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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. 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

  3. 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

  4. 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

  5. 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.

  6. Real-time GMAW quality classification using an artificial neural network with airborne acoustic signals as inputs

    SciTech Connect

    Matteson, A.; Morris, R.; Tate, R.

    1993-12-31

    The acoustic signal produced by the gas metal arc welding (GMAW) arc contains information about the behavior of the arc column, the molten pool and droplet transfer. It is possible to detect some defect producing conditions from the acoustic signal from the GMAW arc. An intelligent sensor, called the Weld Acoustic Monitor (WAM) has been developed to take advantage of this acoustic information in order to provide real-time quality assessment information for process control. The WAM makes use of an Artificial Neural Network (ANN) to classify the characteristic arc acoustic signals of acceptable and unacceptable welds. The ANN used in the Weld Acoustic Monitor developed its own set of rules for this classification problem by learning a data base of known GMAW acoustic signals.

  7. 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.

  8. 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

  9. 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

  10. 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.

  11. Acoustic Signal Processing

    NASA Astrophysics Data System (ADS)

    Hartmann, William M.; Candy, James V.

    Signal processing refers to the acquisition, storage, display, and generation of signals - also to the extraction of information from signals and the re-encoding of information. As such, signal processing in some form is an essential element in the practice of all aspects of acoustics. Signal processing algorithms enable acousticians to separate signals from noise, to perform automatic speech recognition, or to compress information for more efficient storage or transmission. Signal processing concepts are the building blocks used to construct models of speech and hearing. Now, in the 21st century, all signal processing is effectively digital signal processing. Widespread access to high-speed processing, massive memory, and inexpensive software make signal processing procedures of enormous sophistication and power available to anyone who wants to use them. Because advanced signal processing is now accessible to everybody, there is a need for primers that introduce basic mathematical concepts that underlie the digital algorithms. The present handbook chapter is intended to serve such a purpose.

  12. 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

  13. 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.

  14. 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.

  15. 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.

  16. 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.

  17. 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.

  18. Object classification and acoustic imaging with active sonar.

    PubMed

    Kelly, J G; Carpenter, R N; Tague, J A

    1992-04-01

    The theoretical underpinnings of underwater acoustic classification and imaging using high-frequency active sonar are studied. All essential components of practical classification systems are incorporated in a Bayesian theoretic framework. The optimum decision rules and array processing are presented and evaluated. A systematic performance evaluation methodology is derived. New results quantify the relationship between classifier performance and object geometry, acoustic imaging, and the accuracy of a priori knowledge infused into the processor.

  19. Generation of Acoustic Signals from Buried Explosions

    NASA Astrophysics Data System (ADS)

    Bonner, J. L.; Reinke, R.; Waxler, R.; Lenox, E. A.

    2012-12-01

    Buried explosions generate both seismic and acoustic signals. The mechanism for the acoustic generation is generally assumed to be large ground motions above the source region that cause atmospheric pressure disturbances which can propagate locally or regionally depending on source size and weather conditions. In order to better understand the factors that control acoustic generation from buried explosions, we conducted a series of 200 lb explosions detonated in and above the dry alluvium and limestones of Kirtland AFB, New Mexico. In this experiment, nicknamed HUMBLE REDWOOD III, we detonated charges at heights of burst of 2 m (no crater) and depths of burst of 7 m (fully confined). The seismic and acoustic signals were recorded on a network of near-source (< 90 m) co-located accelerometer and overpressure sensors, circular rings of acoustic sensors at 0.3 and 1 km, and multiple seismic and infrasound sensors at local-to-regional distances. Near-source acoustic signals for the 200 lb buried explosion in limestone show an impulsive, short-duration (0.04 s) initial peak, followed by a broad duration (0.3 s) negative pressure trough, and finally a second positive pulse (0.18 s duration). The entire width of the acoustic signal generated by this small buried explosion is 0.5 s and results in a 2 Hz peak in spectral energy. High-velocity wind conditions quickly attenuate the signal with few observations beyond 1 km. We have attempted to model these acoustic waveforms by estimating near-source ground motion using synthetic spall seismograms. Spall seismograms have similar characteristics as the observed acoustics and usually include an initial positive motion P wave, followed by -1 g acceleration due to the ballistic free fall of the near surface rock units, and ends with positive accelerations due to "slapdown" of the material. Spall seismograms were synthesized using emplacement media parameters and high-speed video observations of the surface movements. We present a

  20. Dimensional analysis of acoustically propagated signals

    NASA Technical Reports Server (NTRS)

    Hansen, Scott D.; Thomson, Dennis W.

    1993-01-01

    Traditionally, long term measurements of atmospherically propagated sound signals have consisted of time series of multiminute averages. Only recently have continuous measurements with temporal resolution corresponding to turbulent time scales been available. With modern digital data acquisition systems we now have the capability to simultaneously record both acoustical and meteorological parameters with sufficient temporal resolution to allow us to examine in detail relationships between fluctuating sound and the meteorological variables, particularly wind and temperature, which locally determine the acoustic refractive index. The atmospheric acoustic propagation medium can be treated as a nonlinear dynamical system, a kind of signal processor whose innards depend on thermodynamic and turbulent processes in the atmosphere. The atmosphere is an inherently nonlinear dynamical system. In fact one simple model of atmospheric convection, the Lorenz system, may well be the most widely studied of all dynamical systems. In this paper we report some results of our having applied methods used to characterize nonlinear dynamical systems to study the characteristics of acoustical signals propagated through the atmosphere. For example, we investigate whether or not it is possible to parameterize signal fluctuations in terms of fractal dimensions. For time series one such parameter is the limit capacity dimension. Nicolis and Nicolis were among the first to use the kind of methods we have to study the properties of low dimension global attractors.

  1. 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.

  2. Seabed classification from acoustic profiling data using the similarity index.

    PubMed

    Kim, Han-Joon; Chang, Jae-Kyeong; Jou, Hyeong-Tae; Park, Gun-Tae; Suk, Bong-Chool; Kim, Ki Young

    2002-02-01

    We introduce the similarity index (SI) for the classification of the sea floor from acoustic profiling data. The essential part of our approach is the singular value decomposition of the data to extract a signal coherent trace-to-trace using the Karhunen-Loeve transform. SI is defined as the percentage of the energy of the coherent part contained in the bottom return signals. Important aspects of SI are that it is easily computed and that it represents the textural roughness of the sea floor as a function of grain size, hardness, and a degree of sediment sorting. In a real data example, we classified a section of the sea floor off Cheju Island south of the Korean Peninsula and compared the result with the sedimentology defined from direct sediment sampling and side scan sonar records. The comparison shows that SI can efficiently discriminate the bottom properties by delineating sediment-type boundaries and transition zones in more detail. Therefore, we propose that SI is an effective parameter for geoacoustic modeling. PMID:11863181

  3. Seabed classification from acoustic profiling data using the similarity index.

    PubMed

    Kim, Han-Joon; Chang, Jae-Kyeong; Jou, Hyeong-Tae; Park, Gun-Tae; Suk, Bong-Chool; Kim, Ki Young

    2002-02-01

    We introduce the similarity index (SI) for the classification of the sea floor from acoustic profiling data. The essential part of our approach is the singular value decomposition of the data to extract a signal coherent trace-to-trace using the Karhunen-Loeve transform. SI is defined as the percentage of the energy of the coherent part contained in the bottom return signals. Important aspects of SI are that it is easily computed and that it represents the textural roughness of the sea floor as a function of grain size, hardness, and a degree of sediment sorting. In a real data example, we classified a section of the sea floor off Cheju Island south of the Korean Peninsula and compared the result with the sedimentology defined from direct sediment sampling and side scan sonar records. The comparison shows that SI can efficiently discriminate the bottom properties by delineating sediment-type boundaries and transition zones in more detail. Therefore, we propose that SI is an effective parameter for geoacoustic modeling.

  4. [Use of self-organizing neural networks (Kohonen maps) for classification of voice acoustic signals exemplified by the infant voice with and without time-delayed auditory feedback].

    PubMed

    Schönweiler, R; Kaese, S; Möller, S; Rinscheid, A; Ptok, M

    1996-04-01

    Subjective and auditory assessment of the voice is now more commonly being replaced by objective voice analysis. Because of the amount of data available from computer-aided voice analysis, subjective selection and interpretation of single data sets remain a matter of experience of the individual investigator. Since neuronal networks are widely used in telecommunication and speech recognition, we applied self-organizing Kohonen networks to classify voice patterns. In the phase of "learning," the Kohonen map is adapted to patterns of the primary signals obtained. If, in the phase of using the map, the input signal hits the field of the primary signals, it will resemble them closely. In this study, we recorded newborn and young infant cries using a DAT recorder and a high-quality microphone. The cries were elicited by wearing uncomfortable headphones ("cries of discomfort"). Spectrographic characteristics of the cries were classified by 20-step bark spectra and then applied to the neuronal networks. It was possible to recognize similarities of different cries of the same children and interindividual differences, as well as cries of children with profound hearing loss. In addition, delayed auditory feedback at 80 dB SL was presented to 27 children via headphone using a three-headed tape-recorder as a model for induced individual cry changes. However, it was not possible to classify short-term changes as in a delayed feedback procedure. Nevertheless, neuronal networks may be helpful as an additional tool in spectrographic voice analysis.

  5. 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.

  6. 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.

  7. 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

  8. 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

  9. Tracheal activity recognition based on acoustic signals.

    PubMed

    Olubanjo, Temiloluwa; Ghovanloo, Maysam

    2014-01-01

    Tracheal activity recognition can play an important role in continuous health monitoring for wearable systems and facilitate the advancement of personalized healthcare. Neck-worn systems provide access to a unique set of health-related data that other wearable devices simply cannot obtain. Activities including breathing, chewing, clearing the throat, coughing, swallowing, speech and even heartbeat can be recorded from around the neck. In this paper, we explore tracheal activity recognition using a combination of promising acoustic features from related work and apply simplistic classifiers including K-NN and Naive Bayes. For wearable systems in which low power consumption is of primary concern, we show that with a sub-optimal sampling rate of 16 kHz, we have achieved average classification results in the range of 86.6% to 87.4% using 1-NN, 3-NN, 5-NN and Naive Bayes. All classifiers obtained the highest recognition rate in the range of 97.2% to 99.4% for speech classification. This is promising to mitigate privacy concerns associated with wearable systems interfering with the user's conversations.

  10. 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.

  11. 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.

  12. 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.

  13. 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

  14. 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.

  15. 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%.

  16. 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.

  17. Munitions classification using an Acoustic Resonance Spectroscopic technique

    SciTech Connect

    Roberts, R.S.; Chen, J.T.; Vela, O.A.; Lewis, P.S.

    1993-12-01

    In support of the Bilateral Chemical Weapons Agreement between the United States and Russia, Los Alamos National Laboratory has developed a nondestructive evaluation (NDE) technique that discriminates between different types of artillery munitions. This NDE classification technique allows on-site inspectors to rapidly classify the munitions as chemical or high explosive, and furthermore discriminates between various subclasses of these types of munitions. This technique, based on acoustic resonance measurements, has been successfully demonstrated on a wide variety of high explosive and chemical munitions. The technique consists of building templates of spectral features from sets of known munitions. Spectral features of unknown munitions are compared with a library of templates, and the degree of match between the features and the templates is used to classify the munition. This paper describes the technique, including the feature extraction, clustering and classification algorithms.

  18. Surface electromyography signal processing and classification techniques.

    PubMed

    Chowdhury, Rubana H; Reaz, Mamun B I; Ali, Mohd Alauddin Bin Mohd; Bakar, Ashrif A A; Chellappan, K; Chang, T G

    2013-09-17

    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.

  19. 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

  20. New Techniques of Acoustic Seabed Classification at Ocean Margins

    NASA Astrophysics Data System (ADS)

    BLONDEL, P.

    2001-12-01

    Ocean margins have become the focus of most current geophysical and environmental surveys, because of their economic, scientific and oceanographic significance. These surveys deliver increasingly larger volumes of data, acquired by many types of techniques and sensors. Despite its importance, most of this data is still interpreted visually and qualitatively by skilled interpreters. The problem is that human interpretation is time-consuming and difficult to standardise. In certain conditions, it can be also be subject to more or less systematic errors. Current research in data processing is shifting toward computer-based interpretation techniques, and in particular seafloor classification. This presentation will review the different notions and objectives of classification. This will be followed with a review of acoustic (mainly sonar) classification techniques, supplemented with actual examples from around the world. In particular, new techniques recently developed will be presented, such as multistatic or multi-aspect 3-D sonar imaging. They provide access to a new wealth of useful parameters, often at extremely high-resolution. Seabed classification, in general and at ocean margins in particular, is fast becoming a major tool in seafloor surveying and monitoring,particularly with the development and increasing use of ROVs and autonomous platforms.

  1. 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

  2. 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

  3. Automatic Acoustic Events Detection, Classification, and Semantic Annotation for Persistent Surveillance Applications

    NASA Astrophysics Data System (ADS)

    Alkilani, Amjad H. I.

    Acoustic surveillance and human behavior analysis represent some of the ongoing research topics in signal processing. Acoustic sensors offer a promising sensing modality, primarily because they can capture a huge amount of information from the environment. Moreover, they can be rapidly deployed and are low-cost. In the past, significant efforts have been devoted to detecting sounds of individual objects or events. However, the issue of understanding human activities based on sporadic acoustic sound events has received unequal attention in the literature and hence is not well understood. This dissertation presents an extensive literature survey on this topic and discusses existing advanced techniques for acoustic signal processing and pattern recognition. A novel theoretic framework (Acoustic Events Detection, Classification, and Annotation (AEDCA)) is proposed which accommodates sound events ontology for improved human activities recognition. Based on a generalized taxonomy, three sound categories signifying interaction of human with each other, with vehicles, and with other objects are introduced. In order to understand different type of human interactions salient sound events are preliminarily identified and classified based on trained set of data. To interlink salient events representing an ontology-based hypothesis, a Hidden Markov Model-Acoustic Activity Recognizer (HMM-AAR) is modeled to recognize spatiotemporally correlated events. Once such a connection is established, an annotation of perceived sound activity is generated. The performance of the AEDCA system was tested and measured experimentally in both indoor and outdoor environments. Appropriate confusion matrices are developed for the assessment of performance reliability, and computational efficiency of the AEDCA system. The obtained results are very promising and strongly demonstrate the AEDCA is both reliable and effective, and can be extended to future surveillance applications.

  4. 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.

  5. 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.

  6. 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.

  7. Amplitude Modulations of Acoustic Communication Signals

    NASA Astrophysics Data System (ADS)

    Turesson, Hjalmar K.

    2011-12-01

    In human speech, amplitude modulations at 3 -- 8 Hz are important for discrimination and detection. Two different neurophysiological theories have been proposed to explain this effect. The first theory proposes that, as a consequence of neocortical synaptic dynamics, signals that are amplitude modulated at 3 -- 8 Hz are propagated better than un-modulated signals, or signals modulated above 8 Hz. This suggests that neural activity elicited by vocalizations modulated at 3 -- 8 Hz is optimally transmitted, and the vocalizations better discriminated and detected. The second theory proposes that 3 -- 8 Hz amplitude modulations interact with spontaneous neocortical oscillations. Specifically, vocalizations modulated at 3 -- 8 Hz entrain local populations of neurons, which in turn, modulate the amplitude of high frequency gamma oscillations. This suggests that vocalizations modulated at 3 -- 8 Hz should induce stronger cross-frequency coupling. Similar to human speech, we found that macaque monkey vocalizations also are amplitude modulated between 3 and 8 Hz. Humans and macaque monkeys share similarities in vocal production, implying that the auditory systems subserving perception of acoustic communication signals also share similarities. Based on the similarities between human speech and macaque monkey vocalizations, we addressed how amplitude modulated vocalizations are processed in the auditory cortex of macaque monkeys, and what behavioral relevance modulations may have. Recording single neuron activity, as well as, the activity of local populations of neurons allowed us to test both of the neurophysiological theories presented above. We found that single neuron responses to vocalizations amplitude modulated at 3 -- 8 Hz resulted in better stimulus discrimination than vocalizations lacking 3 -- 8 Hz modulations, and that the effect most likely was mediated by synaptic dynamics. In contrast, we failed to find support for the oscillation-based model proposing a

  8. 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.

  9. Sleep stage classification based on respiratory signal.

    PubMed

    Tataraidze, Alexander; Anishchenko, Lesya; Korostovtseva, Lyudmila; Kooij, Bert Jan; Bochkarev, Mikhail; Sviryaev, Yurii

    2015-01-01

    One of the research tasks, which should be solved to develop a sleep monitor, is sleep stages classification. This paper presents an algorithm for wakefulness, rapid eye movement sleep (REM) and non-REM sleep detection based on a set of 33 features, extracted from respiratory inductive plethysmography signal, and bagging classifier. Furthermore, a few heuristics based on knowledge about normal sleep structure are suggested. We used the data from 29 subjects without sleep-related breathing disorders who underwent a PSG study at a sleep laboratory. Subjects were directed to the PSG study due to suspected sleep disorders. A leave-one-subject-out cross-validation procedure was used for testing the classification performance. The accuracy of 77.85 ± 6.63 and Cohen's kappa of 0.59 ± 0.11 were achieved for the classifier. Using heuristics we increased the accuracy to 80.38 ± 8.32 and the kappa to 0.65 ± 0.13. We conclude that heuristics may improve the automated sleep structure detection based on the analysis of indirect information such as respiration signal and are useful for the development of home sleep monitoring system. PMID:26736273

  10. 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.

  11. Explosive activity at Mt. Yasur volcano: characterization of acoustic signals

    NASA Astrophysics Data System (ADS)

    Spina, L.; Taddeucci, J.; Scarlato, P.; Freda, C.; Gresta, S.

    2012-04-01

    Mt. Yasur (Vanuatu Islands) is an active volcano characterized by persistent Strombolian to mild Vulcanian explosive activity, well known to generate a broad variety of air pressure waves. Between 9 and 12 July 2011, we recorded explosive activity from the three active vents of Mt. Yasur by means of a multiparametric station, comprising thermal and visual high-speed cameras and two ECM microphones recording both infrasonic and sonic signals at 10 kHz sampling frequency. A total of 106 major acoustic events, lasting on average 5 seconds (up to 20 in some ash-rich explosion), correspond to visually recorded explosions at the vents and exhibit a surprisingly broad waveform variability. Major events intervene between minor transients with strongly repetitive waveforms typical of puffing activity. Spectral analyses have been computed on both major events and whole traces. Analysis of major events, carried out using a 5.12 s long window, reveals peak frequencies mostly beneath 5 Hz, only a few events displaying a notable energy content in the sonic band (up to 100 Hz ca). Peak-to-peak amplitude as well as RMS values (evaluated from event start to end) were computed on both raw and filtered (above and below 20 Hz) signals. Spectrograms of the whole traces, carried out using 1.28, 2.56, and 5.12 seconds long windows with 50% overlap, outline clearly the frequency content of major events and the occurrence of puffing ones. We also evaluated the peak frequency of each spectrum of the spectrogram, in order to detect spectral variation of the puffing signal. Considering their great variability, we classified the major events on the base of their spectral content rather than on waveform, grouping together all events having similar spectra by cross-correlating them. Three spectral families cover most of the dataset, as follows: 1) variable and irregular shaped spectra, with energy mainly below 4 Hz; 2) monochromatic events, with simple spectra corresponding in the time domain to

  12. Correlation of signals of thermal acoustic radiation

    NASA Astrophysics Data System (ADS)

    Anosov, A. A.; Passechnik, V. I.

    2003-03-01

    The spatial correlation function is measured for the pressure of thermal acoustic radiation from a source (a narrow plasticine plate) whose temperature is made both higher and lower than the temperature of the receiver. The spatial correlation function of the pressure of thermal acoustic radiation is found to be oscillatory in character. The oscillation amplitude is determined not by the absolute temperature of the source but by the temperature difference between the source and the receiver. The correlation function changes its sign when a source heated with respect to the receiver is replaced by a cooled one.

  13. Mesoscale variations in acoustic signals induced by atmospheric gravity waves.

    PubMed

    Chunchuzov, Igor; Kulichkov, Sergey; Perepelkin, Vitaly; Ziemann, Astrid; Arnold, Klaus; Kniffka, Anke

    2009-02-01

    The results of acoustic tomographic monitoring of the coherent structures in the lower atmosphere and the effects of these structures on acoustic signal parameters are analyzed in the present study. From the measurements of acoustic travel time fluctuations (periods 1 min-1 h) with distant receivers, the temporal fluctuations of the effective sound speed and wind speed are retrieved along different ray paths connecting an acoustic pulse source and several receivers. By using a coherence analysis of the fluctuations near spatially distanced ray turning points, the internal wave-associated fluctuations are filtered and their spatial characteristics (coherences, horizontal phase velocities, and spatial scales) are estimated. The capability of acoustic tomography in estimating wind shear near ground is shown. A possible mechanism describing the temporal modulation of the near-ground wind field by ducted internal waves in the troposphere is proposed.

  14. 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.

  15. 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.

  16. Acoustic signal characteristics during IR laser ablation and their consequences for acoustic tissue discrimination

    NASA Astrophysics Data System (ADS)

    Nahen, Kester; Vogel, Alfred

    2000-06-01

    IR laser ablation of skin is accompanied by acoustic signals the characteristics of which are closely linked to the ablation dynamics. A discrimination between different tissue layers, for example necrotic and vital tissue during laser burn debridement, is therefore possible by an analysis of the acoustic signal. We were able to discriminate tissue layers by evaluating the acoustic energy. To get a better understanding of the tissue specificity of the ablation noise, we investigated the correlation between sample water content, ablation dynamics, and characteristics of the acoustic signal. A free running Er:YAG laser with a maximum pulse energy of 2 J and a spot diameter of 5 mm was used to ablate gelatin samples with different water content. The ablation noise in air was detected using a piezoelectric transducer with a bandwidth of 1 MHz, and the acoustic signal generated inside the ablated sample was measured simultaneously ba a piezoelectric transducer in contact with the sample. Laser flash Schlieren photography was used to investigate the expansion velocity of the vapor plume and the velocity of the ejected material. We observed large differences between the ablation dynamics and material ejection velocity for gelatin samples with 70% and 90% water content. These differences cannot be explained by the small change of the gelatin absorption coefficient, but are largely related to differences of the mechanical properties of the sample. The different ablation dynamics are responsible for an increase of the acoustic energy by a factor of 10 for the sample with the higher water content.

  17. 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).

  18. 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.

  19. 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

  20. 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

  1. 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

  2. River Bed Sediment Classification Using Acoustic Doppler Profiler

    NASA Astrophysics Data System (ADS)

    Shields, F. D.

    2008-12-01

    Restoration or rehabilitation of degraded stream and river habitats requires definition of a target condition and preferably post-implementation monitoring to gage progress toward the target. Stream habitat has been characterized by computing statistics based on measurements of water depth and velocity at each point of a horizontal grid. In many cases stream bed type and cover, both qualitatively assessed, were included as additional grid variables. Resultant statistics describing the central tendency, variability and spatial distribution of these three or four variables and their combinations have been used to explain key differences between more- and less-degraded streams and to infer biotic responses. Usually the required data are collected by wading observers, but application to larger rivers is problematic. Collection of water depth and velocity information may be automated across a wide range of stream sizes using an acoustic Doppler profiler (aDp). Herein we suggest that aDp data may also be used to infer bed hardness and thus type by extracting the return signal strength from the bottom track signal and using this information to compute the echo intensity at the bed. A method for computing echo intensity, along with key assumptions is presented. Echo intensity is computed for a range of river environments and related to the size and related characteristics of bed material. Habitat maps for river reaches depicting water depth, velocity and bed type developed from aDp data sets are presented.

  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. 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).

  6. Digital Signal Processing in Acoustics--Part 2.

    ERIC Educational Resources Information Center

    Davies, H.; McNeill, D. J.

    1986-01-01

    Reviews the potential of a data acquisition system for illustrating the nature and significance of ideas in digital signal processing. Focuses on the fast Fourier transform and the utility of its two-channel format, emphasizing cross-correlation and its two-microphone technique of acoustic intensity measurement. Includes programing format. (ML)

  7. 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.

  8. The influence of source acceleration on acoustic signals

    NASA Technical Reports Server (NTRS)

    Kelly, Jeffrey J.; Wilson, Mark R.

    1993-01-01

    The effect of aircraft acceleration on acoustic signals is often ignored in both analytical studies and data reduction of flight test measurements. In this study, the influence of source acceleration on acoustic signals is analyzed using computer simulated signals for an accelerating point source. Both rotating and translating sources are considered. Using a known signal allows an assessment of the influence of source acceleration on the received signal. Aircraft acceleration must also be considered in the measurement and reduction of flyover noise. Tracking of the aircraft over an array of microphones enables ensemble averaging of the acoustic signal, thus increasing the confidence in the measured data. This is only valid when both the altitude and velocity remain constant. For an accelerating aircraft, each microphone is exposed to differing flight velocities, Doppler shifts, and smear angles. Thus, averaging across the array in the normal manner is constrained by aircraft acceleration and microphone spacing. In this study computer simulated spectra, containing acceleration, are averaged across a 12 microphone array mimicking a flight test with accelerated profile in which noise data was obtained. Overlapped processing is performed is performed in the flight test measurements in order to alleviate spectral smearing.

  9. 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.

  10. 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.

  11. 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.

  12. A Comparison of Signal Enhancement Methods for Extracting Tonal Acoustic Signals

    NASA Technical Reports Server (NTRS)

    Jones, Michael G.

    1998-01-01

    The measurement of pure tone acoustic pressure signals in the presence of masking noise, often generated by mean flow, is a continual problem in the field of passive liner duct acoustics research. In support of the Advanced Subsonic Technology Noise Reduction Program, methods were investigated for conducting measurements of advanced duct liner concepts in harsh, aeroacoustic environments. This report presents the results of a comparison study of three signal extraction methods for acquiring quality acoustic pressure measurements in the presence of broadband noise (used to simulate the effects of mean flow). The performance of each method was compared to a baseline measurement of a pure tone acoustic pressure 3 dB above a uniform, broadband noise background.

  13. 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.

  14. 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

  15. 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.

  16. 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

  17. Size does not matter: size-invariant echo-acoustic object classification.

    PubMed

    Genzel, Daria; Wiegrebe, Lutz

    2013-02-01

    Echolocating bats can not only extract spatial information from the auditory analysis of their ultrasonic emissions, they can also discriminate, classify and identify the three-dimensional shape of objects reflecting their emissions. Effective object recognition requires the segregation of size and shape information. Previous studies have shown that, like in visual object recognition, bats can transfer an echo-acoustic object discrimination task to objects of different size and that they spontaneously classify scaled versions of virtual echo-acoustic objects according to trained virtual-object standards. The current study aims to bridge the gap between these previous findings using a different class of real objects and a classification-instead of a discrimination paradigm. Echolocating bats (Phyllostomus discolor) were trained to classify an object as either a sphere or an hour-glass shaped object. The bats spontaneously generalised this classification to objects of the same shape. The generalisation cannot be explained based on similarities of the power spectra or temporal structures of the echo-acoustic object images and thus require dedicated neural mechanisms dealing with size-invariant echo-acoustic object analysis. Control experiments with human listeners classifying the echo-acoustic images of the objects confirm the universal validity of auditory size invariance. The current data thus corroborate and extend previous psychophysical evidence for sonar auditory-object normalisation and suggest that the underlying auditory mechanisms following the initial neural extraction of the echo-acoustic images in echolocating bats may be very similar in bats and humans.

  18. Intelligent artifact classification for ambulatory physiological signals.

    PubMed

    Sweeney, Kevin T; Leamy, Darren J; Ward, Tomas E; McLoone, Sean

    2010-01-01

    Connected health represents an increasingly important model for health-care delivery. The concept is heavily reliant on technology and in particular remote physiological monitoring. One of the principal challenges is the maintenance of high quality data streams which must be collected with minimally intrusive, inexpensive sensor systems operating in difficult conditions. Ambulatory monitoring represents one of the most challenging signal acquisition challenges of all in that data is collected as the patient engages in normal activities of everyday living. Data thus collected suffers from considerable corruption as a result of artifact, much of it induced by motion and this has a bearing on its utility for diagnostic purposes. We propose a model for ambulatory signal recording in which the data collected is accompanied by labeling indicating the quality of the collected signal. As motion is such an important source of artifact we demonstrate the concept in this case with a quality of signal measure derived from motion sensing technology viz. accelerometers. We further demonstrate how different types of artifact might be tagged to inform artifact reduction signal processing elements during subsequent signal analysis. This is demonstrated through the use of multiple accelerometers which allow the algorithm to distinguish between disturbance of the sensor relative to the underlying tissue and movement of this tissue. A brain monitoring experiment utilizing EEG and fNIRS is used to illustrate the concept.

  19. 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)].

  20. 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

  1. 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.

  2. 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.

  3. Adaptive plasticity in wild field cricket's acoustic signaling.

    PubMed

    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.

  4. 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

  5. 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

  6. 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

  7. 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.}

  8. 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.

  9. 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.

  10. 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.

  11. 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.

  12. 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.

  13. 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.

  14. 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

  15. 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

  16. 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.

  17. Signal processing and tracking of arrivals in ocean acoustic tomography.

    PubMed

    Dzieciuch, Matthew A

    2014-11-01

    The signal processing for ocean acoustic tomography experiments has been improved to account for the scattering of the individual arrivals. The scattering reduces signal coherence over time, bandwidth, and space. In the typical experiment, scattering is caused by the random internal-wave field and results in pulse spreading (over arrival-time and arrival-angle) and wander. The estimator-correlator is an effective procedure that improves the signal-to-noise ratio of travel-time estimates and also provides an estimate of signal coherence. The estimator-correlator smoothes the arrival pulse at the expense of resolution. After an arrival pulse has been measured, it must be associated with a model arrival, typically a ray arrival. For experiments with thousands of transmissions, this is a tedious task that is error-prone when done manually. An error metric that accounts for peak amplitude as well as travel-time and arrival-angle can be defined. The Viterbi algorithm can then be adapted to the task of automated peak tracking. Repeatable, consistent results are produced that are superior to a manual tracking procedure. The tracking can be adjusted by tuning the error metric in logical, quantifiable manner. PMID:25373953

  18. Signal processing and tracking of arrivals in ocean acoustic tomography.

    PubMed

    Dzieciuch, Matthew A

    2014-11-01

    The signal processing for ocean acoustic tomography experiments has been improved to account for the scattering of the individual arrivals. The scattering reduces signal coherence over time, bandwidth, and space. In the typical experiment, scattering is caused by the random internal-wave field and results in pulse spreading (over arrival-time and arrival-angle) and wander. The estimator-correlator is an effective procedure that improves the signal-to-noise ratio of travel-time estimates and also provides an estimate of signal coherence. The estimator-correlator smoothes the arrival pulse at the expense of resolution. After an arrival pulse has been measured, it must be associated with a model arrival, typically a ray arrival. For experiments with thousands of transmissions, this is a tedious task that is error-prone when done manually. An error metric that accounts for peak amplitude as well as travel-time and arrival-angle can be defined. The Viterbi algorithm can then be adapted to the task of automated peak tracking. Repeatable, consistent results are produced that are superior to a manual tracking procedure. The tracking can be adjusted by tuning the error metric in logical, quantifiable manner.

  19. 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

  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. 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.

  2. 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.

  3. 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.

  4. Model-based sediment classification using single-beam echosounder signals.

    PubMed

    Snellen, Mirjam; Siemes, Kerstin; Simons, Dick G

    2011-05-01

    Acoustic remote sensing techniques for mapping sediment properties are of interest due to their low costs and high coverage. Model-based approaches directly couple the acoustic signals to sediment properties. Despite the limited coverage of the single-beam echosounder (SBES), it is widely used. Having available model-based SBES classification tools, therefore, is important. Here, two model-based approaches of different complexity are compared to investigate their practical applicability. The first approach is based on matching the echo envelope. It maximally exploits the information available in the signal but requires complex modeling and optimization. To minimize computational costs, the efficient differential evolution method is used. The second approach reduces the information of the signal to energy only and directly relates this to the reflection coefficient to obtain quantitative information about the sediment parameters. The first approach provides information over a variety of sediment types. In addition to sediment mean grain size, it also provides estimates for the spectral strength and volume scattering parameter. The need to account for all three parameters is demonstrated, justifying computational expenses. In the second approach, the lack of information on these parameters and the limited SBES beamwidth are demonstrated to hamper the conversion of echo energy to reflection coefficient. PMID:21568391

  5. Model-based sediment classification using single-beam echosounder signals.

    PubMed

    Snellen, Mirjam; Siemes, Kerstin; Simons, Dick G

    2011-05-01

    Acoustic remote sensing techniques for mapping sediment properties are of interest due to their low costs and high coverage. Model-based approaches directly couple the acoustic signals to sediment properties. Despite the limited coverage of the single-beam echosounder (SBES), it is widely used. Having available model-based SBES classification tools, therefore, is important. Here, two model-based approaches of different complexity are compared to investigate their practical applicability. The first approach is based on matching the echo envelope. It maximally exploits the information available in the signal but requires complex modeling and optimization. To minimize computational costs, the efficient differential evolution method is used. The second approach reduces the information of the signal to energy only and directly relates this to the reflection coefficient to obtain quantitative information about the sediment parameters. The first approach provides information over a variety of sediment types. In addition to sediment mean grain size, it also provides estimates for the spectral strength and volume scattering parameter. The need to account for all three parameters is demonstrated, justifying computational expenses. In the second approach, the lack of information on these parameters and the limited SBES beamwidth are demonstrated to hamper the conversion of echo energy to reflection coefficient.

  6. Temporal coherence of acoustic signals in a fluctuating ocean.

    PubMed

    Voronovich, Alexander G; Ostashev, Vladimir E; Colosi, John A

    2011-06-01

    Temporal coherence of acoustic signals propagating in a fluctuating ocean is important for many practical applications and has been studied intensively experimentally. However, only a few theoretical formulations of temporal coherence exist. In the present paper, a three-dimensional (3D) modal theory of sound propagation in a fluctuating ocean is used to derive closed-form equations for the spatial-temporal coherence function of a broadband signal. The theory is applied to the analysis of the temporal coherence of a monochromatic signal propagating in an ocean perturbed by linear internal waves obeying the Garrett-Munk (G-M) spectral model. In particular, the temporal coherence function is calculated for propagation ranges up to 10(4) km and for five sound frequencies: 12, 25, 50, 75, and 100 Hz. Then, the dependence of the coherence time (i.e., the value of the time lag at which the temporal coherence decreases by a factor of e) on range and frequency is studied. The results obtained are compared with experimental data and predictions of the path-integral theory.

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

    USGS Publications Warehouse

    Cochrane, Guy R.; Lafferty, Kevin 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 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.

  8. 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.

  9. 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.

  10. Adaptive classification on brain-computer interfaces using reinforcement signals.

    PubMed

    Llera, A; Gómez, V; Kappen, H J

    2012-11-01

    We introduce a probabilistic model that combines a classifier with an extra reinforcement signal (RS) encoding the probability of an erroneous feedback being delivered by the classifier. This representation computes the class probabilities given the task related features and the reinforcement signal. Using expectation maximization (EM) to estimate the parameter values under such a model shows that some existing adaptive classifiers are particular cases of such an EM algorithm. Further, we present a new algorithm for adaptive classification, which we call constrained means adaptive classifier, and show using EEG data and simulated RS that this classifier is able to significantly outperform state-of-the-art adaptive classifiers.

  11. 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.

  12. 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.

  13. 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

  14. A real-time method for autonomous passive acoustic detection-classification of humpback whales.

    PubMed

    Abbot, Ted A; Premus, Vincent E; Abbot, Philip A

    2010-05-01

    This paper describes a method for real-time, autonomous, joint detection-classification of humpback whale vocalizations. The approach adapts the spectrogram correlation method used by Mellinger and Clark [J. Acoust. Soc. Am. 107, 3518-3529 (2000)] for bowhead whale endnote detection to the humpback whale problem. The objective is the implementation of a system to determine the presence or absence of humpback whales with passive acoustic methods and to perform this classification with low false alarm rate in real time. Multiple correlation kernels are used due to the diversity of humpback song. The approach also takes advantage of the fact that humpbacks tend to vocalize repeatedly for extended periods of time, and identification is declared only when multiple song units are detected within a fixed time interval. Humpback whale vocalizations from Alaska, Hawaii, and Stellwagen Bank were used to train the algorithm. It was then tested on independent data obtained off Kaena Point, Hawaii in February and March of 2009. Results show that the algorithm successfully classified humpback whales autonomously in real time, with a measured probability of correct classification in excess of 74% and a measured probability of false alarm below 1%.

  15. 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.

  16. 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.

  17. 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%.

  18. 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

  19. 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

  20. Ecology of acoustic signalling and the problem of masking interference in insects.

    PubMed

    Schmidt, Arne K D; Balakrishnan, Rohini

    2015-01-01

    The efficiency of long-distance acoustic signalling of insects in their natural habitat is constrained in several ways. Acoustic signals are not only subjected to changes imposed by the physical structure of the habitat such as attenuation and degradation but also to masking interference from co-occurring signals of other acoustically communicating species. Masking interference is likely to be a ubiquitous problem in multi-species assemblages, but successful communication in natural environments under noisy conditions suggests powerful strategies to deal with the detection and recognition of relevant signals. In this review we present recent work on the role of the habitat as a driving force in shaping insect signal structures. In the context of acoustic masking interference, we discuss the ecological niche concept and examine the role of acoustic resource partitioning in the temporal, spatial and spectral domains as sender strategies to counter masking. We then examine the efficacy of different receiver strategies: physiological mechanisms such as frequency tuning, spatial release from masking and gain control as useful strategies to counteract acoustic masking. We also review recent work on the effects of anthropogenic noise on insect acoustic communication and the importance of insect sounds as indicators of biodiversity and ecosystem health.

  1. 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.

  2. 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.

  3. 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.

  4. 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.

  5. 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

  6. 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.

  7. 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.

  8. 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

  9. An improved DS acoustic-seismic modality fusion algorithm based on a new cascaded fuzzy classifier for ground-moving targets classification in wireless sensor networks

    NASA Astrophysics Data System (ADS)

    Pan, Qiang; Wei, Jianming; Cao, Hongbing; Li, Na; Liu, Haitao

    2007-04-01

    A new cascaded fuzzy classifier (CFC) is proposed to implement ground-moving targets classification tasks locally at sensor nodes in wireless sensor networks (WSN). The CFC is composed of three and two binary fuzzy classifiers (BFC) respectively in seismic and acoustic signal channel in order to classify person, Light-wheeled (LW) Vehicle, and Heavywheeled (HW) Vehicle in presence of environmental background noise. Base on the CFC, a new basic belief assignment (bba) function is defined for each component BFC to give out a piece of evidence instead of a hard decision label. An evidence generator is used to synthesize available evidences from BFCs into channel evidences and channel evidences are further temporal-fused. Finally, acoustic-seismic modality fusion using Dempster-Shafer method is performed. Our implementation gives significantly better performance than the implementation with majority-voting fusion method through leave-one-out experiments.

  10. 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.

  11. 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.

  12. 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.

  13. 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.

  14. Scale and translation invariant shape and signal classification and detection

    NASA Astrophysics Data System (ADS)

    Williams, William J.

    2003-12-01

    Highly sophisticated methods for detection and classification of signals and images are available. However, most of these methods are not robust to nonstationary variations such as imposed by Doppler effects or other forms of warping. Fourier methods handle time-shift or frequency shift variations in signals or spatial shifts in images. A number of methods have been developed to overcome these problems. In this paper we discuss some specific approaches that have been motivated by time-frequency analysis. Methodologies developed for images can often be profitably used for time-frequency analysis as well, since these representations are essentially images. The scale transform introduced by Cohen can join Fourier transforms in providing robust representations. Scale changes are common in many signal and image scenarios. We call the representation which results from appropriate transformations of the object of interest the Scale and Translation Invariant Representation or STIR. The STIR method is summarized and results from machine diagnosis, radar, marine mammal sounds, TMJ sounds, speech and word spotting are discussed. Some of the limitations and variations of the method are discussed to provide a rationale for selection of particular elements of the method.

  15. 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

  16. Design of acoustic logging signal source of imitation based on field programmable gate array

    NASA Astrophysics Data System (ADS)

    Zhang, K.; Ju, X. D.; Lu, J. Q.; Men, B. Y.

    2014-08-01

    An acoustic logging signal source of imitation is designed and realized, based on the Field Programmable Gate Array (FPGA), to improve the efficiency of examining and repairing acoustic logging tools during research and field application, and to inspect and verify acoustic receiving circuits and corresponding algorithms. The design of this signal source contains hardware design and software design,and the hardware design uses an FPGA as the control core. Four signals are made first by reading the Random Access Memory (RAM) data which are inside the FPGA, then dealing with the data by digital to analog conversion, amplification, smoothing and so on. Software design uses VHDL, a kind of hardware description language, to program the FPGA. Experiments illustrate that the ratio of signal to noise for the signal source is high, the waveforms are stable, and also its functions of amplitude adjustment, frequency adjustment and delay adjustment are in accord with the characteristics of real acoustic logging waveforms. These adjustments can be used to imitate influences on sonic logging received waveforms caused by many kinds of factors such as spacing and span of acoustic tools, sonic speeds of different layers and fluids, and acoustic attenuations of different cementation planes.

  17. 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.

  18. 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

  19. 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

  20. 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

  1. 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.

  2. 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.

  3. 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.

  4. 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.

  5. [Shape acoustical recognition and characteristics of sonar signals by the dolphin T. truncatus].

    PubMed

    Dziedzic, A; Alcuri, G

    1977-10-17

    During the shape acoustical recognition process, the signal processing reveals two phases in the T. truncatus sonar emission. In the course of the first phase, the wide-band signals are invariant, during the second phase, near the end of the approach, their temporal and spectral characteristics change along with the shape of the objects to identify.

  6. 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.

  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. 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.

  10. 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

  11. The evolutionary origins of ritualized acoustic signals in caterpillars.

    PubMed

    Scott, Jaclyn L; Kawahara, Akito Y; Skevington, Jeffrey H; Yen, Shen-Horn; Sami, Abeer; Smith, Myron L; Yack, Jayne E

    2010-01-01

    Animal communication signals can be highly elaborate, and researchers have long sought explanations for their evolutionary origins. For example, how did signals such as the tail-fan display of a peacock, a firefly flash or a wolf howl evolve? Animal communication theory holds that many signals evolved from non-signalling behaviours through the process of ritualization. Empirical evidence for ritualization is limited, as it is necessary to examine living relatives with varying degrees of signal evolution within a phylogenetic framework. We examine the origins of vibratory territorial signals in caterpillars using comparative and molecular phylogenetic methods. We show that a highly ritualized vibratory signal--anal scraping--originated from a locomotory behaviour--walking. Furthermore, comparative behavioural analysis supports the hypothesis that ritualized vibratory signals derive from physical fighting behaviours. Thus, contestants signal their opponents to avoid the cost of fighting. Our study provides experimental evidence for the origins of a complex communication signal, through the process of ritualization.

  12. 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

  13. 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

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

    NASA Astrophysics Data System (ADS)

    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

    2016-09-01

    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.

  15. 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.

  16. 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

  17. Surface roughness evaluation based on acoustic emission signals in robot assisted polishing.

    PubMed

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

    2014-11-14

    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.

  18. The effect of habitat acoustics on common marmoset vocal signal transmission.

    PubMed

    Morrill, Ryan J; Thomas, A Wren; Schiel, Nicola; Souto, Antonio; Miller, Cory T

    2013-09-01

    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.

  19. 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

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

    SciTech Connect

    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.

  1. A support vector machine approach for classification of welding defects from ultrasonic signals

    NASA Astrophysics Data System (ADS)

    Chen, Yuan; Ma, Hong-Wei; Zhang, Guang-Ming

    2014-07-01

    Defect classification is an important issue in ultrasonic non-destructive evaluation. A layered multi-class support vector machine (LMSVM) classification system, which combines multiple SVM classifiers through a layered architecture, is proposed in this paper. The proposed LMSVM classification system is applied to the classification of welding defects from ultrasonic test signals. The measured ultrasonic defect echo signals are first decomposed into wavelet coefficients by the wavelet packet transform. The energy of the wavelet coefficients at different frequency channels are used to construct the feature vectors. The bees algorithm (BA) is then used for feature selection and SVM parameter optimisation for the LMSVM classification system. The BA-based feature selection optimises the energy feature vectors. The optimised feature vectors are input to the LMSVM classification system for training and testing. Experimental results of classifying welding defects demonstrate that the proposed technique is highly robust, precise and reliable for ultrasonic defect classification.

  2. Acoustic emission-based sensor analysis and damage classification for structural health monitoring of composite structures

    NASA Astrophysics Data System (ADS)

    Uprety, Bibhisha

    Within the aerospace industry the need to detect and locate impact events, even when no visible damage is present, is important both from the maintenance and design perspectives. This research focused on the use of Acoustic Emission (AE) based sensing technologies to identify impact events and characterize damage modes in composite structures for structural health monitoring. Six commercially available piezoelectric AE sensors were evaluated for use with impact location estimation algorithms under development at the University of Utah. Both active and passive testing were performed to estimate the time of arrival and plate wave mode velocities for impact location estimation. Four sensors were recommended for further comparative investigations. Furthermore, instrumented low-velocity impact experiments were conducted on quasi-isotropic carbon/epoxy composite laminates to initiate specific types of damage: matrix cracking, delamination and fiber breakage. AE signal responses were collected during impacting and the test panels were ultrasonically C-scanned after impact to identify the internal damage corresponding to the AE signals. Matrix cracking and delamination damage produced using more compliant test panels and larger diameter impactor were characterized by lower frequency signals while fiber breakage produced higher frequency responses. The results obtained suggest that selected characteristics of sensor response signals can be used both to determine whether damage is produced during impacting and to characterize the types of damage produced in an impacted composite structure.

  3. 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)

  4. 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

  5. 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.

  6. Search for acoustic signals from high energy cascades

    NASA Astrophysics Data System (ADS)

    Bell, R.; Bowen, T.

    1985-08-01

    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.

  7. 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.

  8. 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.

  9. 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

  10. Spectrum analysis of photoacoustic signals for tissue classification

    NASA Astrophysics Data System (ADS)

    Chitnis, Parag V.; Mamou, Jonathan; Sampathkumar, Ashwin; Feleppa, Ernest J.

    2014-03-01

    Quantitative ultrasound (QUS) estimates derived from power spectra of pulse-echo signals are sensitive to mi- crostructure and potentially can differentiate among tissues. However, QUS estimates do not provide molecular specificity. We investigated the feasibility of obtaining quantitative photoacoustic (QPA) estimates for sensi- tivity to microstructure and chromophores for tissue classification. QPA methods were tested using gel-based phantoms containing uniformly dispersed, black polyethylene spheres (1E5 particles/ml) with nominal mean diameters of 23.5, 29.5, 42.0, and 58.0 μm. A pulsed, 532-nm laser excited the photoacoustic (PA) response. A single-element, 34-MHz transducer with a 12-mm focal length was raster scanned over the phantom to acquire 3D PA data. Normalized power spectra were generated from the PA signals within 2079, moving (50% overlap), 1-mm-cube regions-of-interest (ROIs) to provide three QPA estimates: spectral slope (SS), spectral intercept (SI), and effective absorber size (EAS). SS and SI were computed using a linear-regression approximation to the normalized spectrum in the -6-dB band. EAS was computed by fitting the normalized spectrum in the -20-dB band to the multi-sphere analytical solution. All estimates were correlated with the size of particles dispersed in the phantoms. SS decreased while SI increased with an increase in particle size. EAS was correlated with nominal particle diameter, but particles aggregation and the finite bandwidth of the PAI system resulted in outliers. SS, SI, and EAS for the 23.5-μm-phantom were -0.14+/--0.04 dB/MHz, 4.8+/-1.3 dB, and 25.4+/-6.3 μm, respectively; the corresponding values for the 58.0-μm phantom were -0.47+/--0.03 dB/MHz, 15.6+/-0.9 dB, and 82.7+/-0.9 μm.

  11. 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.

  12. Acoustic/magnetic fusion system architecture variants and their classification performance

    NASA Astrophysics Data System (ADS)

    Bello, Martin G.

    1997-07-01

    Research in FY-95 first addressed the problem of combining high-frequency (HF) side-scan sonar imagery, low-frequency (LF) side-scan Sonar imagery, and magnetic gradiometer data in order to detect/classify undersea mines. The first approach developed, termed the "Blob-Pair" based acoustic/magnetic (AM) Fusion system architecture, implicitly assumed that a target manifests itself in both HF,LF imagery, and was based on the fusion of single-sensor derived neural network classifier discriminants at a collection of three "decision" nodes, identified with magnetic (M), HF/LF, and HF/LF/M - data fusion cases, respectively. In order, to remove the restrictive assumption of a target manifesting in both }IF,LF data, the "Generalized" AM Fusion Architecture was developed, with a total of 7 "decision" nodes, identified with M, HF, HF/LF, LF, HF/M, HF/LFIM, and LF/M data fusion cases, respectively. However, the "Generalized" AM-Fusion architecture was found empirically to have significantly increased number of false alarms, relative to the "Blob-Pair" based system. Hence, through two-additional AM-Fusion architecture varaints, involving first the use of Classification Token "Post-Processing", and then both Token "Post-Processing" and decision node statistic modification, the performance "gap" between "Blob-Pair" and "Generalized" AM-Fusion Architecture performance was closed.

  13. Hierarchical multilayer perceptron network-based fusion algorithms for detection/classification of mines using multiple acoustic images and magnetic data

    NASA Astrophysics Data System (ADS)

    Bello, Martin G.

    1996-05-01

    Hierarchical neural network approaches have been developed first for combining high and low frequency (HF and LF) Side Scan Sonar imagery, and then for the combination of both acoustic images and Magnetic data. The adopted acoustic data fusion approach consists in a image-screening/HF, LF blob matching stage, followed by an information fusion/classification stage. Three variants of the information fusion/classification algorithm were conceived and evaluated based on `aggregate-feature-combining', `neural-network-discriminant-combining', and individual classifier `decision-based-combining', respectively. The `discriminant- combining' case yielded the best classification performance, and when compared with individual HF, LF classifier performance resulted in at least an order of magnitude reduction in the density of false alarms. Next, results are obtained for combining both acoustic and magnetic data using the described high and low frequency side scan sonar discriminant combining fusion algorithm as a starting point. In the next step, acoustic image pair `tokens' are associated with magnetic `tokens', resulting in three classes of resulting `tokens': `associated' acoustic-pair and magnetic tokens, isolated acoustic-pair tokens, and isolated magnetic `tokens'. Neural network output discriminants are derived for each of the three types of tokens mentioned above, and are employed to make classification decisions. The resulting Detection/Classification Algorithm is evaluated based on a combined ground truth obtained from both acoustic and magnetic sources.

  14. 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

  15. 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...

  16. A comparative study on classification of sleep stage based on EEG signals using feature selection and classification algorithms.

    PubMed

    Şen, Baha; Peker, Musa; Çavuşoğlu, Abdullah; Çelebi, Fatih V

    2014-03-01

    Sleep scoring is one of the most important diagnostic methods in psychiatry and neurology. Sleep staging is a time consuming and difficult task undertaken by sleep experts. This study aims to identify a method which would classify sleep stages automatically and with a high degree of accuracy and, in this manner, will assist sleep experts. This study consists of three stages: feature extraction, feature selection from EEG signals, and classification of these signals. In the feature extraction stage, it is used 20 attribute algorithms in four categories. 41 feature parameters were obtained from these algorithms. Feature selection is important in the elimination of irrelevant and redundant features and in this manner prediction accuracy is improved and computational overhead in classification is reduced. Effective feature selection algorithms such as minimum redundancy maximum relevance (mRMR); fast correlation based feature selection (FCBF); ReliefF; t-test; and Fisher score algorithms are preferred at the feature selection stage in selecting a set of features which best represent EEG signals. The features obtained are used as input parameters for the classification algorithms. At the classification stage, five different classification algorithms (random forest (RF); feed-forward neural network (FFNN); decision tree (DT); support vector machine (SVM); and radial basis function neural network (RBF)) classify the problem. The results, obtained from different classification algorithms, are provided so that a comparison can be made between computation times and accuracy rates. Finally, it is obtained 97.03 % classification accuracy using the proposed method. The results show that the proposed method indicate the ability to design a new intelligent assistance sleep scoring system.

  17. 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.}

  18. 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

  19. 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

  20. 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

  1. 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.

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

    PubMed

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

    2015-12-01

    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.

  3. 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.

  4. 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)].

  5. [Research on Time-frequency Characteristics of Magneto-acoustic Signal of Different Thickness Medium Based on Wave Summing Method].

    PubMed

    Zhang, Shunqi; Yin, Tao; Ma, Ren; Liu, Zhipeng

    2015-08-01

    Functional imaging method of biological electrical characteristics based on magneto-acoustic effect gives valuable information of tissue in early tumor diagnosis, therein time and frequency characteristics analysis of magneto-acoustic signal is important in image reconstruction. This paper proposes wave summing method based on Green function solution for acoustic source of magneto-acoustic effect. Simulations and analysis under quasi 1D transmission condition are carried out to time and frequency characteristics of magneto-acoustic signal of models with different thickness. Simulation results of magneto-acoustic signal were verified through experiments. Results of the simulation with different thickness showed that time-frequency characteristics of magneto-acoustic signal reflected thickness of sample. Thin sample, which is less than one wavelength of pulse, and thick sample, which is larger than one wavelength, showed different summed waveform and frequency characteristics, due to difference of summing thickness. Experimental results verified theoretical analysis and simulation results. This research has laid a foundation for acoustic source and conductivity reconstruction to the medium with different thickness in magneto-acoustic imaging.

  6. 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

  7. Seafloor roughness estimation employing bathymetric systems: An appraisal of the classification and characterization of high-frequency acoustic data

    NASA Astrophysics Data System (ADS)

    Chakraborty, Bishwajit; Haris, K.

    2012-11-01

    The study of the seafloor is important for living and non-living resource estimation along with the related processes identification. To understand the fine-scale seafloor processes, various methods such as application of acoustic remote sensing, seafloor photographic and geological samplings are well established. Among these, the high-frequency single beam echo-sounding system (SBES) and multi-beam echo-sounding system (MBES) became more familiar due to their rapid data acquisition advantages. These systems are extensively used to study the seafloor morphology etc. Seafloor acoustic backscatter information provides fine-scale seafloor roughness and associated sediment processes. The angular and normal incidence backscatter strength data can be utilized to estimate seafloor roughness parameters using physics based numerical inversion models. However, for such applications, the segmentation of the backscatter data is essential, especially before initiating any numerical based models to characterize the seafloor. Under such situations, the employment of the soft-computational techniques e.g., artificial neural networks (ANNs) are found to be suitable for seafloor acoustic data segmentation and classifications. Seafloor studies are carried out at the National Institute of Oceanography, Goa during the last two decades employing similar techniques, and study results related to the seafloor classification and characterizations are documented in this research review.

  8. Acoustic cardiac signals analysis: a Kalman filter-based approach.

    PubMed

    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.

  9. Biological invasions and the acoustic niche: the effect of bullfrog calls on the acoustic signals of white-banded tree frogs.

    PubMed

    Both, Camila; Grant, Taran

    2012-10-23

    Invasive species are known to affect native species in a variety of ways, but the effect of acoustic invaders has not been examined previously. We simulated an invasion of the acoustic niche by exposing calling native male white-banded tree frogs (Hypsiboas albomarginatus) to recorded invasive American bullfrog (Lithobates catesbeianus) calls. In response, tree frogs immediately shifted calls to significantly higher frequencies. In the post-stimulus period, they continued to use higher frequencies while also decreasing signal duration. Acoustic signals are the primary basis of mate selection in many anurans, suggesting that such changes could negatively affect the reproductive success of native species. The effects of bullfrog vocalizations on acoustic communities are expected to be especially severe due to their broad frequency band, which masks the calls of multiple species simultaneously. PMID:22675139

  10. Biological invasions and the acoustic niche: the effect of bullfrog calls on the acoustic signals of white-banded tree frogs

    PubMed Central

    Both, Camila; Grant, Taran

    2012-01-01

    Invasive species are known to affect native species in a variety of ways, but the effect of acoustic invaders has not been examined previously. We simulated an invasion of the acoustic niche by exposing calling native male white-banded tree frogs (Hypsiboas albomarginatus) to recorded invasive American bullfrog (Lithobates catesbeianus) calls. In response, tree frogs immediately shifted calls to significantly higher frequencies. In the post-stimulus period, they continued to use higher frequencies while also decreasing signal duration. Acoustic signals are the primary basis of mate selection in many anurans, suggesting that such changes could negatively affect the reproductive success of native species. The effects of bullfrog vocalizations on acoustic communities are expected to be especially severe due to their broad frequency band, which masks the calls of multiple species simultaneously. PMID:22675139

  11. Biological invasions and the acoustic niche: the effect of bullfrog calls on the acoustic signals of white-banded tree frogs.

    PubMed

    Both, Camila; Grant, Taran

    2012-10-23

    Invasive species are known to affect native species in a variety of ways, but the effect of acoustic invaders has not been examined previously. We simulated an invasion of the acoustic niche by exposing calling native male white-banded tree frogs (Hypsiboas albomarginatus) to recorded invasive American bullfrog (Lithobates catesbeianus) calls. In response, tree frogs immediately shifted calls to significantly higher frequencies. In the post-stimulus period, they continued to use higher frequencies while also decreasing signal duration. Acoustic signals are the primary basis of mate selection in many anurans, suggesting that such changes could negatively affect the reproductive success of native species. The effects of bullfrog vocalizations on acoustic communities are expected to be especially severe due to their broad frequency band, which masks the calls of multiple species simultaneously.

  12. 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.

  13. Calibration techniques and sampling resolution requirements for groundtruthing multibeam acoustic backscatter (EM3000) and QTC VIEW™ classification technology

    NASA Astrophysics Data System (ADS)

    Sutherland, T. F.; Galloway, J.; Loschiavo, R.; Levings, C. D.; Hare, R.

    2007-12-01

    Both acoustic and sediment surveys were carried out in the Broughton Archipelago, British Columbia, in order to map a former aquaculture site and calibrate acoustic surveys with georeferenced sediment properties. The acoustic surveys included EM3000 Multibeam (including backscatter) and QTC VIEW™ (Series IV) technologies, while the geotechnical survey entailed Van Veen grab sampling of surface sediments and associated analyses. The two acoustic technologies were consistent in their ability to identify distinct regions of seafloor characterized by rock outcrops, consolidated substrates, or gel-mud depositional fields. Both multibeam backscatter data and QTC VIEW™ number-coded classifications were extracted across a range of circular areas located at each georeferenced sampling station (radii: 2, 3, 4, 5, 8, 12, 16, 20 m). Statistical correlations were observed between backscatter and certain geotechnical properties, such as sediment porosity, sediment grain size fractions (<2 μm, silt content), and particulate sulfur concentration. The areal resolution of backscatter extraction was explored in terms of determining a sensitive calibration technique between backscatter and sediment properties. In general the highest r2 values between backscatter and sediment variables were observed across extraction radii between 8 and 20 m. Such groundtruthing techniques could be used to interpolate seafloor characteristics between sampling stations and provide a steering tool for sampling designs associated with benthic monitoring programs.

  14. 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

  15. Time delay and Doppler estimation for wideband acoustic signals in multipath environments.

    PubMed

    Jiang, Xue; Zeng, Wen-Jun; Li, Xi-Lin

    2011-08-01

    Estimation of the parameters of a multipath underwater acoustic channel is of great interest for a variety of applications. This paper proposes a high-resolution method for jointly estimating the multipath time delays, Doppler scales, and attenuation amplitudes of a time-varying acoustical channel. The proposed method formulates the estimation of channel parameters into a sparse representation problem. With the [script-l](1)-norm as the measure of sparsity, the proposed method makes use of the basis pursuit (BP) criterion to find the sparse solution. The ill-conditioning can be effectively reduced by the [script-l](1)-norm regularization. Unlike many existing methods that are only applicable to narrowband signals, the proposed method can handle both narrowband and wideband signals. Simulation results are provided to verify the performance and effectiveness of the proposed algorithm, indicating that it has a super-resolution in both delay and Doppler domain, and it is robust to noise.

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

    USGS Publications Warehouse

    Petersen, T.

    2007-01-01

    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.

  17. Lateralization of acoustic signals by dichotically listening budgerigars (Melopsittacus undulatus).

    PubMed

    Welch, Thomas E; Dent, Micheal L

    2011-10-01

    Sound localization allows humans and animals to determine the direction of objects to seek or avoid and indicates the appropriate position to direct visual attention. Interaural time differences (ITDs) and interaural level differences (ILDs) are two primary cues that humans use to localize or lateralize sound sources. There is limited information about behavioral cue sensitivity in animals, especially animals with poor sound localization acuity and small heads, like budgerigars. ITD and ILD thresholds were measured behaviorally in dichotically listening budgerigars equipped with headphones in an identification task. Budgerigars were less sensitive than humans and cats, and more similar to rabbits, barn owls, and monkeys, in their abilities to lateralize dichotic signals. Threshold ITDs were relatively constant for pure tones below 4 kHz, and were immeasurable at higher frequencies. Threshold ILDs were relatively constant over a wide range of frequencies, similar to humans. Thresholds in both experiments were best for broadband noise stimuli. These lateralization results are generally consistent with the free field localization abilities of these birds, and add support to the idea that budgerigars may be able to enhance their cues to directional hearing (e.g., via connected interaural pathways) beyond what would be expected based on head size. PMID:21973385

  18. 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.

  19. 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.

  20. 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.

  1. 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

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

    PubMed

    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 (Pseudorca crassidens) and a Risso's dolphin (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' threshold to the actual ATOC coded signal. The results indicate that small odontocetes such as the Pseudorca and 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 170-180 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.

  3. Classification of eddy current signals using fuzzy logic and neural networks

    NASA Astrophysics Data System (ADS)

    Ewald, Hartmut; Stieper, Michael

    1996-11-01

    The nondestructive eddy current methods are commonly used for automated defect inspection to detect cracks in materials which are used in cars, power and aircraft industries. The eddy current signal from a infinitely long crack can be classified with the help of the fuzzy logic and the neural network techniques. A rule based fuzzy logic classification guarantees better results than fuzzy-cluster- means algorithm, because the classification results can be increased in this case step by step. By using the neural network for the classification of the crack signals it is very important to have a good 'learning pattern.' The advantage of time-delay networks in this application is the fact that the network can 'learn' the eddy-current time signal; a signal preprocessing is not necessary.

  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. 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. 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

  7. 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.

  8. 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.

  9. 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

  10. Variability of spike trains and the processing of temporal patterns of acoustic signals-problems, constraints, and solutions.

    PubMed

    Ronacher, B; Franz, A; Wohlgemuth, S; Hennig, R M

    2004-04-01

    Object recognition and classification by sensory pathways is rooted in spike trains provided by sensory neurons. Nervous systems had to evolve mechanisms to extract information about relevant object properties, and to separate these from spurious features. In this review, problems caused by spike train variability and counterstrategies are exemplified for the processing of acoustic signals in orthopteran insects. Due to size limitations of their nervous system we expect to find solutions that are stripped to the computational basics. A key feature of auditory systems is temporal resolution, which is likely limited by spike train variability. Basic strategies to reduce such variability are to integrate over time, or to average across several neurons. The first strategy is constrained by its possible interference with temporal resolution. Grasshoppers do not seem to explore temporal integration much, in spite of the repetitive structure of their songs, which invites for 'multiple looks' at the signal. The benefits of averaging across neurons depend on uncorrelated responses, a factor that may be crucial for the performance and evolution of small nervous systems. In spite of spike train variability the temporal information necessary for the recognition of conspecifics is preserved to a remarkable degree in the auditory pathway.

  11. 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.}

  12. EMD-Based Temporal and Spectral Features for the Classification of EEG Signals Using Supervised Learning.

    PubMed

    Riaz, Farhan; Hassan, Ali; Rehman, Saad; Niazi, Imran Khan; Dremstrup, Kim

    2016-01-01

    This paper presents a novel method for feature extraction from electroencephalogram (EEG) signals using empirical mode decomposition (EMD). Its use is motivated by the fact that the EMD gives an effective time-frequency analysis of nonstationary signals. The intrinsic mode functions (IMF) obtained as a result of EMD give the decomposition of a signal according to its frequency components. We present the usage of upto third order temporal moments, and spectral features including spectral centroid, coefficient of variation and the spectral skew of the IMFs for feature extraction from EEG signals. These features are physiologically relevant given that the normal EEG signals have different temporal and spectral centroids, dispersions and symmetries when compared with the pathological EEG signals. The calculated features are fed into the standard support vector machine (SVM) for classification purposes. The performance of the proposed method is studied on a publicly available dataset which is designed to handle various classification problems including the identification of epilepsy patients and detection of seizures. Experiments show that good classification results are obtained using the proposed methodology for the classification of EEG signals. Our proposed method also compares favorably to other state-of-the-art feature extraction methods.

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

    DOEpatents

    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.

  14. 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

  15. 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.

  16. 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.

  17. Studies of horizontal refraction and scattering of low-frequency acoustic signals using a modal approach in signal processing of NPAL data

    NASA Astrophysics Data System (ADS)

    Voronovich, Alexander G.; Ostashev, Vladimir E.

    2003-04-01

    In our previous paper [J. Acoust. Soc. Am. 112, 2232], we obtained a time dependence of the horizontal refraction angle (HRA) of acoustic signals propagating over a range of about 4000 km in the ocean. This dependence was computed by processing of acoustic signals recorded during the North Pacific Acoustic Laboratory (NPAL) experiment using a ray-type approach. In the present paper, we consider the results obtained in signal processing of the same data using a modal approach. In this approach, the acoustic field is represented as a sum of local acoustic modes with amplitudes depending on a frequency and arrival angle. We obtained a time dependence of HRA for a time interval of about a year. Time evolution of HRA exhibits long-period variations which could be associated with seasonal trends in the sound speed profiles. The results are consistent with those obtained by the ray approach. Different horizontal angles within arrivals were impossible to resolve due to sound scattering by internal waves. A theoretical estimate of the angular width of the acoustic signals in a horizontal plane was obtained. It appears to be consistent with the observed variance of HRA data. [Work supported by ONR.] a)J. A. Colosi, B. D. Cornuelle, B. D. Dushaw, M. A. Dzieciuch, B. M. Howe, J. A. Mercer, R. C. Spindel, and P. F. Worcester.

  18. Mental task classifications using prefrontal cortex electroencephalograph signals.

    PubMed

    Chai, Rifai; Ling, Sai Ho; Hunter, Gregory P; Nguyen, Hung T

    2012-01-01

    For an electroencephalograph (EEG)-based brain computer interface (BCI) application, the use of gel on the hair area of the scalp is needed for low impedance electrical contact. This causes the set up procedure to be time consuming and inconvenient for a practical BCI system. Moreover, studies of other cortical areas are useful for BCI development. As a more convenient alternative, this paper presents the EEG based-BCI using the prefrontal cortex non-hair area to classify mental tasks at three electrodes position: Fp1, Fpz and Fp2. The relevant mental tasks used are mental arithmetic, ringtone, finger tapping and words composition with additional tasks which are baseline and eyes closed. The feature extraction is based on the Hilbert Huang Transform (HHT) energy method and the classification algorithm is based on an artificial neural network (ANN) with genetic algorithm (GA) optimization. The results show that the dominant alpha wave during eyes closed can still clearly be detected in the prefrontal cortex. The classification accuracy for five subjects, mental tasks vs. baseline task resulted in average accuracy is 73% and the average accuracy for pairs of mental task combinations is 72%.

  19. 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

  20. Classification of surface EMG signals using optimal wavelet packet method based on Davies-Bouldin criterion.

    PubMed

    Wang, Gang; Wang, Zhizhong; Chen, Weiting; Zhuang, Jun

    2006-10-01

    In this paper we present an optimal wavelet packet (OWP) method based on Davies-Bouldin criterion for the classification of surface electromyographic signals. To reduce the feature dimensionality of the outputs of the OWP decomposition, the principle components analysis was employed. Then we chose a neural network classifier to discriminate four types of prosthesis movements. The proposed method achieved a mean classification accuracy of 93.75%, which outperformed the method using the energy of wavelet packet coefficients (with mean classification accuracy 86.25%) and the fuzzy wavelet packet method (87.5%).

  1. Generation of desired signals from acoustic drivers. [for aircraft engine internal noise propagation experiment

    NASA Technical Reports Server (NTRS)

    Ramakrishnan, R.; Salikuddin, M.; Ahuja, K. K.

    1982-01-01

    A procedure to control transient signal generation is developed for the study of internal noise propagation from aircraft engines. A simple algorithm incorporating transform techniques is used to produce signals of any desired waveform from acoustic drivers. The accurate driver response is then calculated, and from this the limiting frequency characteristics are determined and the undesirable frequencies where the driver response is poor are eliminated from the analysis. A synthesized signal is then produced by convolving the inverse of the response function with the desired signal. Although the shape of the synthesized signal is in general quite awkward, the driver generates the desired signal when the distorted signal is fed into the driver. The results of operating the driver in two environments, in a free field and in a duct, are presented in order to show the impedance matching effect of the driver. In addition, results using a high frequency cut-off value as a parameter is presented in order to demonstrate the extent of the applicability of the synthesis procedure. It is concluded that the desired signals can be generated through the signal synthesis procedure.

  2. 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

  3. Acoustic signal perception in a noisy habitat: lessons from synchronising insects.

    PubMed

    Hartbauer, M; Siegert, M E; Fertschai, I; Römer, H

    2012-06-01

    Acoustically communicating animals often have to cope with ambient noise that has the potential to interfere with the perception of conspecific signals. Here we use the synchronous display of mating signals in males of the tropical katydid Mecopoda elongata in order to assess the influence of nocturnal rainforest noise on signal perception. Loud background noise may disturb chorus synchrony either by masking the signals of males or by interaction of noisy events with the song oscillator. Phase-locked synchrony of males was studied under various signal-to-noise ratios (SNRs) using either native noise or the audio component of noise (<9 kHz). Synchronous entrainment was lost at a SNR of -3 dB when native noise was used, whereas with the audio component still 50% of chirp periods matched the pacer period at a SNR of -7 dB. Since the chirp period of solo singing males remained almost unaffected by noise, our results suggest that masking interference limits chorus synchrony by rendering conspecific signals ambiguous. Further, entrainment with periodic artificial signals indicates that synchrony is achieved by ignoring heterospecific signals and attending to a conspecific signal period. Additionally, the encoding of conspecific chirps was studied in an auditory neuron under the same background noise regimes.

  4. 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

  5. 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

  6. 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.

  7. 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

  8. 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

  9. Demodulation of acoustic telemetry binary phase shift keying signal based on high-order Duffing system

    NASA Astrophysics Data System (ADS)

    Yan, Bing-Nan; Liu, Chong-Xin; Ni, Jun-Kang; Zhao, Liang

    2016-10-01

    In order to grasp the downhole situation immediately, logging while drilling (LWD) technology is adopted. One of the LWD technologies, called acoustic telemetry, can be successfully applied to modern drilling. It is critical for acoustic telemetry technology that the signal is successfully transmitted to the ground. In this paper, binary phase shift keying (BPSK) is used to modulate carrier waves for the transmission and a new BPSK demodulation scheme based on Duffing chaos is investigated. Firstly, a high-order system is given in order to enhance the signal detection capability and it is realized through building a virtual circuit using an electronic workbench (EWB). Secondly, a new BPSK demodulation scheme is proposed based on the intermittent chaos phenomena of the new Duffing system. Finally, a system variable crossing zero-point equidistance method is proposed to obtain the phase difference between the system and the BPSK signal. Then it is determined that the digital signal transmitted from the bottom of the well is ‘0’ or ‘1’. The simulation results show that the demodulation method is feasible. Project supported by the National Natural Science Foundation of China (Grant No. 51177117) and the National Key Science & Technology Special Projects, China (Grant No. 2011ZX05021-005).

  10. 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

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

    PubMed

    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

  12. 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.

  13. Study of Doppler Shift Correction for Underwater Acoustic Communication Using Orthogonal Signal Division Multiplexing

    NASA Astrophysics Data System (ADS)

    Ebihara, Tadashi; Mizutani, Keiichi

    2011-07-01

    In this study, we apply Doppler shift correction schemes for underwater acoustic (UWA) communication with orthogonal signal division multiplexing (OSDM) to achieve stable communication in underwater acoustic channels. Three Doppler correction schemes, which exploit the guard interval, are applied to UWA communication with OSDM and evaluated in simulations. Through a simulation in which only the Doppler effect is considered, we confirmed that by adapting schemes to UWA communication with OSDM, we can correct large Doppler shifts, which addresses the usual speed of vehicles and ships. Moreover, by considering both the Doppler effect and channel reverberation, we propose the best possible combination of Doppler correction schemes for UWA communication with OSDM. The results suggest that UWA communication with OSDM may lead to high-quality communication by considering channel reverberation and large Doppler shifts.

  14. 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

  15. 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.

  16. Sea-bottom classification across a shallow-water bar channel and near-shore shelf, using single-beam acoustics

    NASA Astrophysics Data System (ADS)

    Freitas, Rosa; Sampaio, Leandro; Rodrigues, Ana Maria; Quintino, Victor

    2005-12-01

    An acoustic ground discrimination system (QTC VIEW, Series IV) was used to identify and map the bottom acoustic diversity in the bar channel of Ria de Aveiro, Western Portugal. The majority of the survey area presented shallow depth for this type of equipment, ranging mainly from 5 to 15 m. Depth occasionally reached 25 m in specific areas located across the entrance channel, dug by the strong tidal currents, reaching 3 m/s. The acoustic data were submitted to manual and auto-cluster and the results obtained from both procedures were coherent. Using aids to the acoustic classification and ground-truth sediment data, a final solution consisting of four acoustic classes was reached. Their geographical distribution was coincident with the spatial distribution of the major bottom types and sediment groups (hard bottom, coarse sand, medium sand and fine sand), identified through multivariate analysis of the grain-size data, and reflected the complex hydrodynamics of the entrance channel. The acoustic pattern was coincident at the intersections of the acoustic survey lines, assuring the repeatability of the acoustic procedure. Overall, the acoustic approach showed consistent results for the assessment and mapping of the benthic habitats in this shallow-water coastal area, providing a very valuable tool in an area where conventional sediment sampling is less favourable, namely due to strong tidal currents and frequent ship traffic, such as the entrance channel of Ria de Aveiro and the near-shore adjacent shelf.

  17. Feature Extraction from Subband Brain Signals and Its Classification

    NASA Astrophysics Data System (ADS)

    Mukul, Manoj Kumar; Matsuno, Fumitoshi

    This paper considers both the non-stationarity as well as independence/uncorrelated criteria along with the asymmetry ratio over the electroencephalogram (EEG) signals and proposes a hybrid approach of the signal preprocessing methods before the feature extraction. A filter bank approach of the discrete wavelet transform (DWT) is used to exploit the non-stationary characteristics of the EEG signals and it decomposes the raw EEG signals into the subbands of different center frequencies called as rhythm. A post processing of the selected subband by the AMUSE algorithm (a second order statistics based ICA/BSS algorithm) provides the separating matrix for each class of the movement imagery. In the subband domain the orthogonality as well as orthonormality criteria over the whitening matrix and separating matrix do not come respectively. The human brain has an asymmetrical structure. It has been observed that the ratio between the norms of the left and right class separating matrices should be different for better discrimination between these two classes. The alpha/beta band asymmetry ratio between the separating matrices of the left and right classes will provide the condition to select an appropriate multiplier. So we modify the estimated separating matrix by an appropriate multiplier in order to get the required asymmetry and extend the AMUSE algorithm in the subband domain. The desired subband is further subjected to the updated separating matrix to extract subband sub-components from each class. The extracted subband sub-components sources are further subjected to the feature extraction (power spectral density) step followed by the linear discriminant analysis (LDA).

  18. 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

  19. 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.

  20. 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.

  1. Physiological Sensor Signals Classification for Healthcare Using Sensor Data Fusion and Case-Based Reasoning

    PubMed Central

    Begum, Shahina; Barua, Shaibal; Ahmed, Mobyen Uddin

    2014-01-01

    Today, clinicians often do diagnosis and classification of diseases based on information collected from several physiological sensor signals. However, sensor signal could easily be vulnerable to uncertain noises or interferences and due to large individual variations sensitivity to different physiological sensors could also vary. Therefore, multiple sensor signal fusion is valuable to provide more robust and reliable decision. This paper demonstrates a physiological sensor signal classification approach using sensor signal fusion and case-based reasoning. The proposed approach has been evaluated to classify Stressed or Relaxed individuals using sensor data fusion. Physiological sensor signals i.e., Heart Rate (HR), Finger Temperature (FT), Respiration Rate (RR), Carbon dioxide (CO2) and Oxygen Saturation (SpO2) are collected during the data collection phase. Here, sensor fusion has been done in two different ways: (i) decision-level fusion using features extracted through traditional approaches; and (ii) data-level fusion using features extracted by means of Multivariate Multiscale Entropy (MMSE). Case-Based Reasoning (CBR) is applied for the classification of the signals. The experimental result shows that the proposed system could classify Stressed or Relaxed individual 87.5% accurately compare to an expert in the domain. So, it shows promising result in the psychophysiological domain and could be possible to adapt this approach to other relevant healthcare systems. PMID:24995374

  2. Epileptic seizure classification in EEG signals using second-order difference plot of intrinsic mode functions.

    PubMed

    Pachori, Ram Bilas; Patidar, Shivnarayan

    2014-02-01

    Epilepsy is a neurological disorder which is characterized by transient and unexpected electrical disturbance of the brain. The electroencephalogram (EEG) is a commonly used signal for detection of epileptic seizures. This paper presents a new method for classification of ictal and seizure-free EEG signals. The proposed method is based on the empirical mode decomposition (EMD) and the second-order difference plot (SODP). The EMD method decomposes an EEG signal into a set of symmetric and band-limited signals termed as intrinsic mode functions (IMFs). The SODP of IMFs provides elliptical structure. The 95% confidence ellipse area measured from the SODP of IMFs has been used as a feature in order to discriminate seizure-free EEG signals from the epileptic seizure EEG signals. The feature space obtained from the ellipse area parameters of two IMFs has been used for classification of ictal and seizure-free EEG signals using the artificial neural network (ANN) classifier. It has been shown that the feature space formed using ellipse area parameters of first and second IMFs has given good classification performance. Experimental results on EEG database available by the University of Bonn, Germany, are included to illustrate the effectiveness of the proposed method.

  3. Pipe wall damage detection by electromagnetic acoustic transducer generated guided waves in absence of defect signals.

    PubMed

    Vasiljevic, Milos; Kundu, Tribikram; Grill, Wolfgang; Twerdowski, Evgeny

    2008-05-01

    Most investigators emphasize the importance of detecting the reflected signal from the defect to determine if the pipe wall has any damage and to predict the damage location. However, often the small signal from the defect is hidden behind the other arriving wave modes and signal noise. To overcome the difficulties associated with the identification of the small defect signal in the time history plots, in this paper the time history is analyzed well after the arrival of the first defect signal, and after different wave modes have propagated multiple times through the pipe. It is shown that the defective pipe can be clearly identified by analyzing these late arriving diffuse ultrasonic signals. Multiple reflections and scattering of the propagating wave modes by the defect and pipe ends do not hamper the defect detection capability; on the contrary, it apparently stabilizes the signal and makes it easier to distinguish the defective pipe from the defect-free pipe. This paper also highlights difficulties associated with the interpretation of the recorded time histories due to mode conversion by the defect. The design of electro-magnetic acoustic transducers used to generate and receive the guided waves in the pipe is briefly described in the paper.

  4. 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.

  5. 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.

  6. 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.

  7. 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

  8. 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

  9. 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

  10. 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

  11. Combining data fusion with multiresolution analysis for improving the classification accuracy of uterine EMG signals

    NASA Astrophysics Data System (ADS)

    Moslem, Bassam; Diab, Mohamad; Khalil, Mohamad; Marque, Catherine

    2012-12-01

    Multisensor data fusion is a powerful solution for solving difficult pattern recognition problems such as the classification of bioelectrical signals. It is the process of combining information from different sensors to provide a more stable and more robust classification decisions. We combine here data fusion with multiresolution analysis based on the wavelet packet transform (WPT) in order to classify real uterine electromyogram (EMG) signals recorded by 16 electrodes. Herein, the data fusion is done at the decision level by using a weighted majority voting (WMV) rule. On the other hand, the WPT is used to achieve significant enhancement in the classification performance of each channel by improving the discrimination power of the selected feature. We show that the proposed approach tested on our recorded data can improve the recognition accuracy in labor prediction and has a competitive and promising performance.

  12. 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

  13. 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

  14. Multichannel signal processing at Bell Labs Acoustics Research-Sampled by a postdoc

    NASA Astrophysics Data System (ADS)

    Kellermann, Walter

    2001-05-01

    In the mid 1980's, the first large microphone arrays for audio capture were designed and realized by Jim Flanagan and Gary Elko. After the author joined Bell Labs in 1989, the first real-time digital beamformer for teleconferencing applications was implemented and formed a starting point for the development of several novel beamforming techniques. In parallel, multichannel loudspeaker systems were already investigated and research on acoustic echo cancellation, small-aperture directional microphones, and sensor technology complemented the research scenario aiming at seamless hands-free acoustic communication. Arrays of many sensors and loudspeakers for sampling the spatial domain combined with advanced signal processing sparked new concepts that are still fueling ongoing research around the world-including the author's research group. Here, robust adaptive beamforming has found its way from large-scale arrays into many applications using smaller apertures. Blind source separation algorithms allow for effective spatial filtering without a priori information on source positions. Full-duplex communication using multiple channels for both reproduction and recording is enabled by multichannel acoustic echo cancellation combined with beamforming. Recently, wave domain adaptive filtering, a new concept for handling many sensors and many loudspeakers, has been verified for arrays that may well remind some observers of former Bell Labs projects.

  15. 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

  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. Improving transient state myoelectric signal recognition in hand movement classification using gyroscopes.

    PubMed

    Boschmann, Alexander; Nofen, Barbara; Platzner, Marco

    2013-01-01

    Pattern recognition of myoelectric signals in upper-limb prosthesis control has been subject to intense research for several years. However, few systems have yet been successfully clinically implemented. One possible explanation for this discrepancy is that published reports mostly focus on classification accuracy of myoelectric signals recorded under laboratory conditions as the metric for the system's performance. These data are usually acquired only during the static state of the contraction in a fixed seated position. This supports the test subject in performing repeatable contractions throughout the experiment and generally results in an unrealistically high classification accuracy. In clinical testing however, subjects have to perform various activities of daily living, causing the limb to move in different positions. These variations in limb positions can significantly decrease robustness and usability of myoelectric control systems. Recent reports have shown that the so-called limb position effect can be resolved for the static state of the signal by adding accelerometer data to the feature vector. Including data from the transient state of the signals for classifier training generally significantly increases the classification error so it is mostly not considered in published reports. In this paper, we investigate the classification accuracy of transient EMG data, taking into account the limb position effect. We demonstrate that a classifier trained with features from EMG, accelerometer and gyroscope outperforms classifiers using only EMG or EMG and accelerometer data when classifying transient EMG data.

  18. 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.

  19. 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

  20. 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

  1. 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.

  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. Experimental Research Into Generation of Acoustic Emission Signals in the Process of Friction of Hadfield Steel Single Crystals

    NASA Astrophysics Data System (ADS)

    Lychagin, D. V.; Filippov, A. V.; Novitskaia, O. S.; Kolubaev, E. A.; Sizova, O. V.

    2016-08-01

    The results of experimental research into dry sliding friction of Hadfield steel single crystals involving registration of acoustic emission are presented in the paper. The images of friction surfaces of Hadfield steel single crystals and wear grooves of the counterbody surface made after completion of three serial experiments conducted under similar conditions and friction regimes are given. The relation of the acoustic emission waveform envelope to the changing friction factor is revealed. Amplitude-frequency characteristics of acoustic emission signal frames are determined on the base of Fast Fourier Transform and Short Time Fourier Transform during the run-in stage of tribounits and in the process of stable friction.

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

    PubMed

    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. 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.

  7. Acoustic seabed classification using QTC IMPACT on single-beam echo sounder data from the Norwegian Channel, northern North Sea

    NASA Astrophysics Data System (ADS)

    Eidem, Ellen Johanne; Landmark, Knut

    2013-10-01

    Sediment mapping is important for understanding the physical processes, the impact of human activity, and the conditions for marine life on the seabed. For this purpose, the seabed classification tool QTC IMPACT analyses statistical variations in single-beam echo sounder data. QTC was applied in a large and physically diverse area of the Norwegian Channel, between 59°30‧N and 61°N, to produce a new sediment map and to verify the QTC algorithm. The results were interpreted using ground truth (grain size analyses of 40 gravity cores and five grab samples), multi-beam echo sounder bathymetry (MBES), and seismo-acoustic profiles. Surficial sediments were divided into five classes: (1) mud and silt, (2) a variety of clay, silt and sand, (3) sandy mud with gravel, (4) sand with gravel, and (5) clay and sandy clay. Along the Norwegian coast, where MBES imagery shows evidence of glacial erosion, the surficial sediment distribution is variable. The echo shape analysis of QTC did not produce a natural partition of the data, and statistical assumptions did not always hold. Sediment classification was therefore sensitive to the choice of cluster algorithm. However, QTC produced the most physically plausible results on a large scale compared to other cluster algorithms. Class boundaries were consistent with supporting data. One exception is a transition from muddy to sandy sediments not visible in seismo-acoustic data. A possible explanation is that seabed fluid seepage and water current erosion cause sand particle transport into the western part of the channel. The study confirms the capability of QTC in a complex environment, but there are some possible improvements.

  8. 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.

  9. 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

  10. 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.

  11. 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

  12. 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

  13. 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.

  14. Classification of EEG signals using a multiple kernel learning support vector machine.

    PubMed

    Li, Xiaoou; Chen, Xun; Yan, Yuning; Wei, Wenshi; Wang, Z Jane

    2014-07-17

    In this study, a multiple kernel learning support vector machine algorithm is proposed for the identification of EEG signals including mental and cognitive tasks, which is a key component in EEG-based brain computer interface (BCI) systems. The presented BCI approach included three stages: (1) a pre-processing step was performed to improve the general signal quality of the EEG; (2) the features were chosen, including wavelet packet entropy and Granger causality, respectively; (3) a multiple kernel learning support vector machine (MKL-SVM) based on a gradient descent optimization algorithm was investigated to classify EEG signals, in which the kernel was defined as a linear combination of polynomial kernels and radial basis function kernels. Experimental results showed that the proposed method provided better classification performance compared with the SVM based on a single kernel. For mental tasks, the average accuracies for 2-class, 3-class, 4-class, and 5-class classifications were 99.20%, 81.25%, 76.76%, and 75.25% respectively. Comparing stroke patients with healthy controls using the proposed algorithm, we achieved the average classification accuracies of 89.24% and 80.33% for 0-back and 1-back tasks respectively. Our results indicate that the proposed approach is promising for implementing human-computer interaction (HCI), especially for mental task classification and identifying suitable brain impairment candidates.

  15. 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.

  16. 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

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

    PubMed

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

    2015-10-21

    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.

  18. 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

  19. 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

  20. 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

  1. 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

  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. Acoustic events semantic detection, classification, and annotation for persistent surveillance applications

    NASA Astrophysics Data System (ADS)

    Alkilani, Amjad; Shirkhodaie, Amir

    2014-06-01

    Understanding of group activity based on analysis of spatiotemporally correlated acoustic sound events has received a minimum attention in the literature and hence is not well understood. Identification of group sub-activities such as: Human-Vehicle Interactions (HVI), Human-Object Interactions (HOI), and Human-Human Interactions (HHI) can significantly improve Situational Awareness (SA) in Persistent Surveillance Systems (PSS). In this paper, salient sound events associated with group activities are preliminary identified and applied for training a Gaussian Mixture Model (GMM) whose features are employed as feature vectors for training of algorithms for acoustic sound recognition. In this paper, discrimination of salient sounds associated with the HVI, HHI, and HOI events is achieved via a Correlation Based Template Matching (CMTM) classifier. To interlinked salient events representing an ontology-based hypothesis, a Hidden Markov Model (HMM) is employed to recognize spatiotemporally correlated events. Once such a connection is established, then, the system generates an annotation of each perceived sound event. This paper discusses the technical aspects of this approach and presents the experimental results for several outdoor group activities monitored by an array of acoustic sensors.

  4. 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

  5. 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

  6. 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.

  7. The polarimetric entropy classification of SAR based on the clustering and signal noise ration

    NASA Astrophysics Data System (ADS)

    Shi, Lei; Yang, Jie; Lang, Fengkai

    2009-10-01

    Usually, Wishart H/α/A classification is an effective unsupervised classification method. However, the anisotropy parameter (A) is an unstable factor in the low signal noise ration (SNR) areas; at the same time, many clusters are useless to manually recognize. In order to avoid too many clusters to affect the manual recognition and the convergence of iteration and aiming at the drawback of the Wishart classification, in this paper, an enhancive unsupervised Wishart classification scheme for POLSAR data sets is introduced. The anisotropy parameter A is used to subdivide the target after H/α classification, this parameter has the ability to subdivide the homogeneity area in high SNR condition which can not be classified by using H/α. It is very useful to enhance the adaptability in difficult areas. Yet, the target polarimetric decomposition is affected by SNR before the classification; thus, the local homogeneity area's SNR evaluation is necessary. After using the direction of the edge detection template to examine the direction of POL-SAR images, the results can be processed to estimate SNR. The SNR could turn to a powerful tool to guide H/α/A classification. This scheme is able to correct the mistake judging of using A parameter such as eliminating much insignificant spot on the road and urban aggregation, even having a good performance in the complex forest. To convenience the manual recognition, an agglomerative clustering algorithm basing on the method of deviation-class is used to consolidate some clusters which are similar in 3by3 polarimetric coherency matrix. This classification scheme is applied to full polarimetric L band SAR image of Foulum area, Denmark.

  8. 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.

  9. 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.

  10. 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.

  11. 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

  12. 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.

  13. Signal analysis and classification methods for the calcium transient data of stem cell-derived cardiomyocytes.

    PubMed

    Juhola, Martti; Penttinen, Kirsi; Joutsijoki, Henry; Varpa, Kirsi; Saarikoski, Jyri; Rasku, Jyrki; Siirtola, Harri; Iltanen, Kati; Laurikkala, Jorma; Hyyrö, Heikki; Hyttinen, Jari; Aalto-Setälä, Katriina

    2015-06-01

    Calcium cycling is crucial in the excitation-contraction coupling of cardiomyocytes, and therefore has a key role in cardiac functionality. Cardiac disorders and different drugs alter the calcium transients of cardiomyocytes and can cause serious dysfunction of the heart. New insights into this biochemical phenomena can be achieved by studying and analyzing calcium transients. Calcium transients of spontaneously beating human induced pluripotent stem cell-derived cardiomyocytes were recorded for a data set of 280 signals. Our objective was to develop and program procedures: (1) to automatically detect cycling peaks from signals and to classify the peaks of signals as either normal or abnormal, and (2) on the basis of the preceding peak detection results, to classify the entire signals into either a normal class or an abnormal class. We obtained a classification accuracy of approximately 80% compared to class decisions made separately by an experienced researcher, which is promising for the further development of an automatic classification approach. Automated classification software would be beneficial in the future for analyzing cardiomyocyte functionality on a large scale when screening for the adverse cardiac effects of new potential compounds, and also in future clinical applications.

  14. 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

  15. The broadband social acoustic signaling behavior of spinner and spotted dolphins.

    PubMed

    Lammers, Marc O; Au, Whitlow W L; Herzing, Denise L

    2003-09-01

    Efforts to study the social acoustic signaling behavior of delphinids have traditionally been restricted to audio-range (<20 kHz) analyses. To explore the occurrence of communication signals at ultrasonic frequencies, broadband recordings of whistles and burst pulses were obtained from two commonly studied species of delphinids, the Hawaiian spinner dolphin (Stenella longirostris) and the Atlantic spotted dolphin (Stenella frontalis). Signals were quantitatively analyzed to establish their full bandwidth, to identify distinguishing characteristics between each species, and to determine how often they occur beyond the range of human hearing. Fundamental whistle contours were found to extend beyond 20 kHz only rarely among spotted dolphins, but with some regularity in spinner dolphins. Harmonics were present in the majority of whistles and varied considerably in their number, occurrence, and amplitude. Many whistles had harmonics that extended past 50 kHz and some reached as high as 100 kHz. The relative amplitude of harmonics and the high hearing sensitivity of dolphins to equivalent frequencies suggest that harmonics are biologically relevant spectral features. The burst pulses of both species were found to be predominantly ultrasonic, often with little or no energy below 20 kHz. The findings presented reveal that the social signals produced by spinner and spotted dolphins span the full range of their hearing sensitivity, are spectrally quite varied, and in the case of burst pulses are probably produced more frequently than reported by audio-range analyses. PMID:14514216

  16. The broadband social acoustic signaling behavior of spinner and spotted dolphins

    NASA Astrophysics Data System (ADS)

    Lammers, Marc O.; Au, Whitlow W. L.; Herzing, Denise L.

    2003-09-01

    Efforts to study the social acoustic signaling behavior of delphinids have traditionally been restricted to audio-range (<20 kHz) analyses. To explore the occurrence of communication signals at ultrasonic frequencies, broadband recordings of whistles and burst pulses were obtained from two commonly studied species of delphinids, the Hawaiian spinner dolphin (Stenella longirostris) and the Atlantic spotted dolphin (Stenella frontalis). Signals were quantitatively analyzed to establish their full bandwidth, to identify distinguishing characteristics between each species, and to determine how often they occur beyond the range of human hearing. Fundamental whistle contours were found to extend beyond 20 kHz only rarely among spotted dolphins, but with some regularity in spinner dolphins. Harmonics were present in the majority of whistles and varied considerably in their number, occurrence, and amplitude. Many whistles had harmonics that extended past 50 kHz and some reached as high as 100 kHz. The relative amplitude of harmonics and the high hearing sensitivity of dolphins to equivalent frequencies suggest that harmonics are biologically relevant spectral features. The burst pulses of both species were found to be predominantly ultrasonic, often with little or no energy below 20 kHz. The findings presented reveal that the social signals produced by spinner and spotted dolphins span the full range of their hearing sensitivity, are spectrally quite varied, and in the case of burst pulses are probably produced more frequently than reported by audio-range analyses.

  17. 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

  18. 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…

  19. Divergence of Acoustic Signals in a Widely Distributed Frog: Relevance of Inter-Male Interactions

    PubMed Central

    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

  20. 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

  1. 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.

  2. 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

  3. 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.

  4. Classification of acousto-optic correlation signatures of spread spectrum signals using artificial neural networks

    NASA Astrophysics Data System (ADS)

    Deberry, John W.

    1989-05-01

    The primary goal of this research was to determine if artificial Neural Networks (ANNs) can be trained to classify the correlation signatures of direct sequence and frequency-hopped spread-spectrum signals. Secondary goals were to determine: (1) if network classification performance can be modeled with a conditional probability matrix, (2) if the symmetry of the matrices can be controlled, and (3) if using a majority vote rule over independently trained networks improves classification performance. Correlation signatures of the spread-spectrum signals were obtained from United States Army Harry Diamond Laboratories. The signatures were preprocessed and separated into various training and testing data sets. Thirty samples of network responses for several sets of training conditions were gathered using a neural network simulator.

  5. 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

  6. 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.

  7. Experimental Study of Doppler Effect for Underwater Acoustic Communication Using Orthogonal Signal Division Multiplexing

    NASA Astrophysics Data System (ADS)

    Ebihara, Tadashi; Mizutani, Keiichi

    2012-07-01

    This paper is about the underwater acoustic (UWA) communication using orthogonal signal division multiplexing (OSDM) in shallow water, whose environment is time spread and frequency spread. In this paper, the Doppler effect - Doppler shift and spread - for UWA communication using OSDM is mainly considered. The effects of Doppler shift and Doppler spread are evaluated in a test tank with a moving platform on a stable water surface and with a stable platform with a moving water surface, respectively. Doppler shift correction, which has been considered in simulation-based studies, is found to work effectively. In relation to the effect of Doppler spread, the experimental result well agrees with the simulation result. Through this study, it is confirmed that a smaller frame length is preferable because it enables the measurement of the UWA channel frequently so that it can keep up with channel changes.

  8. EEG signal classification based on artificial neural networks and amplitude spectra features

    NASA Astrophysics Data System (ADS)

    Chojnowski, K.; FrÄ czek, J.

    BCI (called Brain-Computer Interface) is an interface that allows direct communication between human brain and an external device. It bases on EEG signal collection, processing and classification. In this paper a complete BCI system is presented which classifies EEG signal using artificial neural networks. For this purpose we used a multi-layered perceptron architecture trained with the RProp algorithm. Furthermore a simple multi-threaded method for automatic network structure optimizing was shown. We presented the results of our system in the opening and closing eyes recognition task. We also showed how our system could be used for controlling devices basing on imaginary hand movements.

  9. 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.

  10. 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.

  11. Localisation of an acoustic signal in a noisy environment: the display call of the king penguin Aptenodytes patagonicus.

    PubMed

    Aubin, Thierry; Jouventin, Pierre

    2002-12-01

    King penguin chicks identify their parents by an acoustic signal, the display call. This call consists of a succession of similar syllables. Each syllable has two harmonic series, strongly modulated in frequency and amplitude, with added beats of varying amplitude generated by a two-voice system. Previous work showed that only one syllable of the call is needed for the chick to identify the calling adult. Both the frequency modulation pattern of the syllable and the two-voice system play a role in the call identification. The syllabic organisation of the call, the harmonic structure and the amplitude modulations of the syllables apparently do not contribute to individual recognition. Are these acoustic features useless? To answer to this question, playback experiments were conducted using three categories of experimental signals: (i) signal with only the fundamental frequencies of the natural call, (ii) signal with the amplitude of each syllable kept at a constant level and (iii) signals with only one syllable, repeated or not. The responses of chicks to these experimental signals were compared to those obtained with the calls of their natural parents. We found that these acoustic features, while not directly implicated in the individual recognition process, help the chicks to better localise the signal of their parents. In addition, the redundant syllabic organisation of the call is a means of counteracting the masking effect of the background noise of the colony. PMID:12432003

  12. 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.

  13. Periodic shock-emission from acoustically driven cavitation clouds: a source of the subharmonic signal.

    PubMed

    Johnston, Keith; Tapia-Siles, Cecilia; Gerold, Bjoern; Postema, Michiel; Cochran, Sandy; Cuschieri, Alfred; Prentice, Paul

    2014-12-01

    Single clouds of cavitation bubbles, driven by 254kHz focused ultrasound at pressure amplitudes in the range of 0.48-1.22MPa, have been observed via high-speed shadowgraphic imaging at 1×10(6) frames per second. Clouds underwent repetitive growth, oscillation and collapse (GOC) cycles, with shock-waves emitted periodically at the instant of collapse during each cycle. The frequency of cloud collapse, and coincident shock-emission, was primarily dependent on the intensity of the focused ultrasound driving the activity. The lowest peak-to-peak pressure amplitude of 0.48MPa generated shock-waves with an average period of 7.9±0.5μs, corresponding to a frequency of f0/2, half-harmonic to the fundamental driving. Increasing the intensity gave rise to GOC cycles and shock-emission periods of 11.8±0.3, 15.8±0.3, 19.8±0.2μs, at pressure amplitudes of 0.64, 0.92 and 1.22MPa, corresponding to the higher-order subharmonics of f0/3, f0/4 and f0/5, respectively. Parallel passive acoustic detection, filtered for the fundamental driving, revealed features that correlated temporally to the shock-emissions observed via high-speed imaging, p(two-tailed) < 0.01 (r=0.996, taken over all data). Subtracting the isolated acoustic shock profiles from the raw signal collected from the detector, demonstrated the removal of subharmonic spectral peaks, in the frequency domain. The larger cavitation clouds (>200μm diameter, at maximum inflation), that developed under insonations of peak-to-peak pressure amplitudes >1.0MPa, emitted shock-waves with two or more fronts suggesting non-uniform collapse of the cloud. The observations indicate that periodic shock-emissions from acoustically driven cavitation clouds provide a source for the cavitation subharmonic signal, and that shock structure may be used to study intra-cloud dynamics at sub-microsecond timescales.

  14. Shared developmental and evolutionary origins for neural basis of vocal–acoustic and pectoral–gestural signaling

    PubMed Central

    Bass, Andrew H.; Chagnaud, Boris P.

    2012-01-01

    Acoustic signaling behaviors are widespread among bony vertebrates, which include the majority of living fishes and tetrapods. Developmental studies in sound-producing fishes and tetrapods indicate that central pattern generating networks dedicated to vocalization originate from the same caudal hindbrain rhombomere (rh) 8-spinal compartment. Together, the evidence suggests that vocalization and its morphophysiological basis, including mechanisms of vocal–respiratory coupling that are widespread among tetrapods, are ancestral characters for bony vertebrates. Premotor-motor circuitry for pectoral appendages that function in locomotion and acoustic signaling develops in the same rh8-spinal compartment. Hence, vocal and pectoral phenotypes in fishes share both developmental origins and roles in acoustic communication. These findings lead to the proposal that the coupling of more highly derived vocal and pectoral mechanisms among tetrapods, including those adapted for nonvocal acoustic and gestural signaling, originated in fishes. Comparative studies further show that rh8 premotor populations have distinct neurophysiological properties coding for equally distinct behavioral attributes such as call duration. We conclude that neural network innovations in the spatiotemporal patterning of vocal and pectoral mechanisms of social communication, including forelimb gestural signaling, have their evolutionary origins in the caudal hindbrain of fishes. PMID:22723366

  15. 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...

  16. 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.

  17. 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.

  18. 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

  19. 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.

  20. 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

  1. 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.

  2. Application of nonlinearly demodulated acoustic signals for the measurement of the acoustical coefficient of reflection for air saturated porous materials

    NASA Astrophysics Data System (ADS)

    Saeid, Mohamed; Castagnède, Bernard; Moussatov, Alexei; Tournat, Vincent; Gusev, Vitalyi

    2004-10-01

    The present Note describes work related to the measurement of the coefficient of reflection in automotive felt materials, by using a mixed ultrasonic/audio range technique. Powerful 162 kHz ultrasonic waves are amplitude modulated in the audio range. By applying appropriate procedures borrowed from underwater nonlinear ultrasonic methods (the so-called parametric antennae), one produces low frequency (i.e. in the 5-30 kHz range) acoustical waves which are generated in the pulse echo mode by short bursts. The coefficient of reflection of various felt materials are measured, and the results are compared to the standard 'fluid-equivalent' model which describes the propagation of acoustic waves in poroelastic air-saturated materials. To cite this article: M. Saeid et al., C. R. Mecanique 332 (2004).

  3. 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

  4. 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.

  5. Classification of the electrocardiogram signals using supervised classifiers and efficient features.

    PubMed

    Zadeh, Ataollah Ebrahim; Khazaee, Ali; Ranaee, Vahid

    2010-08-01

    Automatic classification of electrocardiogram (ECG) signals is vital for clinical diagnosis of heart disease. This paper investigates the design of an efficient system for recognition of the premature ventricular contraction from the normal beats and other heart diseases. This system includes three main modules: denoising module, feature extraction module and classifier module. In the denoising module, it is proposed the stationary wavelet transform for noise reduction of the electrocardiogram signals. In the feature extraction module a proper combination of the morphological-based features and timing interval-based features are proposed. As the classifier, several supervised classifiers are investigated; they are: a number of multi-layer perceptron neural networks with different number of layers and training algorithms, support vector machines with different kernel types, radial basis function and probabilistic neural networks. Also, for comparison the proposed features, we have considered the wavelet-based features. It has done comprehensive simulations in order to achieve a high efficient system for ECG beat classification from 12 files obtained from the MIT-BIH arrhythmia database. Simulation results show that best results are achieved about 97.14% for classification of ECG beats.

  6. 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.

  7. 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

  8. 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.

  9. 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.

  10. 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.

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

    NASA Astrophysics Data System (ADS)

    Bowman, B. C.; Dowla, F.

    1992-05-01

    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.

  12. Decision making and preferences for acoustic signals in choice situations by female crickets.

    PubMed

    Gabel, Eileen; Kuntze, Janine; Hennig, R Matthias

    2015-08-01

    Multiple attributes usually have to be assessed when choosing a mate. Efficient choice of the best mate is complicated if the available cues are not positively correlated, as is often the case during acoustic communication. Because of varying distances of signalers, a female may be confronted with signals of diverse quality at different intensities. Here, we examined how available cues are weighted for a decision by female crickets. Two songs with different temporal patterns and/or sound intensities were presented in a choice paradigm and compared with female responses from a no-choice test. When both patterns were presented at equal intensity, preference functions became wider in choice situations compared with a no-choice paradigm. When the stimuli in two-choice tests were presented at different intensities, this effect was counteracted as preference functions became narrower compared with choice tests using stimuli of equal intensity. The weighting of intensity differences depended on pattern quality and was therefore non-linear. A simple computational model based on pattern and intensity cues reliably predicted female decisions. A comparison of processing schemes suggested that the computations for pattern recognition and directionality are performed in a network with parallel topology. However, the computational flow of information corresponded to serial processing.

  13. 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

  14. Improvement of Power Efficiency for Underwater Acoustic Communication Using Orthogonal Signal Division Multiplexing over Multiple Transducers

    NASA Astrophysics Data System (ADS)

    Ebihara, Tadashi

    2013-07-01

    In underwater acoustic (UWA) communication, power efficiency is one of the important characteristics. This paper is about multistream transmission using orthogonal signal division multiplexing (OSDM) as a technique to increase power efficiency. In this work, the performance of multistream transmission using OSDM is evaluated both experimentally in a test tank and by numerical simulation. Through this study, it is confirmed that the multistream transmission scheme is effective in enhancing the power efficiency compared with the single-stream transmission using higher order modulation. Moreover, the performance of multistream transmission using OSDM is compared with the existing scheme, multistream transmission using orthogonal frequency division multiplexing (OFDM). The obtained results suggest that multistream transmission using OSDM is attractive because it can achieve the same bit-error rate (BER) and the same data rate with less power of the signal, compared with the reference. Although the calculation cost of OSDM in the receiver remains as an issue, multistream transmission using OSDM may contribute to high-speed UWA communication because of its excellent power efficiency.

  15. Assessing the horizontal refraction of ocean acoustic tomography signals using high-resolution ocean state estimates.

    PubMed

    Dushaw, Brian D

    2014-07-01

    The analysis of signals for acoustic tomography sent between a source and a receiver most often uses the unrefracted geodesic path, an approximation that is justified from theoretical considerations, relying on estimates of horizontal gradients of sound speed, or on simple theoretical models. To quantify the effects of horizontal refraction caused by a realistic ocean environment, horizontal refractions of long-range signals were computed using global ocean state estimates for 2004 from the Estimating the Circulation and Climate of the Ocean (ECCO2) project. Basin-scale paths in the eastern North Pacific Ocean and regional-scale paths in the Philippine Sea were used as examples. At O(5 Mm) basin scales, refracted geodesic and geodesic paths differed by only about 5 km. Gyre-scale features had the greatest refractive influence, but the precise refractive effects depended on the path geometry with respect to oceanographic features. Refraction decreased travel times by 5-10 ms and changed azimuthal angles by about 0.2°. At O(500 km) regional scales, paths deviated from the geodesic by only 250 m, and travel times deviated by less than 0.5 ms. Such effects are of little consequence in the analysis of tomographic data. Refraction details depend only slightly on mode number and frequency. PMID:24993200

  16. Time-frequency analysis of acoustic signals in the audio-frequency range generated during Hadfield's steel friction

    NASA Astrophysics Data System (ADS)

    Dobrynin, S. A.; Kolubaev, E. A.; Smolin, A. Yu.; Dmitriev, A. I.; Psakhie, S. G.

    2010-07-01

    Time-frequency analysis of sound waves detected by a microphone during the friction of Hadfield’s steel has been performed using wavelet transform and window Fourier transform methods. This approach reveals a relationship between the appearance of quasi-periodic intensity outbursts in the acoustic response signals and the processes responsible for the formation of wear products. It is shown that the time-frequency analysis of acoustic emission in a tribosystem can be applied, along with traditional approaches, to studying features in the wear and friction process.

  17. 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 ...

  18. 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 A.; Kirillov, Sergey A.; Rysgaard, Søren; Falk-Petersen, Stig; Barber, David G.; Boone, Wieter; Ehn, Jens K.

    2016-07-01

    Six and a half month records from three ice-tethered Acoustic Doppler Current Profilers deployed in October 2013 in Young Sound fjord in Northeast Greenland are used to analyze the acoustic backscatter signal. The acoustic data suggest a systematic diel vertical migration (DVM) of scatters below the land-fast ice during polar night. The scatters were likely composed of zooplankton. The acoustic signal pattern typical to DVM persisted in Young Sound 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 favoring 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 remain in the upper 40 m layer where relatively warmer water temperatures associated with upward heat flux during enhanced estuarine-like circulation could be energetically favorable. 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. Finally, by 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 of Arctic zooplankton species.

  19. 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.

  20. Feature selection using angle modulated simulated Kalman filter for peak classification of EEG signals.

    PubMed

    Adam, Asrul; Ibrahim, Zuwairie; Mokhtar, Norrima; Shapiai, Mohd Ibrahim; Mubin, Marizan; Saad, Ismail

    2016-01-01

    In the existing electroencephalogram (EEG) signals peak classification research, the existing models, such as Dumpala, Acir, Liu, and Dingle peak models, employ different set of features. However, all these models may not be able to offer good performance for various applications and it is found to be problem dependent. Therefore, the objective of this study is to combine all the associated features from the existing models before selecting the best combination of features. A new optimization algorithm, namely as angle modulated simulated Kalman filter (AMSKF) will be employed as feature selector. Also, the neural network random weight method is utilized in the proposed AMSKF technique as a classifier. In the conducted experiment, 11,781 samples of peak candidate are employed in this study for the validation purpose. The samples are collected from three different peak event-related EEG signals of 30 healthy subjects; (1) single eye blink, (2) double eye blink, and (3) eye movement signals. The experimental results have shown that the proposed AMSKF feature selector is able to find the best combination of features and performs at par with the existing related studies of epileptic EEG events classification. PMID:27652153

  1. EEG signals classification of epileptic patients via feature selection and voting criteria in intelligent method.

    PubMed

    Ghaffari, Ali; Ebrahimi Orimi, H

    2014-04-01

    Epileptic disease can be diagnosed by using intelligent methods on the Electroencephalograph (EEG) signals. In this paper, wavelet packet transform (WPT) was used in each of the frequency bands and wavelet coefficients were obtained, then the energy and entropy function was done on the wavelet coefficients and used as initial feature vectors. In the next step, eight and 15 features from 30 initial energy and entropy features were selected as the final features because their receiver operating characteristic (ROC) curve areas were higher than others. There were seven classifier inputs. These seven classifiers consisted of four artificial neural networks (ANN) with different structures, support vector machines (SVM), K-nearest neighbours (KNN) and a hybrid network. Each classifier was trained by 0.5, 0.8 and 0.9 EEG signals. After the training process, a fusion network based on a voting criteria was used to make the algorithm robust against the possible changes in each classifier and increase the classification accuracy. Finally, the algorithm was tested by other EEG signals. As a result, normal and epileptic classes were detected with total classification accuracy of 99-100%.

  2. Feature selection using angle modulated simulated Kalman filter for peak classification of EEG signals.

    PubMed

    Adam, Asrul; Ibrahim, Zuwairie; Mokhtar, Norrima; Shapiai, Mohd Ibrahim; Mubin, Marizan; Saad, Ismail

    2016-01-01

    In the existing electroencephalogram (EEG) signals peak classification research, the existing models, such as Dumpala, Acir, Liu, and Dingle peak models, employ different set of features. However, all these models may not be able to offer good performance for various applications and it is found to be problem dependent. Therefore, the objective of this study is to combine all the associated features from the existing models before selecting the best combination of features. A new optimization algorithm, namely as angle modulated simulated Kalman filter (AMSKF) will be employed as feature selector. Also, the neural network random weight method is utilized in the proposed AMSKF technique as a classifier. In the conducted experiment, 11,781 samples of peak candidate are employed in this study for the validation purpose. The samples are collected from three different peak event-related EEG signals of 30 healthy subjects; (1) single eye blink, (2) double eye blink, and (3) eye movement signals. The experimental results have shown that the proposed AMSKF feature selector is able to find the best combination of features and performs at par with the existing related studies of epileptic EEG events classification.

  3. Acoustical standards in engineering acoustics

    NASA Astrophysics Data System (ADS)

    Burkhard, Mahlon D.

    2001-05-01

    The Engineering Acoustics Technical Committee is concerned with the evolution and improvement of acoustical techniques and apparatus, and with the promotion of new applications of acoustics. As cited in the Membership Directory and Handbook (2002), the interest areas include transducers and arrays; underwater acoustic systems; acoustical instrumentation and monitoring; applied sonics, promotion of useful effects, information gathering and transmission; audio engineering; acoustic holography and acoustic imaging; acoustic signal processing (equipment and techniques); and ultrasound and infrasound. Evident connections between engineering and standards are needs for calibration, consistent terminology, uniform presentation of data, reference levels, or design targets for product development. Thus for the acoustical engineer standards are both a tool for practices, for communication, and for comparison of his efforts with those of others. Development of many standards depends on knowledge of the way products are put together for the market place and acoustical engineers provide important input to the development of standards. Acoustical engineers and members of the Engineering Acoustics arm of the Society both benefit from and contribute to the Acoustical Standards of the Acoustical Society.

  4. [Genetic aspects of sexual behavior in malaria mosquitoes on the basis of specific acoustic signals at mating].

    PubMed

    Perevozkin, V P; Printseva, A A; Maslennikov, P V; Bondarchuk, S S

    2012-06-01

    Acoustic characteristics were studied in two species of the "Anopheles maculipennis" species complex, A. messeae and A. atroparvus. The species were found to clearly differ in sound frequencies, which was assumed to play a key role in species identification during mating in regions of their sympatric distribution. The sound spectrum in A. messeae was far more diverse than in A. atroparvus, which was associated with intraspecific inversion polymorphism of the former. Mosquitoes with the inversion combinations that were most common in populations of the central region of the A. messeae species area specifically differed in acoustic signal spectrum from each other. Hence, sound communication within the species was considered to be the main mechanism that is responsible for sexual partner selection and determines the chromosome associations observed earlier in individual karyotypes. Since males carrying different inversion combinations significantly differed in acoustic characteristics, females were assumed to play a main role in selecting the sexual partner.

  5. 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

  6. A multidimensional signal processing approach for classification of microwave measurements with application to stroke type diagnosis.

    PubMed

    Mesri, Hamed Yousefi; Najafabadi, Masoud Khazaeli; McKelvey, Tomas

    2011-01-01

    A multidimensional signal processing method is described for detection of bleeding stroke based on microwave measurements from an antenna array placed around the head of the patient. The method is data driven and the algorithm uses samples from a healthy control group to calculate the feature used for classification. The feature is derived using a tensor approach and the higher order singular value decomposition is a key component. A leave-one-out validation method is used to evaluate the properties of the method using clinical data.

  7. 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

  8. 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

  9. 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.

  10. Analysis of Electromyographic Signals from Rats' Stomaches for Detection and Classification of Motility

    PubMed Central

    Jiménez, Laura Ivoone Garay; Rodríguez, Pablo Rogelio Hernández; Guerrero, Roberto Muñoz; Ramírez, Emma Gloria Ramos

    2008-01-01

    This paper presents the analysis of the electromyographic signals from rat stomachs to identify and classify contractions. The results were validated with both visual identification and an ultrasonic system to guarantee the reference. Some parameters were defined and associated to the energy of the signal in frequency domain and grouped in a P vector. The parameters were statistically analyzed and according to the results, an artificial neuronal network was designed to use the P vectors as inputs to classify the electrical signals related to the contraction conditions. A first approach classification was performed with and without contraction classes (CR and NCR), then the same database were subdivided in four classes: with induced contraction (ICR), spontaneous contraction (SCR), without contraction due a post mortem condition (PMR) or under physiological conditions (PNCR). In a two-class classifier, performance was 86%, 93% and 91% of detections for each electrogastromyografic (EGMG) signal from each of three pairs of electrodes considered. Because in the four-class classifier, enough data was not collected for the first pair, then a three-class classifier with 82% of performance was used. For the other two EGMG signals electrode pairs, performance was of 76% and 86% respectively. Based in the results, the analysis of P vectors could be used as a contraction detector in motility studies due to different stimuli in a rat model.

  11. 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.

  12. 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

  13. 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

  14. 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.

  15. 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

  16. 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

  17. 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

  18. 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.

  19. 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.

  20. 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

  1. 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.

  2. 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.

  3. 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.

  4. 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.

  5. Analysis of the development and possibilities of the acoustic emission method

    NASA Astrophysics Data System (ADS)

    Malecki, Ignacy

    The phenomenon of acoustic emission has been known for ages, but its practical use only dates back to the early 1960's to 'microseismic observations,' or farther back to the analysis of the acoustic emission generated by metals under stress. Discussed is the expansion of the measurement range by the detection of high frequency acoustic emission signals, the generation of acoustic emission by dislocation movements in metals and the brittle fracture of ceramics, the effect of material fatigue on acoustic emission activity, promising new applications in mining and construction, and efforts to improve acoustic emission transducers. A comparative analysis of trends in the development of acoustic emission techniques over the last 25 years and conclusions concerning the directions of future research are given. A description of ways to improve acoustic emission techniques which primarily focuses on electronic acoustic emission signal processing, extraction, and separation is presented. Phases of acoustic emission activity under conditions of rising stress, the 'life span' and fatigue of a material determined by means of acoustic emission, classification of acoustic emission sources, and analysis of the possibilities of acoustic emission for raw materials, processed materials, mechanical engineering, electronics, power generation, construction, and chemicals and for diagnosing motor vehicles and engineering systems are discussed. The authors also discuss the possibility of using acoustic emission in biology and medicine and the possible applications of acoustic emissions for basic research in physics and chemistry.

  6. 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.

  7. SEPARATING THE EFFECTS OF ACOUSTIC AND PHONETIC FACTORS IN LINGUISTIC PROCESSING WITH IMPOVERISHED SIGNALS BY ADULTS AND CHILDREN

    PubMed Central

    Nittrouer, Susan; Lowenstein, Joanna H.

    2012-01-01

    Cochlear implants allow many individuals with profound hearing loss to understand spoken language, even though the impoverished signals provided by these devices poorly preserve acoustic attributes long believed to support recovery of phonetic structure. Consequently questions may be raised regarding whether traditional psycholinguistic theories rely too heavily on phonetic segments to explain linguistic processing while ignoring potential roles of other forms of acoustic structure. This study tested that possibility. Adults and children (8 years old) performed two tasks: one involving explicit segmentation, phonemic awareness, and one involving a linguistic task thought to operate more efficiently with well-defined phonetic segments, short-term memory. Stimuli were unprocessed signals (UP), amplitude envelopes (AE) analogous to implant signals, and unprocessed signals in noise (NOI) which provided a degraded signal for comparison. Adults’ results for short-term recall were similar for UP and NOI, but worse for AE stimuli. The phonemic awareness task revealed the opposite pattern across AE and NOI. Children’s results for short-term recall showed similar decrements in performance for AE and NOI compared to UP, even though only NOI stimuli showed diminished results for segmentation. Conclusions were that perhaps traditional accounts are too focused on phonetic segments, something implant designers and clinicians need to consider. PMID:24729642

  8. Measurement of Acoustic-to-Seismic Conversion Using T-wave Signals Recorded at Ascension Island and Diego Garcia

    NASA Astrophysics Data System (ADS)

    Pulli, J. J.; Kofford, A. S.; Newman, K. R.; Krumhansl, P. A.

    2012-12-01

    T-wave signals from sub-sea earthquakes are often recorded on coastal or island seismic stations (Linehan, 1940; Okal, 2008). The physical process of the acoustic-to-seismic conversion is poorly understood but likely depends on factors such as seafloor relief and sediment thickness at the location where the interaction occurs. Quantification of the conversion process is necessary to understand and interpret the seismic recordings, and allow for the calculation of in-water acoustic levels from these recordings where no in-water sensor recordings are available. Applications for this knowledge would include the calculation of in-water explosion yields and seismic airgun source levels. Here we present the measurement of the acoustic-to-seismic transfer functions at Ascension Island and Diego Garcia using hydroacoustic data from the International Monitoring System and broadband seismic data from the Global Seismic Network. For Ascension Island, a volcanic island formed above magmatic plumes, we used T-wave signals from earthquakes on the Central Mid-Atlantic Ridge and associated fracture zones. For Diego Garcia, an atoll of carbonate sequences and no volcanism, we used T-wave signals from earthquakes along the Sumatran Subduction Zone, the Indian Ocean Ridges, and the Chagos Arch. The methodology is based on the smoothed cross-spectra over a frequency band that is common to the acoustic and seismic recordings, typically 2-18 Hz. Preliminary results indicate that at 5 Hz the acoustic-to-seismic conversion is 2-4 times more efficient at Ascension Island than at Diego Garcia (124 nm/s/Pa vs. 51 nm/s/Pa, respectively), but nearly equal at 10 Hz (20 nm/s/Pa). At 15 Hz the conversion is more efficient at Diego Garcia (13 nm/s/Pa vs. 8 nm/s/Pa at Ascension). We also investigate the azimuthal variance of this transfer function, as well as the differences between the three components of seismic motion. As a verification of the methodology, we use the equivalent time domain

  9. 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

  10. 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

  11. 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.

  12. 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)

  13. Acoustic emission descriptors

    NASA Astrophysics Data System (ADS)

    Witos, Franciszek; Malecki, Ignacy

    The authors present selected problems associated with acoustic emission interpreted as a physical phenomenon and as a measurement technique. The authors examine point sources of acoustic emission in isotropic, homogeneous linearly elastic media of different shapes. In the case of an unbounded medium the authors give the analytical form of the stress field and the wave shift field of the acoustic emission. In the case of a medium which is unbounded plate the authors give a form for the equations which is suitable for numerical calculation of the changes over time of selected acoustic emission values. For acoustic emission as a measurement technique, the authors represent the output signal as the resultant of a mechanical input value which describes the source, the transient function of the medium, and the transient function of specific components of the measurement loop. As an effect of this notation, the authors introduce the distinction between an acoustic measurement signal and an acoustic measurement impulse. The authors define the basic parameters of an arbitrary impulse. The authors extensively discuss the signal functions of acoustic emission impulses and acoustic emission signals defined in this article as acoustic emission descriptors (or signal functions of acoustic emission impulses) and advanced acoustic emission descriptors (which are either descriptors associated with acoustic emission applications or the signal functions of acoustic emission signals). The article also contains the results of experimental research on three different problems in which acoustic emission descriptors associated with acoustic emission pulses, acoustic emission applications, and acoustic emission signals are used. These problems are respectively: a problem of the amplitude-load characteristics of acoustic emission pulses in carbon samples subjected to compound uniaxial compression, the use of acoustic emission to predict the durability characteristics of conveyor belts, and

  14. 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

  15. 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.

  16. Improved discrete Fourier transform based spectral feature for surface electromyogram signal classification.

    PubMed

    He, Jiayuan; Zhang, Dingguo; Sheng, Xinjun; Meng, Jianjun; Zhu, Xiangyang

    2013-01-01

    An improved discrete Fourier transform (iDFT) is presented in this study as a novel feature for surface electromyogram (sEMG) pattern classification. It employs the principle that the spectrum of sEMG signals changes regarding different motions. iDFT feature focuses on global information of local bands to increase the inter-class distance. The experiment results showed that iDFT feature had a better separability than two other spectral features, auto regression (AR) and Power spectral density (PSD), both on experienced and inexperienced subjects. The optimal bandwidth is between 30 and 50 Hz and influence of division methods is not significant. With the low computation cost and property of insensitivity to sampling frequency, our proposed method provides a competitive choice for prosthetic control.

  17. Bioelectric signal classification using a recurrent probabilistic neural network with time-series discriminant component analysis.

    PubMed

    Hayashi, Hideaki; Shima, Keisuke; Shibanoki, Taro; Kurita, Yuichi; Tsuji, Toshio

    2013-01-01

    This paper outlines a probabilistic neural network developed on the basis of time-series discriminant component analysis (TSDCA) that can be used to classify high-dimensional time-series patterns. TSDCA involves the compression of high-dimensional time series into a lower-dimensional space using a set of orthogonal transformations and the calculation of posterior probabilities based on a continuous-density hidden Markov model that incorporates a Gaussian mixture model expressed in the reduced-dimensional space. The analysis can be incorporated into a neural network so that parameters can be obtained appropriately as network coefficients according to backpropagation-through-time-based training algorithm. The network is considered to enable high-accuracy classification of high-dimensional time-series patterns and to reduce the computation time taken for network training. In the experiments conducted during the study, the validity of the proposed network was demonstrated for EEG signals.

  18. Classification of ECG signals using LDA with factor analysis method as feature reduction technique.

    PubMed

    Kaur, Manpreet; Arora, A S

    2012-11-01

    The analysis of ECG signal, especially the QRS complex as the most characteristic wave in ECG, is a widely accepted approach to study and to classify cardiac dysfunctions. In this paper, first wavelet coefficients calculated for QRS complex are taken as features. Next, factor analysis procedures without rotation and with orthogonal rotation (varimax, equimax and quartimax) are used for feature reduction. The procedure uses the 'Principal Component Method' to estimate component loadings. Further, classification has been done with a LDA classifier. The MIT-BIH arrhythmia database is used and five types of beats (normal, PVC, paced, LBBB and RBBB) are considered for analysis. Accuracy, sensitivity and positive predictivity are performance parameters used for comparing performance of feature reduction techniques. Results demonstrate that the equimax rotation method yields maximum average accuracy of 99.056% for unknown data sets among other used methods.

  19. 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

  20. 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.

  1. 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.

  2. 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

  3. 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.

  4. Classification of reflected signals from cavitated tooth surfaces using an artificial intelligence technique incorporating a fiber optic displacement sensor.

    PubMed

    Rahman, Husna Abdul; Harun, Sulaiman Wadi; Arof, Hamzah; Irawati, Ninik; Musirin, Ismail; Ibrahim, Fatimah; Ahmad, Harith

    2014-05-01

    An enhanced dental cavity diameter measurement mechanism using an intensity-modulated fiber optic displacement sensor (FODS) scanning and imaging system, fuzzy logic as well as a single-layer perceptron (SLP) neural network, is presented. The SLP network was employed for the classification of the reflected signals, which were obtained from the surfaces of teeth samples and captured using FODS. Two features were used for the classification of the reflected signals with one of them being the output of a fuzzy logic. The test results showed that the combined fuzzy logic and SLP network methodology contributed to a 100% classification accuracy of the network. The high-classification accuracy significantly demonstrates the suitability of the proposed features and classification using SLP networks for classifying the reflected signals from teeth surfaces, enabling the sensor to accurately measure small diameters of tooth cavity of up to 0.6 mm. The method remains simple enough to allow its easy integration in existing dental restoration support systems.

  5. Classification of reflected signals from cavitated tooth surfaces using an artificial intelligence technique incorporating a fiber optic displacement sensor

    NASA Astrophysics Data System (ADS)

    Rahman, Husna Abdul; Harun, Sulaiman Wadi; Arof, Hamzah; Irawati, Ninik; Musirin, Ismail; Ibrahim, Fatimah; Ahmad, Harith

    2014-05-01

    An enhanced dental cavity diameter measurement mechanism using an intensity-modulated fiber optic displacement sensor (FODS) scanning and imaging system, fuzzy logic as well as a single-layer perceptron (SLP) neural network, is presented. The SLP network was employed for the classification of the reflected signals, which were obtained from the surfaces of teeth samples and captured using FODS. Two features were used for the classification of the reflected signals with one of them being the output of a fuzzy logic. The test results showed that the combined fuzzy logic and SLP network methodology contributed to a 100% classification accuracy of the network. The high-classification accuracy significantly demonstrates the suitability of the proposed features and classification using SLP networks for classifying the reflected signals from teeth surfaces, enabling the sensor to accurately measure small diameters of tooth cavity of up to 0.6 mm. The method remains simple enough to allow its easy integration in existing dental restoration support systems.

  6. [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.

  7. 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.

  8. 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.

  9. 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

  10. Application of pulse compression signal processing techniques to electromagnetic acoustic transducers for noncontact thickness measurements and imaging

    SciTech Connect

    Ho, K.S.; Gan, T.H.; Billson, D.R.; Hutchins, D.A.

    2005-05-15

    A pair of noncontact Electromagnetic Acoustic Transducers (EMATs) has been used for thickness measurements and imaging of metallic plates. This was performed using wide bandwidth EMATs and pulse-compression signal processing techniques, using chirp excitation. This gives a greatly improved signal-to-noise ratio for air-coupled experiments, increasing the speed of data acquisition. A numerical simulation of the technique has confirmed the performance. Experimental results indicate that it is possible to perform noncontact ultrasonic imaging and thickness gauging in a wide range of metal plates. An accuracy of up to 99% has been obtained for aluminum, brass, and copper samples. The resolution of the image obtained using the pulse compression approach was also improved compared to a transient pulse signal from conventional pulser(receiver). It is thus suggested that the combination of EMATs and pulse compression can lead to a wide range of online applications where fast time acquisition is required.

  11. 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.

  12. 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

  13. Thermally induced acoustic emissions in thermal barrier coatings

    SciTech Connect

    Voyer, J.; Gitzhofer, F.; Boulos, M.I.; Durham, S.

    1995-12-31

    In this study, acoustic emission signals are used to monitor the degradation of plasma sprayed Thermal Barrier Coatings (TBC) under thermal cycling conditions. Signal analysis both in time and frequency domains is carried out in order to identify the key parameters which can be used to classify the acoustic emission signals as a function of the damage mechanisms. This classification offers a means of prediction of the long-term behavior of the thermal barrier coating based on the acoustic emission signal signature at the early stages of bench testing. The tests were carried out using an experimental rig that was developed to reproduce thermal conditions encountered inside a combustion chamber. Twelve infrared lamps, each with a power rating of 1,200 W, are used as a heat source. The samples consist of an alloy blade coated with a duplex TBC made of a 150 {micro}m thick bond coat covered with a 300 {micro}m thick partially-stabilized zirconia coating. The maximum surface temperature of the sample was measured to be around 1,000 C. Two broadband transducers are used for acquisition of acoustic emission signals. Measuring the time between signal detection by each of the two transducers provides a means of determination of the location of the source of the acoustic signals. A classification of the signals based on their energy and their maximum peak frequency is presented.

  14. Turboprop and rotary-wing aircraft flight parameter estimation using both narrow-band and broadband passive acoustic signal-processing methods.

    PubMed

    Ferguson, B G; Lo, K W

    2000-10-01

    Flight parameter estimation methods for an airborne acoustic source can be divided into two categories, depending on whether the narrow-band lines or the broadband component of the received signal spectrum is processed to estimate the flight parameters. This paper provides a common framework for the formulation and test of two flight parameter estimation methods: one narrow band, the other broadband. The performances of the two methods are evaluated by applying them to the same acoustic data set, which is recorded by a planar array of passive acoustic sensors during multiple transits of a turboprop fixed-wing aircraft and two types of rotary-wing aircraft. The narrow-band method, which is based on a kinematic model that assumes the source travels in a straight line at constant speed and altitude, requires time-frequency analysis of the acoustic signal received by a single sensor during each aircraft transit. The broadband method is based on the same kinematic model, but requires observing the temporal variation of the differential time of arrival of the acoustic signal at each pair of sensors that comprises the planar array. Generalized cross correlation of each pair of sensor outputs using a cross-spectral phase transform prefilter provides instantaneous estimates of the differential times of arrival of the signal as the acoustic wavefront traverses the array.

  15. Single-channel blind separation using L₁-sparse complex non-negative matrix factorization for acoustic signals.

    PubMed

    Parathai, P; Woo, W L; Dlay, S S; Gao, Bin

    2015-01-01

    An innovative method of single-channel blind source separation is proposed. The proposed method is a complex-valued non-negative matrix factorization with probabilistically optimal L1-norm sparsity. This preserves the phase information of the source signals and enforces the inherent structures of the temporal codes to be optimally sparse, thus resulting in more meaningful parts factorization. An efficient algorithm with closed-form expression to compute the parameters of the model including the sparsity has been developed. Real-time acoustic mixtures recorded from a single-channel are used to verify the effectiveness of the proposed method. PMID:25618092

  16. Information Theory Filters for Wavelet Packet Coefficient Selection with Application to Corrosion Type Identification from Acoustic Emission Signals

    PubMed Central

    Van Dijck, Gert; Van Hulle, Marc M.

    2011-01-01

    The damage caused by corrosion in chemical process installations can lead to unexpected plant shutdowns and the leakage of potentially toxic chemicals into the environment. When subjected to corrosion, structural changes in the material occur, leading to energy releases as acoustic waves. This acoustic activity can in turn be used for corrosion monitoring, and even for predicting the type of corrosion. Here we apply wavelet packet decomposition to extract features from acoustic emission signals. We then use the extracted wavelet packet coefficients for distinguishing between the most important types of corrosion processes in the chemical process industry: uniform corrosion, pitting and stress corrosion cracking. The local discriminant basis selection algorithm can be considered as a standard for the selection of the most discriminative wavelet coefficients. However, it does not take the statistical dependencies between wavelet coefficients into account. We show that, when these dependencies are ignored, a lower accuracy is obtained in predicting the corrosion type. We compare several mutual information filters to take these dependencies into account in order to arrive at a more accurate prediction. PMID:22163921

  17. Acoustic-emission signal-processing analog unit for locating flaws in large tanks

    NASA Technical Reports Server (NTRS)

    Moskal, F. J.; Fageol, J. D.

    1973-01-01

    Technique monitors structural flaws in 105-in. diameter tanks. Tank surface is divided into many areas and each area is sectioned into 20 equilateral triangles that form icosahedron. Twelve transducers are equally positioned on tank surface at vertex of each triangle. Transducers monitor area for flaws by detecting any increase in acoustical activity.

  18. 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

  19. 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,…

  20. 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…

  1. Environmental variability and acoustic signals: a multi-level approach in songbirds.

    PubMed

    Medina, Iliana; Francis, Clinton D

    2012-12-23

    Among songbirds, growing evidence suggests that acoustic adaptation of song traits occurs in response to habitat features. Despite extensive study, most research supporting acoustic adaptation has only considered acoustic traits averaged for species or populations, overlooking intraindividual variation of song traits, which may facilitate effective communication in heterogeneous and variable environments. Fewer studies have explicitly incorporated sexual selection, which, if strong, may favour variation across environments. Here, we evaluate the prevalence of acoustic adaptation among 44 species of songbirds by determining how environmental variability and sexual selection intensity are associated with song variability (intraindividual and intraspecific) and short-term song complexity. We show that variability in precipitation can explain short-term song complexity among taxonomically diverse songbirds, and that precipitation seasonality and the intensity of sexual selection are related to intraindividual song variation. Our results link song complexity to environmental variability, something previously found for mockingbirds (Family Mimidae). Perhaps more importantly, our results illustrate that individual variation in song traits may be shaped by both environmental variability and strength of sexual selection.

  2. Environmental variability and acoustic signals: a multi-level approach in songbirds

    PubMed Central

    Medina, Iliana; Francis, Clinton D.

    2012-01-01

    Among songbirds, growing evidence suggests that acoustic adaptation of song traits occurs in response to habitat features. Despite extensive study, most research supporting acoustic adaptation has only considered acoustic traits averaged for species or populations, overlooking intraindividual variation of song traits, which may facilitate effective communication in heterogeneous and variable environments. Fewer studies have explicitly incorporated sexual selection, which, if strong, may favour variation across environments. Here, we evaluate the prevalence of acoustic adaptation among 44 species of songbirds by determining how environmental variability and sexual selection intensity are associated with song variability (intraindividual and intraspecific) and short-term song complexity. We show that variability in precipitation can explain short-term song complexity among taxonomically diverse songbirds, and that precipitation seasonality and the intensity of sexual selection are related to intraindividual song variation. Our results link song complexity to environmental variability, something previously found for mockingbirds (Family Mimidae). Perhaps more importantly, our results illustrate that individual variation in song traits may be shaped by both environmental variability and strength of sexual selection. PMID:22859557

  3. 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

  4. 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

  5. [The reflection of the motivational status in the spectral characteristics of the species-specific acoustic signals of the domestic cat].

    PubMed

    Sokolova, N N; Liakso, E E

    1989-01-01

    Spectral characteristics of species-specific acoustic signals were analyzed in cats under various unfavourable conditions: hunger, isolation, pain stimulation, agony. The increase in the need to get rid of the discomfort accompanied by the development of emotional excitation was reflected in spectral characteristics of produced signals. The frequency and duration of signals increased, their spectrum widened accompanied by spectral maxima shifted towards the high-frequency area similar to the range of formant frequencies in the signals of newborn kittens. The similarity between spectral characteristics of the above signals in adult and newborn cats might indicate the appearance of infantile features in adult cats under conditions of a marked desire to change the existing situation. The fact that motivational state was reflected in spectral characteristics of acoustic signals along with stable responses to the signals, spoke in favour of a considerable contribution made by communication to the organization of intraspecific relations.

  6. 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.

  7. Application of the empirical mode decomposition to the extraction of features from EEG signals for mental task classification.

    PubMed

    Diez, Pablo F; Mut, Vicente; Laciar, Eric; Torres, Abel; Avila, Enrique

    2009-01-01

    In this work, it is proposed a technique for the feature extraction of electroencephalographic (EEG) signals for classification of mental tasks which is an important part in the development of Brain Computer Interfaces (BCI). The Empirical Mode Decomposition (EMD) is a method capable to process nonstationary and nonlinear signals as the EEG. This technique was applied in EEG signals of 7 subjects performing 5 mental tasks. For each mode obtained from the EMD and each EEG channel were computed six features: Root Mean Square (RMS), Variance, Shannon Entropy, Lempel-Ziv Complexity Value, and Central and Maximum Frequencies, obtaining a feature vector of 180 components. The Wilks' lambda parameter was applied for the selection of the most important variables reducing the dimensionality of the feature vector. The classification of mental tasks was performed using Linear Discriminate Analysis (LD) and Neural Networks (NN). With this method, the average classification over all subjects in database was 91+/-5% and 87+/-5% using LD and NN, respectively. It was concluded that the EMD allows getting better performances in the classification of mental tasks than the obtained with other traditional methods, like spectral analysis.

  8. A novel acoustic emission monitoring and signal processing to elucidate the fracture dynamics of hydrogen assisted cracking

    SciTech Connect

    Hayashi, Yasuhisa; Takemoto, Makoto; Takemoto, Mikio

    1994-12-31

    An advanced Acoustic Emission (AE) monitoring and signal processing system was developed and applied to elucidate the fracture dynamics of hydrogen assisted cracking (HAC) of quenched-tempered low alloy steel. The developed system enables one to monitor an initiation of microcrack correctly and also to elucidate the dynamics of microcracks when multi-channel moment tensor analysis is jointly used. The system consists of 8-channel monitoring. One channel monitors the surface displacement in loading direction excited by the propagation of elastic wave, and gives the source wave by the deconvolution integral of it with the Green`s function of the second kind. Another 7 channels were designed to measure arrival time and relative amplitude of the P-waves, and to determine both the source location and the crack kinematics by tensor analysis. This paper introduces the developed monitoring system and signal processing method, and fracture dynamics of microcracks in HAC.

  9. 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.

  10. Splitting a droplet with oil encapsulation using surface acoustic wave excited by electric signal with low power

    NASA Astrophysics Data System (ADS)

    Zhang, Anliang; Zha, Yan; Fu, Xingting

    2013-07-01

    A new method for splitting a droplet with oil encapsulation is presented. An interdigital transducer and a reflector are fabricated on a 128° yx-LiNbO3 piezoelectric substrate using microelectric technology. An electric signal with the power of 12.3 dBm is applied to the interdigital transducer to generate surface acoustic wave, which is radiated into a droplet with oil encapsulation, leading to surface acoustic wave streaming force. When the electric signal is suddenly moved off, the breakup of the droplet occurs due to inertial force. Color dye solution droplets encapsulated by oil droplets are demonstrated. The effects of electric power, the volume ratio of color dye solution to oil, and the volume of mother droplet on the breakup of droplets are studied. As applications, the presented method is successfully applied to mixture operation and color development reaction of two droplets. The method provides a new sample preparation technique, which is helpful for microfluidic biochemical analysis in a piezoelectric microfluidic system.

  11. 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

  12. 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.

  13. 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.

  14. 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

  15. 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

  16. 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

  17. 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.

  18. 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

  19. A new method for the extraction and classification of single motor unit action potentials from surface EMG signals.

    PubMed

    Gazzoni, Marco; Farina, Dario; Merletti, Roberto

    2004-07-30

    It has been shown that multi-channel surface EMG allows assessment of anatomical and physiological single motor unit (MU) properties. To get this information, the action potentials of single MUs should be extracted from the interference EMG signals. This study describes an automatic system for the detection and classification of MU action potentials from multi-channel surface EMG signals. The methods for the identification and extraction of action potentials from the raw signals and for their clustering into the MUs to which they belong are described. The segmentation phase is based on the matched Continuous Wavelet Transform (CWT) while the classification is performed by a multi-channel neural network that is a modified version of the multi-channel Adaptive Resonance Theory networks. The neural network can adapt to slow changes in the shape of the MU action potentials. The method does not require any interaction of the operator. The technique proposed was validated on simulated signals, at different levels of force, generated by a structure based surface EMG model. The MUs identified from the simulated signals covered almost the entire recruitment curve. Thus, the proposed algorithm was able to identify a MU sample representative of the muscle. Results on experimental signals recorded from different muscles and conditions are reported, showing the possibility of investigating anatomical and physiological properties of the detected MUs in a variety of practical cases. The main limitation of the approach is that complete firing patterns can be obtained only in specific cases due to MU action potential superpositions.

  20. Classification of the mechanomyogram signal using a wavelet packet transform and singular value decomposition for multifunction prosthesis control.

    PubMed

    Xie, Hong-Bo; Zheng, Yong-Ping; Guo, Jing-Yi

    2009-05-01

    Previous works have resulted in some practical achievements for mechanomyogram (MMG) to control powered prostheses. This work presents the investigation of classifying the hand motion using MMG signals for multifunctional prosthetic control. MMG is thought to reflect the intrinsic mechanical activity of muscle from the lateral oscillations of fibers during contraction. However, external mechanical noise sources such as a movement artifact are known to cause considerable interference to MMG, compromising the classification accuracy. To solve this noise problem, we proposed a new scheme to extract robust MMG features by the integration of the wavelet packet transform (WPT), singular value decomposition (SVD) and a feature selection technique based on distance evaluation criteria for the classification of hand motions. The WPT was first adopted to provide an effective time-frequency representation of non-stationary MMG signals. Then, the SVD and the distance evaluation technique were utilized to extract and select the optimal feature representing the hand motion patterns from the MMG time-frequency representation matrix. Experimental results of 12 subjects showed that four different motions of the forearm and hand could be reliably differentiated using the proposed method when two channels of MMG signals were used. Compared with three previously reported time-frequency decomposition methods, i.e. short-time Fourier transform, stationary wavelet transform and S-transform, the proposed classification system gave the highest average classification accuracy up to 89.7%. The results indicated that MMG could potentially serve as an alternative source of electromyogram for multifunctional prosthetic control using the proposed classification method.

  1. 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.

  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. Classification of penetration--aspiration versus healthy swallows using dual-axis swallowing accelerometry signals in dysphagic subjects.

    PubMed

    Sejdić, Ervin; Steele, Catriona M; Chau, Tom

    2013-07-01

    Swallowing accelerometry is a promising noninvasive approach for the detection of swallowing difficulties. In this paper, we propose an approach for classification of swallowing accelerometry recordings containing either healthy swallows or penetration-aspiration (entry of material into the airway) in dysphagic patients. The proposed algorithm is based on the wavelet packet decomposition of swallowing accelerometry signals in combination with linear discriminant analysis as a feature reduction method and Bayes classification. The proposed algorithm was tested using swallowing accelerometry signals collected from 40 patients during the regularly scheduled videoflouroscopy exam. The participants were instructed to swallow several 5-mL sips of thin liquid barium in a head neutral position. The results of our numerical analysis showed that the proposed algorithm can differentiate healthy swallows from aspiration swallows with an accuracy greater than 90%. These results position swallowing accelerometry as a valid approach for the detection of swallowing difficulties, particularly penetration-aspiration in patients suspected of dysphagia. PMID:23372074

  4. 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.

  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. 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.

  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. Adaptive significance of synchronous chorusing in an acoustically signalling wolf spider.

    PubMed

    Kotiaho, Janne S; Alatalo, Rauno V; Mappes, Johanna; Parri, Silja

    2004-09-01

    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.

  9. 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.

  10. 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,...

  11. 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

  12. DCT domain feature extraction scheme based on motor unit action potential of EMG signal for neuromuscular disease classification.

    PubMed

    Doulah, Abul Barkat Mollah Sayeed Ud; Fattah, Shaikh Anowarul; Zhu, Wei-Ping; Ahmad, M Omair

    2014-01-01

    A feature extraction scheme based on discrete cosine transform (DCT) of electromyography (EMG) signals is proposed for the classification of normal event and a neuromuscular disease, namely the amyotrophic lateral sclerosis. Instead of employing DCT directly on EMG data, it is employed on the motor unit action potentials (MUAPs) extracted from the EMG signal via a template matching-based decomposition technique. Unlike conventional MUAP-based methods, only one MUAP with maximum dynamic range is selected for DCT-based feature extraction. Magnitude and frequency values of a few high-energy DCT coefficients corresponding to the selected MUAP are used as the desired feature which not only reduces computational burden, but also offers better feature quality with high within-class compactness and between-class separation. For the purpose of classification, the K-nearest neighbourhood classifier is employed. Extensive analysis is performed on clinical EMG database and it is found that the proposed method provides a very satisfactory performance in terms of specificity, sensitivity and overall classification accuracy.

  13. DCT domain feature extraction scheme based on motor unit action potential of EMG signal for neuromuscular disease classification

    PubMed Central

    Doulah, Abul Barkat Mollah Sayeed Ud; Zhu, Wei-Ping; Ahmad, M. Omair

    2014-01-01

    A feature extraction scheme based on discrete cosine transform (DCT) of electromyography (EMG) signals is proposed for the classification of normal event and a neuromuscular disease, namely the amyotrophic lateral sclerosis. Instead of employing DCT directly on EMG data, it is employed on the motor unit action potentials (MUAPs) extracted from the EMG signal via a template matching-based decomposition technique. Unlike conventional MUAP-based methods, only one MUAP with maximum dynamic range is selected for DCT-based feature extraction. Magnitude and frequency values of a few high-energy DCT coefficients corresponding to the selected MUAP are used as the desired feature which not only reduces computational burden, but also offers better feature quality with high within-class compactness and between-class separation. For the purpose of classification, the K-nearest neighbourhood classifier is employed. Extensive analysis is performed on clinical EMG database and it is found that the proposed method provides a very satisfactory performance in terms of specificity, sensitivity and overall classification accuracy. PMID:26609372

  14. 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.

  15. Thermal and Acoustic Signals associated to Vulcanian Explosions at Soufrière Hills Volcano, Montserrat

    NASA Astrophysics Data System (ADS)

    Delle Donne, D.; Ripepe, M.; De Angelis, S.; Cole, P.; Lacanna, G.; Stewart, R. C.

    2012-12-01

    Soufrière Hills volcano (SHV) at Montserrat (WI) offers the opportunity to study a large variety of processes related to large Vulcanian eruptions. We show how a thermal camera and an infrasonic array can be used to constrain the eruptive onset, plume exit velocity and volumetric flux. This information is more difficult to be derived by seismic signals alone and thus thermal images and infrasound can help in their interpretation in terms of volcanic dynamics. The thermal and infrasonic integrated analysis applied to the large Vulcanian eruption of 5th February 2010, reveals a temperature increase above the dome lasting for ~20 seconds which coincides with the onset and the duration of the positive compressive infrasonic signal (14 Pa at 5600 m of distance) in the low frequency band <1 Hz. Besides, thermal decomposition method shows a rapid deceleration of the plume velocity from the initial ~170 m/s to a more stationary ascent rate at ~27 m/s. We interpret this initial eruptive phase as dominated by the gas thrust feeding gas and ash in the atmosphere at a volumetric discharge rate of 3.3x104 m3/s, giving a total discharged bulk volume of 8.5x105 m3. The seismic signal associated to this gas thrust phase becomes visible only when filtered in the 0.03 - 0.1 very long period (VLP) frequency band. The maximum amplitude of the VLP seismic signal coincides with the positive infrasonic peak, indicating that the VLP seismic signal originated during the initial gas thrust phase of the eruption. The fragmentation of overpressurized magmatic foam could be responsible for the rapid expansion in the conduit of the gas driving upward hot tephra out the vent in the atmosphere. The ground will react to the upward momentum induced by the mass discharge with a downward oriented counter force, which is probably the source of the VLP seismic signal. The striking correlation of seismic VLP with infrasound and the plume velocity derived by thermal image analysis seems to support this

  16. 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

  17. 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.

  18. Effect of sound on gap-junction-based intercellular signaling: Calcium waves under acoustic irradiation.

    PubMed

    Deymier, P A; Swinteck, N; Runge, K; Deymier-Black, A; Hoying, J B

    2015-01-01

    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.

  19. 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.

  20. 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

  1. 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

  2. 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

  3. Computational principles underlying recognition of acoustic signals in grasshoppers and crickets.

    PubMed

    Ronacher, Bernhard; Hennig, R Matthias; Clemens, Jan

    2015-01-01

    Grasshoppers and crickets independently evolved hearing organs and acoustic communication. They differ considerably in the organization of their auditory pathways, and the complexity of their songs, which are essential for mate attraction. Recent approaches aimed at describing the behavioral preference functions of females in both taxa by a simple modeling framework. The basic structure of the model consists of three processing steps: (1) feature extraction with a bank of 'LN models'-each containing a linear filter followed by a nonlinearity, (2) temporal integration, and (3) linear combination. The specific properties of the filters and nonlinearities were determined using a genetic learning algorithm trained on a large set of different song features and the corresponding behavioral response scores. The model showed an excellent prediction of the behavioral responses to the tested songs. Most remarkably, in both taxa the genetic algorithm found Gabor-like functions as the optimal filter shapes. By slight modifications of Gabor filters several types of preference functions could be modeled, which are observed in different cricket species. Furthermore, this model was able to explain several so far enigmatic results in grasshoppers. The computational approach offered a remarkably simple framework that can account for phenotypically rather different preference functions across several taxa.

  4. 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

  5. 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

  6. 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.

  7. 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.

  8. 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

  9. 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.

  10. 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.

  11. [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.

  12. [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

  13. 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.

  14. 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.

  15. Signal processing techniques for acoustic measurement of sperm whale body lengths.

    PubMed

    Goold, J C

    1996-11-01

    Waveform cross correlation and cepstrum analysis were used to demonstrate possible techniques to measure pulse intervals within sperm whale sonar clicks. The structure of sperm whale clicks takes the form of a series of decaying broadband pulses separated by a time interval that is a function of sound velocity in spermaceti oil and the length of the spermaceti sac within the whales' head. Click signals were bandpass filtered and waveform cross correlation used on the filtered signals to obtain maxima in the correlation function. Such maxima occur when successive pulses within the filtered click waveforms align after time shifting of the replica waveform by integer multiples of the interpulse interval. As an alternative approach, cepstrum analysis was used on the spectra of individual clicks, which were found to contain ripples with periods corresponding to the reciprocal of the interpulse interval. Variable signal quality lead to the conclusion that neither method was reliable for spot measurements of IPIs from individual clicks. However, calculating IPIs by either method for several hundred clicks in 6-min sequences, and smoothing the results with moving averages, allowed realistic mean values to be obtained and interpulse interval trends to be observed with dive time. Interpulse intervals were generally found to decrease with dive time, in accordance with known sound velocity characteristics of spermaceti oil under increasing pressure. Mean values of interpulse intervals obtained by cepstrum analysis for each click sequence were used to estimate body lengths of the respective animals.

  16. 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.).

  17. 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.

  18. 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

  19. Time-varying autoregressive modelling for nonstationary acoustic signal and its frequency analysis

    NASA Astrophysics Data System (ADS)

    Sodsri, Chukiet

    2003-06-01

    A time-varying autoregressive (TVAR) approach is used for modeling nonstationary signals, and frequency information is then extracted from the TVAR parameters. Two methods may be used for estimating the TVAR parameters: the adaptive algorithm approach and the basis function approach. Adaptive algorithms, such as the least mean square (LMS) and the recursive least square (RLS), use a dynamic model for adapting the TVAR parameters and are capable of tracking time-varying frequency, provided that the variation is slow. It is observed that, if the signals have a single time-frequency component, the RLS with a fixed pole on the unit circle yields the fastest convergence. The basis function method employs an explicit model for the TVAR parameter variation, and model parameters are estimated via a block calculation. We proposed a modification to the basis function method by utilizing both forward and backward predictors for estimating the time-varying spectral density of nonstationary signals. It is shown that our approach yields better accuracy than the existing basis function approach, which uses only the forward predictor. The selection of the basis functions and limitations are also discussed in this thesis. Finally, the proposed approach is applied to analyze violin vibrato. Our results showed superior frequency resolution and spectral line smoothness using the proposed approach, compared to conventional analysis with the short time Fourier transform (STFT) whose frequency resolution is very limited. It was also found that frequency modulation of vibrato occurs at the rate of 6 Hz, and the frequency variations for each partial are different and increase nonlinearly with the partial number.

  20. [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

  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.

  2. Corruption of ant acoustical signals by mimetic social parasites: Maculinea butterflies achieve elevated status in host societies by mimicking the acoustics of queen ants.

    PubMed

    Thomas, Jeremy A; Schönrogge, Karsten; Bonelli, Simona; Barbero, Francesca; Balletto, Emilio

    2010-03-01

    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.

  3. 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.

  4. 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

  5. Are mussels able to distinguish underwater sounds? Assessment of the reactions of Mytilus galloprovincialis after exposure to lab-generated acoustic signals.

    PubMed

    Vazzana, Mirella; Celi, Monica; Maricchiolo, Giulia; Genovese, Lucrezia; Corrias, Valentina; Quinci, Enza Maria; de Vincenzi, Giovanni; Maccarrone, Vincenzo; Cammilleri, Gaetano; Mazzola, Salvatore; Buscaino, Giuseppa; Filiciotto, Francesco

    2016-11-01

    This study examined the effects of lab-generated acoustic signals on the behaviour and biochemistry of Mediterranean mussels (Mytilus galloprovincialis). The experiment was carried out in a tank equipped with a video-recording system using six groups of five mussels exposed to five acoustic treatments (each treatment was replicated three times) for 30min. The acoustic signals, with a maximum sound pressure level of 150dB rms re 1μPa, differed in frequency range as follows: low (0.1-5kHz), mid-low (5-10kHz), mid (10-20kHz), mid-high (20-40kHz) and high (40-60kHz). The exposure to sweeps did not produce any significant changes in the mussels' behaviour. Conversely, the specimens exposed to the low frequency band treatment showed significantly higher values of the following biochemical stress parameters measured in their plasma and tissues: glucose, total proteins, total haemocyte number (THC), heat shock protein 70 (Hsp70) expression, and Acetylcholinesterase (AChE) activity. The responses observed in the mussels exposed to low frequency sweeps enable us to suppose a biological and ecological role for this sound, which contains the main frequencies produced by both shipping traffic and the acoustic emissions of fish. PMID:27371112

  6. Are mussels able to distinguish underwater sounds? Assessment of the reactions of Mytilus galloprovincialis after exposure to lab-generated acoustic signals.

    PubMed

    Vazzana, Mirella; Celi, Monica; Maricchiolo, Giulia; Genovese, Lucrezia; Corrias, Valentina; Quinci, Enza Maria; de Vincenzi, Giovanni; Maccarrone, Vincenzo; Cammilleri, Gaetano; Mazzola, Salvatore; Buscaino, Giuseppa; Filiciotto, Francesco

    2016-11-01

    This study examined the effects of lab-generated acoustic signals on the behaviour and biochemistry of Mediterranean mussels (Mytilus galloprovincialis). The experiment was carried out in a tank equipped with a video-recording system using six groups of five mussels exposed to five acoustic treatments (each treatment was replicated three times) for 30min. The acoustic signals, with a maximum sound pressure level of 150dB rms re 1μPa, differed in frequency range as follows: low (0.1-5kHz), mid-low (5-10kHz), mid (10-20kHz), mid-high (20-40kHz) and high (40-60kHz). The exposure to sweeps did not produce any significant changes in the mussels' behaviour. Conversely, the specimens exposed to the low frequency band treatment showed significantly higher values of the following biochemical stress parameters measured in their plasma and tissues: glucose, total proteins, total haemocyte number (THC), heat shock protein 70 (Hsp70) expression, and Acetylcholinesterase (AChE) activity. The responses observed in the mussels exposed to low frequency sweeps enable us to suppose a biological and ecological role for this sound, which contains the main frequencies produced by both shipping traffic and the acoustic emissions of fish.

  7. Use of the discriminant Fourier-derived cepstrum with feature-level post-processing for surface electromyographic signal classification.

    PubMed

    Chen, Xinpu; Zhu, Xiangyang; Zhang, Dingguo

    2009-12-01

    Myoelectrical pattern classification is a crucial part in multi-functional prosthesis control. This paper investigates a discriminant Fourier-derived cepstrum (DFC) and feature-level post-processing (FLPP) to discriminate hand and wrist motions using the surface electromyographic signal. The Fourier-derived cepstrum takes advantage of the Fourier magnitude or sub-band power energy of signals directly and provides flexible use of spectral information changing with different motions. Appropriate cepstral coefficients are selected by a proposed separability criterion to construct DFC features. For the post-processing, FLPP which combines features from several analysis windows is used to improve the feature performance further. In this work, two classifiers (a linear discriminant classifier and quadratic discriminant classifier) without hyper-parameter optimization are employed to simplify the training procedure and avoid the possible bias of feature evaluation. Experimental results of the 11-motion problem show that the proposed DFC feature outperforms traditional features such as time-domain statistics and autoregressive-derived cepstrum in terms of the classification accuracy, and it is a promising method for the multi-functionality and high-accuracy control of myoelectric prostheses.

  8. Designing a robust feature extraction method based on optimum allocation and principal component analysis for epileptic EEG signal classification.

    PubMed

    Siuly, Siuly; Li, Yan

    2015-04-01

    The aim of this study is to design a robust feature extraction method for the classification of multiclass EEG signals to determine valuable features from original epileptic EEG data and to discover an efficient classifier for the features. An optimum allocation based principal component analysis method named as OA_PCA is developed for the feature extraction from epileptic EEG data. As EEG data from different channels are correlated and huge in number, the optimum allocation (OA) scheme is used to discover the most favorable representatives with minimal variability from a large number of EEG data. The principal component analysis (PCA) is applied to construct uncorrelated components and also to reduce the dimensionality of the OA samples for an enhanced recognition. In order to choose a suitable classifier for the OA_PCA feature set, four popular classifiers: least square support vector machine (LS-SVM), naive bayes classifier (NB), k-nearest neighbor algorithm (KNN), and linear discriminant analysis (LDA) are applied and tested. Furthermore, our approaches are also compared with some recent research work. The experimental results show that the LS-SVM_1v1 approach yields 100% of the overall classification accuracy (OCA), improving up to 7.10% over the existing algorithms for the epileptic EEG data. The major finding of this research is that the LS-SVM with the 1v1 system is the best technique for the OA_PCA features in the epileptic EEG signal classification that outperforms all the recent reported existing methods in the literature.

  9. 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.

  10. Tracking Signal Subspace Invariance for Blind Separation and Classification of Nonorthogonal Sources in Correlated Noise

    NASA Astrophysics Data System (ADS)

    Oweiss, Karim G.; Anderson, David J.

    2006-12-01

    We investigate a new approach for the problem of source separation in correlated multichannel signal and noise environments. The framework targets the specific case when nonstationary correlated signal sources contaminated by additive correlated noise impinge on an array of sensors. Existing techniques targeting this problem usually assume signal sources to be independent, and the contaminating noise to be spatially and temporally white, thus enabling orthogonal signal and noise subspaces to be separated using conventional eigendecomposition. In our context, we propose a solution to the problem when the sources are nonorthogonal, and the noise is correlated with an unknown temporal and spatial covariance. The approach is based on projecting the observations onto a nested set of multiresolution spaces prior to eigendecomposition. An inherent invariance property of the signal subspace is observed in a subset of the multiresolution spaces that depends on the degree of approximation expressed by the orthogonal basis. This feature, among others revealed by the algorithm, is eventually used to separate the signal sources in the context of "best basis" selection. The technique shows robustness to source nonstationarities as well as anisotropic properties of the unknown signal propagation medium under no constraints on the array design, and with minimal assumptions about the underlying signal and noise processes. We illustrate the high performance of the technique on simulated and experimental multichannel neurophysiological data measurements.

  11. 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.

  12. 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.

  13. 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

  14. 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.

  15. Signal/Image Processing of Acoustic Flaw Signatures for Detection and Localization

    SciTech Connect

    Candy, J V; Meyer, A W

    2001-06-01

    The timely, nondestructive evaluation (NDE) of critical optics in high energy, pulsed laser experiments is a crucial analysis that must be performed for the experiment to be successful. Failure to detect flaws of critical sizes in vacuum-loaded optical windows can result in a catastrophic failure jeopardizing the safety of both personnel and costly equipment. We discuss the development of signal/image processing techniques to both detect critical flaws and locate their position on the window. The data measured from two Orthogonal arrays of narrow beamwidth ultrasonic transducers are preprocessed using a model-based scheme based on the Green's function of the medium providing individual channel signatures. These signatures are then transformed to the two-dimensional image space using a power-based estimator. A 2D-replicant is then constructed based on the underlying physics of the material along with the geometry of the window. Correlating the replicant with the enhanced power image leads to the optimal 2D-matched filter solution detecting and localizing the flaw. Controlled experimental results on machined flaws are discussed.

  16. Introduction to acoustic emission

    NASA Technical Reports Server (NTRS)

    Possa, G.

    1983-01-01

    Typical acoustic emission signal characteristics are described and techniques which localize the signal source by processing the acoustic delay data from multiple sensors are discussed. The instrumentation, which includes sensors, amplifiers, pulse counters, a minicomputer and output devices is examined. Applications are reviewed.

  17. 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

  18. True katydids (Pseudophyllinae) from Guadeloupe: acoustic signals and functional considerations of song production.

    PubMed

    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

  19. 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).

  20. 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).

  1. 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

  2. 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 with