Science.gov

Sample records for acoustic signal classification

  1. Detection and Classification of Whale Acoustic Signals

    NASA Astrophysics Data System (ADS)

    Xian, Yin

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

  2. Perceptually-Driven Signal Analysis for Acoustic Event Classification

    DTIC Science & Technology

    2007-09-26

    study of musical timbre . Defined as "the subjective attribute of sound which differentiates two or more sounds that have the same loudness, pitch and...therefore a better estimate of the likelihood function. 56 Bibliography [1] J. M. Grey, -AMultidimensional perceptual scaling of musical timbres ...Display, 2005. [10] J. M. Grey, "Perceptual effects of spectral modifications on musical timbres ," Journal of the Acoustical Society of America, vol. 63

  3. Signal classification and event reconstruction for acoustic neutrino detection in sea water with KM3NeT

    NASA Astrophysics Data System (ADS)

    Kießling, Dominik

    2017-03-01

    The research infrastructure KM3NeT will comprise a multi cubic kilometer neutrino telescope that is currently being constructed in the Mediterranean Sea. Modules with optical and acoustic sensors are used in the detector. While the main purpose of the acoustic sensors is the position calibration of the detection units, they can be used as instruments for studies on acoustic neutrino detection, too. In this article, methods for signal classification and event reconstruction for acoustic neutrino detectors will be presented, which were developed using Monte Carlo simulations. For the signal classification the disk-like emission pattern of the acoustic neutrino signal is used. This approach improves the suppression of transient background by several orders of magnitude. Additionally, an event reconstruction is developed based on the signal classification. An overview of these algorithms will be presented and the efficiency of the classification will be discussed. The quality of the event reconstruction will also be presented.

  4. 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.

  5. Military Vehicle Classification via Acoustic and Seismic Signals Using Statistical Learning Methods

    NASA Astrophysics Data System (ADS)

    Xiao, Hanguang; Cai, Congzhong; Chen, Yuzong

    It is a difficult and important task to classify the types of military vehicles using the acoustic and seismic signals generated by military vehicles. For improving the classification accuracy and reducing the computing time and memory size, we investigated different pre-processing technology, feature extraction and selection methods. Short Time Fourier Transform (STFT) was employed for feature extraction. Genetic Algorithms (GA) and Principal Component Analysis (PCA) were used for feature selection and extraction further. A new feature vector construction method was proposed by uniting PCA and another feature selection method. K-Nearest Neighbor Classifier (KNN) and Support Vector Machines (SVM) were used for classification. The experimental results showed the accuracies of KNN and SVM were affected obviously by the window size which was used to frame the time series of the acoustic and seismic signals. The classification results indicated the performance of SVM was superior to that of KNN. The comparison of the four feature selection and extraction methods showed the proposed method is a simple, none time-consuming, and reliable technique for feature selection and helps the classifier SVM to achieve more better results than solely using PCA, GA, or combination.

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

    NASA Astrophysics Data System (ADS)

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

    2016-03-01

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

  7. Sparsity-Based Representation for Classification Algorithms and Comparison Results for Transient Acoustic Signals

    DTIC Science & Technology

    2016-05-01

    large but correlated noise and signal interference (i.e., low-rank interference). Another contribution is the implementation of deep learning ...representation, low rank, deep learning 52 Tung-Duong Tran-Luu 301-394-3082Unclassified Unclassified Unclassified UU ii Approved for public release; distribution...is unlimited. Contents List of Figures v List of Tables vi 1. Introduction 1 1.1 Motivations 1 1.2 Sparsity-Based Representation for Transient Acoustic

  8. Application of Polynomial Neural Networks to Classification of Acoustic Warfare Signals

    DTIC Science & Technology

    1993-04-01

    pronounced if, as is often the case , a threat is operating quietly in an attempt to avoid detection. Additionally, due to the relatively infrequent...when interrogated, produces an output vector, ,i in response to a given input vector, 2. In the case of static networks, the output vector is a single...modeling, and classification such is certainly the case , but there are other instances in which the network output is not intended to be the best

  9. 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.

  10. Acoustic Target Classification Using Multiscale Methods

    DTIC Science & Technology

    1998-01-01

    other vehicular activities well; because it represents dominant spectral peaks better than a short time Fourier transform. In the wavelet transform based...approach; multiscale features are obtained with a wavelet transform . Multiscale classification methods were applied to acoustic data collected at...This study considers the classification of acoustic signatures using features extracted at multiple scales from hierarchical models and a wavelet

  11. Wavelet Preprocessing of Acoustic Signals

    DTIC Science & Technology

    1991-12-01

    wavelet transform to preprocess acoustic broadband signals in a system that discriminates between different classes of acoustic bursts. This is motivated by the similarity between the proportional bandwidth filters provided by the wavelet transform and those found in biological hearing systems. The experiment involves comparing statistical pattern classifier effects of wavelet and FFT preprocessed acoustic signals. The data used was from the DARPA Phase I database, which consists of artificially generated signals with real ocean background. The

  12. Acoustic Event Detection and Classification

    NASA Astrophysics Data System (ADS)

    Temko, Andrey; Nadeu, Climent; Macho, Dušan; Malkin, Robert; Zieger, Christian; Omologo, Maurizio

    The human activity that takes place in meeting rooms or classrooms is reflected in a rich variety of acoustic events (AE), produced either by the human body or by objects handled by humans, so the determination of both the identity of sounds and their position in time may help to detect and describe that human activity. Indeed, speech is usually the most informative sound, but other kinds of AEs may also carry useful information, for example, clapping or laughing inside a speech, a strong yawn in the middle of a lecture, a chair moving or a door slam when the meeting has just started. Additionally, detection and classification of sounds other than speech may be useful to enhance the robustness of speech technologies like automatic speech recognition.

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

    SciTech Connect

    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.

  14. Wavelet preprocessing of acoustic signals

    NASA Astrophysics Data System (ADS)

    Huang, W. Y.; Solorzano, M. R.

    1991-12-01

    This paper describes results using the wavelet transform to preprocess acoustic broadband signals in a system that discriminates between different classes of acoustic bursts. This is motivated by the similarity between the proportional bandwidth filters provided by the wavelet transform and those found in biological hearing systems. The experiment involves comparing statistical pattern classifier effects of wavelet and FFT preprocessed acoustic signals. The data used was from the DARPA Phase 1 database, which consists of artificially generated signals with real ocean background. The results show that the wavelet transform did provide improved performance when classifying in a frame-by-frame basis. The DARPA Phase 1 database is well matched to proportional bandwidth filtering; i.e., signal classes that contain high frequencies do tend to have shorter duration in this database. It is also noted that the decreasing background levels at high frequencies compensate for the poor match of the wavelet transform for long duration (high frequency) signals.

  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. Multiexpert automatic speech recognition using acoustic and myoelectric signals.

    PubMed

    Chan, Adrian D C; Englehart, Kevin B; Hudgins, Bernard; Lovely, Dennis F

    2006-04-01

    Classification accuracy of conventional automatic speech recognition (ASR) systems can decrease dramatically under acoustically noisy conditions. To improve classification accuracy and increase system robustness a multiexpert ASR system is implemented. In this system, acoustic speech information is supplemented with information from facial myoelectric signals (MES). A new method of combining experts, known as the plausibility method, is employed to combine an acoustic ASR expert and a MES ASR expert. The plausibility method of combining multiple experts, which is based on the mathematical framework of evidence theory, is compared to the Borda count and score-based methods of combination. Acoustic and facial MES data were collected from 5 subjects, using a 10-word vocabulary across an 18-dB range of acoustic noise. As expected the performance of an acoustic expert decreases with increasing acoustic noise; classification accuracies of the acoustic ASR expert are as low as 11.5%. The effect of noise is significantly reduced with the addition of the MES ASR expert. Classification accuracies remain above 78.8% across the 18-dB range of acoustic noise, when the plausibility method is used to combine the opinions of multiple experts. In addition, the plausibility method produced classification accuracies higher than any individual expert at all noise levels, as well as the highest classification accuracies, except at the 9-dB noise level. Using the Borda count and score-based multiexpert systems, classification accuracies are improved relative to the acoustic ASR expert but are as low as 51.5% and 59.5%, respectively.

  18. Multidimensional signal processing for ultrasonic signal classification

    NASA Astrophysics Data System (ADS)

    Kim, J.; Ramuhalli, P.; Udpa, L.; Udpa, S.

    2001-04-01

    Neural network based signal classification systems are being used increasingly in the analysis of large volumes of data obtained in NDE applications. One example is in the interpretation on ultrasonic signals obtained from inspection of welds where signals can be due to porosity, slag, lack of fusion and cracks in the weld region. Standard techniques rely on differences in individual A-scans to classify the signals. This paper proposes an ultrasonic signal classification technique based on the information in a group of signals and examining the statistical characteristics of the signals. The method was 2-dimensional signal processing algorithms to analyze the information in B- and B'-scan images. In this paper, 2-dimensional transform based coefficients of the images are used as features and a multilayer perceptron is used to classify them. These results are then combined to get the final classification for the inspected region. Results of applying the technique to data obtained from the inspection of welds are presented.

  19. Acoustical classification of woods for string instruments.

    PubMed

    Yoshikawa, Shigeru

    2007-07-01

    Two basic types of wood are used to make stringed musical instruments: woods for soundboards (top plates) and those for frame boards (back and side plates). A new way to classify the acoustical properties of woods and clearly separate these two groups is proposed in this paper. The transmission parameter (product of propagation speed and Q value of the longitudinal wave along the wood grain) and the antivibration parameter (wood density divided by the propagation speed along the wood grain) are introduced in the proposed classification scheme. Two regression lines, drawn for traditional woods, show the distinctly different functions required by soundboards and frame boards. These regression lines can serve as a reference to select the best substitute woods when traditional woods are not available. Moreover, some peculiarities of Japanese string instruments, which are made clear by comparing woods used for them with woods used for Western and Chinese instruments, are briefly discussed.

  20. Lake bed classification using acoustic data

    USGS Publications Warehouse

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

    1998-01-01

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

  1. Vehicle acoustic classification in netted sensor systems using Gaussian mixture models

    NASA Astrophysics Data System (ADS)

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

    2005-05-01

    Acoustic vehicle classification is a difficult problem due to the non-stationary nature of the signals, and especially the lack of strong harmonic structure for most civilian vehicles with highly muffled exhausts. Acoustic signatures will also vary largely depending on speed, acceleration, gear position, and even the aspect angle of the sensor. The problem becomes more complicated when the deployed acoustic sensors have less than ideal characteristics, in terms of both the frequency response of the transducers, and hardware capabilities which determine the resolution and dynamic range. In a hierarchical network topology, less capable Tier 1 sensors can be tasked with reasonably sophisticated signal processing and classification algorithms, reducing energy-expensive communications with the upper layers. However, at Tier 2, more sophisticated classification algorithms exceeding the Tier 1 sensor/processor capabilities can be deployed. The focus of this paper is the investigation of a Gaussian mixture model (GMM) based classification approach for these upper nodes. The use of GMMs is motivated by their ability to model arbitrary distributions, which is very relevant in the case of motor vehicles with varying operation modes and engines. Tier 1 sensors acquire the acoustic signal and transmit computed feature vectors up to Tier 2 processors for maximum-likelihood classification using GMMs. In a binary classification task of light-vs-heavy vehicles, the GMM based approach achieves 7% equal error rate, providing an approximate error reduction of 49% over Tier 1 only approaches.

  2. Frequency Spreading in Underwater Acoustic Signal Transmission.

    DTIC Science & Technology

    1980-04-15

    acoustic signal transmitted and received underwater J-2 J.2 Signal spectrum computing block diagram. J-3 Chapter I. Frequency spreading 1.0 Introduction... transmitted frequency can be expected in the received signal [1] - [18]. This frequency spreading behavior is the result of the amplitude and phase...result of phase modulation of the transmitted sinusoid by the moving surface, and the separation between the spectral lines at the receiving point is

  3. Acoustic classification of battlefield transient events using wavelet sub-band features

    NASA Astrophysics Data System (ADS)

    Azimi-Sadjadi, M. R.; Jiang, Y.; Srinivasan, S.

    2007-04-01

    Detection, localization and classification of battlefield acoustic transient events are of great importance especially for military operations in urban terrain (MOUT). Generally, there can be a wide variety of battlefield acoustic transient events such as different caliber gunshots, artillery fires, and mortar fires. The discrimination of different types of transient sources is plagued by highly non-stationary nature of these signals, which makes the extraction of representative features a challenging task. This is compounded by the variations in the environmental and operating conditions and existence of a wide range of possible interference. This paper presents new approaches for transient signal estimation and feature extraction from acoustic signatures collected by several distributed sensor nodes. A maximum likelihood (ML)-based method is developed to remove noise/interference and fading effects and restore the acoustic transient signals. The estimated transient signals are then represented using wavelets. The multi-resolution property of the wavelets allows for capturing fine details in the transient signals that can be utilized to successfully classify them. Wavelet sub-band higher order moments and energy-based features are used to characterize the transient signals. The discrimination ability of the subband features for transient signal classification has been demonstrated on several different caliber gunshots. Important findings and observations on these results are also presented.

  4. Neural Network Classification of Cerebral Embolic Signals

    DTIC Science & Technology

    2007-11-02

    application of new signal processing techniques to the analysis and classification of embolic signals. We applied a Wavelet Neural Network algorithm...to approximate the embolic signals, with the parameters of the wavelet nodes being used to train a Neural Network to classify these signals as resulting from normal flow, or from gaseous or solid emboli.

  5. 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.

  6. 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.

  7. Hot topics: Signal processing in acoustics

    NASA Astrophysics Data System (ADS)

    Candy, James

    2002-05-01

    Signal processing represents a technology that provides the mechanism to extract the desired information from noisy acoustical measurement data. The desired result can range from extracting a single number like sound intensity level in the case of marine mammals to the seemingly impossible task of imaging the complex bottom in a hostile ocean environment. Some of the latest approaches to solving acoustical processing problems including sophisticated Bayesian processors in architectural acoustics, iterative flaw removal processing for non-destructive evaluation, time-reversal imaging for buried objects and time-reversal receivers in communications as well as some of the exciting breakthroughs using so-called blind processing techniques for deconvolution are discussed. Processors discussed range from the simple to the sophisticated as dictated by the particular application. It is shown how processing techniques are crucial to extracting the required information for success in the underlying application.

  8. Fatigue Level Estimation of Bill Based on Acoustic Signal Feature by Supervised SOM

    NASA Astrophysics Data System (ADS)

    Teranishi, Masaru; Omatu, Sigeru; Kosaka, Toshihisa

    Fatigued bills have harmful influence on daily operation of Automated Teller Machine(ATM). To make the fatigued bills classification more efficient, development of an automatic fatigued bill classification method is desired. We propose a new method to estimate bending rigidity of bill from acoustic signal feature of banking machines. The estimated bending rigidities are used as continuous fatigue level for classification of fatigued bill. By using the supervised Self-Organizing Map(supervised SOM), we estimate the bending rigidity from only the acoustic energy pattern effectively. The experimental result with real bill samples shows the effectiveness of the proposed method.

  9. Acoustic signal processing toolbox for array processing

    NASA Astrophysics Data System (ADS)

    Pham, Tien; Whipps, Gene T.

    2003-08-01

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

  10. Model-based acoustic characterization and classification of irregular-shaped targets: Application to fisheries and zooplankton acoustics

    NASA Astrophysics Data System (ADS)

    Chu, Dezhang; Stanton, Timothy K.; Wiebe, Peter H.

    2002-11-01

    Acoustic scattering by fish and zooplankton is a complicated function of the geometrical and physical properties of the targets, as well as the environmental and sonar system parameters. The shape and anatomy of zooplankton vary significantly from taxa to taxa and their dominant scattering mechanisms can be completely different. As a result, the acoustic classification of such targets is extremely difficult and often nonunique. To reduce the ambiguity and nonuniqueness, a number of model-based methods are presented. These methods use the temporal, spatial, spectral, and statistical signatures of acoustical scattering signals and can be applied to a variety of acoustic systems, including narrow-band, broadband, and multifrequency systems. The methods also depend strongly on whether or not the targets are resolved. Individual targets with different shapes and material properties have their unique characteristics and can be classified acoustically in terms of their size, orientation, scattering mechanisms, as well as their material properties. Results of applying these methods to the laboratory and field data will be presented and analyzed. [Work supported by ONR, NSF, and the Comer Science and Education Foundation.

  11. Acoustic signal propagation characterization of conduit networks

    NASA Astrophysics Data System (ADS)

    Khan, Muhammad Safeer

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

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

    NASA Astrophysics Data System (ADS)

    Yegireddi, Satyanarayana; Thomas, Nitheesh

    2014-06-01

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

  13. Seismic and acoustic signal identification algorithms

    SciTech Connect

    LADD,MARK D.; ALAM,M. KATHLEEN; SLEEFE,GERARD E.; GALLEGOS,DANIEL E.

    2000-04-03

    This paper will describe an algorithm for detecting and classifying seismic and acoustic signals for unattended ground sensors. The algorithm must be computationally efficient and continuously process a data stream in order to establish whether or not a desired signal has changed state (turned-on or off). The paper will focus on describing a Fourier based technique that compares the running power spectral density estimate of the data to a predetermined signature in order to determine if the desired signal has changed state. How to establish the signature and the detection thresholds will be discussed as well as the theoretical statistics of the algorithm for the Gaussian noise case with results from simulated data. Actual seismic data results will also be discussed along with techniques used to reduce false alarms due to the inherent nonstationary noise environments found with actual data.

  14. 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

  15. Automated Classification of Power Signals

    DTIC Science & Technology

    2008-06-01

    the classification code of the n th event. Boolean EVC [n] The ‘Event file created?’ Boolean is set to 1 if the event has had an event file created...indicate the type of event. int EVC [MAX_EVENTS]; // Boolean to indicate whether an event has had an .evt file created int local_det=0...i; // CLEAN THE EVENT TEXT DATA. for (i=0;i<MAX_EVENTS;i++) { Class[i]="Empty." Class_ID[i]=0; EVC [i]=FALSE; event_class_status[i

  16. 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

  17. Signals Intelligence - Processing - Analysis - Classification

    DTIC Science & Technology

    2009-10-01

    Example: Language identification from audio signals. In a certain mission, a set of languages seems important beforehand. These languages will – with a...tasks to be performed. • OCR: determine the text parts in an image – language dependent approach, quality depends on the language. • Steganography

  18. 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.

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

    NASA Astrophysics Data System (ADS)

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

    2015-12-01

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

  20. 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

  1. Acoustic censusing using automatic vocalization classification and identity recognition.

    PubMed

    Adi, Kuntoro; Johnson, Michael T; Osiejuk, Tomasz S

    2010-02-01

    This paper presents an advanced method to acoustically assess animal abundance. The framework combines supervised classification (song-type and individual identity recognition), unsupervised classification (individual identity clustering), and the mark-recapture model of abundance estimation. The underlying algorithm is based on clustering using hidden Markov models (HMMs) and Gaussian mixture models (GMMs) similar to methods used in the speech recognition community for tasks such as speaker identification and clustering. Initial experiments using a Norwegian ortolan bunting (Emberiza hortulana) data set show the feasibility and effectiveness of the approach. Individually distinct acoustic features have been observed in a wide range of animal species, and this combined with the widespread success of speaker identification and verification methods for human speech suggests that robust automatic identification of individuals from their vocalizations is attainable. Only a few studies, however, have yet attempted to use individual acoustic distinctiveness to directly assess population density and structure. The approach introduced here offers a direct mechanism for using individual vocal variability to create simpler and more accurate population assessment tools in vocally active species.

  2. Automatic Genre Classification of Musical Signals

    NASA Astrophysics Data System (ADS)

    Barbedo, Jayme Garcia sArnal; Lopes, Amauri

    2006-12-01

    We present a strategy to perform automatic genre classification of musical signals. The technique divides the signals into 21.3 milliseconds frames, from which 4 features are extracted. The values of each feature are treated over 1-second analysis segments. Some statistical results of the features along each analysis segment are used to determine a vector of summary features that characterizes the respective segment. Next, a classification procedure uses those vectors to differentiate between genres. The classification procedure has two main characteristics: (1) a very wide and deep taxonomy, which allows a very meticulous comparison between different genres, and (2) a wide pairwise comparison of genres, which allows emphasizing the differences between each pair of genres. The procedure points out the genre that best fits the characteristics of each segment. The final classification of the signal is given by the genre that appears more times along all signal segments. The approach has shown very good accuracy even for the lowest layers of the hierarchical structure.

  3. Multi-View Acoustic Sizing and Classification of Individual Fish

    NASA Astrophysics Data System (ADS)

    Roberts, P. L. D.; Jaffe, J. S.

    Estimating biophysical parameters of fish populations in situ such as size, orientation, shape, and taxa is a fundamental goal in oceanography. Towards this end, acoustics is a natural choice due to its rapid, non-invasive capabilities. Here, multi-view methods are explored for classification, size and orientation estimation, and 2D image reconstruction for individual fish. Size- and shape-based classification using multi-view data is shown to be accurate (~10% error) using kernel methods and discriminant analysis. For species-based classification in the absence of significant differences in size or shape, multi-view methods offer significant (~40%) reduction in error, but absolute error rates remain high (~20%) due to the lack of discriminant information in acoustic scatter. Length and orientation estimation are investigated using a parameter-based approach with a simple ellipsoidal scattering model. Good accuracy is obtained when the views span the full 360°. When the span is limited to less than 60°, incorporating a prior constraint on possible body shapes can lead to reduced uncertainty in the estimated parameters. Finally, using views that span the full 360°, sparse Bayesian learning coupled with a conventional Radon transform yields accurate two-dimensional, projected images of the fish.

  4. 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.

  5. 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%.

  6. 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.

  7. 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

  8. 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

  9. 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

  10. Evolution of acoustic and visual signals in Asian barbets.

    PubMed

    Gonzalez-Voyer, A; den Tex, R-J; Castelló, A; Leonard, J A

    2013-03-01

    The study of animal communication systems is an important step towards gaining greater understanding of the processes influencing diversification because signals often play an important role in mate choice and can lead to reproductive isolation. Signal evolution can be influenced by a diversity of factors such as biophysical constraints on the emitter, the signalling environment, or selection to avoid heterospecific matings. Furthermore, because signals can be costly to produce, trade-offs may exist between different types of signals. Here, we apply phylogenetic comparative analyses to study the evolution of acoustic and visual signals in Asian barbets, a clade of non-Passerine, forest-dependent birds. Our results suggest that evolution of acoustic and visual signals in barbets is influenced by diverse factors, such as morphology and signalling environment, suggesting a potential effect of sensory drive. We found no trade-offs between visual and acoustic signals. Quite to the contrary, more colourful species sing significantly longer songs. Song characteristics presented distinct patterns of evolution. Song frequency diverged early on and the rate of evolution of this trait appears to be constrained by body size. On the other hand, characteristics associated with length of the song presented evidence for more recent divergence. Finally, our results indicate that there is a spatial component to the evolution of visual signals, and that visual signals are more divergent between closely related taxa than acoustic signals. Hence, visual signals in these species could play a role in speciation or reinforcement of reproductive isolation following secondary contacts.

  11. 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.

  12. Acoustic signals of Chinese alligators (Alligator sinensis): social communication.

    PubMed

    Wang, Xianyan; Wang, Ding; Wu, Xiaobing; Wang, Renping; Wang, Chaolin

    2007-05-01

    This paper reports the first systematic study of acoustic signals during social interactions of the Chinese alligator (Alligator sinensis). Sound pressure level (SPL) measurements revealed that Chinese alligators have an elaborate acoustic communication system with both long-distance signal-bellowing-and short-distance signals that include tooting, bubble blowing, hissing, mooing, head slapping and whining. Bellows have high SPL and appear to play an important role in the alligator's long range intercommunion. Sounds characterized by low SPL are short-distance signals used when alligators are in close spatial proximity to one another. The signal spectrographic analysis showed that the acoustic signals of Chinese alligators have a very low dominant frequency, less than 500 Hz. These frequencies are consistent with adaptation to a habitat with high density vegetation. Low dominant frequency sound attenuates less and could therefore cover a larger spatial range by diffraction in a densely vegetated environment relative to a higher dominant frequency sound.

  13. 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.

  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. Signal Processing Aspects of Nonlinear Acoustics.

    DTIC Science & Technology

    1980-07-07

    D. F., and Widener, M. W.: 1979, " PARRAY Technology Papers Presented at Scientific and Technical Meetings," Applied Research Laboratories Technical...Report No. 79-4, Applied Research Laboratories, The University of Texas at Austin. AD A077 726. 19. Goldsberry, T. G.: 1979, "The PARRAY as an Acoustic

  17. 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.

  18. 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).

  19. Environmental Acoustic Transfer Functions and the Filtering of Acoustic Signals

    DTIC Science & Technology

    2005-03-21

    function by the Sturm - Liouville Theorem (7). Then the left-hand side of the inner product equation is*X l;m;n Kl;m;nFl (z;H)Fm (y;L)Fn (x;W )l;m;nc...results of this thesis enable us to determine under which conditions a �ltering operation can successfully be performed on a set of received signals...signal being propagated at a location ~x0, and so the use of the Dirac delta function is appropriate in the use of a forcing function. A time-dependent

  20. Fluctuations of Broadband Acoustic Signals in Shallow Water

    DTIC Science & Technology

    2015-09-30

    Signals in Shallow Water Mohsen Badiey College of Earth, Ocean, and Environment University of Delaware Newark, DE 19716 Phone: (302) 831-3687 Fax...refraction, and scattering in shallow water and coastal regions in the presence of temporal and spatial ocean variability. OBJECTIVES The scientific...of water column and dynamic sea surface variability, as well as source/receiver motion on acoustic wave propagation for underwater acoustic

  1. 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

  2. Workshop on the Detection, Classification, Localization and Density Estimation of Marine Mammals Using Passive Acoustics - 2015

    DTIC Science & Technology

    2015-09-30

    and Density Estimation of Marine Mammals Using Passive Acoustics - 2015 John A. Hildebrand Scripps Institution of Oceanography UCSD La Jolla...classification, localization and density estimation of marine mammals using passive acoustics , and by doing so advance the state of the art in this field...Passive Acoustics was organized and held at the Scripps Institution of Oceanography (SIO) in July 2015. The objective of ONR support for the

  3. Fifth International Workshop on Detection, Classification, Localization and Density Estimation of Marine Mammals using Passive Acoustics

    DTIC Science & Technology

    2013-09-30

    spring 2011 in Seattle) • The Fourth International Conference on Detection and Classification of Marine Mammals using Passive Acoustics ( Pavia ...Italy, 2009) • The International BioAcoustic Congress ( Pavia , Italy, 2009) Report Documentation Page Form ApprovedOMB No. 0704-0188 Public reporting

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

    PubMed

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

    2011-09-01

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

  5. 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.

  6. Signal Classification in Fading Channels Using Cyclic Spectral Analysis

    DTIC Science & Technology

    2009-07-01

    efficient algorithms to detect and classify an OFDM signal based on its cyclic prefix through the use of a simple autocorrelation procedure [21–23...we focus on the case of an OFDM signal transmitted with no cyclic prefix. Therefore, an intermediate stage is needed between the SOF- based ...classifications and the HOCS- based classifications. A simple yet effective method to distinguish OFDM signals from the single carrier signals in question is

  7. 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…

  8. PREDICTIVE MODELING OF ACOUSTIC SIGNALS FROM THERMOACOUSTIC POWER SENSORS (TAPS)

    SciTech Connect

    Dumm, Christopher M.; Vipperman, Jeffrey S.

    2016-06-30

    Thermoacoustic Power Sensor (TAPS) technology offers the potential for self-powered, wireless measurement of nuclear reactor core operating conditions. TAPS are based on thermoacoustic engines, which harness thermal energy from fission reactions to generate acoustic waves by virtue of gas motion through a porous stack of thermally nonconductive material. TAPS can be placed in the core, where they generate acoustic waves whose frequency and amplitude are proportional to the local temperature and radiation flux, respectively. TAPS acoustic signals are not measured directly at the TAPS; rather, they propagate wirelessly from an individual TAPS through the reactor, and ultimately to a low-power receiver network on the vessel’s exterior. In order to rely on TAPS as primary instrumentation, reactor-specific models which account for geometric/acoustic complexities in the signal propagation environment must be used to predict the amplitude and frequency of TAPS signals at receiver locations. The reactor state may then be derived by comparing receiver signals to the reference levels established by predictive modeling. In this paper, we develop and experimentally benchmark a methodology for predictive modeling of the signals generated by a TAPS system, with the intent of subsequently extending these efforts to modeling of TAPS in a liquid sodium environmen

  9. Inversion of High Frequency Acoustic Data for Sediment Properties Needed for the Detection and Classification of UXOs

    DTIC Science & Technology

    2015-05-26

    FINAL REPORT Inversion of High Frequency Acoustic Data for Sediment Properties Needed for the Detection and Classification of UXOs SERDP...2015 Inversion of High Frequency Acoustic Data for Sediment Properties Needed for the Detection and Classification of UXO’s W912HQ-12-C-0049 MR...the acoustic response of the environment as well as the environment’s effect on the acoustic response of munitions [1]. Simulation tools and

  10. Fluctuations of Broadband Acoustic Signals in Shallow Water

    DTIC Science & Technology

    2010-09-30

    DISTRIBUTION STATEMENT A: Distribution approved...for public release; distribution is unlimited. Fluctuations of Broadband Acoustic Signals in Shallow Water Mohsen Badiey College of Earth, Ocean...AND ADDRESS(ES) 10. SPONSOR/MONITOR’S ACRONYM(S) 11. SPONSOR/MONITOR’S REPORT NUMBER(S) 12. DISTRIBUTION /AVAILABILITY STATEMENT Approved for

  11. Acoustic and electric signals from lightning

    NASA Technical Reports Server (NTRS)

    Balachandran, N. K.

    1983-01-01

    Observations of infrasound apparently generated by the collapse of the electrostatic field in the thundercloud, are presented along with electric field measurements and high-frequency thunder signals. The frequency of the infrasound pulse is about 1 Hz and amplitude a few microbars. The observations seem to confirm some of the theoretical predictions of Wilson (1920) and Dessler (1973). The signal is predominated by a compressional phase and seems to be beamed vertically. Calculation of the parameters of the charged region using the infrasound signal give reasonable values.

  12. Link Budget Analysis for Undersea Acoustic Signaling

    DTIC Science & Technology

    2002-06-01

    wireless communications for estimating signal-to- noise ratio ( SNR ) at the receiver. Link-budget analysis considers transmitter power, transmitter...is represented as an intermediate result called the channel SNR . The channel SNR includes ambient-noise and transmission-loss components. Several...to satellite and wireless communications for estimating signal-to-noise ratio ( SNR ) at the receiver. Link-budget analysis considers transmitter

  13. 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).

  14. 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.

  15. Using acoustic emission signals for monitoring of production processes.

    PubMed

    Tönshoff, H K; Jung, M; Männel, S; Rietz, W

    2000-07-01

    The systems for in-process quality assurance offer the possibility of estimating the workpiece quality during machining. Especially for finishing processes like grinding or turning of hardened steels, it is important to control the process continuously in order to avoid rejects and refinishing. This paper describes the use of on-line monitoring systems with process-integrated measurement of acoustic emission to evaluate hard turning and grinding processes. The correlation between acoustic emission signals and subsurface integrity is determined to analyse the progression of the processes and the workpiece quality.

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

    NASA Astrophysics Data System (ADS)

    Johnson, Spencer Joseph

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

  17. 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.

  18. The effects of acoustic attenuation in optoacoustic signals.

    PubMed

    Deán-Ben, X Luís; Razansky, Daniel; Ntziachristos, Vasilis

    2011-09-21

    In this paper, it is demonstrated that the effects of acoustic attenuation may play a significant role in establishing the quality of tomographic optoacoustic reconstructions. Accordingly, spatially dependent reduction of signal amplitude leads to quantification errors in the reconstructed distribution of the optical absorption coefficient while signal broadening causes loss of image resolution. Here we propose a correction algorithm for accounting for attenuation effects, which is applicable in both the time and frequency domains. It is further investigated which part of the optoacoustic signal spectrum is practically affected by those effects in realistic imaging scenarios. The validity and benefits of the suggested modelling and correction approaches are experimentally validated in phantom measurements.

  19. Biosonar acoustic images for target localization and classification by bats

    NASA Astrophysics Data System (ADS)

    Simmons, James A.

    1997-07-01

    Echolocating bats use sonar to guide interception of insects, recognize objects by shape, and even track prey in clutter. Broadcasts of the big brown bat are 0.5 to 20 ms FM signals in the 20-100 kHz ultrasonic band. Insects consist of several reflecting glints, each equivalent in cross- section to a small sphere of 2 mm to 2 cm radius, while clutter is typically composed of numerous glints distributed over a large volume. The bats' signals extend in space for many target lengths, while ka values for each glint are 0.5 to 30 across the broadcast band. Bats perceive acoustic images having echo delay as their primary dimension, and space is perceived in terms of the distribution of target glints in range. Range disparities between the ears provide two 'looks' at each target from slightly different locations as well as information about azimuth. The bats auditory system encodes the FM sweeps of broadcasts and echoes as linear-period spectrograms with integration-times of 300-400 micrometers . Bats nevertheless perceive individual glints in targets for echo-delay separations well inside the integration-time window. Deconvolution is achieved by spectrogram correlation in the time domain and spectral shape transformation in the frequency-domain, with all output evidently being displayed in the time domina. Neural responses in the bat's auditory system seem limited in time precision to 20-50 micrometers at best and 300 microsecond(s) to 3 ms in a broader sample, and stimulus phase is thought to be lost for frequencies above 1-3 kHz. Yet bats perceive echo delay with an accuracy of 10-15 ns and have two-echo resolution of about 2 microsecond(s) . Moreover, bats perceive echo phase-shifts as the correctly corresponding shifts in echo delay. Successive images are subtracted to enhance perception of shape from multiple 'looks', and echo phase is an integral part of this critical process. Utterly novel time-scale magnification appears in the bat's neural responses to

  20. 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.

  1. Coupled Research in Ocean Acoustics and Signal Processing for the Next Generation of Underwater Acoustic Communication Systems

    DTIC Science & Technology

    2015-08-09

    Coupled Research in Ocean Acoustics and Signal Processing for the Next Generation of Underwater Acoustic Communication Systems 5a. CONTRACT NUMBER 5b...Processing for the Next Generation of Underwater Acoustic Communication Systems Principal Investigator’s Name: Dr. James Preisig Period Covered By...correlation structure of received communications signals after they have been converted to the frequency domain via Fourier Transforms as de- scribed in

  2. Coupled Research in Ocean Acoustics and Signal Processing for the Next Generation of Underwater Acoustic Communication Systems

    DTIC Science & Technology

    2016-08-05

    JPAnalytics LLC CC: DCMA Boston DTIC Director, NRL Progress Report #9 Coupled Research in Ocean Acoustics and Signal Processing for the Next Generation...of Underwater Acoustic Communication Systems Principal Investigator’s Name: Dr. James Preisig Period Covered By Report: 4/20/2016 to 7/19/2016 Report...lower dimensional structures in acoustic communications data, specifically fre- quency domain transformations of received communications signals, to

  3. Cross Spectral Analysis of Acoustic Signals

    DTIC Science & Technology

    1978-03-01

    this -- for ground flashes they measured peaks (after correc- tion for wind -noise) in the 40 to 80 HZ range. Some attenua- tion occurs due to...betweer r 2 - rI - v0 T and k: cos a = Cr2 - 1r - t(7) v0 is the wind velocity and usually is neglected. If this method is applied to signals received at... wind velocity, V t ,is ignored, and P(r 2 ,t) = P(rl,t + T), once again one can estimate the time lag 7 and use the time laq to find the source, using

  4. Cavitating vortex characterization based on acoustic signal detection

    NASA Astrophysics Data System (ADS)

    Digulescu, A.; Murgan, I.; Candel, I.; Bunea, F.; Ciocan, G.; Bucur, D. M.; Dunca, G.; Ioana, C.; Vasile, G.; Serbanescu, A.

    2016-11-01

    In hydraulic turbines operating at part loads, a cavitating vortex structure appears at runner outlet. This helical vortex, called vortex rope, can be cavitating in its core if the local pressure is lower that the vaporization pressure. An actual concern is the detection of the cavitation apparition and the characterization of its level. This paper presents a potentially innovative method for the detection of the cavitating vortex presence based on acoustic methods. The method is tested on a reduced scale facility using two acoustic transceivers positioned in ”V” configuration. The received signals were continuously recorded and their frequency content was chosen to fit the flow and the cavitating vortex. Experimental results showed that due to the increasing flow rate, the signal - vortex interaction is observed as modifications on the received signal's high order statistics and bandwidth. Also, the signal processing results were correlated with the data measured with a pressure sensor mounted in the cavitating vortex section. Finally it is shown that this non-intrusive acoustic approach can indicate the apparition, development and the damping of the cavitating vortex. For real scale facilities, applying this method is a work in progress.

  5. Acoustic Response of Underwater Munitions near a Sediment Interface: Measurement Model Comparisons and Classification Schemes

    DTIC Science & Technology

    2015-04-23

    FINAL REPORT Acoustic Response of Underwater Munitions near a Sediment Interface: Measurement Model Comparisons and Classification Schemes SERDP...6 Figure 2. Effect of fish on acoustic color templates during GULFEX12 …………… 8 Figure 3. Selection of targets deployed during TREX13 and BAYEX14...deployed during TREX13 and BAYEX14 …… 29 Figure 16. Ray diagrams for the acoustic ray model …………………………… 29 Figure 17. Model-model and data-model

  6. 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.

  7. 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.

  8. Angle of Arrival Estimation for Saturated Acoustic Signals

    DTIC Science & Technology

    2013-03-01

    to close proximity to a large transient event, which can render target localization difficult with many standard algorithms. Our goal is to develop an...defined threshold on multiple channels. However, close proximity to an 2 acoustic source can result in signal saturation, where data reach a...KINGMAN RD STE 0944 FT BELVOIR VA 22060-6218 4 PDFS US ARMY ARDEC FUZE PRECISION ARMAMENT TECHNOLOGY DIV ATTN A MORCOS ATTN H VANPELT

  9. 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

  10. Acoustic classification of multiple simultaneous bird species: a multi-instance multi-label approach.

    PubMed

    Briggs, Forrest; Lakshminarayanan, Balaji; Neal, Lawrence; Fern, Xiaoli Z; Raich, Raviv; Hadley, Sarah J K; Hadley, Adam S; Betts, Matthew G

    2012-06-01

    Although field-collected recordings typically contain multiple simultaneously vocalizing birds of different species, acoustic species classification in this setting has received little study so far. This work formulates the problem of classifying the set of species present in an audio recording using the multi-instance multi-label (MIML) framework for machine learning, and proposes a MIML bag generator for audio, i.e., an algorithm which transforms an input audio signal into a bag-of-instances representation suitable for use with MIML classifiers. The proposed representation uses a 2D time-frequency segmentation of the audio signal, which can separate bird sounds that overlap in time. Experiments using audio data containing 13 species collected with unattended omnidirectional microphones in the H. J. Andrews Experimental Forest demonstrate that the proposed methods achieve high accuracy (96.1% true positives/negatives). Automated detection of bird species occurrence using MIML has many potential applications, particularly in long-term monitoring of remote sites, species distribution modeling, and conservation planning.

  11. 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.

  12. 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

  13. 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)].

  14. Bioacoustic Signal Classification in Cat Auditory Cortex

    DTIC Science & Technology

    1994-01-01

    representation as the input ( front end ) to a self- organizing signal classifier and as training pattern for the output of a dynamic neural network. In the...potential use as a front end for a biological based signal classifier, their use as a trainer for network models, and their ability to predict spatial...usually contributing more spikes, at best level, than monotonic neurons. d) One region in the center of the dorsal-ventral extent of czt Al appears to have

  15. 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.

  16. Fatigue crack localization with near-field acoustic emission signals

    NASA Astrophysics Data System (ADS)

    Zhou, Changjiang; Zhang, Yunfeng

    2013-04-01

    This paper presents an AE source localization technique using near-field acoustic emission (AE) signals induced by crack growth and propagation. The proposed AE source localization technique is based on the phase difference in the AE signals measured by two identical AE sensing elements spaced apart at a pre-specified distance. This phase difference results in canceling-out of certain frequency contents of signals, which can be related to AE source direction. Experimental data from simulated AE source such as pencil breaks was used along with analytical results from moment tensor analysis. It is observed that the theoretical predictions, numerical simulations and the experimental test results are in good agreement. Real data from field monitoring of an existing fatigue crack on a bridge was also used to test this system. Results show that the proposed method is fairly effective in determining the AE source direction in thick plates commonly encountered in civil engineering structures.

  17. 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.

  18. 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.

  19. Acoustic mapping and classification of benthic habitat using unsupervised learning in artificial reef water

    NASA Astrophysics Data System (ADS)

    Li, Dong; Tang, Cheng; Xia, Chunlei; Zhang, Hua

    2017-02-01

    Artificial reefs (ARs) are effective means to maintain fishery resources and to restore ecological environment in coastal waters. ARs have been widely constructed along the Chinese coast. However, understanding of benthic habitats in the vicinity of ARs is limited, hindering effective fisheries and aquacultural management. Multibeam echosounder (MBES) is an advanced acoustic instrument capable of efficiently generating large-scale maps of benthic environments at fine resolutions. The objective of this study is to develop a technical approach to characterize, classify, and map shallow coastal areas with ARs using an MBES. An automated classification method is designed and tested to process bathymetric and backscatter data from MBES and transform the variables into simple, easily visualized maps. To reduce the redundancy in acoustic variables, a principal component analysis (PCA) is used to condense the highly collinear dataset. An acoustic benthic map of bottom sediments is classified using an iterative self-organizing data analysis technique (ISODATA). The approach is tested with MBES surveys in a 1.15 km2 fish farm with a high density of ARs off the Yantai coast in northern China. Using this method, 3 basic benthic habitats (sandy bottom, muddy sediments, and ARs) are distinguished. The results of the classification are validated using sediment samples and underwater surveys. Our study shows that the use of MBES is an effective method for acoustic mapping and classification of ARs.

  20. 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

  1. 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

  2. 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.}

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

    NASA Astrophysics Data System (ADS)

    Kremliovsky, Michael; Kadtke, James

    1996-06-01

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

  4. 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.

  5. 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.

  6. Observer-based beamforming algorithm for acoustic array signal processing.

    PubMed

    Bai, Long; Huang, Xun

    2011-12-01

    In the field of noise identification with microphone arrays, conventional delay-and-sum (DAS) beamforming is the most popular signal processing technique. However, acoustic imaging results that are generated by DAS beamforming are easily influenced by background noise, particularly for in situ wind tunnel tests. Even when arithmetic averaging is used to statistically remove the interference from the background noise, the results are far from perfect because the interference from the coherent background noise is still present. In addition, DAS beamforming based on arithmetic averaging fails to deliver real-time computational capability. An observer-based approach is introduced in this paper. This so-called observer-based beamforming method has a recursive form similar to the state observer in classical control theory, thus holds a real-time computational capability. In addition, coherent background noise can be gradually rejected in iterations. Theoretical derivations of the observer-based beamforming algorithm are carefully developed in this paper. Two numerical simulations demonstrate the good coherent background noise rejection and real-time computational capability of the observer-based beamforming, which therefore can be regarded as an attractive algorithm for acoustic array signal processing.

  7. Classification of Underwater Signals Using Wavelet-Based Decompositions

    DTIC Science & Technology

    1998-06-01

    proposed by Learned and Willsky [21], uses the SVD information obtained from the power mapping, the second one selects the most within-a-class...34 SPIE, Vol. 2242, pp. 792-802, Wavelet Applications, 1994 [14] R. Coifman and D. Donoho, "Translation-Invariant Denoising ," Internal Report...J. Barsanti, Jr., Denoising of Ocean Acoustic Signals Using Wavelet-Based Techniques, MSEE Thesis, Naval Postgraduate School, Monterey, California

  8. 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.

  9. Deconvolution and signal extraction in geophysics and acoustics

    NASA Astrophysics Data System (ADS)

    Sibul, Leon H.; Roan, Michael J.; Erling, Josh

    2002-11-01

    Deconvolution and signal extraction are fundamental signal processing techniques in geophysics and acoustics. An introductory overview of the standard second-order methods and minimum entropy deconvolution is presented. Limitations of the second-order methods are discussed and the need for more general methods is established. The minimum entropy deconvolution (MED), as proposed by Wiggins in 1977, is a technique for the deconvolution of seismic signals that overcomes limitations of the second-order method of deconvolution. The unifying conceptual framework MED, as presented in the Donoho's classical paper (1981) is discussed. The basic assumption of MED is that input signals to the forward filter are independent, identically distributed non-Gaussian random processes. A forward convolution filter ''makes'' the output of the forward filter more Gaussian which increases its entropy. The minimization of entropy restores the original non-Gaussian input. We also give an overview of recent developments in blind deconvolution (BDC), blind source separation (BSS), and blind signal extraction (BSE). The recent research in these areas uses information theoretic (IT) criteria (entropy, mutual information, K-L divergence, etc.) for optimization objective functions. Gradients of these objective functions are nonlinear functions, resulting in nonlinear algorithms. Some of the recursive algorithms for nonlinear optimization are reviewed.

  10. 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.

  11. Signal Classification Using The Mean Separator Neural Network

    DTIC Science & Technology

    2000-03-01

    and investigated. One modification involved input data preconditioning prior to neural network processing. A second modification incorporated...decision-making capacity. More data is not needed; enhanced information and knowledge are essential. This study built upon the Mean Separator Neural ... Network (MSNN) signal classification tool originally proposed by Duzenli (1998) and modified it for increased robustness. MSNN variants were developed

  12. Autonomous Non-Linear Classification of LPI Radar Signal Modulations

    DTIC Science & Technology

    2007-09-01

    database of important LPI radar waveform modulations including Frequency Modulation Continuous Waveform ( FMCW ), Phase Shift Keying (PSK), Frequency...important LPI radar waveform modulations including Frequency Modulation Continuous Waveform ( FMCW ), Phase Shift Keying (PSK), Frequency Shift Keying (FSK...LINEAR CLASSIFICATION OF LPI RADAR SIGNAL MODULATIONS by Taylan O. Gulum September 2007 Thesis Co-Advisors: Phillip E. Pace Roberto

  13. Floc Growth and Changes in ADV Acoustic Backscatter Signal

    NASA Astrophysics Data System (ADS)

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

    2013-12-01

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

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

    NASA Astrophysics Data System (ADS)

    Suh, Youngjoo; Kim, Hoirin

    2014-12-01

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

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

    PubMed

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

    2007-06-01

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

  16. 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

  17. Advanced Methods for Passive Acoustic Detection, Classification, and Localization of Marine Mammals

    DTIC Science & Technology

    2013-09-30

    click classification. Algorithms are being developed and tested for several species of beaked whales and small odontocetes. 2. Tonal signal detection... Tonal signal detection and classification “ Tonal signal” is a generic term for frequency-modulated calls such as baleen whale moans or...methods to be developed here determine the species associated with odontocete whistles that are extracted automatically via the Silbido tonal contour

  18. 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.

  19. 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

  20. 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.

  1. 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.

  2. 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.

  3. 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.

  4. Detection of Gear Failures via Vibration and Acoustic Signals Using Wavelet Transform

    NASA Astrophysics Data System (ADS)

    Baydar, N.; Ball, A.

    2003-07-01

    Vibration analysis is widely used in machinery diagnostics and the wavelet transform has also been implemented in many applications in the condition monitoring of machinery. In contrast to previous applications, this paper examines whether acoustic signal can be used effectively along vibration signal to detect the various local faults in gearboxes using the wavelet transform. Two commonly encountered local faults, tooth breakage and tooth crack, were simulated. The results from acoustic signals were compared with vibration signals. The results suggest that acoustic signals are very affective for the early detection of faults and may provide a powerful tool to indicate the various types of progressing faults in gearboxes.

  5. Acoustic model adaptation for ortolan bunting (Emberiza hortulana L.) song-type classification.

    PubMed

    Tao, Jidong; Johnson, Michael T; Osiejuk, Tomasz S

    2008-03-01

    Automatic systems for vocalization classification often require fairly large amounts of data on which to train models. However, animal vocalization data collection and transcription is a difficult and time-consuming task, so that it is expensive to create large data sets. One natural solution to this problem is the use of acoustic adaptation methods. Such methods, common in human speech recognition systems, create initial models trained on speaker independent data, then use small amounts of adaptation data to build individual-specific models. Since, as in human speech, individual vocal variability is a significant source of variation in bioacoustic data, acoustic model adaptation is naturally suited to classification in this domain as well. To demonstrate and evaluate the effectiveness of this approach, this paper presents the application of maximum likelihood linear regression adaptation to ortolan bunting (Emberiza hortulana L.) song-type classification. Classification accuracies for the adapted system are computed as a function of the amount of adaptation data and compared to caller-independent and caller-dependent systems. The experimental results indicate that given the same amount of data, supervised adaptation significantly outperforms both caller-independent and caller-dependent systems.

  6. Advanced Methods for Passive Acoustic Detection, Classification, and Localization of Marine Mammals

    DTIC Science & Technology

    2012-09-30

    auditory system has similar units for detecting frequency changes in tonal signals at specific frequencies ( Mendelson and Cynader 1985). Mellinger...contours. J. Acoust. Soc. Am. 129:4055-4061. Mendelson , J.R., and M.S. Cynader. (1985) Sensitivity of cat auditory primary cortex (AI) neurons to the

  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. Multiple Signal Classification for Gravitational Wave Burst Search

    NASA Astrophysics Data System (ADS)

    Cao, Junwei; He, Zhengqi

    2013-01-01

    This work is mainly focused on the application of the multiple signal classification (MUSIC) algorithm for gravitational wave burst search. This algorithm extracts important gravitational wave characteristics from signals coming from detectors with arbitrary position, orientation and noise covariance. In this paper, the MUSIC algorithm is described in detail along with the necessary adjustments required for gravitational wave burst search. The algorithm's performance is measured using simulated signals and noise. MUSIC is compared with the Q-transform for signal triggering and with Bayesian analysis for direction of arrival (DOA) estimation, using the Ω-pipeline. Experimental results show that MUSIC has a lower resolution but is faster. MUSIC is a promising tool for real-time gravitational wave search for multi-messenger astronomy.

  9. 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.

  10. 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%.

  11. Impacts of Underwater Turbulence on Acoustical and Optical Signals and Their Linkage

    DTIC Science & Technology

    2013-02-12

    convected quantities like temperature in turbulence fluid," J. Fluid Mech. 5,113-133(1959). 26. J. W. Goodman , Introduction to Fourier Optics (Roberts...Turbulence on Acoustical and Optical Signals and Their Linkage 5a. CONTRACT NUMBER 5b. GRANT NUMBER 5c. PROGRAM ELEMENT NUMBER 0602782N 6...Acoustical and optical signal transmission underwater is of vital interest for both civilian and military applications. The range and signal to noise

  12. Cumulative and Synergistic Effects of Physical, Biological and Acoustic Signals on Marine Mammal Habitat Use

    DTIC Science & Technology

    2009-09-30

    rather than animals. Note that some animals do utilize the higher frequency bands, e.g. killer and beluga whales , but these animals are only...NOAA-supported projects, including Passive Acoustic monitoring of killer and beluga whales at the Barren Islands, Alaska, the Bering Sea Acoustic...physical, biological and acoustic signals impact marine mammal habitat use. In particular, what are the effects of manmade underwater sound on

  13. 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%.

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

    SciTech Connect

    Wang Qiao; Zhan Hu

    2013-05-10

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

  15. Reliable Classification of High Explosive and Chemical/Biological Artillery Using Acoustic Sensors

    DTIC Science & Technology

    2006-10-01

    high pass filtering of the input signal is used to implement the DWT [5]. The resulting banks of dyadic multirate filters split the input signal...Figure 4 – A Multirate Filter Bank used as a Five Level Wavelet Decomposition Tree 2.2 Wavelet Features for Classification Figures 5 (a-c...resulting banks of dyadic multirate filters separate the frequency components into different subbands. – Each pass through gives you resolution of

  16. 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.

  17. Advanced Methods for Passive Acoustic Detection, Classification, and Localization of Marine Mammals

    DTIC Science & Technology

    2011-09-30

    signals at specific frequencies ( Mendelson and Cynader 1985). Mellinger and Martin will lead the effort to test some feature extraction and classification...Oceanogr. Atmos. Admin. Tech. Memo. OAR–PMEL–120 (NOAA PMEL, Seattle). 30 pp. Mendelson , J.R., and M.S. Cynader. (1985) Sensitivity of cat auditory

  18. Classification of acoustic emission sources produced by carbon/epoxy composite based on support vector machine

    NASA Astrophysics Data System (ADS)

    Ding, Peng; Li, Qin; Huang, Xunlei

    2015-07-01

    Carbon/epoxy specimens were made and stretched to fracture. In the process, acoustic emission (AE) signals were collected and their parameters were set as the input parameters of the neural network. Results show that using support vector machine (SVM) network can recognize the difference of AE sources more accurately than using the BP neural network. In addition, the accuracy of the SVM increases when the number of the training set increases. It is proved that using AE signal parameters and SVM network can recognize the AE sources’ pattern well.

  19. 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.

  20. 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.

  1. Courtship Initiation Is Stimulated by Acoustic Signals in Drosophila melanogaster

    PubMed Central

    Ejima, Aki; Griffith, Leslie C.

    2008-01-01

    Finding a mating partner is a critical task for many organisms. It is in the interest of males to employ multiple sensory modalities to search for females. In Drosophila melanogaster, vision is thought to be the most important courtship stimulating cue at long distance, while chemosensory cues are used at relatively short distance. In this report, we show that when visual cues are not available, sounds produced by the female allow the male to detect her presence in a large arena. When the target female was artificially immobilized, the male spent a prolonged time searching before starting courtship. This delay in courtship initiation was completely rescued by playing either white noise or recorded fly movement sounds to the male, indicating that the acoustic and/or seismic stimulus produced by movement stimulates courtship initiation, most likely by increasing the general arousal state of the male. Mutant males expressing tetanus toxin (TNT) under the control of Gr68a-GAL4 had a defect in finding active females and a delay in courtship initiation in a large arena, but not in a small arena. Gr68a-GAL4 was found to be expressed pleiotropically not only in putative gustatory pheromone receptor neurons but also in mechanosensory neurons, suggesting that Gr68a-positive mechanosensory neurons, not gustatory neurons, provide motion detection necessary for courtship initiation. TNT/Gr68a males were capable of discriminating the copulation status and age of target females in courtship conditioning, indicating that female discrimination and formation of olfactory courtship memory are independent of the Gr68a-expressing neurons that subserve gustation and mechanosensation. This study suggests for the first time that mechanical signals generated by a female fly have a prominent effect on males' courtship in the dark and leads the way to studying how multimodal sensory information and arousal are integrated in behavioral decision making. PMID:18802468

  2. 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.

  3. 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

  4. Multiple signal classification algorithm for super-resolution fluorescence microscopy

    PubMed Central

    Agarwal, Krishna; Macháň, Radek

    2016-01-01

    Single-molecule localization techniques are restricted by long acquisition and computational times, or the need of special fluorophores or biologically toxic photochemical environments. Here we propose a statistical super-resolution technique of wide-field fluorescence microscopy we call the multiple signal classification algorithm which has several advantages. It provides resolution down to at least 50 nm, requires fewer frames and lower excitation power and works even at high fluorophore concentrations. Further, it works with any fluorophore that exhibits blinking on the timescale of the recording. The multiple signal classification algorithm shows comparable or better performance in comparison with single-molecule localization techniques and four contemporary statistical super-resolution methods for experiments of in vitro actin filaments and other independently acquired experimental data sets. We also demonstrate super-resolution at timescales of 245 ms (using 49 frames acquired at 200 frames per second) in samples of live-cell microtubules and live-cell actin filaments imaged without imaging buffers. PMID:27934858

  5. Multiple signal classification algorithm for super-resolution fluorescence microscopy

    NASA Astrophysics Data System (ADS)

    Agarwal, Krishna; Macháň, Radek

    2016-12-01

    Single-molecule localization techniques are restricted by long acquisition and computational times, or the need of special fluorophores or biologically toxic photochemical environments. Here we propose a statistical super-resolution technique of wide-field fluorescence microscopy we call the multiple signal classification algorithm which has several advantages. It provides resolution down to at least 50 nm, requires fewer frames and lower excitation power and works even at high fluorophore concentrations. Further, it works with any fluorophore that exhibits blinking on the timescale of the recording. The multiple signal classification algorithm shows comparable or better performance in comparison with single-molecule localization techniques and four contemporary statistical super-resolution methods for experiments of in vitro actin filaments and other independently acquired experimental data sets. We also demonstrate super-resolution at timescales of 245 ms (using 49 frames acquired at 200 frames per second) in samples of live-cell microtubules and live-cell actin filaments imaged without imaging buffers.

  6. Neural network classification of myoelectric signal for prosthesis control.

    PubMed

    Kelly, M F; Parker, P A; Scott, R N

    1991-12-01

    An alternate approach to deriving control for multidegree of freedom prosthetic arms is considered. By analyzing a single-channel myoelectric signal (MES), we can extract information that can be used to identify different contraction patterns in the upper arm. These contraction patterns are generated by subjects without previous training and are naturally associated with specific functions. Using a set of normalized MES spectral features, we can identify contraction patterns for four arm functions, specifically extension and flexion of the elbow and pronation and supination of the forearm. Performing identification independent of signal power is advantageous because this can then be used as a means for deriving proportional rate control for a prosthesis. An artificial neural network implementation is applied in the classification task. By using three single-layer perceptron networks, the MES is classified, with the spectral representations as input features. Trials performed on five subjects with normal limbs resulted in an average classification performance level of 85% for the four functions.

  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. Modulation of Radio Frequency Signals by Nonlinearly Generated Acoustic Fields

    DTIC Science & Technology

    2014-01-01

    Kirchhoff’s theorem, typically applied to EM waves, determining the far-field patterns of an acoustic source from amplitude and phase measurements made in...two noncollinear ultrasonic baffled piston sources. The theory is extended to the modeling of the sound beams generated by parametric transducer arrays ...typically applied to EM waves, determining the far-field patterns of an acoustic source from amplitude and phase measurements made in the near-field by

  9. 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.

  10. 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.

  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.

    2006-08-08

    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.

  13. 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.

  14. 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.

  15. 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

  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. 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

  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. 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.

  1. Signal feature extraction by multi-scale PCA and its application to respiratory sound classification.

    PubMed

    Xie, Shengkun; Jin, Feng; Krishnan, Sridhar; Sattar, Farook

    2012-07-01

    Respiratory sound (RS) signals carry significant information about the underlying functioning of the pulmonary system by the presence of adventitious sounds. Although many studies have addressed the problem of pathological RS classification, only a limited number of scientific works have focused in multi-scale analysis. This paper proposes a new signal classification scheme for various types of RS based on multi-scale principal component analysis as a signal enhancement and feature extraction method to capture major variability of Fourier power spectra of the signal. Since we classify RS signals in a high dimensional feature subspace, a new classification method, called empirical classification, is developed for further signal dimension reduction in the classification step and has been shown to be more robust and outperform other simple classifiers. An overall accuracy of 98.34% for the classification of 689 real RS recording segments shows the promising performance of the presented method.

  2. Cerebral Processing of Emotionally Loaded Acoustic Signals by Tinnitus Patients.

    PubMed

    Georgiewa, Petra; Szczepek, Agnieszka J; Rose, Matthias; Klapp, Burghard F; Mazurek, Birgit

    2016-01-01

    This exploratory study determined the activation pattern in nonauditory brain areas in response to acoustic, emotionally positive, negative or neutral stimuli presented to tinnitus patients and control subjects. Ten patients with chronic tinnitus and without measurable hearing loss and 13 matched control subjects were included in the study and subjected to fMRI with a 1.5-tesla scanner. During the scanning procedure, acoustic stimuli of different emotional value were presented to the subjects. Statistical analyses were performed using statistical parametric mapping (SPM 99). The activation pattern induced by emotionally loaded acoustic stimuli differed significantly within and between both groups tested, depending on the kind of stimuli used. Within-group differences included the limbic system, prefrontal regions, temporal association cortices and striatal regions. Tinnitus patients had a pronounced involvement of limbic regions involved in the processing of chimes (positive stimulus) and neutral words (neutral stimulus), strongly suggesting improperly functioning inhibitory mechanisms that were functioning well in the control subjects. This study supports the hypothesis about the existence of a tinnitus-specific brain network. Such a network could respond to any acoustic stimuli by activating limbic areas involved in stress reactivity and emotional processing and by reducing activation of areas responsible for attention and acoustic filtering (thalamus, frontal regions), possibly reinforcing negative effects of tinnitus.

  3. 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.

  4. Acoustic evaluation of cementing quality using obliquely incident ultrasonic signals

    NASA Astrophysics Data System (ADS)

    Duan, Wen-Xing; Qiao, Wen-Xiao; Che, Xiao-Hua; Xie, Hui

    2014-09-01

    Ultrasonic cement bond logging is a widely used method for evaluating cementing quality. Conventional ultrasonic cement bond logging uses vertical incidence and cannot accurately evaluate lightweight cement bonding. Oblique incidence is a new technology for evaluating cement quality with improved accuracy for lightweight cements. In this study, we simulated models of acoustic impedance of cement and cementing quality using ultrasonic oblique incidence, and we obtained the relation between cementing quality, acoustic impedance of cement, and the acoustic attenuation coefficient of the A0-mode and S0-mode Lamb waves. Then, we simulated models of different cement thickness and we obtained the relation between cement thickness and the time difference of the arrival between the A0 and A0' modes.

  5. 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.

  6. 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.

  7. Classification of biological signals using linear and nonlinear features.

    PubMed

    Balli, T; Palaniappan, R

    2010-07-01

    This paper investigates the characterization ability of linear and nonlinear features and proposes combining such features in order to improve the classification of biological signals, in particular single-trial electroencephalogram (EEG) and electrocardiogram (ECG) data. For this purpose, three data sets composed of ECG, epileptic EEG and finger-movement EEG were utilized. The characterization ability of seven nonlinear features namely the approximate entropy, largest Lyapunov exponents, correlation dimension, nonlinear prediction error, Hurst exponent, higher order autocovariance and asymmetry due to time reversal are compared with two linear features namely the autoregressive (AR) reflection coefficients and AR model coefficients. The features were tested by their ability to differentiate between different classes of data using a linear discriminant analysis (LDA) method with tenfold cross-validation. The class separability of combined linear and nonlinear features was assessed using sequential floating forward search with linear discriminant analysis method (SFFS-LDA). The results demonstrated that linear and nonlinear features on their own provided comparable results for the ECG data set and the finger-movement EEG data set whilst the linear features provided a better class separability compared to nonlinear features for the epileptic EEG data set. Combining linear and nonlinear features demonstrated a significant improvement in the class separability for all of the data sets where an average improvement of 20.56% was obtained with the ECG data set, 7.45% with finger-movement data set and 6.62% with the epileptic EEG data set. Overall results suggest that the use of combined linear and nonlinear feature sets would be a better approach for the characterization and classification of biological signals such as EEG and ECG.

  8. 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.

  9. Functional coupling of acoustic and chemical signals in the courtship behaviour of the male Drosophila melanogaster.

    PubMed Central

    Rybak, F; Sureau, G; Aubin, T

    2002-01-01

    During courtship, the male Drosophila melanogaster sends signals to the female through two major sensory channels: chemical and acoustic. These signals are involved in the stimulation of the female to accept copulation. In order to determine the respective importance in the courtship of these signals, their production was controlled using genetical and surgical techniques. Males deprived of the ability to emit both signals are unable to mate, demonstrating that other (e.g. visual or tactile) signals are not sufficient to stimulate the female. If either acoustic or chemical signals are lacking, the courtship success is strongly reduced, the lack of the former having significantly more drastic effects. However, the accelerated matings of males observed with males bearing wild-type hydrocarbons compared with defective ones, whichever the modality of acoustic performance (wing vibration or playback), strongly support the role of cuticular compounds to stimulate females. We can conclude that among the possible factors involved in communication during courtship, acoustic and chemical signals may act in a synergistic way and not separately in D. melanogaster. PMID:11934360

  10. 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

  11. 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.

  12. An enhanced sparse representation strategy for signal classification

    NASA Astrophysics Data System (ADS)

    Zhou, Yin; Gao, Jinglun; Barner, Kenneth E.

    2012-06-01

    Sparse representation based classification (SRC) has achieved state-of-the-art results on face recognition. It is hence desired to extend its power to a broader range of classification tasks in pattern recognition. SRC first encodes a query sample as a linear combination of a few atoms from a predefined dictionary. It then identifies the label by evaluating which class results in the minimum reconstruction error. The effectiveness of SRC is limited by an important assumption that data points from different classes are not distributed along the same radius direction. Otherwise, this approach will lose their discrimination ability, even though data from different classes are actually well-separated in terms of Euclidean distance. This assumption is reasonable for face recognition as images of the same subject under different intensity levels are still considered to be of same-class. However, the assumption is not always satisfied when dealing with many other real-world data, e.g., the Iris dataset, where classes are stratified along the radius direction. In this paper, we propose a new coding strategy, called Nearest- Farthest Neighbors based SRC (NF-SRC), to effectively overcome the limitation within SRC. The dictionary is composed of both the Nearest Neighbors and the Farthest Neighbors. While the Nearest Neighbors are used to narrow the selection of candidate samples, the Farthest Neighbors are employed to make the dictionary more redundant. NF-SRC encodes each query signal in a greedy way similar to OMP. The proposed approach is evaluated over extensive experiments. The encouraging results demonstrate the feasibility of the proposed method.

  13. 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.

  14. 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

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

    DOEpatents

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

    2015-08-18

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

  16. Classification of Acousto-Optic Correlation Signatures of Spread Spectrum Signals Using Artificial Neural Networks

    DTIC Science & Technology

    1989-12-01

    Ohio ’aPw iorlipuab muo i 0I2, AFIT/GE/ENG/89D-10 CLASSIFICATION OF ACOUSTO - OPTIC CORRELATION SIGNATURES OF SPREAD SPECTRUM SIGNALS USING ARTIFICIAL...ENG/89D- 10 CLASSIFICATION OF ACOUSTO - OPTIC CORRELATION SIGNATURES OF SPREAD SPECTRUM SIGNALS USING ARTIFICIAL NEURAL NETWORKS THESIS John W. DeBerry...Captain, USAF AFIT/GE/ENG/89D- 10 Approved for public release; distribution unlimited. AFIT/GE/ENG/89D-10 CLASSIFICATION OF ACOUSTO - OPTIC CORRELATION

  17. 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.

  18. Investigation of Volcanic Seismo-Acoustic Signals: Applying Subspace Detection to Lava Fountain Activity at Etna Volcano

    NASA Astrophysics Data System (ADS)

    Sciotto, M.; Rowe, C. A.; Cannata, A.; Arrowsmith, S.; Privitera, E.; Gresta, S.

    2011-12-01

    The current eruption of Mount Etna, which began in January, 2011, has produced numerous energetic episodes of lava fountaining, which have bee recorded by the INGV seismic and acoustic sensors located on and around the volcano. The source of these events was the pit crater on the east flank of the Southeast crater of Etna. Simultaneously, small levels of activity were noted in the Bocca Nuova as well, prior to its lava fountaining activity. We will present an analysis of seismic and acoustic signals related to the 2011 activity wherein we apply the method of subspace detection to determine whether the source exhibits a temporal evolution within or between fountaining events, or otherwise produces repeating, classifiable events occurring through the continuous explosive degassing. We will examine not only the raw waveforms, but also spectral variations in time as well as time-varying statistical functions such as signal skewness and kurtosis. These results will be compared to straightforward cross-correlation analysis. In addition to classification performance, the subspace method has promise to outperform standard STA/LTA methods for real-time event detection in cases where similar events can be expected.

  19. Acousto-Optic Interaction in Surface Acoustic Waves and Its Application to Real Time Signal Processing.

    DTIC Science & Technology

    1977-12-30

    ACOUSTO - OPTIC INTERACTION IN SURFACE ACOUSTIC WAVES AND ITS APP--ETC(U) DEC 77 0 SCHUMER, P DAS NOOOIJ -75-C-0772 NCLASSIFIED MA-ONR-30 Nt.EE E’h...CHART NAT*NAL BUREAU OF STANDARDS 1-63- ACOUSTO - OPTIC INTERACTION IN SURFACE ACOUSTIC WAVES AND ITS APPLICATION TO REAL TIME SIGNAL PROCESSING By 00 D... Acousto - optics , Integrated optics, Optical Signal Processing. 20. AbSKTRACT (Continue an reverse side it neceary and idewnt& by block mum ber) The

  20. 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.

  1. Third International Conference on Acoustic Communication by Animals

    DTIC Science & Technology

    2011-09-30

    predominant aim of this conference is to consider acoustic communication, its mechanisms, and the detection of acoustic signals, particularly in noisy ...frogs (6). Topics covered included cognition/language; song and call classification; rule learning; acoustic ecology; communication in noisy ...at the Statler Hotel and Conference Center on the Cornell University campus. Evening programs included a networking dinner (“Bioacoustics and Pizza

  2. 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.

  3. Speaker Race Identification from Acoustic Cues in the Vocal Signal.

    NASA Astrophysics Data System (ADS)

    Walton, Julie Hart

    Sustained /a/ sounds were tape recorded from 50 adult male African-American and 50 adult male European -American speakers. A one-second acoustic sample was extracted from the mid-portion of each sustained vowel. Vowel samples from each African-American subject were randomly paired with those from European-American subjects. A one-second inter-stimulus interval of silence separated the two voices in the pair; the order of the voices in each pair was randomly selected. When presented with a tape of the 50 voice pairs, listeners could determine the race of the speaker with 60% accuracy. An acoustic analysis of the voices revealed that African-American speakers had a tendency toward greater frequency perturbation, significantly greater amplitude perturbation, and a significantly lower harmonics-to-noise ratio than the European-American speakers. An analysis of the listeners' responses revealed that the listeners may have relied on a combination of increased frequency perturbation, increased amplitude perturbation, and a lower harmonics-to-noise ratio to identify the African-American speakers.

  4. Acoustic signal characteristics of laser induced cavitation in DDFP droplet: Spectrum and time-frequency analysis.

    PubMed

    Feng, Yi; Qin, Dui; Zhang, Jun; Ma, Chenxiang; Wan, Mingxi

    2015-01-01

    Cavitation has great application potential in microvessel damage and targeted drug delivery. Concerning cavitation, droplet vaporization has been widely investigated in vitro and in vivo with plasmonic nanoparticles. Droplets with a liquid dodecafluoropentane (DDFP) core enclosed in an albumin shell have a stable and simple structure with good characteristics of laser absorbing; thus, DDFP droplets could be an effective aim for laser-induced cavitation. The DDPF droplet was prepared and perfused in a mimic microvessel in the optical microscopic system with a passive acoustic detection module. Three patterns of laser-induced cavitation in the droplets were observed. The emitted acoustic signals showed specific spectrum components at specific time points. It was suggested that a nanosecond laser pulse could induce cavitation in DDPF droplets, and specific acoustic signals would be emitted. Analyzing its characteristics could aid in monitoring the laser-induced cavitation process in droplets, which is meaningful to theranostic application.

  5. 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

  6. Analysis of acoustic emission signals and monitoring of machining processes

    PubMed

    Govekar; Gradisek; Grabec

    2000-03-01

    Monitoring of a machining process on the basis of sensor signals requires a selection of informative inputs in order to reliably characterize and model the process. In this article, a system for selection of informative characteristics from signals of multiple sensors is presented. For signal analysis, methods of spectral analysis and methods of nonlinear time series analysis are used. With the aim of modeling relationships between signal characteristics and the corresponding process state, an adaptive empirical modeler is applied. The application of the system is demonstrated by characterization of different parameters defining the states of a turning machining process, such as: chip form, tool wear, and onset of chatter vibration. The results show that, in spite of the complexity of the turning process, the state of the process can be well characterized by just a few proper characteristics extracted from a representative sensor signal. The process characterization can be further improved by joining characteristics from multiple sensors and by application of chaotic characteristics.

  7. 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.

  8. Analysis of acoustic signals on CO{sub 2} arc welding

    SciTech Connect

    Ogawa, Y.; Morita, T.; Sumitomo, T.; Koga, H.

    1995-12-31

    The sound emitted during the arc welding process is closely related to the welding phenomenon, and sometimes it provides useful information for monitoring and controlling the welding process. It is important to use different kinds of information to control the welding process to improve the quality of controlling system, especially for underwater welding. Because the recovery process of weld defects is a time and money consuming matter, and sometimes it is difficult to monitor the arc condition by a visual system. The fundamental analysis of acoustic signals and their relations with the other parameters such as arc voltage, arc current and a vibration of weld plate had been carried out in order to understand the feature of acoustic signals and to develop effective signal processing algorithm. All of the data were recorded by the cassette recorder. After the experiment was completed, the analysis of recorded data was carried out by using of a signal processor and a computer system.

  9. A novel multipitch measurement algorithm for acoustic signals of moving targets

    NASA Astrophysics Data System (ADS)

    Huang, Jingchang; Guo, Feng; Zu, Xingshui; Li, Haiyan; Liu, Huawei; Li, Baoqing

    2016-12-01

    In this paper, a novel multipitch measurement (MPM) method is proposed for acoustic signals. Starting from the analysis of moving targets' acoustic signatures, a pitch-based harmonics representation model of acoustic signal is put forward. According to the proposed harmonics model, a modified greatest common divisor (MGCD) method is developed to obtain an initial multipitch set (IMS). Subsequently, the harmonic number vector (HNV) associated with the IMS is determined by maximizing the objective function formulated as a multi-impulse-train weighted symmetric average magnitude sum function (SAMSF) of the observed signal. The frequencies of SAMSF are determined by the target acoustic signal, the periods of the multi-impulse-train are governed by the estimated IMS harmonics and the maximization of the objective function is figured out through a time-domain matching of periodicities of the multi-impulse-train with that of the SAMSF. Finally, by using the obtained IMS and its HNV, a precise fundamental frequency set is achieved. Evaluation of the algorithm performances in comparison with state-of-the-art methods indicates that MPM is practical for the multipitch extraction of moving targets.

  10. 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...

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

    NASA Astrophysics Data System (ADS)

    Ghica, D.; Ionescu, C.

    2012-04-01

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

  12. Cumulative and Synergistic Effects of Physical, Biological, and Acoustic Signals on Marine Mammal Habitat Use

    DTIC Science & Technology

    2011-09-30

    Biological, and Acoustic Signals on Marine Mammal Habitat Use Jennifer L. Miksis-Olds Applied Research Laboratory The Pennsylvania State University PO...signals impact marine mammal prey and resulting marine mammal habitat use. This is especially critical in areas like the Bering Sea where global climate...animal presence and habitat use. Objective 1: What effect do changing sea ice dynamics have on zooplankton populations? a) How does zooplankton

  13. Automatic classification of sleep stages based on the time-frequency image of EEG signals.

    PubMed

    Bajaj, Varun; Pachori, Ram Bilas

    2013-12-01

    In this paper, a new method for automatic sleep stage classification based on time-frequency image (TFI) of electroencephalogram (EEG) signals is proposed. Automatic classification of sleep stages is an important part for diagnosis and treatment of sleep disorders. The smoothed pseudo Wigner-Ville distribution (SPWVD) based time-frequency representation (TFR) of EEG signal has been used to obtain the time-frequency image (TFI). The segmentation of TFI has been performed based on the frequency-bands of the rhythms of EEG signals. The features derived from the histogram of segmented TFI have been used as an input feature set to multiclass least squares support vector machines (MC-LS-SVM) together with the radial basis function (RBF), Mexican hat wavelet, and Morlet wavelet kernel functions for automatic classification of sleep stages from EEG signals. The experimental results are presented to show the effectiveness of the proposed method for classification of sleep stages from EEG signals.

  14. 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.}

  15. 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.

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

    NASA Astrophysics Data System (ADS)

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

    2012-12-01

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

  17. 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

  18. Time-frequency Analysis for Acoustic Emission Signals of Hypervelocity Impact

    NASA Astrophysics Data System (ADS)

    Liu, W. G.; Pang, B. J.; Zhang, W.; Sun, F.; Guan, G. S.

    The risk of collision of man-made orbital debris with spacecraft in near Earth orbits continues to increase A major of the space debris between 1mm and 10mm can t be well tracked in Earth orbits Damage from these un-tracked debris impacts is a serious hazard to aircraft and spacecraft These on-orbit collisions occur at velocities exceeding 10km s and at these velocities even very small particles can create significant damage The development of in-situ impact detecting system is indispensable for protecting the spacecraft from tragedy malfunction by the debris Acoustic Emission AE detecting technique has been recognized as an important technology for non-destructive detecting due to the AE signals offering a potentially useful additional means of non-invasively gathering concerning the state of spacecrafts Also Acoustic emission health monitoring is able to detect locate and assess impact damage when the spacecrafts is impacted by hypervelocity space debris and micrometeoroids This information can help operators and designers at the ground station take effective measures to maintain the function of spacecraft In this article Acoustic emission AE is used for characterization and location for hypervelocity Impacts Two different Acoustic Emission AE sensors were used to detect the arrival time and signals of the hits Hypervelocity Impacts were generated with a two-stage light-gas gun firing small Aluminum ball projectiles 4mm 6 4mm In the impact studies the signals were recorded with Disp AEwin PAC instruments by the conventional crossing

  19. 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.

  20. 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.

  1. 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)].

  2. 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.

  3. [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.

  4. (A new time of flight) Acoustic flow meter using wide band signals and adaptive beamforming techniques

    NASA Astrophysics Data System (ADS)

    Murgan, I.; Ioana, C.; Candel, I.; Anghel, A.; Ballester, J. L.; Reeb, B.; Combes, G.

    2016-11-01

    In this paper we present the result of our research concerning the improvement of acoustic time of flight flow metering for water pipes. Current flow meters are based on the estimation of direct time of flight by matched filtering of the received and emitted signals by acoustic transducers. Currently, narrow band signals are used, as well as a single emitter/receptor transducer configuration. Although simple, this configuration presents a series of limitations such as energy losses due to pipe wall/water interface, pressure/flow transients, sensitivity to flow induced vibrations, acoustic beam deformations and shift due to changes in flow velocity and embedded turbulence in the flow. The errors associated with these limitations reduce the overall robustness of existing flow meters, as well as the measured flow rate range and lower accuracy. In order to overcome these limitations, two major innovations were implemented at the signal processing level. The first one concerns the use of wide band signals that optimise the power transfer throughout the acoustic path and also increase the number of velocity/flow readings per second. Using wide band signals having a high duration-bandwidth product increases the precision in terms of time of flight measurements and, in the same time, improves the system robustness. The second contribution consists in the use of a multiple emitter - multiple receivers configuration (for one path) in order to compensate the emitted acoustic beam shift, compensate the time of flight estimation errors and thus increase the flow meter's robustness in case of undesired effects such as the “flow blow” and transient/rapid flow rate/velocity changes. Using a new signal processing algorithm that take advantage of the controlled wide band content coming from multiple receivers, the new flow meters achieves a higher accuracy in terms of flow velocity over a wider velocity range than existing systems. Tests carried out on real scale experimental

  5. Audio Signal Processing Using Time-Frequency Approaches: Coding, Classification, Fingerprinting, and Watermarking

    NASA Astrophysics Data System (ADS)

    Umapathy, K.; Ghoraani, B.; Krishnan, S.

    2010-12-01

    Audio signals are information rich nonstationary signals that play an important role in our day-to-day communication, perception of environment, and entertainment. Due to its non-stationary nature, time- or frequency-only approaches are inadequate in analyzing these signals. A joint time-frequency (TF) approach would be a better choice to efficiently process these signals. In this digital era, compression, intelligent indexing for content-based retrieval, classification, and protection of digital audio content are few of the areas that encapsulate a majority of the audio signal processing applications. In this paper, we present a comprehensive array of TF methodologies that successfully address applications in all of the above mentioned areas. A TF-based audio coding scheme with novel psychoacoustics model, music classification, audio classification of environmental sounds, audio fingerprinting, and audio watermarking will be presented to demonstrate the advantages of using time-frequency approaches in analyzing and extracting information from audio signals.

  6. Classification of Communication Signals and Detection of Unknown Formats Using Artificial Neural Networks

    DTIC Science & Technology

    2006-12-01

    Using Artificial Neural Networks Alexander Iversen, Nicholas K. Taylor and Keith E. Brown Intelligent Systems Laboratory Heriot-Watt University...Brown, K.E. (2006) Classification of Communication Signals and Detection of Unknown Formats Using Artificial Neural Networks . In Military...Classification of Communication Signals and Detection of Unknown Formats Using Artificial Neural Networks 5a. CONTRACT NUMBER 5b. GRANT NUMBER 5c. PROGRAM

  7. 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

  8. Control of deviations and prediction of surface roughness from micro machining of THz waveguides using acoustic emission signals

    NASA Astrophysics Data System (ADS)

    Griffin, James M.; Diaz, Fernanda; Geerling, Edgar; Clasing, Matias; Ponce, Vicente; Taylor, Chris; Turner, Sam; Michael, Ernest A.; Patricio Mena, F.; Bronfman, Leonardo

    2017-02-01

    By using acoustic emission (AE) it is possible to control deviations and surface quality during micro milling operations. The method of micro milling is used to manufacture a submillimetre waveguide where micro machining is employed to achieve the required superior finish and geometrical tolerances. Submillimetre waveguide technology is used in deep space signal retrieval where highest detection efficiencies are needed and therefore every possible signal loss in the receiver has to be avoided and stringent tolerances achieved. With a sub-standard surface finish the signals travelling along the waveguides dissipate away faster than with perfect surfaces where the residual roughness becomes comparable with the electromagnetic skin depth. Therefore, the higher the radio frequency the more critical this becomes. The method of time-frequency analysis (STFT) is used to transfer raw AE into more meaningful salient signal features (SF). This information was then correlated against the measured geometrical deviations and, the onset of catastrophic tool wear. Such deviations can be offset from different AE signals (different deviations from subsequent tests) and feedback for a final spring cut ensuring the geometrical accuracies are met. Geometrical differences can impact on the required transfer of AE signals (change in cut off frequencies and diminished SNR at the interface) and therefore errors have to be minimised to within 1 μm. Rules based on both Classification and Regression Trees (CART) and Neural Networks (NN) were used to implement a simulation displaying how such a control regime could be used as a real time controller, be it corrective measures (via spring cuts) over several initial machining passes or, with a micron cut introducing a level plain measure for allowing setup corrective measures (similar to a spirit level).

  9. Investigation of ELF Signals Associated with Mine Warfare: A University of Idaho and Acoustic Research Detachment Collaboration, Phase Three

    DTIC Science & Technology

    2012-07-01

    with Mine Warfare: A University of Idaho and Acoustic Research Detachment Collaboration, Phase Three 5a. CONTRACT NUMBER 5b. GRANT NUMBER...Warfare, A University of Idaho and Acoustic Research Detachment Collaboration, Phase Three.” Phase Three is a continuation of the Phase One and Two...of ELF Signals Associated with Mine Warfare: A University of Idaho and Acoustic Research Detachment Collaboration, Phase Three By Jeffrey L

  10. 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.

  11. Research on the characteristic of acoustic signal induced by thermoelastic mechanism

    NASA Astrophysics Data System (ADS)

    Zhou, Ju; Lei, Li Hua; Zhang, Jian Jun; Xue, Ming

    2016-10-01

    When a laser irradiates into the liquid medium, the medium absorbs the laser energy and induces sound source. As a new method to generate underwater sound wave, laser-acoustic has a variety of commercial and oceanographic applications on the information transmission between aerial and underwater platform, underwater target detection, marine environment measurement etc. due to its merits such as high acoustic intensity, spike pulse and wide frequency spectrum. According to different energy intensity of the laser pulse and the spatial and temporal distribution of energy interaction region, the mechanism of the laser interacting with water that generating sound are classified as thermoelastic, vaporization and optical breakdown mainly. Thermoelastic is an important mechanism of laser-acoustics. The characteristics of photoacoustic signal that induced by thermoelastic mechanism was summarized and analyzed comprehensively. According to different induce conditions, theoretical models of the photoacoustic signal induced by a δ pulse and a long pulse laser are summarized respectively, and its nature characteristic in the time domain and frequency domain were analyzed. Through simulation, the theoretical curve of the sound directivity was drawn. These studies will provide a reference for the practical application of laser-acoustics technology.

  12. 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.

  13. Automatic detection of the dominant melody in acoustic musical signals

    NASA Astrophysics Data System (ADS)

    Klapuri, Anssi P.

    2005-09-01

    An auditory-model based method is described for estimating the fundamental frequency contour of the dominant melody in complex music signals. The core method consists of a conventional cochlear model followed by a novel periodicity analysis mechanism within the subbands. As the output, the method computes the salience (i.e., strength) of different fundamental frequency candidates in successive time frames. The maximum value of this vector in each frame can be used to indicate the dominant fundamental frequency directly. In addition, however, it was noted that the first-order time differential of the salience vector leads to an efficient use of temporal features which improve the performance in the presence of a large number of concurrent sounds. These temporal features include particularly the common amplitude or frequency modulation of the partials of the sound that is used to communicate the melody. A noise-suppression mechanism is described which improves the robustness of estimation in the presence of drums and percussive instruments. In evaluations, a database of complex music signals was used where the melody was manually annotated. Use of the method for music information retrieval and music summarization is discussed.

  14. 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.

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

    NASA Astrophysics Data System (ADS)

    Milner, G. Martin

    2005-05-01

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

  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. 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.

  18. Application of Acoustic Signal Processing Techniques for Improved Underwater Source Detection and Localization

    DTIC Science & Technology

    1988-08-31

    Systems Center, San Diego; the Electric Boat Division of General ambiguities in the beam patterns, provided the bearmforming is done with Dynamics. ] the...Am. Suppl. 1. Vol. 60. Fall 1986 112th Meeting: Acoustical Socity of America A wearable multichannel signal processor for stimulation of single... electrical dynamic range 1Hi4 & Channel interaction measured by forward-masked "pla of the patient. Several processor configurations with different resonator

  19. 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.

  20. 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.

  1. 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.

  2. 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.

  3. Acoustic Classification and Optimization for Multi-Modal Rendering of Real-World Scenes.

    PubMed

    Schissler, Carl; Loftin, Christian; Manocha, Dinesh

    2017-02-09

    We present a novel algorithm to generate virtual acoustic effects in captured 3D models of real-world scenes for multimodal augmented reality. We leverage recent advances in 3D scene reconstruction in order to automatically compute acoustic material properties. Our technique consists of a two-step procedure that first applies a convolutional neural network (CNN) to estimate the acoustic material properties, including frequency-dependent absorption coefficients, that are used for interactive sound propagation. In the second step, an iterative optimization algorithm is used to adjust the materials determined by the CNN until a virtual acoustic simulation converges to measured acoustic impulse responses. We have applied our algorithm to many reconstructed real-world indoor scenes and evaluated its fidelity for augmented reality applications.

  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. 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

  6. 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.

  7. 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.

  8. 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.}

  9. 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

  10. Influences of an acoustic signal with ultrasound components on the acquisition of a defensive conditioned reflex in Wistar rats.

    PubMed

    Loseva, E V; Alekseeva, T G

    2007-06-01

    The effects of short (90 sec) exposures to a complex acoustic signal with ultrasound components on the acquisition of a defensive conditioned two-way avoidance reflex using an electric shock as the unconditioned stimulus in a shuttle box were studied in female Wistar rats. This stimulus induced audiogenic convulsions of different severities in 59% of the animals. A scale for assessing the ability of rats to acquire the conditioned two-way avoidance reflex was developed. Presentation of the complex acoustic signal was found to be a powerful stressor for Wistar rats, preventing the acquisition of the reflex in the early stages (four and six days) after presentation. This effect was independent of the presence and severity of audiogenic convulsions in the rats during presentation of the acoustic signal. On repeat training nine days after the acoustic signal (with the first session after four days), acquisition of the reflex was hindered (as compared with controls not presented with the acoustic signal). However, on repeat training at later time points (1.5 months after the complex acoustic signal, with the first session after six days), the rats rapidly achieved the learning criterion (10 correct avoidance responses in a row). On the other hand, if the acoustic signal was presented at different times (immediately or at three or 45 days) after the first training session, the animals' ability to acquire the reflex on repeat training was not impaired at either the early or late periods after exposure to the stressor. These results suggest that the complex acoustic signal impairs short-term memory (the process of acquisition of the conditioned two-way avoidance reflex at the early post-presentation time point) but has no effect on long-term memory or consolidation of the memory trace.

  11. Similarity assessment of acoustic emission signals and its application in source localization.

    PubMed

    Chen, Shiwan; Yang, Chunhe; Wang, Guibin; Liu, Wei

    2017-03-01

    In conventional AE source localization acoustic emission (AE) signals are applied directly to localize the source without any waveform identification or quality evaluation, which always leads to large errors in source localization. To improve the reliability and accuracy of acoustic emission source localization, an identification procedure is developed to assess the similarity of AE signals to select signals with high quality to localize the AE source. Magnitude square coherence (MSC), wavelet coherence and dynamic timing warping (DTW) are successively applied for similarity assessment. Results show that cluster analysis based on DTW distance is effective to select AE signals with high similarity. Similarity assessment results of the proposed method are almost completely consistent with manual identification. A novel AE source localization procedure is developed combining the selected AE signals with high quality and a direct source localization algorithm. AE data from thermal-cracking tests in Beishan granite are analyzed to demonstrate the effectiveness of the proposed AE localization procedure. AE events are re-localized by the proposed AE localization procedure. And the accuracy of events localization has been improved significantly. The reliability and credibility of AE source localization will be improved by the proposed method.

  12. 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.

  13. Processing of acoustic signals in grasshoppers - a neuroethological approach towards female choice.

    PubMed

    Ronacher, Bernhard; Stange, Nicole

    2013-01-01

    Acoustic communication is a major factor for mate attraction in many grasshopper species and thus plays a vital role in a grasshopper's life. First of all, the recognition of the species-specific sound patterns is crucial for preventing hybridization with other species, which would result in a drastic fitness loss. In addition, there is evidence that females are choosy with respect to conspecific males and prefer or reject the songs of some individuals, thereby exerting a sexual selection on males. Remarkably, the preferences of females are preserved even under masking noise. To discriminate between the basically similar signals of conspecifics is obviously a challenge for small nervous systems. We therefore ask how the acoustic signals are processed and represented in the grasshopper's nervous system, to allow for a fine discrimination and assessment of individual songs. The discrimination of similar signals may be impeded not only by signal masking due to external noise sources, but also by intrinsic noise due to the inherent variability of spike trains. Using a spike train metric we could estimate how well, in principle, the songs of different individuals can be discriminated on the basis of neuronal responses, and found a remarkable potential for discrimination performance at the first stage, but not on higher stages of the auditory pathway. Next, we ask which benefits a grasshopper female may earn from being choosy. New results, which revealed correlations between specific song features and the size and immunocompetence of the males, suggest that females may derive from acoustic signals clues about condition and health of the sending male. However, we observed substantial differences between the preference functions of individual females and it may be particularly rewarding to relate the variations in female preferences to individual differences in the responses of identified neurons.

  14. Sparse approximation of long-term biomedical signals for classification via dynamic PCA.

    PubMed

    Xie, Shengkun; Jin, Feng; Krishnan, Sridhar

    2011-01-01

    Sparse approximation is a novel technique in applications of event detection problems to long-term complex biomedical signals. It involves simplifying the extent of resources required to describe a large set of data sufficiently for classification. In this paper, we propose a multivariate statistical approach using dynamic principal component analysis along with the non-overlapping moving window technique to extract feature information from univariate long-term observational signals. Within the dynamic PCA framework, a few principal components plus the energy measure of signals in principal component subspace are highly promising for applying event detection problems to both stationary and non-stationary signals. The proposed method has been first tested using synthetic databases which contain various representative signals. The effectiveness of the method is then verified with real EEG signals for the purpose of epilepsy diagnosis and epileptic seizure detection. This sparse method produces a 100% classification accuracy for both synthetic data and real single channel EEG data.

  15. Classification of EMG signals using PSO optimized SVM for diagnosis of neuromuscular disorders.

    PubMed

    Subasi, Abdulhamit

    2013-06-01

    Support vector machine (SVM) is an extensively used machine learning method with many biomedical signal classification applications. In this study, a novel PSO-SVM model has been proposed that hybridized the particle swarm optimization (PSO) and SVM to improve the EMG signal classification accuracy. This optimization mechanism involves kernel parameter setting in the SVM training procedure, which significantly influences the classification accuracy. The experiments were conducted on the basis of EMG signal to classify into normal, neurogenic or myopathic. In the proposed method the EMG signals were decomposed into the frequency sub-bands using discrete wavelet transform (DWT) and a set of statistical features were extracted from these sub-bands to represent the distribution of wavelet coefficients. The obtained results obviously validate the superiority of the SVM method compared to conventional machine learning methods, and suggest that further significant enhancements in terms of classification accuracy can be achieved by the proposed PSO-SVM classification system. The PSO-SVM yielded an overall accuracy of 97.41% on 1200 EMG signals selected from 27 subject records against 96.75%, 95.17% and 94.08% for the SVM, the k-NN and the RBF classifiers, respectively. PSO-SVM is developed as an efficient tool so that various SVMs can be used conveniently as the core of PSO-SVM for diagnosis of neuromuscular disorders.

  16. Classification of signals for blocking apoptosis in vascular endothelial cells.

    PubMed

    Hase, M; Araki, S; Kaji, K; Hayashi, H

    1994-10-01

    The survival and death of human umbilical vascular endothelial cells in culture are affected by several factors, such as fibroblast growth factor (FGF), serum, phorbol ester (TPA), and vanadate. In order to identify common aspects of the various signal-transduction processes during the course of apoptotic or programmed cell death, we designed experiments to distinguish between these factors in terms of the pathway that is responsible for the processing of each stimulus. We found, for example, that the effect of removal of FGF was specifically overcome by the addition of the phorbol ester. Our results indicated that two distinct pathways were operative, one specific for signal transduction initiated by FGF and phorbol ester and another specific for signal transduction initiated by serum and vanadate. These two pathways merged down-stream of the individual signal-processing pathways.

  17. 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.

  18. Identification of blasting sources in the Dobrogea seismogenic region, Romania using seismo-acoustic signals

    NASA Astrophysics Data System (ADS)

    Ghica, Daniela Veronica; Grecu, Bogdan; Popa, Mihaela; Radulian, Mircea

    2016-10-01

    In order to discriminate between quarry blasts and earthquakes observed in the Dobrogea seismogenic region, a seismo-acoustic analysis was performed on 520 events listed in the updated Romanian seismic catalogue from January 2011 to December 2012. During this time interval, 104 seismo-acoustic events observed from a distance between 110 and 230 km and backazimuth interval of 110-160° from the IPLOR infrasound array were identified as explosions by associating with infrasonic signals. WinPMCC software for interactive analysis was applied to detect and characterize infrasonic signals in terms of backazimuth, speed and frequency content. The measured and expected values of both backazimuths and arrival times for the study events were compared in order to identify the sources of infrasound. Two predominant directions for seismo-acoustic sources' aligning were observed, corresponding to the northern and central parts of Dobrogea, and these directions are further considered as references in the process of discriminating explosions from earthquakes. A predominance of high-frequency detections (above 1 Hz) is also observed in the infrasound data. The strong influence of seasonally dependent stratospheric winds on the IPLOR detection capability limits the efficiency of the discrimination procedure, as proposed by this study.

  19. 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.

  20. 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

  1. 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.

  2. Brain estrogen signaling and acute modulation of acoustic communication behaviors: a working hypothesis

    PubMed Central

    Remage-Healey, Luke

    2013-01-01

    Summary Although estrogens are widely considered circulating ‘sex steroid hormones’ typically associated with female reproduction, recent evidence suggests that estrogens can act as local modulators of brain circuits in both males and females. Functional implications of this newly-characterized estrogen signaling system have begun to emerge. This essay summarizes evidence in support of the hypothesis that the rapid production of estrogens in brain circuits can drive acute changes in both the production and perception of acoustic communication behaviors. These studies reveal two fundamental neurobiological concepts: 1) estrogens can be produced locally in brain circuits independent of levels in nearby circuits and in the circulation, and 2) estrogens can have very rapid effects within these brain circuits to modulate social vocalizations, acoustic processing, and sensorimotor integration. This research relies on a vertebrate-wide span of investigations, including vocalizing fishes, amphibians and birds, emphasizing the importance of comparative model systems in understanding principles of neurobiology. PMID:23065844

  3. 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.

  4. 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

  5. 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

  6. 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).

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

    NASA Astrophysics Data System (ADS)

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

    2015-08-01

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

  8. Nondestructive evaluation of steels using acoustic and magnetic Barkhausen signals - I. Effect of carbide precipitation and hardness

    SciTech Connect

    Kameda, J.; Ranjan, R.

    1987-07-01

    The effect of microstructures on acoustic and magnetic Barkhausen signals has been investigated in a quenched and tempered steel and spheroidized steels with various carbon contents. A major peak of the acoustic Barkhausen signal was induced when a magnetic field was increased from zero to the saturation state. A minor peak of the acoustic signal and a single peak of the magnetic signal appeared during the decreasing field. The peak value of the acoustic Barkhausen signal shows a linear dependence on the sweep rate of a magnetic field while that of the magnetic Barkhausen shows a nonlinear one. The increasing tempering temperature which gives rise to a decrease in hardness and an increase in carbide size and spacing caused the acoustic and magnetic Barkhausen peak voltages to increase precipitously and gradually, respectively. In the spheroidized steels, the acoustic peak voltage monotonically decreased with increasing carbon content from 0.17 to 0.96 wt% and the magnetic peak voltage was greatest when the carbon content was 0.46 wt%.

  9. Classification of surface electromyographic signals by means of multifractal singularity spectrum.

    PubMed

    Wang, Gang; Ren, Doutian

    2013-03-01

    In order to effectively control a prosthetic system, considerable attempts have been made in recent years to improve the classification accuracy of surface electromyographic (SEMG) signals. However, the extraction of effective features is still a primary challenge for the classification of SEMG signals. This study tried to solve the problem by applying the multifractal analysis. It was found that the SEMG signals were characterized by multifractality during forearm movements and different types of forearm movements were related to different multifractal singularity spectra. To quantitatively evaluate the multifractal singularity spectra of the SEMG signals, the areas of the singularity spectrum curves were calculated by integrating the spectrum curves with respect to the singularity strengths. Our results showed that there were several separate clusters resulting from singularity spectrum areas of different forearm movements when two channels of SEMG signals were used in this experimental research, which demonstrated that the multifractal analysis approach was suitable for identifying different types of forearm movements. By comparing with other feature extraction techniques, the multifractal singularity spectrum approach provided higher classification accuracy in terms of the classification of SEMG signals.

  10. Automated Method of Frequency Determination in Software Metric Data Through the Use of the Multiple Signal Classification (MUSIC) Algorithm

    DTIC Science & Technology

    1998-06-26

    METHOD OF FREQUENCY DETERMINATION 4 IN SOFTWARE METRIC DATA THROUGH THE USE OF THE 5 MULTIPLE SIGNAL CLASSIFICATION ( MUSIC ) ALGORITHM 6 7 STATEMENT OF...graph showing the estimated power spectral 12 density (PSD) generated by the multiple signal classification 13 ( MUSIC ) algorithm from the data set used...implemented in this module; however, it is preferred to use 1 the Multiple Signal Classification ( MUSIC ) algorithm. The MUSIC 2 algorithm is

  11. Analysis of Acoustic Emission Signals During Laser Spot Welding of SS304 Stainless Steel

    NASA Astrophysics Data System (ADS)

    Lee, Seounghwan; Ahn, Suneung; Park, Changsoon

    2014-03-01

    In this article, an in-process monitoring scheme for a pulsed Nd:YAG laser spot welding (LSW) is presented. Acoustic emission (AE) was selected for the feedback signal, and the AE data during LSW were sampled and analyzed for varying process conditions such as laser power and pulse duration. In the analysis, possible AE generation sources such as melting and solidification mechanism during welding were investigated using both the time- and frequency-domain signal processings. The results, which show close relationships between LSW and AE signals, were adopted in the feature (input) selection of a back-propagation artificial neural network, to predict the weldability of stainless steel sheets. Processed outputs agree well with LSW experimental data, which confirms the usefulness of the proposed scheme.

  12. 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.

  13. 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.

  14. Acoustic emission signals frequency-amplitude characteristics of sandstone after thermal treated under uniaxial compression

    NASA Astrophysics Data System (ADS)

    Kong, Biao; Wang, Enyuan; Li, Zenghua; Wang, Xiaoran; Niu, Yue; Kong, Xiangguo

    2017-01-01

    Thermally treated sandstone deformation and fracture produced abundant acoustic emission (AE) signals. The AE signals waveform contained plentiful precursor information of sandstone deformation and fracture behavior. In this paper, uniaxial compression tests of sandstone after different temperature treatments were conducted, the frequency-amplitude characteristics of AE signals were studied, and the main frequency distribution at different stress level was analyzed. The AE signals frequency-amplitude characteristics had great difference after different high temperature treatment. Significant differences existed of the main frequency distribution of AE signals during thermal treated sandstone deformation and fracture. The main frequency band of the largest waveforms proportion was not unchanged after different high temperature treatments. High temperature caused thermal damage to the sandstone, and sandstone deformation and fracture was obvious than the room temperature. The number of AE signals was larger than the room temperature during the initial loading stage. The low frequency AE signals had bigger proportion when the stress was 0.1, and the maximum value of the low frequency amplitude was larger than high frequency signals. With the increase of stress, the low and high frequency AE signals were gradually increase, which indicated that different scales ruptures were broken in sandstone. After high temperature treatment, the number of high frequency AE signals was significantly bigger than the low frequency AE signals during the latter loading stage, this indicates that the small scale rupture rate of recurrence and frequency were more than large scale rupture. The AE ratio reached the maximum during the sandstone instability failure period, and large scale rupture was dominated in the failure process. AE amplitude increase as the loading increases, the deformation and fracture of sandstone was increased gradually. By comparison, the value of the low frequency

  15. 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).

  16. Myoelectric Signal Segmentation and Classification Using Wavelets Based Neural Networks

    DTIC Science & Technology

    2007-11-02

    MES are signals recorded using surface electrodes that reflect the localized neuromuscular activity. They have been used in various aspects of medical ...and biomedical applications [1]. For example, they are used for the diagnosis of neuromuscular diseases such as polymyositics [2]. One of the uses...training patterns and N is the number of output nodes. dk and zk are the desired and actual responses for output node k, respectively. Arm movement

  17. 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

  18. Long recording sequences: how to track the intra-individual variability of acoustic signals.

    PubMed

    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.

  19. 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

  20. Surface Reflection Phase in Two Way Acoustic Signal in Oceanic Crustal Deformation Measurement

    NASA Astrophysics Data System (ADS)

    Ikuta, R.; Tadokoro, K.; Watanabe, T.; Nagai, S.; Okuda, T.

    2011-12-01

    We are developing a geodetic method of monitoring crustal deformation under the ocean using kinematic GPS and acoustic ranging. The measurements are done by measuring two way traveltime of supersonic signal between a vessel, whose position is precisely determined by kinematic GPS, and transponders array (benchmark) on the ocean bottom. The goal of our research is to achieve sub-centimeter accuracy in measuring position of the benchmark by a very short-time measurement like 10 hours. In this study, we focused the under-water acoustic part of the system to improve data acquisition rate and then number of observation equations to solve the position of the benchmark with better accuracy. The measurements have started in Suruga Bay in 2003 and in Kumano Basin in 2004, which have been repeated a few times in a year. The accuracy of the benchmark positioning depends on the quality and quantity of the acoustic signal data. We are using M-sequence signal because of its robustness against ambient noises (The signal length is 14.322ms, Carrier frequency is 12.987kHz). We calculate cross-correlation between emitted and received signal and then accept the signal with cross correlation coefficient higher than a threshold. However, we often failed to achieve well correlated signals and then obtain very few traveltime data through one cruise. Sometimes in the cruise of good condition, 70 % of acoustic data have correlation coefficient above 0.7, on the other hand, only 10 % of all the data have correlation coefficient of 0.7 in bad condition cruise. We found that increase of ambient noise and contamination of later phase resembling to the main signal occurs independently each other. The ambient noise should be due to screw noise of the vessel because the noise grew up when sailing against the wind and current. On the other hand the later phases have following features: 1. Arrive in between 1 and 2 ms after the main signal arrival 2. The cross-correlation coefficient sometimes

  1. Detection and classification of underwater targets in background noise acoustic daylight

    NASA Astrophysics Data System (ADS)

    Goo, Gee-In

    2003-09-01

    It has been reported that underwater target models, spheres and cylinders can be detected and classified in background acoustic noise. In this paper, the author presents his recent finding that underwater target is detectable in acoustic background noise in open waters. Using a resonance detection technique, G-Transform, the noise background of a number of AUTEC sample data files with mammal clicks were analyzed. From the noise backgrounds in these data files, a number of possible target signatures were observed. It suggests that real underwater targets may be detected and classified passively in background noise.

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

    PubMed

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

    2014-12-01

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

  3. 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

  4. Physiological sensor signals classification for healthcare using sensor data fusion and case-based reasoning.

    PubMed

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

    2014-07-03

    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.

  5. Traits of acoustic signalization and generation of sounds by some schooling physostomous fish

    NASA Astrophysics Data System (ADS)

    Kuznetsov, M. Yu.

    2009-11-01

    The results of experimental investigations of acoustic activity of schooling physostomous fish are discussed, made with reference to chum salmon, pink salmon, Pacific herring, and sardine. Dynamic spectra of most investigated fish are concentrated within two subranges of frequency, according to each investigated fish species. Direct participation of the swimming bladder in sound formation in the investigated fish is shown. Morphological traits of sound-producing organs of salmons and herrings are considered. Mechanisms of generation of signals in physotmous fish involving the muscular sphincter and swimming bladder are analyzed.

  6. Oscillating bubble as a sensor of low frequency electro-acoustic signals in electrolytes.

    PubMed

    Tankovsky, N; Baerner, K; Barey, Dooa Abdel

    2006-08-16

    Small air-bubble deformations, caused by electro-acoustic signals generated in electrolytic solutions have been detected by angle-modulation of a refracted He-Ne laser beam. The observed electromechanical resonance at low frequency, below 100 Hz, has proved to be directly related to the oscillations of characteristic ion-doped water structures when driven by an external electric field. The presence of structure-breaking or structure-making ions modifies the water structure, which varies the mechanical losses of the oscillating system and can be registered as changes in the width of the observed resonance curves.

  7. 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.

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

    NASA Astrophysics Data System (ADS)

    Kellermann, Walter

    2004-05-01

    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.

  9. 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

  10. 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.

  11. 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

  12. 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.

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

    SciTech Connect

    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.

  14. 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.

  15. Comparison of linear, nonlinear, and feature selection methods for EEG signal classification.

    PubMed

    Garrett, Deon; Peterson, David A; Anderson, Charles W; Thaut, Michael H

    2003-06-01

    The reliable operation of brain-computer interfaces (BCIs) based on spontaneous electroencephalogram (EEG) signals requires accurate classification of multichannel EEG. The design of EEG representations and classifiers for BCI are open research questions whose difficulty stems from the need to extract complex spatial and temporal patterns from noisy multidimensional time series obtained from EEG measurements. The high-dimensional and noisy nature of EEG may limit the advantage of nonlinear classification methods over linear ones. This paper reports the results of a linear (linear discriminant analysis) and two nonlinear classifiers (neural networks and support vector machines) applied to the classification of spontaneous EEG during five mental tasks, showing that nonlinear classifiers produce only slightly better classification results. An approach to feature selection based on genetic algorithms is also presented with preliminary results of application to EEG during finger movement.

  16. 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

  17. Leg Motion Classification with Artificial Neural Networks Using Wavelet-Based Features of Gyroscope Signals

    PubMed Central

    Ayrulu-Erdem, Birsel; Barshan, Billur

    2011-01-01

    We extract the informative features of gyroscope signals using the discrete wavelet transform (DWT) decomposition and provide them as input to multi-layer feed-forward artificial neural networks (ANNs) for leg motion classification. Since the DWT is based on correlating the analyzed signal with a prototype wavelet function, selection of the wavelet type can influence the performance of wavelet-based applications significantly. We also investigate the effect of selecting different wavelet families on classification accuracy and ANN complexity and provide a comparison between them. The maximum classification accuracy of 97.7% is achieved with the Daubechies wavelet of order 16 and the reverse bi-orthogonal (RBO) wavelet of order 3.1, both with similar ANN complexity. However, the RBO 3.1 wavelet is preferable because of its lower computational complexity in the DWT decomposition and reconstruction. PMID:22319378

  18. Leg motion classification with artificial neural networks using wavelet-based features of gyroscope signals.

    PubMed

    Ayrulu-Erdem, Birsel; Barshan, Billur

    2011-01-01

    We extract the informative features of gyroscope signals using the discrete wavelet transform (DWT) decomposition and provide them as input to multi-layer feed-forward artificial neural networks (ANNs) for leg motion classification. Since the DWT is based on correlating the analyzed signal with a prototype wavelet function, selection of the wavelet type can influence the performance of wavelet-based applications significantly. We also investigate the effect of selecting different wavelet families on classification accuracy and ANN complexity and provide a comparison between them. The maximum classification accuracy of 97.7% is achieved with the Daubechies wavelet of order 16 and the reverse bi-orthogonal (RBO) wavelet of order 3.1, both with similar ANN complexity. However, the RBO 3.1 wavelet is preferable because of its lower computational complexity in the DWT decomposition and reconstruction.

  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.

  20. Classification of EMG signals using PCA and FFT.

    PubMed

    Güler, Nihal Fatma; Koçer, Sabri

    2005-06-01

    In this study, the fast Fourier transform (FFT) analysis was applied to EMG signals recorded from ulnar nerves of 59 patients to interpret data. The data of the patients were diagnosed by the neurologists as 19 patients were normal, 20 patients had neuropathy and 20 patients had myopathy. The amount of FFT coefficients had been reduced by using principal components analysis (PCA). This would facilitate calculation and storage of EMG data. PCA coefficients were applied to multilayer perceptron (MLP) and support vector machine (SVM) and both classified systems of performance values were computed. Consequently, the results show that SVM has high anticipation level in the diagnosis of neuromuscular disorders. It is proved that its test performance is high compared with MLP.

  1. Automatic and adaptive classification of electroencephalographic signals for brain computer interfaces.

    PubMed

    Rodríguez-Bermúdez, Germán; García-Laencina, Pedro J

    2012-11-01

    Extracting knowledge from electroencephalographic (EEG) signals has become an increasingly important research area in biomedical engineering. In addition to its clinical diagnostic purposes, in recent years there have been many efforts to develop brain computer interface (BCI) systems, which allow users to control external devices only by using their brain activity. Once the EEG signals have been acquired, it is necessary to use appropriate feature extraction and classification methods adapted to the user in order to improve the performance of the BCI system and, also, to make its design stage easier. This work introduces a novel fast adaptive BCI system for automatic feature extraction and classification of EEG signals. The proposed system efficiently combines several well-known feature extraction procedures and automatically chooses the most useful features for performing the classification task. Three different feature extraction techniques are applied: power spectral density, Hjorth parameters and autoregressive modelling. The most relevant features for linear discrimination are selected using a fast and robust wrapper methodology. The proposed method is evaluated using EEG signals from nine subjects during motor imagery tasks. Obtained experimental results show its advantages over the state-of-the-art methods, especially in terms of classification accuracy and computational cost.

  2. Automatic classification of infant sleep based on instantaneous frequencies in a single-channel EEG signal.

    PubMed

    Čić, Maja; Šoda, Joško; Bonković, Mirjana

    2013-12-01

    This study presents a novel approach for the electroencephalogram (EEG) signal quantification in which the empirical mode decomposition method, a time-frequency method designated for nonlinear and non-stationary signals, decomposes the EEG signal into intrinsic mode functions (IMF) with corresponding frequency ranges that characterize the appropriate oscillatory modes embedded in the brain neural activity acquired using EEG. To calculate the instantaneous frequency of IMFs, an algorithm was developed using the Generalized Zero Crossing method. From the resulting frequencies, two different novel features were generated: the median instantaneous frequencies and the number of instantaneous frequency changes during a 30s segment for seven IMFs. The sleep stage classification for the daytime sleep of 20 healthy babies was determined using the Support Vector Machine classification algorithm. The results were evaluated using the cross-validation method to achieve an approximately 90% accuracy and with new examinee data to achieve 80% average accuracy of classification. The obtained results were higher than the human experts' agreement and were statistically significant, which positioned the method, based on the proposed features, as an efficient procedure for automatic sleep stage classification. The uniqueness of this study arises from newly proposed features of the time-frequency domain, which bind characteristics of the sleep signals to the oscillation modes of brain activity, reflecting the physical characteristics of sleep, and thus have the potential to highlight the congruency of twin pairs with potential implications for the genetic determination of sleep.

  3. A novel deep learning approach for classification of EEG motor imagery signals

    NASA Astrophysics Data System (ADS)

    Rezaei Tabar, Yousef; Halici, Ugur

    2017-02-01

    Objective. Signal classification is an important issue in brain computer interface (BCI) systems. Deep learning approaches have been used successfully in many recent studies to learn features and classify different types of data. However, the number of studies that employ these approaches on BCI applications is very limited. In this study we aim to use deep learning methods to improve classification performance of EEG motor imagery signals. Approach. In this study we investigate convolutional neural networks (CNN) and stacked autoencoders (SAE) to classify EEG Motor Imagery signals. A new form of input is introduced to combine time, frequency and location information extracted from EEG signal and it is used in CNN having one 1D convolutional and one max-pooling layers. We also proposed a new deep network by combining CNN and SAE. In this network, the features that are extracted in CNN are classified through the deep network SAE. Main results. The classification performance obtained by the proposed method on BCI competition IV dataset 2b in terms of kappa value is 0.547. Our approach yields 9% improvement over the winner algorithm of the competition. Significance. Our results show that deep learning methods provide better classification performance compared to other state of art approaches. These methods can be applied successfully to BCI systems where the amount of data is large due to daily recording.

  4. Clustering technique-based least square support vector machine for EEG signal classification.

    PubMed

    Siuly; Li, Yan; Wen, Peng Paul

    2011-12-01

    This paper presents a new approach called clustering technique-based least square support vector machine (CT-LS-SVM) for the classification of EEG signals. Decision making is performed in two stages. In the first stage, clustering technique (CT) has been used to extract representative features of EEG data. In the second stage, least square support vector machine (LS-SVM) is applied to the extracted features to classify two-class EEG signals. To demonstrate the effectiveness of the proposed method, several experiments have been conducted on three publicly available benchmark databases, one for epileptic EEG data, one for mental imagery tasks EEG data and another one for motor imagery EEG data. Our proposed approach achieves an average sensitivity, specificity and classification accuracy of 94.92%, 93.44% and 94.18%, respectively, for the epileptic EEG data; 83.98%, 84.37% and 84.17% respectively, for the motor imagery EEG data; and 64.61%, 58.77% and 61.69%, respectively, for the mental imagery tasks EEG data. The performance of the CT-LS-SVM algorithm is compared in terms of classification accuracy and execution (running) time with our previous study where simple random sampling with a least square support vector machine (SRS-LS-SVM) was employed for EEG signal classification. We also compare the proposed method with other existing methods in the literature for the three databases. The experimental results show that the proposed algorithm can produce a better classification rate than the previous reported methods and takes much less execution time compared to the SRS-LS-SVM technique. The research findings in this paper indicate that the proposed approach is very efficient for classification of two-class EEG signals.

  5. Classification of Benign and Malignant Breast Tumors in Ultrasound Images with Posterior Acoustic Shadowing Using Half-Contour Features.

    PubMed

    Zhou, Zhuhuang; Wu, Shuicai; Chang, King-Jen; Chen, Wei-Ren; Chen, Yung-Sheng; Kuo, Wen-Hung; Lin, Chung-Chih; Tsui, Po-Hsiang

    Posterior acoustic shadowing (PAS) can bias breast tumor segmentation and classification in ultrasound images. In this paper, half-contour features are proposed to classify benign and malignant breast tumors with PAS, considering the fact that the upper half of the tumor contour is less affected by PAS. Adaptive thresholding and disk expansion are employed to detect tumor contours. Based on the detected full contour, the upper half contour is extracted. For breast tumor classification, six quantitative feature parameters are analyzed for both full contours and half contours, including standard deviation of degree (SDD), which is proposed to describe tumor irregularity. Fifty clinical cases (40 with PAS and 10 without PAS) were used. Tumor circularity (TC) and SDD were both effective full- and half-contour parameters in classifying images without PAS. Half-contour TC [74 % accuracy, 72 % sensitivity, 76 % specificity, 0.78 area under the receiver operating characteristic curve (AUC), p > 0.05] significantly improved the classification of breast tumors with PAS compared to that with full-contour TC (54 % accuracy, 56 % sensitivity, 52 % specificity, 0.52 AUC, p > 0.05). Half-contour SDD (72 % accuracy, 76 % sensitivity, 68 % specificity, 0.81 AUC, p < 0.05) improved the classification of breast tumors with PAS compared to that with full-contour SDD (62 % accuracy, 80 % sensitivity, 44 % specificity, 0.61 AUC, p > 0.05). The proposed half-contour TC and SDD may be useful in classifying benign and malignant breast tumors in ultrasound images affected by PAS.

  6. Holographic matched filtering of acoustic signals with the application of a membrane modulator

    NASA Astrophysics Data System (ADS)

    Larkin, A. I.; Minialga, V. L.; Petropavlovskii, V. M.

    1986-04-01

    The results of preliminary experiments on a holographic-matched-filtering space-time light modulator for use in the real-time digital analysis of acoustic signals (such as those from the multiple hydrophones of the DUMAND project) are reported. The modulator is based on a transverse-displacement traveling-wave membrane (in this case a taut metal ribbon with a diffusely reflective coating) illuminated by an electrooptic-shutter-pulsed laser beam to record Fresnel holograms. The effects of varying the illumination optics, the ribbon temperature and characteristics, and other device parameters are investigated, and the feasibility of analyzing signals from 0.1 to 100 kHz with a base of 1000 is demonstrated.

  7. 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

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

    PubMed

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

    2014-12-07

    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.

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

    PubMed

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

    2015-01-07

    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.

  10. A practical approach for quantifying acoustic emission signals using diffuse field measurements

    NASA Astrophysics Data System (ADS)

    Scholey, Jonathan J.; Wilcox, Paul D.

    2009-03-01

    Acoustic Emission (AE) testing is capable of detecting a wide range of defects using a relatively sparse sensor array and as a result is a candidate structural health monitoring technology. The widespread application of the technology is restricted by a lack of predictive modelling capability and quantitative source characteristic information. Most AE tests are conducted on small coupons where source characteristics are estimated using the early arriving part of the AE signal. The early arriving part of an AE signal, and therefore the source characteristics, are dependent on the source location, source orientation and specimen geometry making them unsuitable for use in predictive models. The work in this paper is concerned with making source characterisation measurements based on the diffuse field of an AE signal. A practical approach for calibrating the diffuse field amplitude is proposed and is demonstrated on AE signals from electrochemically accelerated corrosion of a 316L stainless steel plate. The diffuse field amplitude of several AE events is calculated and reported as an equivalent absolute force. The low signal to noise ratio and high attenuation of elastic wave energy are found to reduce the accuracy of the results.

  11. 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.

  12. Coupled Research in Ocean Acoustics and Signal Processing for the Next Generation of Underwater Acoustic Communication Systems

    DTIC Science & Technology

    2015-03-18

    explicitly model the time vari- ability of acoustic channels and using this to predict underwater acoustic com- munications systems performance. Prior...methods have accommodated time variability by assuming that the channel is time invariant over an appropri- ately short interval of time. By explicitly...with the rate of channel fluctuations, the number and configuration of hydrophone array elements, the size of fil- ters in subsequent equalizers, and

  13. Advanced Methods for Passive Acoustic Detection, Classification, and Localization of Marine Mammals

    DTIC Science & Technology

    2014-09-30

    in the case of aerial surveys, significantly dangerous . In both the areas critical to the Navy and in other areas critical to marine mammals, PAM... animal calls via hyperbolic methods, Journal of the Acoustical Society of merica 97, 3352–3353 (1995). Morrissey, R. P., J. Ward, N. DiMarzio, S... animal as it follows its prey just prior to capture. Figure 6: Example of tracking highly ambiguous localizations. 15 Figure 7

  14. 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.

  15. Genetic algorithm for the optimization of features and neural networks in ECG signals classification

    NASA Astrophysics Data System (ADS)

    Li, Hongqiang; Yuan, Danyang; Ma, Xiangdong; Cui, Dianyin; Cao, Lu

    2017-01-01

    Feature extraction and classification of electrocardiogram (ECG) signals are necessary for the automatic diagnosis of cardiac diseases. In this study, a novel method based on genetic algorithm-back propagation neural network (GA-BPNN) for classifying ECG signals with feature extraction using wavelet packet decomposition (WPD) is proposed. WPD combined with the statistical method is utilized to extract the effective features of ECG signals. The statistical features of the wavelet packet coefficients are calculated as the feature sets. GA is employed to decrease the dimensions of the feature sets and to optimize the weights and biases of the back propagation neural network (BPNN). Thereafter, the optimized BPNN classifier is applied to classify six types of ECG signals. In addition, an experimental platform is constructed for ECG signal acquisition to supply the ECG data for verifying the effectiveness of the proposed method. The GA-BPNN method with the MIT-BIH arrhythmia database achieved a dimension reduction of nearly 50% and produced good classification results with an accuracy of 97.78%. The experimental results based on the established acquisition platform indicated that the GA-BPNN method achieved a high classification accuracy of 99.33% and could be efficiently applied in the automatic identification of cardiac arrhythmias.

  16. Genetic algorithm for the optimization of features and neural networks in ECG signals classification

    PubMed Central

    Li, Hongqiang; Yuan, Danyang; Ma, Xiangdong; Cui, Dianyin; Cao, Lu

    2017-01-01

    Feature extraction and classification of electrocardiogram (ECG) signals are necessary for the automatic diagnosis of cardiac diseases. In this study, a novel method based on genetic algorithm-back propagation neural network (GA-BPNN) for classifying ECG signals with feature extraction using wavelet packet decomposition (WPD) is proposed. WPD combined with the statistical method is utilized to extract the effective features of ECG signals. The statistical features of the wavelet packet coefficients are calculated as the feature sets. GA is employed to decrease the dimensions of the feature sets and to optimize the weights and biases of the back propagation neural network (BPNN). Thereafter, the optimized BPNN classifier is applied to classify six types of ECG signals. In addition, an experimental platform is constructed for ECG signal acquisition to supply the ECG data for verifying the effectiveness of the proposed method. The GA-BPNN method with the MIT-BIH arrhythmia database achieved a dimension reduction of nearly 50% and produced good classification results with an accuracy of 97.78%. The experimental results based on the established acquisition platform indicated that the GA-BPNN method achieved a high classification accuracy of 99.33% and could be efficiently applied in the automatic identification of cardiac arrhythmias. PMID:28139677

  17. 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.

  18. 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.

  19. Classification of EEG Signals Using a Multiple Kernel Learning Support Vector Machine

    PubMed Central

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

    2014-01-01

    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. PMID:25036334

  20. 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.

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

    NASA Astrophysics Data System (ADS)

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

    2012-10-01

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

  2. 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

  3. 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.

  4. 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

  5. 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

  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

    2016-03-05

    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. Reproducible Data Processing Research for the CABRI R.I.A. experiments Acoustic Emission signal analysis

    SciTech Connect

    Pantera, Laurent

    2015-07-01

    The CABRI facility is an experimental nuclear reactor of the French Atomic Energy Commission (CEA) designed to study the behaviour of fuel rods at high burnup under Reactivity Initiated Accident (R.I.A.) conditions such as the scenario of a control rod ejection. During the experimental phase, the behaviour of the fuel element generates acoustic waves which can be detected by two microphones placed upstream and downstream from the test device. Studies carried out on the last fourteen tests showed the interest in carrying out temporal and spectral analyses on these signals by showing the existence of signatures which can be correlated with physical phenomena. We want presently to return to this rich data in order to have a new point of view by applying modern signal processing methods. Such an antecedent works resumption leads to some difficulties. Although all the raw data are accessible in the form of text files, analyses and graphics representations were not clear in reproducing from the former studies since the people who were in charge of the original work have left the laboratory and it is not easy when time passes, even with our own work, to be able to remember the steps of data manipulations and the exact setup. Thus we decided to consolidate the availability of the data and its manipulation in order to provide a robust data processing workflow to the experimentalists before doing any further investigations. To tackle this issue of strong links between data, treatments and the generation of documents, we adopted a Reproducible Research paradigm. We shall first present the tools chosen in our laboratory to implement this workflow and, then we shall describe the global perception carried out to continue the study of the Acoustic Emission signals recorded by the two microphones during the last fourteen CABRI R.I.A. tests. (authors)

  9. Advanced Methods for Passive Acoustic Detection, Classification, and Localization of Marine Mammals

    DTIC Science & Technology

    2015-09-30

    marine mammal vocalizations and ultimately, in some cases, provide data for estimating the population density of the species present. In recent years...pose significant challenges. In this project, we are developing improved methods for detection, classification, and localization of many types of marine mammal sounds.

  10. Monitoring of global acoustic transmissions: Signal processing and preliminary data analysis

    NASA Astrophysics Data System (ADS)

    Frogner, Gary R.

    1991-09-01

    A great deal of controversy exists concerning the possible global warming trend which may occur as a result of a documented increase in atmospheric greenhouse gasses. The 1991 Heard Island Feasibility Experiment tested the feasibility of using transmissions of acoustic energy through major ocean basins of the world to monitor spatially averaged global temperature trends. This thesis documents the Naval Postgraduate School's reception of the phase encoded signal transmitted from the Southern Indian Ocean, development of real-time signal processing software, and preliminary data analysis. Data, received from a 32-channel vertical array suspended in the deep sound channel off the coast of Monterey, CA, was processed using real-time capable software. Data processing to reduce noise, determine SNR, and remove the m-sequence coding was found to be quite sensitive to Doppler frequency shifts. Although the SNR of the raw data was only about -27.5 dB for individual hydrophones, the transmitted signal was detected in both the frequency and time domains. However, the maximum processed signal peak in the time domain had an SNR of only +9 dB which is insufficient for use in a long term global temperature monitoring project. The hydrophone provides inadequate arrival time resolution.

  11. Influence of intermediate aminodextran layers on the signal response of surface acoustic wave biosensors.

    PubMed

    Länge, Kerstin; Rapp, Michael

    2008-06-15

    Surface acoustic wave (SAW) devices based on horizontally polarized surface shear waves enable direct and label-free detection of proteins in real time. Binding reactions on the sensor surface are detected by determining changes in surface wave velocity caused mainly by mass adsorption or change of viscoelasticity in the sensing layer. Intermediate hydrogel layers have been proven to be useful to immobilize capture molecules or ligands corresponding to the analyte. However, the SAW signal response strongly depends on the morphology of the hydrogel due to different relative changes of its acoustomechanical parameters such as viscoelasticity and density. In this work five aminodextrans (AMD) and one diamino polyethylene glycol (DA-PEG) were used as intermediate hydrogel layers. Sensors with immobilized streptavidin and samples containing biotinylated bovine serum albumin were used to exemplify affinity assays based on immobilized capture molecules for protein detection. The effects of the three-dimensional AMDs and the two-dimensional (2D) DA-PEG on the SAW signal response were investigated. The signal height decreased with increasing molar mass and increasing amount of immobilized AMD. Consequently, thin hydrogel layers are ideal to obtain optimum signal responses in this type of assay, whereas it is not necessarily a 2D hydrogel that gives the best results.

  12. 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

  13. 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.

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

    PubMed

    Rusconi, Marco; Valleriani, Angelo

    2016-06-20

    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.

  15. 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

  16. 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

  17. 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.

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

    PubMed Central

    Kalmijn, A D

    2000-01-01

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

  19. Classification

    ERIC Educational Resources Information Center

    Clary, Renee; Wandersee, James

    2013-01-01

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

  20. A Statistical Model-Based Speech Enhancement Using Acoustic Noise Classification for Robust Speech Communication

    NASA Astrophysics Data System (ADS)

    Choi, Jae-Hun; Chang, Joon-Hyuk

    In this paper, we present a speech enhancement technique based on the ambient noise classification that incorporates the Gaussian mixture model (GMM). The principal parameters of the statistical model-based speech enhancement algorithm such as the weighting parameter in the decision-directed (DD) method and the long-term smoothing parameter of the noise estimation, are set according to the classified context to ensure best performance under each noise. For real-time context awareness, the noise classification is performed on a frame-by-frame basis using the GMM with the soft decision framework. The speech absence probability (SAP) is used in detecting the speech absence periods and updating the likelihood of the GMM.

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

    NASA Technical Reports Server (NTRS)

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

    2005-01-01

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

  2. Use of high frequency analysis of acoustic emission signals to determine rolling element bearing condition

    NASA Astrophysics Data System (ADS)

    Cockerill, A.; Holford, K. M.; Bradshaw, T.; Cole, P.; Pullin, R.; Clarke, A.

    2015-07-01

    Acoustic Emission (AE) sensors were used to detect signals arising from a cylindrical roller bearing with artificial defects seeded onto the outer raceway. An SKF N204ECP roller bearing was placed between two double row spherical roller bearings, type SKF 22202E, and loaded between 0.29 and 1.79kN. Speed was constant at 5780rpm. High frequency analysis allowed insight into the condition of the bearings through the determination of an increase in the structural resonances of the system as the size of an artificial defect was increased. As higher loads were applied, frequencies around 100kHz were excited, indicating the release of AE possibly attributed to friction and the plastic deformation as peaks, induced through engraving of the raceway, were flattened and worn down. Sensitivity of AE to this level in bearings indicates the potential of the technique to detect the early stages of bearing failure during life tests.

  3. 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.

  4. Channel noise enhances signal detectability in a model of acoustic neuron through the stochastic resonance paradigm.

    PubMed

    Liberti, M; Paffi, A; Maggio, F; De Angelis, A; Apollonio, F; d'Inzeo, G

    2009-01-01

    A number of experimental investigations have evidenced the extraordinary sensitivity of neuronal cells to weak input stimulations, including electromagnetic (EM) fields. Moreover, it has been shown that biological noise, due to random channels gating, acts as a tuning factor in neuronal processing, according to the stochastic resonant (SR) paradigm. In this work the attention is focused on noise arising from the stochastic gating of ionic channels in a model of Ranvier node of acoustic fibers. The small number of channels gives rise to a high noise level, which is able to cause a spike train generation even in the absence of stimulations. A SR behavior has been observed in the model for the detection of sinusoidal signals at frequencies typical of the speech.

  5. Method for Simultaneously Making a Plurality of Acoustic Signal Sensor Elements

    NASA Technical Reports Server (NTRS)

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

    2005-01-01

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

  6. Comparison of alternatives to amplitude thresholding for onset detection of acoustic emission signals

    NASA Astrophysics Data System (ADS)

    Bai, F.; Gagar, D.; Foote, P.; Zhao, Y.

    2017-02-01

    Acoustic Emission (AE) monitoring can be used to detect the presence of damage as well as determine its location in Structural Health Monitoring (SHM) applications. Information on the time difference of the signal generated by the damage event arriving at different sensors in an array is essential in performing localisation. Currently, this is determined using a fixed threshold which is particularly prone to errors when not set to optimal values. This paper presents three new methods for determining the onset of AE signals without the need for a predetermined threshold. The performance of the techniques is evaluated using AE signals generated during fatigue crack growth and compared to the established Akaike Information Criterion (AIC) and fixed threshold methods. It was found that the 1D location accuracy of the new methods was within the range of < 1 - 7.1 % of the monitored region compared to 2.7% for the AIC method and a range of 1.8-9.4% for the conventional Fixed Threshold method at different threshold levels.

  7. Identification and Classification of OFDM Based Signals Using Preamble Correlation and Cyclostationary Feature Extraction

    DTIC Science & Technology

    2009-09-01

    rapidly advancing technologies of wireless communication networks are providing enormous opportunities. A large number of users in emerging markets ...base element of the 802.16 frame is the physical slot, having the duration 4ps s t f  (2.10) where sf is the sampling frequency. The number of ...CLASSIFICATION OF OFDM BASED SIGNALS USING PREAMBLE CORRELATION AND CYCLOSTATIONARY FEATURE EXTRACTION by Steven R. Schnur September 2009

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

    NASA Astrophysics Data System (ADS)

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

    2012-12-01

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

  9. 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.

  10. 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.

  11. 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

  12. 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...

  13. Shared developmental and evolutionary origins for neural basis of vocal-acoustic and pectoral-gestural signaling.

    PubMed

    Bass, Andrew H; Chagnaud, Boris P

    2012-06-26

    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.

  14. 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.

  15. 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.

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

    NASA Astrophysics Data System (ADS)

    Torres, Juan Felix

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

  17. 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.

  18. 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.

  19. 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.

  20. Food intake monitoring: an acoustical approach to automated food intake activity detection and classification of consumed food.

    PubMed

    Päßler, Sebastian; Wolff, Matthias; Fischer, Wolf-Joachim

    2012-06-01

    Obesity and nutrition-related diseases are currently growing challenges for medicine. A precise and timesaving method for food intake monitoring is needed. For this purpose, an approach based on the classification of sounds produced during food intake is presented. Sounds are recorded non-invasively by miniature microphones in the outer ear canal. A database of 51 participants eating seven types of food and consuming one drink has been developed for algorithm development and model training. The database is labeled manually using a protocol with introductions for annotation. The annotation procedure is evaluated using Cohen's kappa coefficient. The food intake activity is detected by the comparison of the signal energy of in-ear sounds to environmental sounds recorded by a reference microphone. Hidden Markov models are used for the recognition of single chew or swallowing events. Intake cycles are modeled as event sequences in finite-state grammars. Classification of consumed food is realized by a finite-state grammar decoder based on the Viterbi algorithm. We achieved a detection accuracy of 83% and a food classification accuracy of 79% on a test set of 10% of all records. Our approach faces the need of monitoring the time and occurrence of eating. With differentiation of consumed food, a first step toward the goal of meal weight estimation is taken.

  1. 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 ...

  2. Coupled Research in Ocean Acoustics and Signal Processing for the Next Generation of Underwater Acoustic Communication Systems

    DTIC Science & Technology

    2015-11-20

    environments. The second area of work is that of characterizing the performance of adaptive equalizers in order to evaluate di↵erent system configuration trade...the optimal partition- ing of a large-N array of hydrophones into subarrays for coherent processing by adaptive equalizers before combining the...Underwater Acoustic Communications. (Pajovic and Preisig) and lends new insights into the roles of subarrays and feedback filters play in adaptive

  3. Simultaneous measurement of temperature, hydrostatic pressure and acoustic signal using a single distributed Bragg reflector fiber laser

    NASA Astrophysics Data System (ADS)

    Tan, Yan-Nan; Zhang, Yang; Guan, Bai-Ou

    2011-05-01

    A fiber-optic sensor based on a dual polarization fiber grating laser for simultaneous measurement of temperature, hydrostatic pressure and acoustic signal is proposed and experimentally demonstrated. The acoustic wave induces a frequency modulation (FM) of the carrier in radio frequency (RF) range generated by the fiber laser and can be easily extracted by using the FM demodulation technique. The temperature can be determined by the laser wavelength. The hydrostatic pressure can be determined by monitoring the static shift of the carrier frequency and deducting the effect of the temperature.

  4. How effective are acoustic signals in territorial defence in the Lusitanian toadfish?

    PubMed

    Conti, Carlotta; Fonseca, Paulo J; Picciulin, Marta; Amorim, M Clara P

    2015-03-01

    The function of fish sounds in territorial defence, in particular its influence on the intruder's behaviour during territorial invasions, is poorly known. Breeding Lusitanian toadfish males (Halobatrachus didactylus) use sounds (boatwhistles) to defend nests from intruders. Results from a previous study suggest that boatwhistles function as a 'keep-out signal' during territorial defence. To test this hypothesis we performed territorial intrusion experiments with muted Lusitanian toadfish. Males were muted by making a cut and deflating the swimbladder (the sound-producing apparatus) under anaesthesia. Toadfish nest-holder males reacted to intruders mainly by emitting sounds (sham-operated and control groups) and less frequently with escalated bouts of fighting. When the nest-holder produced a boatwhistle, the intruder fled more frequently than expected by chance alone. Muted males experienced a higher number of intrusions than the other groups, probably because of their inability to vocalise. Together, our results show that fish acoustic signals are effective deterrents in nest/territorial intrusions, similar to bird song.

  5. Measuring the 2D baryon acoustic oscillation signal of galaxies in WiggleZ: cosmological constraints

    NASA Astrophysics Data System (ADS)

    Hinton, Samuel R.; Kazin, Eyal; Davis, Tamara M.; Blake, Chris; Brough, Sarah; Colless, Matthew; Couch, Warrick J.; Drinkwater, Michael J.; Glazebrook, Karl; Jurek, Russell J.; Parkinson, David; Pimbblet, Kevin A.; Poole, Gregory B.; Pracy, Michael; Woods, David

    2017-02-01

    We present results from the 2D anisotropic baryon acoustic oscillation (BAO) signal present in the final data set from the WiggleZ Dark Energy Survey. We analyse the WiggleZ data in two ways: first using the full shape of the 2D correlation function and secondly focusing only on the position of the BAO peak in the reconstructed data set. When fitting for the full shape of the 2D correlation function we use a multipole expansion to compare with theory. When we use the reconstructed data we marginalize over the shape and just measure the position of the BAO peak, analysing the data in wedges separating the signal along the line of sight from that parallel to the line of sight. We verify our method with mock data and find the results to be free of bias or systematic offsets. We also redo the pre-reconstruction angle-averaged (1D) WiggleZ BAO analysis with an improved covariance and present an updated result. The final results are presented in the form of Ωc h2, H(z), and DA(z) for three redshift bins with effective redshifts z = 0.44, 0.60, and 0.73. Within these bins and methodologies, we recover constraints between 5 and 22 per cent error. Our cosmological constraints are consistent with flat ΛCDM cosmology and agree with results from the Baryon Oscillation Spectroscopic Survey.

  6. Measuring the 2D baryon acoustic oscillation signal of galaxies in WiggleZ: cosmological constraints.

    PubMed

    Hinton, Samuel R; Kazin, Eyal; Davis, Tamara M; Blake, Chris; Brough, Sarah; Colless, Matthew; Couch, Warrick J; Drinkwater, Michael J; Glazebrook, Karl; Jurek, Russell J; Parkinson, David; Pimbblet, Kevin A; Poole, Gregory B; Pracy, Michael; Woods, David

    2017-02-01

    We present results from the 2D anisotropic baryon acoustic oscillation (BAO) signal present in the final data set from the WiggleZ Dark Energy Survey. We analyse the WiggleZ data in two ways: first using the full shape of the 2D correlation function and secondly focusing only on the position of the BAO peak in the reconstructed data set. When fitting for the full shape of the 2D correlation function we use a multipole expansion to compare with theory. When we use the reconstructed data we marginalize over the shape and just measure the position of the BAO peak, analysing the data in wedges separating the signal along the line of sight from that parallel to the line of sight. We verify our method with mock data and find the results to be free of bias or systematic offsets. We also redo the pre-reconstruction angle-averaged (1D) WiggleZ BAO analysis with an improved covariance and present an updated result. The final results are presented in the form of Ω c  h(2), H(z), and DA (z) for three redshift bins with effective redshifts z = 0.44, 0.60, and 0.73. Within these bins and methodologies, we recover constraints between 5 and 22 per cent error. Our cosmological constraints are consistent with flat ΛCDM cosmology and agree with results from the Baryon Oscillation Spectroscopic Survey.

  7. 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.

  8. Measuring the 2D baryon acoustic oscillation signal of galaxies in WiggleZ: cosmological constraints

    PubMed Central

    Hinton, Samuel R.; Kazin, Eyal; Davis, Tamara M.; Blake, Chris; Brough, Sarah; Colless, Matthew; Couch, Warrick J.; Drinkwater, Michael J.; Glazebrook, Karl; Jurek, Russell J.; Parkinson, David; Pimbblet, Kevin A.; Poole, Gregory B.; Pracy, Michael; Woods, David

    2016-01-01

    We present results from the 2D anisotropic baryon acoustic oscillation (BAO) signal present in the final data set from the WiggleZ Dark Energy Survey. We analyse the WiggleZ data in two ways: first using the full shape of the 2D correlation function and secondly focusing only on the position of the BAO peak in the reconstructed data set. When fitting for the full shape of the 2D correlation function we use a multipole expansion to compare with theory. When we use the reconstructed data we marginalize over the shape and just measure the position of the BAO peak, analysing the data in wedges separating the signal along the line of sight from that parallel to the line of sight. We verify our method with mock data and find the results to be free of bias or systematic offsets. We also redo the pre-reconstruction angle-averaged (1D) WiggleZ BAO analysis with an improved covariance and present an updated result. The final results are presented in the form of Ωc h2, H(z), and DA(z) for three redshift bins with effective redshifts z = 0.44, 0.60, and 0.73. Within these bins and methodologies, we recover constraints between 5 and 22 per cent error. Our cosmological constraints are consistent with flat ΛCDM cosmology and agree with results from the Baryon Oscillation Spectroscopic Survey. PMID:28066154

  9. 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.

  10. 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.

  11. 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.

  12. 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.

  13. 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.

  14. 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.

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

    PubMed

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

    2016-01-15

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

  16. Innovations in motoneuron synchrony drive rapid temporal modulations in vertebrate acoustic signaling

    PubMed Central

    Chagnaud, Boris P.; Zee, Michele C.; Baker, Robert

    2012-01-01

    Rapid temporal modulation of acoustic signals among several vertebrate lineages has recently been shown to depend on the actions of superfast muscles. We hypothesized that such fast events, known to require synchronous activation of muscle fibers, would rely on motoneuronal properties adapted to generating a highly synchronous output to sonic muscles. Using intracellular in vivo recordings, we identified a suite of premotor network inputs and intrinsic motoneuronal properties synchronizing the oscillatory-like, simultaneous activation of superfast muscles at high gamma frequencies in fish. Motoneurons lacked spontaneous activity, firing synchronously only at the frequency of premotor excitatory input. Population-level motoneuronal output generated a spike-like, vocal nerve volley that directly determines muscle contraction rate and, in turn, natural call frequency. In the absence of vocal output, motoneurons showed low excitability and a weak afterhyperpolarization, leading to rapid accommodation in firing rate. By contrast, vocal activity was accompanied by a prominent afterhyperpolarization, indicating a dependency on network activity. Local injection of a GABAA receptor antagonist demonstrated the necessity of electrophysiologically and immunohistochemically confirmed inhibitory GABAergic input for motoneuronal synchrony and vocalization. Numerous transneuronally labeled motoneurons following single-cell neurobiotin injection together with electrophysiological collision experiments confirmed gap junctional coupling, known to contribute to synchronous activity in other neural networks. Motoneuronal synchrony at the premotor input frequency was maintained during differential recruitment of variably sized motoneurons. Differential motoneuron recruitment led, however, to amplitude modulation (AM) of vocal output and, hence, natural call AM. In summary, motoneuronal intrinsic properties, in particular low excitability, predisposed vocal motoneurons to the

  17. 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.

  18. 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.

  19. Classification of ankle joint movements based on surface electromyography signals for rehabilitation robot applications.

    PubMed

    Al-Quraishi, Maged S; Ishak, Asnor J; Ahmad, Siti A; Hasan, Mohd K; Al-Qurishi, Muhammad; Ghapanchizadeh, Hossein; Alamri, Atif

    2016-08-02

    Electromyography (EMG)-based control is the core of prostheses, orthoses, and other rehabilitation devices in recent research. Nonetheless, EMG is difficult to use as a control signal given the complex nature of the signal. To overcome this problem, the researchers employed a pattern recognition technique. EMG pattern recognition mainly involves four stages: signal detection, preprocessing feature extraction, dimensionality reduction, and classification. In particular, the success of any pattern recognition technique depends on the feature extraction stage. In this study, a modified time-domain features set and logarithmic transferred time-domain features (LTD) were evaluated and compared with other traditional time-domain features set (TTD). Three classifiers were employed to assess the two feature sets, namely linear discriminant analysis (LDA), k nearest neighborhood, and Naïve Bayes. Results indicated the superiority of the new time-domain feature set LTD, on conventional time-domain features TTD with the average classification accuracy of 97.23 %. In addition, the LDA classifier outperformed the other two classifiers considered in this study.

  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.

  1. Detection and Classification of Low Probability of Intercept Radar Signals Using Parallel Filter Arrays and Higher Order Statistics

    DTIC Science & Technology

    2002-09-01

    Resulting Plots for Different LPI Radar Signals (1) FMCW Table 9 shows a FMCW signal with carrier frequency equal to 1 KHz, sampling frequency equal to...REPORT TYPE AND DATES COVERED Master’s Thesis 4. TITLE AND SUBTITLE: Detection and Classification of LPI Radar Signals using Parallel Filter...In order to detect LPI radar waveforms new signal processing techniques are required. This thesis first develops a MATLAB® toolbox to generate

  2. Classification of Anticipatory Signals for Grasp and Release from Surface Electromyography.

    PubMed

    Siu, Ho Chit; Shah, Julie A; Stirling, Leia A

    2016-10-25

    Surface electromyography (sEMG) is a technique for recording natural muscle activation signals, which can serve as control inputs for exoskeletons and prosthetic devices. Previous experiments have incorporated these signals using both classical and pattern-recognition control methods in order to actuate such devices. We used the results of an experiment incorporating grasp and release actions with object contact to develop an intent-recognition system based on Gaussian mixture models (GMM) and continuous-emission hidden Markov models (HMM) of sEMG data. We tested this system with data collected from 16 individuals using a forearm band with distributed sEMG sensors. The data contain trials with shifted band alignments to assess robustness to sensor placement. This study evaluated and found that pattern-recognition-based methods could classify transient anticipatory sEMG signals in the presence of shifted sensor placement and object contact. With the best-performing classifier, the effect of label lengths in the training data was also examined. A mean classification accuracy of 75.96% was achieved through a unigram HMM method with five mixture components. Classification accuracy on different sub-movements was found to be limited by the length of the shortest sub-movement, which means that shorter sub-movements within dynamic sequences require larger training sets to be classified correctly. This classification of user intent is a potential control mechanism for a dynamic grasping task involving user contact with external objects and noise. Further work is required to test its performance as part of an exoskeleton controller, which involves contact with actuated external surfaces.

  3. Classification of Anticipatory Signals for Grasp and Release from Surface Electromyography

    PubMed Central

    Siu, Ho Chit; Shah, Julie A.; Stirling, Leia A.

    2016-01-01

    Surface electromyography (sEMG) is a technique for recording natural muscle activation signals, which can serve as control inputs for exoskeletons and prosthetic devices. Previous experiments have incorporated these signals using both classical and pattern-recognition control methods in order to actuate such devices. We used the results of an experiment incorporating grasp and release actions with object contact to develop an intent-recognition system based on Gaussian mixture models (GMM) and continuous-emission hidden Markov models (HMM) of sEMG data. We tested this system with data collected from 16 individuals using a forearm band with distributed sEMG sensors. The data contain trials with shifted band alignments to assess robustness to sensor placement. This study evaluated and found that pattern-recognition-based methods could classify transient anticipatory sEMG signals in the presence of shifted sensor placement and object contact. With the best-performing classifier, the effect of label lengths in the training data was also examined. A mean classification accuracy of 75.96% was achieved through a unigram HMM method with five mixture components. Classification accuracy on different sub-movements was found to be limited by the length of the shortest sub-movement, which means that shorter sub-movements within dynamic sequences require larger training sets to be classified correctly. This classification of user intent is a potential control mechanism for a dynamic grasping task involving user contact with external objects and noise. Further work is required to test its performance as part of an exoskeleton controller, which involves contact with actuated external surfaces. PMID:27792155

  4. Signal processing Model/Method for Recovering Acoustic Reflectivity of Spot Weld

    SciTech Connect

    Davis, William B.

    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.

  5. Channel Modeling and Threshold Signal Processing in Underwater Acoustics: An Analytical Overview

    DTIC Science & Technology

    1986-12-19

    Introduction to Statistical Communication Theory, McGraw-Hill (New York), 1960, (Part IV). [21]. J. R. Breton and D. Middleton, "General Theory of Acoustic Prop...5), pp. 1245-1260, May 1981. See also, Breton , J. R., A General Theory of Acoustic Propaation and Applications to Strong Acoustic Scattering in the...IV, ibid., Vol. IT-18, 35-67; 68-90 (1972). [32]. , Invited lectures, at Acoustics Institute N.N. Andr ~ev, Acad. Sci. USSR (Moscow), 1973, 1976, 1979

  6. Award 1 Title: Acoustic Communications 2011 Experiment: Deployment Support and Post Experiment Data Handling and Analysis. Award 2 Title: Exploiting Structured Dependencies in the Design of Adaptive Algorithms for Underwater Communication Award. 3 Title: Coupled Research in Ocean Acoustics and Signal Processing for the Next Generation of Underwater Acoustic Communication Systems

    DTIC Science & Technology

    2015-09-30

    Exploiting Structured Dependencies in the Design of Adaptive Algorithms for Underwater Communication Award #3 Title Coupled Research in Ocean Acoustics...depend on the physical oceanography and pushing the state of the art in our understanding of adaptive signal processing algorithms relevant to...deployable VHF acoustic data transmission and acquisition system. 3. Develop signal models and processing algorithms that reduce to the extent

  7. Discrimination of acoustic communication signals by grasshoppers (Chorthippus biguttulus): temporal resolution, temporal integration, and the impact of intrinsic noise.

    PubMed

    Ronacher, Bernhard; Wohlgemuth, Sandra; Vogel, Astrid; Krahe, Rüdiger

    2008-08-01

    A characteristic feature of hearing systems is their ability to resolve both fast and subtle amplitude modulations of acoustic signals. This applies also to grasshoppers, which for mate identification rely mainly on the characteristic temporal patterns of their communication signals. Usually the signals arriving at a receiver are contaminated by various kinds of noise. In addition to extrinsic noise, intrinsic noise caused by stochastic processes within the nervous system contributes to making signal recognition a difficult task. The authors asked to what degree intrinsic noise affects temporal resolution and, particularly, the discrimination of similar acoustic signals. This study aims at exploring the neuronal basis for sexual selection, which depends on exploiting subtle differences between basically similar signals. Applying a metric, by which the similarities of spike trains can be assessed, the authors investigated how well the communication signals of different individuals of the same species could be discriminated and correctly classified based on the responses of auditory neurons. This spike train metric yields clues to the optimal temporal resolution with which spike trains should be evaluated.

  8. Improved Maturity and Ripeness Classifications of Magnifera Indica cv. Harumanis Mangoes through Sensor Fusion of an Electronic Nose and Acoustic Sensor

    PubMed Central

    Zakaria, Ammar; Shakaff, Ali Yeon Md; Masnan, Maz Jamilah; Saad, Fathinul Syahir Ahmad; Adom, Abdul Hamid; Ahmad, Mohd Noor; Jaafar, Mahmad Nor; Abdullah, Abu Hassan; Kamarudin, Latifah Munirah

    2012-01-01

    In recent years, there have been a number of reported studies on the use of non-destructive techniques to evaluate and determine mango maturity and ripeness levels. However, most of these reported works were conducted using single-modality sensing systems, either using an electronic nose, acoustics or other non-destructive measurements. This paper presents the work on the classification of mangoes (Magnifera Indica cv. Harumanis) maturity and ripeness levels using fusion of the data of an electronic nose and an acoustic sensor. Three groups of samples each from two different harvesting times (week 7 and week 8) were evaluated by the e-nose and then followed by the acoustic sensor. Principal Component Analysis (PCA) and Linear Discriminant Analysis (LDA) were able to discriminate the mango harvested at week 7 and week 8 based solely on the aroma and volatile gases released from the mangoes. However, when six different groups of different maturity and ripeness levels were combined in one classification analysis, both PCA and LDA were unable to discriminate the age difference of the Harumanis mangoes. Instead of six different groups, only four were observed using the LDA, while PCA showed only two distinct groups. By applying a low level data fusion technique on the e-nose and acoustic data, the classification for maturity and ripeness levels using LDA was improved. However, no significant improvement was observed using PCA with data fusion technique. Further work using a hybrid LDA-Competitive Learning Neural Network was performed to validate the fusion technique and classify the samples. It was found that the LDA-CLNN was also improved significantly when data fusion was applied. PMID:22778629

  9. International Workshop on Detection, Classification and Localization of Marine Mammals Using Passive Acoustics (4th). International Workshop on Density Estimation of Marine Mammals Using Passive Acoustics (1st)

    DTIC Science & Technology

    2009-09-13

    Mars 55 Characterisation of sound subunits for humpback whale song analysis. F. Pace, P.R. White, O. Adam 56 Passive acoustic detection of Minke...International Monitoring System. Samaran Flore, Adam Olivier, Guinet Christophe 58 Detection of Minke whale sounds in the Stellwagen Bank National Marine...September 8.40 Characterisation of sound subunits for humpback whale song analysis. F. Pace, P.R. White, O. Adam 9.00 Passive acoustic detection of

  10. 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

  11. 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

  12. 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.

  13. Classification of heart rate signals of healthy and pathological subjects using threshold based symbolic entropy.

    PubMed

    Aziz, Wajid; Rafique, M; Ahmad, I; Arif, M; Habib, Nazneen; Nadeem, M S A

    2014-09-01

    The dynamical fluctuations of biological signals provide a unique window to construe the underlying mechanism of the biological systems in health and disease. Recent research evidences suggest that a wide class of diseases appear to degrade the biological complexity and adaptive capacity of the system. Heart rate signals are one of the most important biological signals that have widely been investigated during the last two and half decades. Recent studies suggested that heart rate signals fluctuate in a complex manner. Various entropy based complexity analysis measures have been developed for quantifying the valuable information that may be helpful for clinical monitoring and for early intervention. This study is focused on determining HRV dynamics to distinguish healthy subjects from patients with certain cardiac problems using symbolic time series analysis technique. For that purpose, we have employed recently developed threshold based symbolic entropy to cardiac inter-beat interval time series of healthy, congestive heart failure and atrial fibrillation subjects. Normalized Corrected Shannon Entropy (NCSE) was used to quantify the dynamics of heart rate signals by continuously varying threshold values. A rule based classifier was implemented for classification of different groups by selecting threshold values for the optimal separation. The findings indicated that there is reduction in the complexity of pathological subjects as compared to healthy ones at wide range of threshold values. The results also demonstrated that complexity decreased with disease severity.

  14. 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. PMID:27879860

  15. 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.

  16. Experimental study of the structural characteristics of Al melts on the basis of Fourier analysis of acoustic emission signals

    NASA Astrophysics Data System (ADS)

    Vorontsov, Vadim; Zhuravlev, Danil; Cherepanov, Alexander

    2014-09-01

    This scientific work is devoted to the study of the genetic connection structures of solid and liquid phases. Fourier analysis of signals of acoustic emission (AE) accompanying the melting of high purity aluminum from the melting point up to t=860°C was performed. The experimental data allowed for following the dynamics of the range order of the disorder zones in the melt with increasing melt temperature until their complete destruction.

  17. 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.

  18. 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

  19. Classification.

    PubMed

    Tuxhorn, Ingrid; Kotagal, Prakash

    2008-07-01

    In this article, we review the practical approach and diagnostic relevance of current seizure and epilepsy classification concepts and principles as a basic framework for good management of patients with epileptic seizures and epilepsy. Inaccurate generalizations about terminology, diagnosis, and treatment may be the single most important factor, next to an inadequately obtained history, that determines the misdiagnosis and mismanagement of patients with epilepsy. A stepwise signs and symptoms approach for diagnosis, evaluation, and management along the guidelines of the International League Against Epilepsy and definitions of epileptic seizures and epilepsy syndromes offers a state-of-the-art clinical approach to managing patients with epilepsy.

  20. Tools to evaluate seafloor integrity: comparison of multi-device acoustic seafloor classifications for benthic macrofauna-driven patterns in the German Bight, southern North Sea

    NASA Astrophysics Data System (ADS)

    Holler, Peter; Markert, Edith; Bartholomä, Alexander; Capperucci, Ruggero; Hass, H. Christian; Kröncke, Ingrid; Mielck, Finn; Reimers, H. Christian

    2016-12-01

    To determine the spatial resolution of sediment properties and benthic macrofauna communities in acoustic backscatter, the suitability of four acoustic seafloor classification devices (single-beam echosounder with RoxAnn and QTC 5.5 seafloor classification system, sidescan sonar with QTC Swathview seafloor classification, and multi-beam echosounder with QTC Swathview seafloor classification) was compared in a study area of approx. 6 km2 northwest of the island of Helgoland in the German Bight, southern North Sea. This was based on a simple similarity index between simultaneous sidescan sonar, single-beam echosounder and multi-beam echosounder profiling spanning the period 2011-2014. The results show a high similarity between seafloor classifications based on sidescan sonar and RoxAnn single-beam systems, in turn associated with a lower similarity for the multi-beam echosounder system. Analyses of surface sediment samples at 39 locations along four transects (0.1 m2 Van Veen grab) revealed the presence of sandy mud (southern and western parts), coarse sand, gravel and cobbles. Rock outcrops were identified in the north-eastern and eastern parts. A typical Nucula nitidosa-Abra alba community was found in sandy muds to muddy sands in the northern part, whereas the southern part is characterised by widespread occurrence of the ophiuroid brittle star Amphiura filiformis. A transitional N. nitidosa-A. filiformis community was detected in the central part. Moreover, the southern part is characterised by a high abundance of A. filiformis and its commensal bivalve Kurtiella bidentata. The high number of A. filiformis feeding arms (up to ca. 6,800 per m2) can largely explain the gentle change of backscatter intensity along the tracks, because sediment composition and/or seafloor structures showed no significant variability.

  1. Tools to evaluate seafloor integrity: comparison of multi-device acoustic seafloor classifications for benthic macrofauna-driven patterns in the German Bight, southern North Sea

    NASA Astrophysics Data System (ADS)

    Holler, Peter; Markert, Edith; Bartholomä, Alexander; Capperucci, Ruggero; Hass, H. Christian; Kröncke, Ingrid; Mielck, Finn; Reimers, H. Christian

    2017-04-01

    To determine the spatial resolution of sediment properties and benthic macrofauna communities in acoustic backscatter, the suitability of four acoustic seafloor classification devices (single-beam echosounder with RoxAnn and QTC 5.5 seafloor classification system, sidescan sonar with QTC Swathview seafloor classification, and multi-beam echosounder with QTC Swathview seafloor classification) was compared in a study area of approx. 6 km2 northwest of the island of Helgoland in the German Bight, southern North Sea. This was based on a simple similarity index between simultaneous sidescan sonar, single-beam echosounder and multi-beam echosounder profiling spanning the period 2011-2014. The results show a high similarity between seafloor classifications based on sidescan sonar and RoxAnn single-beam systems, in turn associated with a lower similarity for the multi-beam echosounder system. Analyses of surface sediment samples at 39 locations along four transects (0.1 m2 Van Veen grab) revealed the presence of sandy mud (southern and western parts), coarse sand, gravel and cobbles. Rock outcrops were identified in the north-eastern and eastern parts. A typical Nucula nitidosa- Abra alba community was found in sandy muds to muddy sands in the northern part, whereas the southern part is characterised by widespread occurrence of the ophiuroid brittle star Amphiura filiformis. A transitional N. nitidosa- A. filiformis community was detected in the central part. Moreover, the southern part is characterised by a high abundance of A. filiformis and its commensal bivalve Kurtiella bidentata. The high number of A. filiformis feeding arms (up to ca. 6,800 per m2) can largely explain the gentle change of backscatter intensity along the tracks, because sediment composition and/or seafloor structures showed no significant variability.

  2. 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.

  3. 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)

  4. 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.

  5. 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

    2015-01-07

    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.

  6. 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.

  7. 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

  8. 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

  9. Coupled Research in Ocean Acoustics and Signal Processing for the Next Generation of Underwater Acoustic Communication Systems

    DTIC Science & Technology

    2016-08-05

    order of millimeters in the tank with wind speeds up to 16 meters per second. The data from the experiment was all quality checked and found to be of...mechanism which remains stable (i.e., no vibration) at the wind speeds used in the experiment. The second purpose was to conduct the tests up to higher wind ...speeds to determine of the apparent plateauing of some quantitative signal characteristics with increasing wind speed at the upper limit of the usable

  10. Low-power analog processing for sensing applications: low-frequency harmonic signal classification.

    PubMed

    White, Daniel J; William, Peter E; Hoffman, Michael W; Balkir, Sina

    2013-07-25

    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.

  11. 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.

  12. Neural networks for automated classification of ionospheric irregularities in HF radar backscattered signals

    NASA Astrophysics Data System (ADS)

    Wing, S.; Greenwald, R. A.; Meng, C.-I.; Sigillito, V. G.; Hutton, L. V.

    2003-08-01

    The classification of high frequency (HF) radar backscattered signals from the ionospheric irregularities (clutters) into those suitable, or not, for further analysis, is a time-consuming task even by experts in the field. We tested several different feedforward neural networks on this task, investigating the effects of network type (single layer versus multilayer) and number of hidden nodes upon performance. As expected, the multilayer feedforward networks (MLFNs) outperformed the single-layer networks. The MLFNs achieved performance levels of 100% correct on the training set and up to 98% correct on the testing set. Comparable figures for the single-layer networks were 94.5% and 92%, respectively. When measures of sensitivity, specificity, and proportion of variance accounted for by the model are considered, the superiority of the MLFNs over the single-layer networks is much more striking. Our results suggest that such neural networks could aid many HF radar operations such as frequency search, space weather, etc.

  13. 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.

  14. 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

  15. 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.

  16. Acoustic and Electrical Signal Emission recordings when marble specimens are subjected to compressional mechanical stress

    NASA Astrophysics Data System (ADS)

    Triantis, Dimos; Stavrakas, Ilias; Hloupis, George; Ninos, Konstantinos; Vallianatos, Filippos

    2013-04-01

    The detection of Acoustic Emissions (AE) and Electrical Signals (ES) has been proved as a valuable experimental method to characterize the mechanical status of marble specimens when subjected to mechanical stress. In this work, marble specimens with dimensions 10cm x 4cm x 4cm where subjected to sequential loading cycles. The maximum stress of each loading was near the vicinity of fracture and was maintained for a relatively long time (th=200s). Concurrently to the mechanical tests, AE and ES were recorded. Specifically, two AE sensors and five ES sensors were installed on the surface of the specimens and the detected emissions were stored on a PC. The recordings show that AE and ES provide information regarding the damage spreading and location in the bulk of the specimen. Specifically, when the mechanical stress was maintained constant at the high stress value during each loading cycle the cumulative number of the AE hits become gradually less reaching a minimum after the first three loading cycles, indicating the existence of the Kaiser effect. During the eighth loading cycle the AE hits show a significant increase that became maximum at the ninth cycle before where failure occured. A similar behavior was observed for the cumulative energy. A b-value analysis was conducted following both Aki's and Gutenberg-Richter relations on the amplitudes of the AE hits. The b-values were found to increase during the three first loading cycles while consequently they were practically constant until reaching the two final loading cycles where they became gradually lower. The ES significantly increases during the stress increase of each cycle and gradually restores at a background level when the applied stress is maintained constant near the vicinity of fracture. It was observed that the background restoration level becomes gradually higher during the first four loading cycles. Consequently, during the next three loading cycles the background level is maintained practically

  17. Impact of Ion Acoustic Wave Instabilities in the Flow Field of a Hypersonic Vehicle on EM Signals

    NASA Astrophysics Data System (ADS)

    Mudaliar, Saba; Sotnikov, Vladimir

    2016-10-01

    Flow associated with a high speed air vehicle (HSAV) can get partially ionized. In the absence of external magnetic field the flow field turbulence is due to ion acoustic wave (IAW) instabilities. Our interest is in studying the impact of this turbulence on the radiation characteristics of EM signals from the HSAV. We decompose the radiated signal into coherent and diffuse parts. We find that the coherent part has the same spectrum as that of the source signal, but it is distorted because of dispersive coherent attenuation. The diffuse part is expressed as a convolution (in wavenumber and frequency) of the source signal with the spectrum of electron density fluctuations. This is a constrained convolution in the sense that the spectrum has to satisfy the IAW dispersion relation. A quantity that characterizes the flow is the mean free path (MFP). When the MFP is large compared to the thickness of the flow the coherent part is significant. If the MFP is larger than the thickness of the flow the diffuse part is the dominant part of the received signal. In the special case when the source signal frequency is close the electron plasma frequency, there can exist in the flow region Langmuir modes in addition to the EM modes. The radiation characteristics of EM source signals from the HSAV in this case are quite different.

  18. Massively parallel classification of single-trial EEG signals using a min-max modular neural network.

    PubMed

    Lu, Bao-Liang; Shin, Jonghan; Ichikawa, Michinori

    2004-03-01

    This paper presents a method for classifying single-trial electroencephalogram (EEG) signals using min-max modular neural networks implemented in a massively parallel way. The method has three main steps. First, a large-scale, complex EEG classification problem is simply divided into a reasonable number of two-class subproblems, as small as needed. Second, the two-class subproblems are simply learned by individual smaller network modules in parallel. Finally, all the individual trained network modules are integrated into a hierarchical, parallel, and modular classifier according to two module combination laws. To demonstrate the effectiveness of the method, we perform simulations on fifteen different four-class EEG classification tasks, each of which consists of 1491 training and 636 test data. These EEG classification tasks were created using a set of non-averaged, single-trial hippocampal EEG signals recorded from rats; the features of the EEG signals are extracted using wavelet transform techniques. The experimental results indicate that the proposed method has several attractive features. 1) The method is appreciably faster than the existing approach that is based on conventional multilayer perceptrons. 2) Complete learning of complex EEG classification problems can be easily realized, and better generalization performance can be achieved. 3) The method scales up to large-scale, complex EEG classification problems.

  19. 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.

  20. 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.

  1. 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

  2. Classification of a Driver's cognitive workload levels using artificial neural network on ECG signals.

    PubMed

    Tjolleng, Amir; Jung, Kihyo; Hong, Wongi; Lee, Wonsup; Lee, Baekhee; You, Heecheon; Son, Joonwoo; Park, Seikwon

    2017-03-01

    An artificial neural network (ANN) model was developed in the present study to classify the level of a driver's cognitive workload based on electrocardiography (ECG). ECG signals were measured on 15 male participants while they performed a simulated driving task as a primary task with/without an N-back task as a secondary task. Three time-domain ECG measures (mean inter-beat interval (IBI), standard deviation of IBIs, and root mean squared difference of adjacent IBIs) and three frequencydomain ECG measures (power in low frequency, power in high frequency, and ratio of power in low and high frequencies) were calculated. To compensate for individual differences in heart response during the driving tasks, a three-step data processing procedure was performed to ECG signals of each participant: (1) selection of two most sensitive ECG measures, (2) definition of three (low, medium, and high) cognitive workload levels, and (3) normalization of the selected ECG measures. An ANN model was constructed using a feed-forward network and scaled conjugate gradient as a back-propagation learning rule. The accuracy of the ANN classification model was found satisfactory for learning data (95%) and testing data (82%).

  3. 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.

  4. 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.

  5. [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.

  6. Classification of prefrontal and motor cortex signals for three-class fNIRS-BCI.

    PubMed

    Hong, Keum-Shik; Naseer, Noman; Kim, Yun-Hee

    2015-02-05

    Functional near-infrared spectroscopy (fNIRS) is an optical imaging method that can be used for a brain-computer interface (BCI). In the present study, we concurrently measure and discriminate fNIRS signals evoked by three different mental activities, that is, mental arithmetic (MA), right-hand motor imagery (RI), and left-hand motor imagery (LI). Ten healthy subjects were asked to perform the MA, RI, and LI during a 10s task period. Using a continuous-wave NIRS system, signals were acquired concurrently from the prefrontal and the primary motor cortices. Multiclass linear discriminant analysis was utilized to classify MA vs. RI vs. LI with an average classification accuracy of 75.6% across the ten subjects, for a 2-7s time window during the a 10s task period. These results demonstrate the feasibility of implementing a three-class fNIRS-BCI using three different intentionally-generated cognitive tasks as inputs.

  7. Mate call as reward: Acoustic communication signals can acquire positive reinforcing values during adulthood in female zebra finches (Taeniopygia guttata).

    PubMed

    Hernandez, Alexandra M; Perez, Emilie C; Mulard, Hervé; Mathevon, Nicolas; Vignal, Clémentine

    2016-02-01

    Social stimuli can have rewarding properties and promote learning. In birds, conspecific vocalizations like song can act as a reinforcer, and specific song variants can acquire particular rewarding values during early life exposure. Here we ask if, during adulthood, an acoustic signal simpler and shorter than song can become a reward for a female songbird because of its particular social value. Using an operant choice apparatus, we showed that female zebra finches display a preferential response toward their mate's calls. This reinforcing value of mate's calls could be involved in the maintenance of the monogamous pair-bond of the zebra finch.

  8. Information theory filters for wavelet packet coefficient selection with application to corrosion type identification from acoustic emission signals.

    PubMed

    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.

  9. 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

  10. 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

  11. 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

  12. 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,…

  13. Estimation of Ocean and Seabed Parameters and Processes Using Low Frequency Acoustic Signals

    DTIC Science & Technology

    2012-09-30

    Ambient habitat noise and vibration at the Georgia Aquarium ,” J. Acoust. Soc. Am. 132, EL88 (2012), [published, refereed]. 8 9. Scheifele, P. M...Clark, J. G., Sonstrom,K., Kim, H., Potty, G. R., Miller, J. H., and Gaglione, E., “BallroomMusic Spillover into a BelugaWhale Aquarium Exhibit

  14. 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.

  15. Detection and Resolvability of Pulsed Acoustic Signals Through the South China Sea Basin: A Modeling Analysis

    DTIC Science & Technology

    2005-09-01

    of the internal wave distribution developed by Hsu and Liu (2000) compiled from hundreds of Synthetic Aperture Radar (SAR) images from the First...Hamiltonian Acoustic Raytracing Program for the Ocean (Jones et al., 1986). HARPO traces rays by numerically integrating Hamilton’s equations of motion

  16. 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…

  17. The effect of climate on acoustic signals: does atmospheric sound absorption matter for bird song and bat echolocation?

    PubMed

    Snell-Rood, Emilie C

    2012-02-01

    The divergence of signals along ecological gradients may lead to speciation. The current research tests the hypothesis that variation in sound absorption selects for divergence in acoustic signals along climatic gradients, which has implications for understanding not only diversification, but also how organisms may respond to climate change. Because sound absorption varies with temperature, humidity, and the frequency of sound, individuals or species may vary signal structure with changes in climate over space or time. In particular, signals of lower frequency, narrower bandwidth, and longer duration should be more detectable in environments with high sound absorption. Using both North American wood warblers (Parulidae) and bats of the American Southwest, this work found evidence of associations between signal structure and sound absorption. Warbler species with higher mean absorption across their range were more likely to have narrow bandwidth songs. Bat species found in higher absorption habitats were more likely to have lower frequency echolocation calls. In addition, bat species changed echolocation call structure across seasons, using longer duration, lower frequency calls in the higher absorption rainy season. These results suggest that signals may diverge along climatic gradients due to variation in sound absorption, although the effects of absorption are modest.

  18. 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.

  19. Study on generation mechanisms of second-order nonlinear signals in surface acoustic wave devices and their suppression

    NASA Astrophysics Data System (ADS)

    Nakagawa, Ryo; Kyoya, Haruki; Shimizu, Hiroshi; Kihara, Takashi; Hashimoto, Ken-ya

    2015-07-01

    In this study, we examine the generation mechanisms of the second-order nonlinear signals in surface acoustic wave resonators/duplexers fabricated on a 42°YX-LiTaO3 substrate. It is shown that the crystal asymmetry of the substrate can generate the second-order nonlinear signals. The following two mechanisms mainly contribute to their generation: (a) self-mixing of the electrostatic field and (b) mixing of the electrostatic field with the strain field associated with laterally propagating modes. Both of them occur at the gaps between the electrode tip and the dummy electrode. In addition, an interdigital transducer design that cancels this asymmetry is proposed. The design is applied to a one-port resonator and a duplexer, and the effectiveness of this technique is demonstrated.

  20. High-frequency underwater acoustic communications using FH-FSK signaling in a reverberant shallow water environment

    NASA Astrophysics Data System (ADS)

    Yang, Wen-Bin; Yang, T. C.

    2003-10-01

    This paper describes the experimental results of frequency-hopped frequency-shift-key (FH-FSK) signaling operated at 20 kHz with a 4 kHz bandwidth for underwater acoustic communications in a reverberant shallow water environment. The data were collected during the RDS4 (Rapidly Deployable Systems) experiment in a shallow water (<80 m depth) near Halifax, Canada. The measured impulse response function showed multipaths lasting over a second, which is an order of magnitude longer than the symbol length. Time-varying Doppler shifts of 30-70 Hz were found in the data. The long multipath delay and high Doppler shift are found to have a significant impact on data processing. For example, using conventional processing that detects the symbol energy over the symbol duration, the bit error rates (BER) are of the order 30-40%. Using a longer time window allowing integration of multipath energy and using Doppler estimated from trigger signals, the uncoded BER is reduced to 10-15%. The data are error-free after error decoding using a convolutional code with a rate and constraint length of 9. Consequences for acoustic networking will be discussed. [Work supported by ONR.

  1. 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.

  2. How females of chirping and trilling field crickets integrate the 'what' and 'where' of male acoustic signals during decision making.

    PubMed

    Gabel, Eileen; Gray, David A; Matthias Hennig, R

    2016-11-01

    In crickets acoustic communication serves mate selection. Female crickets have to perceive and integrate male cues relevant for mate choice while confronted with several different signals in an acoustically diverse background. Overall female decisions are based on the attractiveness of the temporal pattern (informative about the 'what') and on signal intensity (informative about the 'where') of male calling songs. Here, we investigated how the relevant cues for mate choice are integrated during the decision process by females of five different species of chirping and trilling field crickets. Using a behavioral design, female preferences in no-choice and choice situations for male calling songs differing in pulse rate, modulation depth, intensities, chirp/trill arrangements and temporal shifts were examined. Sensory processing underlying decisions in female field crickets is rather similar as combined evidence suggested that incoming song patterns were analyzed separately by bilaterally paired networks for pattern attractiveness and pattern intensity. A downstream gain control mechanism leads to a weighting of the intensity cue by pattern attractiveness. While remarkable differences between species were observed with respect to specific processing steps, closely related species exhibited more similar preferences than did more distantly related species.

  3. 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.

  4. Cumulative and Synergistic Effects of Physical, Biological, and Acoustic Signals on Marine Mammal Habitat Use

    DTIC Science & Technology

    2009-09-30

    beluga whales at the Barren Islands, Alaska, the Bering Sea Acoustic Report, Marine Mammal Monitoring for NW Fisheries, and Monitoring killer whale ...distribution, physical oceanographic process, and sound levels to marine mammal habitat use on the eastern Bering Sea shelf. Integrated data such...individual parameters. 3) A mixed-model analysis will be performed to identify relationships between marine mammal presence and environmental sound

  5. Classification of emotional states from electrocardiogram signals: a non-linear approach based on hurst

    PubMed Central

    2013-01-01

    Background Identifying the emotional state is helpful in applications involving patients with autism and other intellectual disabilities; computer-based training, human computer interaction etc. Electrocardiogram (ECG) signals, being an activity of the autonomous nervous system (ANS), reflect the underlying true emotional state of a person. However, the performance of various methods developed so far lacks accuracy, and more robust methods need to be developed to identify the emotional pattern associated with ECG signals. Methods Emotional ECG data was obtained from sixty participants by inducing the six basic emotional states (happiness, sadness, fear, disgust, surprise and neutral) using audio-visual stimuli. The non-linear feature ‘Hurst’ was computed using Rescaled Range Statistics (RRS) and Finite Variance Scaling (FVS) methods. New Hurst features were proposed by combining the existing RRS and FVS methods with Higher Order Statistics (HOS). The features were then classified using four classifiers – Bayesian Classifier, Regression Tree, K- nearest neighbor and Fuzzy K-nearest neighbor. Seventy percent of the features were used for training and thirty percent for testing the algorithm. Results Analysis of Variance (ANOVA) conveyed that Hurst and the proposed features were statistically significant (p < 0.001). Hurst computed using RRS and FVS methods showed similar classification accuracy. The features obtained by combining FVS and HOS performed better with a maximum accuracy of 92.87% and 76.45% for classifying the six emotional states using random and subject independent validation respectively. Conclusions The results indicate that the combination of non-linear analysis and HOS tend to capture the finer emotional changes that can be seen in healthy ECG data. This work can be further fine tuned to develop a real time system. PMID:23680041

  6. Seismo-acoustic signals of the 2013 Russian meteor recorded across Central and Northern Europe

    NASA Astrophysics Data System (ADS)

    Koch, Karl

    2014-05-01

    The meteor over Russia entering the Earth's atmosphere on 15 February 2013 around 03:20UT near the city of Chelyabinsk was the largest since the 1908 Tunguska fireball. As such the shock waves generated by this event were observed at infrasonic stations globally, in particular the network of some 45 of the planned 60 infrasound systems of the International Monitoring System (IMS) being deployed for the verification of the Comprehensive Nuclear Test-Ban Treaty (CTBT). Furthermore the shock waves coupling into the ground near the source location were observed as Rayleigh waves at seismic stations to distances of more than 4000 km. Beyond the acoustic observations that were made at infrasound sensors we report here on additional observations of the acoustic waves which have coupled into the Earth at the receiver. The corresponding observations were made in Central Europe, in particular at the Gräfenberg broad-band array, as well as in Northern Europe (NORSAR in Scandinavia and on Spitsbergen), where also broad-band seismic array stations are located. That indeed the acoustic arrival from the bolide was recorded can be confirmed by frequency-wavenumber analyses giving compatible velocities and back-azimuths for the ground-truth source location over Russia. Theses observations are compatible with IMS station observations and also with shock wave arrivals on seismic stations on the Eurasian platform.

  7. A methodology for time-frequency image processing applied to the classification of non-stationary multichannel signals using instantaneous frequency descriptors with application to newborn EEG signals

    NASA Astrophysics Data System (ADS)

    Boashash, Boualem; Boubchir, Larbi; Azemi, Ghasem

    2012-12-01

    This article presents a general methodology for processing non-stationary signals for the purpose of classification and localization. The methodology combines methods adapted from three complementary areas: time-frequency signal analysis, multichannel signal analysis and image processing. The latter three combine in a new methodology referred to as multichannel time-frequency image processing which is applied to the problem of classifying electroencephalogram (EEG) abnormalities in both adults and newborns. A combination of signal related features and image related features are used by merging key instantaneous frequency descriptors which characterize the signal non-stationarities. The results obtained show that, firstly, the features based on time-frequency image processing techniques such as image segmentation, improve the performance of EEG abnormalities detection in the classification systems based on multi-SVM and neural network classifiers. Secondly, these discriminating features are able to better detect the correlation between newborn EEG signals in a multichannel-based newborn EEG seizure detection for the purpose of localizing EEG abnormalities on the scalp.

  8. Auditory object salience: human cortical processing of non-biological action sounds and their acoustic signal attributes.

    PubMed

    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 of the

  9. 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

  10. 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.

  11. 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

  12. 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.

  13. 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.

  14. 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.

  15. Evaluating Acoustic Emission Signals as an in situ process monitoring technique for Selective Laser Melting (SLM)

    SciTech Connect

    Fisher, Karl A.; Candy, Jim V.; Guss, Gabe; Mathews, M. J.

    2016-10-14

    In situ real-time monitoring of the Selective Laser Melting (SLM) process has significant implications for the AM community. The ability to adjust the SLM process parameters during a build (in real-time) can save time, money and eliminate expensive material waste. Having a feedback loop in the process would allow the system to potentially ‘fix’ problem regions before a next powder layer is added. In this study we have investigated acoustic emission (AE) phenomena generated during the SLM process, and evaluated the results in terms of a single process parameter, of an in situ process monitoring technique.

  16. Flow velocity profiling using acoustic time of flight flow metering based on wide band signals and adaptive beam-forming techniques

    NASA Astrophysics Data System (ADS)

    Murgan, I.; Candel, I.; Ioana, C.; Digulescu, A.; Bunea, F.; Ciocan, G. D.; Anghel, A.; Vasile, G.

    2016-11-01

    In this paper, we present a novel approach to non-intrusive flow velocity profiling technique using multi-element sensor array and wide-band signal's processing methods. Conventional techniques for the measurements of the flow velocity profiles are usually based on intrusive instruments (current meters, acoustic Doppler profilers, Pitot tubes, etc.) that take punctual velocity readings. Although very efficient, these choices are limited in terms of practical cases of applications especially when non-intrusive measurements techniques are required and/or a spatial accuracy of the velocity profiling is required This is due to factors related to hydraulic machinery down time, the often long time duration needed to explore the entire section area, the frequent cumbersome number of devices that needs to be handled simultaneously, or the impossibility to perform intrusive tests. In the case of non-intrusive flow profiling methods based on acoustic techniques, previous methods concentrated on using a large number of acoustic transducers placed around the measured section. Although feasible, this approach presents several major drawbacks such as a complicated signal timing, transmission, acquisition and recording system, resulting in a relative high cost of operation. In addition, because of the geometrical constraints, a desired number of sensors may not be installed. Recent results in acoustic flow metering based on wide band signals and adaptive beamforming proved that it is possible to achieve flow velocity profiles using less acoustic transducers. In a normal acoustic time of flight path the transducers are both emitters and receivers, sequentially changing their roles. In the new configuration, proposed in this paper, two new receivers are added on each side. Since the beam angles of each acoustic transducer are wide enough the newly added transducers can receive the transmitted signals and additional time of flight estimation can be done. Thus, several flow

  17. Acoustic Signaling by Singing Humpback Whales (Megaptera novaeangliae): What Role Does Reverberation Play?

    PubMed Central

    Mercado, Eduardo

    2016-01-01

    When humpback whales (Megaptera novaeangliae) sing in coastal waters, the units they produce can generate reverberation. Traditionally, such reverberant acoustic energy has been viewed as an incidental side-effect of high-amplitude, long-distance, sound transmission in the ocean. An alternative possibility, however, is that reverberation actually contributes to the structure and function of songs. In the current study, this possibility was assessed by analyzing reverberation generated by humpback whale song units, as well as the spectral structure of unit sequences, produced by singers from different regions. Acoustical analyses revealed that: (1) a subset of units within songs generated narrowband reverberant energy that in some cases persisted for periods longer than the interval between units; (2) these highly reverberant units were regularly repeated throughout the production of songs; and (3) units occurring before and after these units often contained spectral energy peaks at non-overlapping, adjacent frequencies that were systematically related to the bands of reverberant energy generated by the units. These findings strongly suggest that some singing humpback whales not only produce sounds conducive to long-duration reverberation, but also may sequentially structure songs to avoid spectral overlap between units and ongoing reverberation. Singer-generated reverberant energy that is received simultaneously with directly transmitted song units can potentially provide listening whales with spatial cues that may enable them to more accurately determine a singer’s position. PMID:27907182

  18. Acoustical inverse problems regularization: Direct definition of filter factors using Signal-to-Noise Ratio

    NASA Astrophysics Data System (ADS)

    Gauthier, P.-A.; Gérard, A.; Camier, C.; Berry, A.

    2014-02-01

    Acoustic imaging aims at localization and characterization of sound sources using microphone arrays. In this paper a new regularization method for acoustic imaging by inverse approach is proposed. The method first relies on the singular value decomposition of the plant matrix and on the projection of the measured data on the corresponding singular vectors. In place of regularization using classical methods such as truncated singular value decomposition and Tikhonov regularization, the proposed method involves the direct definition of the filter factors on the basis of a thresholding operation, defined from the estimated measurement noise. The thresholding operation is achieved using modified filter functions. The originality of the approach is to propose the definition of a filter factor which provides more damping to the singular components dominated by noise than that given by the Tikhonov filter. This has the advantage of potentially simplifying the selection of the best regularization amount in inverse problems. Theoretical results show that this method is comparatively more accurate than Tikhonov regularization and truncated singular value decomposition.

  19. 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).

  20. A Review of Signal Detection Using the Bispectrum with Applications in Underwater Acoustics

    DTIC Science & Technology

    1994-01-01

    or polyspectra) technique. Examples comparing power spectral and bpspectral analysis include the following topics: the identification of signals...HOS) or polyspectra) technique. Examples comparing power Spectral and b-spectral analysis include the following topics: the identification of signals...that no pocessing gain is derived from bispectral analysis . It is shown that the resolution for direct methods using no formal cumulant construction

  1. French Research in Acoustics and Signal Processing: Report on Introductory Visit

    DTIC Science & Technology

    1986-05-01

    improves the reception of a transmitted signal and resolves the multipath transit times. The work includes scale-model experiments in the laboratory...Definition d’une Densite Energetique et Realisation Physique de Filtres Bidimensionnels," Dixieme Colloque sur le Traitement du Signal et ses

  2. Excavation Equipment Recognition Based on Novel Acoustic Statistical Features.

    PubMed

    Cao, Jiuwen; Wang, Wei; Wang, Jianzhong; Wang, Ruirong

    2016-09-30

    Excavation equipment recognition attracts increasing attentions in recent years due to its significance in underground pipeline network protection and civil construction management. In this paper, a novel classification algorithm based on acoustics processing is proposed for four representative excavation equipments. New acoustic statistical features, namely, the short frame energy ratio, concentration of spectrum amplitude ratio, truncated energy range, and interval of pulse are first developed to characterize acoustic signals. Then, probability density distributions of these acoustic features are analyzed and a novel classifier is presented. Experiments on real recorded acoustics of the four excavation devices are conducted to demonstrate the effectiveness of the proposed algorithm. Comparisons with two popular machine learning methods, support vector machine and extreme learning machine, combined with the popular linear prediction cepstral coefficients are provided to show the generalization capability of our method. A real surveillance system using our algorithm is developed and installed in a metro construction site for real-time recognition performance validation.

  3. Applications of signal multiplexing in fiber optic-based acoustic and seismic sensors

    NASA Astrophysics Data System (ADS)

    Costley, R. D.; Folks, William R.; Kirkendall, Clay K.; Galan-Comas, Gustavo; Smith, Eric W.; Parker, Michael W.; Hathaway, Kent K.

    2016-05-01

    Fiber optic systems are deployed in a variety of settings as strain sensors to locate small disturbances along the length of the optical fiber cable, which is often tens of kilometers long. This technology has the advantages of low cost and design simplicity, as the sensor is its own source of telemetry and may be easily repaired or replaced. One of the limitations of current technology is noise from optical backscatter events in the fiber resulting in a degraded signal in individual spatial zones leading to signal fading. Detection within these zones along the length of the fiber is then obscured. Signal multiplexing may be used to increase sensitivity and signal-to-noise ratio and reduce signal fading. In such an architecture, multiple channels are multiplexed together and transmitted along the fiber. In this article, we report on results from two different systems that were tested using such techniques. Results are then compared with a single channel system.

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

    PubMed

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

    2004-09-07

    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.

  5. Acoustic emission signals can discriminate between compressive bone fractures and tensile ligament injuries in the spine during dynamic loading.

    PubMed

    Van Toen, C; Street, J; Oxland, T R; Cripton, P A

    2012-06-01

    Acoustic emission (AE) sensors are a reliable tool in detecting fracture; however they have not been used to differentiate between compressive osseous and tensile ligamentous failures in the spine. This study evaluated the effectiveness of AE data in detecting the time of injury of ligamentum flavum (LF) and vertebral body (VB) specimens tested in tension and compression, respectively, and in differentiating between these failures. AE signals were collected while LF (n=7) and VB (n=7) specimens from human cadavers were tested in tension and compression (0.4m/s), respectively. Times of injury (time of peak AE amplitude) were compared to those using traditional methods (VB: time of peak force, LF: visual evidence in high speed video). Peak AE signal amplitudes and frequencies (using Fourier and wavelet transformations) for the LF and VB specimens were compared. In each group, six specimens failed (VB, fracture; LF, periosteal stripping or attenuation) and one did not. Time of injury using AE signals for VB and LF specimens produced average absolute differences to traditional methods of 0.7 (SD=0.2) ms and 2.4 (SD=1.5) ms (representing 14% and 20% of the average loading time), respectively. AE signals from VB fractures had higher amplitudes and frequencies than those from LF failures (average peak amplitude 87.7 (SD=6.9) dB vs. 71.8 (SD=9.8)dB for the inferior sensor, p<0.05; median characteristic frequency from the inferior sensor 97 (interquartile range, IQR, 41) kHz vs. 31 (IQR 2) kHz, p<0.05). These findings demonstrate that AE signals could be used to delineate complex failures of the spine.

  6. Method and apparatus for background signal reduction in opto-acoustic absorption measurement

    NASA Technical Reports Server (NTRS)

    Rosengren, L. G. (Inventor)

    1976-01-01

    The sensitivity of an opto-acoustic absorption detector is increased to make it possible to measure trace amounts of constituent gases. A second beam radiation path is created through the sample cell identical to a first path except as to length, alternating the beam through the two paths and minimizing the detected pressure difference for the two paths while the beam wavelength is tuned away from the absorption lines of the sample. Then with the beam wavelength tuned to the absorption line of any constituent of interest, the pressure difference is a measure of trace amounts of the constituent. The same improved detector may also be used for measuring the absorption coefficient of known concentrations of absorbing gases.

  7. Processing of acoustic signals via wavelet & Choi - Williams analysis in three-point bending load of carbon/epoxy and glass/epoxy composites.

    PubMed

    Beheshtizadeh, Nima; Mostafapour, Amir

    2017-04-05

    In this article, acoustic emission method was used for monitoring of flexural loading of GFRP (Glass fiber/epoxy composite) and CFRP (Carbon fiber/epoxy composite) via one acoustical sensor. In order to signal processing, various methods were employed such as wavelet transform, Short time Fourier transform, Choi - Williams transform and etc. Using two signal processing methods, wavelet transform and Choi - Williams transform, for monitoring of GFRP and CFRP specimens, determines strengths and weaknesses of each method and appointed the best analysis for signal processing of three point bending load of this type of composites. Based on information obtained from comparing of CFRP and GFRP, it is resulted that, the ratio of elastic modules and maximum load bearing of CFRP to GFRP is 1.36 and 3.25 respectively. Moreover, based on comparing of two analysis method results, Wavelet analysis was appointed better signal processing method for this type of load and material.

  8. 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,...

  9. Acoustic underwater signals with a probable function during competitive feeding in a tadpole.

    PubMed

    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. 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.

  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).

  12. Efficient Processing of Acoustic Signals for High Rate Information Transmission over Sparse Underwater Channels

    DTIC Science & Technology

    2016-09-02

    real- time implementation. To reduce computational complexity of signal processing and improve performance of data detection, receiver structures that...the fractionally-spaced channel estimators and the short feedforward equalizer filters. Receiver algorithm is applied to real data transmitted at 10...on minimization of the mean-squared error in data symbol estimation. This tap selection method is not optimal because the input signal to the

  13. Classification of patients undergoing weaning from mechanical ventilation using the coherence between heart rate variability and respiratory flow signal.

    PubMed

    Arcentales, A; Caminal, P; Diaz, I; Benito, S; Giraldo, B F

    2015-07-01

    Weaning from mechanical ventilation is still one of the most challenging problems in intensive care. Unnecessary delays in discontinuation and weaning trials that are undertaken too early are both undesirable. This study investigated the contribution of spectral signals of heart rate variability (HRV) and respiratory flow, and their coherence to classifying patients on weaning process from mechanical ventilation. A total of 121 candidates for weaning, undergoing spontaneous breathing tests, were analyzed: 73 were successfully weaned (GSucc), 33 failed to maintain spontaneous breathing so were reconnected (GFail), and 15 were extubated after the test but reintubated within 48 h (GRein). The power spectral density and magnitude squared coherence (MSC) of HRV and respiratory flow signals were estimated. Dimensionality reduction was performed using principal component analysis (PCA) and sequential floating feature selection. The patients were classified using a fuzzy K-nearest neighbour method. PCA of the MSC gave the best classification with the highest accuracy of 92% classifying GSucc versus GFail patients, and 86% classifying GSucc versus GRein patients. PCA of the respiratory flow signal gave the best classification between GFail and GRein patients (79% accuracy). These classifiers showed a good balance between sensitivity and specificity. Besides, the spectral coherence between HRV and the respiratory flow signal, in patients on weaning trial process, can contribute to the extubation decision.

  14. 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

  15. 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

  16. Elaborate visual and acoustic signals evolve independently in a large, phenotypically diverse radiation of songbirds.

    PubMed

    Mason, Nicholas A; Shultz, Allison J; Burns, Kevin J

    2014-08-07

    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.

  17. Real-Time Speech/Music Classification With a Hierarchical Oblique Decision Tree

    DTIC Science & Technology

    2008-04-01

    REAL-TIME SPEECH/ MUSIC CLASSIFICATION WITH A HIERARCHICAL OBLIQUE DECISION TREE Jun Wang, Qiong Wu, Haojiang Deng, Qin Yan Institute of Acoustics...time speech/ music classification with a hierarchical oblique decision tree. A set of discrimination features in frequency domain are selected...handle signals without discrimination and can not work properly in the existence of multimedia signals. This paper proposes a real-time speech/ music

  18. 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.

  19. Automated Acoustic Identification of Bats

    DTIC Science & Technology

    2011-10-01

    discrimination for such acoustically similar and cryptic species. Quantitative methods depend upon automatically extracting call descriptive parameters...constructing a dichotomous key, we iterated the highest performing classification choices at each step in a hierarchical classification scheme to...prevents unambiguous discrimination (Figure 31). For such acoustically cryptic species, identification remains in the realm of calculating a statistical

  20. Shallow-Water Mud Acoustics

    DTIC Science & Technology

    2015-09-30

    1 DISTRIBUTION STATEMENT A. Approved for public release; distribution is unlimited. Shallow- Water Mud Acoustics William L. Siegmann...shallow water over mud sediments and of acoustic detection, localization, and classification of objects buried in mud. OBJECTIVES • Develop...including long-range conveyance of information; detection, localization, and classification of objects buried in mud; and improvement of shallow water

  1. 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.

  2. Automatic parameter optimization in epsilon-filter for acoustical signal processing utilizing correlation coefficient.

    PubMed

    Abe, Tomomi; Hashimoto, Shuji; Matsumoto, Mitsuharu

    2010-02-01

    epsilon-filter can reduce most kinds of noise from a single-channel noisy signal while preserving signals that vary drastically such as speech signals. It can reduce not only stationary noise but also nonstationary noise. However, it has some parameters whose values are set empirically. So far, there have been few studies to evaluate the appropriateness of the parameter settings for epsilon-filter. This paper employs the correlation coefficient of the filter output and the difference between the filter input and output as the evaluation function of the parameter setting. This paper also describes the algorithm to set the optimal parameter value of epsilon-filter automatically. To evaluate the adequateness of the obtained parameter, the mean absolute error is calculated. The experimental results show that the adequate parameter in epsilon-filter can be obtained automatically by using the proposed method.

  3. 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.

  4. 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.

  5. 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.

  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 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

  8. Wing Morphometry and Acoustic Signals in Sterile and Wild Males: Implications for Mating Success in Ceratitis capitata

    PubMed Central

    de Souza, João Maria Gomes Alencar; Molina, Wagner Franco; de Almeida, Lúcia Maria; de Gouveia, Milson Bezerra; de Macêdo, Francisco Pepino; Laumann, Raul Alberto; Paranhos, Beatriz Aguiar Jordão

    2015-01-01

    The sterile insect technique (SIT) is widely utilized in the biological control of fruit flies of the family Tephritidae, particularly against the Mediterranean fruit fly. This study investigated the interaction between mating success and morphometric variation in the wings and the production of acoustic signals among three male groups of Ceratitis capitata (Wiedemann): (1) wild males, (2) irradiated with Co-60 (steriles), and (3) irradiated (steriles) and treated with ginger oil. The canonical variate analysis discriminated two groups (males irradiated and males wild), based on the morphological shape of the wings. Among males that emit buzz signals, wild males obtained copulation more frequently than males in Groups 2 and 3. The individuals of Group 3 achieved more matings than those in Group 2. Wild males displayed lower pulse duration, higher intervals between pulses, and higher dominant frequency. Regarding the reproductive success, the morphological differences in the wings' shape between accepted and nonaccepted males are higher in wild males than in the irradiated ones. The present results can be useful in programs using the sterile insect technique for biological control of C. capitata. PMID:26075293

  9. 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

  10. 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.

  11. 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

  12. Inhibition in the auditory brainstem enhances signal representation and regulates gain in complex acoustic environments

    PubMed Central

    Keine, Christian; Rübsamen, Rudolf; Englitz, Bernhard

    2016-01-01

    Inhibition plays a crucial role in neural signal processing, shaping and limiting responses. In the auditory system, inhibition already modulates second order neurons in the cochlear nucleus, e.g. spherical bushy cells (SBCs). While the physiological basis of inhibition and excitation is well described, their functional interaction in signal processing remains elusive. Using a combination of in vivo loose-patch recordings, iontophoretic drug application, and detailed signal analysis in the Mongolian Gerbil, we demonstrate that inhibition is widely co-tuned with excitation, and leads only to minor sharpening of the spectral response properties. Combinations of complex stimuli and neuronal input-output analysis based on spectrotemporal receptive fields revealed inhibition to render the neuronal output temporally sparser and more reproducible than the input. Overall, inhibition plays a central role in improving the temporal response fidelity of SBCs across a wide range of input intensities and thereby provides the basis for high-fidelity signal processing. DOI: http://dx.doi.org/10.7554/eLife.19295.001 PMID:27855778

  13. 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.

  14. Analysis and classification of sleep stages based on difference visibility graphs from a single-channel EEG signal.

    PubMed

    Zhu, Guohun; Li, Yan; Wen, Peng Paul

    2014-11-01

    The existing sleep stages classification methods are mainly based on time or frequency features. This paper classifies the sleep stages based on graph domain features from a single-channel electroencephalogram (EEG) signal. First, each epoch (30 s) EEG signal is mapped into a visibility graph (VG) and a horizontal VG (HVG). Second, a difference VG (DVG) is obtained by subtracting the edges set of the HVG from the edges set of the VG to extract essential degree sequences and to detect the gait-related movement artifact recordings. The mean degrees (MDs) and degree distributions (DDs) P (k) on HVGs and DVGs are analyzed epoch-by-epoch from 14,963 segments of EEG signals. Then, the MDs of each DVG and HVG and seven distinguishable DD values of P (k) from each DVG are extracted. Finally, nine extracted features are forwarded to a support vector machine to classify the sleep stages into two, three, four, five, and six states. The accuracy and kappa coefficients of six-state classification are 87.5% and 0.81, respectively. It was found that the MDs of the VGs on the deep sleep stage are higher than those on the awake and light sleep stages, and the MDs of the HVGs are just the reverse.

  15. [EFFECTS OF MUSIC-ACOUSTIC SIGNALS, ONLINE CONTROLLED BY EEG OSCILLATORS OF THE SUBJECT].

    PubMed

    Fedotchev, A I; Bondar, A T; Bakhchina, A V; Parin, S B; Polevaya, S A; Radchenko, G S

    2015-08-01

    The effects of 2 variants of the method of musical EEG neurofeedback, in which the dominant spectral components of subject's EEG (EEG oscillators) are online converted to music-like signals similar by timbre to flute sounds, have been studied. In the first case, these music-like signals were smoothly varying by the pitch and intensity in accordance with the current amplitude of the EEG oscillator. In the second case, the same variations of flute-like sound were accompanied by such musical element as rhythm. After the single exposure, the modifications of subject's brain activity and positive changes in psycho-physiological state of the subject have been found. Particularly pronounced effects were observed under rhythmically organized music-like stimuli.

  16. Signal Processing Using Surface Acoustic Wave Devices and Its Application to Spread Spectrum Communication Systems.

    DTIC Science & Technology

    1979-12-13

    its properties and relationships, was derived specifically for this work and is found in its complete form in Appendix A. Although no such analysis...surface waves beneath the semiconductor tend to remain collimated by the waveguiding properties of the semiconductor/ground plane combination and even...particular radar signal can be obtained using SAW convolvers as the main element, it would be worthwhile to present a few of the major properties of this

  17. 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.

  18. Size and quality information in acoustic signals of Rhinolophus ferrumequinum in distress situations.

    PubMed

    Jiang, Tinglei; Huang, Xiaobin; Wu, Hui; Feng, Jiang

    2017-05-01

    Many animals produce alarm or distress calls when they encounter predators. Previous studies have shown that the distress calls of some birds can also signal the quality of the bird as prey to predators. In this case, both predator and prey may benefit from sharing information about prey's ability to escape. However, little is known about whether echolocation pulses and distress calls in bats convey size and quality information in distress situations. This study investigates the relationship between echolocation, distress calls, and the health of the callers to determine whether these signals are reliable indicators of sender's attributes and quality. The spectro-temporal structure of echolocation pulses and distress calls from captured greater horseshoe bats, Rhinolophus ferrumequinum, were found to be correlated to their body size, body condition, and T-cell-mediated immunocompetence. The peak frequency of echolocation pulses was found to be positively correlated with the bats' forearm length. However, regression analysis has shown that no significant relationship exists between distress calls and overall body size, or between distress calls and overall health. These results suggest that the peak frequency of echolocation pulses may be a reliable index signal to attract conspecifics, but distress calls of bats may not convey information about their size or overall quality as conspecifics or prey. These results indicate that distress calls in bats may only convey their emotional state, to attract conspecifics and facilitate estimation of predation risk.

  19. Feature Selection and Classification of Electroencephalographic Signals: An Artificial Neural Network and Genetic Algorithm Based Approach.

    PubMed

    Erguzel, Turker Tekin; Ozekes, Serhat; Tan, Oguz; Gultekin, Selahattin

    2015-10-01

    Feature selection is an important step in many pattern recognition systems aiming to overcome the so-called curse of dimensionality. In this study, an optimized classification method was tested in 147 patients with major depressive disorder (MDD) treated with repetitive transcranial magnetic stimulation (rTMS). The performance of the combination of a genetic algorithm (GA) and a back-propagation (BP) neural network (BPNN) was evaluated using 6-channel pre-rTMS electroencephalographic (EEG) patterns of theta and delta frequency bands. The GA was first used to eliminate the redundant and less discriminant features to maximize classification performance. The BPNN was then applied to test the performance of the feature subset. Finally, classification performance using the subset was evaluated using 6-fold cross-validation. Although the slow bands of the frontal electrodes are widely used to collect EEG data for patients with MDD and provide quite satisfactory classification results, the outcomes of the proposed approach indicate noticeably increased overall accuracy of 89.12% and an area under the receiver operating characteristic (ROC) curve (AUC) of 0.904 using the reduced feature set.

  20. Continuous measurements of suspended sediment loads using dual frequency acoustic Doppler profile signals

    NASA Astrophysics Data System (ADS)

    Antonini, Alessandro; Guerrero, Massimo; Rüther, Nils; Stokseth, Siri

    2016-04-01

    A huge thread to Hydropower plants (HPP) is incoming sediments in suspension from the rivers upstream. The sediments settle in the reservoir and reduce the effective head as well as the volume and reduce consequently the lifetime of the reservoir. In addition are the fine sediments causing severe damages to turbines and infrastructure of a HPP. For estimating the amount of in-coming sediments in suspension and the consequent planning of efficient counter measures, it is essential to monitor the rivers within the catchment of the HPP for suspended sediments. This work is considerably time consuming and requires highly educated personnel and is therefore expensive. Surrogate-indirect methods using acoustic and optic devices have bee developed since the last decades that may be efficiently applied for the continuous monitoring of suspended sediment loads. The presented study proposes therefore to establish a research station at a cross section of a river which is the main tributary to a reservoir of a HPP and equip this station with surrogate as well as with common method of measuring suspended load concentrations and related flow discharge and level. The logger at the research station delivers data automatically to a server. Therefore it is ensured that also large flood events are covered. Data during flood are of high interest to the HPP planners since they carried the most part of the sediment load in a hydrological year. Theses peaks can hardly be measured with common measurement methods. Preliminary results of the wet season 2015/2016 are presented. The data gives insight in the applicable range, in terms of scattering particles concentration-average size and corresponding flow discharge and level, eventually enabling the study of suspended sediment load-water flow correlations during peak events. This work is carried out as part of a larger research project on sustainable hydro power plants exposed to high sediment yield, SediPASS. SediPASS is funded by the

  1. 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.

  2. [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.

  3. Detection of single-nucleotide polymorphisms with novel leaky surface acoustic wave biosensors, DNA ligation and enzymatic signal amplification.

    PubMed

    Xu, Qinghua; Chang, Kai; Lu, Weiping; Chen, Wei; Ding, Yi; Jia, Shuangrong; Zhang, Kejun; Li, Fake; Shi, Jianfeng; Cao, Liang; Deng, Shaoli; Chen, Ming

    2012-03-15

    This manuscript describes a new technique for detecting single-nucleotide polymorphisms (SNPs) by integrating a leaky surface acoustic wave (LSAW) biosensor, enzymatic DNA ligation and enzymatic signal amplification. In this technique, the DNA target is hybridized with a capture probe immobilized on the surface of a LSAW biosensor. Then, the hybridized sequence is ligated to biotinylated allele-specific detection probe using Taq DNA ligase. The ligation does not take place if there is a single-nucleotide mismatch between the target and the capture probe. The ligated detection probe is transformed into a streptavidin-horseradish peroxidase (SA-HRP) terminal group via a biotin-streptavidin complex. Then, the SA-HRP group catalyzes the polymerization of 3,3-diaminobenzidine (DAB) to form a surface precipitate, thus effectively increasing the sensitivity of detecting surface mass changes and allowing detection of SNPs. Optimal detection conditions were found to be: 0.3 mol/L sodium ion concentration in PBS, pH 7.6, capture probe concentration 0.5 μmol/L and target sequence concentration 1.0 μmol/L. The detection limit was found to be 1 × 10(-12)mol/L. Using this technique, we were able to detect a single-point mutation at nucleotide A2293G in Japanese encephalitis virus.

  4. An Integrated Processing Method for Fatigue Damage Identification in a Steel Structure Based on Acoustic Emission Signals

    NASA Astrophysics Data System (ADS)

    Zhang, Yubo; Luo, Hongyun; Li, Junrong; Lv, Jinlong; Zhang, Zheng; Ma, Yue

    2017-03-01

    This paper presents an integrated processing method that applies principal component analysis (PCA), artificial neural network (ANN), information entropy and information fusion technique to analyze acoustic emission signals for identifying fatigue damage in a steel structure. Firstly, PCA is used to build different data spaces based on the damage patterns. Input information from each sensor is diagnosed locally through ANN in the data space. The output of the ANNs is used for basic probability assignment. Secondly, the first fusion operation adopts Dempster-Shafer (D-S) evidence theory to combine the basic probability assignment value of ANNs in the different data space of a sensor. Finally, the fusion results of each sensor are combined by D-S evidence theory for the second fusion operation. In this paper, information entropy is used to calculate the uncertainty and construct basic probability assignment function. The damage identification method is verified through four-point bending fatigue tests of Q345 steel. Validation results show that the damage identification method can reduce the uncertainty of the system and has a certain extent of fault tolerance. Compared with ANN and ANN combined with information fusion methods, the proposed method shows a higher fatigue damage identification accuracy and is a potential for fatigue damage identification.

  5. A neural network-based analysis of acoustic courtship signals and female responses in Chorthippus biguttulus grasshoppers.

    PubMed

    Wittmann, Jan P; Kolss, Munjong; Reinhold, Klaus

    2011-08-01

    In many animal species, male acoustic courtship signals are evaluated by females for mate choice. At the behavioural level, this phenomenon has been well studied. However, although several song characteristics have been determined to affect the attractiveness of a given song, the mechanisms of the evaluation process remain largely unclear. Here, we present a simple neural network model for analysing and evaluating courtship songs of Chorthippus biguttulus males in real-time. The model achieves a high predictive power of the attractiveness of artificial songs as assigned by real Chorthippus biguttulus females: about 87% of the variance can be explained. It also allows us to determine the relative contribution of different song characteristics to overall attractiveness and how each of the song components influences female responsiveness. In general, the obtained results closely match those of empirical studies. Therefore, our model may be used to obtain a first estimate of male song attractiveness and may thus complement actual testing of female responsiveness in the laboratory. In addition, the model allows including and testing novel song parameters to generate new hypotheses for further experimental studies. The supplemental material of this article contains the article's data in an active, re-usable format.

  6. 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.

  7. Acoustic neuroma

    MedlinePlus

    Vestibular schwannoma; Tumor - acoustic; Cerebellopontine angle tumor; Angle tumor; Hearing loss - acoustic; Tinnitus - acoustic ... Acoustic neuromas have been linked with the genetic disorder neurofibromatosis type 2 (NF2). Acoustic neuromas are uncommon.

  8. 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.

  9. 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.

  10. 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.

  11. Analysis of EEG-fMRI data in focal epilepsy based on automated spike classification and Signal Space Projection.

    PubMed

    Liston, Adam D; De Munck, Jan C; Hamandi, Khalid; Laufs, Helmut; Ossenblok, Pauly; Duncan, John S; Lemieux, Louis

    2006-07-01

    Simultaneous acquisition of EEG and fMRI data enables the investigation of the hemodynamic correlates of interictal epileptiform discharges (IEDs) during the resting state in patients with epilepsy. This paper addresses two issues: (1) the semi-automation of IED classification in statistical modelling for fMRI analysis and (2) the improvement of IED detection to increase experimental fMRI efficiency. For patients with multiple IED generators, sensitivity to IED-correlated BOLD signal changes can be improved when the fMRI analysis model distinguishes between IEDs of differing morphology and field. In an attempt to reduce the subjectivity of visual IED classification, we implemented a semi-automated system, based on the spatio-temporal clustering of EEG events. We illustrate the technique's usefulness using EEG-fMRI data from a subject with focal epilepsy in whom 202 IEDs were visually identified and then clustered semi-automatically into four clusters. Each cluster of IEDs was modelled separately for the purpose of fMRI analysis. This revealed IED-correlated BOLD activations in distinct regions corresponding to three different IED categories. In a second step, Signal Space Projection (SSP) was used to project the scalp EEG onto the dipoles corresponding to each IED cluster. This resulted in 123 previously unrecognised IEDs, the inclusion of which, in the General Linear Model (GLM), increased the experimental efficiency as reflected by significant BOLD activations. We have also shown that the detection of extra IEDs is robust in the face of fluctuations in the set of visually detected IEDs. We conclude that automated IED classification can result in more objective fMRI models of IEDs and significantly increased sensitivity.

  12. Deficit in acoustic signal-in-noise detection in glycine receptor α3 subunit knockout mice.

    PubMed

    Tziridis, Konstantin; Buerbank, Stefanie; Eulenburg, Volker; Dlugaiczyk, Julia; Schulze, Holger

    2017-02-01

    Hearing is an essential sense for communication in animals and humans. Normal function of the cochlea of higher vertebrates relies on a fine-tuned interplay of afferent and efferent innervation of both inner and outer hair cells. Efferent inhibition is controlled via olivocochlear feedback loops, mediated mainly by acetylcholine, γ-aminobutyric acid (GABA) and glycine, and is one of the first sites affected by synapto- and neuropathy in the development of hearing loss. While the functions of acetylcholine, GABA and other inhibitory transmitters within these feedback loops are at least partially understood, especially the function of glycine still remains elusive. To address this question, we investigated hearing in glycine receptor (GlyR) α3 knockout (KO) and wildtype (WT) mice. We found no differences in pure tone hearing thresholds at 11.3 and 16 kHz between the two groups as assessed by auditory brainstem response (ABR) measurements. Detailed analysis of the ABR waves at 11.3 kHz, however, revealed a latency decrease of wave III and an amplitude increase of wave IV in KO compared to WT animals. GlyRα3 KO animals showed significantly impaired prepulse inhibition of the auditory startle response in a noisy environment, indicating that GlyRα3-mediated glycinergic inhibition is important for signal-in-noise detection.

  13. Artificial neural network-based classification of body movements in ambulatory ECG signal.

    PubMed

    Darji, Sachin T; Kher, Rahul K

    2013-11-01

    Abstract Ambulatory ECG monitoring provides electrical activity of the heart when a person is involved in doing normal routine activities. Thus, the recorded ECG signal consists of cardiac signal along with motion artifacts introduced due to a person's body movements during routine activities. Detection of motion artifacts due to different physical activities might help in further cardiac diagnosis. Ambulatory ECG signal analysis for detection of various motion artifacts using adaptive filtering approach is addressed in this paper. We have used BIOPAC MP 36 system for acquiring ECG signal. The ECG signals of five healthy subjects (aged between 22-30 years) were recorded while the person performed various body movements like up and down movement of the left hand, up and down movement of the right hand, waist twisting movement while standing and change from sitting down on a chair to standing up movement in lead I configuration. An adaptive filter-based approach has been used to extract the motion artifact component from the ambulatory ECG signal. The features of motion artifact signal, extracted using Gabor transform, have been used to train the artificial neural network (ANN) for classifying body movements.

  14. Preferences based on spectral differences in acoustic signals in four species of treefrogs (Anura: Hylidae).

    PubMed

    Gerhardt, H Carl; Martínez-Rivera, Carlos C; Schwartz, Joshua J; Marshall, Vincent T; Murphy, Christopher G

    2007-09-01

    Frogs have two inner ear organs, each tuned to a different range of frequencies. Female treefrogs (Hylidae) of three species in which males produce calls with a bimodal spectrum (Hyla chrysoscelis, H. versicolor, H. arenicolor) preferred alternatives with a bimodal spectrum to alternatives with a single high-frequency peak. By contrast, females of H. avivoca, in which males produce calls with a single, high-frequency peak, preferred synthetic calls with a single high-frequency peak to calls with a bimodal spectrum. These results are consistent with the expectations of the matched-filter hypothesis and run counter to the predictions of the pre-existing bias hypothesis. At moderate to high playback levels (85-90 dB), females of H. avivoca and of two of three mtDNA-defined lineages of H. versicolor preferred unimodal signals with a high-frequency peak to those with a low-frequency peak. Females of H. chrysoscelis, H. arenicolor and the third lineage of H. versicolor did not show a preference, indicating that receiver mechanisms may be at least as evolutionarily labile as call structure. Spectral-peak preferences of gray treefrogs from Missouri, USA were intensity-dependent. Whereas females chose low-frequency calls at 65 dB spl, there was either no preference (H. chrysoscelis) or a preference for high-frequency calls (H. versicolor) at 85 and 90 dB spl. These non-linear effects indicate that there is an increasing influence of high-frequency energy on preferences as females approach calling males, and these results serve to emphasize that playback experiments conducted at a single level may have limited generality.

  15. 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.

  16. 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 dimens