do not explicitly represent these two components as distinct entities, as their joint estimation from the acoustic speech signal becomes an ill-defined blind deconvolution problem. Nevertheless, because of the desire to understand glottal behavior and how it relates to perceived voice quality, there has been continued interest in explicitly estimating the glottal component of the speech signal. To this end, several inverse filtering (IF) algorithms have been proposed, but they are unreliable in practice because of the blind formulation of the separation problem. In an effort to develop a method that can bypass the challenging IF process, this thesis proposes a new glottal source information extraction method that relies on supervised machine learning to transform smoothed spectral representations of speech, which are already used in some of the most widely deployed and successful speech analysis applications, into a set of glottal source features. A transformation method based on Gaussian mixture regression (GMR) is presented and compared to current IF methods in terms of feature similarity, reliability, and speaker discrimination capability on a large speech corpus, and potential representations of the spectral envelope of speech are investigated for their ability represent glottal source variation in a predictable manner. The proposed system was found to produce glottal source features that reasonably matched their IF counterparts in many cases, while being less susceptible to spurious errors. The development of the proposed method entailed a study into the aspects

  20. Acoustic signal propagation and measurement in natural stream channels for application to surrogate bed load measurements: Halfmoon Creek, Colorado.

    Technology Transfer Automated Retrieval System (TEKTRAN)

    Monitoring sediment-generated noise using submerged hydrophones is a surrogate method for measuring bed load transport in streams with the potential for improving estimates of bed load transport through widespread, inexpensive monitoring. Understanding acoustic signal propagation in natural stream e...

  1. pySPACE—a signal processing and classification environment in Python

    PubMed Central

    Krell, Mario M.; Straube, Sirko; Seeland, Anett; Wöhrle, Hendrik; Teiwes, Johannes; Metzen, Jan H.; Kirchner, Elsa A.; Kirchner, Frank

    2013-01-01

    In neuroscience large amounts of data are recorded to provide insights into cerebral information processing and function. The successful extraction of the relevant signals becomes more and more challenging due to increasing complexities in acquisition techniques and questions addressed. Here, automated signal processing and machine learning tools can help to process the data, e.g., to separate signal and noise. With the presented software pySPACE (http://pyspace.github.io/pyspace), signal processing algorithms can be compared and applied automatically on time series data, either with the aim of finding a suitable preprocessing, or of training supervised algorithms to classify the data. pySPACE originally has been built to process multi-sensor windowed time series data, like event-related potentials from the electroencephalogram (EEG). The software provides automated data handling, distributed processing, modular build-up of signal processing chains and tools for visualization and performance evaluation. Included in the software are various algorithms like temporal and spatial filters, feature generation and selection, classification algorithms, and evaluation schemes. Further, interfaces to other signal processing tools are provided and, since pySPACE is a modular framework, it can be extended with new algorithms according to individual needs. In the presented work, the structural hierarchies are described. It is illustrated how users and developers can interface the software and execute offline and online modes. Configuration of pySPACE is realized with the YAML format, so that programming skills are not mandatory for usage. The concept of pySPACE is to have one comprehensive tool that can be used to perform complete signal processing and classification tasks. It further allows to define own algorithms, or to integrate and use already existing libraries. PMID:24399965

  2. pySPACE-a signal processing and classification environment in Python.

    PubMed

    Krell, Mario M; Straube, Sirko; Seeland, Anett; Wöhrle, Hendrik; Teiwes, Johannes; Metzen, Jan H; Kirchner, Elsa A; Kirchner, Frank

    2013-01-01

    In neuroscience large amounts of data are recorded to provide insights into cerebral information processing and function. The successful extraction of the relevant signals becomes more and more challenging due to increasing complexities in acquisition techniques and questions addressed. Here, automated signal processing and machine learning tools can help to process the data, e.g., to separate signal and noise. With the presented software pySPACE (http://pyspace.github.io/pyspace), signal processing algorithms can be compared and applied automatically on time series data, either with the aim of finding a suitable preprocessing, or of training supervised algorithms to classify the data. pySPACE originally has been built to process multi-sensor windowed time series data, like event-related potentials from the electroencephalogram (EEG). The software provides automated data handling, distributed processing, modular build-up of signal processing chains and tools for visualization and performance evaluation. Included in the software are various algorithms like temporal and spatial filters, feature generation and selection, classification algorithms, and evaluation schemes. Further, interfaces to other signal processing tools are provided and, since pySPACE is a modular framework, it can be extended with new algorithms according to individual needs. In the presented work, the structural hierarchies are described. It is illustrated how users and developers can interface the software and execute offline and online modes. Configuration of pySPACE is realized with the YAML format, so that programming skills are not mandatory for usage. The concept of pySPACE is to have one comprehensive tool that can be used to perform complete signal processing and classification tasks. It further allows to define own algorithms, or to integrate and use already existing libraries. PMID:24399965

  3. Automated Classification of Medical Percussion Signals for the Diagnosis of Pulmonary Injuries

    NASA Astrophysics Data System (ADS)

    Bhuiyan, Md Moinuddin

    Used for centuries in the clinical practice, audible percussion is a method of eliciting sounds by areas of the human body either by finger tips or by a percussion hammer. Despite its advantages, pulmonary diagnostics by percussion is still highly subjective, depends on the physician's skills, and requires quiet surroundings. Automation of this well-established technique could help amplify its existing merits while removing the above drawbacks. In this study, an attempt is made to automatically decompose clinical percussion signals into a sum of Exponentially Damped Sinusoids (EDS) using Matrix Pencil Method, which in this case form a more natural basis than Fourier harmonics and thus allow for a more robust representation of the signal in the parametric space. It is found that some EDS represent transient oscillation modes of the thorax/abdomen excited by the percussion event, while others are associated with the noise. It is demonstrated that relatively few EDS are usually enough to accurately reconstruct the original signal. It is shown that combining the frequency and damping parameters of these most significant EDS allows for efficient classification of percussion signals into the two main types historically known as "resonant" and "tympanic". This classification ability can provide a basis for the automated objective diagnostics of various pulmonary pathologies including pneumothorax.

  4. Matched signal detection on graphs: Theory and application to brain imaging data classification.

    PubMed

    Hu, Chenhui; Sepulcre, Jorge; Johnson, Keith A; Fakhri, Georges E; Lu, Yue M; Li, Quanzheng

    2016-01-15

    Motivated by recent progress in signal processing on graphs, we have developed a matched signal detection (MSD) theory for signals with intrinsic structures described by weighted graphs. First, we regard graph Laplacian eigenvalues as frequencies of graph-signals and assume that the signal is in a subspace spanned by the first few graph Laplacian eigenvectors associated with lower eigenvalues. The conventional matched subspace detector can be applied to this case. Furthermore, we study signals that may not merely live in a subspace. Concretely, we consider signals with bounded variation on graphs and more general signals that are randomly drawn from a prior distribution. For bounded variation signals, the test is a weighted energy detector. For the random signals, the test statistic is the difference of signal variations on associated graphs, if a degenerate Gaussian distribution specified by the graph Laplacian is adopted. We evaluate the effectiveness of the MSD on graphs both with simulated and real data sets. Specifically, we apply MSD to the brain imaging data classification problem of Alzheimer's disease (AD) based on two independent data sets: 1) positron emission tomography data with Pittsburgh compound-B tracer of 30 AD and 40 normal control (NC) subjects, and 2) resting-state functional magnetic resonance imaging (R-fMRI) data of 30 early mild cognitive impairment and 20 NC subjects. Our results demonstrate that the MSD approach is able to outperform the traditional methods and help detect AD at an early stage, probably due to the success of exploiting the manifold structure of the data. PMID:26481679

  5. Sleep apnea classification using ECG-signal wavelet-PCA features.

    PubMed

    Rachim, Vega Pradana; Li, Gang; Chung, Wan-Young

    2014-01-01

    Sleep apnea is often diagnosed using an overnight sleep test called a polysomnography (PSG). Unfortunately, though it is the gold standard of sleep disorder diagnosis, a PSG is time consuming, inconvenient, and expensive. Many researchers have tried to ameliorate this problem by developing other reliable methods, such as using electrocardiography (ECG) as an observed signal source. Respiratory rate interval, ECG-derived respiration, and heart rate variability analysis have been studied recently as a means of detecting apnea events using ECG during normal sleep, but these methods have performance weaknesses. Thus, the aim of this study is to classify the subject into normal- or apnea-subject based on their single-channel ECG measurement in regular sleep. In this proposed study, ECG is decomposed into five levels using wavelet decomposition for the initial processing to determine the detail coefficients (D3-D5) of the signal. Approximately 15 features were extracted from every minute of ECG. Principal component analysis and a support vector machine are used for feature dimension reduction and classification, respectively. According to classification that been done from a data set consisting of thirty-five patients, the proposed minute-to-minute classifier specificity, sensitivity, and subject-based classification accuracy are 95.20%, 92.65%, and 94.3%, respectively. Furthermore, the proposed system can be used as a basis for future development of sleep apnea screening tools. PMID:25226993

  6. Support-vector-machines-based multidimensional signal classification for fetal activity characterization

    NASA Astrophysics Data System (ADS)

    Ribes, S.; Voicu, I.; Girault, J. M.; Fournier, M.; Perrotin, F.; Tranquart, F.; Kouamé, D.

    2011-03-01

    Electronic fetal monitoring may be required during the whole pregnancy to closely monitor specific fetal and maternal disorders. Currently used methods suffer from many limitations and are not sufficient to evaluate fetal asphyxia. Fetal activity parameters such as movements, heart rate and associated parameters are essential indicators of the fetus well being, and no current device gives a simultaneous and sufficient estimation of all these parameters to evaluate the fetus well-being. We built for this purpose, a multi-transducer-multi-gate Doppler system and developed dedicated signal processing techniques for fetal activity parameter extraction in order to investigate fetus's asphyxia or well-being through fetal activity parameters. To reach this goal, this paper shows preliminary feasibility of separating normal and compromised fetuses using our system. To do so, data set consisting of two groups of fetal signals (normal and compromised) has been established and provided by physicians. From estimated parameters an instantaneous Manning-like score, referred to as ultrasonic score was introduced and was used together with movements, heart rate and associated parameters in a classification process using Support Vector Machines (SVM) method. The influence of the fetal activity parameters and the performance of the SVM were evaluated using the computation of sensibility, specificity, percentage of support vectors and total classification accuracy. We showed our ability to separate the data into two sets : normal fetuses and compromised fetuses and obtained an excellent matching with the clinical classification performed by physician.

  7. Seismic Signal Classification with Offshore/Amphibious Networks Using an Artificial Neural Network

    NASA Astrophysics Data System (ADS)

    Williams, M. C.; Trehu, A. M.

    2011-12-01

    The amphibious Central Oregon Locked Zone Array (COLZA) of seismic stations was deployed from 2007-2009 to record earthquakes occurring in the seismogenic zone offshore central Oregon. This array included two year-long deployments of ocean bottom seismometers (OBS's) from the NSF OBSIP. In addition to local and distant earthquakes, the OBS array recorded thousands of impulsive local signals, which are not easily filtered out by a standard STA/LTA detection algorithm. Many of these signals are likely of biological origin (informally referred to as "bio-bumps"). These signals have a wide range of amplitudes, can mask local earthquake phase arrivals, and make automatic detection more difficult. We show that signal characteristics derived from 3-component seismic data at each station can be used to filter out event detections that are unlikely to be earthquake-generated. A decision-making algorithm is run using a joint set of signal characteristics to identify possible local events and classify detections that are likely to be "bumps". We present results on the effectiveness of this classification technique using various combinations of input parameters applied to the onshore/offshore COLZA array dataset. The classification algorithm is a multilayer perceptron (MLP) artificial neural network, trained through backpropagation using human-identified examples of both earthquake phases and impulsive "bumps". The effectiveness of a neural network is highly dependent on the data space consisting of the inputs calculated for each signal, which represent its main characteristics and differentiate it from other events. As inputs to the neural network, for each event detection, in addition to the STA/LTA value, we determine three signal characteristics from 3-component waveform data: the variance of the power cepstrum calculated from a portion of the signal spectrum, the rectilinearity of particle motion, and the ratio of particle motion orthogonal to the principle direction of

  8. Real-time classification of signals from three-component seismic sensors using neural nets

    SciTech Connect

    Bowman, B.C.; Dowla, F.

    1992-05-05

    Adaptive seismic data acquisition systems with capabilities of signal discrimination and event classification are important in treaty monitoring, proliferation, and earthquake early detection systems. Potential applications include monitoring underground chemical explosions, as well as other military, cultural, and natural activities where characteristics of signals change rapidly and without warning. In these applications, the ability to detect and interpret events rapidly without falling behind the influx of the data is critical. We developed a system for real-time data acquisition, analysis, learning, and classification of recorded events employing some of the latest technology in computer hardware, software, and artificial neural networks methods. The system is able to train dynamically, and updates its knowledge based on new data. The software is modular and hardware-independent; i.e., the front-end instrumentation is transparent to the analysis system. The software is designed to take advantage of the multiprocessing environment of the Unix operating system. The Unix System V shared memory and static RAM protocols for data access and the semaphore mechanism for interprocess communications were used. As the three-component sensor detects a seismic signal, it is displayed graphically on a color monitor using X11/Xlib graphics with interactive screening capabilities. For interesting events, the triaxial signal polarization is computed, a fast Fourier Transform (FFT) algorithm is applied, and the normalized power spectrum is transmitted to a backpropagation neural network for event classification. The system is currently capable of handling three data channels with a sampling rate of 500 Hz, which covers the bandwidth of most seismic events. The system has been tested in laboratory setting with artificial events generated in the vicinity of a three-component sensor.

  9. Feature extraction and classification of sEMG signals applied to a virtual hand prosthesis.

    PubMed

    Tello, Richard M G; Bastos-Filho, Teodiano; Frizera-Neto, Anselmo; Arjunan, Sridhar; Kumar, Dinesh K

    2013-01-01

    This paper presents the classification of motor tasks, using surface electromyography (sEMG) to control a virtual prosthetic hand for rehabilitation of amputees. Two types of classifiers are compared: k-Nearest Neighbor (k-NN) and Bayesian (Discriminant Analysis). Motor tasks are divided into four groups correlated. The volunteers were people without amputation and several analyzes of each of the signals were conducted. The online simulations use the sliding window technique and for feature extraction RMS (Root Mean Square), VAR (Variance) and WL (Waveform Length) values were used. A model is proposed for reclassification using cross-validation in order to validate the classification, and a visualization in Sammon Maps is provided in order to observe the separation of the classes for each set of motor tasks. Finally, the proposed method can be implemented in a computer interface providing a visual feedback through an virtual hand prosthetic developed in Visual C++ and MATLAB commands. PMID:24110086

  10. Automated Detection and Classification of Rockfall Induced Seismic Signals with Hidden-Markov-Models

    NASA Astrophysics Data System (ADS)

    Zeckra, M.; Hovius, N.; Burtin, A.; Hammer, C.

    2015-12-01

    Originally introduced in speech recognition, Hidden Markov Models are applied in different research fields of pattern recognition. In seismology, this technique has recently been introduced to improve common detection algorithms, like STA/LTA ratio or cross-correlation methods. Mainly used for the monitoring of volcanic activity, this study is one of the first applications to seismic signals induced by geomorphologic processes. With an array of eight broadband seismometers deployed around the steep Illgraben catchment (Switzerland) with high-level erosion, we studied a sequence of landslides triggered over a period of several days in winter. A preliminary manual classification led us to identify three main seismic signal classes that were used as a start for the HMM automated detection and classification: (1) rockslide signal, including a failure source and the debris mobilization along the slope, (2) rockfall signal from the remobilization of debris along the unstable slope, and (3) single cracking signal from the affected cliff observed before the rockslide events. Besides the ability to classify the whole dataset automatically, the HMM approach reflects the origin and the interactions of the three signal classes, which helps us to understand this geomorphic crisis and the possible triggering mechanisms for slope processes. The temporal distribution of crack events (duration > 5s, frequency band [2-8] Hz) follows an inverse Omori law, leading to the catastrophic behaviour of the failure mechanisms and the interest for warning purposes in rockslide risk assessment. Thanks to a dense seismic array and independent weather observations in the landslide area, this dataset also provides information about the triggering mechanisms, which exhibit a tight link between rainfall and freezing level fluctuations.

  11. Phenotypic covariance structure and its divergence for acoustic mate attraction signals among four cricket species

    PubMed Central

    Bertram, Susan M; Fitzsimmons, Lauren P; McAuley, Emily M; Rundle, Howard D; Gorelick, Root

    2012-01-01

    The phenotypic variance–covariance matrix (P) describes the multivariate distribution of a population in phenotypic space, providing direct insight into the appropriateness of measured traits within the context of multicollinearity (i.e., do they describe any significant variance that is independent of other traits), and whether trait covariances restrict the combinations of phenotypes available to selection. Given the importance of P, it is therefore surprising that phenotypic covariances are seldom jointly analyzed and that the dimensionality of P has rarely been investigated in a rigorous statistical framework. Here, we used a repeated measures approach to quantify P separately for populations of four cricket species using seven acoustic signaling traits thought to enhance mate attraction. P was of full or almost full dimensionality in all four species, indicating that all traits conveyed some information that was independent of the other traits, and that phenotypic trait covariances do not constrain the combinations of signaling traits available to selection. P also differed significantly among species, although the dominant axis of phenotypic variation (pmax) was largely shared among three of the species (Acheta domesticus, Gryllus assimilis, G. texensis), but different in the fourth (G. veletis). In G. veletis and A. domesticus, but not G. assimilis and G. texensis, pmax was correlated with body size, while pmax was not correlated with residual mass (a condition measure) in any of the species. This study reveals the importance of jointly analyzing phenotypic traits. PMID:22408735

  12. Wintertime water dynamics and moonlight disruption of the acoustic backscatter diurnal signal in an ice-covered Northeast Greenland fjord

    NASA Astrophysics Data System (ADS)

    Petrusevich, Vladislav; Dmitrenko, Igor; Kirillov, Sergey; Rysgaard, Søren; Falk-Petersen, Stig; Barber, David; Ehn, Jens

    2016-04-01

    Six and a half month time series of acoustic backscatter and velocity from three ice-tethered Acoustic Doppler Current Profilers deployed in the Young Sound fjord in Northeast Greenland were used to analyse the acoustic signal. During period of civil polar night below the land-fast ice, the acoustic data suggest a systematic diel vertical migration (DVM) of backscatters likely comprised of zooplankton. The acoustic backscatter and vertical velocity data were also arranged in a form of actograms. Results show that the acoustic signal pattern typical to DVM in Young Sound persists throughout the entire winter including the period of civil polar night. However, polynya-enhanced estuarine-like cell circulation that occurred during winter disrupted the DVM signal favouring zooplankton to occupy the near-surface water layer. This suggests that zooplankton avoided spending additional energy crossing the interface with a relatively strong velocity gradient comprised by fjord inflow in the intermediate layer and outflow in the subsurface layer. Instead the zooplankton tended to favour remaining in the upper 40 m layer where also the relatively warmer water temperatures associated with upward heat flux during enhanced estuarine-like circulation could be energetically favourable. Furthermore, our data show moonlight disruption of DVM in the subsurface layer and weaker intensity of vertical migration beneath snow covered land-fast ice during polar night. Using existing models for lunar illuminance and light transmission through sea ice and snow cover we estimated under ice illuminance and compared it with known light sensitivity for Arctic zooplankton species.

  13. Classification of alkali-silica reaction and corrosion distress using acoustic emission

    NASA Astrophysics Data System (ADS)

    Abdelrahman, Marwa; ElBatanouny, Mohamed; Serrato, Michael; Dixon, Kenneth; Larosche, Carl; Ziehl, Paul

    2016-02-01

    The Nuclear Regulatory Commission regulates approximately 100 commercial nuclear power reactor facilities that contribute about 20% of the total electric energy produced in the United States. Half of these reactor facilities are over 30 years old and are approaching their original design service life. Due to economic and durability considerations, significant portions of many of the facilities were constructed with reinforced concrete, including the containment facilities, cooling towers, and foundations. While most of these concrete facilities have performed exceptionally well throughout their initial expected service life, some are beginning to exhibit different forms of concrete deterioration. In this study, acoustic emission (AE) is used to monitor two main concrete deterioration mechanisms; alkali-silica reaction (ASR) distress and corrosion of reinforcing steel. An accelerated ASR test was conducted where specimens were continuously monitored with AE. The results show that AE can detect and classify damage due to ASR distress in the specimens. AE was also used to remotely monitor active corrosion regions in a reactor facility. AE monitoring of accelerated corrosion testing was also conducted on a concrete block specimen cut from a similar reactor building. Electrochemical measurements were conducted to correlate AE activity to quantifiable corrosion measurements and to enhance capabilities for service life prediction.

  14. Classification of large acoustic datasets using machine learning and crowdsourcing: application to whale calls.

    PubMed

    Shamir, Lior; Yerby, Carol; Simpson, Robert; von Benda-Beckmann, Alexander M; Tyack, Peter; Samarra, Filipa; Miller, Patrick; Wallin, John

    2014-02-01

    Vocal communication is a primary communication method of killer and pilot whales, and is used for transmitting a broad range of messages and information for short and long distance. The large variation in call types of these species makes it challenging to categorize them. In this study, sounds recorded by audio sensors carried by ten killer whales and eight pilot whales close to the coasts of Norway, Iceland, and the Bahamas were analyzed using computer methods and citizen scientists as part of the Whale FM project. Results show that the computer analysis automatically separated the killer whales into Icelandic and Norwegian whales, and the pilot whales were separated into Norwegian long-finned and Bahamas short-finned pilot whales, showing that at least some whales from these two locations have different acoustic repertoires that can be sensed by the computer analysis. The citizen science analysis was also able to separate the whales to locations by their sounds, but the separation was somewhat less accurate compared to the computer method. PMID:25234903

  15. Identification of Damaged Wheat Kernels and Cracked-Shell Hazelnuts with Impact Acoustics Time-Frequency Patterns

    Technology Transfer Automated Retrieval System (TEKTRAN)

    A new adaptive time-frequency (t-f) analysis and classification procedure is applied to impact acoustic signals for detecting hazelnuts with cracked shells and three types of damaged wheat kernels. Kernels were dropped onto a steel plate, and the resulting impact acoustic signals were recorded with ...

  16. In vitro microemboli classification using neural network models and RF signals.

    PubMed

    Benoudjit, N; Ferroudji, K; Bahaz, M; Bouakaz, A

    2011-04-01

    Emboli classification is of high clinical importance for selecting appropriate treatment for patients. Several ultrasonic (US) methods using Doppler processing have been used for emboli detection and classification as solid or gaseous matter. We suggest in this experimental study exploiting the Radio-Frequency (RF) signal backscattered by the emboli since they contain additional information on the embolus than the Doppler signal. The aim of the study is the analysis of RF signals using Multilayer Perceptron (MLP) and Radial-Basis Function Network (RBFN) in order to classify emboli. Anthares scanner with RF access was used with a transmit frequency of 1.82MHz at two mechanical indices (MI) 0.2 and 0.6. The mechanical index is given as the peak negative pressure (in MPa) divided by the square root of the frequency (in MHz). A Doppler flow phantom was used containing a 0.8mm diameter vessel surrounded by a tissue mimicking material. To imitate gas emboli US behaviour, Sonovue microbubbles were injected at two different doses (10μl and 5μl) in a nonrecirculating at a constant flow. The surrounding tissue was assumed to behave as a solid emboli. In order to mimic real clinical pathological situations, Sonovue concentration was chosen such that the fundamental scattering from the tissue and from the contrast were identical. The amplitudes and bandwidths of the fundamental and the 2nd harmonic components were selected as input parameters to the MLP and RBFN models. Moreover the frequency bandwidths of the fundamental and the 2nd harmonic echoes were approximated by Gaussian functions and the coefficients were used as a third input parameter to the neural network models. The results show that the Gaussian coefficients provide the highest rate of classification in comparison to the amplitudes and the bandwidths of the fundamental and the 2nd harmonic components. The classification rates reached 89.28% and 92.85% with MLP and RBFN models respectively. This short

  17. Acoustic signal associated with the bursting of a soap film which initially closes an overpressurized cavity . Experiment and theory

    NASA Astrophysics Data System (ADS)

    Vidal, V.; Géminard, J.-C.; Divoux, T.; Melo, F.

    2006-12-01

    We report an experimental study of the sound produced by the bursting of a thin liquid film, which initially closes an overpressurized cylindrical cavity. There is a need for a deep understanding of the phenomenon, which can be very useful in numerous practical cases. For instance, in the nature, the volcanologists observe the bursting of large, elongated, gas-bubbles at the surface of lava lakes and record the associated sound emission. One can wonder which pieces of information they can get from such acoustic measurements. For a didactic purpose, we provide also the reader with all the theoretical background necessary for the understanding of the physical processes that govern the various characteristics of the acoustic signals: the cavity geometry governs the frequency; the viscous dissipation and the radiation are responsible for the damping; the acoustic energy informs about the characteristic time associated with the film-rupture more than about the energy initially loaded in the cavity.

  18. Flow Visualization and Acoustic Signal Detection in the Process of Drop Impact on the Surface of a Liquid

    NASA Astrophysics Data System (ADS)

    Prohorov, V. E.

    2012-04-01

    An experimental study of hydrophysical and acoustic phenomena produced by drop falling on the free water surface is of great practical importance with regard to rain intensity measurement and preparation of oceanic acoustic noises model. Key features of underwater flow associated with an acoustic emission can be revealed in the laboratory experiments under controllable reproducible conditions. The current paper describes the experiments in which the drops detach from a nozzle of 0.4 cm in diameter. The flows impact area is visualized by high speed video camera CR3000×2 whose frame rate varies from 4000 to 20000 fps. Acoustic signals are measured by calibrated hydrophone (bandpass from 2 Hz to 125 kHz) which is synchronized with the video camera by means of special PC interface supplied with multichannel 12-bit AD-convertor. The accuracy of synchronization is supported on the levels 1 µS. The total acoustic signal produced by drop consists of the initial (impact) pulse followed by one or more resonant sound packets emitted by air bubbles separating from the underwater cavity. Maximal number of packets fixed in the experiments is 4. Comparison of the video- and acoustic data show that resonant packets radiation is strongly timed to the moments of detachment of the air cavity from the underwater cavern formed in the process of absorption of the drop by intaking liquid. The detachment is followed by extremely high accelerations of the underwater cavity tip when it tears off the basic cavern. Acceleration is estimated at level 1000 m/S that matches pressure gradient jump initiated by accelerations is of an order of 10 Pa/m. Detached cavity is initially of irregular form but then turns to regular (elliptic or spherical) shape within some period during which the sound packet is emitted. The work is supported by Ministry of Education and Science RF (Goscontract No. 16.518.11.7059).

  19. Direct classification of all American English phonemes using signals from functional speech motor cortex

    NASA Astrophysics Data System (ADS)

    Mugler, Emily M.; Patton, James L.; Flint, Robert D.; Wright, Zachary A.; Schuele, Stephan U.; Rosenow, Joshua; Shih, Jerry J.; Krusienski, Dean J.; Slutzky, Marc W.

    2014-06-01

    Objective. Although brain-computer interfaces (BCIs) can be used in several different ways to restore communication, communicative BCI has not approached the rate or efficiency of natural human speech. Electrocorticography (ECoG) has precise spatiotemporal resolution that enables recording of brain activity distributed over a wide area of cortex, such as during speech production. In this study, we sought to decode elements of speech production using ECoG. Approach. We investigated words that contain the entire set of phonemes in the general American accent using ECoG with four subjects. Using a linear classifier, we evaluated the degree to which individual phonemes within each word could be correctly identified from cortical signal. Main results. We classified phonemes with up to 36% accuracy when classifying all phonemes and up to 63% accuracy for a single phoneme. Further, misclassified phonemes follow articulation organization described in phonology literature, aiding classification of whole words. Precise temporal alignment to phoneme onset was crucial for classification success. Significance. We identified specific spatiotemporal features that aid classification, which could guide future applications. Word identification was equivalent to information transfer rates as high as 3.0 bits s-1 (33.6 words min-1), supporting pursuit of speech articulation for BCI control.

  20. Signal processing Model/Method for Recovering Acoustic Reflectivity of Spot Weld

    2005-09-08

    empirically. For fast estimation of R using only observations beta(1, ..., T) a receiver state equation has been derived, and is attached as Eq. (3). This equation has the further advantage that the initial impulse S need not be known, rather it is estimated simultaneously. This is necessary because element failure and coupling can cause large variations in S. Constrained nonlinear least squares techniques can be applied to this equation to recover reflectivity (and initial impulse) [4]. In particular, the Gauss-Newton algorithm on the log of the sum of squared errors based on the receiver state equation is recommended. To summarize, it is the model described in Eqs. (2) and (3) that is novel, and that enables the recovery of acoustic reflectivity from the ultrasound signals. It has been verified that this reflectivity estimate provides a better indicator of weld veracity than other features previously derived from such signals.« less

  1. Single-beam acoustic seabed classification in coral reef environments with application to the assessment of grouper and snapper habitat in the upper Florida Keys, USA

    NASA Astrophysics Data System (ADS)

    Gleason, Arthur C. R.

    A single-beam acoustic seabed classification system was used to map coral reef environments in the upper Florida Keys, USA, and the Bahamas. The system consisted of two components, both produced by the Quester Tangent Corporation. A QTCView Series V, operating with a 50 kHz sounder, was used for data acquisition, and IMPACT software was used for data processing and classification. First, methodological aspects of system performance were evaluated. Second, the system was applied to the assessment of grouper and snapper habitat. Two methodological properties were explored: transferability (i.e. mapping the same classes at multiple sites) and reproducibility (i.e. surveying one site multiple times). The transferability results showed that a two-class scheme of hard bottom and sediment could be mapped at four sites with overall accuracy ranging from 73% to 86%. The locations of most misclassified echoes had one of two characteristics: a thin sediment veneer overlying hard bottom or within-footprint relief on the order of 0.5 m or greater. Reproducibility experiments showed that consistency of acoustic classes between repeat transects over the same area on different days varied, for the most part, between 50% and 65%. Consistency increased to between 78% and 92% when clustering was limited to two acoustic classes, to between approximately 70% and 100% when only echoes acquired within two degrees of nadir in the pitch direction were used, and to between 81% and 87% when a limited set of features was used for classification. The assessment of grouper and snapper habitat addressed the question whether areas of high fish abundance were associated with characteristic acoustic or geomorphological signatures. The results showed, first, that the hard bottom/sediment classification scheme was a useful first step for stratifying survey areas to increase efficiency of grouper census efforts. Second, an index of acoustic variability complemented the hard bottom

  2. A different approach to use narrowband super-resolution multiple signal classification algorithm on wideband sources.

    PubMed

    Asgari, Mohammad; Soltani, Nasim Yahya; Riahi, Ali

    2010-01-01

    There are varieties of wideband direction-of-arrival (DOA) estimation algorithms. Their structure comprises a number of narrowband ones, each performs in one frequency in a given bandwidth, and then different responses should be combined in a proper way to yield true DOAs. Hence, wideband algorithms are always complex and so non-real-time. This paper investigates a method to derive a flat response of narrowband multiple signal classification (MUSIC) [R. O. Schmidt, IEEE Trans. Antennas Propag., 34, 276-280 (1986)] algorithm in the whole frequencies of given band. Therefore, required conditions of applying narrowband algorithm on wideband impinging signals will be given through a concrete analysis. It could be found out that array sensor locations are able to compensate the frequency variations to reach a flat response of DOAs in a specified wideband frequency. PMID:20058975

  3. An electromagnetic finite difference time domain analog treatment of small signal acoustic interactions

    NASA Astrophysics Data System (ADS)

    Kunz, K.; Steich, D.; Lewis, K.; Landrum, C.; Barth, M.

    1994-03-01

    Hyperbolic partial differential equations encompass an extremely important set of physical phenomena including electromagnetics and acoustics. Small amplitude acoustic interactions behave much the same as electromagnetic interactions for longitudinal acoustic waves because of the similar nature of the governing hyperbolic equations. Differences appear when transverse acoustic waves are considered; nonetheless, the strong analogy between the acoustic and electromagnetic phenomena prompted the development of a Finite Difference Time Domain (FDTD) acoustic analog to the existing electromagnetic FDTD technique. The advantages of an acoustic FDTD (AFDTD) code are as follows: (1) boundary condition-free treatment of the acoustic scatterer--only the intrinsic properties of the scatterer's material are needed, no shell treatment or other set of special equations describing the macroscopic behavior of a sheet of material or a junction, etc. are required; this allows completely general geometries and materials in the model. (2) Advanced outer radiation boundary condition analogs--in the electromagnetics arena, highly absorbing outer radiation boundary conditions were developed that can be applied with little modification to the acoustics arena with equal success. (3) A suite of preexisting capabilities related to electromagnetic modeling--this includes automated model generation and interaction visualization as its most important components and is best exemplified by the capabilities of the LLNL generated TSAR electromagnetic FDTD code.

  4. Comparison of Standard and Novel Signal Analysis Approaches to Obstructive Sleep Apnea Classification

    PubMed Central

    Roebuck, Aoife; Clifford, Gari D.

    2015-01-01

    Obstructive sleep apnea (OSA) is a disorder characterized by repeated pauses in breathing during sleep, which leads to deoxygenation and voiced chokes at the end of each episode. OSA is associated by daytime sleepiness and an increased risk of serious conditions such as cardiovascular disease, diabetes, and stroke. Between 2 and 7% of the adult population globally has OSA, but it is estimated that up to 90% of those are undiagnosed and untreated. Diagnosis of OSA requires expensive and cumbersome screening. Audio offers a potential non-contact alternative, particularly with the ubiquity of excellent signal processing on every phone. Previous studies have focused on the classification of snoring and apneic chokes. However, such approaches require accurate identification of events. This leads to limited accuracy and small study populations. In this work, we propose an alternative approach which uses multiscale entropy (MSE) coefficients presented to a classifier to identify disorder in vocal patterns indicative of sleep apnea. A database of 858 patients was used, the largest reported in this domain. Apneic choke, snore, and noise events encoded with speech analysis features were input into a linear classifier. Coefficients of MSE derived from the first 4 h of each recording were used to train and test a random forest to classify patients as apneic or not. Standard speech analysis approaches for event classification achieved an out-of-sample accuracy (Ac) of 76.9% with a sensitivity (Se) of 29.2% and a specificity (Sp) of 88.7% but high variance. For OSA severity classification, MSE provided an out-of-sample Ac of 79.9%, Se of 66.0%, and Sp = 88.8%. Including demographic information improved the MSE-based classification performance to Ac = 80.5%, Se = 69.2%, and Sp = 87.9%. These results indicate that audio recordings could be used in screening for OSA, but are generally under-sensitive. PMID:26380256

  5. EEG data classification through signal spatial redistribution and optimized linear discriminants.

    PubMed

    Gutiérrez, David; Escalona-Vargas, Diana I

    2010-01-01

    This paper presents a preprocessing technique for improving the classification of electroencephalographic (EEG) data in brain-computer interfaces (BCI) for the case of realistic measuring conditions, such as low signal-to-noise ratio (SNR), reduced number of measuring electrodes, and reduced amount of data used to train the classifier. The proposed method is based on a linear minimum mean squared error (LMMSE) spatial filter specifically designed to improve the SNR of the signals before being classified. The design parameters of the spatial filter are obtained through an optimized version of Fisher's linear discriminant (FLD) whose area under the receiver operating characteristics (ROC) curve is maximized. The combination of the spatial filter and the optimized FLD increases the SNR and changes the spatial distribution of the measured signals. As a result, the signals can be more easily discriminated by means of a simple sign detector or threshold-based classifier. A series of experiments on simulated EEG data compare the performance of the proposed classification scheme to the performance of the Mahalanobis distance-based classifier, which is widely used in BCI systems. Numerical results show that the proposed preprocessing technique enhances the classifier's performance even for low SNR conditions and few measurements, while the Mahalanobis classifier is not reliable under such realistic operating conditions. Furthermore, real EEG data from a self-paced key typing experiment is used to demonstrate the applicability of the preprocessing technique. The proposed method has the potential of improving the efficiency of real-life BCI systems, as well as reducing the computational complexity associated with their implementation. PMID:19523710

  6. Neural network classification of autoregressive features from electroencephalogram signals for brain computer interface design

    NASA Astrophysics Data System (ADS)

    Huan, Nai-Jen; Palaniappan, Ramaswamy

    2004-09-01

    In this paper, we have designed a two-state brain-computer interface (BCI) using neural network (NN) classification of autoregressive (AR) features from electroencephalogram (EEG) signals extracted during mental tasks. The main purpose of the study is to use Keirn and Aunon's data to investigate the performance of different mental task combinations and different AR features for BCI design for individual subjects. In the experimental study, EEG signals from five mental tasks were recorded from four subjects. Different combinations of two mental tasks were studied for each subject. Six different feature extraction methods were used to extract the features from the EEG signals: AR coefficients computed with Burg's algorithm, AR coefficients computed with a least-squares (LS) algorithm and adaptive autoregressive (AAR) coefficients computed with a least-mean-square (LMS) algorithm. All the methods used order six applied to 125 data points and these three methods were repeated with the same data but with segmentation into five segments in increments of 25 data points. The multilayer perceptron NN trained by the back-propagation algorithm (MLP-BP) and linear discriminant analysis (LDA) were used to classify the computed features into different categories that represent the mental tasks. We compared the classification performances among the six different feature extraction methods. The results showed that sixth-order AR coefficients with the LS algorithm without segmentation gave the best performance (93.10%) using MLP-BP and (97.00%) using LDA. The results also showed that the segmentation and AAR methods are not suitable for this set of EEG signals. We conclude that, for different subjects, the best mental task combinations are different and proper selection of mental tasks and feature extraction methods are essential for the BCI design.

  7. Acoustic signal characteristic detection by neurons in ventral nucleus of the lateral lemniscus in mice

    PubMed Central

    LIU, Hui-Hua; HUANG, Cai-Fei; WANG, Xin

    2014-01-01

    Under free field conditions, we used single unit extracellular recording to study the detection of acoustic signals by neurons in the ventral nucleus of the lateral lemniscus (VNLL) in Kunming mouse (Mus musculus). The results indicate two types of firing patterns in VNLL neurons: onset and sustained. The first spike latency (FSL) of onset neurons was shorter than that of sustained neurons. With increasing sound intensity, the FSL of onset neurons remained stable and that of sustained neurons was shortened, indicating that onset neurons are characterized by precise timing. By comparing the values of Q10 and Q30 of the frequency tuning curve, no differences between onset and sustained neurons were found, suggesting that firing pattern and frequency tuning are not correlated. Among the three types of rate-intensity function (RIF) found in VNLL neurons, the proportion of monotonic RIF is the largest, followed by saturated RIF, and non-monotonic RIF. The dynamic range (DR) in onset neurons was shorter than in sustained neurons, indicating different capabilities in intensity tuning of different firing patterns and that these differences are correlated with the type of RIF. Our results also show that the best frequency of VNLL neurons was negatively correlated with depth, supporting the view point that the VNLL has frequency topologic organization. PMID:25465088

  8. Chelyabinsk meteoroid entry: analysis of acoustic signals in the area of direct sound propagation

    NASA Astrophysics Data System (ADS)

    Podobnaya, Elena; Popova, Olga; Glazachev, Dmitry; Rybnov, Yurij; Shuvalov, Valery; Jenniskens, Peter; Kharlamov, Vladimir

    E.Podobnaya, Yu.Rybnov, O.Popova, V. Shuvalov, P. Jenniskens, V.Kharlamov, D.Glazachev The Chelyabinsk airburst of 15 February 2013, was exceptional because of the large kinetic energy of the impacting body and the airburst that was generated, creating significant damage and injuries in a populated area. The meteor and the effects of the airburst were extraordinarily well documented. Numerous video records provided an accurate record of the trajectory and orbit of the cosmic body as well as features of its interaction with the atmosphere (Borovicka et al., 2013; Popova et al. 2013). In this presentation, we discuss the information on shock wave arrival times. Arrival times of the shock wave were derived from the shaking of the camera, the movement of smoke or car exhaust, and the movement of cables in the field of view, as well as directly from the audio record. From the analysis of these shock wave arrival times, the altitudes of the energy deposition were derived (Popova et al. 2013). Borovicka et al (2013) suggested that subsequent acoustic arrivals corresponded to separate fragmentation events. The observed arrival times will be compared with model estimates taking into account the real wind and atmospheric conditions (i.e. sound velocity changes with altitude). Results of numerical simulations will be compared with recorded sound signals. References Borovicka J. et al., 2013, Nature 503, 235 Popova O. et al., 2013, Science, 342, 1096

  9. Underwater acoustic communication using orthogonal signal division multiplexing scheme with time diversity

    NASA Astrophysics Data System (ADS)

    Ebihara, Tadashi; Ogasawara, Hanako; Mizutani, Koichi

    2016-03-01

    In this paper, an underwater acoustic (UWA) communication scheme for mobile platforms is proposed. The proposed scheme is based on the orthogonal signal division multiplexing (OSDM) scheme, which offers highly reliable UWA communication. However, OSDM is not suitable for mobile platforms as it is — it requires a receiver array and a large calculation cost for equalization. To establish a reliable link with small communication platforms, we design OSDM that can perform reliable communication without the need for an array and can reduce receiver complexity using the time-diversity technique (TD), and evaluate its performance in experiments. The experimental results suggest that OSDM-TD can simultaneously achieve power-efficient communications and receiver complexity reduction, and can realize small-scale communication platforms. In detail, OSDM-TD achieved almost the same communication quality as conventional OSDM, in exchange for an effective data rate. Moreover, the power efficiency of OSDM-TD was almost the same as that of conventional OSDM with two receiver array elements, although the calculation cost of OSDM-TD was far below that of conventional OSDM. As a result, it was found that OSDM-TD is suitable for UWA communication for mobile nodes whose capacity and computational resources are severely limited.

  10. ECG signal compression and classification algorithm with quad level vector for ECG holter system.

    PubMed

    Kim, Hyejung; Yazicioglu, Refet Firat; Merken, Patrick; Van Hoof, Chris; Yoo, Hoi-Jun

    2010-01-01

    An ECG signal processing method with quad level vector (QLV) is proposed for the ECG holter system. The ECG processing consists of the compression flow and the classification flow, and the QLV is proposed for both flows to achieve better performance with low-computation complexity. The compression algorithm is performed by using ECG skeleton and the Huffman coding. Unit block size optimization, adaptive threshold adjustment, and 4-bit-wise Huffman coding methods are applied to reduce the processing cost while maintaining the signal quality. The heartbeat segmentation and the R-peak detection methods are employed for the classification algorithm. The performance is evaluated by using the Massachusetts Institute of Technology-Boston's Beth Israel Hospital Arrhythmia Database, and the noise robust test is also performed for the reliability of the algorithm. Its average compression ratio is 16.9:1 with 0.641% percentage root mean square difference value and the encoding rate is 6.4 kbps. The accuracy performance of the R-peak detection is 100% without noise and 95.63% at the worst case with -10-dB SNR noise. The overall processing cost is reduced by 45.3% with the proposed compression techniques. PMID:19775975

  11. Digital seismo-acoustic signal processing aboard a wireless sensor platform

    NASA Astrophysics Data System (ADS)

    Marcillo, O.; Johnson, J. B.; Lorincz, K.; Werner-Allen, G.; Welsh, M.

    2006-12-01

    We are developing a low power, low-cost wireless sensor array to conduct real-time signal processing of earthquakes at active volcanoes. The sensor array, which integrates data from both seismic and acoustic sensors, is based on Moteiv TMote Sky wireless sensor nodes (www.moteiv.com). The nodes feature a Texas Instruments MSP430 microcontroller, 48 Kbytes of program memory, 10 Kbytes of static RAM, 1 Mbyte of external flash memory, and a 2.4-GHz Chipcon CC2420 IEEE 802.15.4 radio. The TMote Sky is programmed in TinyOS. Basic signal processing occurs on an array of three peripheral sensor nodes. These nodes are tied into a dedicated GPS receiver node, which is focused on time synchronization, and a central communications node, which handles data integration and additional processing. The sensor nodes incorporate dual 12-bit digitizers sampling a seismic sensor and a pressure transducer at 100 samples per second. The wireless capabilities of the system allow flexible array geometry, with a maximum aperture of 200m. We have already developed the digital signal processing routines on board the Moteiv Tmote sensor nodes. The developed routines accomplish Real-time Seismic-Amplitude Measurement (RSAM), Seismic Spectral- Amplitude Measurement (SSAM), and a user-configured Short Term Averaging / Long Term Averaging (STA LTA ratio), which is used to calculate first arrivals. The processed data from individual nodes are transmitted back to a central node, where additional processing may be performed. Such processing will include back azimuth determination and other wave field analyses. Future on-board signal processing will focus on event characterization utilizing pattern recognition and spectral characterization. The processed data is intended as low bandwidth information which can be transmitted periodically and at low cost through satellite telemetry to a web server. The processing is limited by the computational capabilities (RAM, ROM) of the nodes. Nevertheless, we

  12. Maintaining acoustic communication at a cocktail party: heterospecific masking noise improves signal detection through frequency separation

    PubMed Central

    Siegert, M. E.; Römer, H.; Hartbauer, M.

    2014-01-01

    SUMMARY We examined acoustic masking in a chirping katydid species of the Mecopoda elongata complex due to interference with a sympatric Mecopoda species where males produce continuous trills at high amplitudes. Frequency spectra of both calling songs range from 1 to 80 kHz; the chirper species has more energy in a narrow frequency band at 2 kHz and above 40 kHz. Behaviourally, chirper males successfully phase-locked their chirps to playbacks of conspecific chirps under masking conditions at signal-to-noise ratios (SNRs) of −8 dB. After the 2 kHz band in the chirp had been equalised to the level in the masking trill, the breakdown of phase-locked synchrony occurred at a SNR of +7 dB. The remarkable receiver performance is partially mirrored in the selective response of a first-order auditory interneuron (TN1) to conspecific chirps under these masking conditions. However, the selective response is only maintained for a stimulus including the 2 kHz component, although this frequency band has no influence on the unmasked TN1 response. Remarkably, the addition of masking noise at 65 dB sound pressure level (SPL) to threshold response levels of TN1 for pure tones of 2 kHz enhanced the sensitivity of the response by 10 dB. Thus, the spectral dissimilarity between masker and signal at a rather low frequency appears to be of crucial importance for the ability of the chirping species to communicate under strong masking by the trilling species. We discuss the possible properties underlying the cellular/synaptic mechanisms of the ‘novelty detector’. PMID:24307713

  13. Plant acoustics: in the search of a sound mechanism for sound signaling in plants.

    PubMed

    Mishra, Ratnesh Chandra; Ghosh, Ritesh; Bae, Hanhong

    2016-08-01

    Being sessile, plants continuously deal with their dynamic and complex surroundings, identifying important cues and reacting with appropriate responses. Consequently, the sensitivity of plants has evolved to perceive a myriad of external stimuli, which ultimately ensures their successful survival. Research over past centuries has established that plants respond to environmental factors such as light, temperature, moisture, and mechanical perturbations (e.g. wind, rain, touch, etc.) by suitably modulating their growth and development. However, sound vibrations (SVs) as a stimulus have only started receiving attention relatively recently. SVs have been shown to increase the yields of several crops and strengthen plant immunity against pathogens. These vibrations can also prime the plants so as to make them more tolerant to impending drought. Plants can recognize the chewing sounds of insect larvae and the buzz of a pollinating bee, and respond accordingly. It is thus plausible that SVs may serve as a long-range stimulus that evokes ecologically relevant signaling mechanisms in plants. Studies have suggested that SVs increase the transcription of certain genes, soluble protein content, and support enhanced growth and development in plants. At the cellular level, SVs can change the secondary structure of plasma membrane proteins, affect microfilament rearrangements, produce Ca(2+) signatures, cause increases in protein kinases, protective enzymes, peroxidases, antioxidant enzymes, amylase, H(+)-ATPase / K(+) channel activities, and enhance levels of polyamines, soluble sugars and auxin. In this paper, we propose a signaling model to account for the molecular episodes that SVs induce within the cell, and in so doing we uncover a number of interesting questions that need to be addressed by future research in plant acoustics. PMID:27342223

  14. Improved Maturity and Ripeness Classifications of Magnifera Indica cv. Harumanis Mangoes through Sensor Fusion of an Electronic Nose and Acoustic Sensor

    PubMed Central

    Zakaria, Ammar; Shakaff, Ali Yeon Md; Masnan, Maz Jamilah; Saad, Fathinul Syahir Ahmad; Adom, Abdul Hamid; Ahmad, Mohd Noor; Jaafar, Mahmad Nor; Abdullah, Abu Hassan; Kamarudin, Latifah Munirah

    2012-01-01

    In recent years, there have been a number of reported studies on the use of non-destructive techniques to evaluate and determine mango maturity and ripeness levels. However, most of these reported works were conducted using single-modality sensing systems, either using an electronic nose, acoustics or other non-destructive measurements. This paper presents the work on the classification of mangoes (Magnifera Indica cv. Harumanis) maturity and ripeness levels using fusion of the data of an electronic nose and an acoustic sensor. Three groups of samples each from two different harvesting times (week 7 and week 8) were evaluated by the e-nose and then followed by the acoustic sensor. Principal Component Analysis (PCA) and Linear Discriminant Analysis (LDA) were able to discriminate the mango harvested at week 7 and week 8 based solely on the aroma and volatile gases released from the mangoes. However, when six different groups of different maturity and ripeness levels were combined in one classification analysis, both PCA and LDA were unable to discriminate the age difference of the Harumanis mangoes. Instead of six different groups, only four were observed using the LDA, while PCA showed only two distinct groups. By applying a low level data fusion technique on the e-nose and acoustic data, the classification for maturity and ripeness levels using LDA was improved. However, no significant improvement was observed using PCA with data fusion technique. Further work using a hybrid LDA-Competitive Learning Neural Network was performed to validate the fusion technique and classify the samples. It was found that the LDA-CLNN was also improved significantly when data fusion was applied. PMID:22778629

  15. Classification

    NASA Astrophysics Data System (ADS)

    Oza, Nikunj

    2012-03-01

    A supervised learning task involves constructing a mapping from input data (normally described by several features) to the appropriate outputs. A set of training examples— examples with known output values—is used by a learning algorithm to generate a model. This model is intended to approximate the mapping between the inputs and outputs. This model can be used to generate predicted outputs for inputs that have not been seen before. Within supervised learning, one type of task is a classification learning task, in which each output is one or more classes to which the input belongs. For example, we may have data consisting of observations of sunspots. In a classification learning task, our goal may be to learn to classify sunspots into one of several types. Each example may correspond to one candidate sunspot with various measurements or just an image. A learning algorithm would use the supplied examples to generate a model that approximates the mapping between each supplied set of measurements and the type of sunspot. This model can then be used to classify previously unseen sunspots based on the candidate’s measurements. The generalization performance of a learned model (how closely the target outputs and the model’s predicted outputs agree for patterns that have not been presented to the learning algorithm) would provide an indication of how well the model has learned the desired mapping. More formally, a classification learning algorithm L takes a training set T as its input. The training set consists of |T| examples or instances. It is assumed that there is a probability distribution D from which all training examples are drawn independently—that is, all the training examples are independently and identically distributed (i.i.d.). The ith training example is of the form (x_i, y_i), where x_i is a vector of values of several features and y_i represents the class to be predicted.* In the sunspot classification example given above, each training example

  16. Lithological control on gas hydrate saturation as revealed by signal classification of NMR logging data

    NASA Astrophysics Data System (ADS)

    Bauer, Klaus; Kulenkampff, Johannes; Henninges, Jan; Spangenberg, Erik

    2015-09-01

    In this paper, nuclear magnetic resonance (NMR) downhole logging data are analyzed with a new strategy to study gas hydrate-bearing sediments in the Mackenzie Delta (NW Canada). In NMR logging, transverse relaxation time (T2) distribution curves are usually used to determine single-valued parameters such as apparent total porosity or hydrocarbon saturation. Our approach analyzes the entire T2 distribution curves as quasi-continuous signals to characterize the rock formation. We apply self-organizing maps, a neural network clustering technique, to subdivide the data set of NMR curves into classes with a similar and distinctive signal shape. The method includes (1) preparation of data vectors, (2) unsupervised learning, (3) cluster definition, and (4) classification and depth mapping of all NMR signals. Each signal class thus represents a specific pore size distribution which can be interpreted in terms of distinct lithologies and reservoir types. A key step in the interpretation strategy is to reconcile the NMR classes with other log data not considered in the clustering analysis, such as gamma ray, hydrate saturation, and other logs. Our results defined six main lithologies within the target zone. Gas hydrate layers were recognized by their low signal amplitudes for all relaxation times. Most importantly, two subtypes of hydrate-bearing shaly sands were identified. They show distinct NMR signals and differ in hydrate saturation and gamma ray values. An inverse linear relationship between hydrate saturation and clay content was concluded. Finally, we infer that the gas hydrate is not grain coating, but rather, pore filling with matrix support is the preferred growth habit model for the studied formation.

  17. Dynamic Agent Classification and Tracking Using an Ad Hoc Mobile Acoustic Sensor Network

    NASA Astrophysics Data System (ADS)

    Friedlander, David; Griffin, Christopher; Jacobson, Noah; Phoha, Shashi; Brooks, Richard R.

    2003-12-01

    Autonomous networks of sensor platforms can be designed to interact in dynamic and noisy environments to determine the occurrence of specified transient events that define the dynamic process of interest. For example, a sensor network may be used for battlefield surveillance with the purpose of detecting, identifying, and tracking enemy activity. When the number of nodes is large, human oversight and control of low-level operations is not feasible. Coordination and self-organization of multiple autonomous nodes is necessary to maintain connectivity and sensor coverage and to combine information for better understanding the dynamics of the environment. Resource conservation requires adaptive clustering in the vicinity of the event. This paper presents methods for dynamic distributed signal processing using an ad hoc mobile network of microsensors to detect, identify, and track targets in noisy environments. They seamlessly integrate data from fixed and mobile platforms and dynamically organize platforms into clusters to process local data along the trajectory of the targets. Local analysis of sensor data is used to determine a set of target attribute values and classify the target. Sensor data from a field test in the Marine base at Twentynine Palms, Calif, was analyzed using the techniques described in this paper. The results were compared to "ground truth" data obtained from GPS receivers on the vehicles.

  18. Effect of reflected and refracted signals on coherent underwater acoustic communication: results from the Kauai experiment (KauaiEx 2003).

    PubMed

    Rouseff, Daniel; Badiey, Mohsen; Song, Aijun

    2009-11-01

    The performance of a communications equalizer is quantified in terms of the number of acoustic paths that are treated as usable signal. The analysis uses acoustical and oceanographic data collected off the Hawaiian Island of Kauai. Communication signals were measured on an eight-element vertical array at two different ranges, 1 and 2 km, and processed using an equalizer based on passive time-reversal signal processing. By estimating the Rayleigh parameter, it is shown that all paths reflected by the sea surface at both ranges undergo incoherent scattering. It is demonstrated that some of these incoherently scattered paths are still useful for coherent communications. At range of 1 km, optimal communications performance is achieved when six acoustic paths are retained and all paths with more than one reflection off the sea surface are rejected. Consistent with a model that ignores loss from near-surface bubbles, the performance improves by approximately 1.8 dB when increasing the number of retained paths from four to six. The four-path results though are more stable and require less frequent channel estimation. At range of 2 km, ray refraction is observed and communications performance is optimal when some paths with two sea-surface reflections are retained. PMID:19894819

  19. Classification

    NASA Technical Reports Server (NTRS)

    Oza, Nikunj C.

    2011-01-01

    A supervised learning task involves constructing a mapping from input data (normally described by several features) to the appropriate outputs. Within supervised learning, one type of task is a classification learning task, in which each output is one or more classes to which the input belongs. In supervised learning, a set of training examples---examples with known output values---is used by a learning algorithm to generate a model. This model is intended to approximate the mapping between the inputs and outputs. This model can be used to generate predicted outputs for inputs that have not been seen before. For example, we may have data consisting of observations of sunspots. In a classification learning task, our goal may be to learn to classify sunspots into one of several types. Each example may correspond to one candidate sunspot with various measurements or just an image. A learning algorithm would use the supplied examples to generate a model that approximates the mapping between each supplied set of measurements and the type of sunspot. This model can then be used to classify previously unseen sunspots based on the candidate's measurements. This chapter discusses methods to perform machine learning, with examples involving astronomy.

  20. Real-Time Subject-Independent Pattern Classification of Overt and Covert Movements from fNIRS Signals

    PubMed Central

    Rana, Mohit; Prasad, Vinod A.; Guan, Cuntai; Birbaumer, Niels; Sitaram, Ranganatha

    2016-01-01

    Recently, studies have reported the use of Near Infrared Spectroscopy (NIRS) for developing Brain–Computer Interface (BCI) by applying online pattern classification of brain states from subject-specific fNIRS signals. The purpose of the present study was to develop and test a real-time method for subject-specific and subject-independent classification of multi-channel fNIRS signals using support-vector machines (SVM), so as to determine its feasibility as an online neurofeedback system. Towards this goal, we used left versus right hand movement execution and movement imagery as study paradigms in a series of experiments. In the first two experiments, activations in the motor cortex during movement execution and movement imagery were used to develop subject-dependent models that obtained high classification accuracies thereby indicating the robustness of our classification method. In the third experiment, a generalized classifier-model was developed from the first two experimental data, which was then applied for subject-independent neurofeedback training. Application of this method in new participants showed mean classification accuracy of 63% for movement imagery tasks and 80% for movement execution tasks. These results, and their corresponding offline analysis reported in this study demonstrate that SVM based real-time subject-independent classification of fNIRS signals is feasible. This method has important applications in the field of hemodynamic BCIs, and neuro-rehabilitation where patients can be trained to learn spatio-temporal patterns of healthy brain activity. PMID:27467528

  1. Real-Time Subject-Independent Pattern Classification of Overt and Covert Movements from fNIRS Signals.

    PubMed

    Robinson, Neethu; Zaidi, Ali Danish; Rana, Mohit; Prasad, Vinod A; Guan, Cuntai; Birbaumer, Niels; Sitaram, Ranganatha

    2016-01-01

    Recently, studies have reported the use of Near Infrared Spectroscopy (NIRS) for developing Brain-Computer Interface (BCI) by applying online pattern classification of brain states from subject-specific fNIRS signals. The purpose of the present study was to develop and test a real-time method for subject-specific and subject-independent classification of multi-channel fNIRS signals using support-vector machines (SVM), so as to determine its feasibility as an online neurofeedback system. Towards this goal, we used left versus right hand movement execution and movement imagery as study paradigms in a series of experiments. In the first two experiments, activations in the motor cortex during movement execution and movement imagery were used to develop subject-dependent models that obtained high classification accuracies thereby indicating the robustness of our classification method. In the third experiment, a generalized classifier-model was developed from the first two experimental data, which was then applied for subject-independent neurofeedback training. Application of this method in new participants showed mean classification accuracy of 63% for movement imagery tasks and 80% for movement execution tasks. These results, and their corresponding offline analysis reported in this study demonstrate that SVM based real-time subject-independent classification of fNIRS signals is feasible. This method has important applications in the field of hemodynamic BCIs, and neuro-rehabilitation where patients can be trained to learn spatio-temporal patterns of healthy brain activity. PMID:27467528

  2. Considerations on nonlinearity measurement with high signal-to-noise ratio for RF surface and bulk acoustic wave devices

    NASA Astrophysics Data System (ADS)

    Kodaira, Ryosuke; Omori, Tatsuya; Hashimoto, Ken-ya; Kyoya, Haruki; Nakagawa, Ryo

    2015-07-01

    This paper discusses the measurement setup of non-linearity caused in radio frequency (RF) surface and bulk acoustic wave (SAW/BAW) devices with high signal-to-noise ratio (SNR). It is shown that when some important points are considered, the background level can be suppressed better than -135 dBm, and the non-linearity signals can be measured in high SNR. Finally, measured results are compared with those measured independently by Murata Manufacturing, and validity of the measurement is cross-checked.

  3. Correlation of infrared thermographic patterns and acoustic emission signals with tensile deformation and fracture processes

    NASA Astrophysics Data System (ADS)

    Venkataraman, B.; Raj, Baldev; Mukhopadhyay, C. K.; Jayakumar, T.

    2001-04-01

    During tensile deformation, part of the mechanical work done on the specimen is transformed into heat and acoustic activity. The amount of acoustic activity and the thermal emissions depend on the test conditions and the deformation behavior of the specimen during loading. Authors have used thermography and acoustic emission (AE) simultaneously for monitoring tensile deformation in AISI type 316 SS. Tensile testing was carried out at 298 K at three different strain rates. It has been shown that the simultaneous use of these techniques can provide complementary information for characterizing the tensile deformation and fracture processes.

  4. Low-Power Analog Processing for Sensing Applications: Low-Frequency Harmonic Signal Classification

    PubMed Central

    White, Daniel J.; William, Peter E.; Hoffman, Michael W.; Balkir, Sina

    2013-01-01

    A low-power analog sensor front-end is described that reduces the energy required to extract environmental sensing spectral features without using Fast Fouriér Transform (FFT) or wavelet transforms. An Analog Harmonic Transform (AHT) allows selection of only the features needed by the back-end, in contrast to the FFT, where all coefficients must be calculated simultaneously. We also show that the FFT coefficients can be easily calculated from the AHT results by a simple back-substitution. The scheme is tailored for low-power, parallel analog implementation in an integrated circuit (IC). Two different applications are tested with an ideal front-end model and compared to existing studies with the same data sets. Results from the military vehicle classification and identification of machine-bearing fault applications shows that the front-end suits a wide range of harmonic signal sources. Analog-related errors are modeled to evaluate the feasibility of and to set design parameters for an IC implementation to maintain good system-level performance. Design of a preliminary transistor-level integrator circuit in a 0.13 μm complementary metal-oxide-silicon (CMOS) integrated circuit process showed the ability to use online self-calibration to reduce fabrication errors to a sufficiently low level. Estimated power dissipation is about three orders of magnitude less than similar vehicle classification systems that use commercially available FFT spectral extraction. PMID:23892765

  5. Low-power analog processing for sensing applications: low-frequency harmonic signal classification.

    PubMed

    White, Daniel J; William, Peter E; Hoffman, Michael W; Balkir, Sina

    2013-01-01

    A low-power analog sensor front-end is described that reduces the energy required to extract environmental sensing spectral features without using Fast Fouriér Transform (FFT) or wavelet transforms. An Analog Harmonic Transform (AHT) allows selection of only the features needed by the back-end, in contrast to the FFT, where all coefficients must be calculated simultaneously. We also show that the FFT coefficients can be easily calculated from the AHT results by a simple back-substitution. The scheme is tailored for low-power, parallel analog implementation in an integrated circuit (IC). Two different applications are tested with an ideal front-end model and compared to existing studies with the same data sets. Results from the military vehicle classification and identification of machine-bearing fault applications shows that the front-end suits a wide range of harmonic signal sources. Analog-related errors are modeled to evaluate the feasibility of and to set design parameters for an IC implementation to maintain good system-level performance. Design of a preliminary transistor-level integrator circuit in a 0.13 µm complementary metal-oxide-silicon (CMOS) integrated circuit process showed the ability to use online self-calibration to reduce fabrication errors to a sufficiently low level. Estimated power dissipation is about three orders of magnitude less than similar vehicle classification systems that use commercially available FFT spectral extraction. PMID:23892765

  6. WE-D-BRF-02: Acoustic Signal From the Bragg Peak for Range Verification in Proton Therapy

    SciTech Connect

    Reinhardt, S; Assmann, W; Fink, A; Thirolf, P; Parodi, K; Kellnberger, S; Omar, M; Ntziachristos, V; Gaebisch, C; Moser, M; Dollinger, G; Sergiadis, G

    2014-06-15

    Purpose: Range verification in ion beam therapy relies to date on nuclear imaging techniques which require complex and costly detector systems. A different approach is the detection of thermoacoustic signals that are generated due to localized energy loss of ion beams. Aim of this work is to study the feasibility of determining the ion range with sub-mm accuracy by use of high frequency ultrasonic (US) transducers and to image the Bragg peak by tomography. Methods: A water phantom was irradiated by a pulsed 20 MeV proton beam with varying pulse intensity, length and repetition rate. The acoustic signal of single proton pulses was measured by different PZT-based US detectors (3.5 MHz and 10 MHz central frequencies). For tomography a 64 channel US detector array was used and moved along the ion track by a remotely controlled motor stage. Results: A clear signal of the Bragg peak was visible for an energy deposition as low as 10{sup 12} eV. The signal amplitude showed a linear increase with particle number per pulse and thus, dose. Range measurements were reproducible within +/− 20 micrometer and agreed well with Geant4 simulations. The tomographic reconstruction does not only allow to measure the ion range but also the beam spot size at the Bragg peak position. Conclusion: Range verification by acoustic means is a promising new technique for treatment modalities where the tumor can be localized by US imaging. Further improvement of sensitivity is required to account for higher attenuation of the US signal in tissue, as well as lower energy density in the Bragg peak in realistic treatment cases due to higher particle energy and larger spot sizes. Nevertheless, the acoustic range verification approach could offer the possibility of combining anatomical US imaging with Bragg Peak imaging in the near future. The work was funded by the DFG cluster of excellence Munich Centre for Advanced Photonics (MAP)

  7. Neural networks for automated classification of ionospheric irregularities in HF radar backscattered signals

    NASA Astrophysics Data System (ADS)

    Wing, S.; Greenwald, R. A.; Meng, C.-I.; Sigillito, V. G.; Hutton, L. V.

    2003-08-01

    The classification of high frequency (HF) radar backscattered signals from the ionospheric irregularities (clutters) into those suitable, or not, for further analysis, is a time-consuming task even by experts in the field. We tested several different feedforward neural networks on this task, investigating the effects of network type (single layer versus multilayer) and number of hidden nodes upon performance. As expected, the multilayer feedforward networks (MLFNs) outperformed the single-layer networks. The MLFNs achieved performance levels of 100% correct on the training set and up to 98% correct on the testing set. Comparable figures for the single-layer networks were 94.5% and 92%, respectively. When measures of sensitivity, specificity, and proportion of variance accounted for by the model are considered, the superiority of the MLFNs over the single-layer networks is much more striking. Our results suggest that such neural networks could aid many HF radar operations such as frequency search, space weather, etc.

  8. Wireless acoustic-electric feed-through for power and signal transmission

    NASA Technical Reports Server (NTRS)

    Sherrit, Stewart (Inventor); Bar-Cohen, Yoseph (Inventor); Bao, Xiaoqi (Inventor); Doty, Benjamin (Inventor); Badescu, Mircea (Inventor); Chang, Zensheu (Inventor)

    2011-01-01

    An embodiment provides electrical energy from a source on one side of a medium to a load on the other side of the medium, the embodiment including a first piezoelectric to generate acoustic energy in response to electrical energy from the source, and a second piezoelectric to convert the received acoustic energy to electrical energy used by the load. Other embodiments are described and claimed.

  9. Acoustic Data Processing and Transient Signal Analysis for the Hybrid Wing Body 14- by 22-Foot Subsonic Wind Tunnel Test

    NASA Technical Reports Server (NTRS)

    Bahr, Christopher J.; Brooks, Thomas F.; Humphreys, William M.; Spalt, Taylor B.; Stead, Daniel J.

    2014-01-01

    An advanced vehicle concept, the HWB N2A-EXTE aircraft design, was tested in NASA Langley's 14- by 22-Foot Subsonic Wind Tunnel to study its acoustic characteristics for var- ious propulsion system installation and airframe con gurations. A signi cant upgrade to existing data processing systems was implemented, with a focus on portability and a re- duction in turnaround time. These requirements were met by updating codes originally written for a cluster environment and transferring them to a local workstation while en- abling GPU computing. Post-test, additional processing of the time series was required to remove transient hydrodynamic gusts from some of the microphone time series. A novel automated procedure was developed to analyze and reject contaminated blocks of data, under the assumption that the desired acoustic signal of interest was a band-limited sta- tionary random process, and of lower variance than the hydrodynamic contamination. The procedure is shown to successfully identify and remove contaminated blocks of data and retain the desired acoustic signal. Additional corrections to the data, mainly background subtraction, shear layer refraction calculations, atmospheric attenuation and microphone directivity corrections, were all necessary for initial analysis and noise assessments. These were implemented for the post-processing of spectral data, and are shown to behave as expected.

  10. Grey seals use anthropogenic signals from acoustic tags to locate fish: evidence from a simulated foraging task

    PubMed Central

    Stansbury, Amanda L.; Götz, Thomas; Deecke, Volker B.; Janik, Vincent M.

    2015-01-01

    Anthropogenic noise can have negative effects on animal behaviour and physiology. However, noise is often introduced systematically and potentially provides information for navigation or prey detection. Here, we show that grey seals (Halichoerus grypus) learn to use sounds from acoustic fish tags as an indicator of food location. In 20 randomized trials each, 10 grey seals individually explored 20 foraging boxes, with one box containing a tagged fish, one containing an untagged fish and all other boxes being empty. The tagged box was found after significantly fewer non-tag box visits across trials, and seals revisited boxes containing the tag more often than any other box. The time and number of boxes needed to find both fish decreased significantly throughout consecutive trials. Two additional controls were conducted to investigate the role of the acoustic signal: (i) tags were placed in one box, with no fish present in any boxes and (ii) additional pieces of fish, inaccessible to the seal, were placed in the previously empty 18 boxes, making possible alternative chemosensory cues less reliable. During these controls, the acoustically tagged box was generally found significantly faster than the control box. Our results show that animals learn to use information provided by anthropogenic signals to enhance foraging success. PMID:25411449

  11. Grey seals use anthropogenic signals from acoustic tags to locate fish: evidence from a simulated foraging task.

    PubMed

    Stansbury, Amanda L; Götz, Thomas; Deecke, Volker B; Janik, Vincent M

    2015-01-01

    Anthropogenic noise can have negative effects on animal behaviour and physiology. However, noise is often introduced systematically and potentially provides information for navigation or prey detection. Here, we show that grey seals (Halichoerus grypus) learn to use sounds from acoustic fish tags as an indicator of food location. In 20 randomized trials each, 10 grey seals individually explored 20 foraging boxes, with one box containing a tagged fish, one containing an untagged fish and all other boxes being empty. The tagged box was found after significantly fewer non-tag box visits across trials, and seals revisited boxes containing the tag more often than any other box. The time and number of boxes needed to find both fish decreased significantly throughout consecutive trials. Two additional controls were conducted to investigate the role of the acoustic signal: (i) tags were placed in one box, with no fish present in any boxes and (ii) additional pieces of fish, inaccessible to the seal, were placed in the previously empty 18 boxes, making possible alternative chemosensory cues less reliable. During these controls, the acoustically tagged box was generally found significantly faster than the control box. Our results show that animals learn to use information provided by anthropogenic signals to enhance foraging success. PMID:25411449

  12. Single-point nonlinearity indicators for the propagation of high-amplitude acoustic signals

    NASA Astrophysics Data System (ADS)

    Falco, Lauren E.

    . Both single-frequency signals and band-limited noise were used as sources, and waveforms were recorded at all four propagation distances. The second set of data was obtained at the model-scale jet facility at the University of Mississippi's National Center for Physical Acoustics. A computer controlled microphone boom was constructed to hold an array of six microphones. The array was rotated about the presumed location of the acoustic source center (4 jet diameters downstream of the nozzle exit), and two stationary microphones were mounted on the walls. Measurements were made for several jet conditions; data presented here represent Mach 0.85 and Mach 2 conditions. Application of the four candidate nonlinearity indicators to the experimental data reveals that each indicator has advantages and disadvantages. Qneg/Qpos does not detect the presence of shocks as postulated, but it does conform to expectations in the shock-free region and support the use of Qpos as an indicator. The main advantage of Qpos/p3rms is that it can be used for band-limited measurements. Increased indicator values are seen for signals with higher source frequencies and amplitudes that are expected to undergo stronger nonlinear evolution. However, no physical meaning can yet be derived from the numerical value of the indicator. The spectral Gol'dberg number Gammas is the most promising of the candidate quantities. It has the ability to indicate the direction of nonlinear energy transfer as well as provide a comparison between the strengths of linear and nonlinear effects. These attributes allow it to be used to qualitatively predict the evolution of a spectrum. The coherence indicator gammaQ also specifies the direction of nonlinear energy transfer, but its numerical value holds less meaning. However, it is bounded between -1 and 1, so values near zero denote very weak or no nonlinearity, and values near -1 or 1 denote strong nonlinearity. Further, because it is bounded, it does not become unstable

  13. Arrhythmia recognition and classification using combined linear and nonlinear features of ECG signals.

    PubMed

    Elhaj, Fatin A; Salim, Naomie; Harris, Arief R; Swee, Tan Tian; Ahmed, Taqwa

    2016-04-01

    Arrhythmia is a cardiac condition caused by abnormal electrical activity of the heart, and an electrocardiogram (ECG) is the non-invasive method used to detect arrhythmias or heart abnormalities. Due to the presence of noise, the non-stationary nature of the ECG signal (i.e. the changing morphology of the ECG signal with respect to time) and the irregularity of the heartbeat, physicians face difficulties in the diagnosis of arrhythmias. The computer-aided analysis of ECG results assists physicians to detect cardiovascular diseases. The development of many existing arrhythmia systems has depended on the findings from linear experiments on ECG data which achieve high performance on noise-free data. However, nonlinear experiments characterize the ECG signal more effectively sense, extract hidden information in the ECG signal, and achieve good performance under noisy conditions. This paper investigates the representation ability of linear and nonlinear features and proposes a combination of such features in order to improve the classification of ECG data. In this study, five types of beat classes of arrhythmia as recommended by the Association for Advancement of Medical Instrumentation are analyzed: non-ectopic beats (N), supra-ventricular ectopic beats (S), ventricular ectopic beats (V), fusion beats (F) and unclassifiable and paced beats (U). The characterization ability of nonlinear features such as high order statistics and cumulants and nonlinear feature reduction methods such as independent component analysis are combined with linear features, namely, the principal component analysis of discrete wavelet transform coefficients. The features are tested for their ability to differentiate different classes of data using different classifiers, namely, the support vector machine and neural network methods with tenfold cross-validation. Our proposed method is able to classify the N, S, V, F and U arrhythmia classes with high accuracy (98.91%) using a combined support

  14. Parallel feedback active noise control of MRI acoustic noise with signal decomposition using hybrid RLS-NLMS adaptive algorithms.

    PubMed

    Ganguly, Anshuman; Krishna Vemuri, Sri Hari; Panahi, Issa

    2014-01-01

    This paper presents a cost-effective adaptive feedback Active Noise Control (FANC) method for controlling functional Magnetic Resonance Imaging (fMRI) acoustic noise by decomposing it into dominant periodic components and residual random components. Periodicity of fMRI acoustic noise is exploited by using linear prediction (LP) filtering to achieve signal decomposition. A hybrid combination of adaptive filters-Recursive Least Squares (RLS) and Normalized Least Mean Squares (NLMS) are then used to effectively control each component separately. Performance of the proposed FANC system is analyzed and Noise attenuation levels (NAL) up to 32.27 dB obtained by simulation are presented which confirm the effectiveness of the proposed FANC method. PMID:25570676

  15. [Automatic Classification of Epileptic Electroencephalogram Signal Based on Improved Multivariate Multiscale Entropy].

    PubMed

    Xu, Yonghong; Cui, Jie; Hong, Wenxue; Liang, Huijuan

    2015-04-01

    Traditional sample entropy fails to quantify inherent long-range dependencies among real data. Multiscale sample entropy (MSE) can detect intrinsic correlations in data, but it is usually used in univariate data. To generalize this method for multichannel data, we introduced multivariate multiscale entropy into multiscale signals as a reflection of the nonlinear dynamic correlation. But traditional multivariate multiscale entropy has a large quantity of computation and costs a large period of time and space for more channel system, so that it can not reflect the correlation between variables timely and accurately. In this paper, therefore, an improved multivariate multiscale entropy embeds on all variables at the same time, instead of embedding on a single variable as in the traditional methods, to solve the memory overflow while the number of channels rise, and it is more suitable for the actual multivariate signal analysis. The method was tested in simulation data and Bonn epilepsy dataset. The simulation results showed that the proposed method had a good performance to distinguish correlation data. Bonn epilepsy dataset experiment also showed that the method had a better classification accuracy among the five data set, especially with an accuracy of 100% for data collection of Z and S. PMID:26211236

  16. Classification of RF transients in space using digital signal processing and neural network techniques

    SciTech Connect

    Moore, K.R.; Blain, P.C.; Briles, S.D.; Jones, R.G.

    1995-02-01

    The FORTE{prime} (Fast On-Orbit Recording of Transient Events) small satellite experiment scheduled for launch in October, 1995 will attempt to measure and classify electromagnetic transients as sensed from space. The FORTE{prime} payload will employ an Event Classifier to perform onboard classification of radio frequency transients from terrestrial sources such as lightning. These transients are often dominated by a constantly changing assortment of man-made ``clutter`` such as TV, FM, and radar signals. The FORTE{prime} Event Classifier, or EC, uses specialized hardware to implement various signal processing and neural network algorithms. The resulting system can process and classify digitized records of several thousand samples onboard the spacecraft at rates of about a second per record. In addition to reducing dowlink rates, the EC minimizes command uplink data by normally using uploaded algorithm sequences rather than full code modules (although it is possible for full code modules to be uploaded from the ground). The FORTE{prime} Event Classifier experiment combines science and engineering in an evolutionary step toward useful and robust adaptive processing systems in space.

  17. Information Theoretic Approach Based on Entropy for Classification of Bioacoustics Signals

    NASA Astrophysics Data System (ADS)

    Han, Ng Chee; Muniandy, Sithi V.; Dayou, Jedol; Mun, Ho Chong; Ahmad, Abdul Hamid; Dalimin, Mohd. Noh

    2010-07-01

    A new hybrid method for automated frog sound identification by incorporating entropy and spectral centroid concept is proposed. Entropy has important physical implications as the amount of "disorder" of a system. This study explores the use of various definitions of entropies such as the Shannon entropy, Kolmogorov-Rényi entropy and Tsallis entropy as measure of information contents or complexity for the purpose of the pattern recognition of bioacoustics signal. Each of these definitions of entropies characterizes different aspects of the signal. The entropies are combined with other standard pattern recognition tools such as the Fourier spectral analysis to form a hybrid spectral-entropic classification scheme. The efficiency of the system is tested using a database of sound syllables are obtained from a number of species of Microhylidae frogs. Nonparametric k-NN classifier is used to recognize the frog species based on the spectral-entropic features. The result showed that the k-NN classifier based on the selected features is able to identify the species of the frogs with relativity good accuracy compared to features relying on spectral contents alone. The robustness of the developed system is also tested for different noise levels.

  18. Lithological controls on gas hydrate saturation: Insights from signal classification of NMR downhole data

    NASA Astrophysics Data System (ADS)

    Bauer, Klaus; Kulenkampff, Johannes; Henninges, Jan; Spangenberg, Erik

    2016-04-01

    Nuclear magnetic resonance (NMR) downhole data are analyzed with a new strategy to study gas hydrate-bearing sediments in the Mackenzie Delta (NW Canada). NMR logging is a powerful tool to study geological reservoir formations. The measurements are based on interactions between the magnetic moments of protons in geological formation water and an external magnetic field. Inversion of the measured raw data provides so-called transverse relaxation time (T2) distribution curves or spectra. Different parts of the T2 curve are related with distinct pore radii and corresponding fluid components. A common practice in the analysis of T2 distribution curves is to extract single-valued parameters such as apparent total porosity. Moreover, the derived total NMR apparent porosity and the gamma-gamma density log apparent porosity can be combined to estimate gas hydrate saturation in hydrate-bearing sediments. To avoid potential loss of information, in our new approach we analyze the entire T2 distribution curves as quasi-continuous signals to characterize the rock formation. The approach is applied to NMR data measured in gas hydrate research well Mallik 5L-38. We use self-organizing maps, a neural network clustering technique, to subdivide the data set of NMR T2 distribution curves into classes with a similar and distinctive signal shape. The method includes (1) preparation of data vectors, (2) unsupervised learning, (3) cluster definition, and (4) classification and depth mapping of all NMR signals. Each signal class thus represents a specific pore size distribution which can be interpreted in terms of distinct lithologies and reservoir types. A key step in the interpretation strategy is to reconcile the NMR classes with other log data not considered in the clustering analysis, such as gamma ray, photo-electric factor, hydrate saturation, and other logs. Our results defined six main lithologies within the target zone. Gas hydrate layers were recognized by their low signal

  19. Continuous automatic classification of seismic signals of volcanic origin at Mt. Merapi, Java, Indonesia

    NASA Astrophysics Data System (ADS)

    Ohrnberger, Matthias

    2001-07-01

    Merapi volcano is one of the most active and dangerous volcanoes of the earth. Located in central part of Java island (Indonesia), even a moderate eruption of Merapi poses a high risk to the highly populated area. Due to the close relationship between the volcanic unrest and the occurrence of seismic events at Mt. Merapi, the monitoring of Merapi's seismicity plays an important role for recognizing major changes in the volcanic activity. An automatic seismic event detection and classification system, which is capable to characterize the actual seismic activity in near real-time, is an important tool which allows the scientists in charge to take immediate decisions during a volcanic crisis. In order to accomplish the task of detecting and classifying volcano-seismic signals automatically in the continuous data streams, a pattern recognition approach has been used. It is based on the method of hidden Markov models (HMM), a technique, which has proven to provide high recognition rates at high confidence levels in classification tasks of similar complexity (e.g. speech recognition). Any pattern recognition system relies on the appropriate representation of the input data in order to allow a reasonable class-decision by means of a mathematical test function. Based on the experiences from seismological observatory practice, a parametrization scheme of the seismic waveform data is derived using robust seismological analysis techniques. The wavefield parameters are summarized into a real-valued feature vector per time step. The time series of this feature vector build the basis for the HMM-based classification system. In order to make use of discrete hidden Markov (DHMM) techniques, the feature vectors are further processed by applying a de-correlating and prewhitening transformation and additional vector quantization. The seismic wavefield is finally represented as a discrete symbol sequence with a finite alphabet. This sequence is subject to a maximum likelihood test

  20. Multichannel analysis of surface-waves and integration of downhole acoustic televiewer imaging, ultrasonic Vs and Vp, and vertical seismic profiling in an NEHRP-standard classification, South of Concordia, Kansas, USA

    NASA Astrophysics Data System (ADS)

    Raef, Abdelmoneam; Gad, Sabreen; Tucker-Kulesza, Stacey

    2015-10-01

    Seismic site characteristics, as pertaining to earthquake hazard reduction, are a function of the subsurface elastic moduli and the geologic structures. This study explores how multiscale (surface, downhole, and laboratory) datasets can be utilized to improve "constrained" average Vs30 (shear-wave velocity to a 30-meter depth). We integrate borehole, surface and laboratory measurements for a seismic site classification based on the standards of the National Earthquake Hazard Reduction Program (NEHRP). The seismic shear-wave velocity (Vs30) was derived from a geophysical inversion workflow that utilized multichannel analysis of surface-waves (MASW) and downhole acoustic televiewer imaging (DATI). P-wave and S-wave velocities, based on laboratory measurements of arrival times of ultrasonic-frequency signals, supported the workflow by enabling us to calculate Poisson's ratio, which was incorporated in building an initial model for the geophysical inversion of MASW. Extraction of core samples from two boreholes provided lithology and thickness calibration of the amplitudes of the acoustic televiewer imaging for each layer. The MASW inversion, for calculating Vs sections, was constrained with both ultrasonic laboratory measurements (from first arrivals of Vs and Vp waveforms at simulated in situ overburden stress conditions) and the downhole acoustic televiewer (DATV) amplitude logs. The Vs30 calculations enabled categorizing the studied site as NEHRP-class "C" - very dense soil and soft rock. Unlike shallow fractured carbonates in the studied area, S-wave and P-wave velocities at ultrasonic frequency for the deeper intact shale core-samples from two boreholes were in better agreement with the corresponding velocities from both a zero-offset vertical seismic profiling (VSP) and inversion of Rayleigh-wave velocity dispersion curves.

  1. A signal processing approach for enhanced Acoustic Emission data analysis in high activity systems: Application to organic matrix composites

    NASA Astrophysics Data System (ADS)

    Kharrat, M.; Ramasso, E.; Placet, V.; Boubakar, M. L.

    2016-03-01

    Structural elements made of Organic Matrix Composites (OMC) under complex loading may suffer from high Acoustic Emission (AE) activity caused by the emergence of different emission sources at high rates with high noise level, which finally engender continuous emissions. The detection of hits in this situation becomes a challenge particularly during fatigue tests. This work suggests an approach based on the Discrete Wavelet Transform (DWT) denoising applied on signal segments. A particular attention is paid to the adjustment of the denoising parameters based on pencil lead breaks and their influence on the quality of the denoised AE signals. The validation of the proposed approach is performed on a ring-shaped Carbon Fiber Reinforced Plastics (CFRP) under in-service-like conditions involving continuous emissions with superimposed damage-related transients. It is demonstrated that errors in hit detection are greatly reduced leading to a better identification of the natural damage scenario based on AE signals.

  2. Improved Neural Signal Classification in a Rapid Serial Visual Presentation Task Using Active Learning.

    PubMed

    Marathe, Amar R; Lawhern, Vernon J; Wu, Dongrui; Slayback, David; Lance, Brent J

    2016-03-01

    The application space for brain-computer interface (BCI) technologies is rapidly expanding with improvements in technology. However, most real-time BCIs require extensive individualized calibration prior to use, and systems often have to be recalibrated to account for changes in the neural signals due to a variety of factors including changes in human state, the surrounding environment, and task conditions. Novel approaches to reduce calibration time or effort will dramatically improve the usability of BCI systems. Active Learning (AL) is an iterative semi-supervised learning technique for learning in situations in which data may be abundant, but labels for the data are difficult or expensive to obtain. In this paper, we apply AL to a simulated BCI system for target identification using data from a rapid serial visual presentation (RSVP) paradigm to minimize the amount of training samples needed to initially calibrate a neural classifier. Our results show AL can produce similar overall classification accuracy with significantly less labeled data (in some cases less than 20%) when compared to alternative calibration approaches. In fact, AL classification performance matches performance of 10-fold cross-validation (CV) in over 70% of subjects when training with less than 50% of the data. To our knowledge, this is the first work to demonstrate the use of AL for offline electroencephalography (EEG) calibration in a simulated BCI paradigm. While AL itself is not often amenable for use in real-time systems, this work opens the door to alternative AL-like systems that are more amenable for BCI applications and thus enables future efforts for developing highly adaptive BCI systems. PMID:26600162

  3. Mate call as reward: Acoustic communication signals can acquire positive reinforcing values during adulthood in female zebra finches (Taeniopygia guttata).

    PubMed

    Hernandez, Alexandra M; Perez, Emilie C; Mulard, Hervé; Mathevon, Nicolas; Vignal, Clémentine

    2016-02-01

    Social stimuli can have rewarding properties and promote learning. In birds, conspecific vocalizations like song can act as a reinforcer, and specific song variants can acquire particular rewarding values during early life exposure. Here we ask if, during adulthood, an acoustic signal simpler and shorter than song can become a reward for a female songbird because of its particular social value. Using an operant choice apparatus, we showed that female zebra finches display a preferential response toward their mate's calls. This reinforcing value of mate's calls could be involved in the maintenance of the monogamous pair-bond of the zebra finch. PMID:26881942

  4. Acoustic duetting in Drosophila virilis relies on the integration of auditory and tactile signals.

    PubMed

    LaRue, Kelly M; Clemens, Jan; Berman, Gordon J; Murthy, Mala

    2015-01-01

    Many animal species, including insects, are capable of acoustic duetting, a complex social behavior in which males and females tightly control the rate and timing of their courtship song syllables relative to each other. The mechanisms underlying duetting remain largely unknown across model systems. Most studies of duetting focus exclusively on acoustic interactions, but the use of multisensory cues should aid in coordinating behavior between individuals. To test this hypothesis, we develop Drosophila virilis as a new model for studies of duetting. By combining sensory manipulations, quantitative behavioral assays, and statistical modeling, we show that virilis females combine precisely timed auditory and tactile cues to drive song production and duetting. Tactile cues delivered to the abdomen and genitalia play the larger role in females, as even headless females continue to coordinate song production with courting males. These data, therefore, reveal a novel, non-acoustic, mechanism for acoustic duetting. Finally, our results indicate that female-duetting circuits are not sexually differentiated, as males can also produce 'female-like' duets in a context-dependent manner. PMID:26046297

  5. Perturbation and Nonlinear Dynamic Analysis of Acoustic Phonatory Signal in Parkinsonian Patients Receiving Deep Brain Stimulation

    ERIC Educational Resources Information Center

    Lee, Victoria S.; Zhou, Xiao Ping; Rahn, Douglas A., III; Wang, Emily Q.; Jiang, Jack J.

    2008-01-01

    Nineteen PD patients who received deep brain stimulation (DBS), 10 non-surgical (control) PD patients, and 11 non-pathologic age- and gender-matched subjects performed sustained vowel phonations. The following acoustic measures were obtained on the sustained vowel phonations: correlation dimension (D[subscript 2]), percent jitter, percent shimmer,…

  6. Acoustic duetting in Drosophila virilis relies on the integration of auditory and tactile signals

    PubMed Central

    LaRue, Kelly M; Clemens, Jan; Berman, Gordon J; Murthy, Mala

    2015-01-01

    Many animal species, including insects, are capable of acoustic duetting, a complex social behavior in which males and females tightly control the rate and timing of their courtship song syllables relative to each other. The mechanisms underlying duetting remain largely unknown across model systems. Most studies of duetting focus exclusively on acoustic interactions, but the use of multisensory cues should aid in coordinating behavior between individuals. To test this hypothesis, we develop Drosophila virilis as a new model for studies of duetting. By combining sensory manipulations, quantitative behavioral assays, and statistical modeling, we show that virilis females combine precisely timed auditory and tactile cues to drive song production and duetting. Tactile cues delivered to the abdomen and genitalia play the larger role in females, as even headless females continue to coordinate song production with courting males. These data, therefore, reveal a novel, non-acoustic, mechanism for acoustic duetting. Finally, our results indicate that female-duetting circuits are not sexually differentiated, as males can also produce ‘female-like’ duets in a context-dependent manner. DOI: http://dx.doi.org/10.7554/eLife.07277.001 PMID:26046297

  7. Reflex Modification by Acoustic Signals in Newborn Infants and in Adults.

    ERIC Educational Resources Information Center

    Hoffman, Howard S.; And Others

    1985-01-01

    Five experiments using identical reflex modification procedures on neonates and adults suggest developmental differences in processing auditory stimuli. Neonates failed to exhibit reflex inhibition by either prior acoustic or tactile stimuli. Adults exhibited robust reflex inhibition to these same stimuli. Developmental processes implied by these…

  8. West Texas array experiment: Noise and source characterization of short-range infrasound and acoustic signals, along with lab and field evaluation of Intermountain Laboratories infrasound microphones

    NASA Astrophysics Data System (ADS)

    Fisher, Aileen

    spatial wind noise filtering hoses or pipes. The grid was within the distance limits of a single gauge's normal hose array, and data were used to perform a spatial noise correlation study. The highest correlation values were not found in the lower frequencies as anticipated, owing to a lack of sources in the lower range and the uncorrelated nature of wind noise. The highest values, with cross-correlation averages between 0.4 and 0.7 from 3 to 17 m between gauges, were found at night from 10 and 20 Hz due to a continuous local noise source and low wind. Data from the larger array were used to identify continuous and impulsive signals in the area that comprise the ambient noise field. Ground truth infrasound and acoustic, time and location data were taken for a highway site, a wind farm, and a natural gas compressor. Close-range sound data were taken with a single IML "traveler" gauge. Spectrograms and spectrum peaks were used to identify their source signatures. Two regional location techniques were also tested with data from the large array by using a propane cannon as a controlled, impulsive source. A comparison is presented of the Multiple Signal Classification Algorithm (MUSIC) to a simple, quadratic, circular wavefront algorithm. MUSIC was unable to effectively separate noise and source eignenvalues and eigenvectors due to spatial aliasing of the propane cannon signal and a lack of incoherent noise. Only 33 out of 80 usable shots were located by MUSIC within 100 m. Future work with the algorithm should focus on location of impulsive and continuous signals with development of methods for accurate separation of signal and noise eigenvectors in the presence of coherent noise and possible spatial aliasing. The circular wavefront algorithm performed better with our specific dataset and successfully located 70 out of 80 propane cannon shots within 100 m of the original location, 66 of which were within 20 m. This method has low computation requirements, making it well

  9. Classification of emotional states from electrocardiogram signals: a non-linear approach based on hurst

    PubMed Central

    2013-01-01

    Background Identifying the emotional state is helpful in applications involving patients with autism and other intellectual disabilities; computer-based training, human computer interaction etc. Electrocardiogram (ECG) signals, being an activity of the autonomous nervous system (ANS), reflect the underlying true emotional state of a person. However, the performance of various methods developed so far lacks accuracy, and more robust methods need to be developed to identify the emotional pattern associated with ECG signals. Methods Emotional ECG data was obtained from sixty participants by inducing the six basic emotional states (happiness, sadness, fear, disgust, surprise and neutral) using audio-visual stimuli. The non-linear feature ‘Hurst’ was computed using Rescaled Range Statistics (RRS) and Finite Variance Scaling (FVS) methods. New Hurst features were proposed by combining the existing RRS and FVS methods with Higher Order Statistics (HOS). The features were then classified using four classifiers – Bayesian Classifier, Regression Tree, K- nearest neighbor and Fuzzy K-nearest neighbor. Seventy percent of the features were used for training and thirty percent for testing the algorithm. Results Analysis of Variance (ANOVA) conveyed that Hurst and the proposed features were statistically significant (p < 0.001). Hurst computed using RRS and FVS methods showed similar classification accuracy. The features obtained by combining FVS and HOS performed better with a maximum accuracy of 92.87% and 76.45% for classifying the six emotional states using random and subject independent validation respectively. Conclusions The results indicate that the combination of non-linear analysis and HOS tend to capture the finer emotional changes that can be seen in healthy ECG data. This work can be further fine tuned to develop a real time system. PMID:23680041

  10. Model-based inversion algorithm for ground penetration radar signal processing with correlation for target classification

    NASA Astrophysics Data System (ADS)

    Patz, Mark David

    A non-intrusive buried object classifier for a ground penetrating radar (GPR) system is developed. Various GPR data sets and the implemented processing are described. A model based inversion algorithm that utilizes correlation methodology for target classification is introduced. Experimental data was collected with a continuous wave GPR. Synthetic data was generated with a newly developed software package that implements mathematical models to predict the electromagnetic returns from an underground object. Sample targets and geometries were chosen to produce nine configurations/scenarios for analysis. The real measurement sets for each configuration and the synthetic sets for a family of similar configurations were imaged with the same state-of-the-art signal processing algorithms. The imaged results for the real data measurements were correlated with the imaged results for the synthetic data sets to produce performance measurements, thus producing a procedure that provides a non-invasive assessment of the object and medium determined by the synthetic data set that maximally correlated with the real data return. Synthetic results and experiment results showed good correlations. For the synthetic data, a mathematical model was developed for electromagnetic returns from an object shape (i.e., cylinder, parallelepiped, sphere) composed of a uniform construction (i.e., metal, wood, plastic, clay) within a uniform dielectric material (i.e., air, sand, loam, clay, water). This model was then implemented within a software package, thus providing the ability to generate simulated measurements from any combination of object, construction, and dielectric.

  11. Application of an Aligned and Unaligned Signal Processing Technique to Investigate Tones and Broadband Noise in Fan and Contra-Rotating Open Rotor Acoustic Spectra

    NASA Technical Reports Server (NTRS)

    Miles, Jeffrey Hilton; Hultgren, Lennart S.

    2015-01-01

    The study of noise from a two-shaft contra-rotating open rotor (CROR) is challenging since the shafts are not phase locked in most cases. Consequently, phase averaging of the acoustic data keyed to a single shaft rotation speed is not meaningful. An unaligned spectrum procedure that was developed to estimate a signal coherence threshold and reveal concealed spectral lines in turbofan engine combustion noise is applied to fan and CROR acoustic data in this paper.

  12. Overview on the Diversity of Sounds Produced by Clownfishes (Pomacentridae): Importance of Acoustic Signals in Their Peculiar Way of Life

    PubMed Central

    Colleye, Orphal; Parmentier, Eric

    2012-01-01

    Background Clownfishes (Pomacentridae) are brightly colored coral reef fishes well known for their mutualistic symbiosis with tropical sea anemones. These fishes live in social groups in which there is a size-based dominance hierarchy. In this structure where sex is socially controlled, agonistic interactions are numerous and serve to maintain size differences between individuals adjacent in rank. Clownfishes are also prolific callers whose sounds seem to play an important role in the social hierarchy. Here, we aim to review and to synthesize the diversity of sounds produced by clownfishes in order to emphasize the importance of acoustic signals in their way of life. Methodology/Principal Findings Recording the different acoustic behaviors indicated that sounds are divided into two main categories: aggressive sounds produced in conjunction with threat postures (charge and chase), and submissive sounds always emitted when fish exhibited head shaking movements (i.e. a submissive posture). Both types of sounds showed size-related intraspecific variation in dominant frequency and pulse duration: smaller individuals produce higher frequency and shorter duration pulses than larger ones, and inversely. Consequently, these sonic features might be useful cues for individual recognition within the group. This observation is of significant importance due to the size-based hierarchy in clownfish group. On the other hand, no acoustic signal was associated with the different reproductive activities. Conclusions/Significance Unlike other pomacentrids, sounds are not produced for mate attraction in clownfishes but to reach and to defend the competition for breeding status, which explains why constraints are not important enough for promoting call diversification in this group. PMID:23145114

  13. Discussion about generation mechanisms of third-order nonlinear signals in surface acoustic wave resonators based on simulation

    NASA Astrophysics Data System (ADS)

    Nakagawa, Ryo; Suzuki, Takanao; Shimizu, Hiroshi; Kyoya, Haruki; Nako, Katsuhiro; Hashimoto, Ken-ya

    2016-07-01

    In this paper, we discuss the generation mechanisms of third-order nonlinearity in surface acoustic wave (SAW) devices on the basis of simulation results, which are obtained by a proposed method for this discussion. First, eight nonlinear terms are introduced to the piezoelectric constitutive equations, and nonlinear stress and electric flux fields are estimated using linear strain and electric fields calculated by a linear analysis, i.e., the coupling of mode simulation. Then, their contributions are embedded as voltage and current sources, respectively, in an equivalent circuit model, and nonlinear signals appearing at external ports are estimated. It is shown that eight coefficients of the nonlinear terms can be determined from a series of experiments carried out at various driving and resulting frequencies. This is because the effect of each nonlinear term on the nonlinear signal outputs changes markedly with the conditions. When the coefficients are determined properly, the simulations agree well with some measurement results under various conditions.

  14. Underwater Acoustics

    NASA Astrophysics Data System (ADS)

    Kuperman, William A.; Roux, Philippe

    It is well underwater established that sound waves, compared to electromagnetic waves, propagate long distances in the ocean. Hence, in the ocean as opposed to air or a vacuum, one uses sound navigation and ranging (SONAR) instead navigation and ranging (SONAR) of radar, acoustic communication instead of radio, and acoustic imaging and tomography instead of microwave or optical imaging or X-ray tomography. Underwater acoustics is the science of sound in water (most commonly in the ocean) and encompasses not only the study of sound propagation, but also the masking of sound signals by interfering phenomenon and signal processing for extracting these signals from interference. This chapter we will present the basics physics of ocean acoustics and then discuss applications.

  15. Observations and Bayesian location methodology of transient acoustic signals (likely blue whales) in the Indian Ocean, using a hydrophone triplet.

    PubMed

    Le Bras, Ronan J; Kuzma, Heidi; Sucic, Victor; Bokelmann, Götz

    2016-05-01

    A notable sequence of calls was encountered, spanning several days in January 2003, in the central part of the Indian Ocean on a hydrophone triplet recording acoustic data at a 250 Hz sampling rate. This paper presents signal processing methods applied to the waveform data to detect, group, extract amplitude and bearing estimates for the recorded signals. An approximate location for the source of the sequence of calls is inferred from extracting the features from the waveform. As the source approaches the hydrophone triplet, the source level (SL) of the calls is estimated at 187 ± 6 dB re: 1 μPa-1 m in the 15-60 Hz frequency range. The calls are attributed to a subgroup of blue whales, Balaenoptera musculus, with a characteristic acoustic signature. A Bayesian location method using probabilistic models for bearing and amplitude is demonstrated on the calls sequence. The method is applied to the case of detection at a single triad of hydrophones and results in a probability distribution map for the origin of the calls. It can be extended to detections at multiple triads and because of the Bayesian formulation, additional modeling complexity can be built-in as needed. PMID:27250159

  16. Normalization and source separation of acoustic emission signals for condition monitoring and fault detection of multi-cylinder diesel engines

    NASA Astrophysics Data System (ADS)

    Wu, Weiliang; Lin, Tian Ran; Tan, Andy C. C.

    2015-12-01

    A signal processing technique is presented in this paper to normalize and separate the source of non-linear acoustic emission (AE) signals of a multi-cylinder diesel engine for condition monitoring applications and fault detection. The normalization technique presented in the paper overcomes the long-existing non-linearity problem of AE sensors so that responses measured by different AE sensors can be quantitatively analysed and compared. A source separation algorithm is also developed in the paper to separate the mixture of the normalized AE signals produced by a multi-cylinder diesel engine by utilising the system parameters (i.e., wave attenuation constant and the arrival time delay) of AE wave propagation determined by a standard pencil lead break test on the engine cylinder head. It is shown that the source separation algorithm is able to separate the signal interference of adjacent cylinders from the monitored cylinder once the wave attenuation constant and the arrival time delay along the propagation path are known. The algorithm is particularly useful in the application of AE technique for condition monitoring of small-size diesel engines where signal interference from the neighbouring cylinders is strong.

  17. Visualizing detecting low-frequency underwater acoustic signals by means of optical diffraction.

    PubMed

    Ren, Yao; Miao, Runcai; Su, Xiaoming; Chen, Hua

    2016-03-10

    A novel and simple technique based on the light diffraction effect for visualization of low-frequency underwater acoustic waves (LFUAWs) in real time has been developed in this paper. A cylindrical object has been put on the surface of the water. A low-frequency underwater longitudinal wave can be generated into a water surface transversal capillary wave around the cylinder by our technique. Modulating the phase of a laser beam reflected from a water surface by surface acoustic waves (SAWs) realizes the acousto-optic effect. Then, a steady and visible diffraction pattern is experimentally observed. A physical model of the SAW is established to verify the feasibility of our technique. An analytical expression of wavelength, wave amplitude, and excitation frequency has been derived to study the physical properties of LFUAWs, and it explains the experimental phenomenon very well. As a result, the technique is effective, easy, and practical for visualizing LFUAWs and has significance for applications. PMID:26974797

  18. The ionospheric response to the acoustic signal from submarine earthquakes according to the GPS data

    NASA Astrophysics Data System (ADS)

    Gokhberg, M. B.; Ol'shanskaya, E. V.; Steblov, G. M.; Shalimov, S. L.

    2014-01-01

    The ionospheric response to the transit of acoustic waves from a number of the strongest submarine earthquakes with magnitudes M w ≥ 7.7, which occurred during the past few years, is analyzed. The amplitude of the response in the detrended TEC is studied as a function of the magnitude and vertical component of the surface deformation. It is shown that the geomagnetic field can significantly modulate the shape of the ionospheric response, depending on whether the perturbation propagates equatorward or polarward.

  19. Ensemble of classifiers for confidence-rated classification of NDE signal

    NASA Astrophysics Data System (ADS)

    Banerjee, Portia; Safdarnejad, Seyed; Udpa, Lalita; Udpa, Satish

    2016-02-01

    Ensemble of classifiers in general, aims to improve classification accuracy by combining results from multiple weak hypotheses into a single strong classifier through weighted majority voting. Improved versions of ensemble of classifiers generate self-rated confidence scores which estimate the reliability of each of its prediction and boost the classifier using these confidence-rated predictions. However, such a confidence metric is based only on the rate of correct classification. In existing works, although ensemble of classifiers has been widely used in computational intelligence, the effect of all factors of unreliability on the confidence of classification is highly overlooked. With relevance to NDE, classification results are affected by inherent ambiguity of classifica-tion, non-discriminative features, inadequate training samples and noise due to measurement. In this paper, we extend the existing ensemble classification by maximizing confidence of every classification decision in addition to minimizing the classification error. Initial results of the approach on data from eddy current inspection show improvement in classification performance of defect and non-defect indications.

  20. Multiple feature extraction and classification of electroencephalograph signal for Alzheimers' with spectrum and bispectrum

    NASA Astrophysics Data System (ADS)

    Wang, Ruofan; Wang, Jiang; Li, Shunan; Yu, Haitao; Deng, Bin; Wei, Xile

    2015-01-01

    In this paper, we have combined experimental neurophysiologic recording and statistical analysis to investigate the nonlinear characteristic and the cognitive function of the brain. Spectrum and bispectrum analyses are proposed to extract multiple effective features of electroencephalograph (EEG) signals from Alzheimer's disease (AD) patients and further applied to distinguish AD patients from the normal controls. Spectral analysis based on autoregressive Burg method is first used to quantify the power distribution of EEG series in the frequency domain. Compared to the control group, the relative power spectral density of AD group is significantly higher in the theta frequency band, while lower in the alpha frequency bands. In addition, median frequency of spectrum is decreased, and spectral entropy ratio of these two frequency bands undergoes drastic changes at the P3 electrode in the central-parietal brain region, implying that the electrophysiological behavior in AD brain is much slower and less irregular. In order to explore the nonlinear high order information, bispectral analysis which measures the complexity of phase-coupling is further applied to P3 electrode in the whole frequency band. It is demonstrated that less bispectral peaks appear and the amplitudes of peaks fall, suggesting a decrease of non-Gaussianity and nonlinearity of EEG in ADs. Notably, the application of this method to five brain regions shows higher concentration of the weighted center of bispectrum and lower complexity reflecting phase-coupling by bispectral entropy. Based on spectrum and bispectrum analyses, six efficient features are extracted and then applied to discriminate AD from the normal in the five brain regions. The classification results indicate that all these features could differentiate AD patients from the normal controls with a maximum accuracy of 90.2%. Particularly, different brain regions are sensitive to different features. Moreover, the optimal combination of

  1. Auditory object salience: human cortical processing of non-biological action sounds and their acoustic signal attributes

    PubMed Central

    Lewis, James W.; Talkington, William J.; Tallaksen, Katherine C.; Frum, Chris A.

    2012-01-01

    Whether viewed or heard, an object in action can be segmented as a distinct salient event based on a number of different sensory cues. In the visual system, several low-level attributes of an image are processed along parallel hierarchies, involving intermediate stages wherein gross-level object form and/or motion features are extracted prior to stages that show greater specificity for different object categories (e.g., people, buildings, or tools). In the auditory system, though relying on a rather different set of low-level signal attributes, meaningful real-world acoustic events and “auditory objects” can also be readily distinguished from background scenes. However, the nature of the acoustic signal attributes or gross-level perceptual features that may be explicitly processed along intermediate cortical processing stages remain poorly understood. Examining mechanical and environmental action sounds, representing two distinct non-biological categories of action sources, we had participants assess the degree to which each sound was perceived as object-like versus scene-like. We re-analyzed data from two of our earlier functional magnetic resonance imaging (fMRI) task paradigms (Engel et al., 2009) and found that scene-like action sounds preferentially led to activation along several midline cortical structures, but with strong dependence on listening task demands. In contrast, bilateral foci along the superior temporal gyri (STG) showed parametrically increasing activation to action sounds rated as more “object-like,” independent of sound category or task demands. Moreover, these STG regions also showed parametric sensitivity to spectral structure variations (SSVs) of the action sounds—a quantitative measure of change in entropy of the acoustic signals over time—and the right STG additionally showed parametric sensitivity to measures of mean entropy and harmonic content of the environmental sounds. Analogous to the visual system, intermediate stages

  2. Acoustic Emission Signal Processing Technique to Characterize Reactor In-Pile Phenomena

    SciTech Connect

    Vivek Agarwal; Magdy Samy Tawfik; James A Smith

    2014-07-01

    Existing and developing advanced sensor technologies and instrumentation will allow non-intrusive in-pile measurement of temperature, extension, and fission gases when coupled with advanced signal processing algorithms. The transmitted measured sensor signals from inside to the outside of containment structure are corrupted by noise and are attenuated, thereby reducing the signal strength and signal-to-noise ratio. Identification and extraction of actual signal (representative of an in-pile phenomenon) is a challenging and complicated process. In this paper, empirical mode decomposition technique is proposed to reconstruct actual sensor signal by partially combining intrinsic mode functions. Reconstructed signal corresponds to phenomena and/or failure modes occurring inside the reactor. In addition, it allows accurate non-intrusive monitoring and trending of in-pile phenomena.

  3. Acoustic emission signal processing technique to characterize reactor in-pile phenomena

    SciTech Connect

    Agarwal, Vivek; Tawfik, Magdy S.; Smith, James A.

    2015-03-31

    Existing and developing advanced sensor technologies and instrumentation will allow non-intrusive in-pile measurement of temperature, extension, and fission gases when coupled with advanced signal processing algorithms. The transmitted measured sensor signals from inside to the outside of containment structure are corrupted by noise and are attenuated, thereby reducing the signal strength and the signal-to-noise ratio. Identification and extraction of actual signal (representative of an in-pile phenomenon) is a challenging and complicated process. In the paper, empirical mode decomposition technique is utilized to reconstruct actual sensor signal by partially combining intrinsic mode functions. Reconstructed signal will correspond to phenomena and/or failure modes occurring inside the reactor. In addition, it allows accurate non-intrusive monitoring and trending of in-pile phenomena.

  4. A Comparison of Supervised Classification Methods for the Prediction of Substrate Type Using Multibeam Acoustic and Legacy Grain-Size Data

    PubMed Central

    Stephens, David; Diesing, Markus

    2014-01-01

    Detailed seabed substrate maps are increasingly in demand for effective planning and management of marine ecosystems and resources. It has become common to use remotely sensed multibeam echosounder data in the form of bathymetry and acoustic backscatter in conjunction with ground-truth sampling data to inform the mapping of seabed substrates. Whilst, until recently, such data sets have typically been classified by expert interpretation, it is now obvious that more objective, faster and repeatable methods of seabed classification are required. This study compares the performances of a range of supervised classification techniques for predicting substrate type from multibeam echosounder data. The study area is located in the North Sea, off the north-east coast of England. A total of 258 ground-truth samples were classified into four substrate classes. Multibeam bathymetry and backscatter data, and a range of secondary features derived from these datasets were used in this study. Six supervised classification techniques were tested: Classification Trees, Support Vector Machines, k-Nearest Neighbour, Neural Networks, Random Forest and Naive Bayes. Each classifier was trained multiple times using different input features, including i) the two primary features of bathymetry and backscatter, ii) a subset of the features chosen by a feature selection process and iii) all of the input features. The predictive performances of the models were validated using a separate test set of ground-truth samples. The statistical significance of model performances relative to a simple baseline model (Nearest Neighbour predictions on bathymetry and backscatter) were tested to assess the benefits of using more sophisticated approaches. The best performing models were tree based methods and Naive Bayes which achieved accuracies of around 0.8 and kappa coefficients of up to 0.5 on the test set. The models that used all input features didn't generally perform well, highlighting the need for

  5. Statistically Optimal Approximations of Astronomical Signals: Implications to Classification and Advanced Study of Variable Stars

    NASA Astrophysics Data System (ADS)

    Andronov, I. L.; Chinarova, L. L.; Kudashkina, L. S.; Marsakova, V. I.; Tkachenko, M. G.

    2016-06-01

    We have elaborated a set of new algorithms and programs for advanced time series analysis of (generally) multi-component multi-channel observations with irregularly spaced times of observations, which is a common case for large photometric surveys. Previous self-review on these methods for periodogram, scalegram, wavelet, autocorrelation analysis as well as on "running" or "sub-interval" local approximations were self-reviewed in (2003ASPC..292..391A). For an approximation of the phase light curves of nearly-periodic pulsating stars, we use a Trigonometric Polynomial (TP) fit of the statistically optimal degree and initial period improvement using differential corrections (1994OAP.....7...49A). For the determination of parameters of "characteristic points" (minima, maxima, crossings of some constant value etc.) we use a set of methods self-reviewed in 2005ASPC..335...37A, Results of the analysis of the catalogs compiled using these programs are presented in 2014AASP....4....3A. For more complicated signals, we use "phenomenological approximations" with "special shapes" based on functions defined on sub-intervals rather on the complete interval. E. g. for the Algol-type stars we developed the NAV ("New Algol Variable") algorithm (2012Ap.....55..536A, 2012arXiv1212.6707A, 2015JASS...32..127A), which was compared to common methods of Trigonometric Polynomial Fit (TP) or local Algebraic Polynomial (A) fit of a fixed or (alternately) statistically optimal degree. The method allows determine the minimal set of parameters required for the "General Catalogue of Variable Stars", as well as an extended set of phenomenological and astrophysical parameters which may be used for the classification. Totally more that 1900 variable stars were studied in our group using these methods in a frame of the "Inter-Longitude Astronomy" campaign (2010OAP....23....8A) and the "Ukrainian Virtual Observatory" project (2012KPCB...28...85V).

  6. Selective and Efficient Neural Coding of Communication Signals Depends on Early Acoustic and Social Environment

    PubMed Central

    Amin, Noopur; Gastpar, Michael; Theunissen, Frédéric E.

    2013-01-01

    Previous research has shown that postnatal exposure to simple, synthetic sounds can affect the sound representation in the auditory cortex as reflected by changes in the tonotopic map or other relatively simple tuning properties, such as AM tuning. However, their functional implications for neural processing in the generation of ethologically-based perception remain unexplored. Here we examined the effects of noise-rearing and social isolation on the neural processing of communication sounds such as species-specific song, in the primary auditory cortex analog of adult zebra finches. Our electrophysiological recordings reveal that neural tuning to simple frequency-based synthetic sounds is initially established in all the laminae independent of patterned acoustic experience; however, we provide the first evidence that early exposure to patterned sound statistics, such as those found in native sounds, is required for the subsequent emergence of neural selectivity for complex vocalizations and for shaping neural spiking precision in superficial and deep cortical laminae, and for creating efficient neural representations of song and a less redundant ensemble code in all the laminae. Our study also provides the first causal evidence for ‘sparse coding’, such that when the statistics of the stimuli were changed during rearing, as in noise-rearing, that the sparse or optimal representation for species-specific vocalizations disappeared. Taken together, these results imply that a layer-specific differential development of the auditory cortex requires patterned acoustic input, and a specialized and robust sensory representation of complex communication sounds in the auditory cortex requires a rich acoustic and social environment. PMID:23630587

  7. Body Morphology, Energy Stores, and Muscle Enzyme Activity Explain Cricket Acoustic Mate Attraction Signaling Variation

    PubMed Central

    Thomson, Ian R.; Darveau, Charles-A.; Bertram, Susan M.

    2014-01-01

    High mating success in animals is often dependent on males signalling attractively with high effort. Since males should be selected to maximize their reproductive success, female preferences for these traits should result in minimal signal variation persisting in the population. However, extensive signal variation persists. The genic capture hypothesis proposes genetic variation persists because fitness-conferring traits depend on an individual's basic processes, including underlying physiological, morphological, and biochemical traits, which are themselves genetically variable. To explore the traits underlying signal variation, we quantified among-male differences in signalling, morphology, energy stores, and the activities of key enzymes associated with signalling muscle metabolism in two species of crickets, Gryllus assimilis (chirper: <20 pulses/chirp) and G. texensis (triller: >20 pulses/chirp). Chirping G. assimilis primarily fuelled signalling with carbohydrate metabolism: smaller individuals and individuals with increased thoracic glycogen stores signalled for mates with greater effort; individuals with greater glycogen phosphorylase activity produced more attractive mating signals. Conversely, the more energetic trilling G. texensis fuelled signalling with both lipid and carbohydrate metabolism: individuals with increased β-hydroxyacyl-CoA dehydrogenase activity and increased thoracic free carbohydrate content signalled for mates with greater effort; individuals with higher thoracic and abdominal carbohydrate content and higher abdominal lipid stores produced more attractive signals. Our findings suggest variation in male reproductive success may be driven by hidden physiological trade-offs that affect the ability to uptake, retain, and use essential nutrients, although the results remain correlational in nature. Our findings indicate that a physiological perspective may help us to understand some of the causes of variation in behaviour. PMID:24608102

  8. In-flight fiber optic acoustic emission sensor (FAESense) system for the real time detection, localization, and classification of damage in composite aircraft structures

    NASA Astrophysics Data System (ADS)

    Mendoza, Edgar; Prohaska, John; Kempen, Connie; Esterkin, Yan; Sun, Sunjian

    2013-05-01

    Acoustic emission sensing is a leading structural health monitoring technique use for the early warning detection of structural damage associated with impacts, cracks, fracture, and delaminations in advanced materials. Current AE systems based on electronic PZT transducers suffer from various limitations that prevent its wide dynamic use in practical avionics and aerospace applications where weight, size and power are critical for operation. This paper describes progress towards the development of a wireless in-flight distributed fiber optic acoustic emission monitoring system (FAESense™) suitable for the onboard-unattended detection, localization, and classification of damage in avionics and aerospace structures. Fiber optic AE sensors offer significant advantages over its counterpart electronic AE sensors by using a high-density array of micron-size AE transducers distributed and multiplex over long lengths of a standard single mode optical fiber. Immediate SHM applications are found in commercial and military aircraft, helicopters, spacecraft, wind mil turbine blades, and in next generation weapon systems, as well as in the petrochemical and aerospace industries, civil structures, power utilities, and a wide spectrum of other applications.

  9. In situ monitoring the pulse CO 2 laser interaction with 316-L stainless steel using acoustical signals and plasma analysis

    NASA Astrophysics Data System (ADS)

    Khosroshahi, M. E.; pour, F. Anoosheh; Hadavi, M.; Mahmoodi, M.

    2010-10-01

    In most laser material processing, material removal by different mechanisms is involved. Here, application of acoustic signals with thermoelastic (below threshold) and breakdown origin (above threshold) together with plasma plume analysis as a simple monitoring system of interaction process is suggested. In this research the interaction of pulse CO 2 laser with 200 ns duration and maximum energy of 1.3 J operating at 1 Hz with austenitic stainless steel (316-L) is reported. The results showed that the non-linear point of the curve can serve as a useful indicator of melting fluence threshold (in this case ≈830 J cm -2) with corresponding temperature calculated using plasma plume analysis. Higher acoustic amplitudes and larger plasma plume volume indicates more intense interaction. Also, analysis showed that a phase explosion process with material removal (ejecta) in the form of non-adiabatic (i.e., dt ≫ α-1) is at play after laser pulse is ended. Also, SEM photographs show different surface quality medication at different laser intensities, which indicates the importance of recoil momentum pressure and possibly electrons and ions densities in heat transfer. Finally, electrochemical test indicate an improved corrosion resistance for laser treated samples compared to untreated ones.

  10. Adaptive significance of synchronous chorusing in an acoustically signalling wolf spider.

    PubMed Central

    Kotiaho, Janne S.; Alatalo, Rauno V.; Mappes, Johanna; Parri, Silja

    2004-01-01

    Synchronous sexual signalling is a behavioural phenomenon that has received considerable theoretical interest, but surprisingly few empirical tests have been conducted. Here, we present a set of experiments designed to determine (i) whether the sexual signalling of the drumming wolf spider Hygrolycosa rubrofasciata is synchronous, and (ii) whether the synchrony may have evolved through female preference. Using controlled playback experiments, we found that males actively synchronized their drumming bouts with other males and females significantly preferred closely synchronized drumming clusters compared with loose clusters. In loose clusters, the first drumming signals attracted the most female responses, whereas in close clusters, the last drumming signals were the most heeded. We suggest that this female preference for the last drummer can maintain male synchronous signalling in H. rubrofasciata. PMID:15315901

  11. Acoustic emission frequency discrimination

    NASA Technical Reports Server (NTRS)

    Sugg, Frank E. (Inventor); Graham, Lloyd J. (Inventor)

    1988-01-01

    In acoustic emission nondestructive testing, broadband frequency noise is distinguished from narrow banded acoustic emission signals, since the latter are valid events indicative of structural flaws in the material being examined. This is accomplished by separating out those signals which contain frequency components both within and beyond (either above or below) the range of valid acoustic emission events. Application to acoustic emission monitoring during nondestructive bond verification and proof loading of undensified tiles on the Space Shuttle Orbiter is considered.

  12. A robust H∞ learning approach to blind separation of slowly time-varying mixture of acoustic electromechanical signals

    NASA Astrophysics Data System (ADS)

    Das, Niva; Routray, Aurobinda; Dash, Pradipta Kishor

    2009-08-01

    Although many techniques have been developed for solving the blind source separation (BSS) problem, some issues related to robustness of BSS algorithms are yet to be addressed. Most of the BSS algorithms developed assume the mixing system to be stationary. In this paper, we present a robust approach based on H∞ learning to address the instantaneous BSS problem in a non-stationary mixing environment. The motivation behind applying H∞ filter is that these are robust to errors arising out of model uncertainties, parameter variations and additive noise. Acoustic electromechanical signals have been considered for simulation purpose. Simulation results demonstrate that the H∞ filter performs superior to Kalman filter and VS-NGA algorithm. To ensure practicability of the proposed approach, the H∞ learning algorithm has been implemented and tested on Texas Instrument's TMS320C6713 floating point DSP platform successfully.

  13. Method and apparatus for background signal reduction in opto-acoustic absorption measurement

    NASA Technical Reports Server (NTRS)

    Rosengren, L. G. (Inventor)

    1976-01-01

    The sensitivity of an opto-acoustic absorption detector is increased to make it possible to measure trace amounts of constituent gases. A second beam radiation path is created through the sample cell identical to a first path except as to length, alternating the beam through the two paths and minimizing the detected pressure difference for the two paths while the beam wavelength is tuned away from the absorption lines of the sample. Then with the beam wavelength tuned to the absorption line of any constituent of interest, the pressure difference is a measure of trace amounts of the constituent. The same improved detector may also be used for measuring the absorption coefficient of known concentrations of absorbing gases.

  14. Acoustic underwater signals with a probable function during competitive feeding in a tadpole

    NASA Astrophysics Data System (ADS)

    Reeve, Erik; Ndriantsoa, Serge Herilala; Strauß, Axel; Randrianiaina, Roger-Daniel; Rasolonjatovo Hiobiarilanto, Tahiry; Glaw, Frank; Glos, Julian; Vences, Miguel

    2011-02-01

    Acoustic communication is widespread among adult stages of terrestrial animals and fish and has also been observed in insect larvae. We report underwater acoustic communication in the larvae of a frog, Gephyromantis azzurrae, from Isalo, a sandstone massif in western Madagascar. According to our field data, these tadpoles live in streams and prefer habitats characterized by comparatively low temperatures, shallow water depth, and a relatively fast current. Feeding experiments indicated that the tadpoles are carnivorous and macrophagous. They consumed insect larvae and, to a lesser extent, small shrimps, and conspecific as well as heterospecific tadpoles. Calls of these tadpoles consisted either of single click notes or of irregular series of various clicks. Some complex calls have a pulsed structure with three to nine indistinct energy pulses. Production of the pulses coincided with rapid closure of the jaw sheaths and often with an upward movement of the body. Calls were emitted while attacking prey and occurred significantly more often when attacking conspecifics. Tadpoles that had not been fed for some time emitted sounds more frequently than those that had been regularly fed. The spectral frequency of the calls differed in tadpole groups of different size and was higher in groups of smaller tadpoles, suggesting that spectral frequency carries some information about tadpole size which might be important during competitive feeding to assess size and strength of competitors. This report differs from those for the larvae of South American horned frogs, Ceratophrys ornata. These are the only other tadpoles for which sound production has reliably been reported but the calls of Ceratophrys tadpoles occur mainly in a defensive context.

  15. [Analysis of species specific acoustic signals in the mesencephalic, diencephalic, and neostriatal structures of the brain].

    PubMed

    Shliafer, T P; Aleksandrova, Zh G

    1979-10-01

    In chronic experiments, the neuronal activity in structures of the auditory analyzer was studied during perception of species-specific signals in the chicken. The majority of the neostriatum cells responded to territorial vocalizations of the rooster and to squeaking of the chicken; in the midbrain structures the maximal responses occurred to signals of distress and alarm. The greatest number of cells responded most obviously to the cardinal component of the chicken vocalization spectrum. PMID:510592

  16. Frequency and time pattern differences in acoustic signals produced by Prostephanus truncatus (Horn) (Coleoptera: Bostrichidae) and Sitophilus zeamais (Motschulsky) (Coleoptera: Curculionidae)in stored maize

    Technology Transfer Automated Retrieval System (TEKTRAN)

    The acoustic signals emitted by the last stage larval instars and adults of Prostephanus truncatus and Sitophilus zeamais in stored maize were investigated. Analyses were performed to identify brief, 1-10-ms broadband sound impulses of five different frequency patterns produced by larvae and adults,...

  17. The vocal repertoire of the domesticated zebra finch: a data-driven approach to decipher the information-bearing acoustic features of communication signals.

    PubMed

    Elie, Julie E; Theunissen, Frédéric E

    2016-03-01

    Although a universal code for the acoustic features of animal vocal communication calls may not exist, the thorough analysis of the distinctive acoustical features of vocalization categories is important not only to decipher the acoustical code for a specific species but also to understand the evolution of communication signals and the mechanisms used to produce and understand them. Here, we recorded more than 8000 examples of almost all the vocalizations of the domesticated zebra finch, Taeniopygia guttata: vocalizations produced to establish contact, to form and maintain pair bonds, to sound an alarm, to communicate distress or to advertise hunger or aggressive intents. We characterized each vocalization type using complete representations that avoided any a priori assumptions on the acoustic code, as well as classical bioacoustics measures that could provide more intuitive interpretations. We then used these acoustical features to rigorously determine the potential information-bearing acoustical features for each vocalization type using both a novel regularized classifier and an unsupervised clustering algorithm. Vocalization categories are discriminated by the shape of their frequency spectrum and by their pitch saliency (noisy to tonal vocalizations) but not particularly by their fundamental frequency. Notably, the spectral shape of zebra finch vocalizations contains peaks or formants that vary systematically across categories and that would be generated by active control of both the vocal organ (source) and the upper vocal tract (filter). PMID:26581377

  18. Adaptive neuro-fuzzy inference system for classification of background EEG signals from ESES patients and controls.

    PubMed

    Yang, Zhixian; Wang, Yinghua; Ouyang, Gaoxiang

    2014-01-01

    Background electroencephalography (EEG), recorded with scalp electrodes, in children with electrical status epilepticus during slow-wave sleep (ESES) syndrome and control subjects has been analyzed. We considered 10 ESES patients, all right-handed and aged 3-9 years. The 10 control individuals had the same characteristics of the ESES ones but presented a normal EEG. Recordings were undertaken in the awake and relaxed states with their eyes open. The complexity of background EEG was evaluated using the permutation entropy (PE) and sample entropy (SampEn) in combination with the ANOVA test. It can be seen that the entropy measures of EEG are significantly different between the ESES patients and normal control subjects. Then, a classification framework based on entropy measures and adaptive neuro-fuzzy inference system (ANFIS) classifier is proposed to distinguish ESES and normal EEG signals. The results are promising and a classification accuracy of about 89% is achieved. PMID:24790547

  19. Adaptive Neuro-Fuzzy Inference System for Classification of Background EEG Signals from ESES Patients and Controls

    PubMed Central

    Yang, Zhixian; Wang, Yinghua; Ouyang, Gaoxiang

    2014-01-01

    Background electroencephalography (EEG), recorded with scalp electrodes, in children with electrical status epilepticus during slow-wave sleep (ESES) syndrome and control subjects has been analyzed. We considered 10 ESES patients, all right-handed and aged 3–9 years. The 10 control individuals had the same characteristics of the ESES ones but presented a normal EEG. Recordings were undertaken in the awake and relaxed states with their eyes open. The complexity of background EEG was evaluated using the permutation entropy (PE) and sample entropy (SampEn) in combination with the ANOVA test. It can be seen that the entropy measures of EEG are significantly different between the ESES patients and normal control subjects. Then, a classification framework based on entropy measures and adaptive neuro-fuzzy inference system (ANFIS) classifier is proposed to distinguish ESES and normal EEG signals. The results are promising and a classification accuracy of about 89% is achieved. PMID:24790547

  20. Elaborate visual and acoustic signals evolve independently in a large, phenotypically diverse radiation of songbirds.

    PubMed

    Mason, Nicholas A; Shultz, Allison J; Burns, Kevin J

    2014-08-01

    The concept of a macroevolutionary trade-off among sexual signals has a storied history in evolutionary biology. Theory predicts that if multiple sexual signals are costly for males to produce or maintain and females prefer a single, sexually selected trait, then an inverse correlation between sexual signal elaborations is expected among species. However, empirical evidence for what has been termed the 'transfer hypothesis' is mixed, which may reflect different selective pressures among lineages, evolutionary covariates or methodological differences among studies. Here, we examine interspecific correlations between song and plumage elaboration in a phenotypically diverse, widespread radiation of songbirds, the tanagers. The tanagers (Thraupidae) are the largest family of songbirds, representing nearly 10% of all songbirds. We assess variation in song and plumage elaboration across 301 species, representing the largest scale comparative study of multimodal sexual signalling to date. We consider whether evolutionary covariates, including habitat, structural and carotenoid-based coloration, and subfamily groupings influence the relationship between song and plumage elaboration. We find that song and plumage elaboration are uncorrelated when considering all tanagers, although the relationship between song and plumage complexity varies among subfamilies. Taken together, we find that elaborate visual and vocal sexual signals evolve independently among tanagers. PMID:24943371

  1. Elaborate visual and acoustic signals evolve independently in a large, phenotypically diverse radiation of songbirds

    PubMed Central

    Mason, Nicholas A.; Shultz, Allison J.; Burns, Kevin J.

    2014-01-01

    The concept of a macroevolutionary trade-off among sexual signals has a storied history in evolutionary biology. Theory predicts that if multiple sexual signals are costly for males to produce or maintain and females prefer a single, sexually selected trait, then an inverse correlation between sexual signal elaborations is expected among species. However, empirical evidence for what has been termed the ‘transfer hypothesis’ is mixed, which may reflect different selective pressures among lineages, evolutionary covariates or methodological differences among studies. Here, we examine interspecific correlations between song and plumage elaboration in a phenotypically diverse, widespread radiation of songbirds, the tanagers. The tanagers (Thraupidae) are the largest family of songbirds, representing nearly 10% of all songbirds. We assess variation in song and plumage elaboration across 301 species, representing the largest scale comparative study of multimodal sexual signalling to date. We consider whether evolutionary covariates, including habitat, structural and carotenoid-based coloration, and subfamily groupings influence the relationship between song and plumage elaboration. We find that song and plumage elaboration are uncorrelated when considering all tanagers, although the relationship between song and plumage complexity varies among subfamilies. Taken together, we find that elaborate visual and vocal sexual signals evolve independently among tanagers. PMID:24943371

  2. Coupled High Speed Imaging and Seismo-Acoustic Recordings of Strombolian Explosions at Etna, July 2014: Implications for Source Processes and Signal Inversions.

    NASA Astrophysics Data System (ADS)

    Taddeucci, J.; Del Bello, E.; Scarlato, P.; Ricci, T.; Andronico, D.; Kueppers, U.; Cannata, A.; Sesterhenn, J.; Spina, L.

    2015-12-01

    Seismic and acoustic surveillance is routinely performed at several persistent activity volcanoes worldwide. However, interpretation of the signals associated with explosive activity is still equivocal, due to both source variability and the intrinsically limited information carried by the waves. Comparison and cross-correlation of the geophysical quantities with other information in general and visual recording in particular is therefore actively sought. At Etna (Italy) in July 2014, short-lived Strombolian explosions ejected bomb- to lapilli-sized, molten pyroclasts at a remarkably repeatable time interval of about two seconds, offering a rare occasion to systematically investigate the seismic and acoustic fields radiated by this common volcanic source. We deployed FAMoUS (FAst, MUltiparametric Setup for the study of explosive activity) at 260 meters from the vents, recording more than 60 explosions in thermal and visible high-speed videos (50 to 500 frames per second) and broadband seismic and acoustic instruments (1 to 10000 Hz for the acoustic and from 0.01 to 30 Hz for the seismic). Analysis of this dataset highlights nonlinear relationships between the exit velocity and mass of ejecta and the amplitude and frequency of the acoustic signals. It also allows comparing different methods to estimate source depth, and to validate existing theory on the coupling of airwaves with ground motion.

  3. Through-container measurement of acoustic signatures for classification/discrimination of liquid explosives (LEs) and precursor threat liquids

    NASA Astrophysics Data System (ADS)

    Diaz, Aaron A.; Samuel, Todd J.; Tucker, Brian J.; Cinson, Anthony D.; Valencia, Juan D.; Gervais, Kevin L.; Thompson, Jason S.

    2008-03-01

    Work at the Pacific Northwest National Laboratory has demonstrated that ultrasonic property measurements can be effectively employed for the rapid and accurate classification/discrimination of liquids in small, carry-on, standard "stream-of-commerce" containers. This paper focuses on a set of laboratory measurements acquired with the PNNL prototype device as applied to several types of liquids (including threat liquids and precursor chemicals) to the manufacture of LEs in small commercially available plastic containers.

  4. Effect of sound on gap-junction-based intercellular signaling: Calcium waves under acoustic irradiation

    NASA Astrophysics Data System (ADS)

    Deymier, P. A.; Swinteck, N.; Runge, K.; Deymier-Black, A.; Hoying, J. B.

    2015-11-01

    We present a previously unrecognized effect of sound waves on gap-junction-based intercellular signaling such as in biological tissues composed of endothelial cells. We suggest that sound irradiation may, through temporal and spatial modulation of cell-to-cell conductance, create intercellular calcium waves with unidirectional signal propagation associated with nonconventional topologies. Nonreciprocity in calcium wave propagation induced by sound wave irradiation is demonstrated in the case of a linear and a nonlinear reaction-diffusion model. This demonstration should be applicable to other types of gap-junction-based intercellular signals, and it is thought that it should be of help in interpreting a broad range of biological phenomena associated with the beneficial therapeutic effects of sound irradiation and possibly the harmful effects of sound waves on health.

  5. Decomposition of frequency characteristics of acoustic emission signals for different types of partial discharges sources

    NASA Astrophysics Data System (ADS)

    Witos, F.; Gacek, Z.; Paduch, P.

    2006-11-01

    The problem touched in the article is decomposition of frequency characteristic of AE signals into elementary form of three-parametrical Gauss function. At the first stage, for modelled curves in form of sum of three-parametrical Gauss peaks, accordance of modelled curve and a curve resulting from a solutions obtained using method with dynamic windows, Levenberg-Marquardt algorithm, genetic algorithms and differential evolution algorithm are discussed. It is founded that analyses carried out by means differential evolution algorithm are effective and the computer system served an analysis of AE signal frequency characteristics was constructed. Decomposition of frequency characteristics for selected AE signals coming from modelled PD sources using different ends of the bushing, and real PD sources in generator coil bars are carried out.

  6. Habituation of Auditory Steady State Responses Evoked by Amplitude-Modulated Acoustic Signals in Rats

    PubMed Central

    Prado-Gutierrez, Pavel; Castro-Fariñas, Anisleidy; Morgado-Rodriguez, Lisbet; Velarde-Reyes, Ernesto; Martínez, Agustín D.; Martínez-Montes, Eduardo

    2015-01-01

    Generation of the auditory steady state responses (ASSR) is commonly explained by the linear combination of random background noise activity and the stationary response. Based on this model, the decrease of amplitude that occurs over the sequential averaging of epochs of the raw data has been exclusively linked to the cancelation of noise. Nevertheless, this behavior might also reflect the non-stationary response of the ASSR generators. We tested this hypothesis by characterizing the ASSR time course in rats with different auditory maturational stages. ASSR were evoked by 8-kHz tones of different supra-threshold intensities, modulated in amplitude at 115 Hz. Results show that the ASSR amplitude habituated to the sustained stimulation and that dishabituation occurred when deviant stimuli were presented. ASSR habituation increased as animals became adults, suggesting that the ability to filter acoustic stimuli with no-relevant temporal information increased with age. Results are discussed in terms of the current model of the ASSR generation and analysis procedures. They might have implications for audiometric tests designed to assess hearing in subjects who cannot provide reliable results in the psychophysical trials. PMID:26557360

  7. Error surface topology in the data analysis of laser-induced thermal acoustics signals

    NASA Astrophysics Data System (ADS)

    Schlamp, Stefan; Schmid, Lukas

    2001-12-01

    Laser-induced thermal acoustics (LITA) promises remote, instantaneous and non-intrusive point-measurements of the speed of sound (temperature), thermal diffusivity (density), flow velocity (Mach number) and species concentration simultaneously in harsh environments. The data analysis relies on a nonlinear fit of an analytical model to the acquired data. The measured quantities are parameters in the model. Computational cost and convergence behaviour depend on the dimensionality of the parameter space, the initial guesses for the parameters and on whether the data analysis is performed in the time or frequency domain. The topology of the four-dimensional error surface is discussed and a characteristic allowable distance of the initial guesses from the global minimum is defined and quantified for typical configurations. Noise has no significant influence on the convergence neighbourhood or the computational cost. If improved initial guesses (10% maximum error) for the speed of sound and the flow velocity are obtained by data preprocessing, convergence of the fitting algorithm is ensured.

  8. Wing Morphometry and Acoustic Signals in Sterile and Wild Males: Implications for Mating Success in Ceratitis capitata

    PubMed Central

    de Souza, João Maria Gomes Alencar; Molina, Wagner Franco; de Almeida, Lúcia Maria; de Gouveia, Milson Bezerra; de Macêdo, Francisco Pepino; Laumann, Raul Alberto; Paranhos, Beatriz Aguiar Jordão

    2015-01-01

    The sterile insect technique (SIT) is widely utilized in the biological control of fruit flies of the family Tephritidae, particularly against the Mediterranean fruit fly. This study investigated the interaction between mating success and morphometric variation in the wings and the production of acoustic signals among three male groups of Ceratitis capitata (Wiedemann): (1) wild males, (2) irradiated with Co-60 (steriles), and (3) irradiated (steriles) and treated with ginger oil. The canonical variate analysis discriminated two groups (males irradiated and males wild), based on the morphological shape of the wings. Among males that emit buzz signals, wild males obtained copulation more frequently than males in Groups 2 and 3. The individuals of Group 3 achieved more matings than those in Group 2. Wild males displayed lower pulse duration, higher intervals between pulses, and higher dominant frequency. Regarding the reproductive success, the morphological differences in the wings' shape between accepted and nonaccepted males are higher in wild males than in the irradiated ones. The present results can be useful in programs using the sterile insect technique for biological control of C. capitata. PMID:26075293

  9. Mountain chickadees from different elevations sing different songs: acoustic adaptation, temporal drift or signal of local adaptation?

    PubMed

    Branch, Carrie L; Pravosudov, Vladimir V

    2015-04-01

    Song in songbirds is widely thought to function in mate choice and male-male competition. Song is also phenotypically plastic and typically learned from local adults; therefore, it varies across geographical space and can serve as a cue for an individual's location of origin, with females commonly preferring males from their respective location. Geographical variation in song dialect may reflect acoustic adaptation to different environments and/or serve as a signal of local adaptation. In montane environments, environmental differences can occur over an elevation gradient, favouring local adaptations across small spatial scales. We tested whether food caching mountain chickadees, known to exhibit elevation-related differences in food caching intensity, spatial memory and the hippocampus, also sing different dialects despite continuous distribution and close proximity. Male songs were collected from high and low elevations at two different mountains (separated by 35 km) to test whether song differs between elevations and/or between adjacent populations at each mountain. Song structure varied significantly between high and low elevation adjacent populations from the same mountain and between populations from different mountains at the same elevations, despite a continuous distribution across each mountain slope. These results suggest that elevation-related differences in song structure in chickadees might serve as a signal for local adaptation. PMID:26064641

  10. Wing Morphometry and Acoustic Signals in Sterile and Wild Males: Implications for Mating Success in Ceratitis capitata.

    PubMed

    de Souza, João Maria Gomes Alencar; de Lima-Filho, Paulo Augusto; Molina, Wagner Franco; de Almeida, Lúcia Maria; de Gouveia, Milson Bezerra; de Macêdo, Francisco Pepino; Laumann, Raul Alberto; Paranhos, Beatriz Aguiar Jordão

    2015-01-01

    The sterile insect technique (SIT) is widely utilized in the biological control of fruit flies of the family Tephritidae, particularly against the Mediterranean fruit fly. This study investigated the interaction between mating success and morphometric variation in the wings and the production of acoustic signals among three male groups of Ceratitis capitata (Wiedemann): (1) wild males, (2) irradiated with Co-60 (steriles), and (3) irradiated (steriles) and treated with ginger oil. The canonical variate analysis discriminated two groups (males irradiated and males wild), based on the morphological shape of the wings. Among males that emit buzz signals, wild males obtained copulation more frequently than males in Groups 2 and 3. The individuals of Group 3 achieved more matings than those in Group 2. Wild males displayed lower pulse duration, higher intervals between pulses, and higher dominant frequency. Regarding the reproductive success, the morphological differences in the wings' shape between accepted and nonaccepted males are higher in wild males than in the irradiated ones. The present results can be useful in programs using the sterile insect technique for biological control of C. capitata. PMID:26075293

  11. Alarm signals of the great gerbil: Acoustic variation by predator context, sex, age, individual, and family group

    NASA Astrophysics Data System (ADS)

    Randall, Jan A.; McCowan, Brenda; Collins, Kellie C.; Hooper, Stacie L.; Rogovin, Konstantin

    2005-10-01

    The great gerbil, Rhombomys opinus, is a highly social rodent that usually lives in family groups consisting of related females, their offspring, and an adult male. The gerbils emit alarm vocalizations in the presence of diverse predators with different hunting tactics. Alarm calls were recorded in response to three predators, a monitor lizard, hunting dog, and human, to determine whether the most common call type, the rhythmic call, is functionally referential with regard to type of predator. Results show variation in the alarm calls of both adults and subadults with the type of predator. Discriminant function analysis classified an average of 70% of calls to predator type. Call variation, however, was not limited to the predator context, because signal structure also differed by sex, age, individual callers, and family groups. These variations illustrate the flexibility of the rhythmic alarm call of the great gerbil and how it might have multiple functions and communicate in multiple contexts. Three alarm calls, variation in the rhythmic call, and vibrational signals generated from foot-drumming provide the gerbils with a varied and multi-channel acoustic repertoire.

  12. Mountain chickadees from different elevations sing different songs: acoustic adaptation, temporal drift or signal of local adaptation?

    PubMed Central

    Branch, Carrie L.; Pravosudov, Vladimir V.

    2015-01-01

    Song in songbirds is widely thought to function in mate choice and male–male competition. Song is also phenotypically plastic and typically learned from local adults; therefore, it varies across geographical space and can serve as a cue for an individual's location of origin, with females commonly preferring males from their respective location. Geographical variation in song dialect may reflect acoustic adaptation to different environments and/or serve as a signal of local adaptation. In montane environments, environmental differences can occur over an elevation gradient, favouring local adaptations across small spatial scales. We tested whether food caching mountain chickadees, known to exhibit elevation-related differences in food caching intensity, spatial memory and the hippocampus, also sing different dialects despite continuous distribution and close proximity. Male songs were collected from high and low elevations at two different mountains (separated by 35 km) to test whether song differs between elevations and/or between adjacent populations at each mountain. Song structure varied significantly between high and low elevation adjacent populations from the same mountain and between populations from different mountains at the same elevations, despite a continuous distribution across each mountain slope. These results suggest that elevation-related differences in song structure in chickadees might serve as a signal for local adaptation. PMID:26064641

  13. Comparison of Methods for Identifying Noise Sources in Far-Field Acoustic Signals

    NASA Astrophysics Data System (ADS)

    Tenney, Andrew; Lewalle, Jacques

    2013-11-01

    Three different methods of extracting intermittent wave packets from unstructured background within complex time series signals were analyzed and compared. The algorithms are denoted ``cross correlation,'' ``denoising,'' and ``TFLE (Time-Frequency-Lag event)'' methods respectively. All three methods utilize Mexican Hat or Morlet wavelets for the transformation of time domain signals into time-frequency domain signals. Within the denoising and cross correlation algorithms, events are identified through comparison of high energy excerpts of each signal captured by individual far-field microphones, while the TFLE algorithm simply defines events by their contributions to positive correlation values. The goal of this analysis is to quantify the advantages and disadvantages of each of these methods. The results lend themselves to determining the validity of these methods as noise source identification algorithms to be used in jet noise characterization. This work is supported in part by Spectral Energies LLC, under an SBIR grant from AFRL; and by the Department of Mechanical and Aerospace Engineering REU Program at SU.

  14. A High Performance Pocket-Size System for Evaluations in Acoustic Signal Processing

    NASA Astrophysics Data System (ADS)

    Rass, Uwe; Steeger, Gerhard H.

    2001-12-01

    Custom-made hardware is attractive for sophisticated signal processing in wearable electroacoustic devices, but has a high initial cost overhead. Thus, signal processing algorithms should be tested thoroughly in real application environments by potential end users prior to the hardware implementation. In addition, the algorithms should be easily alterable during this test phase. A wearable system which meets these requirements has been developed and built. The system is based on the high performance signal processor Motorola DSP56309. This device also includes high quality stereo analog-to-digital-(ADC)- and digital-to-analog-(DAC)-converters with 20 bit word length each. The available dynamic range exceeds 88 dB. The input and output gains can be adjusted by digitally controlled potentiometers. The housing of the unit is small enough to carry it in a pocket (dimensions 150 × 80 × 25 mm). Software tools have been developed to ease the development of new algorithms. A set of configurable Assembler code modules implements all hardware dependent software routines and gives easy access to the peripherals and interfaces. A comfortable fitting interface allows easy control of the signal processing unit from a PC, even by assistant personnel. The device has proven to be a helpful means for development and field evaluations of advanced new hearing aid algorithms, within interdisciplinary research projects. Now it is offered to the scientific community.

  15. [EFFECTS OF MUSIC-ACOUSTIC SIGNALS, ONLINE CONTROLLED BY EEG OSCILLATORS OF THE SUBJECT].

    PubMed

    Fedotchev, A I; Bondar, A T; Bakhchina, A V; Parin, S B; Polevaya, S A; Radchenko, G S

    2015-08-01

    The effects of 2 variants of the method of musical EEG neurofeedback, in which the dominant spectral components of subject's EEG (EEG oscillators) are online converted to music-like signals similar by timbre to flute sounds, have been studied. In the first case, these music-like signals were smoothly varying by the pitch and intensity in accordance with the current amplitude of the EEG oscillator. In the second case, the same variations of flute-like sound were accompanied by such musical element as rhythm. After the single exposure, the modifications of subject's brain activity and positive changes in psycho-physiological state of the subject have been found. Particularly pronounced effects were observed under rhythmically organized music-like stimuli. PMID:26591592

  16. Signal Analysis of Helicopter Blade-Vortex-Interaction Acoustic Noise Data

    NASA Technical Reports Server (NTRS)

    Rogers, James C.; Dai, Renshou

    1998-01-01

    Blade-Vortex-Interaction (BVI) produces annoying high-intensity impulsive noise. NASA Ames collected several sets of BVI noise data during in-flight and wind tunnel tests. The goal of this work is to extract the essential features of the BVI signals from the in-flight data and examine the feasibility of extracting those features from BVI noise recorded inside a large wind tunnel. BVI noise generating mechanisms and BVI radiation patterns an are considered and a simple mathematical-physical model is presented. It allows the construction of simple synthetic BVI events that are comparable to free flight data. The boundary effects of the wind tunnel floor and ceiling are identified and more complex synthetic BVI events are constructed to account for features observed in the wind tunnel data. It is demonstrated that improved recording of BVI events can be attained by changing the geometry of the rotor hub, floor, ceiling and microphone. The Euclidean distance measure is used to align BVI events from each blade and improved BVI signals are obtained by time-domain averaging the aligned data. The differences between BVI events for individual blades are then apparent. Removal of wind tunnel background noise by optimal Wiener-filtering is shown to be effective provided representative noise-only data have been recorded. Elimination of wind tunnel reflections by cepstral and optimal filtering deconvolution is examined. It is seen that the cepstral method is not applicable but that a pragmatic optimal filtering approach gives encouraging results. Recommendations for further work include: altering measurement geometry, real-time data observation and evaluation, examining reflection signals (particularly those from the ceiling) and performing further analysis of expected BVI signals for flight conditions of interest so that microphone placement can be optimized for each condition.

  17. Technique for the suppression of three-pass signals in surface-acoustic-wave filters

    NASA Astrophysics Data System (ADS)

    Paskhin, V. M.; Sandler, M. S.; Sveshnikov, B. V.

    1981-12-01

    It is shown analytically that for any thickness of the interdigital transducer (IDT) electrodes, the level of three-pass signal suppression can be made appreciable by the proper choice of complex electrical loads of the transducers. These loads are shown to depend on the IDT electrode thickness. The theoretical conclusion is verified experimentally by studying an SAW filter with aluminum IDT on an ST-cut quartz substrate.

  18. A method of construction of information images of the acoustic signals of the human bronchopulmonary system

    NASA Astrophysics Data System (ADS)

    Bureev, A. Sh.; Zhdanov, D. S.; Zemlyakov, I. Yu.; Kiseleva, E. Yu.; Khokhlova, L. A.

    2015-11-01

    The present study focuses on the development of a method of identification of respiratory sounds and noises of a human naturally and in various pathological conditions. The existing approaches based on a simple method of frequency and time signal analysis, have insufficient specificity, efficiency and unambiguous interpretation of the results of a clinical study. An algorithm for a phase selection of respiratory cycles and analysis of respiratory sounds resulting from bronchi examination of a patient has been suggested. The algorithm is based on the method of phase timing analysis of bronchi phonograms. The results of the phase-frequency algorithm with high resolution reflects a time position of the traceable signals and the individual structure of recorded signals. This allows using the proposed method for the formation of information images (models) of the diagnostically significant fragments. A weight function, frequency parameters of which can be selectively modified, is used for this purpose. The vision of the weighting function is specific to each type of respiratory noise, traditionally referred to quality characteristics (wet or dry noise, crackling, etc.).

  19. Application of the multiple signal classification (MUSIC) method for one-pulse burst-echo Doppler sonar data

    NASA Astrophysics Data System (ADS)

    Iwata, Tetsuo; Goto, Yoji; Susaki, Hironori

    2001-12-01

    In order to estimate ship velocity, we have applied the multiple signal classification (MUSIC) method to one-pulse burst-echo Doppler sonar data. The MUSIC method enabled us to estimate the Doppler frequency shift precisely under a low-signal-to-noise ratio (SNR) situation even from a one-pulse burst-echo signal with small data points. In simulation experiments, a signal frequency component of f = 16 Hz could be extracted from N = 128 data-point data under a 40% Gaussian-distributed additive noise with a sampling frequency fs = 2048 Hz. From actual one-pulse burst-echo signal data of N = 128 points, a Doppler frequency shift of Δf = 1.45 kHz, corresponding to a ship velocity 1.76 knots, was clearly detected, the frequency resolution of which was almost impossible to attain by the conventional Fourier transform (FFT) method. We found that the MUSIC method was useful especially for estimating the ship velocity at a very low speed.

  20. Continuous measurements of suspended sediment loads using dual frequency acoustic Doppler profile signals

    NASA Astrophysics Data System (ADS)

    Antonini, Alessandro; Guerrero, Massimo; Rüther, Nils; Stokseth, Siri

    2016-04-01

    A huge thread to Hydropower plants (HPP) is incoming sediments in suspension from the rivers upstream. The sediments settle in the reservoir and reduce the effective head as well as the volume and reduce consequently the lifetime of the reservoir. In addition are the fine sediments causing severe damages to turbines and infrastructure of a HPP. For estimating the amount of in-coming sediments in suspension and the consequent planning of efficient counter measures, it is essential to monitor the rivers within the catchment of the HPP for suspended sediments. This work is considerably time consuming and requires highly educated personnel and is therefore expensive. Surrogate-indirect methods using acoustic and optic devices have bee developed since the last decades that may be efficiently applied for the continuous monitoring of suspended sediment loads. The presented study proposes therefore to establish a research station at a cross section of a river which is the main tributary to a reservoir of a HPP and equip this station with surrogate as well as with common method of measuring suspended load concentrations and related flow discharge and level. The logger at the research station delivers data automatically to a server. Therefore it is ensured that also large flood events are covered. Data during flood are of high interest to the HPP planners since they carried the most part of the sediment load in a hydrological year. Theses peaks can hardly be measured with common measurement methods. Preliminary results of the wet season 2015/2016 are presented. The data gives insight in the applicable range, in terms of scattering particles concentration-average size and corresponding flow discharge and level, eventually enabling the study of suspended sediment load-water flow correlations during peak events. This work is carried out as part of a larger research project on sustainable hydro power plants exposed to high sediment yield, SediPASS. SediPASS is funded by the

  1. [Music-Acoustic Signals Controlled by Subject's Brain Potentials in the Correction of Unfavorable Functional States].

    PubMed

    Fedotchev, A I; Bondar, A T; Bakhchina, A V; Parin, S B; Polevaya, S A; Radchenko, G S

    2016-01-01

    Literature review and the results of own studies on the development and experimental testing of musical EEG neurofeedback technology are presented. The technology is based on exposure of subjects to music or music-like signals that are organized in strict accordance with the current values of brain potentials of the patient. The main attention is paid to the analysis of the effectiveness of several versions of the technology, using specific and meaningful for the individual narrow-frequency EEG oscillators during the correction of unfavorable changes of the functional state. PMID:27149824

  2. Classification of BMI control commands from rat's neural signals using extreme learning machine.

    PubMed

    Lee, Youngbum; Lee, Hyunjoo; Kim, Jinkwon; Shin, Hyung-Cheul; Lee, Myoungho

    2009-01-01

    A recently developed machine learning algorithm referred to as Extreme Learning Machine (ELM) was used to classify machine control commands out of time series of spike trains of ensembles of CA1 hippocampus neurons (n = 34) of a rat, which was performing a target-to-goal task on a two-dimensional space through a brain-machine interface system. Performance of ELM was analyzed in terms of training time and classification accuracy. The results showed that some processes such as class code prefix, redundancy code suffix and smoothing effect of the classifiers' outputs could improve the accuracy of classification of robot control commands for a brain-machine interface system. PMID:19860924

  3. Acoustic analysis of explosions in high noise environment

    NASA Astrophysics Data System (ADS)

    Man, Hong; Desai, Sachi

    2008-04-01

    Explosion detection and recognition is a critical capability to provide situational awareness to the war-fighters in battlefield. Acoustic sensors are frequently deployed to detect such events and to trigger more expensive sensing/sensor modalities (i.e. radar, laser spectroscope, IR etc.). Acoustic analysis of explosions has been intensively studied to reliably discriminate mortars, artillery, round variations, and type of blast (i.e. chemical/biological or high-explosive). One of the major challenges is high level of noise, which may include non-coherent noise generated from the environmental background and coherent noise induced by possible mobile acoustic sensor platform. In this work, we introduce a new acoustic scene analysis method to effectively enhance explosion classification reliability and reduce the false alarm rate at low SNR and with high coherent noise. The proposed method is based on acoustic signature modeling using Hidden Markov Models (HMMs). Special frequency domain acoustic features characterizing explosions as well as coherent noise are extracted from each signal segment, which forms an observation vector for HMM training and test. Classification is based on a unique model similarity measure between the HMM estimated from the test observations and the trained HMMs. Experimental tests are based on the acoustic explosion dataset from US ARMY ARDEC, and experimental results have demonstrated the effectiveness of the proposed method.

  4. Complex multivariate sexual selection on male acoustic signaling in a wild population of Teleogryllus commodus.

    PubMed

    Bentsen, Caroline L; Hunt, John; Jennions, Michael D; Brooks, Robert

    2006-04-01

    Mate choice may impose both linear (i.e., directional) and nonlinear (i.e., quadratic and correlational) sexual selection on advertisement traits. Traditionally, mate recognition and sensory tuning have been thought to impose stabilizing (i.e., negative quadratic) sexual selection, whereas adaptive mate choice effects directional selection. It has been suggested that adaptive choice may exert positive quadratic and/or correlational sexual selection. Earlier, we showed that five structural components of the advertisement call of male field crickets (Teleogryllus commodus) were under multivariate stabilizing selection under laboratory conditions. Here we experimentally estimate selection on these five traits plus a measure of calling activity (the number of repeats in a looped bout of calling) in the field. There was general support for multivariate stabilizing selection on call structure, and calling activity was under strong positive directional selection, as predicted for a signal of genetic quality. There was, however, also appreciable correlational selection, suggesting an interaction between male call structure and calling effort. Interestingly, selection for short interbout durations of silence favored longer intercall durations in the field, in contrast to results from continuous looped call playback in the laboratory. We discuss the general importance of nonlinear selection in the honest signaling of genetic quality. PMID:16670989

  5. Signal Analysis Algorithms for Optimized Fitting of Nonresonant Laser Induced Thermal Acoustics Damped Sinusoids

    NASA Technical Reports Server (NTRS)

    Balla, R. Jeffrey; Miller, Corey A.

    2008-01-01

    This study seeks a numerical algorithm which optimizes frequency precision for the damped sinusoids generated by the nonresonant LITA technique. It compares computed frequencies, frequency errors, and fit errors obtained using five primary signal analysis methods. Using variations on different algorithms within each primary method, results from 73 fits are presented. Best results are obtained using an AutoRegressive method. Compared to previous results using Prony s method, single shot waveform frequencies are reduced approx.0.4% and frequency errors are reduced by a factor of approx.20 at 303K to approx. 0.1%. We explore the advantages of high waveform sample rates and potential for measurements in low density gases.

  6. An evaluation of acoustic seabed classification techniques for marine biotope monitoring over broad-scales (>1 km 2) and meso-scales (10 m 2-1 km 2)

    NASA Astrophysics Data System (ADS)

    van Rein, H.; Brown, C. J.; Quinn, R.; Breen, J.; Schoeman, D.

    2011-07-01

    Acoustic seabed classification is a useful tool for monitoring marine benthic habitats over broad-scales (>1 km 2) and meso-scales (10 m 2-1 km 2). Its utility in this context was evaluated using two approaches: by describing natural changes in the temporal distribution of marine biotopes across the broad-scale (4 km 2), and by attempting to detect specific experimentally-induced changes to kelp-dominated biotopes across the meso-scale (100 m 2). For the first approach, acoustic backscatter mosaics were constructed using sidescan sonar and multibeam echosounder data collected from Church Bay (Rathlin Island, Northern Ireland) in 1999, 2008 and 2009. The mosaics were manually segmented into acoustic facies, which were ground-truthed using a drop-video camera. Biotopes were classified from the video by multivariate exploratory analysis and cross-tabulated with the acoustic facies, showing a positive correlation. These results were integrated with bathymetric data to map the distribution of seven unique biotopes in Church Bay. Kappa analysis showed the biotope distribution was highly similar between the biotope maps, possibly due to the stability of bedforms shaped by the tidal regime around Rathlin Island. The greatest biotope change in this approach was represented by seasonal and annual changes in the growth of the seagrass, Zostera marina. In the second approach, sidescan sonar data were collected before and after the removal of 100 m 2 of kelp from three sites. Comparison of the data revealed no differences between the high-resolution backscatter imagery. It is concluded that acoustic seabed classification can be used to monitor change over broad- and meso-scales but not necessarily for all biotopes; its success depends on the type of acoustic system employed and the biological characteristics of the target biotope.

  7. Contribution of the 3-D visualization of acoustic borehole signals (full waveforms) to a quick formation evaluation

    NASA Astrophysics Data System (ADS)

    Rousseau, André; Jeantet, Dominique

    1994-02-01

    Logging and continuous coring are carried out when drilling and looking for materials such as gravels, sand, and clay or in order to evaluate the fracture state of a deep site intended for waste storage. However, in some cases of non-consolidated formations, the results may be disappointing because of the borehole conditions. Full waveforms, as seismic signals, provide information about physical parameters of the grounds crossed by the sonic tool, and this information is almost independent of borehole conditions. Traditional displays in variable area or density show the wave arrival times and the frequencies with depth; for variable density, a color scale permits to see clustered instantaneous phases. In order to determine precisely and simultaneously the three signal parameters (arrival time, frequency, and amplitude) in the depth-propagation time domain, a 3-D visualization software has been developed. The "view" parameters, which give a nice display of the 3-D→2-D projection of the signals in a parallel perspectives relative to depth, are estimated on the monitor screen in an interactive way. A larger size version of the software is available for displaying in detail the acoustic signals for the whole borehole. However, this program needs a large computer, and the maximum size of the drawing depends on the computer memory available for use. The comparison between traditional and 3-D displays shows that without previous preprocessing, the 3-D visualization (1) shows the very small and continuous variations of amplitude (and, therefore, of attenuation) with depth better; and (2) can bring out interferences and "energetic peaks" by simply changing the "view" parameters. As the attenuation of the different waves is directly determined, fresh zones can be distinguished immediately from fracture zones and hard ground from soft ground. The geometry of major fracturing can be deduced directly from graphical representation; i.e. open or closed, and horizontal or

  8. High signal-to-noise ratio acoustic sensor using phase shifted gratings interrogated by the Pound-Drever-Hall technique

    NASA Astrophysics Data System (ADS)

    Kung, Peter; Comanici, Maria I.

    2015-03-01

    Optical fiber is made of glass, an insulator, and thus it is immune to strong electromagnetic interference. Therefore, fiber optics is a technology ideally suitable for sensing of partial discharge (PD) both in transformers and generators. Extensive efforts have been used to develop a cost effective solution for detecting partial discharge, which generates acoustic emission, with signals ranging from 30 kHz to 200 kHz. The requirement is similar to fiber optics Hydro Phone, but at higher frequencies. There are several keys to success: there must be at least 60 dB signal-to-noise ratio (SNR) performance, which will ensure not only PD detection but later on provide diagnostics and also the ability to locate the origin of the events. Defects that are stationary would gradually degrade the insulation and result in total breakdown. Transformers currently need urgent attention: most of them are oil filled and are at least 30 to 50 years old, close to the end of life. In this context, an issue to be addressed is the safety of the personnel working close to the assets and collateral damage that could be caused by a tank explosion (with fire spilling over the whole facility). This paper will describe the latest achievement in fiber optics PD sensor technology: the use of phase shifted-fiber gratings with a very high speed interrogation method that uses the Pound-Drever-Hall technique. More importantly, this is based on a technology that could be automated, easy to install, and, eventually, available at affordable prices.

  9. High signal-to-noise acoustic sensor using phase-shifted gratings interrogated by the Pound-Drever-Hall technique

    NASA Astrophysics Data System (ADS)

    Kung, Peter; Comanici, Maria I.

    2014-11-01

    Optical fiber is made of glass, an insulator, and thus it is immune to strong electromagnetic interference. Therefore, fiber optics is a technology ideally suitable for sensing of partial discharge (PD) both in transformers and generators. Extensive efforts have been used to develop a cost effective solution for detecting partial discharge, which generates acoustic emission, with signals ranging from 30 kHz to 200 kHz. The requirement is similar to fiber optics Hydro Phone, but at higher frequencies. There are several keys to success: there must be at least 60 dB signal-to-noise ratio (SNR) performance, which will ensure not only PD detection but later on provide diagnostics and also the ability to locate the origin of the events. Defects that are stationary would gradually degrade the insulation and result in total breakdown. Transformers currently need urgent attention: most of them are oil filled and are at least 30 to 50 years old, close to the end of life. In this context, an issue to be addressed is the safety of the personnel working close to the assets and collateral damage that could be caused by a tank explosion (with fire spilling over the whole facility). This paper will describe the latest achievement in fiber optics PD sensor technology: the use of phase shifted-fiber gratings with a very high speed interrogation method that uses the Pound-Drever-Hall technique. More importantly, this is based on a technology that could be automated, easy to install, and, eventually, available at affordable prices.

  10. Fast contactless vibrating structure characterization using real time field programmable gate array-based digital signal processing: demonstrations with a passive wireless acoustic delay line probe and vision.

    PubMed

    Goavec-Mérou, G; Chrétien, N; Friedt, J-M; Sandoz, P; Martin, G; Lenczner, M; Ballandras, S

    2014-01-01

    Vibrating mechanical structure characterization is demonstrated using contactless techniques best suited for mobile and rotating equipments. Fast measurement rates are achieved using Field Programmable Gate Array (FPGA) devices as real-time digital signal processors. Two kinds of algorithms are implemented on FPGA and experimentally validated in the case of the vibrating tuning fork. A first application concerns in-plane displacement detection by vision with sampling rates above 10 kHz, thus reaching frequency ranges above the audio range. A second demonstration concerns pulsed-RADAR cooperative target phase detection and is applied to radiofrequency acoustic transducers used as passive wireless strain gauges. In this case, the 250 ksamples/s refresh rate achieved is only limited by the acoustic sensor design but not by the detection bandwidth. These realizations illustrate the efficiency, interest, and potentialities of FPGA-based real-time digital signal processing for the contactless interrogation of passive embedded probes with high refresh rates. PMID:24517814

  11. Fast contactless vibrating structure characterization using real time field programmable gate array-based digital signal processing: Demonstrations with a passive wireless acoustic delay line probe and vision

    NASA Astrophysics Data System (ADS)

    Goavec-Mérou, G.; Chrétien, N.; Friedt, J.-M.; Sandoz, P.; Martin, G.; Lenczner, M.; Ballandras, S.

    2014-01-01

    Vibrating mechanical structure characterization is demonstrated using contactless techniques best suited for mobile and rotating equipments. Fast measurement rates are achieved using Field Programmable Gate Array (FPGA) devices as real-time digital signal processors. Two kinds of algorithms are implemented on FPGA and experimentally validated in the case of the vibrating tuning fork. A first application concerns in-plane displacement detection by vision with sampling rates above 10 kHz, thus reaching frequency ranges above the audio range. A second demonstration concerns pulsed-RADAR cooperative target phase detection and is applied to radiofrequency acoustic transducers used as passive wireless strain gauges. In this case, the 250 ksamples/s refresh rate achieved is only limited by the acoustic sensor design but not by the detection bandwidth. These realizations illustrate the efficiency, interest, and potentialities of FPGA-based real-time digital signal processing for the contactless interrogation of passive embedded probes with high refresh rates.

  12. Comment on "The directionality of acoustic T-phase signals from small magnitude submarine earthquakes" [J. Acoust. Soc. Am. 119, 3669-3675 (2006)].

    PubMed

    Bohnenstiehl, Delwayne R

    2007-03-01

    In a recent paper, Chapman and Marrett [J. Acoust. Soc. Am. 119, 3669-3675 (2006)] examined the tertiary (T-) waves associated with three subduction-related earthquakes within the South Fiji Basin. In that paper it is argued that acoustic energy is radiated into the sound channel by downslope propagation along abyssal seamounts and ridges that lie distant to the epicenter. A reexamination of the travel-time constraints indicates that this interpretation is not well supported. Rather, the propagation model that is described would require the high-amplitude T-wave components to be sourced well to the east of the region identified, along a relatively flat-lying seafloor. PMID:17407863

  13. Study of wavelet packet energy entropy for emotion classification in speech and glottal signals

    NASA Astrophysics Data System (ADS)

    He, Ling; Lech, Margaret; Zhang, Jing; Ren, Xiaomei; Deng, Lihua

    2013-07-01

    The automatic speech emotion recognition has important applications in human-machine communication. Majority of current research in this area is focused on finding optimal feature parameters. In recent studies, several glottal features were examined as potential cues for emotion differentiation. In this study, a new type of feature parameter is proposed, which calculates energy entropy on values within selected Wavelet Packet frequency bands. The modeling and classification tasks are conducted using the classical GMM algorithm. The experiments use two data sets: the Speech Under Simulated Emotion (SUSE) data set annotated with three different emotions (angry, neutral and soft) and Berlin Emotional Speech (BES) database annotated with seven different emotions (angry, bored, disgust, fear, happy, sad and neutral). The average classification accuracy achieved for the SUSE data (74%-76%) is significantly higher than the accuracy achieved for the BES data (51%-54%). In both cases, the accuracy was significantly higher than the respective random guessing levels (33% for SUSE and 14.3% for BES).

  14. True Katydids (Pseudophyllinae) from Guadeloupe: Acoustic Signals and Functional Considerations of Song Production

    PubMed Central

    Stumpner, Andreas; Dann, Angela; Schink, Matthias; Gubert, Silvia; Hugel, Sylvain

    2013-01-01

    Guadeloupe, the largest of the Leeward Islands, harbors three species of Pseudophyllinae (Orthoptera: Tettigoniidae) belonging to distinct tribes. This study examined the basic aspects of sound production and acousto-vibratory behavior of these species. As the songs of many Pseudophyllinae are complex and peak at high frequencies, they require high quality recordings. Wild specimens were therefore recorded ex situ. Collected specimens were used in structure-function experiments. Karukerana aguilari Bonfils (Pterophyllini) is a large species with a mirror in each tegmen and conspicuous folds over the mirror. It sings 4–6 syllables, each comprising 10–20 pulses, with several peaks in the frequency spectrum between 4 and 20 kHz. The song is among the loudest in Orthoptera (> 125 dB SPL in 10 cm distance). The folds are protective and have no function in song production. Both mirrors may work independently in sound radiation. Nesonotus reticulatus (Fabricius) (Cocconotini) produces verses from two syllables at irregular intervals. The song peaks around 20 kHz. While singing, the males often produce a tremulation signal with the abdomen at about 8–10 Hz. To our knowledge, it is the first record of simultaneous calling song and tremulation in Orthoptera. Other males reply to the tremulation with their own tremulation. Xerophyllopteryx fumosa (Brunner von Wattenwyl) (Pleminiini) is a large, bark-like species, producing a syllable of around 20 pulses. The syllables are produced with irregular rhythms (often two with shorter intervals). The song peaks around 2–3 kHz and 10 kHz. The hind wings are relatively thick and are held between the half opened tegmina during singing. Removal of the hind wings reduces song intensity by about 5 dB, especially of the low frequency component, suggesting that the hind wings have a role in amplifying the song. PMID:24785151

  15. Estimation of source location and ground impedance using a hybrid multiple signal classification and Levenberg-Marquardt approach

    NASA Astrophysics Data System (ADS)

    Tam, Kai-Chung; Lau, Siu-Kit; Tang, Shiu-Keung

    2016-07-01

    A microphone array signal processing method for locating a stationary point source over a locally reactive ground and for estimating ground impedance is examined in detail in the present study. A non-linear least square approach using the Levenberg-Marquardt method is proposed to overcome the problem of unknown ground impedance. The multiple signal classification method (MUSIC) is used to give the initial estimation of the source location, while the technique of forward backward spatial smoothing is adopted as a pre-processer of the source localization to minimize the effects of source coherence. The accuracy and robustness of the proposed signal processing method are examined. Results show that source localization in the horizontal direction by MUSIC is satisfactory. However, source coherence reduces drastically the accuracy in estimating the source height. The further application of Levenberg-Marquardt method with the results from MUSIC as the initial inputs improves significantly the accuracy of source height estimation. The present proposed method provides effective and robust estimation of the ground surface impedance.

  16. Single-trial classification of near-infrared spectroscopy signals arising from multiple cortical regions.

    PubMed

    Schudlo, Larissa C; Chau, Tom

    2015-09-01

    Near-infrared spectroscopy (NIRS) brain-computer interface (BCI) studies have primarily made use of measurements taken from a single cortical area. In particular, the anterior prefrontal cortex has been the key area used for detecting higher-level cognitive task performance. However, mental task execution typically requires coordination between several, spatially-distributed brain regions. We investigated the value of expanding the area of interrogation to include NIRS measurements from both the prefrontal and parietal cortices to decode mental states. Hemodynamic activity was monitored at 46 locations over the prefrontal and parietal cortices using a continuous-wave near-infrared spectrometer while 11 able-bodied adults rested or performed either the verbal fluency task (VFT) or Stroop task. Offline classification was performed for the three possible binary problems using 25 iterations of bagging with a linear discriminant base classifier. Classifiers were trained on a 10 dimensional feature set. When all 46 measurement locations were considered for classification, average accuracies of 80.4±7.0%, 82.4±7.6%, and 82.8±5.9% in differentiating VFT vs rest, Stroop vs rest and VFT vs Stroop, respectively, were obtained. Relative to using measurements from the anterior PFC alone, an overall average improvement of 11.3% was achieved. Utilizing NIRS measurements from the prefrontal and parietal cortices can be of value in classifying mental states involving working memory and attention. NIRS-BCI accuracies may be improved by incorporating measurements from several, distinct cortical regions, rather than a single area alone. Further development of an NIRS-BCI supporting combinations of VFT, Stroop task and rest states is also warranted. PMID:25960315

  17. Evaluation of a rubber-compound diaphragm for acoustic fisheries surveys: Effects on dual-beam signal intensity and beam patterns

    USGS Publications Warehouse

    Fleischer, Guy W.; Argyle, R.L.; Nester, R.T.; Dawson, J.J.

    2002-01-01

    The use of rubber-compound windows for fisheries acoustics must consider operating frequency and ambient water temperatures. Signal attenuation by the rubber becomes pronounced with increased frequency and decreased temperature. Based on our results, a 420 k Hz system could be expected to lose up to 3-4 dB in colder water through a 5.1-cm thick rubber diaphragm. At 120 k Hz, signal loss was negligible and would undoubtedly also be inconsequential for even lower frequencies used in fisheries applications (e.g., 70, 38 k Hz).

  18. R-Peak Detection using Daubechies Wavelet and ECG Signal Classification using Radial Basis Function Neural Network

    NASA Astrophysics Data System (ADS)

    Rai, H. M.; Trivedi, A.; Chatterjee, K.; Shukla, S.

    2014-01-01

    This paper employed the Daubechies wavelet transform (WT) for R-peak detection and radial basis function neural network (RBFNN) to classify the electrocardiogram (ECG) signals. Five types of ECG beats: normal beat, paced beat, left bundle branch block (LBBB) beat, right bundle branch block (RBBB) beat and premature ventricular contraction (PVC) were classified. 500 QRS complexes were arbitrarily extracted from 26 records in Massachusetts Institute of Technology-Beth Israel Hospital (MIT-BIH) arrhythmia database, which are available on Physionet website. Each and every QRS complex was represented by 21 points from p1 to p21 and these QRS complexes of each record were categorized according to types of beats. The system performance was computed using four types of parameter evaluation metrics: sensitivity, positive predictivity, specificity and classification error rate. The experimental result shows that the average values of sensitivity, positive predictivity, specificity and classification error rate are 99.8%, 99.60%, 99.90% and 0.12%, respectively with RBFNN classifier. The overall accuracy achieved for back propagation neural network (BPNN), multilayered perceptron (MLP), support vector machine (SVM) and RBFNN classifiers are 97.2%, 98.8%, 99% and 99.6%, respectively. The accuracy levels and processing time of RBFNN is higher than or comparable with BPNN, MLP and SVM classifiers.

  19. Pattern classification of Myo-Electrical signal during different Maximum Voluntary Contractions: A study using BSS techniques

    NASA Astrophysics Data System (ADS)

    Naik, Ganesh R.; Kumar, Dinesh K.; Arjunan, Sridhar P.

    2010-01-01

    The presence of noise and cross-talk from closely located and simultaneously active muscles is exaggerated when the level of muscle contraction is very low. Due to this the current applications of surface electromyogram (sEMG) are infeasible and unreliable in pattern classification. This research reports a new technique of sEMG using Independent Component Analysis (ICA). The technique uses blind source separation (BSS) methods to classify the patterns of Myo-electrical signals during different Maximum Voluntary Contraction (MVCs) at different low level finger movements. The results of the experiments indicate that patterns using ICA of sEMG is a reliable (p<0.001) measure of strength of muscle contraction even when muscle activity is only 20% MVC. The authors propose that ICA is a useful indicator of muscle properties and is a useful indicator of the level of muscle activity.

  20. A Review of Classification Techniques of EMG Signals during Isotonic and Isometric Contractions.

    PubMed

    Nazmi, Nurhazimah; Abdul Rahman, Mohd Azizi; Yamamoto, Shin-Ichiroh; Ahmad, Siti Anom; Zamzuri, Hairi; Mazlan, Saiful Amri

    2016-01-01

    In recent years, there has been major interest in the exposure to physical therapy during rehabilitation. Several publications have demonstrated its usefulness in clinical/medical and human machine interface (HMI) applications. An automated system will guide the user to perform the training during rehabilitation independently. Advances in engineering have extended electromyography (EMG) beyond the traditional diagnostic applications to also include applications in diverse areas such as movement analysis. This paper gives an overview of the numerous methods available to recognize motion patterns of EMG signals for both isotonic and isometric contractions. Various signal analysis methods are compared by illustrating their applicability in real-time settings. This paper will be of interest to researchers who would like to select the most appropriate methodology in classifying motion patterns, especially during different types of contractions. For feature extraction, the probability density function (PDF) of EMG signals will be the main interest of this study. Following that, a brief explanation of the different methods for pre-processing, feature extraction and classifying EMG signals will be compared in terms of their performance. The crux of this paper is to review the most recent developments and research studies related to the issues mentioned above. PMID:27548165

  1. Brain source localization: A new method based on MUltiple SIgnal Classification algorithm and spatial sparsity of the field signal for electroencephalogram measurements

    NASA Astrophysics Data System (ADS)

    Vergallo, P.; Lay-Ekuakille, A.

    2013-08-01

    Brain activity can be recorded by means of EEG (Electroencephalogram) electrodes placed on the scalp of the patient. The EEG reflects the activity of groups of neurons located in the head, and the fundamental problem in neurophysiology is the identification of the sources responsible of brain activity, especially if a seizure occurs and in this case it is important to identify it. The studies conducted in order to formalize the relationship between the electromagnetic activity in the head and the recording of the generated external field allow to know pattern of brain activity. The inverse problem, that is given the sampling field at different electrodes the underlying asset must be determined, is more difficult because the problem may not have a unique solution, or the search for the solution is made difficult by a low spatial resolution which may not allow to distinguish between activities involving sources close to each other. Thus, sources of interest may be obscured or not detected and known method in source localization problem as MUSIC (MUltiple SIgnal Classification) could fail. Many advanced source localization techniques achieve a best resolution by exploiting sparsity: if the number of sources is small as a result, the neural power vs. location is sparse. In this work a solution based on the spatial sparsity of the field signal is presented and analyzed to improve MUSIC method. For this purpose, it is necessary to set a priori information of the sparsity in the signal. The problem is formulated and solved using a regularization method as Tikhonov, which calculates a solution that is the better compromise between two cost functions to minimize, one related to the fitting of the data, and another concerning the maintenance of the sparsity of the signal. At the first, the method is tested on simulated EEG signals obtained by the solution of the forward problem. Relatively to the model considered for the head and brain sources, the result obtained allows to

  2. Helicopter detection and classification demonstrator

    NASA Astrophysics Data System (ADS)

    van Koersel, Antonius C.

    2000-07-01

    A technology demonstrator that detects and classifies different helicopter types automatically, was developed at TNO-FEL. The demonstrator is based on a PC, which receives its acoustic input from an all-weather microphone. The demonstrator uses commercial off-the-shelf hardware to digitize the acoustic signal. The user-interface and the signal processing software are written in MatLabTM. The demonstrator detects the noise from helicopters; the classification is performed using a database with helicopter-specific features. The demonstrator currently contains information of 11 different helicopter types, but can easily be expanded to include additional types of helicopters. The input signal is analyzed in real time, the result is a classification ranging from `no target' to `helicopter type x', e.g. Lynx Mk2. If the helicopter is classified, its relative speed is estimated as well. The algorithm was developed and tested using a database of different helicopters (hovering and moving) recorded at distances ranging from 90 meter up to 8 kilometer. The sensitivity to noise was investigated using jet, tank, artillery and environmental (wind and turbulence) noise as input.

  3. Acoustic biosensors

    PubMed Central

    Fogel, Ronen; Seshia, Ashwin A.

    2016-01-01

    Resonant and acoustic wave devices have been researched for several decades for application in the gravimetric sensing of a variety of biological and chemical analytes. These devices operate by coupling the measurand (e.g. analyte adsorption) as a modulation in the physical properties of the acoustic wave (e.g. resonant frequency, acoustic velocity, dissipation) that can then be correlated with the amount of adsorbed analyte. These devices can also be miniaturized with advantages in terms of cost, size and scalability, as well as potential additional features including integration with microfluidics and electronics, scaled sensitivities associated with smaller dimensions and higher operational frequencies, the ability to multiplex detection across arrays of hundreds of devices embedded in a single chip, increased throughput and the ability to interrogate a wider range of modes including within the same device. Additionally, device fabrication is often compatible with semiconductor volume batch manufacturing techniques enabling cost scalability and a high degree of precision and reproducibility in the manufacturing process. Integration with microfluidics handling also enables suitable sample pre-processing/separation/purification/amplification steps that could improve selectivity and the overall signal-to-noise ratio. Three device types are reviewed here: (i) bulk acoustic wave sensors, (ii) surface acoustic wave sensors, and (iii) micro/nano-electromechanical system (MEMS/NEMS) sensors. PMID:27365040

  4. Acoustic biosensors.

    PubMed

    Fogel, Ronen; Limson, Janice; Seshia, Ashwin A

    2016-06-30

    Resonant and acoustic wave devices have been researched for several decades for application in the gravimetric sensing of a variety of biological and chemical analytes. These devices operate by coupling the measurand (e.g. analyte adsorption) as a modulation in the physical properties of the acoustic wave (e.g. resonant frequency, acoustic velocity, dissipation) that can then be correlated with the amount of adsorbed analyte. These devices can also be miniaturized with advantages in terms of cost, size and scalability, as well as potential additional features including integration with microfluidics and electronics, scaled sensitivities associated with smaller dimensions and higher operational frequencies, the ability to multiplex detection across arrays of hundreds of devices embedded in a single chip, increased throughput and the ability to interrogate a wider range of modes including within the same device. Additionally, device fabrication is often compatible with semiconductor volume batch manufacturing techniques enabling cost scalability and a high degree of precision and reproducibility in the manufacturing process. Integration with microfluidics handling also enables suitable sample pre-processing/separation/purification/amplification steps that could improve selectivity and the overall signal-to-noise ratio. Three device types are reviewed here: (i) bulk acoustic wave sensors, (ii) surface acoustic wave sensors, and (iii) micro/nano-electromechanical system (MEMS/NEMS) sensors. PMID:27365040

  5. Acoustic communication in the Greater Sage-Grouse (Centrocercus urophasianus) an examination into vocal sacs, sound propagation, and signal directionality

    NASA Astrophysics Data System (ADS)

    Dantzker, Marc Steven

    The thesis is an inquiry into the acoustic communication of a very unusual avian species, the Greater Sage-Grouse, Centrocercus urophasianus. One of the most outstanding features of this animal's dynamic mating display is its use of paired air sacs that emerge explosively from an esophageal pouch. My first line of inquiry into this system is a review of the form and function of similar vocal apparatuses, collectively called vocal sacs, in birds. Next, with a combination of mathematical models and field measurements, My collaborator and I investigate the acoustic environment where the Greater Sage-Grouse display. The complexities of this acoustic environment are relevant both to the birds and to the subsequent examinations of the display's properties. Finally, my collaborators and I examine a cryptic component of the acoustic display --- directionality --- which we measured simultaneously from multiple locations around free moving grouse on their mating grounds.

  6. A novel approach for SEMG signal classification with adaptive local binary patterns.

    PubMed

    Ertuğrul, Ömer Faruk; Kaya, Yılmaz; Tekin, Ramazan

    2016-07-01

    Feature extraction plays a major role in the pattern recognition process, and this paper presents a novel feature extraction approach, adaptive local binary pattern (aLBP). aLBP is built on the local binary pattern (LBP), which is an image processing method, and one-dimensional local binary pattern (1D-LBP). In LBP, each pixel is compared with its neighbors. Similarly, in 1D-LBP, each data in the raw is judged against its neighbors. 1D-LBP extracts feature based on local changes in the signal. Therefore, it has high a potential to be employed in medical purposes. Since, each action or abnormality, which is recorded in SEMG signals, has its own pattern, and via the 1D-LBP these (hidden) patterns may be detected. But, the positions of the neighbors in 1D-LBP are constant depending on the position of the data in the raw. Also, both LBP and 1D-LBP are very sensitive to noise. Therefore, its capacity in detecting hidden patterns is limited. To overcome these drawbacks, aLBP was proposed. In aLBP, the positions of the neighbors and their values can be assigned adaptively via the down-sampling and the smoothing coefficients. Therefore, the potential to detect (hidden) patterns, which may express an illness or an action, is really increased. To validate the proposed feature extraction approach, two different datasets were employed. Achieved accuracies by the proposed approach were higher than obtained results by employed popular feature extraction approaches and the reported results in the literature. Obtained accuracy results were brought out that the proposed method can be employed to investigate SEMG signals. In summary, this work attempts to develop an adaptive feature extraction scheme that can be utilized for extracting features from local changes in different categories of time-varying signals. PMID:26718556

  7. Application of linear graph embedding as a dimensionality reduction technique and sparse representation classifier as a post classifier for the classification of epilepsy risk levels from EEG signals

    NASA Astrophysics Data System (ADS)

    Prabhakar, Sunil Kumar; Rajaguru, Harikumar

    2015-12-01

    The most common and frequently occurring neurological disorder is epilepsy and the main method useful for the diagnosis of epilepsy is electroencephalogram (EEG) signal analysis. Due to the length of EEG recordings, EEG signal analysis method is quite time-consuming when it is processed manually by an expert. This paper proposes the application of Linear Graph Embedding (LGE) concept as a dimensionality reduction technique for processing the epileptic encephalographic signals and then it is classified using Sparse Representation Classifiers (SRC). SRC is used to analyze the classification of epilepsy risk levels from EEG signals and the parameters such as Sensitivity, Specificity, Time Delay, Quality Value, Performance Index and Accuracy are analyzed.

  8. Low frequency acoustic microscope

    DOEpatents

    Khuri-Yakub, Butrus T.

    1986-11-04

    A scanning acoustic microscope is disclosed for the detection and location of near surface flaws, inclusions or voids in a solid sample material. A focused beam of acoustic energy is directed at the sample with its focal plane at the subsurface flaw, inclusion or void location. The sample is scanned with the beam. Detected acoustic energy specularly reflected and mode converted at the surface of the sample and acoustic energy reflected by subsurface flaws, inclusions or voids at the focal plane are used for generating an interference signal which is processed and forms a signal indicative of the subsurface flaws, inclusions or voids.

  9. Multimodal fusion framework: a multiresolution approach for emotion classification and recognition from physiological signals.

    PubMed

    Verma, Gyanendra K; Tiwary, Uma Shanker

    2014-11-15

    The purpose of this paper is twofold: (i) to investigate the emotion representation models and find out the possibility of a model with minimum number of continuous dimensions and (ii) to recognize and predict emotion from the measured physiological signals using multiresolution approach. The multimodal physiological signals are: Electroencephalogram (EEG) (32 channels) and peripheral (8 channels: Galvanic skin response (GSR), blood volume pressure, respiration pattern, skin temperature, electromyogram (EMG) and electrooculogram (EOG)) as given in the DEAP database. We have discussed the theories of emotion modeling based on i) basic emotions, ii) cognitive appraisal and physiological response approach and iii) the dimensional approach and proposed a three continuous dimensional representation model for emotions. The clustering experiment on the given valence, arousal and dominance values of various emotions has been done to validate the proposed model. A novel approach for multimodal fusion of information from a large number of channels to classify and predict emotions has also been proposed. Discrete Wavelet Transform, a classical transform for multiresolution analysis of signal has been used in this study. The experiments are performed to classify different emotions from four classifiers. The average accuracies are 81.45%, 74.37%, 57.74% and 75.94% for SVM, MLP, KNN and MMC classifiers respectively. The best accuracy is for 'Depressing' with 85.46% using SVM. The 32 EEG channels are considered as independent modes and features from each channel are considered with equal importance. May be some of the channel data are correlated but they may contain supplementary information. In comparison with the results given by others, the high accuracy of 85% with 13 emotions and 32 subjects from our proposed method clearly proves the potential of our multimodal fusion approach. PMID:24269801

  10. Aircraft Operations Classification System

    NASA Technical Reports Server (NTRS)

    Harlow, Charles; Zhu, Weihong

    2001-01-01

    Accurate data is important in the aviation planning process. In this project we consider systems for measuring aircraft activity at airports. This would include determining the type of aircraft such as jet, helicopter, single engine, and multiengine propeller. Some of the issues involved in deploying technologies for monitoring aircraft operations are cost, reliability, and accuracy. In addition, the system must be field portable and acceptable at airports. A comparison of technologies was conducted and it was decided that an aircraft monitoring system should be based upon acoustic technology. A multimedia relational database was established for the study. The information contained in the database consists of airport information, runway information, acoustic records, photographic records, a description of the event (takeoff, landing), aircraft type, and environmental information. We extracted features from the time signal and the frequency content of the signal. A multi-layer feed-forward neural network was chosen as the classifier. Training and testing results were obtained. We were able to obtain classification results of over 90 percent for training and testing for takeoff events.

  11. Acoustic neuroma

    MedlinePlus

    Vestibular schwannoma; Tumor - acoustic; Cerebellopontine angle tumor; Angle tumor ... Acoustic neuromas have been linked with the genetic disorder neurofibromatosis type 2 (NF2). Acoustic neuromas are uncommon.

  12. Measures of voiced frication for automatic classification

    NASA Astrophysics Data System (ADS)

    Jackson, Philip J. B.; Jesus, Luis M. T.; Shadle, Christine H.; Pincas, Jonathan

    2001-05-01

    As an approach to understanding the characteristics of the acoustic sources in voiced fricatives, it seems apt to draw on knowledge of vowels and voiceless fricatives, which have been relatively well studied. However, the presence of both phonation and frication in these mixed-source sounds offers the possibility of mutual interaction effects, with variations across place of articulation. This paper examines the acoustic and articulatory consequences of these interactions and explores automatic techniques for finding parametric and statistical descriptions of these phenomena. A reliable and consistent set of such acoustic cues could be used for phonetic classification or speech recognition. Following work on devoicing of European Portuguese voiced fricatives [Jesus and Shadle, in Mamede et al. (eds.) (Springer-Verlag, Berlin, 2003), pp. 1-8]. and the modulating effect of voicing on frication [Jackson and Shadle, J. Acoust. Soc. Am. 108, 1421-1434 (2000)], the present study focuses on three types of information: (i) sequences and durations of acoustic events in VC transitions, (ii) temporal, spectral and modulation measures from the periodic and aperiodic components of the acoustic signal, and (iii) voicing activity derived from simultaneous EGG data. Analysis of interactions observed in British/American English and European Portuguese speech corpora will be compared, and the principal findings discussed.

  13. Exceeding chance level by chance: The caveat of theoretical chance levels in brain signal classification and statistical assessment of decoding accuracy.

    PubMed

    Combrisson, Etienne; Jerbi, Karim

    2015-07-30

    Machine learning techniques are increasingly used in neuroscience to classify brain signals. Decoding performance is reflected by how much the classification results depart from the rate achieved by purely random classification. In a 2-class or 4-class classification problem, the chance levels are thus 50% or 25% respectively. However, such thresholds hold for an infinite number of data samples but not for small data sets. While this limitation is widely recognized in the machine learning field, it is unfortunately sometimes still overlooked or ignored in the emerging field of brain signal classification. Incidentally, this field is often faced with the difficulty of low sample size. In this study we demonstrate how applying signal classification to Gaussian random signals can yield decoding accuracies of up to 70% or higher in two-class decoding with small sample sets. Most importantly, we provide a thorough quantification of the severity and the parameters affecting this limitation using simulations in which we manipulate sample size, class number, cross-validation parameters (k-fold, leave-one-out and repetition number) and classifier type (Linear-Discriminant Analysis, Naïve Bayesian and Support Vector Machine). In addition to raising a red flag of caution, we illustrate the use of analytical and empirical solutions (binomial formula and permutation tests) that tackle the problem by providing statistical significance levels (p-values) for the decoding accuracy, taking sample size into account. Finally, we illustrate the relevance of our simulations and statistical tests on real brain data by assessing noise-level classifications in Magnetoencephalography (MEG) and intracranial EEG (iEEG) baseline recordings. PMID:25596422

  14. Semi-supervised Bayesian classification of materials with impact-echo signals.

    PubMed

    Igual, Jorge; Salazar, Addisson; Safont, Gonzalo; Vergara, Luis

    2015-01-01

    The detection and identification of internal defects in a material require the use of some technology that translates the hidden interior damages into observable signals with different signature-defect correspondences. We apply impact-echo techniques for this purpose. The materials are classified according to their defective status (homogeneous, one defect or multiple defects) and kind of defect (hole or crack, passing through or not). Every specimen is impacted by a hammer, and the spectrum of the propagated wave is recorded. This spectrum is the input data to a Bayesian classifier that is based on the modeling of the conditional probabilities with a mixture of Gaussians. The parameters of the Gaussian mixtures and the class probabilities are estimated using an extended expectation-maximization algorithm. The advantage of our proposal is that it is flexible, since it obtains good results for a wide range of models even under little supervision; e.g., it obtains a harmonic average of precision and recall value of 92.38% given only a 10% supervision ratio. We test the method with real specimens made of aluminum alloy. The results show that the algorithm works very well. This technique could be applied in many industrial problems, such as the optimization of the marble cutting process. PMID:25996512

  15. Semi-Supervised Bayesian Classification of Materials with Impact-Echo Signals

    PubMed Central

    Igual, Jorge; Salazar, Addisson; Safont, Gonzalo; Vergara, Luis

    2015-01-01

    The detection and identification of internal defects in a material require the use of some technology that translates the hidden interior damages into observable signals with different signature-defect correspondences. We apply impact-echo techniques for this purpose. The materials are classified according to their defective status (homogeneous, one defect or multiple defects) and kind of defect (hole or crack, passing through or not). Every specimen is impacted by a hammer, and the spectrum of the propagated wave is recorded. This spectrum is the input data to a Bayesian classifier that is based on the modeling of the conditional probabilities with a mixture of Gaussians. The parameters of the Gaussian mixtures and the class probabilities are estimated using an extended expectation-maximization algorithm. The advantage of our proposal is that it is flexible, since it obtains good results for a wide range of models even under little supervision; e.g., it obtains a harmonic average of precision and recall value of 92.38% given only a 10% supervision ratio. We test the method with real specimens made of aluminum alloy. The results show that the algorithm works very well. This technique could be applied in many industrial problems, such as the optimization of the marble cutting process. PMID:25996512

  16. Emotion classification in Parkinson's disease by higher-order spectra and power spectrum features using EEG signals: a comparative study.

    PubMed

    Yuvaraj, R; Murugappan, M; Ibrahim, Norlinah Mohamed; Omar, Mohd Iqbal; Sundaraj, Kenneth; Mohamad, Khairiyah; Palaniappan, R; Satiyan, M

    2014-03-01

    Deficits in the ability to process emotions characterize several neuropsychiatric disorders and are traits of Parkinson's disease (PD), and there is need for a method of quantifying emotion, which is currently performed by clinical diagnosis. Electroencephalogram (EEG) signals, being an activity of central nervous system (CNS), can reflect the underlying true emotional state of a person. This study applied machine-learning algorithms to categorize EEG emotional states in PD patients that would classify six basic emotions (happiness and sadness, fear, anger, surprise and disgust) in comparison with healthy controls (HC). Emotional EEG data were recorded from 20 PD patients and 20 healthy age-, education level- and sex-matched controls using multimodal (audio-visual) stimuli. The use of nonlinear features motivated by the higher-order spectra (HOS) has been reported to be a promising approach to classify the emotional states. In this work, we made the comparative study of the performance of k-nearest neighbor (kNN) and support vector machine (SVM) classifiers using the features derived from HOS and from the power spectrum. Analysis of variance (ANOVA) showed that power spectrum and HOS based features were statistically significant among the six emotional states (p < 0.0001). Classification results shows that using the selected HOS based features instead of power spectrum based features provided comparatively better accuracy for all the six classes with an overall accuracy of 70.10% ± 2.83% and 77.29% ± 1.73% for PD patients and HC in beta (13-30 Hz) band using SVM classifier. Besides, PD patients achieved less accuracy in the processing of negative emotions (sadness, fear, anger and disgust) than in processing of positive emotions (happiness, surprise) compared with HC. These results demonstrate the effectiveness of applying machine learning techniques to the classification of emotional states in PD patients in a user independent manner using EEG signals. The

  17. Block design enhances classification of 3D reach targets from electroencephalographic signals.

    PubMed

    Sosnik, Ronen; Tadipatri, Vijay Aditya; Tewfik, Ahmed H; Pellizzer, Giuseppe

    2016-08-01

    To date, decoding accuracy of actual or imagined pointing movements to targets in 3D space from electroencephalographic (EEG) signals has remained modest. The reason may pertain to the fact that these movements activate essentially the same neural networks. In this study, we aimed at testing whether repetitive pointing movements to each of the targets promotes the development of segregated neural patterns, resulting in enhanced decoding accuracy. Six human subjects generated slow or fast repetitive pointing movements with their right dominant arm to one of five targets distributed in 3D space, followed by repetitive imagery of movements to the same target or to a different target. Nine naive subjects generated both repetitive and non-repetitive slow actual movements to each of the five targets to test the effect of block design on decoding accuracy. In order to assure that base line drift and low frequency motion artifacts do not contaminate the data, the data were high-pass filtered in 4-30Hz, leaving out the delta and gamma band. For the repetitive trials, the model decoded target location with 81% accuracy, which is significantly higher than chance level. The average decoding rate of target location was only 30% for the non-repetitive trials, which is not significantly different than chance level. A subset of electrodes, mainly over the contralateral sensorimotor areas, was found to provide most of the discriminative features for all tested conditions. Time proximity between trained and tested blocks was found to enhance decoding accuracy of target location both by target non-specific and specific mechanisms. Our findings suggest that movement repetition promotes the development of distinct neural patterns, presumably by the formation of target-specific kinesthetic memory. PMID:27223628

  18. Pipe Leaks Classification by Using a Data-driven Approach Based on Features from Cross-Correlated Piezo-vibration Signals

    NASA Astrophysics Data System (ADS)

    Camacho-Navarro, Jhonatan; Ruiz, Magda; Perez, Oscar; Villamizar, Rodolfo; Mujica, Luis

    2015-07-01

    This work presents a data driven approach for pipe leaks classification, validated on a steel carbon pipe section conditioned with leaks of different sizes and locations in order to emulate abnormal conditions. The tested structure was instrumented with piezoelectric devices attached at different locations over the surface, in order to induce guided waves and to record its behaviour along the structure. For each experiment, one piezo device is excited by means of a high frequency burst type signal and the other ones are used as sensors. A blind bridle is connected to one of the extremes and an air source is coupled to the other extreme to emulate operational conditions. Statistical indices of correlated piezoelectric signals are obtained by using principal component analysis to distinguish different leak scenarios. Next, a selforganizing map is used to classify them. The experimental results show an improvement of the classification-learning rate when correlated signals are used instead of uncorrelated ones

  19. A Novel Variable Selection Method Based on a Partial KL Information Measure and Its Application to Channel Selection for Bioelectric Signal Classification

    NASA Astrophysics Data System (ADS)

    Shibanoki, Taro; Shima, Keisuke; Tsuji, Toshio; Takaki, Takeshi; Otsuka, Akira; Chin, Takaaki

    This paper proposes a novel variable selection method based on the KL information measure, and applies it to optimal channel selection for bioelectric signal classification. Generally, the accuracy of classifcation for bioelectric signals is greatly influenced by measuring positions of the signals as well as individual physical abilities of a user. Therefore, it is effective for classification to select optimal positions for each user in advance. In the proposed method, the probability density functions (pdfs) of measured data are estimated through learning of a multidimensional probabilistic neural network (PNN) based on the KL information theory. Then, a partial KL information measure is newly defined to evaluate contribution of each dimension in the data. The effective dimensions can be selected eliminating ineffective ones based on the partial KL information in a one-by-one manner. In the experiments, the proposed method was applied to EMG electrode selection with six subjects (including an amputee), and the effective channels were selected from all channels attached to each subject's forearm. Experimental results showed that the number of channels was reduced with 36.1±12.5 [%], and the average classification rate using selected channels by the proposed method was 98.99±1.31 [%]. These results indicated that the proposed method is capable to select effective channels (optimal or semi-optimal) for accurate classification.

  20. Morphometric, acoustic and lithofacies characterization of mud volcanoes in the Eastern Mediterranean: Toward a new approach and classification to constrain the regional distribution and activity of mud volcanoes?

    NASA Astrophysics Data System (ADS)