Classification of underwater target echoes based on auditory perception characteristics

NASA Astrophysics Data System (ADS)

Li, Xiukun; Meng, Xiangxia; Liu, Hang; Liu, Mingye

2014-06-01

In underwater target detection, the bottom reverberation has some of the same properties as the target echo, which has a great impact on the performance. It is essential to study the difference between target echo and reverberation. In this paper, based on the unique advantage of human listening ability on objects distinction, the Gammatone filter is taken as the auditory model. In addition, time-frequency perception features and auditory spectral features are extracted for active sonar target echo and bottom reverberation separation. The features of the experimental data have good concentration characteristics in the same class and have a large amount of differences between different classes, which shows that this method can effectively distinguish between the target echo and reverberation.

Sonar target enhancement by shrinkage of incoherent wavelet coefficients.

PubMed

Hunter, Alan J; van Vossen, Robbert

2014-01-01

Background reverberation can obscure useful features of the target echo response in broadband low-frequency sonar images, adversely affecting detection and classification performance. This paper describes a resolution and phase-preserving means of separating the target response from the background reverberation noise using a coherence-based wavelet shrinkage method proposed recently for de-noising magnetic resonance images. The algorithm weights the image wavelet coefficients in proportion to their coherence between different looks under the assumption that the target response is more coherent than the background. The algorithm is demonstrated successfully on experimental synthetic aperture sonar data from a broadband low-frequency sonar developed for buried object detection.

Alluvial substrate mapping by automated texture segmentation of recreational-grade side scan sonar imagery.

PubMed

Hamill, Daniel; Buscombe, Daniel; Wheaton, Joseph M

2018-01-01

Side scan sonar in low-cost 'fishfinder' systems has become popular in aquatic ecology and sedimentology for imaging submerged riverbed sediment at coverages and resolutions sufficient to relate bed texture to grain-size. Traditional methods to map bed texture (i.e. physical samples) are relatively high-cost and low spatial coverage compared to sonar, which can continuously image several kilometers of channel in a few hours. Towards a goal of automating the classification of bed habitat features, we investigate relationships between substrates and statistical descriptors of bed textures in side scan sonar echograms of alluvial deposits. We develop a method for automated segmentation of bed textures into between two to five grain-size classes. Second-order texture statistics are used in conjunction with a Gaussian Mixture Model to classify the heterogeneous bed into small homogeneous patches of sand, gravel, and boulders with an average accuracy of 80%, 49%, and 61%, respectively. Reach-averaged proportions of these sediment types were within 3% compared to similar maps derived from multibeam sonar.

Acoustic Facies Analysis of Side-Scan Sonar Data

NASA Astrophysics Data System (ADS)

Dwan, Fa Shu

Acoustic facies analysis methods have allowed the generation of system-independent values for the quantitative seafloor acoustic parameter, backscattering strength, from GLORIA and (TAMU) ^2 side-scan sonar data. The resulting acoustic facies parameters enable quantitative comparisons of data collected by different sonar systems, data from different environments, and measurements made with survey geometries. Backscattering strength values were extracted from the sonar amplitude data by inversion based on the sonar equation. Image processing products reveal seafloor features and patterns of relative intensity. To quantitatively compare data collected at different times or by different systems, and to ground truth-measurements and geoacoustic models, quantitative corrections must be made on any given data set for system source level, beam pattern, time-varying gain, processing gain, transmission loss, absorption, insonified area contribution, and grazing angle effects. In the sonar equation, backscattering strength is the sonar parameter which is directly related to seafloor properties. The GLORIA data used in this study are from the edge of a distal lobe of the Monterey Fan. An interfingered region of strong and weak seafloor signal returns from a flat seafloor region provides an ideal data set for this study. Inversion of imagery data from the region allows the quantitative definition of different acoustic facies. The (TAMU) ^2 data used are from a calibration site near the Green Canyon area of the Gulf of Mexico. Acoustic facies analysis techniques were implemented to generate statistical information for acoustic facies based on the estimates of backscattering strength. The backscattering strength values have been compared with Lambert's Law and other functions to parameterize the description of the acoustic facies. The resulting Lambertian constant values range from -26 dB to -36 dB. A modified Lambert relationship, which consists of both intercept and slope terms, appears to represent the BSS versus grazing angle profiles better based on chi^2 testing and error ellipse generation. Different regression functions, composed of trigonometric functions, were analyzed for different segments of the BSS profiles. A cotangent or sine/cosine function shows promising results for representing the entire grazing angle span of the BSS profiles.

Applying multibeam sonar and mathematical modeling for mapping seabed substrate and biota of offshore shallows

NASA Astrophysics Data System (ADS)

Herkül, Kristjan; Peterson, Anneliis; Paekivi, Sander

2017-06-01

Both basic science and marine spatial planning are in a need of high resolution spatially continuous data on seabed habitats and biota. As conventional point-wise sampling is unable to cover large spatial extents in high detail, it must be supplemented with remote sensing and modeling in order to fulfill the scientific and management needs. The combined use of in situ sampling, sonar scanning, and mathematical modeling is becoming the main method for mapping both abiotic and biotic seabed features. Further development and testing of the methods in varying locations and environmental settings is essential for moving towards unified and generally accepted methodology. To fill the relevant research gap in the Baltic Sea, we used multibeam sonar and mathematical modeling methods - generalized additive models (GAM) and random forest (RF) - together with underwater video to map seabed substrate and epibenthos of offshore shallows. In addition to testing the general applicability of the proposed complex of techniques, the predictive power of different sonar-based variables and modeling algorithms were tested. Mean depth, followed by mean backscatter, were the most influential variables in most of the models. Generally, mean values of sonar-based variables had higher predictive power than their standard deviations. The predictive accuracy of RF was higher than that of GAM. To conclude, we found the method to be feasible and with predictive accuracy similar to previous studies of sonar-based mapping.

A Robust and Fast Method for Sidescan Sonar Image Segmentation Using Nonlocal Despeckling and Active Contour Model.

PubMed

Huo, Guanying; Yang, Simon X; Li, Qingwu; Zhou, Yan

2017-04-01

Sidescan sonar image segmentation is a very important issue in underwater object detection and recognition. In this paper, a robust and fast method for sidescan sonar image segmentation is proposed, which deals with both speckle noise and intensity inhomogeneity that may cause considerable difficulties in image segmentation. The proposed method integrates the nonlocal means-based speckle filtering (NLMSF), coarse segmentation using k -means clustering, and fine segmentation using an improved region-scalable fitting (RSF) model. The NLMSF is used before the segmentation to effectively remove speckle noise while preserving meaningful details such as edges and fine features, which can make the segmentation easier and more accurate. After despeckling, a coarse segmentation is obtained by using k -means clustering, which can reduce the number of iterations. In the fine segmentation, to better deal with possible intensity inhomogeneity, an edge-driven constraint is combined with the RSF model, which can not only accelerate the convergence speed but also avoid trapping into local minima. The proposed method has been successfully applied to both noisy and inhomogeneous sonar images. Experimental and comparative results on real and synthetic sonar images demonstrate that the proposed method is robust against noise and intensity inhomogeneity, and is also fast and accurate.

Optimization of Adaboost Algorithm for Sonar Target Detection in a Multi-Stage ATR System

NASA Technical Reports Server (NTRS)

Lin, Tsung Han (Hank)

2011-01-01

JPL has developed a multi-stage Automated Target Recognition (ATR) system to locate objects in images. First, input images are preprocessed and sent to a Grayscale Optical Correlator (GOC) filter to identify possible regions-of-interest (ROIs). Second, feature extraction operations are performed using Texton filters and Principal Component Analysis (PCA). Finally, the features are fed to a classifier, to identify ROIs that contain the targets. Previous work used the Feed-forward Back-propagation Neural Network for classification. In this project we investigate a version of Adaboost as a classifier for comparison. The version we used is known as GentleBoost. We used the boosted decision tree as the weak classifier. We have tested our ATR system against real-world sonar images using the Adaboost approach. Results indicate an improvement in performance over a single Neural Network design.

A novel underwater dam crack detection and classification approach based on sonar images

PubMed Central

Shi, Pengfei; Fan, Xinnan; Ni, Jianjun; Khan, Zubair; Li, Min

2017-01-01

Underwater dam crack detection and classification based on sonar images is a challenging task because underwater environments are complex and because cracks are quite random and diverse in nature. Furthermore, obtainable sonar images are of low resolution. To address these problems, a novel underwater dam crack detection and classification approach based on sonar imagery is proposed. First, the sonar images are divided into image blocks. Second, a clustering analysis of a 3-D feature space is used to obtain the crack fragments. Third, the crack fragments are connected using an improved tensor voting method. Fourth, a minimum spanning tree is used to obtain the crack curve. Finally, an improved evidence theory combined with fuzzy rule reasoning is proposed to classify the cracks. Experimental results show that the proposed approach is able to detect underwater dam cracks and classify them accurately and effectively under complex underwater environments. PMID:28640925

A novel underwater dam crack detection and classification approach based on sonar images.

PubMed

Shi, Pengfei; Fan, Xinnan; Ni, Jianjun; Khan, Zubair; Li, Min

2017-01-01

Underwater dam crack detection and classification based on sonar images is a challenging task because underwater environments are complex and because cracks are quite random and diverse in nature. Furthermore, obtainable sonar images are of low resolution. To address these problems, a novel underwater dam crack detection and classification approach based on sonar imagery is proposed. First, the sonar images are divided into image blocks. Second, a clustering analysis of a 3-D feature space is used to obtain the crack fragments. Third, the crack fragments are connected using an improved tensor voting method. Fourth, a minimum spanning tree is used to obtain the crack curve. Finally, an improved evidence theory combined with fuzzy rule reasoning is proposed to classify the cracks. Experimental results show that the proposed approach is able to detect underwater dam cracks and classify them accurately and effectively under complex underwater environments.

Size Constancy in Bat Biosonar? Perceptual Interaction of Object Aperture and Distance

PubMed Central

Heinrich, Melina; Wiegrebe, Lutz

2013-01-01

Perception and encoding of object size is an important feature of sensory systems. In the visual system object size is encoded by the visual angle (visual aperture) on the retina, but the aperture depends on the distance of the object. As object distance is not unambiguously encoded in the visual system, higher computational mechanisms are needed. This phenomenon is termed “size constancy”. It is assumed to reflect an automatic re-scaling of visual aperture with perceived object distance. Recently, it was found that in echolocating bats, the ‘sonar aperture’, i.e., the range of angles from which sound is reflected from an object back to the bat, is unambiguously perceived and neurally encoded. Moreover, it is well known that object distance is accurately perceived and explicitly encoded in bat sonar. Here, we addressed size constancy in bat biosonar, recruiting virtual-object techniques. Bats of the species Phyllostomus discolor learned to discriminate two simple virtual objects that only differed in sonar aperture. Upon successful discrimination, test trials were randomly interspersed using virtual objects that differed in both aperture and distance. It was tested whether the bats spontaneously assigned absolute width information to these objects by combining distance and aperture. The results showed that while the isolated perceptual cues encoding object width, aperture, and distance were all perceptually well resolved by the bats, the animals did not assign absolute width information to the test objects. This lack of sonar size constancy may result from the bats relying on different modalities to extract size information at different distances. Alternatively, it is conceivable that familiarity with a behaviorally relevant, conspicuous object is required for sonar size constancy, as it has been argued for visual size constancy. Based on the current data, it appears that size constancy is not necessarily an essential feature of sonar perception in bats. PMID:23630598

Size constancy in bat biosonar? Perceptual interaction of object aperture and distance.

PubMed

Heinrich, Melina; Wiegrebe, Lutz

2013-01-01

Perception and encoding of object size is an important feature of sensory systems. In the visual system object size is encoded by the visual angle (visual aperture) on the retina, but the aperture depends on the distance of the object. As object distance is not unambiguously encoded in the visual system, higher computational mechanisms are needed. This phenomenon is termed "size constancy". It is assumed to reflect an automatic re-scaling of visual aperture with perceived object distance. Recently, it was found that in echolocating bats, the 'sonar aperture', i.e., the range of angles from which sound is reflected from an object back to the bat, is unambiguously perceived and neurally encoded. Moreover, it is well known that object distance is accurately perceived and explicitly encoded in bat sonar. Here, we addressed size constancy in bat biosonar, recruiting virtual-object techniques. Bats of the species Phyllostomus discolor learned to discriminate two simple virtual objects that only differed in sonar aperture. Upon successful discrimination, test trials were randomly interspersed using virtual objects that differed in both aperture and distance. It was tested whether the bats spontaneously assigned absolute width information to these objects by combining distance and aperture. The results showed that while the isolated perceptual cues encoding object width, aperture, and distance were all perceptually well resolved by the bats, the animals did not assign absolute width information to the test objects. This lack of sonar size constancy may result from the bats relying on different modalities to extract size information at different distances. Alternatively, it is conceivable that familiarity with a behaviorally relevant, conspicuous object is required for sonar size constancy, as it has been argued for visual size constancy. Based on the current data, it appears that size constancy is not necessarily an essential feature of sonar perception in bats.

Alluvial substrate mapping by automated texture segmentation of recreational-grade side scan sonar imagery

PubMed Central

Buscombe, Daniel; Wheaton, Joseph M.

2018-01-01

Side scan sonar in low-cost ‘fishfinder’ systems has become popular in aquatic ecology and sedimentology for imaging submerged riverbed sediment at coverages and resolutions sufficient to relate bed texture to grain-size. Traditional methods to map bed texture (i.e. physical samples) are relatively high-cost and low spatial coverage compared to sonar, which can continuously image several kilometers of channel in a few hours. Towards a goal of automating the classification of bed habitat features, we investigate relationships between substrates and statistical descriptors of bed textures in side scan sonar echograms of alluvial deposits. We develop a method for automated segmentation of bed textures into between two to five grain-size classes. Second-order texture statistics are used in conjunction with a Gaussian Mixture Model to classify the heterogeneous bed into small homogeneous patches of sand, gravel, and boulders with an average accuracy of 80%, 49%, and 61%, respectively. Reach-averaged proportions of these sediment types were within 3% compared to similar maps derived from multibeam sonar. PMID:29538449

Description and Evaluation of a Four-Channel, Coherent 100-kHz Sidescan Sonar

DTIC Science & Technology

2004-12-01

document contains color images. 14. ABSTRACT This report documents the design and features of a new, four-channel, coherent 100-kHz sidescan sonar...Atlantic Technical Memorandum DRDC Atlantic TM 2004-204 December 2004 Abstract This report documents the design and features of a new...Results This report documents the design and features of this new high-frequency sonar system. These initial field trial results demonstrate some of

Qualitative and quantitative processing of side-scan sonar data

DOE Office of Scientific and Technical Information (OSTI.GOV)

Dwan, F.S.; Anderson, A.L.; Hilde, T.W.C.

1990-06-01

Modern side-scan sonar systems allow vast areas of seafloor to be rapidly imaged and quantitatively mapped in detail. The application of remote sensing image processing techniques can be used to correct for various distortions inherent in raw sonography. Corrections are possible for water column, slant-range, aspect ratio, speckle and striping noise, multiple returns, power drop-off, and for georeferencing. The final products reveal seafloor features and patterns that are geometrically correct, georeferenced, and have improved signal/noise ratio. These products can be merged with other georeferenced data bases for further database management and information extraction. In order to compare data collected bymore » different systems from a common area and to ground truth measurements and geoacoustic models, quantitative correction must be made for calibrated sonar system and bathymetry effects. Such data inversion must account for system source level, beam pattern, time-varying gain, processing gain, transmission loss, absorption, insonified area, and grazing angle effects. Seafloor classification can then be performed on the calculated back-scattering strength using Lambert's Law and regression analysis. Examples are given using both approaches: image analysis and inversion of data based on the sonar equation.« less

Bats' avoidance of real and virtual objects: implications for the sonar coding of object size.

PubMed

Goerlitz, Holger R; Genzel, Daria; Wiegrebe, Lutz

2012-01-01

Fast movement in complex environments requires the controlled evasion of obstacles. Sonar-based obstacle evasion involves analysing the acoustic features of object-echoes (e.g., echo amplitude) that correlate with this object's physical features (e.g., object size). Here, we investigated sonar-based obstacle evasion in bats emerging in groups from their day roost. Using video-recordings, we first show that the bats evaded a small real object (ultrasonic loudspeaker) despite the familiar flight situation. Secondly, we studied the sonar coding of object size by adding a larger virtual object. The virtual object echo was generated by real-time convolution of the bats' calls with the acoustic impulse response of a large spherical disc and played from the loudspeaker. Contrary to the real object, the virtual object did not elicit evasive flight, despite the spectro-temporal similarity of real and virtual object echoes. Yet, their spatial echo features differ: virtual object echoes lack the spread of angles of incidence from which the echoes of large objects arrive at a bat's ears (sonar aperture). We hypothesise that this mismatch of spectro-temporal and spatial echo features caused the lack of virtual object evasion and suggest that the sonar aperture of object echoscapes contributes to the sonar coding of object size. Copyright © 2011 Elsevier B.V. All rights reserved.

Dolphin sonar detection and discrimination capabilities

NASA Astrophysics Data System (ADS)

Au, Whitlow W. L.

2004-05-01

Dolphins have a very sophisticated short range sonar that surpasses all technological sonar in its capabilities to perform complex target discrimination and recognition tasks. The system that the U.S. Navy has for detecting mines buried under ocean sediment is one that uses Atlantic bottlenose dolphins. However, close examination of the dolphin sonar system will reveal that the dolphin acoustic hardware is fairly ordinary and not very special. The transmitted signals have peak-to-peak amplitudes as high as 225-228 dB re 1 μPa which translates to an rms value of approximately 210-213 dB. The transmit beamwidth is fairly broad at about 10o in both the horizontal and vertical planes and the receiving beamwidth is slightly broader by several degrees. The auditory filters are not very narrow with Q values of about 8.4. Despite these fairly ordinary features of the acoustic system, these animals still demonstrate very unusual and astonishing capabilities. Some of the capabilities of the dolphin sonar system will be presented and the reasons for their keen sonar capabilities will be discussed. Important features of their sonar include the broadband clicklike signals used, adaptive sonar search capabilities and large dynamic range of its auditory system.

Using side-scan sonar to characterize and map physical habitat and anthropogenic underwater features in the St. Louis River.

EPA Science Inventory

Characterizing underwater habitat and other features is difficult and costly, especially in the large St. Louis River Estuary. We are using side-scan sonar (SSS), first developed in the 1960s to remotely sense underwater habitat features from reflected acoustic signals (backscatt...

Observing Ocean Ecosystems with Sonar

DOE Office of Scientific and Technical Information (OSTI.GOV)

Matzner, Shari; Maxwell, Adam R.; Ham, Kenneth D.

2016-12-01

We present a real-time processing system for sonar to detect and track animals, and to extract water column biomass statistics in order to facilitate continuous monitoring of an underwater environment. The Nekton Interaction Monitoring System (NIMS) is built to connect to an instrumentation network, where it consumes a real-time stream of sonar data and archives tracking and biomass data.

Automatic Detection of Sand Ripple Features in Sidescan Sonar Imagery

DTIC Science & Technology

2014-07-09

Among the features used in forensic scientific fingerprint analysis are terminations or bifurcations of print ridges. Sidescan sonar imagery of ripple...always be pathological cases. The size of the blocks of pixels used in determining the ripple wavelength is evident in the output images on the right in

Audible sonar images generated with proprioception for target analysis.

PubMed

Kuc, Roman B

2017-05-01

Some blind humans have demonstrated the ability to detect and classify objects with echolocation using palatal clicks. An audible-sonar robot mimics human click emissions, binaural hearing, and head movements to extract interaural time and level differences from target echoes. Targets of various complexity are examined by transverse displacements of the sonar and by target pose rotations that model movements performed by the blind. Controlled sonar movements executed by the robot provide data that model proprioception information available to blind humans for examining targets from various aspects. The audible sonar uses this sonar location and orientation information to form two-dimensional target images that are similar to medical diagnostic ultrasound tomograms. Simple targets, such as single round and square posts, produce distinguishable and recognizable images. More complex targets configured with several simple objects generate diffraction effects and multiple reflections that produce image artifacts. The presentation illustrates the capabilities and limitations of target classification from audible sonar images.

A man-made object detection for underwater TV

NASA Astrophysics Data System (ADS)

Cheng, Binbin; Wang, Wenwu; Chen, Yao

2018-03-01

It is a great challenging task to complete an automatic search of objects underwater. Usually the forward looking sonar is used to find the target, and then the initial identification of the target is completed by the side-scan sonar, and finally the confirmation of the target is accomplished by underwater TV. This paper presents an efficient method for automatic extraction of man-made sensitive targets in underwater TV. Firstly, the image of underwater TV is simplified with taking full advantage of the prior knowledge of the target and the background; then template matching technology is used for target detection; finally the target is confirmed by extracting parallel lines on the target contour. The algorithm is formulated for real-time execution on limited-memory commercial-of-the-shelf platforms and is capable of detection objects in underwater TV.

Bottlenose dolphins perceive object features through echolocation.

PubMed

Harley, Heidi E; Putman, Erika A; Roitblat, Herbert L

2003-08-07

How organisms (including people) recognize distant objects is a fundamental question. The correspondence between object characteristics (distal stimuli), like visual shape, and sensory characteristics (proximal stimuli), like retinal projection, is ambiguous. The view that sensory systems are 'designed' to 'pick up' ecologically useful information is vague about how such mechanisms might work. In echolocating dolphins, which are studied as models for object recognition sonar systems, the correspondence between echo characteristics and object characteristics is less clear. Many cognitive scientists assume that object characteristics are extracted from proximal stimuli, but evidence for this remains ambiguous. For example, a dolphin may store 'sound templates' in its brain and identify whole objects by listening for a particular sound. Alternatively, a dolphin's brain may contain algorithms, derived through natural endowments or experience or both, which allow it to identify object characteristics based on sounds. The standard method used to address this question in many species is indirect and has led to equivocal results with dolphins. Here we outline an appropriate method and test it to show that dolphins extract object characteristics directly from echoes.

Nekton Interaction Monitoring System

DOE Office of Scientific and Technical Information (OSTI.GOV)

2017-03-15

The software provides a real-time processing system for sonar to detect and track animals, and to extract water column biomass statistics in order to facilitate continuous monitoring of an underwater environment. The Nekton Interaction Monitoring System (NIMS) extracts and archives tracking and backscatter statistics data from a real-time stream of data from a sonar device. NIMS also sends real-time tracking messages over the network that can be used by other systems to generate other metrics or to trigger instruments such as an optical video camera. A web-based user interface provides remote monitoring and control. NIMS currently supports three popular sonarmore » devices: M3 multi-beam sonar (Kongsberg), EK60 split-beam echo-sounder (Simrad) and BlueView acoustic camera (Teledyne).« less

Echolocating bats rely on audiovocal feedback to adapt sonar signal design.

PubMed

Luo, Jinhong; Moss, Cynthia F

2017-10-10

Many species of bat emit acoustic signals and use information carried by echoes reflecting from nearby objects to navigate and forage. It is widely documented that echolocating bats adjust the features of sonar calls in response to echo feedback; however, it remains unknown whether audiovocal feedback contributes to sonar call design. Audiovocal feedback refers to the monitoring of one's own vocalizations during call production and has been intensively studied in nonecholocating animals. Audiovocal feedback not only is a necessary component of vocal learning but also guides the control of the spectro-temporal structure of vocalizations. Here, we show that audiovocal feedback is directly involved in the echolocating bat's control of sonar call features. As big brown bats tracked targets from a stationary position, we played acoustic jamming signals, simulating calls of another bat, timed to selectively perturb audiovocal feedback or echo feedback. We found that the bats exhibited the largest call-frequency adjustments when the jamming signals occurred during vocal production. By contrast, bats did not show sonar call-frequency adjustments when the jamming signals coincided with the arrival of target echoes. Furthermore, bats rapidly adapted sonar call design in the first vocalization following the jamming signal, revealing a response latency in the range of 66 to 94 ms. Thus, bats, like songbirds and humans, rely on audiovocal feedback to structure sonar signal design.

Characteristics and Use of a Parametric End-Fired Array for Acoustics in Air

DTIC Science & Technology

2007-03-01

as a sonar application for underwater use. The vast majority of the research for parametric arrays was devoted to underwater applications until the...and also for the calibration of hydrophones and receivers for wide band sonar . All of the researchers mentioned above mainly focused their efforts on...features, which include very high directivity at low frequencies without unwanted side lobes. They are generally used as a wide band sonar system

Detecting the presence-absence of bluefin tuna by automated analysis of medium-range sonars on fishing vessels.

PubMed

Uranga, Jon; Arrizabalaga, Haritz; Boyra, Guillermo; Hernandez, Maria Carmen; Goñi, Nicolas; Arregui, Igor; Fernandes, Jose A; Yurramendi, Yosu; Santiago, Josu

2017-01-01

This study presents a methodology for the automated analysis of commercial medium-range sonar signals for detecting presence/absence of bluefin tuna (Tunnus thynnus) in the Bay of Biscay. The approach uses image processing techniques to analyze sonar screenshots. For each sonar image we extracted measurable regions and analyzed their characteristics. Scientific data was used to classify each region into a class ("tuna" or "no-tuna") and build a dataset to train and evaluate classification models by using supervised learning. The methodology performed well when validated with commercial sonar screenshots, and has the potential to automatically analyze high volumes of data at a low cost. This represents a first milestone towards the development of acoustic, fishery-independent indices of abundance for bluefin tuna in the Bay of Biscay. Future research lines and additional alternatives to inform stock assessments are also discussed.

Detecting the presence-absence of bluefin tuna by automated analysis of medium-range sonars on fishing vessels

PubMed Central

Uranga, Jon; Arrizabalaga, Haritz; Boyra, Guillermo; Hernandez, Maria Carmen; Goñi, Nicolas; Arregui, Igor; Fernandes, Jose A.; Yurramendi, Yosu; Santiago, Josu

2017-01-01

This study presents a methodology for the automated analysis of commercial medium-range sonar signals for detecting presence/absence of bluefin tuna (Tunnus thynnus) in the Bay of Biscay. The approach uses image processing techniques to analyze sonar screenshots. For each sonar image we extracted measurable regions and analyzed their characteristics. Scientific data was used to classify each region into a class (“tuna” or “no-tuna”) and build a dataset to train and evaluate classification models by using supervised learning. The methodology performed well when validated with commercial sonar screenshots, and has the potential to automatically analyze high volumes of data at a low cost. This represents a first milestone towards the development of acoustic, fishery-independent indices of abundance for bluefin tuna in the Bay of Biscay. Future research lines and additional alternatives to inform stock assessments are also discussed. PMID:28152032

The Dolphin Sonar: Excellent Capabilities In Spite of Some Mediocre Properties

NASA Astrophysics Data System (ADS)

Au, Whitlow W. L.

2004-11-01

Dolphin sonar research has been conducted for several decades and much has been learned about the capabilities of echolocating dolphins to detect, discriminate and recognize underwater targets. The results of these research projects suggest that dolphins possess the most sophisticated of all sonar for short ranges and shallow water where reverberation and clutter echoes are high. The critical feature of the dolphin sonar is the capability of discriminating and recognizing complex targets in a highly reverberant and noisy environment. The dolphin's detection threshold in reverberation occurs at a echo-to reverberation ratio of approximately 4 dB. Echolocating dolphins also have the capability to make fine discriminate of target properties such as wall thickness difference of water-filled cylinders and material differences in metallic plates. The high-resolution property of the animal's echolocation signals and the high dynamic range of its auditory system are important factors in their outstanding discrimination capabilities. In the wall thickness discrimination of cylinder experiment, time differences between echo highlights at small as 500-600 ns can be resolved by echolocating dolphins. Measurements of the targets used in the metallic plate composition experiment suggest that dolphins attended to echo components that were 20-30 dB below the maximum level for a specific target. It is interesting to realize that some of the properties of the dolphin sonar system are fairly mediocre, yet the total performance of the system is often outstanding. When compared to some technological sonar, the energy content of the dolphin sonar signal is not very high, the transmission and receiving beamwidths are fairly large, and the auditory filters are not very narrow. Yet the dolphin sonar has demonstrated excellent capabilities in spite the mediocre features of its "hardware." Reasons why dolphins can perform complex sonar task will be discussed in light of the "equipment" they possess.

Distant touch hydrodynamic imaging with an artificial lateral line.

PubMed

Yang, Yingchen; Chen, Jack; Engel, Jonathan; Pandya, Saunvit; Chen, Nannan; Tucker, Craig; Coombs, Sheryl; Jones, Douglas L; Liu, Chang

2006-12-12

Nearly all underwater vehicles and surface ships today use sonar and vision for imaging and navigation. However, sonar and vision systems face various limitations, e.g., sonar blind zones, dark or murky environments, etc. Evolved over millions of years, fish use the lateral line, a distributed linear array of flow sensing organs, for underwater hydrodynamic imaging and information extraction. We demonstrate here a proof-of-concept artificial lateral line system. It enables a distant touch hydrodynamic imaging capability to critically augment sonar and vision systems. We show that the artificial lateral line can successfully perform dipole source localization and hydrodynamic wake detection. The development of the artificial lateral line is aimed at fundamentally enhancing human ability to detect, navigate, and survive in the underwater environment.

Frequency organization and responses to complex sounds in the medial geniculate body of the mustached bat.

PubMed

Wenstrup, J J

1999-11-01

The auditory cortex of the mustached bat (Pteronotus parnellii) displays some of the most highly developed physiological and organizational features described in mammalian auditory cortex. This study examines response properties and organization in the medial geniculate body (MGB) that may contribute to these features of auditory cortex. About 25% of 427 auditory responses had simple frequency tuning with single excitatory tuning curves. The remainder displayed more complex frequency tuning using two-tone or noise stimuli. Most of these were combination-sensitive, responsive to combinations of different frequency bands within sonar or social vocalizations. They included FM-FM neurons, responsive to different harmonic elements of the frequency modulated (FM) sweep in the sonar signal, and H1-CF neurons, responsive to combinations of the bat's first sonar harmonic (H1) and a higher harmonic of the constant frequency (CF) sonar signal. Most combination-sensitive neurons (86%) showed facilitatory interactions. Neurons tuned to frequencies outside the biosonar range also displayed combination-sensitive responses, perhaps related to analyses of social vocalizations. Complex spectral responses were distributed throughout dorsal and ventral divisions of the MGB, forming a major feature of this bat's analysis of complex sounds. The auditory sector of the thalamic reticular nucleus also was dominated by complex spectral responses to sounds. The ventral division was organized tonotopically, based on best frequencies of singly tuned neurons and higher best frequencies of combination-sensitive neurons. Best frequencies were lowest ventrolaterally, increasing dorsally and then ventromedially. However, representations of frequencies associated with higher harmonics of the FM sonar signal were reduced greatly. Frequency organization in the dorsal division was not tonotopic; within the middle one-third of MGB, combination-sensitive responses to second and third harmonic CF sonar signals (60-63 and 90-94 kHz) occurred in adjacent regions. In the rostral one-third, combination-sensitive responses to second, third, and fourth harmonic FM frequency bands predominated. These FM-FM neurons, thought to be selective for delay between an emitted pulse and echo, showed some organization of delay selectivity. The organization of frequency sensitivity in the MGB suggests a major rewiring of the output of the central nucleus of the inferior colliculus, by which collicular neurons tuned to the bat's FM sonar signals mostly project to the dorsal, not the ventral, division. Because physiological differences between collicular and MGB neurons are minor, a major role of the tecto-thalamic projection in the mustached bat may be the reorganization of responses to provide for cortical representations of sonar target features.

Analysis of seafloor backscatter strength dependence on the survey azimuth using multibeam echosounder data

NASA Astrophysics Data System (ADS)

Lurton, Xavier; Eleftherakis, Dimitrios; Augustin, Jean-Marie

2018-06-01

The sediment backscatter strength measured by multibeam echosounders is a key feature for seafloor mapping either qualitative (image mosaics) or quantitative (extraction of classifying features). An important phenomenon, often underestimated, is the dependence of the backscatter level on the azimuth angle imposed by the survey line directions: strong level differences at varying azimuth can be observed in case of organized roughness of the seabed, usually caused by tide currents over sandy sediments. This paper presents a number of experimental results obtained from shallow-water cruises using a 300-kHz multibeam echosounder and specially dedicated to the study of this azimuthal effect, with a specific configuration of the survey strategy involving a systematic coverage of reference areas following "compass rose" patterns. The results show for some areas a very strong dependence of the backscatter level, up to about 10-dB differences at intermediate oblique angles, although the presence of these ripples cannot be observed directly—neither from the bathymetry data nor from the sonar image, due to the insufficient resolution capability of the sonar. An elementary modeling of backscattering from rippled interfaces explains and comforts these observations. The consequences of this backscatter dependence upon survey azimuth on the current strategies of backscatter data acquisition and exploitation are discussed.

Timing matters: sonar call groups facilitate target localization in bats.

PubMed

Kothari, Ninad B; Wohlgemuth, Melville J; Hulgard, Katrine; Surlykke, Annemarie; Moss, Cynthia F

2014-01-01

To successfully negotiate a cluttered environment, an echolocating bat must control the timing of motor behaviors in response to dynamic sensory information. Here we detail the big brown bat's adaptive temporal control over sonar call production for tracking prey, moving predictably or unpredictably, under different experimental conditions. We studied the adaptive control of vocal-motor behaviors in free-flying big brown bats, Eptesicus fuscus, as they captured tethered and free-flying insects, in open and cluttered environments. We also studied adaptive sonar behavior in bats trained to track moving targets from a resting position. In each of these experiments, bats adjusted the features of their calls to separate target and clutter. Under many task conditions, flying bats produced prominent sonar sound groups identified as clusters of echolocation pulses with relatively stable intervals, surrounded by longer pulse intervals. In experiments where bats tracked approaching targets from a resting position, bats also produced sonar sound groups, and the prevalence of these sonar sound groups increased when motion of the target was unpredictable. We hypothesize that sonar sound groups produced during flight, and the sonar call doublets produced by a bat tracking a target from a resting position, help the animal resolve dynamic target location and represent the echo scene in greater detail. Collectively, our data reveal adaptive temporal control over sonar call production that allows the bat to negotiate a complex and dynamic environment.

Timing matters: sonar call groups facilitate target localization in bats

PubMed Central

Kothari, Ninad B.; Wohlgemuth, Melville J.; Hulgard, Katrine; Surlykke, Annemarie; Moss, Cynthia F.

2014-01-01

To successfully negotiate a cluttered environment, an echolocating bat must control the timing of motor behaviors in response to dynamic sensory information. Here we detail the big brown bat's adaptive temporal control over sonar call production for tracking prey, moving predictably or unpredictably, under different experimental conditions. We studied the adaptive control of vocal-motor behaviors in free-flying big brown bats, Eptesicus fuscus, as they captured tethered and free-flying insects, in open and cluttered environments. We also studied adaptive sonar behavior in bats trained to track moving targets from a resting position. In each of these experiments, bats adjusted the features of their calls to separate target and clutter. Under many task conditions, flying bats produced prominent sonar sound groups identified as clusters of echolocation pulses with relatively stable intervals, surrounded by longer pulse intervals. In experiments where bats tracked approaching targets from a resting position, bats also produced sonar sound groups, and the prevalence of these sonar sound groups increased when motion of the target was unpredictable. We hypothesize that sonar sound groups produced during flight, and the sonar call doublets produced by a bat tracking a target from a resting position, help the animal resolve dynamic target location and represent the echo scene in greater detail. Collectively, our data reveal adaptive temporal control over sonar call production that allows the bat to negotiate a complex and dynamic environment. PMID:24860509

Adaptive bearing estimation and tracking of multiple targets in a realistic passive sonar scenario

NASA Astrophysics Data System (ADS)

Rajagopal, R.; Challa, Subhash; Faruqi, Farhan A.; Rao, P. R.

1997-06-01

In a realistic passive sonar environment, the received signal consists of multipath arrivals from closely separated moving targets. The signals are contaminated by spatially correlated noise. The differential MUSIC has been proposed to estimate the DOAs in such a scenario. This method estimates the 'noise subspace' in order to estimate the DOAs. However, the 'noise subspace' estimate has to be updated as and when new data become available. In order to save the computational costs, a new adaptive noise subspace estimation algorithm is proposed in this paper. The salient features of the proposed algorithm are: (1) Noise subspace estimation is done by QR decomposition of the difference matrix which is formed from the data covariance matrix. Thus, as compared to standard eigen-decomposition based methods which require O(N3) computations, the proposed method requires only O(N2) computations. (2) Noise subspace is updated by updating the QR decomposition. (3) The proposed algorithm works in a realistic sonar environment. In the second part of the paper, the estimated bearing values are used to track multiple targets. In order to achieve this, the nonlinear system/linear measurement extended Kalman filtering proposed is applied. Computer simulation results are also presented to support the theory.

Computer image processing in marine resource exploration

NASA Technical Reports Server (NTRS)

Paluzzi, P. R.; Normark, W. R.; Hess, G. R.; Hess, H. D.; Cruickshank, M. J.

1976-01-01

Pictographic data or imagery is commonly used in marine exploration. Pre-existing image processing techniques (software) similar to those used on imagery obtained from unmanned planetary exploration were used to improve marine photography and side-scan sonar imagery. Features and details not visible by conventional photo processing methods were enhanced by filtering and noise removal on selected deep-sea photographs. Information gained near the periphery of photographs allows improved interpretation and facilitates construction of bottom mosaics where overlapping frames are available. Similar processing techniques were applied to side-scan sonar imagery, including corrections for slant range distortion, and along-track scale changes. The use of digital data processing and storage techniques greatly extends the quantity of information that can be handled, stored, and processed.

Case Study of Using Resources about Sonar Operators To Teach Instructional Design.

ERIC Educational Resources Information Center

Mclellan, Hilary

1993-01-01

Describes a fictional account of the work of a submarine sonar operator ("The Hunt for Red October" by Tom Clancy) that captures the practitioner in a complex real-world work context featuring sophisticated electronic technologies. Describes how fiction can be adapted for and used as a basis for instructional design students to explore…

Sidescan-Sonar Imagery and Surficial Geologic Interpretations of the Sea Floor in Western Rhode Island Sound

USGS Publications Warehouse

McMullen, K.Y.; Poppe, L.J.; Haupt, T.A.; Crocker, J.M.

2009-01-01

The U.S. Geological Survey (USGS) and National Oceanic and Atmospheric Administration (NOAA) have been working together to interpret sea-floor geology along the northeastern coast of the United States. In 2004, the NOAA Ship RUDE completed survey H11322, a sidescan-sonar and bathymetric survey that covers about 60 square kilometers of the sea floor in western Rhode Island Sound. This report interprets sidescan-sonar and bathymetric data from NOAA survey H11322 to delineate sea-floor features and sedimentary environments in the study area. Paleozoic bedrock and Cretaceous Coastal Plain sediments in Rhode Island Sound underlie Pleistocene glacial drift that affects the distribution of surficial Holocene marine and transgressional sediments. The study area has three bathymetric highs separated by a channel system. Features and patterns in the sidescan-sonar imagery include low, moderate, and high backscatter; sand waves; scarps; erosional outliers; boulders; trawl marks; and dredge spoils. Four sedimentary environments in the study area, based on backscatter and bathymetric features, include those characterized by erosion or nondeposition, coarse-grained bedload transport, sorting and reworking, and deposition. Environments characterized by erosion or nondeposition and coarse-grained bedload transport are located in shallower areas and environments characterized by deposition are located in deeper areas; environments characterized by sorting and reworking processes are generally located at moderate depths.

Forming maps of targets having multiple reflectors with a biomimetic audible sonar.

PubMed

Kuc, Roman

2018-05-01

A biomimetic audible sonar mimics human echolocation by emitting clicks and sensing echoes binaurally to investigate the limitations in acoustic mapping of 2.5 dimensional targets. A monaural sonar that provides only echo time-of-flight values produces biased maps that lie outside the target surfaces. Reflector bearing estimates derived from the first echoes detected by a binaural sonar are employed to form unbiased maps. Multiple echoes from a target introduce phantom-reflector artifacts into its map because later echoes are produced by reflectors at bearings different from those determined from the first echoes. In addition, overlapping echoes interfere to produce bearing errors. Addressing the causes of these bearing errors motivates a processing approach that employs template matching to extract valid echoes. Interfering echoes can mimic a valid echo and also form PR artifacts. These artifacts are eliminated by recognizing the bearing fluctuations that characterize echo interference. Removing PR artifacts produces a map that resembles the physical target shape to within the resolution capabilities of the sonar. The remaining differences between the target shape and the final map are void artifacts caused by invalid or missing echoes.

Multiple Frequency Parametric Sonar

DTIC Science & Technology

2015-09-28

300003 1 MULTIPLE FREQUENCY PARAMETRIC SONAR STATEMENT OF GOVERNMENT INTEREST [0001] The invention described herein may be manufactured and...a method for increasing the bandwidth of a parametric sonar system by using multiple primary frequencies rather than only two primary frequencies...2) Description of Prior Art [0004] Parametric sonar generates narrow beams at low frequencies by projecting sound at two distinct primary

Sonar Detection and Classification of Underwater UXO and Environmental Parameters

DTIC Science & Technology

2012-07-09

For these targets, representations like those in Figs. 10 and 11 may be more useful because they focus on properties of the isolated target signal ... using time-frequency phenomena extracted from strong ROIs in target scattering data. In general, backscattered signals contain specular as well as...database of sonar target signals useful for developing and evaluating C/ID algorithms that separate UXO from bottom clutter and to look for and

Ultrasonic Methods for Human Motion Detection

DTIC Science & Technology

2006-10-01

contacts. The active method utilizes continuous wave ultrasonic Doppler sonar . Human motions have unique Doppler signatures and their combination...The present article reports results of human motion investigations with help of CW ultrasonic Doppler sonar . Low-cost, low-power ultrasonic motion...have been developed for operation in air [10]. Benefits of using ultrasonic CW Doppler sonar included the low-cost, low-electric noise, small size

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.

Evaluating the use of side-scan sonar for detecting freshwater mussel beds in turbid river environments

USGS Publications Warehouse

Powers, Jarrod; Brewer, Shannon K.; Long, James M.; Campbell, Thomas

2015-01-01

Side-scan sonar is a valuable tool for mapping habitat features in many aquatic systems suggesting it may also be useful for locating sedentary biota. The objective of this study was to determine if side-scan sonar could be used to identify freshwater mussel (unionid) beds and the required environmental conditions. We used side-scan sonar to develop a series of mussel-bed reference images by placing mussel shells within homogenous areas of fine and coarse substrates. We then used side-scan sonar to map a 32-km river reach during spring and summer. Using our mussel-bed reference images, several river locations were identified where mussel beds appeared to exist in the scanned images and we chose a subset of sites (n = 17) for field validation. The validation confirmed that ~60% of the sites had mussel beds and ~80% had some mussels or shells present. Water depth was significantly related to our ability to predict mussel-bed locations: predictive ability was greatest at depths of 1–2 m, but decreased in water >2-m deep. We determined side-scan sonar is an effective tool for preliminary assessments of mussel presence during times when they are located at or above the substrate surface and in relatively fine substrates excluding fine silt.

Top/bottom multisensor remote sensing of Arctic sea ice

NASA Technical Reports Server (NTRS)

Comiso, J. C.; Wadhams, P.; Krabill, W. B.; Swift, R. N.; Crawford, J. P.

1991-01-01

Results are presented on the Aircraft/Submarine Sea Ice Project experiment carried out in May 1987 to investigate concurrently the top and the bottom features of the Arctic sea-ice cover. Data were collected nearly simultaneously by instruments aboard two aircraft and a submarine, which included passive and active (SAR) microwave sensors, upward looking and sidescan sonars, a lidar profilometer, and an IR sensor. The results described fall into two classes of correlations: (1) quantitative correlations between profiles, such as ice draft (sonar), ice elevation (laser), SAR backscatter along the track line, and passive microwave brightness temperatures; and (2) qualitative and semiquantitative correlations between corresponding areas of imagery (i.e., passive microwave, AR, and sidescan sonar).

3D Chirp Sonar Images on Fluid Migration Pathways and Their Implications on Seafloor Stability East of the Fangliao Submarine Canyon Offshore SW Taiwan

NASA Astrophysics Data System (ADS)

Lu, Y. W.; Liu, C. S.; Su, C. C.; Hsu, H. H.; Chen, Y. H.

2015-12-01

This study utilizes both chirp sonar images and coring results to investigate the unstable seafloor strata east of the Fangliao Submarine Canyon offshore southwestern Taiwan. We have constructed 3D chirp sonar images from a densely surveyed block to trace the attitude of an acoustic transparent layer and features caused by fluid activities. Based on the distribution of this transparent layer and fluid-related features, we suggest that this transparent layer forms a pathway for fluid migration which induces fluid-related characters such as acoustic blanking and fluid chimneys in the 3D chirp sonar images. Cored seafloor samples are used in this study to investigate the sediment compositions. The 210Pb activity profiles of the cores show oscillating and unsteady values at about 20~25 cm from core top. The bulk densities of the core samples in the same section (about 20~25 cm from core top) give values lower than those at deeper parts of the cores. These results indicate that the water content is much higher in the shallow sediments than in the deeper strata. From core sample analyses, we deduce that the local sediments are disturbed by liquefaction. From the analyses of 3D chirp sonar images and core data, we suggest that the seafloor east of the Fangliao Submarine Canyon is in an unstable condition, if disturbed by earthquakes, submarine landslides and gravity flows could be easily triggered and cause some geohazards, like breaking submarine cables during the 2006 Pingtung earthquake event.

Biomarkers to Assess Possible Biological Effects on Reproductive Potential, Immune Function, and Energetic Fitness of Bottlenose Dolphins Exposed to Sounds Consistent with Naval Sonars

DTIC Science & Technology

2013-09-30

ion modes. The resulting chromatograms were then processed using XCMS (alignment and peak picking ). The data were processed with in-house...UHPLC liquid chromatography Fourier transform ion cyclotron resonance (FT-ICR) mass spectrometry ( MS ). A standard method was developed for rapid analysis...extraction protocols and then implemented LC- MS / MS analyses on our Thermo Fisher Scientific TSQ Vantage triple quadrupole mass spectrometer. This

50 CFR 218.232 - Permissible methods of taking.

Code of Federal Regulations, 2014 CFR

2014-10-01

... Low Frequency Active (SURTASS LFA) Sonar § 218.232 Permissible methods of taking. (a) Under Letters of.... This annual per-stock cap of 12 percent applies regardless of the number of SURTASS LFA sonar vessels...

50 CFR 218.232 - Permissible methods of taking.

Code of Federal Regulations, 2012 CFR

2012-10-01

... Low Frequency Active (SURTASS LFA) Sonar § 218.232 Permissible methods of taking. (a) Under Letters of.... This annual per-stock cap of 12 percent applies regardless of the number of SURTASS LFA sonar vessels...

Validation of a side-scan sonar method for quantifying walleye spawning habitat availability in the littoral zone of northern Wisconsin Lakes

USGS Publications Warehouse

Richter, Jacob T.; Sloss, Brian L.; Isermann, Daniel A.

2016-01-01

Previous research has generally ignored the potential effects of spawning habitat availability and quality on recruitment of Walleye Sander vitreus, largely because information on spawning habitat is lacking for many lakes. Furthermore, traditional transect-based methods used to describe habitat are time and labor intensive. Our objectives were to determine if side-scan sonar could be used to accurately classify Walleye spawning habitat in the nearshore littoral zone and provide lakewide estimates of spawning habitat availability similar to estimates obtained from a transect–quadrat-based method. Based on assessments completed on 16 northern Wisconsin lakes, interpretation of side-scan sonar images resulted in correct identification of substrate size-class for 93% (177 of 191) of selected locations and all incorrect classifications were within ± 1 class of the correct substrate size-class. Gravel, cobble, and rubble substrates were incorrectly identified from side-scan images in only two instances (1% misclassification), suggesting that side-scan sonar can be used to accurately identify preferred Walleye spawning substrates. Additionally, we detected no significant differences in estimates of lakewide littoral zone substrate compositions estimated using side-scan sonar and a traditional transect–quadrat-based method. Our results indicate that side-scan sonar offers a practical, accurate, and efficient technique for assessing substrate composition and quantifying potential Walleye spawning habitat in the nearshore littoral zone of north temperate lakes.

Linear Scour Depressions or Bedforms? Using Interferometric Sonar to Investigate Nearshore Sediment Transport

NASA Astrophysics Data System (ADS)

Borrelli, M.; Giese, G. S.; Dingman, S. L.; Gontz, A. M.; Adams, M. B.; Norton, A. R.; Brown, T. L.

2011-12-01

A series of ambiguous features on the seafloor off the coast of Provincetown, Massachusetts USA has been identified in two bathymetric lidar surveys (2007, 2010) conducted by the US Army Corps of Engineers. Similar features in the area have been described as linear scour depressions by other investigators, but at deeper water depths. These features exhibit some of the characteristics of bedforms, they have migrated tens of meters and maintained similar 3 dimensional morphologies. However, what would be described as the slipface more closely resembles the updrift face of a linear scour depression. The features are in relatively shallow water (9 - 15 m), are 150 - 200 m long, have spacings of 100 - 150 m and are 5-6 m in height. Further investigations are being undertaken to better understand these features and nearshore sediment transport in the area. The features appear along a high energy, accreting coast with both strong wave-driven sediment flux and tidal currents. Mapping of the study area with an interferometric sonar system, which collects coincident swath bathymetry and acoustic backscatter imagery, is ongoing. Interferometric sonar increases bathymetric swath width to depth ratios, in comparison to multibeam systems, and expedites data collection by reducing costs, vessel-time and hazards associated with navigating shallow waters. In addition, sediment grab samples and a series of seismic reflection profiles will also be collected in the area to ground-truth acoustic imagery and provide a subsurface framework for the features, respectively. These datasets will allow investigators to better document bottom conditions, estimate flow velocities needed to create these features and improve our understanding of sediment transport processes and pathways in the area.

Relationships between autofocus methods for SAR and self-survey techniques for SONAR. [Synthetic Aperture Radar (SAR)

DOE Office of Scientific and Technical Information (OSTI.GOV)

Wahl, D.E.; Jakowatz, C.V. Jr.; Ghiglia, D.C.

1991-01-01

Autofocus methods in SAR and self-survey techniques in SONAR have a common mathematical basis in that they both involve estimation and correction of phase errors introduced by sensor position uncertainties. Time delay estimation and correlation methods have been shown to be effective in solving the self-survey problem for towed SONAR arrays. Since it can be shown that platform motion errors introduce similar time-delay estimation problems in SAR imaging, the question arises as to whether such techniques could be effectively employed for autofocus of SAR imagery. With a simple mathematical model for motion errors in SAR, we will show why suchmore » correlation/time-delay techniques are not nearly as effective as established SAR autofocus algorithms such as phase gradient autofocus or sub-aperture based methods. This analysis forms an important bridge between signal processing methodologies for SAR and SONAR. 5 refs., 4 figs.« less

Automated detection of submerged navigational obstructions in freshwater impoundments with hull mounted sidescan sonar

NASA Astrophysics Data System (ADS)

Morris, Phillip A.

The prevalence of low-cost side scanning sonar systems mounted on small recreational vessels has created improved opportunities to identify and map submerged navigational hazards in freshwater impoundments. However, these economical sensors also present unique challenges for automated techniques. This research explores related literature in automated sonar imagery processing and mapping technology, proposes and implements a framework derived from these sources, and evaluates the approach with video collected from a recreational grade sonar system. Image analysis techniques including optical character recognition and an unsupervised computer automated detection (CAD) algorithm are employed to extract the transducer GPS coordinates and slant range distance of objects protruding from the lake bottom. The retrieved information is formatted for inclusion into a spatial mapping model. Specific attributes of the sonar sensors are modeled such that probability profiles may be projected onto a three dimensional gridded map. These profiles are computed from multiple points of view as sonar traces crisscross or come near each other. As lake levels fluctuate over time so do the elevation points of view. With each sonar record, the probability of a hazard existing at certain elevations at the respective grid points is updated with Bayesian mechanics. As reinforcing data is collected, the confidence of the map improves. Given a lake's current elevation and a vessel draft, a final generated map can identify areas of the lake that have a high probability of containing hazards that threaten navigation. The approach is implemented in C/C++ utilizing OpenCV, Tesseract OCR, and QGIS open source software and evaluated in a designated test area at Lake Lavon, Collin County, Texas.

Center for Neural Engineering: applications of pulse-coupled neural networks

NASA Astrophysics Data System (ADS)

Malkani, Mohan; Bodruzzaman, Mohammad; Johnson, John L.; Davis, Joel

1999-03-01

Pulsed-Coupled Neural Network (PCNN) is an oscillatory model neural network where grouping of cells and grouping among the groups that form the output time series (number of cells that fires in each input presentation also called `icon'). This is based on the synchronicity of oscillations. Recent work by Johnson and others demonstrated the functional capabilities of networks containing such elements for invariant feature extraction using intensity maps. PCNN thus presents itself as a more biologically plausible model with solid functional potential. This paper will present the summary of several projects and their results where we successfully applied PCNN. In project one, the PCNN was applied for object recognition and classification through a robotic vision system. The features (icons) generated by the PCNN were then fed into a feedforward neural network for classification. In project two, we developed techniques for sensory data fusion. The PCNN algorithm was implemented and tested on a B14 mobile robot. The PCNN-based features were extracted from the images taken from the robot vision system and used in conjunction with the map generated by data fusion of the sonar and wheel encoder data for the navigation of the mobile robot. In our third project, we applied the PCNN for speaker recognition. The spectrogram image of speech signals are fed into the PCNN to produce invariant feature icons which are then fed into a feedforward neural network for speaker identification.

Input Decimated Ensembles

NASA Technical Reports Server (NTRS)

Tumer, Kagan; Oza, Nikunj C.; Clancy, Daniel (Technical Monitor)

2001-01-01

Using an ensemble of classifiers instead of a single classifier has been shown to improve generalization performance in many pattern recognition problems. However, the extent of such improvement depends greatly on the amount of correlation among the errors of the base classifiers. Therefore, reducing those correlations while keeping the classifiers' performance levels high is an important area of research. In this article, we explore input decimation (ID), a method which selects feature subsets for their ability to discriminate among the classes and uses them to decouple the base classifiers. We provide a summary of the theoretical benefits of correlation reduction, along with results of our method on two underwater sonar data sets, three benchmarks from the Probenl/UCI repositories, and two synthetic data sets. The results indicate that input decimated ensembles (IDEs) outperform ensembles whose base classifiers use all the input features; randomly selected subsets of features; and features created using principal components analysis, on a wide range of domains.

Shallow water benthic imaging and substrate characterization using recreational-grade sidescan-sonar

USGS Publications Warehouse

Buscombe, Daniel D.

2017-01-01

In recent years, lightweight, inexpensive, vessel-mounted ‘recreational grade’ sonar systems have rapidly grown in popularity among aquatic scientists, for swath imaging of benthic substrates. To promote an ongoing ‘democratization’ of acoustical imaging of shallow water environments, methods to carry out geometric and radiometric correction and georectification of sonar echograms are presented, based on simplified models for sonar-target geometry and acoustic backscattering and attenuation in shallow water. Procedures are described for automated removal of the acoustic shadows, identification of bed-water interface for situations when the water is too turbid or turbulent for reliable depth echosounding, and for automated bed substrate classification based on singlebeam full-waveform analysis. These methods are encoded in an open-source and freely-available software package, which should further facilitate use of recreational-grade sidescan sonar, in a fully automated and objective manner. The sequential correction, mapping, and analysis steps are demonstrated using a data set from a shallow freshwater environment.

Processing and Analysis of Multibeam Sonar Data and Images near the Yellow River Estuary

NASA Astrophysics Data System (ADS)

Tang, Q.

2017-12-01

Yellow River Estuary is a typical high-suspended particulate matter estuary in the world. A lot of sediments from Yellow River and other substances produced by human activity cause high-concentration suspended matter and depositional system in the estuary and adjacent water area. Multibeam echo sounder (MBES) was developed in the 1970s, and it not only provided high-precision bathymetric data, but also provided seabed backscatter strength data and water column data with high temporal and spatial resolution. Here, based on high-precision sonar data of the seabed and water column collected by SeaBat7125 MBES system near the Yellow River Estuary, we use advanced data and image processing methods to generate seabed sonar images and water suspended particulate matter acoustic images. By analyzing these data and images, we get a lot of details of the seabed and whole water column features, and we also acquire their shape, size and basic physical characteristics of suspended particulate matters in the experiment area near the Yellow River Estuary. This study shows great potential for monitoring suspended particulate matter use MBES, and the research results will contribute to a comprehensive understanding of sediment transportation, evolution of river trough and shoal in Yellow River Estuary.

An investigation of acoustic beam patterns for the sonar localization problem using a beam based method.

PubMed

Guarato, Francesco; Windmill, James; Gachagan, Anthony; Harvey, Gerald

2013-06-01

Target localization can be accomplished through an ultrasonic sonar system equipped with an emitter and two receivers. Time of flight of the sonar echoes allows the calculation of the distance of the target. The orientation can be estimated from knowledge of the beam pattern of the receivers and the ratio, in the frequency domain, between the emitted and the received signals after compensation for distance effects and air absorption. The localization method is described and, as its performance strongly depends on the beam pattern, the search of the most appropriate sonar receiver in order to ensure the highest accuracy of target orientation estimations is developed in this paper. The structure designs considered are inspired by the ear shapes of some bat species. Parameters like flare rate, truncation angle, and tragus are considered in the design of the receiver structures. Simulations of the localization method allow us to state which combination of those parameters could provide the best real world implementation. Simulation results show the estimates of target orientations are, in the worst case, 2° with SNR = 50 dB using the receiver structure chosen for a potential practical implementation of a sonar system.

Geometry and topology of the space of sonar target echos.

PubMed

Robinson, Michael; Fennell, Sean; DiZio, Brian; Dumiak, Jennifer

2018-03-01

Successful synthetic aperture sonar target classification depends on the "shape" of the scatterers within a target signature. This article presents a workflow that computes a target-to-target distance from persistence diagrams, since the "shape" of a signature informs its persistence diagram in a structure-preserving way. The target-to-target distances derived from persistence diagrams compare favorably against those derived from spectral features and have the advantage of being substantially more compact. While spectral features produce clusters associated to each target type that are reasonably dense and well formed, the clusters are not well-separated from one another. In rather dramatic contrast, a distance derived from persistence diagrams results in highly separated clusters at the expense of some misclassification of outliers.

Implementation and testing of a Deep Water Correlation Velocity Sonar

DOE Office of Scientific and Technical Information (OSTI.GOV)

Dickey, F.R.; Bookheimer, W.C.; Rhoades, K.W.

1983-05-01

The paper describes a new sonar designated the Magnavox MX 810 Deep Water Correlation Sonar which is under development by the General Electric Company and the Magnavox Advanced Products and Systems Company. The sonar measures ship's velocity relative to the bottom but instead of using the conventional doppler effect, it uses the correlation method described by Dickey and Edward in 1978. In this method, the narrow beams required for doppler are not needed and a low frequency that penetrates to the bottom in deep water is used. The sonar was designed with the constraint that it use a transducer thatmore » mounts through a single 12 inch gate valve. Most offshore geophysical surveys at present make use of an integrated navigation system with bottom referenced velocity input from a doppler sonar which, because of limitations on the sonar bottomtracking range, has difficulty in areas where the water depth is greater than about 500 meters. The MX 810 provides bottom tracking in regions of much greater water depth. It also may be applied as an aid in continuous positioning of a vessel over a fixed location. It also should prove useful as a more general navigation aid. The sonar is undergoing a series of tests using Magnavox's facilities for the purpose of verifying the performance and obtaining data to support and quantify planned improvements in both software and hardware. A prototype transducer of only 5 watts power output was used, but in spite of this low power, successful operation to depths of 1900 meters was obtained. Extrapolation to system parameters to be implemented in production models predicts operation to depths of 5000 meters.« less

Automated target classification in high resolution dual frequency sonar imagery

NASA Astrophysics Data System (ADS)

Aridgides, Tom; Fernández, Manuel

2007-04-01

An improved computer-aided-detection / computer-aided-classification (CAD/CAC) processing string has been developed. The classified objects of 2 distinct strings are fused using the classification confidence values and their expansions as features, and using "summing" or log-likelihood-ratio-test (LLRT) based fusion rules. The utility of the overall processing strings and their fusion was demonstrated with new high-resolution dual frequency sonar imagery. Three significant fusion algorithm improvements were made. First, a nonlinear 2nd order (Volterra) feature LLRT fusion algorithm was developed. Second, a Box-Cox nonlinear feature LLRT fusion algorithm was developed. The Box-Cox transformation consists of raising the features to a to-be-determined power. Third, a repeated application of a subset feature selection / feature orthogonalization / Volterra feature LLRT fusion block was utilized. It was shown that cascaded Volterra feature LLRT fusion of the CAD/CAC processing strings outperforms summing, baseline single-stage Volterra and Box-Cox feature LLRT algorithms, yielding significant improvements over the best single CAD/CAC processing string results, and providing the capability to correctly call the majority of targets while maintaining a very low false alarm rate. Additionally, the robustness of cascaded Volterra feature fusion was demonstrated, by showing that the algorithm yields similar performance with the training and test sets.

Accurate facade feature extraction method for buildings from three-dimensional point cloud data considering structural information

NASA Astrophysics Data System (ADS)

Wang, Yongzhi; Ma, Yuqing; Zhu, A.-xing; Zhao, Hui; Liao, Lixia

2018-05-01

Facade features represent segmentations of building surfaces and can serve as a building framework. Extracting facade features from three-dimensional (3D) point cloud data (3D PCD) is an efficient method for 3D building modeling. By combining the advantages of 3D PCD and two-dimensional optical images, this study describes the creation of a highly accurate building facade feature extraction method from 3D PCD with a focus on structural information. The new extraction method involves three major steps: image feature extraction, exploration of the mapping method between the image features and 3D PCD, and optimization of the initial 3D PCD facade features considering structural information. Results show that the new method can extract the 3D PCD facade features of buildings more accurately and continuously. The new method is validated using a case study. In addition, the effectiveness of the new method is demonstrated by comparing it with the range image-extraction method and the optical image-extraction method in the absence of structural information. The 3D PCD facade features extracted by the new method can be applied in many fields, such as 3D building modeling and building information modeling.

Seismic Interface Waves in Coastal Waters: A Review

DTIC Science & Technology

1980-11-15

Being at the low- 4 frequency end of classical sonar activity and at the high-frequency end of seismic research, the propagation of infrasonic energy...water areas. Certainly this and other seismic detection methods will never replace the highly-developed sonar techniques but in coastal waters they...for many sonar purposes [5, 85 to 90) shows that very simple bottom models may already be sufficient to make allowance for the influence of the sea

Application of musical timbre discrimination features to active sonar classification

NASA Astrophysics Data System (ADS)

Young, Victor W.; Hines, Paul C.; Pecknold, Sean

2005-04-01

In musical acoustics significant effort has been devoted to uncovering the physical basis of timbre perception. Most investigations into timbre rely on multidimensional scaling (MDS), in which different musical sounds are arranged as points in multidimensional space. The Euclidean distance between points corresponds to the perceptual distance between sounds and the multidimensional axes are linked to measurable properties of the sounds. MDS has identified numerous temporal and spectral features believed to be important to timbre perception. There is reason to believe that some of these features may have wider application in the disparate field of underwater acoustics, since anecdotal evidence suggests active sonar returns from metallic objects sound different than natural clutter returns when auralized by human operators. This is particularly encouraging since attempts to develop robust automatic classifiers capable of target-clutter discrimination over a wide range of operational conditions have met with limited success. Spectral features relevant to target-clutter discrimination are believed to include click-pitch and envelope irregularity; relevant temporal features are believed to include duration, sub-band attack/decay time, and time separation pitch. Preliminary results from an investigation into the role of these timbre features in target-clutter discrimination will be presented. [Work supported by NSERC and GDC.

Feature extraction for document text using Latent Dirichlet Allocation

NASA Astrophysics Data System (ADS)

Prihatini, P. M.; Suryawan, I. K.; Mandia, IN

2018-01-01

Feature extraction is one of stages in the information retrieval system that used to extract the unique feature values of a text document. The process of feature extraction can be done by several methods, one of which is Latent Dirichlet Allocation. However, researches related to text feature extraction using Latent Dirichlet Allocation method are rarely found for Indonesian text. Therefore, through this research, a text feature extraction will be implemented for Indonesian text. The research method consists of data acquisition, text pre-processing, initialization, topic sampling and evaluation. The evaluation is done by comparing Precision, Recall and F-Measure value between Latent Dirichlet Allocation and Term Frequency Inverse Document Frequency KMeans which commonly used for feature extraction. The evaluation results show that Precision, Recall and F-Measure value of Latent Dirichlet Allocation method is higher than Term Frequency Inverse Document Frequency KMeans method. This shows that Latent Dirichlet Allocation method is able to extract features and cluster Indonesian text better than Term Frequency Inverse Document Frequency KMeans method.

Side-scan sonar imaging of the Colorado River, Grand Canyon

USGS Publications Warehouse

Anima, Roberto; Wong, Florence L.; Hogg, David; Galanis, Peter

2007-01-01

This paper presents data collection methods and side-scan sonar data collected along the Colorado River in Grand Canyon in August and September of 2000. The purpose of the data collection effort was to image the distribution of sand between Glen Canyon Dam and river mile 87.4 before and after the 31,600 cfs flow of September 6-8. The side-scan sonar imaging focused on pools between rapids but included smaller rapids where possible.

Distribution of an Acoustic Scattering Layer, Petermann Fjord, Northwest Greenland

NASA Astrophysics Data System (ADS)

Heffron, E.; Mayer, L. A.; Jakobsson, M.; Hogan, K.; Jerram, K.

2017-12-01

The Petermann 2015 Expedition was a comprehensive paleoceanographic and paleoclimatological study of the marine-terminating Petermann Glacier and its outlet system in Northwest Greenland carried out July-August 2015. The purpose was the reconstruction of glacial history and current glacial processes in Petermann Fjord to better understand the fate of the Petermann Glacier and its floating ice tongue that acts as a critical buttressing force to the outlet glacier draining about 4% of the Greenland Ice Sheet. Seafloor mapping was a critical component of the study and an EM122 multibeam sonar was utilized for this purpose; additionally, water column data were acquired with this sonar and an EK80 split-beam echosounder. During the expedition, the mapping team noted an acoustic scattering layer in the EK80 and EM122 water column data which was observed to change depth in a spatially consistent manner that appeared to be related to location. Initial onboard processing revealed what appears to be a strong spatial coherence in the layer distribution that corresponds to our understanding of the complex circulation pattern in the study area, including inflow of warmer Atlantic waters and outflow of subglacial waters. This initial processing was limited to observations at 46 discrete locations that corresponded to CTD stations, a very small subset of the 4800 line kilometers of data collected by each sonar. Both sonars were run 24 hours per day over the 30-day expedition, providing continuous time-varying acoustic coverage of the study area. Post-cruise additional data has been processed to extract the acoustic returns from the scattering layer using a combination of commercial sonar processing software and specialized MATLAB and Python routines. 3-D surfaces have been generated from the extracted points in order to visualize the continuous spatial and temporal distribution of the scattering layer across the entire study area. Multiple crossings of the same location at different times of day address the question of the temporal stability of the scattering layer while the detailed map of the spatial distribution demonstrates the relationship of the scattering layer to the water masses and implies that continuous acoustic coverage may be a powerful proxy for oceanography.

Target-depth estimation in active sonar: Cramer-Rao bounds for a bilinear sound-speed profile.

PubMed

Mours, Alexis; Ioana, Cornel; Mars, Jérôme I; Josso, Nicolas F; Doisy, Yves

2016-09-01

This paper develops a localization method to estimate the depth of a target in the context of active sonar, at long ranges. The target depth is tactical information for both strategy and classification purposes. The Cramer-Rao lower bounds for the target position as range and depth are derived for a bilinear profile. The influence of sonar parameters on the standard deviations of the target range and depth are studied. A localization method based on ray back-propagation with a probabilistic approach is then investigated. Monte-Carlo simulations applied to a summer Mediterranean sound-speed profile are performed to evaluate the efficiency of the estimator. This method is finally validated on data in an experimental tank.

Multiresolution 3-D reconstruction from side-scan sonar images.

PubMed

Coiras, Enrique; Petillot, Yvan; Lane, David M

2007-02-01

In this paper, a new method for the estimation of seabed elevation maps from side-scan sonar images is presented. The side-scan image formation process is represented by a Lambertian diffuse model, which is then inverted by a multiresolution optimization procedure inspired by expectation-maximization to account for the characteristics of the imaged seafloor region. On convergence of the model, approximations for seabed reflectivity, side-scan beam pattern, and seabed altitude are obtained. The performance of the system is evaluated against a real structure of known dimensions. Reconstruction results for images acquired by different sonar sensors are presented. Applications to augmented reality for the simulation of targets in sonar imagery are also discussed.

Research on the feature extraction and pattern recognition of the distributed optical fiber sensing signal

NASA Astrophysics Data System (ADS)

Wang, Bingjie; Sun, Qi; Pi, Shaohua; Wu, Hongyan

2014-09-01

In this paper, feature extraction and pattern recognition of the distributed optical fiber sensing signal have been studied. We adopt Mel-Frequency Cepstral Coefficient (MFCC) feature extraction, wavelet packet energy feature extraction and wavelet packet Shannon entropy feature extraction methods to obtain sensing signals (such as speak, wind, thunder and rain signals, etc.) characteristic vectors respectively, and then perform pattern recognition via RBF neural network. Performances of these three feature extraction methods are compared according to the results. We choose MFCC characteristic vector to be 12-dimensional. For wavelet packet feature extraction, signals are decomposed into six layers by Daubechies wavelet packet transform, in which 64 frequency constituents as characteristic vector are respectively extracted. In the process of pattern recognition, the value of diffusion coefficient is introduced to increase the recognition accuracy, while keeping the samples for testing algorithm the same. Recognition results show that wavelet packet Shannon entropy feature extraction method yields the best recognition accuracy which is up to 97%; the performance of 12-dimensional MFCC feature extraction method is less satisfactory; the performance of wavelet packet energy feature extraction method is the worst.

Comparative analysis of feature extraction methods in satellite imagery

NASA Astrophysics Data System (ADS)

Karim, Shahid; Zhang, Ye; Asif, Muhammad Rizwan; Ali, Saad

2017-10-01

Feature extraction techniques are extensively being used in satellite imagery and getting impressive attention for remote sensing applications. The state-of-the-art feature extraction methods are appropriate according to the categories and structures of the objects to be detected. Based on distinctive computations of each feature extraction method, different types of images are selected to evaluate the performance of the methods, such as binary robust invariant scalable keypoints (BRISK), scale-invariant feature transform, speeded-up robust features (SURF), features from accelerated segment test (FAST), histogram of oriented gradients, and local binary patterns. Total computational time is calculated to evaluate the speed of each feature extraction method. The extracted features are counted under shadow regions and preprocessed shadow regions to compare the functioning of each method. We have studied the combination of SURF with FAST and BRISK individually and found very promising results with an increased number of features and less computational time. Finally, feature matching is conferred for all methods.

Multibeam Sonar Backscatter Data Acquisition and Processing: Guidelines and Recommendations from the GEOHAB Backscatter Working Group

NASA Astrophysics Data System (ADS)

Heffron, E.; Lurton, X.; Lamarche, G.; Brown, C.; Lucieer, V.; Rice, G.; Schimel, A.; Weber, T.

2015-12-01

Backscatter data acquired with multibeam sonars are now commonly used for the remote geological interpretation of the seabed. The systems hardware, software, and processing methods and tools have grown in numbers and improved over the years, yet many issues linger: there are no standard procedures for acquisition, poor or absent calibration, limited understanding and documentation of processing methods, etc. A workshop organized at the GeoHab (a community of geoscientists and biologists around the topic of marine habitat mapping) annual meeting in 2013 was dedicated to seafloor backscatter data from multibeam sonars and concluded that there was an overwhelming need for better coherence and agreement on the topics of acquisition, processing and interpretation of data. The GeoHab Backscatter Working Group (BSWG) was subsequently created with the purpose of documenting and synthetizing the state-of-the-art in sensors and techniques available today and proposing methods for best practice in the acquisition and processing of backscatter data. Two years later, the resulting document "Backscatter measurements by seafloor-mapping sonars: Guidelines and Recommendations" was completed1. The document provides: An introduction to backscatter measurements by seafloor-mapping sonars; A background on the physical principles of sonar backscatter; A discussion on users' needs from a wide spectrum of community end-users; A review on backscatter measurement; An analysis of best practices in data acquisition; A review of data processing principles with details on present software implementation; and finally A synthesis and key recommendations. This presentation reviews the BSWG mandate, structure, and development of this document. It details the various chapter contents, its recommendations to sonar manufacturers, operators, data processing software developers and end-users and its implication for the marine geology community. 1: Downloadable at https://www.niwa.co.nz/coasts-and-oceans/research-projects/backscatter-measurement-guidelines

Automated Point Cloud Correspondence Detection for Underwater Mapping Using AUVs

NASA Technical Reports Server (NTRS)

Hammond, Marcus; Clark, Ashley; Mahajan, Aditya; Sharma, Sumant; Rock, Stephen

2015-01-01

An algorithm for automating correspondence detection between point clouds composed of multibeam sonar data is presented. This allows accurate initialization for point cloud alignment techniques even in cases where accurate inertial navigation is not available, such as iceberg profiling or vehicles with low-grade inertial navigation systems. Techniques from computer vision literature are used to extract, label, and match keypoints between "pseudo-images" generated from these point clouds. Image matches are refined using RANSAC and information about the vehicle trajectory. The resulting correspondences can be used to initialize an iterative closest point (ICP) registration algorithm to estimate accumulated navigation error and aid in the creation of accurate, self-consistent maps. The results presented use multibeam sonar data obtained from multiple overlapping passes of an underwater canyon in Monterey Bay, California. Using strict matching criteria, the method detects 23 between-swath correspondence events in a set of 155 pseudo-images with zero false positives. Using less conservative matching criteria doubles the number of matches but introduces several false positive matches as well. Heuristics based on known vehicle trajectory information are used to eliminate these.

A Mobile Robot Sonar System with Obstacle Avoidance.

DTIC Science & Technology

1994-03-01

WITH OBSTACLE - AVOIDANCE __ by __ Patrick Gerard Byrne March 1994 Thesis Advisor : Yutaka Kanayama Approved for public release; distribution is...point p is on a line L whose normal has an orientation a and whose distance from the origin is r (Figure 5). This method has an advantage in expressing...sonar(FRONTR); Wine(&pl); while(hitl I >’- 100.0 11 hitl 1 - 0.0 ){ hitl I = sonar(FRONTR); I skipO; line(&p3); gat- robO (&posit 1); while(positl.x

Sea-floor morphology and sedimentary environments in western Block Island Sound, offshore of Fishers Island, New York

USGS Publications Warehouse

McMullen, Katherine Y.; Poppe, Lawrence J.; Danforth, William W.; Blackwood, Dann S.; Winner, William G.; Parker, Castle E.

2015-01-01

Multibeam-bathymetric and sidescan-sonar data, collected by the National Oceanic and Atmospheric Administration in a 114-square-kilometer area of Block Island Sound, southeast of Fishers Island, New York, are combined with sediment samples and bottom photography collected by the U.S. Geological Survey from 36 stations in this area in order to interpret sea-floor features and sedimentary environments. These interpretations and datasets provide base maps for studies on benthic ecology and resource management. The geologic features and sedimentary environments on the sea floor are products of the area’s glacial history and modern processes. These features include bedrock, drumlins, boulders, cobbles, large current-scoured bathymetric depressions, obstacle marks, and glaciolacustrine sediments found in high-energy sedimentary environments of erosion or nondeposition; and sand waves and megaripples in sedimentary environments characterized by coarse-grained bedload transport. Trawl marks are preserved in lower energy environments of sorting and reworking. This report releases the multibeam-bathymetric, sidescan-sonar, sediment, and photographic data and interpretations of the features and sedimentary environments in Block Island Sound, offshore Fishers Island.

Robust and fast-converging level set method for side-scan sonar image segmentation

NASA Astrophysics Data System (ADS)

Liu, Yan; Li, Qingwu; Huo, Guanying

2017-11-01

A robust and fast-converging level set method is proposed for side-scan sonar (SSS) image segmentation. First, the noise in each sonar image is removed using the adaptive nonlinear complex diffusion filter. Second, k-means clustering is used to obtain the initial presegmentation image from the denoised image, and then the distance maps of the initial contours are reinitialized to guarantee the accuracy of the numerical calculation used in the level set evolution. Finally, the satisfactory segmentation is achieved using a robust variational level set model, where the evolution control parameters are generated by the presegmentation. The proposed method is successfully applied to both synthetic image with speckle noise and real SSS images. Experimental results show that the proposed method needs much less iteration and therefore is much faster than the fuzzy local information c-means clustering method, the level set method using a gamma observation model, and the enhanced region-scalable fitting method. Moreover, the proposed method can usually obtain more accurate segmentation results compared with other methods.

Multi-Stage System for Automatic Target Recognition

NASA Technical Reports Server (NTRS)

Chao, Tien-Hsin; Lu, Thomas T.; Ye, David; Edens, Weston; Johnson, Oliver

2010-01-01

A multi-stage automated target recognition (ATR) system has been designed to perform computer vision tasks with adequate proficiency in mimicking human vision. The system is able to detect, identify, and track targets of interest. Potential regions of interest (ROIs) are first identified by the detection stage using an Optimum Trade-off Maximum Average Correlation Height (OT-MACH) filter combined with a wavelet transform. False positives are then eliminated by the verification stage using feature extraction methods in conjunction with neural networks. Feature extraction transforms the ROIs using filtering and binning algorithms to create feature vectors. A feedforward back-propagation neural network (NN) is then trained to classify each feature vector and to remove false positives. The system parameter optimizations process has been developed to adapt to various targets and datasets. The objective was to design an efficient computer vision system that can learn to detect multiple targets in large images with unknown backgrounds. Because the target size is small relative to the image size in this problem, there are many regions of the image that could potentially contain the target. A cursory analysis of every region can be computationally efficient, but may yield too many false positives. On the other hand, a detailed analysis of every region can yield better results, but may be computationally inefficient. The multi-stage ATR system was designed to achieve an optimal balance between accuracy and computational efficiency by incorporating both models. The detection stage first identifies potential ROIs where the target may be present by performing a fast Fourier domain OT-MACH filter-based correlation. Because threshold for this stage is chosen with the goal of detecting all true positives, a number of false positives are also detected as ROIs. The verification stage then transforms the regions of interest into feature space, and eliminates false positives using an artificial neural network classifier. The multi-stage system allows tuning the detection sensitivity and the identification specificity individually in each stage. It is easier to achieve optimized ATR operation based on its specific goal. The test results show that the system was successful in substantially reducing the false positive rate when tested on a sonar and video image datasets.

Text feature extraction based on deep learning: a review.

PubMed

Liang, Hong; Sun, Xiao; Sun, Yunlei; Gao, Yuan

2017-01-01

Selection of text feature item is a basic and important matter for text mining and information retrieval. Traditional methods of feature extraction require handcrafted features. To hand-design, an effective feature is a lengthy process, but aiming at new applications, deep learning enables to acquire new effective feature representation from training data. As a new feature extraction method, deep learning has made achievements in text mining. The major difference between deep learning and conventional methods is that deep learning automatically learns features from big data, instead of adopting handcrafted features, which mainly depends on priori knowledge of designers and is highly impossible to take the advantage of big data. Deep learning can automatically learn feature representation from big data, including millions of parameters. This thesis outlines the common methods used in text feature extraction first, and then expands frequently used deep learning methods in text feature extraction and its applications, and forecasts the application of deep learning in feature extraction.

A design for a dynamic biomimetic sonarhead inspired by horseshoe bats.

PubMed

Caspers, Philip; Mueller, Rolf

2018-05-24

The noseleaf and pinnae of horseshoe bats (Rhinolophus ferrumequinum) have both been shown to actively deform during biosonar operation. Since these baffle structures directly affect the properties of the animal's biosonar system, this work mimics horseshoe bat sonar system with the goal of developing a platform to study the dynamic sensing principles horseshoe bats employ. Consequently, two robotic devices were developed to mimic the dynamic emission and reception characteristics of horseshoe bats. The noseleaf and pinnae shapes were modeled as smooth blanks matched to digital representations of a horseshoe bat specimen's noseleaf and pinnae. Local shape features mimicking structures on the pinnae and noseleaf were added digitally. Flexible baffles with local shape feature combinations were manufactured and paired with actuation mechanisms to mimic pinnae and noseleaf deformations in-vivo. Two noseleaves with and without local shape features were considered. Each noseleaf baffle was mounted to a platform called the dynamic emission head to actuate three surface elements of the baffle. Similarly, 12 pinna realizations composed of combinations of three local shape features were mounted to a platform called the dynamic reception head to deform the left and right pinnae independently. Motion of the noseleaf and pinnae were synchronized to the incoming and outgoing sonar waveform, and the joint time-frequency properties of the noseleaf and pinnae local feature combinations and combinations of the pinnae and noseleaf thereof were characterized across spatial direction. Amplitude modulations to the outgoing and incoming sonar pulse information across spatial direction were observed for all pinnae and noseleaf local shape feature combinations. Peak modulation variance generated by motion of the pinnae and combinations of the noseleaf and pinnae approached a white Gaussian noise variance bound. However, it was found the dynamic emitter generated less modulation than either the combined or reception scenarios. © 2018 IOP Publishing Ltd.

Novel sonar signal processing tool using Shannon entropy

DOE Office of Scientific and Technical Information (OSTI.GOV)

Quazi, A.H.

1996-06-01

Traditionally, conventional signal processing extracts information from sonar signals using amplitude, signal energy or frequency domain quantities obtained using spectral analysis techniques. The object is to investigate an alternate approach which is entirely different than that of traditional signal processing. This alternate approach is to utilize the Shannon entropy as a tool for the processing of sonar signals with emphasis on detection, classification, and localization leading to superior sonar system performance. Traditionally, sonar signals are processed coherently, semi-coherently, and incoherently, depending upon the a priori knowledge of the signals and noise. Here, the detection, classification, and localization technique will bemore » based on the concept of the entropy of the random process. Under a constant energy constraint, the entropy of a received process bearing finite number of sample points is maximum when hypothesis H{sub 0} (that the received process consists of noise alone) is true and decreases when correlated signal is present (H{sub 1}). Therefore, the strategy used for detection is: (I) Calculate the entropy of the received data; then, (II) compare the entropy with the maximum value; and, finally, (III) make decision: H{sub 1} is assumed if the difference is large compared to pre-assigned threshold and H{sub 0} is otherwise assumed. The test statistics will be different between entropies under H{sub 0} and H{sub 1}. Here, we shall show the simulated results for detecting stationary and non-stationary signals in noise, and results on detection of defects in a Plexiglas bar using an ultrasonic experiment conducted by Hughes. {copyright} {ital 1996 American Institute of Physics.}« less

Processing, mosaicking and management of the Monterey Bay digital sidescan-sonar images

USGS Publications Warehouse

Chavez, P.S.; Isbrecht, J.; Galanis, P.; Gabel, G.L.; Sides, S.C.; Soltesz, D.L.; Ross, Stephanie L.; Velasco, M.G.

2002-01-01

Sidescan-sonar imaging systems with digital capabilities have now been available for approximately 20 years. In this paper we present several of the various digital image processing techniques developed by the U.S. Geological Survey (USGS) and used to apply intensity/radiometric and geometric corrections, as well as enhance and digitally mosaic, sidescan-sonar images of the Monterey Bay region. New software run by a WWW server was designed and implemented to allow very large image data sets, such as the digital mosaic, to be easily viewed interactively, including the ability to roam throughout the digital mosaic at the web site in either compressed or full 1-m resolution. The processing is separated into the two different stages: preprocessing and information extraction. In the preprocessing stage, sensor-specific algorithms are applied to correct for both geometric and intensity/radiometric distortions introduced by the sensor. This is followed by digital mosaicking of the track-line strips into quadrangle format which can be used as input to either visual or digital image analysis and interpretation. An automatic seam removal procedure was used in combination with an interactive digital feathering/stenciling procedure to help minimize tone or seam matching problems between image strips from adjacent track-lines. The sidescan-sonar image processing package is part of the USGS Mini Image Processing System (MIPS) and has been designed to process data collected by any 'generic' digital sidescan-sonar imaging system. The USGS MIPS software, developed over the last 20 years as a public domain package, is available on the WWW at: http://terraweb.wr.usgs.gov/trs/software.html.

Image segmentation-based robust feature extraction for color image watermarking

NASA Astrophysics Data System (ADS)

Li, Mianjie; Deng, Zeyu; Yuan, Xiaochen

2018-04-01

This paper proposes a local digital image watermarking method based on Robust Feature Extraction. The segmentation is achieved by Simple Linear Iterative Clustering (SLIC) based on which an Image Segmentation-based Robust Feature Extraction (ISRFE) method is proposed for feature extraction. Our method can adaptively extract feature regions from the blocks segmented by SLIC. This novel method can extract the most robust feature region in every segmented image. Each feature region is decomposed into low-frequency domain and high-frequency domain by Discrete Cosine Transform (DCT). Watermark images are then embedded into the coefficients in the low-frequency domain. The Distortion-Compensated Dither Modulation (DC-DM) algorithm is chosen as the quantization method for embedding. The experimental results indicate that the method has good performance under various attacks. Furthermore, the proposed method can obtain a trade-off between high robustness and good image quality.

Big brown bats (Eptesicus fuscus) reveal diverse strategies for sonar target tracking in clutter.

PubMed

Mao, Beatrice; Aytekin, Murat; Wilkinson, Gerald S; Moss, Cynthia F

2016-09-01

Bats actively adjust the acoustic features of their sonar calls to control echo information specific to a given task and environment. A previous study investigated how bats adapted their echolocation behavior when tracking a moving target in the presence of a stationary distracter at different distances and angular offsets. The use of only one distracter, however, left open the possibility that a bat could reduce the interference of the distracter by turning its head. Here, bats tracked a moving target in the presence of one or two symmetrically placed distracters to investigate adaptive echolocation behavior in a situation where vocalizing off-axis would result in increased interference from distracter echoes. Both bats reduced bandwidth and duration but increased sweep rate in more challenging distracter conditions, and surprisingly, made more head turns in the two-distracter condition compared to one, but only when distracters were placed at large angular offsets. However, for most variables examined, subjects showed distinct strategies to reduce clutter interference, either by (1) changing spectral or temporal features of their calls, or (2) producing large numbers of sonar sound groups and consistent head-turning behavior. The results suggest that individual bats can use different strategies for target tracking in cluttered environments.

Split Bregman's optimization method for image construction in compressive sensing

NASA Astrophysics Data System (ADS)

Skinner, D.; Foo, S.; Meyer-Bäse, A.

2014-05-01

The theory of compressive sampling (CS) was reintroduced by Candes, Romberg and Tao, and D. Donoho in 2006. Using a priori knowledge that a signal is sparse, it has been mathematically proven that CS can defY Nyquist sampling theorem. Theoretically, reconstruction of a CS image relies on the minimization and optimization techniques to solve this complex almost NP-complete problem. There are many paths to consider when compressing and reconstructing an image but these methods have remained untested and unclear on natural images, such as underwater sonar images. The goal of this research is to perfectly reconstruct the original sonar image from a sparse signal while maintaining pertinent information, such as mine-like object, in Side-scan sonar (SSS) images. Goldstein and Osher have shown how to use an iterative method to reconstruct the original image through a method called Split Bregman's iteration. This method "decouples" the energies using portions of the energy from both the !1 and !2 norm. Once the energies are split, Bregman iteration is used to solve the unconstrained optimization problem by recursively solving the problems simultaneously. The faster these two steps or energies can be solved then the faster the overall method becomes. While the majority of CS research is still focused on the medical field, this paper will demonstrate the effectiveness of the Split Bregman's methods on sonar images.

AUTOMATED PRODUCTION OF SEAGRASS MAPS FROM SIDESCAN SONAR IMAGERY: ACCURACY, VARIABILITY AND PATCH RESOLUTION

EPA Science Inventory

Maps of seagrass beds are useful for monitoring estuarine condition, managing habitats, and modeling estuarine processes. We recently developed inexpensive methods for collecting and classifying sidescan sonar (SSS) imagery for seagrass presence in turbid waters as shallow as 1-...

Multi-component separation and analysis of bat echolocation calls.

PubMed

DiCecco, John; Gaudette, Jason E; Simmons, James A

2013-01-01

The vast majority of animal vocalizations contain multiple frequency modulated (FM) components with varying amounts of non-linear modulation and harmonic instability. This is especially true of biosonar sounds where precise time-frequency templates are essential for neural information processing of echoes. Understanding the dynamic waveform design by bats and other echolocating animals may help to improve the efficacy of man-made sonar through biomimetic design. Bats are known to adapt their call structure based on the echolocation task, proximity to nearby objects, and density of acoustic clutter. To interpret the significance of these changes, a method was developed for component separation and analysis of biosonar waveforms. Techniques for imaging in the time-frequency plane are typically limited due to the uncertainty principle and interference cross terms. This problem is addressed by extending the use of the fractional Fourier transform to isolate each non-linear component for separate analysis. Once separated, empirical mode decomposition can be used to further examine each component. The Hilbert transform may then successfully extract detailed time-frequency information from each isolated component. This multi-component analysis method is applied to the sonar signals of four species of bats recorded in-flight by radiotelemetry along with a comparison of other common time-frequency representations.

Using Side-scan Sonar to Characterize and Map Physical Habitat and Anthropogenic Underwater Features in the St. Louis River. (poster)

EPA Science Inventory

Characterizing underwater habitat and other features is difficult and costly, especially in large river systems. The St. Louis River is the largest US tributary to Lake Superior and the lower portion consists of a 48.5 km2 complex of wetlands, tributaries, and bays. We surveyed 8...

Using side-scan sonar to characterize and map physical habitat and anthropogenic underwater features in the St. Louis River

EPA Science Inventory

Characterizing underwater habitat and other features is difficult and costly, especially in large river systems. The St. Louis River is the largest US tributary to Lake Superior and the lower portion consists of a 48.5 km2 complex of wetlands, tributaries, and bays. We surveyed 8...

Algorithms and data structures for automated change detection and classification of sidescan sonar imagery

NASA Astrophysics Data System (ADS)

Gendron, Marlin Lee

During Mine Warfare (MIW) operations, MIW analysts perform change detection by visually comparing historical sidescan sonar imagery (SSI) collected by a sidescan sonar with recently collected SSI in an attempt to identify objects (which might be explosive mines) placed at sea since the last time the area was surveyed. This dissertation presents a data structure and three algorithms, developed by the author, that are part of an automated change detection and classification (ACDC) system. MIW analysts at the Naval Oceanographic Office, to reduce the amount of time to perform change detection, are currently using ACDC. The dissertation introductory chapter gives background information on change detection, ACDC, and describes how SSI is produced from raw sonar data. Chapter 2 presents the author's Geospatial Bitmap (GB) data structure, which is capable of storing information geographically and is utilized by the three algorithms. This chapter shows that a GB data structure used in a polygon-smoothing algorithm ran between 1.3--48.4x faster than a sparse matrix data structure. Chapter 3 describes the GB clustering algorithm, which is the author's repeatable, order-independent method for clustering. Results from tests performed in this chapter show that the time to cluster a set of points is not affected by the distribution or the order of the points. In Chapter 4, the author presents his real-time computer-aided detection (CAD) algorithm that automatically detects mine-like objects on the seafloor in SSI. The author ran his GB-based CAD algorithm on real SSI data, and results of these tests indicate that his real-time CAD algorithm performs comparably to or better than other non-real-time CAD algorithms. The author presents his computer-aided search (CAS) algorithm in Chapter 5. CAS helps MIW analysts locate mine-like features that are geospatially close to previously detected features. A comparison between the CAS and a great circle distance algorithm shows that the CAS performs geospatial searching 1.75x faster on large data sets. Finally, the concluding chapter of this dissertation gives important details on how the completed ACDC system will function, and discusses the author's future research to develop additional algorithms and data structures for ACDC.

EFFECTS OF GREEN MACROALGAE ON CLASSIFICATION OF SEAGRASS IN SIDE SCAN SONAR IMAGERY

EPA Science Inventory

High resolution maps of seagrass beds are useful for monitoring estuarine condition, managing fish habitats, and modeling estuarine processes. Side scan sonar (SSS) is one method for producing spatially accurate seagrass maps, although it has not been used widely. Our team rece...

Detecting submerged objects: the application of side scan sonar to forensic contexts.

PubMed

Schultz, John J; Healy, Carrie A; Parker, Kenneth; Lowers, Bim

2013-09-10

Forensic personnel must deal with numerous challenges when searching for submerged objects. While traditional water search methods have generally involved using dive teams, remotely operated vehicles (ROVs), and water scent dogs for cases involving submerged objects and bodies, law enforcement is increasingly integrating multiple methods that include geophysical technologies. There are numerous advantages for integrating geophysical technologies, such as side scan sonar and ground penetrating radar (GPR), with more traditional search methods. Overall, these methods decrease the time involved searching, in addition to increasing area searched. However, as with other search methods, there are advantages and disadvantages when using each method. For example, in instances with excessive aquatic vegetation or irregular bottom terrain, it may not be possible to discern a submersed body with side scan sonar. As a result, forensic personnel will have the highest rate of success during searches for submerged objects when integrating multiple search methods, including deploying multiple geophysical technologies. The goal of this paper is to discuss the methodology of various search methods that are employed for submerged objects and how these various methods can be integrated as part of a comprehensive protocol for water searches depending upon the type of underwater terrain. In addition, two successful case studies involving the search and recovery of a submerged human body using side scan sonar are presented to illustrate the successful application of integrating a geophysical technology with divers when searching for a submerged object. Copyright © 2013 Elsevier Ireland Ltd. All rights reserved.

Adjustment of Sonar and Laser Acquisition Data for Building the 3D Reference Model of a Canal Tunnel.

PubMed

Moisan, Emmanuel; Charbonnier, Pierre; Foucher, Philippe; Grussenmeyer, Pierre; Guillemin, Samuel; Koehl, Mathieu

2015-12-11

In this paper, we focus on the construction of a full 3D model of a canal tunnel by combining terrestrial laser (for its above-water part) and sonar (for its underwater part) scans collected from static acquisitions. The modeling of such a structure is challenging because the sonar device is used in a narrow environment that induces many artifacts. Moreover, the location and the orientation of the sonar device are unknown. In our approach, sonar data are first simultaneously denoised and meshed. Then, above- and under-water point clouds are co-registered to generate directly the full 3D model of the canal tunnel. Faced with the lack of overlap between both models, we introduce a robust algorithm that relies on geometrical entities and partially-immersed targets, which are visible in both the laser and sonar point clouds. A full 3D model, visually promising, of the entrance of a canal tunnel is obtained. The analysis of the method raises several improvement directions that will help with obtaining more accurate models, in a more automated way, in the limits of the involved technology.

Adjustment of Sonar and Laser Acquisition Data for Building the 3D Reference Model of a Canal Tunnel †

PubMed Central

Moisan, Emmanuel; Charbonnier, Pierre; Foucher, Philippe; Grussenmeyer, Pierre; Guillemin, Samuel; Koehl, Mathieu

2015-01-01

In this paper, we focus on the construction of a full 3D model of a canal tunnel by combining terrestrial laser (for its above-water part) and sonar (for its underwater part) scans collected from static acquisitions. The modeling of such a structure is challenging because the sonar device is used in a narrow environment that induces many artifacts. Moreover, the location and the orientation of the sonar device are unknown. In our approach, sonar data are first simultaneously denoised and meshed. Then, above- and under-water point clouds are co-registered to generate directly the full 3D model of the canal tunnel. Faced with the lack of overlap between both models, we introduce a robust algorithm that relies on geometrical entities and partially-immersed targets, which are visible in both the laser and sonar point clouds. A full 3D model, visually promising, of the entrance of a canal tunnel is obtained. The analysis of the method raises several improvement directions that will help with obtaining more accurate models, in a more automated way, in the limits of the involved technology. PMID:26690444

Underwater sonar image detection: A combination of non-local spatial information and quantum-inspired shuffled frog leaping algorithm.

PubMed

Wang, Xingmei; Liu, Shu; Liu, Zhipeng

2017-01-01

This paper proposes a combination of non-local spatial information and quantum-inspired shuffled frog leaping algorithm to detect underwater objects in sonar images. Specifically, for the first time, the problem of inappropriate filtering degree parameter which commonly occurs in non-local spatial information and seriously affects the denoising performance in sonar images, was solved with the method utilizing a novel filtering degree parameter. Then, a quantum-inspired shuffled frog leaping algorithm based on new search mechanism (QSFLA-NSM) is proposed to precisely and quickly detect sonar images. Each frog individual is directly encoded by real numbers, which can greatly simplify the evolution process of the quantum-inspired shuffled frog leaping algorithm (QSFLA). Meanwhile, a fitness function combining intra-class difference with inter-class difference is adopted to evaluate frog positions more accurately. On this basis, recurring to an analysis of the quantum-behaved particle swarm optimization (QPSO) and the shuffled frog leaping algorithm (SFLA), a new search mechanism is developed to improve the searching ability and detection accuracy. At the same time, the time complexity is further reduced. Finally, the results of comparative experiments using the original sonar images, the UCI data sets and the benchmark functions demonstrate the effectiveness and adaptability of the proposed method.

Underwater sonar image detection: A combination of non-local spatial information and quantum-inspired shuffled frog leaping algorithm

PubMed Central

Liu, Zhipeng

2017-01-01

This paper proposes a combination of non-local spatial information and quantum-inspired shuffled frog leaping algorithm to detect underwater objects in sonar images. Specifically, for the first time, the problem of inappropriate filtering degree parameter which commonly occurs in non-local spatial information and seriously affects the denoising performance in sonar images, was solved with the method utilizing a novel filtering degree parameter. Then, a quantum-inspired shuffled frog leaping algorithm based on new search mechanism (QSFLA-NSM) is proposed to precisely and quickly detect sonar images. Each frog individual is directly encoded by real numbers, which can greatly simplify the evolution process of the quantum-inspired shuffled frog leaping algorithm (QSFLA). Meanwhile, a fitness function combining intra-class difference with inter-class difference is adopted to evaluate frog positions more accurately. On this basis, recurring to an analysis of the quantum-behaved particle swarm optimization (QPSO) and the shuffled frog leaping algorithm (SFLA), a new search mechanism is developed to improve the searching ability and detection accuracy. At the same time, the time complexity is further reduced. Finally, the results of comparative experiments using the original sonar images, the UCI data sets and the benchmark functions demonstrate the effectiveness and adaptability of the proposed method. PMID:28542266

Acoustic detection and quantification of benthic egg beds of the squid Loligo opalescens in Monterey Bay, California.

PubMed

Foote, Kenneth G; Hanlon, Roger T; Lampietro, Pat J; Kvitek, Rikk G

2006-02-01

The squid Loligo opalescens is a key species in the nearshore pelagic community of California, supporting the most valuable state marine fishery, yet the stock biomass is unknown. In southern Monterey Bay, extensive beds occur on a flat, sandy bottom, water depths 20-60 m, thus sidescan sonar is a prima-facie candidate for use in rapid, synoptic, and noninvasive surveying. The present study describes development of an acoustic method to detect, identify, and quantify squid egg beds by means of high-frequency sidescan-sonar imagery. Verification of the method has been undertaken with a video camera carried on a remotely operated vehicle. It has been established that sidescan sonar images can be used to predict the presence or absence of squid egg beds. The lower size limit of detectability of an isolated egg bed is about 0.5 m with a 400-kHz sidescan sonar used with a 50-m range when towed at 3 knots. It is possible to estimate the abundance of eggs in a region of interest by computing the cumulative area covered by the egg beds according to the sidescan sonar image. In a selected quadrat one arc second on each side, the estimated number of eggs was 36.5 million.

Studying seafloor bedforms using autonomous stationary imaging and profiling sonars

USGS Publications Warehouse

Montgomery, Ellyn T.; Sherwood, Christopher R.

2014-01-01

The Sediment Transport Group at the U.S. Geological Survey, Woods Hole Coastal and Marine Science Center uses downward looking sonars deployed on seafloor tripods to assess and measure the formation and migration of bedforms. The sonars have been used in three resolution-testing experiments, and deployed autonomously to observe changes in the seafloor for up to two months in seven field experiments since 2002. The sonar data are recorded concurrently with measurements of waves and currents to: a) relate bedform geometry to sediment and flow characteristics; b) assess hydrodynamic drag caused by bedforms; and c) estimate bedform sediment transport rates, all with the goal of evaluating and improving numerical models of these processes. Our hardware, data processing methods, and test and validation procedures have evolved since 2001. We now employ a standard sonar configuration that provides reliable data for correlating flow conditions with bedform morphology. Plans for the future are to sample more rapidly and improve the precision of our tripod orientation measurements.

Development of a 2 MHz Sonar Sensor for Inspection of Bridge Substructures.

PubMed

Park, Chul; Kim, Youngseok; Lee, Heungsu; Choi, Sangsik; Jung, Haewook

2018-04-16

Hydraulic factors account for a large part of the causes of bridge collapse. Due to the nature of the underwater environment, quick and accurate inspection is required when damage occurs. In this study, we developed a 2 MHz side scan sonar sensor module and effective operation technique by improving the limitations of existing sonar. Through field tests, we analyzed the correlation of factors affecting the resolution of the sonar data such as the angle of survey, the distance from the underwater structure and the water depth. The effect of the distance and the water depth and the structure on the survey angle was 66~82%. We also derived the relationship between these factors as a regression model for effective operating techniques. It is considered that application of the developed 2 MHz side scan sonar and its operation method could contribute to prevention of bridge collapses and disasters by quickly and accurately checking the damage of bridge substructures due to hydraulic factors.

Development of a 2 MHz Sonar Sensor for Inspection of Bridge Substructures

PubMed Central

Park, Chul; Lee, Heungsu; Choi, Sangsik; Jung, Haewook

2018-01-01

Hydraulic factors account for a large part of the causes of bridge collapse. Due to the nature of the underwater environment, quick and accurate inspection is required when damage occurs. In this study, we developed a 2 MHz side scan sonar sensor module and effective operation technique by improving the limitations of existing sonar. Through field tests, we analyzed the correlation of factors affecting the resolution of the sonar data such as the angle of survey, the distance from the underwater structure and the water depth. The effect of the distance and the water depth and the structure on the survey angle was 66~82%. We also derived the relationship between these factors as a regression model for effective operating techniques. It is considered that application of the developed 2 MHz side scan sonar and its operation method could contribute to prevention of bridge collapses and disasters by quickly and accurately checking the damage of bridge substructures due to hydraulic factors. PMID:29659557

High thresholds for avoidance of sonar by free-ranging long-finned pilot whales (Globicephala melas).

PubMed

Antunes, R; Kvadsheim, P H; Lam, F P A; Tyack, P L; Thomas, L; Wensveen, P J; Miller, P J O

2014-06-15

The potential effects of exposing marine mammals to military sonar is a current concern. Dose-response relationships are useful for predicting potential environmental impacts of specific operations. To reveal behavioral response thresholds of exposure to sonar, we conducted 18 exposure/control approaches to 6 long-finned pilot whales. Source level and proximity of sonar transmitting one of two frequency bands (1-2 kHz and 6-7 kHz) were increased during exposure sessions. The 2-dimensional movement tracks were analyzed using a changepoint method to identify the avoidance response thresholds which were used to estimate dose-response relationships. No support for an effect of sonar frequency or previous exposures on the probability of response was found. Estimated response thresholds at which 50% of population show avoidance (SPLmax=170 dB re 1 μPa, SELcum=173 dB re 1 μPa(2) s) were higher than previously found for other cetaceans. The US Navy currently uses a generic dose-response relationship to predict the responses of cetaceans to naval active sonar, which has been found to underestimate behavioural impacts on killer whales and beaked whales. The navy curve appears to match more closely our results with long-finned pilot whales, though it might underestimate the probability of avoidance for pilot-whales at long distances from sonar sources. Copyright © 2014 Elsevier Ltd. All rights reserved.

A novel approach to surveying sturgeon using side-scan sonar and occupancy modeling

USGS Publications Warehouse

Flowers, H. Jared; Hightower, Joseph E.

2013-01-01

Technological advances represent opportunities to enhance and supplement traditional fisheries sampling approaches. One example with growing importance for fisheries research is hydroacoustic technologies such as side-scan sonar. Advantages of side-scan sonar over traditional techniques include the ability to sample large areas efficiently and the potential to survey fish without physical handling-important for species of conservation concern, such as endangered sturgeons. Our objectives were to design an efficient survey methodology for sampling Atlantic Sturgeon Acipenser oxyrinchus by using side-scan sonar and to developmethods for analyzing these data. In North Carolina and South Carolina, we surveyed six rivers thought to contain varying abundances of sturgeon by using a combination of side-scan sonar, telemetry, and video cameras (i.e., to sample jumping sturgeon). Lower reaches of each river near the saltwater-freshwater interface were surveyed on three occasions (generally successive days), and we used occupancy modeling to analyze these data.We were able to detect sturgeon in five of six rivers by using these methods. Side-scan sonar was effective in detecting sturgeon, with estimated gear-specific detection probabilities ranging from 0.2 to 0.5 and river-specific occupancy estimates (per 2-km river segment) ranging from 0.0 to 0.8. Future extensions of this occupancy modeling framework will involve the use of side-scan sonar data to assess sturgeon habitat and abundance in different river systems.

Directional Receiver for Biomimetic Sonar System

NASA Astrophysics Data System (ADS)

Guarato, Francesco; Andrews, Heather; Windmill, James F.; Jackson, Joseph; Gachagan, Anthony

An ultrasonic localization method for a sonar system equipped with an emitter and two directional receivers and inspired by bat echolocation uses knowledge of the beam pattern of the receivers to estimate target orientation. Rousettus leschenaultii's left ear constitutes the model for the design of the optimal receiver for this sonar system and 3D printing was used to fabricate receiver structures comprising of two truncated cones with an elliptical external perimeter and a parabolic flare rate in the upper part. Measurements show one receiver has a predominant lobe in the same region and with similar attenuation values as the bat ear model. The final sonar system is to be mounted on vehicular and aerial robots which require remote control for motion and sensors for estimation of each robot's location.

Use of handheld sonar to locate a missing diver.

PubMed

McGrane, Owen; Cronin, Aaron; Hile, David

2013-03-01

The purpose of this study was to investigate whether a handheld sonar device significantly reduces the mean time needed to locate a missing diver. This institutional review board approved, prospective, crossover study used a voluntary convenience sample of 10 scuba divers. Participants conducted both a standard and modified search to locate a simulated missing diver. The standard search utilized a conventional search pattern starting at the point where the missing diver (simulated) was last seen. The modified search used a sonar beacon to augment the search. For each search method, successful completion of the search was defined as locating the missing diver within 40 minutes. Twenty total dives were completed. Using a standard search pattern, the missing diver was found by only 1 diver (10%), taking 18 minutes and 45 seconds. In the sonar-assisted search group, the missing diver was found by all 10 participants (100%), taking an average of 2 minutes and 47 seconds (SD 1 minute, 20 seconds). Using the nonparametric related samples Wilcoxon signed rank test, actual times between the sonar group and the standard group were significant (P < .01). Using paired samples t tests, the sonar group's self-assessed confidence increased significantly after using the sonar (P < .001), whereas the standard group decreased in confidence (not statistically significant, P = .111). Handheld sonar significantly reduces the mean duration to locate a missing diver as well as increasing users' confidence in their ability to find a missing diver when compared with standard search techniques. Copyright © 2013 Wilderness Medical Society. Published by Elsevier Inc. All rights reserved.

30 CFR 250.1742 - What other methods can I use to verify that a site is clear?

Code of Federal Regulations, 2014 CFR

2014-07-01

... use . . . You must . . . And you must . . . (a) Sonar, cover 100 percent of the appropriate grid area listed in § 250.1741(a), Use a sonar signal with a frequency of at least 500 kHz. (b) A diver, ensure...

30 CFR 250.1742 - What other methods can I use to verify that a site is clear?

Code of Federal Regulations, 2013 CFR

2013-07-01

... use . . . You must . . . And you must . . . (a) Sonar, cover 100 percent of the appropriate grid area listed in § 250.1741(a), Use a sonar signal with a frequency of at least 500 kHz. (b) A diver, ensure...

30 CFR 250.1742 - What other methods can I use to verify that a site is clear?

Code of Federal Regulations, 2012 CFR

2012-07-01

... use . . . You must . . . And you must . . . (a) Sonar, cover 100 percent of the appropriate grid area listed in § 250.1741(a), Use a sonar signal with a frequency of at least 500 kHz. (b) A diver, ensure...

30 CFR 250.1742 - What other methods can I use to verify that a site is clear?

Code of Federal Regulations, 2011 CFR

2011-07-01

... following table: If you use— You must— And you must— (a) Sonar cover 100 percent of the appropriate grid area listed in § 250.1741(a) Use a sonar signal with a frequency of at least 500 kHz. (b) A diver...

30 CFR 250.1740 - How must I verify that the site of a permanently plugged well, removed platform, or other removed...

Code of Federal Regulations, 2010 CFR

2010-07-01

...: (1) Drag a trawl over the site; (2) Scan across the location using sonar equipment; (3) Inspect the...) Drag a trawl over the site; (2) Scan across the site using sonar equipment; or (3) Use another method...

30 CFR 250.1742 - What other methods can I use to verify that a site is clear?

Code of Federal Regulations, 2010 CFR

2010-07-01

...— And you must— (a) Sonar cover 100 percent of the appropriate grid area listed in § 250.1741(a) Use a sonar signal with a frequency of at least 500 kHz. (b) A diver ensure that the diver visually inspects...

Salmon escapement estimates into the Togiak River using sonar, Togiak National Wildlife Refuge, Alaska, 1987, 1988, and 1990

USGS Publications Warehouse

Irving, David B.; Finn, James E.; Larson, James P.

1995-01-01

We began a three year study in 1987 to test the feasibility of using sonar in the Togiak River to estimate salmon escapements. Current methods rely on periodic aerial surveys and a counting tower at river kilometer 97. Escapement estimates are not available until 10 to 14 days after the salmon enter the river. Water depth and turbidity preclude relocating the tower to the lower river and affect the reliability of aerial surveys. To determine whether an alternative method could be developed to improve the timeliness and accuracy of current escapement monitoring, Bendix sonar units were operated during 1987, 1988, and 1990. Two sonar stations were set up opposite each other at river kilometer 30 and were operated 24 hours per day, seven days per week. Catches from gill nets with 12, 14, and 20 cm stretch mesh, a beach seine, and visual observations were used to estimate species composition. Length and sex data were collected from salmon caught in the nets to assess sampling bias.In 1987, sonar was used to select optimal sites and enumerate coho salmon. In 1988 and 1990, the sites identified in 1987 were used to estimate the escapement of five salmon species. Sockeye salmon escapement was estimated at 512,581 and 589,321, chinook at 7,698 and 15,098, chum at 246,144 and 134,958, coho at 78,588 and 28,290, and pink at 96,167 and 131,484. Sonar estimates of sockeye salmon were two to three times the Alaska Department of Fish and Game's escapement estimate based on aerial surveys and tower counts. The source of error was probably a combination of over-estimating the total number of targets counted by the sonar and by incorrectly estimating species composition.Total salmon escapement estimates using sonar may be feasible but several more years of development are needed. Because of the overlapped salmon run timing, estimating species composition appears the most difficult aspect of using sonar for management. Possible improvements include using a larger beach seine or selecting gill net mesh sizes evenly spaced between 10 and 20 cm stretch mesh.Salmon counts at river kilometer 30 would reduce the lag time between salmon river entry and the escapement estimate to 2-5 days. Any further decrease in lag time, however, would require moving the sonar operations downriver into less desirable braided portions of the river.

Sidescan-Sonar Imagery and Surficial Geologic Interpretations of the Sea Floor in Central Rhode Island Sound

USGS Publications Warehouse

McMullen, K.Y.; Poppe, L.J.; Denny, J.F.; Haupt, T.A.; Crocker, J.M.

2008-01-01

The U.S. Geological Survey (USGS) has been working with the National Oceanic and Atmospheric Administration (NOAA) to interpret the surficial geology of areas along the northeastern coast of the United States. During 2004, the NOAA Ship RUDE conducted Hydrographic Survey H11321 in Rhode Island Sound. This sidescan-sonar and bathymetry survey covers an area of 93 km? located 12 km southeast of Brenton Point, RI in water depths of 28-39 m (fig. 1). The purpose of this report is to delineate sea floor features and sedimentary environments of this area in central Rhode Island Sound using sidescan-sonar and bathymetric data from NOAA Survey H11321 and seismic-reflection data from a previous USGS field study (Needell and others, 1983a). This is important for the study of benthic habitats and provides a framework for future research. Prior work in this area includes the mapping of surface sediments and surficial geology. McMaster (1960) collected sediment samples from Rhode Island Sound and Narragansett Bay and mapped our study area as having a sandy sea floor. In addition, one sample of sand from the National Ocean Service (NOS) Hydrographic Database came from a location in the northeast part of our study area in 1939 (fig. 2; Poppe and others, 2003). McMaster and others (1968) used seismic-reflection profiles to map the locations of a cuesta of Cretaceous sediments crossing Rhode Island Sound and post-Cretaceous drainage channels. Knebel and others (1982) identified sedimentary environments in Rhode Island Sound using sidescan sonographs. Needell and others (1983b) studied the Quaternary geology and mapped the structure, sedimentary environments, and geologic hazards in Rhode Island Sound using sidescan-sonar and seismic-reflection data. Sidescan-sonar and bathymetric data from NOAA Survey H11320, which overlaps the far eastern edge of our study area, was interpreted to consist of basins surrounded by a moraine and bathymetric highs composed of till with areas of rocks, sand waves, hummocks, glaciolacustrine erosional outliers, small scarps and elongate hills (fig. 1; McMullen and others, 2007). Some of those features extend into this study area.

Automatic extraction of planetary image features

NASA Technical Reports Server (NTRS)

LeMoigne-Stewart, Jacqueline J. (Inventor); Troglio, Giulia (Inventor); Benediktsson, Jon A. (Inventor); Serpico, Sebastiano B. (Inventor); Moser, Gabriele (Inventor)

2013-01-01

A method for the extraction of Lunar data and/or planetary features is provided. The feature extraction method can include one or more image processing techniques, including, but not limited to, a watershed segmentation and/or the generalized Hough Transform. According to some embodiments, the feature extraction method can include extracting features, such as, small rocks. According to some embodiments, small rocks can be extracted by applying a watershed segmentation algorithm to the Canny gradient. According to some embodiments, applying a watershed segmentation algorithm to the Canny gradient can allow regions that appear as close contours in the gradient to be segmented.

Broadband classification and statistics of echoes from aggregations of fish measured by long-range, mid-frequency sonar.

PubMed

Jones, Benjamin A; Stanton, Timothy K; Colosi, John A; Gauss, Roger C; Fialkowski, Joseph M; Michael Jech, J

2017-06-01

For horizontal-looking sonar systems operating at mid-frequencies (1-10 kHz), scattering by fish with resonant gas-filled swimbladders can dominate seafloor and surface reverberation at long-ranges (i.e., distances much greater than the water depth). This source of scattering, which can be difficult to distinguish from other sources of scattering in the water column or at the boundaries, can add spatio-temporal variability to an already complex acoustic record. Sparsely distributed, spatially compact fish aggregations were measured in the Gulf of Maine using a long-range broadband sonar with continuous spectral coverage from 1.5 to 5 kHz. Observed echoes, that are at least 15 decibels above background levels in the horizontal-looking sonar data, are classified spectrally by the resonance features as due to swimbladder-bearing fish. Contemporaneous multi-frequency echosounder measurements (18, 38, and 120 kHz) and net samples are used in conjunction with physics-based acoustic models to validate this approach. Furthermore, the fish aggregations are statistically characterized in the long-range data by highly non-Rayleigh distributions of the echo magnitudes. These distributions are accurately predicted by a computationally efficient, physics-based model. The model accounts for beam-pattern and waveguide effects as well as the scattering response of aggregations of fish.

The fusion of large scale classified side-scan sonar image mosaics.

PubMed

Reed, Scott; Tena, Ruiz Ioseba; Capus, Chris; Petillot, Yvan

2006-07-01

This paper presents a unified framework for the creation of classified maps of the seafloor from sonar imagery. Significant challenges in photometric correction, classification, navigation and registration, and image fusion are addressed. The techniques described are directly applicable to a range of remote sensing problems. Recent advances in side-scan data correction are incorporated to compensate for the sonar beam pattern and motion of the acquisition platform. The corrected images are segmented using pixel-based textural features and standard classifiers. In parallel, the navigation of the sonar device is processed using Kalman filtering techniques. A simultaneous localization and mapping framework is adopted to improve the navigation accuracy and produce georeferenced mosaics of the segmented side-scan data. These are fused within a Markovian framework and two fusion models are presented. The first uses a voting scheme regularized by an isotropic Markov random field and is applicable when the reliability of each information source is unknown. The Markov model is also used to inpaint regions where no final classification decision can be reached using pixel level fusion. The second model formally introduces the reliability of each information source into a probabilistic model. Evaluation of the two models using both synthetic images and real data from a large scale survey shows significant quantitative and qualitative improvement using the fusion approach.

Target recognition based on convolutional neural network

NASA Astrophysics Data System (ADS)

Wang, Liqiang; Wang, Xin; Xi, Fubiao; Dong, Jian

2017-11-01

One of the important part of object target recognition is the feature extraction, which can be classified into feature extraction and automatic feature extraction. The traditional neural network is one of the automatic feature extraction methods, while it causes high possibility of over-fitting due to the global connection. The deep learning algorithm used in this paper is a hierarchical automatic feature extraction method, trained with the layer-by-layer convolutional neural network (CNN), which can extract the features from lower layers to higher layers. The features are more discriminative and it is beneficial to the object target recognition.

Recreational-Grade Sidescan Sonar: Transforming a Low-Cost Leisure Gadget into a High Resolution Riverbed Remote Sensing Tool

NASA Astrophysics Data System (ADS)

Hamill, D. D.; Buscombe, D.; Wheaton, J. M.; Wilcock, P. R.

2016-12-01

The size and spatial organization of bed material, bed texture, is a fundamental physical attribute of lotic ecosystems. Traditional methods to map bed texture (such as physical samples and underwater video) are limited by low spatial coverage, and poor precision in positioning. Recreational grade sidescan sonar systems now offer the possibility of imaging submerged riverbed sediments at coverages and resolutions sufficient to identify subtle changes in bed texture, in any navigable body of water, with minimal cost, expertise in sonar, or logistical effort, thereby facilitating the democratization of acoustic imaging of benthic environments, to support ecohydrological studies in shallow water, not subject to the rigors of hydrographic standards, nor the preserve of hydroacoustic expertise and proprietary hydrographic industry software. We investigate the possibility of using recreational grade sidescan sonar for sedimentary change detection using a case study of repeat sidescan imaging of mixed sand-gravel-rock riverbeds in a debris-fan dominated canyon river, at a coverage and resolution that meets the objectives of studies of the effects of changing bed substrates on salmonid spawning. A repeat substrate mapping analysis on data collected between 2012 and 2015 on the Colorado River in Glen, Marble, and Grand Canyons will be presented. A detailed method has been developed to interpret and analyze non-survey-grade sidescan sonar data, encoded within an open source software tool developed by the authors. An automated technique to quantify bed texture directly from sidescan sonar imagery is tested against bed sediment observations from underwater video and multibeam sonar. Predictive relationships between known bed sediment observations and bed texture metrics could provide an objective means to quantify bed textures and to relate changes in bed texture to biological components of an aquatic ecosystem, at high temporal frequency, and with minimal logistical effort and cost.

Using Data Crawlers and Semantic Web to Build Financial XBRL Data Generators: The SONAR Extension Approach

PubMed Central

Rodríguez-García, Miguel Ángel; Rodríguez-González, Alejandro; Valencia-García, Rafael; Gómez-Berbís, Juan Miguel

2014-01-01

Precise, reliable and real-time financial information is critical for added-value financial services after the economic turmoil from which markets are still struggling to recover. Since the Web has become the most significant data source, intelligent crawlers based on Semantic Technologies have become trailblazers in the search of knowledge combining natural language processing and ontology engineering techniques. In this paper, we present the SONAR extension approach, which will leverage the potential of knowledge representation by extracting, managing, and turning scarce and disperse financial information into well-classified, structured, and widely used XBRL format-oriented knowledge, strongly supported by a proof-of-concept implementation and a thorough evaluation of the benefits of the approach. PMID:24587726

Using data crawlers and semantic Web to build financial XBRL data generators: the SONAR extension approach.

PubMed

Rodríguez-García, Miguel Ángel; Rodríguez-González, Alejandro; Colomo-Palacios, Ricardo; Valencia-García, Rafael; Gómez-Berbís, Juan Miguel; García-Sánchez, Francisco

2014-01-01

Precise, reliable and real-time financial information is critical for added-value financial services after the economic turmoil from which markets are still struggling to recover. Since the Web has become the most significant data source, intelligent crawlers based on Semantic Technologies have become trailblazers in the search of knowledge combining natural language processing and ontology engineering techniques. In this paper, we present the SONAR extension approach, which will leverage the potential of knowledge representation by extracting, managing, and turning scarce and disperse financial information into well-classified, structured, and widely used XBRL format-oriented knowledge, strongly supported by a proof-of-concept implementation and a thorough evaluation of the benefits of the approach.

Tongue-driven sonar beam steering by a lingual-echolocating fruit bat

PubMed Central

Falk, Benjamin; Chiu, Chen; Krishnan, Anand; Arbour, Jessica H.; Moss, Cynthia F.

2017-01-01

Animals enhance sensory acquisition from a specific direction by movements of head, ears, or eyes. As active sensing animals, echolocating bats also aim their directional sonar beam to selectively “illuminate” a confined volume of space, facilitating efficient information processing by reducing echo interference and clutter. Such sonar beam control is generally achieved by head movements or shape changes of the sound-emitting mouth or nose. However, lingual-echolocating Egyptian fruit bats, Rousettus aegyptiacus, which produce sound by clicking their tongue, can dramatically change beam direction at very short temporal intervals without visible morphological changes. The mechanism supporting this capability has remained a mystery. Here, we measured signals from free-flying Egyptian fruit bats and discovered a systematic angular sweep of beam focus across increasing frequency. This unusual signal structure has not been observed in other animals and cannot be explained by the conventional and widely-used “piston model” that describes the emission pattern of other bat species. Through modeling, we show that the observed beam features can be captured by an array of tongue-driven sound sources located along the side of the mouth, and that the sonar beam direction can be steered parsimoniously by inducing changes to the pattern of phase differences through moving tongue location. The effects are broadly similar to those found in a phased array—an engineering design widely found in human-made sonar systems that enables beam direction changes without changes in the physical transducer assembly. Our study reveals an intriguing parallel between biology and human engineering in solving problems in fundamentally similar ways. PMID:29244805

Tongue-driven sonar beam steering by a lingual-echolocating fruit bat.

PubMed

Lee, Wu-Jung; Falk, Benjamin; Chiu, Chen; Krishnan, Anand; Arbour, Jessica H; Moss, Cynthia F

2017-12-01

Animals enhance sensory acquisition from a specific direction by movements of head, ears, or eyes. As active sensing animals, echolocating bats also aim their directional sonar beam to selectively "illuminate" a confined volume of space, facilitating efficient information processing by reducing echo interference and clutter. Such sonar beam control is generally achieved by head movements or shape changes of the sound-emitting mouth or nose. However, lingual-echolocating Egyptian fruit bats, Rousettus aegyptiacus, which produce sound by clicking their tongue, can dramatically change beam direction at very short temporal intervals without visible morphological changes. The mechanism supporting this capability has remained a mystery. Here, we measured signals from free-flying Egyptian fruit bats and discovered a systematic angular sweep of beam focus across increasing frequency. This unusual signal structure has not been observed in other animals and cannot be explained by the conventional and widely-used "piston model" that describes the emission pattern of other bat species. Through modeling, we show that the observed beam features can be captured by an array of tongue-driven sound sources located along the side of the mouth, and that the sonar beam direction can be steered parsimoniously by inducing changes to the pattern of phase differences through moving tongue location. The effects are broadly similar to those found in a phased array-an engineering design widely found in human-made sonar systems that enables beam direction changes without changes in the physical transducer assembly. Our study reveals an intriguing parallel between biology and human engineering in solving problems in fundamentally similar ways.

Estimation and simulation of multi-beam sonar noise.

PubMed

Holmin, Arne Johannes; Korneliussen, Rolf J; Tjøstheim, Dag

2016-02-01

Methods for the estimation and modeling of noise present in multi-beam sonar data, including the magnitude, probability distribution, and spatial correlation of the noise, are developed. The methods consider individual acoustic samples and facilitate compensation of highly localized noise as well as subtraction of noise estimates averaged over time. The modeled noise is included in an existing multi-beam sonar simulation model [Holmin, Handegard, Korneliussen, and Tjøstheim, J. Acoust. Soc. Am. 132, 3720-3734 (2012)], resulting in an improved model that can be used to strengthen interpretation of data collected in situ at any signal to noise ratio. Two experiments, from the former study in which multi-beam sonar data of herring schools were simulated, are repeated with inclusion of noise. These experiments demonstrate (1) the potentially large effect of changes in fish orientation on the backscatter from a school, and (2) the estimation of behavioral characteristics such as the polarization and packing density of fish schools. The latter is achieved by comparing real data with simulated data for different polarizations and packing densities.

Audio feature extraction using probability distribution function

NASA Astrophysics Data System (ADS)

Suhaib, A.; Wan, Khairunizam; Aziz, Azri A.; Hazry, D.; Razlan, Zuradzman M.; Shahriman A., B.

2015-05-01

Voice recognition has been one of the popular applications in robotic field. It is also known to be recently used for biometric and multimedia information retrieval system. This technology is attained from successive research on audio feature extraction analysis. Probability Distribution Function (PDF) is a statistical method which is usually used as one of the processes in complex feature extraction methods such as GMM and PCA. In this paper, a new method for audio feature extraction is proposed which is by using only PDF as a feature extraction method itself for speech analysis purpose. Certain pre-processing techniques are performed in prior to the proposed feature extraction method. Subsequently, the PDF result values for each frame of sampled voice signals obtained from certain numbers of individuals are plotted. From the experimental results obtained, it can be seen visually from the plotted data that each individuals' voice has comparable PDF values and shapes.

Histogram of gradient and binarized statistical image features of wavelet subband-based palmprint features extraction

NASA Astrophysics Data System (ADS)

Attallah, Bilal; Serir, Amina; Chahir, Youssef; Boudjelal, Abdelwahhab

2017-11-01

Palmprint recognition systems are dependent on feature extraction. A method of feature extraction using higher discrimination information was developed to characterize palmprint images. In this method, two individual feature extraction techniques are applied to a discrete wavelet transform of a palmprint image, and their outputs are fused. The two techniques used in the fusion are the histogram of gradient and the binarized statistical image features. They are then evaluated using an extreme learning machine classifier before selecting a feature based on principal component analysis. Three palmprint databases, the Hong Kong Polytechnic University (PolyU) Multispectral Palmprint Database, Hong Kong PolyU Palmprint Database II, and the Delhi Touchless (IIDT) Palmprint Database, are used in this study. The study shows that our method effectively identifies and verifies palmprints and outperforms other methods based on feature extraction.

Beaked whales respond to simulated and actual navy sonar.

PubMed

Tyack, Peter L; Zimmer, Walter M X; Moretti, David; Southall, Brandon L; Claridge, Diane E; Durban, John W; Clark, Christopher W; D'Amico, Angela; DiMarzio, Nancy; Jarvis, Susan; McCarthy, Elena; Morrissey, Ronald; Ward, Jessica; Boyd, Ian L

2011-03-14

Beaked whales have mass stranded during some naval sonar exercises, but the cause is unknown. They are difficult to sight but can reliably be detected by listening for echolocation clicks produced during deep foraging dives. Listening for these clicks, we documented Blainville's beaked whales, Mesoplodon densirostris, in a naval underwater range where sonars are in regular use near Andros Island, Bahamas. An array of bottom-mounted hydrophones can detect beaked whales when they click anywhere within the range. We used two complementary methods to investigate behavioral responses of beaked whales to sonar: an opportunistic approach that monitored whale responses to multi-day naval exercises involving tactical mid-frequency sonars, and an experimental approach using playbacks of simulated sonar and control sounds to whales tagged with a device that records sound, movement, and orientation. Here we show that in both exposure conditions beaked whales stopped echolocating during deep foraging dives and moved away. During actual sonar exercises, beaked whales were primarily detected near the periphery of the range, on average 16 km away from the sonar transmissions. Once the exercise stopped, beaked whales gradually filled in the center of the range over 2-3 days. A satellite tagged whale moved outside the range during an exercise, returning over 2-3 days post-exercise. The experimental approach used tags to measure acoustic exposure and behavioral reactions of beaked whales to one controlled exposure each of simulated military sonar, killer whale calls, and band-limited noise. The beaked whales reacted to these three sound playbacks at sound pressure levels below 142 dB re 1 µPa by stopping echolocation followed by unusually long and slow ascents from their foraging dives. The combined results indicate similar disruption of foraging behavior and avoidance by beaked whales in the two different contexts, at exposures well below those used by regulators to define disturbance.

Beaked Whales Respond to Simulated and Actual Navy Sonar

PubMed Central

Tyack, Peter L.; Zimmer, Walter M. X.; Moretti, David; Southall, Brandon L.; Claridge, Diane E.; Durban, John W.; Clark, Christopher W.; D'Amico, Angela; DiMarzio, Nancy; Jarvis, Susan; McCarthy, Elena; Morrissey, Ronald; Ward, Jessica; Boyd, Ian L.

2011-01-01

Beaked whales have mass stranded during some naval sonar exercises, but the cause is unknown. They are difficult to sight but can reliably be detected by listening for echolocation clicks produced during deep foraging dives. Listening for these clicks, we documented Blainville's beaked whales, Mesoplodon densirostris, in a naval underwater range where sonars are in regular use near Andros Island, Bahamas. An array of bottom-mounted hydrophones can detect beaked whales when they click anywhere within the range. We used two complementary methods to investigate behavioral responses of beaked whales to sonar: an opportunistic approach that monitored whale responses to multi-day naval exercises involving tactical mid-frequency sonars, and an experimental approach using playbacks of simulated sonar and control sounds to whales tagged with a device that records sound, movement, and orientation. Here we show that in both exposure conditions beaked whales stopped echolocating during deep foraging dives and moved away. During actual sonar exercises, beaked whales were primarily detected near the periphery of the range, on average 16 km away from the sonar transmissions. Once the exercise stopped, beaked whales gradually filled in the center of the range over 2–3 days. A satellite tagged whale moved outside the range during an exercise, returning over 2–3 days post-exercise. The experimental approach used tags to measure acoustic exposure and behavioral reactions of beaked whales to one controlled exposure each of simulated military sonar, killer whale calls, and band-limited noise. The beaked whales reacted to these three sound playbacks at sound pressure levels below 142 dB re 1 µPa by stopping echolocation followed by unusually long and slow ascents from their foraging dives. The combined results indicate similar disruption of foraging behavior and avoidance by beaked whales in the two different contexts, at exposures well below those used by regulators to define disturbance. PMID:21423729

Robust digital image watermarking using distortion-compensated dither modulation

NASA Astrophysics Data System (ADS)

Li, Mianjie; Yuan, Xiaochen

2018-04-01

In this paper, we propose a robust feature extraction based digital image watermarking method using Distortion- Compensated Dither Modulation (DC-DM). Our proposed local watermarking method provides stronger robustness and better flexibility than traditional global watermarking methods. We improve robustness by introducing feature extraction and DC-DM method. To extract the robust feature points, we propose a DAISY-based Robust Feature Extraction (DRFE) method by employing the DAISY descriptor and applying the entropy calculation based filtering. The experimental results show that the proposed method achieves satisfactory robustness under the premise of ensuring watermark imperceptibility quality compared to other existing methods.

Digital sonar system

DOEpatents

Young, K.K.; Wilkes, R.J.

1995-11-21

A transponder of an active digital sonar system identifies a multifrequency underwater activating sonar signal received from a remote sonar transmitter. The transponder includes a transducer that receives acoustic waves, including the activating sonar signal, and generates an analog electrical receipt signal. The analog electrical receipt signal is converted to a digital receipt signal and cross-correlated with a digital transmission signal pattern corresponding to the activating sonar signal. A relative peak in the cross-correlation value is indicative of the activating sonar signal having been received by the transponder. In response to identifying the activating sonar signal, the transponder transmits a responding multifrequency sonar signal. 4 figs.

Digital sonar system

DOEpatents

Young, Kenneth K.; Wilkes, R. Jeffrey

1995-01-01

A transponder of an active digital sonar system identifies a multifrequency underwater activating sonar signal received from a remote sonar transmitter. The transponder includes a transducer that receives acoustic waves, including the activating sonar signal, and generates an analog electrical receipt signal. The analog electrical receipt signal is converted to a digital receipt signal and cross-correlated with a digital transmission signal pattern corresponding to the activating sonar signal. A relative peak in the cross-correlation value is indicative of the activating sonar signal having been received by the transponder. In response to identifying the activating sonar signal, the transponder transmits a responding multifrequency sonar signal.

Gender Recognition from Human-Body Images Using Visible-Light and Thermal Camera Videos Based on a Convolutional Neural Network for Image Feature Extraction

PubMed Central

Nguyen, Dat Tien; Kim, Ki Wan; Hong, Hyung Gil; Koo, Ja Hyung; Kim, Min Cheol; Park, Kang Ryoung

2017-01-01

Extracting powerful image features plays an important role in computer vision systems. Many methods have previously been proposed to extract image features for various computer vision applications, such as the scale-invariant feature transform (SIFT), speed-up robust feature (SURF), local binary patterns (LBP), histogram of oriented gradients (HOG), and weighted HOG. Recently, the convolutional neural network (CNN) method for image feature extraction and classification in computer vision has been used in various applications. In this research, we propose a new gender recognition method for recognizing males and females in observation scenes of surveillance systems based on feature extraction from visible-light and thermal camera videos through CNN. Experimental results confirm the superiority of our proposed method over state-of-the-art recognition methods for the gender recognition problem using human body images. PMID:28335510

Gender Recognition from Human-Body Images Using Visible-Light and Thermal Camera Videos Based on a Convolutional Neural Network for Image Feature Extraction.

PubMed

Nguyen, Dat Tien; Kim, Ki Wan; Hong, Hyung Gil; Koo, Ja Hyung; Kim, Min Cheol; Park, Kang Ryoung

2017-03-20

Extracting powerful image features plays an important role in computer vision systems. Many methods have previously been proposed to extract image features for various computer vision applications, such as the scale-invariant feature transform (SIFT), speed-up robust feature (SURF), local binary patterns (LBP), histogram of oriented gradients (HOG), and weighted HOG. Recently, the convolutional neural network (CNN) method for image feature extraction and classification in computer vision has been used in various applications. In this research, we propose a new gender recognition method for recognizing males and females in observation scenes of surveillance systems based on feature extraction from visible-light and thermal camera videos through CNN. Experimental results confirm the superiority of our proposed method over state-of-the-art recognition methods for the gender recognition problem using human body images.

A distributed pipeline for DIDSON data processing

USGS Publications Warehouse

Li, Liling; Danner, Tyler; Eickholt, Jesse; McCann, Erin L.; Pangle, Kevin; Johnson, Nicholas

2018-01-01

Technological advances in the field of ecology allow data on ecological systems to be collected at high resolution, both temporally and spatially. Devices such as Dual-frequency Identification Sonar (DIDSON) can be deployed in aquatic environments for extended periods and easily generate several terabytes of underwater surveillance data which may need to be processed multiple times. Due to the large amount of data generated and need for flexibility in processing, a distributed pipeline was constructed for DIDSON data making use of the Hadoop ecosystem. The pipeline is capable of ingesting raw DIDSON data, transforming the acoustic data to images, filtering the images, detecting and extracting motion, and generating feature data for machine learning and classification. All of the tasks in the pipeline can be run in parallel and the framework allows for custom processing. Applications of the pipeline include monitoring migration times, determining the presence of a particular species, estimating population size and other fishery management tasks.

Sonar Recognition Training: An Investigation of Whole VS. Part and Analytic VS. Synthetic Procedures.

ERIC Educational Resources Information Center

Annett, John

An experienced person, in such tasks as sonar detection and recognition, has a considerable superiority over a machine recognition system in auditory pattern recognition. However, people require extensive exposure to auditory patterns before achieving a high level of performance. In an attempt to discover a method of training people to recognize…

Digital sonar system

DOE Office of Scientific and Technical Information (OSTI.GOV)

Young, K.K.; Wilkes, R.J.

1995-11-21

A transponder of an active digital sonar system identifies a multifrequency underwater activating sonar signal received from a remote sonar transmitter. The transponder includes a transducer that receives acoustic waves, including the activating sonar signal, and generates an analog electrical receipt signal. The analog electrical receipt signal is converted to a digital receipt signal and cross-correlated with a digital transmission signal pattern corresponding to the activating sonar signal. A relative peak in the cross-correlation value is indicative of the activating sonar signal having been received by the transponder. In response to identifying the activating sonar signal, the transponder transmits amore » responding multifrequency sonar signal. 4 figs.« less

DOE Office of Scientific and Technical Information (OSTI.GOV)

Harmon, S; Jeraj, R; Galavis, P

Purpose: Sensitivity of PET-derived texture features to reconstruction methods has been reported for features extracted from axial planes; however, studies often utilize three dimensional techniques. This work aims to quantify the impact of multi-plane (3D) vs. single-plane (2D) feature extraction on radiomics-based analysis, including sensitivity to reconstruction parameters and potential loss of spatial information. Methods: Twenty-three patients with solid tumors underwent [{sup 18}F]FDG PET/CT scans under identical protocols. PET data were reconstructed using five sets of reconstruction parameters. Tumors were segmented using an automatic, in-house algorithm robust to reconstruction variations. 50 texture features were extracted using two Methods: 2D patchesmore » along axial planes and 3D patches. For each method, sensitivity of features to reconstruction parameters was calculated as percent difference relative to the average value across reconstructions. Correlations between feature values were compared when using 2D and 3D extraction. Results: 21/50 features showed significantly different sensitivity to reconstruction parameters when extracted in 2D vs 3D (wilcoxon α<0.05), assessed by overall range of variation, Rangevar(%). Eleven showed greater sensitivity to reconstruction in 2D extraction, primarily first-order and co-occurrence features (average Rangevar increase 83%). The remaining ten showed higher variation in 3D extraction (average Range{sub var}increase 27%), mainly co-occurence and greylevel run-length features. Correlation of feature value extracted in 2D and feature value extracted in 3D was poor (R<0.5) in 12/50 features, including eight co-occurrence features. Feature-to-feature correlations in 2D were marginally higher than 3D, ∣R∣>0.8 in 16% and 13% of all feature combinations, respectively. Larger sensitivity to reconstruction parameters were seen for inter-feature correlation in 2D(σ=6%) than 3D (σ<1%) extraction. Conclusion: Sensitivity and correlation of various texture features were shown to significantly differ between 2D and 3D extraction. Additionally, inter-feature correlations were more sensitive to reconstruction variation using single-plane extraction. This work highlights a need for standardized feature extraction/selection techniques in radiomics.« less

An Optimal Mean Based Block Robust Feature Extraction Method to Identify Colorectal Cancer Genes with Integrated Data.

PubMed

Liu, Jian; Cheng, Yuhu; Wang, Xuesong; Zhang, Lin; Liu, Hui

2017-08-17

It is urgent to diagnose colorectal cancer in the early stage. Some feature genes which are important to colorectal cancer development have been identified. However, for the early stage of colorectal cancer, less is known about the identity of specific cancer genes that are associated with advanced clinical stage. In this paper, we conducted a feature extraction method named Optimal Mean based Block Robust Feature Extraction method (OMBRFE) to identify feature genes associated with advanced colorectal cancer in clinical stage by using the integrated colorectal cancer data. Firstly, based on the optimal mean and L 2,1 -norm, a novel feature extraction method called Optimal Mean based Robust Feature Extraction method (OMRFE) is proposed to identify feature genes. Then the OMBRFE method which introduces the block ideology into OMRFE method is put forward to process the colorectal cancer integrated data which includes multiple genomic data: copy number alterations, somatic mutations, methylation expression alteration, as well as gene expression changes. Experimental results demonstrate that the OMBRFE is more effective than previous methods in identifying the feature genes. Moreover, genes identified by OMBRFE are verified to be closely associated with advanced colorectal cancer in clinical stage.

Speech Emotion Feature Selection Method Based on Contribution Analysis Algorithm of Neural Network

DOE Office of Scientific and Technical Information (OSTI.GOV)

Wang Xiaojia; Mao Qirong; Zhan Yongzhao

There are many emotion features. If all these features are employed to recognize emotions, redundant features may be existed. Furthermore, recognition result is unsatisfying and the cost of feature extraction is high. In this paper, a method to select speech emotion features based on contribution analysis algorithm of NN is presented. The emotion features are selected by using contribution analysis algorithm of NN from the 95 extracted features. Cluster analysis is applied to analyze the effectiveness for the features selected, and the time of feature extraction is evaluated. Finally, 24 emotion features selected are used to recognize six speech emotions.more » The experiments show that this method can improve the recognition rate and the time of feature extraction.« less

[Feature extraction for breast cancer data based on geometric algebra theory and feature selection using differential evolution].

PubMed

Li, Jing; Hong, Wenxue

2014-12-01

The feature extraction and feature selection are the important issues in pattern recognition. Based on the geometric algebra representation of vector, a new feature extraction method using blade coefficient of geometric algebra was proposed in this study. At the same time, an improved differential evolution (DE) feature selection method was proposed to solve the elevated high dimension issue. The simple linear discriminant analysis was used as the classifier. The result of the 10-fold cross-validation (10 CV) classification of public breast cancer biomedical dataset was more than 96% and proved superior to that of the original features and traditional feature extraction method.

Analysis and Modeling of Echolocation Signals Emitted by Mediterranean Bottlenose Dolphins

NASA Astrophysics Data System (ADS)

Greco, Maria; Gini, Fulvio

2006-12-01

We analyzed the echolocation sounds emitted by Mediterranean bottlenose dolphins. We extracted the click trains by visual inspection of the data files recorded along the coast of the Tuscany with the collaboration of the CETUS Research Center. We modeled the extracted sonar clicks as Gaussian or exponential multicomponent signals, we estimated the characteristic parameters and compared the data with the reconstructed signals based on the estimates. Results about the estimation and the data fitting are largely shown in the paper.

Swath sonar mapping of Earth's submarine plate boundaries

NASA Astrophysics Data System (ADS)

Carbotte, S. M.; Ferrini, V. L.; Celnick, M.; Nitsche, F. O.; Ryan, W. B. F.

2014-12-01

The recent loss of Malaysia Airlines flight MH370 in an area of the Indian Ocean where less than 5% of the seafloor is mapped with depth sounding data (Smith and Marks, EOS 2014) highlights the striking lack of detailed knowledge of the topography of the seabed for much of the worlds' oceans. Advances in swath sonar mapping technology over the past 30 years have led to dramatic improvements in our capability to map the seabed. However, the oceans are vast and only an estimated 10% of the seafloor has been mapped with these systems. Furthermore, the available coverage is highly heterogeneous and focused within areas of national strategic priority and community scientific interest. The major plate boundaries that encircle the globe, most of which are located in the submarine environment, have been a significant focus of marine geoscience research since the advent of swath sonar mapping. While the location of these plate boundaries are well defined from satellite-derived bathymetry, significant regions remain unmapped at the high-resolutions provided by swath sonars and that are needed to study active volcanic and tectonic plate boundary processes. Within the plate interiors, some fossil plate boundary zones, major hotspot volcanoes, and other volcanic provinces have been the focus of dedicated research programs. Away from these major tectonic structures, swath mapping coverage is limited to sparse ocean transit lines which often reveal previously unknown deep-sea channels and other little studied sedimentary structures not resolvable in existing low-resolution global compilations, highlighting the value of these data even in the tectonically quiet plate interiors. Here, we give an overview of multibeam swath sonar mapping of the major plate boundaries of the globe as extracted from public archives. Significant quantities of swath sonar data acquired from deep-sea regions are in restricted-access international archives. Open access to more of these data sets would enable global comparisons of plate boundary structures and processes and could facilitate a more coordinated approach to optimizing the future acquisition of these high-value data by the global research community.

Structural Mapping and Geomorphology of Ireland's Southwest Continental Shelf Using High Resolution Sonar

NASA Astrophysics Data System (ADS)

Bowden, S.; Wireman, R.

2016-02-01

Bathymetric surveys were conducted on the continental shelf off the southwest coast of County Cork, Ireland by the Marine Institute of Ireland, the Geological Survey of Ireland, and the INFOMAR project. Data were collected from July 2006 through September 2014 using a Kongsberg EM2040 multibeam echosounder aboard the R/Vs Celtic Voyager and Keary, and a Kongsberg EM1002 on the R/V Celtic Explorer. Sonar data were post-processed with CARIS HIPS and SIPS 9.0 to create 2D and 3D bathymetric and backscatter intensity surfaces with a resolution of 1 m. The offshore study site is part of the 286 Ma western Variscian orogenic front and has several massive outcrops, exhibiting 5 to 20 m of near-vertical relief. These outcrops were structurally mapped and relatively aged, and exhibit significant folding, rotation, tilting, and joint systems. Google Earth, ArcGIS, and previous terrestrial studies were used to further analyze how geomorphology is controlled by seafloor composition and structural features. Rock type and age were interpreted by comparing fracture analysis of the joints and fold trends to similar onshore outcrops documented previously, to determine an age of 416-299 Ma for the shelf's outcropping strata and associated structural features. The oldest features observed are regional anticlines and synclines containing Upper Devonian Old Red Sandstone and Lower Carboniferous shales. Within the shale layers are NE-SW plunging parasitic chevron folds. Jointing is observed in both sandstone and shale layers and is superimposed on chevron folding, with cross joints appearing to influence shallow current patterns. Rotation of the regional folds is the youngest structural feature, as both the parasitic folds and joint systems are warped. Our study shows that high resolution sonar is an effective tool for offshore structural mapping and is an important resource for understanding the geomorphology and geologic history of submerged outcrops on continental shelf systems.

A novel murmur-based heart sound feature extraction technique using envelope-morphological analysis

NASA Astrophysics Data System (ADS)

Yao, Hao-Dong; Ma, Jia-Li; Fu, Bin-Bin; Wang, Hai-Yang; Dong, Ming-Chui

2015-07-01

Auscultation of heart sound (HS) signals serves as an important primary approach to diagnose cardiovascular diseases (CVDs) for centuries. Confronting the intrinsic drawbacks of traditional HS auscultation, computer-aided automatic HS auscultation based on feature extraction technique has witnessed explosive development. Yet, most existing HS feature extraction methods adopt acoustic or time-frequency features which exhibit poor relationship with diagnostic information, thus restricting the performance of further interpretation and analysis. Tackling such a bottleneck problem, this paper innovatively proposes a novel murmur-based HS feature extraction method since murmurs contain massive pathological information and are regarded as the first indications of pathological occurrences of heart valves. Adapting discrete wavelet transform (DWT) and Shannon envelope, the envelope-morphological characteristics of murmurs are obtained and three features are extracted accordingly. Validated by discriminating normal HS and 5 various abnormal HS signals with extracted features, the proposed method provides an attractive candidate in automatic HS auscultation.

Efficacy Evaluation of Different Wavelet Feature Extraction Methods on Brain MRI Tumor Detection

NASA Astrophysics Data System (ADS)

Nabizadeh, Nooshin; John, Nigel; Kubat, Miroslav

2014-03-01

Automated Magnetic Resonance Imaging brain tumor detection and segmentation is a challenging task. Among different available methods, feature-based methods are very dominant. While many feature extraction techniques have been employed, it is still not quite clear which of feature extraction methods should be preferred. To help improve the situation, we present the results of a study in which we evaluate the efficiency of using different wavelet transform features extraction methods in brain MRI abnormality detection. Applying T1-weighted brain image, Discrete Wavelet Transform (DWT), Discrete Wavelet Packet Transform (DWPT), Dual Tree Complex Wavelet Transform (DTCWT), and Complex Morlet Wavelet Transform (CMWT) methods are applied to construct the feature pool. Three various classifiers as Support Vector Machine, K Nearest Neighborhood, and Sparse Representation-Based Classifier are applied and compared for classifying the selected features. The results show that DTCWT and CMWT features classified with SVM, result in the highest classification accuracy, proving of capability of wavelet transform features to be informative in this application.

Combining split-beam and dual-frequency identification sonars to estimate abundance of anadromous fishes in the Roanoke River, North Carolina

USGS Publications Warehouse

Hughes, Jacob B.; Hightower, Joseph E.

2015-01-01

Riverine hydroacoustic techniques are an effective method for evaluating abundance of upstream migrating anadromous fishes. To use these methods in the Roanoke River, North Carolina, at a wide site with uneven bottom topography, we used a combination of split-beam sonar and dual-frequency identification sonar (DIDSON) deployments. We aimed a split-beam sonar horizontally to monitor midchannel and near-bottom zones continuously over the 3-month spring monitoring periods in 2010 and 2011. The DIDSON was rotated between seven cross-channel locations (using a vertical aim) and nearshore regions (using horizontal aims). Vertical deployment addressed blind spots in split-beam coverage along the bottom and provided reliable information about the cross-channel and vertical distributions of upstream migrants. Using a Bayesian framework, we modeled sonar counts within four cross-channel strata and apportioned counts by species using species proportions from boat electrofishing and gill netting. Modeled estimates (95% credible intervals [CIs]) of total upstream migrants in 2010 and 2011 were 2.5 million (95% CI, 2.4–2.6 million) and 3.6 million (95% CI, 3.4–3.9 million), respectively. Results indicated that upstream migrants are extremely shore- and bottom-oriented, suggesting nearshore DIDSON monitoring improved the accuracy and precision of our estimates. This monitoring protocol and model may be widely applicable to river systems regardless of their cross-sectional width or profile.

Smart Acoustic Network Using Combined FSK-PSK, Adaptive Beamforming and Equalization

DTIC Science & Technology

2002-09-30

sonar data transmission from underwater vehicle during mission. The two-year objectives for the high-reliability acoustic network using multiple... sonar laboratory) and used for acoustic networking during underwater vehicle operation. The joint adaptive coherent path beamformer method consists...broadband communications transducer, while the low noise preamplifier conditions received signals for analog to digital conversion. External user

Smart Acoustic Network Using Combined FSK-PSK, Adaptive, Beamforming and Equalization

DTIC Science & Technology

2001-09-30

sonar data transmission from underwater vehicle during mission. The two-year objectives for the high-reliability acoustic network using multiple... sonar laboratory) and used for acoustic networking during underwater vehicle operation. The joint adaptive coherent path beamformer method consists...broadband communications transducer, while the low noise preamplifier conditions received signals for analog to digital conversion. External user

A PCA aided cross-covariance scheme for discriminative feature extraction from EEG signals.

PubMed

Zarei, Roozbeh; He, Jing; Siuly, Siuly; Zhang, Yanchun

2017-07-01

Feature extraction of EEG signals plays a significant role in Brain-computer interface (BCI) as it can significantly affect the performance and the computational time of the system. The main aim of the current work is to introduce an innovative algorithm for acquiring reliable discriminating features from EEG signals to improve classification performances and to reduce the time complexity. This study develops a robust feature extraction method combining the principal component analysis (PCA) and the cross-covariance technique (CCOV) for the extraction of discriminatory information from the mental states based on EEG signals in BCI applications. We apply the correlation based variable selection method with the best first search on the extracted features to identify the best feature set for characterizing the distribution of mental state signals. To verify the robustness of the proposed feature extraction method, three machine learning techniques: multilayer perceptron neural networks (MLP), least square support vector machine (LS-SVM), and logistic regression (LR) are employed on the obtained features. The proposed methods are evaluated on two publicly available datasets. Furthermore, we evaluate the performance of the proposed methods by comparing it with some recently reported algorithms. The experimental results show that all three classifiers achieve high performance (above 99% overall classification accuracy) for the proposed feature set. Among these classifiers, the MLP and LS-SVM methods yield the best performance for the obtained feature. The average sensitivity, specificity and classification accuracy for these two classifiers are same, which are 99.32%, 100%, and 99.66%, respectively for the BCI competition dataset IVa and 100%, 100%, and 100%, for the BCI competition dataset IVb. The results also indicate the proposed methods outperform the most recently reported methods by at least 0.25% average accuracy improvement in dataset IVa. The execution time results show that the proposed method has less time complexity after feature selection. The proposed feature extraction method is very effective for getting representatives information from mental states EEG signals in BCI applications and reducing the computational complexity of classifiers by reducing the number of extracted features. Copyright © 2017 Elsevier B.V. All rights reserved.

Digitally controlled sonars

NASA Technical Reports Server (NTRS)

Hansen, G. R.

1983-01-01

Sonars are usually designed and constructed as stand alone instruments. That is, all elements or subsystems of the sonar are provided: power conditioning, displays, intercommunications, control, receiver, transmitter, and transducer. The sonars which are a part of the Advanced Ocean Test Development Platform (AOTDP) represent a departure from this manner of implementation and are configured more like an instrumentation system. Only the transducer, transmitter, and receiver which are unique to a particular sonar function; Up, Down, Side Scan, exist as separable subsystems. The remaining functions are reserved to the AOTDP and serve all sonars and other instrumentation in a shared manner. The organization and functions of the common AOTDP elements were described and then the interface with the sonars discussed. The techniques for software control of the sonar parameters were explained followed by the details of the realization of the sonar functions and some discussion of the performance of the side scan sonars.

Neural network modeling of a dolphin's sonar discrimination capabilities.

PubMed

Au, W W; Andersen, L N; Rasmussen, A R; Roitblat, H L; Nachtigall, P E

1995-07-01

The capability of an echolocating dolphin to discriminate differences in the wall thickness of cylinders was previously modeled by a counterpropagation neural network using only spectral information from the echoes. In this study, both time and frequency information were used to model the dolphin discrimination capabilities. Echoes from the same cylinders were digitized using a broadband simulated dolphin sonar signal with the transducer mounted on the dolphin's pen. The echoes were filtered by a bank of continuous constant-Q digital filters and the energy from each filter was computed in time increments of 1/bandwidth. Echo features of the standard and each comparison target were analyzed in pairs by a counterpropagation neural network, a backpropagation neural network, and a model using Euclidean distance measures. The backpropagation network performed better than both the counterpropagation network, and the Euclidean model, using either spectral-only features or combined temporal and spectral features. All models performed better using features containing both temporal and spectral information. The backpropagation network was able to perform better than the dolphins for noise-free echoes with Q values as low as 2 and 3. For a Q of 2, only temporal information was available. However, with noisy data, the network required a Q of 8 in order to perform as well as the dolphin.

A novel feature extraction approach for microarray data based on multi-algorithm fusion

PubMed Central

Jiang, Zhu; Xu, Rong

2015-01-01

Feature extraction is one of the most important and effective method to reduce dimension in data mining, with emerging of high dimensional data such as microarray gene expression data. Feature extraction for gene selection, mainly serves two purposes. One is to identify certain disease-related genes. The other is to find a compact set of discriminative genes to build a pattern classifier with reduced complexity and improved generalization capabilities. Depending on the purpose of gene selection, two types of feature extraction algorithms including ranking-based feature extraction and set-based feature extraction are employed in microarray gene expression data analysis. In ranking-based feature extraction, features are evaluated on an individual basis, without considering inter-relationship between features in general, while set-based feature extraction evaluates features based on their role in a feature set by taking into account dependency between features. Just as learning methods, feature extraction has a problem in its generalization ability, which is robustness. However, the issue of robustness is often overlooked in feature extraction. In order to improve the accuracy and robustness of feature extraction for microarray data, a novel approach based on multi-algorithm fusion is proposed. By fusing different types of feature extraction algorithms to select the feature from the samples set, the proposed approach is able to improve feature extraction performance. The new approach is tested against gene expression dataset including Colon cancer data, CNS data, DLBCL data, and Leukemia data. The testing results show that the performance of this algorithm is better than existing solutions. PMID:25780277

A novel feature extraction approach for microarray data based on multi-algorithm fusion.

PubMed

Jiang, Zhu; Xu, Rong

2015-01-01

Feature extraction is one of the most important and effective method to reduce dimension in data mining, with emerging of high dimensional data such as microarray gene expression data. Feature extraction for gene selection, mainly serves two purposes. One is to identify certain disease-related genes. The other is to find a compact set of discriminative genes to build a pattern classifier with reduced complexity and improved generalization capabilities. Depending on the purpose of gene selection, two types of feature extraction algorithms including ranking-based feature extraction and set-based feature extraction are employed in microarray gene expression data analysis. In ranking-based feature extraction, features are evaluated on an individual basis, without considering inter-relationship between features in general, while set-based feature extraction evaluates features based on their role in a feature set by taking into account dependency between features. Just as learning methods, feature extraction has a problem in its generalization ability, which is robustness. However, the issue of robustness is often overlooked in feature extraction. In order to improve the accuracy and robustness of feature extraction for microarray data, a novel approach based on multi-algorithm fusion is proposed. By fusing different types of feature extraction algorithms to select the feature from the samples set, the proposed approach is able to improve feature extraction performance. The new approach is tested against gene expression dataset including Colon cancer data, CNS data, DLBCL data, and Leukemia data. The testing results show that the performance of this algorithm is better than existing solutions.

Extraction of multi-scale landslide morphological features based on local Gi* using airborne LiDAR-derived DEM

NASA Astrophysics Data System (ADS)

Shi, Wenzhong; Deng, Susu; Xu, Wenbing

2018-02-01

For automatic landslide detection, landslide morphological features should be quantitatively expressed and extracted. High-resolution Digital Elevation Models (DEMs) derived from airborne Light Detection and Ranging (LiDAR) data allow fine-scale morphological features to be extracted, but noise in DEMs influences morphological feature extraction, and the multi-scale nature of landslide features should be considered. This paper proposes a method to extract landslide morphological features characterized by homogeneous spatial patterns. Both profile and tangential curvature are utilized to quantify land surface morphology, and a local Gi* statistic is calculated for each cell to identify significant patterns of clustering of similar morphometric values. The method was tested on both synthetic surfaces simulating natural terrain and airborne LiDAR data acquired over an area dominated by shallow debris slides and flows. The test results of the synthetic data indicate that the concave and convex morphologies of the simulated terrain features at different scales and distinctness could be recognized using the proposed method, even when random noise was added to the synthetic data. In the test area, cells with large local Gi* values were extracted at a specified significance level from the profile and the tangential curvature image generated from the LiDAR-derived 1-m DEM. The morphologies of landslide main scarps, source areas and trails were clearly indicated, and the morphological features were represented by clusters of extracted cells. A comparison with the morphological feature extraction method based on curvature thresholds proved the proposed method's robustness to DEM noise. When verified against a landslide inventory, the morphological features of almost all recent (< 5 years) landslides and approximately 35% of historical (> 10 years) landslides were extracted. This finding indicates that the proposed method can facilitate landslide detection, although the cell clusters extracted from curvature images should be filtered using a filtering strategy based on supplementary information provided by expert knowledge or other data sources.

Ship Detection Based on Multiple Features in Random Forest Model for Hyperspectral Images

NASA Astrophysics Data System (ADS)

Li, N.; Ding, L.; Zhao, H.; Shi, J.; Wang, D.; Gong, X.

2018-04-01

A novel method for detecting ships which aim to make full use of both the spatial and spectral information from hyperspectral images is proposed. Firstly, the band which is high signal-noise ratio in the range of near infrared or short-wave infrared spectrum, is used to segment land and sea on Otsu threshold segmentation method. Secondly, multiple features that include spectral and texture features are extracted from hyperspectral images. Principal components analysis (PCA) is used to extract spectral features, the Grey Level Co-occurrence Matrix (GLCM) is used to extract texture features. Finally, Random Forest (RF) model is introduced to detect ships based on the extracted features. To illustrate the effectiveness of the method, we carry out experiments over the EO-1 data by comparing single feature and different multiple features. Compared with the traditional single feature method and Support Vector Machine (SVM) model, the proposed method can stably achieve the target detection of ships under complex background and can effectively improve the detection accuracy of ships.

An Improved Azimuth Angle Estimation Method with a Single Acoustic Vector Sensor Based on an Active Sonar Detection System.

PubMed

Zhao, Anbang; Ma, Lin; Ma, Xuefei; Hui, Juan

2017-02-20

In this paper, an improved azimuth angle estimation method with a single acoustic vector sensor (AVS) is proposed based on matched filtering theory. The proposed method is mainly applied in an active sonar detection system. According to the conventional passive method based on complex acoustic intensity measurement, the mathematical and physical model of this proposed method is described in detail. The computer simulation and lake experiments results indicate that this method can realize the azimuth angle estimation with high precision by using only a single AVS. Compared with the conventional method, the proposed method achieves better estimation performance. Moreover, the proposed method does not require complex operations in frequencydomain and achieves computational complexity reduction.

Sonar Imaging of Elastic Fluid-Filled Cylindrical Shells.

NASA Astrophysics Data System (ADS)

Dodd, Stirling Scott

1995-01-01

Previously a method of describing spherical acoustic waves in cylindrical coordinates was applied to the problem of point source scattering by an elastic infinite fluid -filled cylindrical shell (S. Dodd and C. Loeffler, J. Acoust. Soc. Am. 97, 3284(A) (1995)). This method is applied to numerically model monostatic oblique incidence scattering from a truncated cylinder by a narrow-beam high-frequency imaging sonar. The narrow beam solution results from integrating the point source solution over the spatial extent of a line source and line receiver. The cylinder truncation is treated by the method of images, and assumes that the reflection coefficient at the truncation is unity. The scattering form functions, calculated using this method, are applied as filters to a narrow bandwidth, high ka pulse to find the time domain scattering response. The time domain pulses are further processed and displayed in the form of a sonar image. These images compare favorably to experimentally obtained images (G. Kaduchak and C. Loeffler, J. Acoust. Soc. Am. 97, 3289(A) (1995)). The impact of the s_{ rm o} and a_{rm o} Lamb waves is vividly apparent in the images.

Seafloor identification in sonar imagery via simulations of Helmholtz equations and discrete optimization

NASA Astrophysics Data System (ADS)

Engquist, Björn; Frederick, Christina; Huynh, Quyen; Zhou, Haomin

2017-06-01

We present a multiscale approach for identifying features in ocean beds by solving inverse problems in high frequency seafloor acoustics. The setting is based on Sound Navigation And Ranging (SONAR) imaging used in scientific, commercial, and military applications. The forward model incorporates multiscale simulations, by coupling Helmholtz equations and geometrical optics for a wide range of spatial scales in the seafloor geometry. This allows for detailed recovery of seafloor parameters including material type. Simulated backscattered data is generated using numerical microlocal analysis techniques. In order to lower the computational cost of the large-scale simulations in the inversion process, we take advantage of a pre-computed library of representative acoustic responses from various seafloor parameterizations.

Built-up Areas Extraction in High Resolution SAR Imagery based on the method of Multiple Feature Weighted Fusion

NASA Astrophysics Data System (ADS)

Liu, X.; Zhang, J. X.; Zhao, Z.; Ma, A. D.

2015-06-01

Synthetic aperture radar in the application of remote sensing technology is becoming more and more widely because of its all-time and all-weather operation, feature extraction research in high resolution SAR image has become a hot topic of concern. In particular, with the continuous improvement of airborne SAR image resolution, image texture information become more abundant. It's of great significance to classification and extraction. In this paper, a novel method for built-up areas extraction using both statistical and structural features is proposed according to the built-up texture features. First of all, statistical texture features and structural features are respectively extracted by classical method of gray level co-occurrence matrix and method of variogram function, and the direction information is considered in this process. Next, feature weights are calculated innovatively according to the Bhattacharyya distance. Then, all features are weighted fusion. At last, the fused image is classified with K-means classification method and the built-up areas are extracted after post classification process. The proposed method has been tested by domestic airborne P band polarization SAR images, at the same time, two groups of experiments based on the method of statistical texture and the method of structural texture were carried out respectively. On the basis of qualitative analysis, quantitative analysis based on the built-up area selected artificially is enforced, in the relatively simple experimentation area, detection rate is more than 90%, in the relatively complex experimentation area, detection rate is also higher than the other two methods. In the study-area, the results show that this method can effectively and accurately extract built-up areas in high resolution airborne SAR imagery.

Side-scan sonar mapping: Pseudo-real-time processing and mosaicking techniques

DOE Office of Scientific and Technical Information (OSTI.GOV)

Danforth, W.W.; Schwab, W.C.; O'Brien, T.F.

1990-05-01

The US Geological Survey (USGS) surveyed 1,000 km{sup 2} of the continental shelf off San Francisco during a 17-day cruise, using a 120-kHz side-scan sonar system, and produced a digitally processed sonar mosaic of the survey area. The data were processed and mosaicked in real time using software developed at the Lamont-Doherty Geological Observatory and modified by the USGS, a substantial task due to the enormous amount of data produced by high-resolution side-scan systems. Approximately 33 megabytes of data were acquired every 1.5 hr. The real-time sonar images were displayed on a PC-based workstation and the data were transferred tomore » a UNIX minicomputer where the sonar images were slant-range corrected, enhanced using an averaging method of desampling and a linear-contrast stretch, merged with navigation, geographically oriented at a user-selected scale, and finally output to a thermal printer. The hard-copy output was then used to construct a mosaic of the survey area. The final product of this technique is a UTM-projected map-mosaic of sea-floor backscatter variations, which could be used, for example, to locate appropriate sites for sediment sampling to ground truth the sonar imagery while still at sea. More importantly, reconnaissance surveys of this type allow for the analysis and interpretation of the mosaic during a cruise, thus greatly reducing the preparation time needed for planning follow-up studies of a particular area.« less

Acoustic⁻Seismic Mixed Feature Extraction Based on Wavelet Transform for Vehicle Classification in Wireless Sensor Networks.

PubMed

Zhang, Heng; Pan, Zhongming; Zhang, Wenna

2018-06-07

An acoustic⁻seismic mixed feature extraction method based on the wavelet coefficient energy ratio (WCER) of the target signal is proposed in this study for classifying vehicle targets in wireless sensor networks. The signal was decomposed into a set of wavelet coefficients using the à trous algorithm, which is a concise method used to implement the wavelet transform of a discrete signal sequence. After the wavelet coefficients of the target acoustic and seismic signals were obtained, the energy ratio of each layer coefficient was calculated as the feature vector of the target signals. Subsequently, the acoustic and seismic features were merged into an acoustic⁻seismic mixed feature to improve the target classification accuracy after the acoustic and seismic WCER features of the target signal were simplified using the hierarchical clustering method. We selected the support vector machine method for classification and utilized the data acquired from a real-world experiment to validate the proposed method. The calculated results show that the WCER feature extraction method can effectively extract the target features from target signals. Feature simplification can reduce the time consumption of feature extraction and classification, with no effect on the target classification accuracy. The use of acoustic⁻seismic mixed features effectively improved target classification accuracy by approximately 12% compared with either acoustic signal or seismic signal alone.

A new automated spectral feature extraction method and its application in spectral classification and defective spectra recovery

NASA Astrophysics Data System (ADS)

Wang, Ke; Guo, Ping; Luo, A.-Li

2017-03-01

Spectral feature extraction is a crucial procedure in automated spectral analysis. This procedure starts from the spectral data and produces informative and non-redundant features, facilitating the subsequent automated processing and analysis with machine-learning and data-mining techniques. In this paper, we present a new automated feature extraction method for astronomical spectra, with application in spectral classification and defective spectra recovery. The basic idea of our approach is to train a deep neural network to extract features of spectra with different levels of abstraction in different layers. The deep neural network is trained with a fast layer-wise learning algorithm in an analytical way without any iterative optimization procedure. We evaluate the performance of the proposed scheme on real-world spectral data. The results demonstrate that our method is superior regarding its comprehensive performance, and the computational cost is significantly lower than that for other methods. The proposed method can be regarded as a new valid alternative general-purpose feature extraction method for various tasks in spectral data analysis.

[A novel method of multi-channel feature extraction combining multivariate autoregression and multiple-linear principal component analysis].

PubMed

Wang, Jinjia; Zhang, Yanna

2015-02-01

Brain-computer interface (BCI) systems identify brain signals through extracting features from them. In view of the limitations of the autoregressive model feature extraction method and the traditional principal component analysis to deal with the multichannel signals, this paper presents a multichannel feature extraction method that multivariate autoregressive (MVAR) model combined with the multiple-linear principal component analysis (MPCA), and used for magnetoencephalography (MEG) signals and electroencephalograph (EEG) signals recognition. Firstly, we calculated the MVAR model coefficient matrix of the MEG/EEG signals using this method, and then reduced the dimensions to a lower one, using MPCA. Finally, we recognized brain signals by Bayes Classifier. The key innovation we introduced in our investigation showed that we extended the traditional single-channel feature extraction method to the case of multi-channel one. We then carried out the experiments using the data groups of IV-III and IV - I. The experimental results proved that the method proposed in this paper was feasible.

Classification and pose estimation of objects using nonlinear features

NASA Astrophysics Data System (ADS)

Talukder, Ashit; Casasent, David P.

1998-03-01

A new nonlinear feature extraction method called the maximum representation and discrimination feature (MRDF) method is presented for extraction of features from input image data. It implements transformations similar to the Sigma-Pi neural network. However, the weights of the MRDF are obtained in closed form, and offer advantages compared to nonlinear neural network implementations. The features extracted are useful for both object discrimination (classification) and object representation (pose estimation). We show its use in estimating the class and pose of images of real objects and rendered solid CAD models of machine parts from single views using a feature-space trajectory (FST) neural network classifier. We show more accurate classification and pose estimation results than are achieved by standard principal component analysis (PCA) and Fukunaga-Koontz (FK) feature extraction methods.

Sample-space-based feature extraction and class preserving projection for gene expression data.

PubMed

Wang, Wenjun

2013-01-01

In order to overcome the problems of high computational complexity and serious matrix singularity for feature extraction using Principal Component Analysis (PCA) and Fisher's Linear Discrinimant Analysis (LDA) in high-dimensional data, sample-space-based feature extraction is presented, which transforms the computation procedure of feature extraction from gene space to sample space by representing the optimal transformation vector with the weighted sum of samples. The technique is used in the implementation of PCA, LDA, Class Preserving Projection (CPP) which is a new method for discriminant feature extraction proposed, and the experimental results on gene expression data demonstrate the effectiveness of the method.

Effective and extensible feature extraction method using genetic algorithm-based frequency-domain feature search for epileptic EEG multiclassification

PubMed Central

Wen, Tingxi; Zhang, Zhongnan

2017-01-01

Abstract In this paper, genetic algorithm-based frequency-domain feature search (GAFDS) method is proposed for the electroencephalogram (EEG) analysis of epilepsy. In this method, frequency-domain features are first searched and then combined with nonlinear features. Subsequently, these features are selected and optimized to classify EEG signals. The extracted features are analyzed experimentally. The features extracted by GAFDS show remarkable independence, and they are superior to the nonlinear features in terms of the ratio of interclass distance and intraclass distance. Moreover, the proposed feature search method can search for features of instantaneous frequency in a signal after Hilbert transformation. The classification results achieved using these features are reasonable; thus, GAFDS exhibits good extensibility. Multiple classical classifiers (i.e., k-nearest neighbor, linear discriminant analysis, decision tree, AdaBoost, multilayer perceptron, and Naïve Bayes) achieve satisfactory classification accuracies by using the features generated by the GAFDS method and the optimized feature selection. The accuracies for 2-classification and 3-classification problems may reach up to 99% and 97%, respectively. Results of several cross-validation experiments illustrate that GAFDS is effective in the extraction of effective features for EEG classification. Therefore, the proposed feature selection and optimization model can improve classification accuracy. PMID:28489789

Effective and extensible feature extraction method using genetic algorithm-based frequency-domain feature search for epileptic EEG multiclassification.

PubMed

Wen, Tingxi; Zhang, Zhongnan

2017-05-01

In this paper, genetic algorithm-based frequency-domain feature search (GAFDS) method is proposed for the electroencephalogram (EEG) analysis of epilepsy. In this method, frequency-domain features are first searched and then combined with nonlinear features. Subsequently, these features are selected and optimized to classify EEG signals. The extracted features are analyzed experimentally. The features extracted by GAFDS show remarkable independence, and they are superior to the nonlinear features in terms of the ratio of interclass distance and intraclass distance. Moreover, the proposed feature search method can search for features of instantaneous frequency in a signal after Hilbert transformation. The classification results achieved using these features are reasonable; thus, GAFDS exhibits good extensibility. Multiple classical classifiers (i.e., k-nearest neighbor, linear discriminant analysis, decision tree, AdaBoost, multilayer perceptron, and Naïve Bayes) achieve satisfactory classification accuracies by using the features generated by the GAFDS method and the optimized feature selection. The accuracies for 2-classification and 3-classification problems may reach up to 99% and 97%, respectively. Results of several cross-validation experiments illustrate that GAFDS is effective in the extraction of effective features for EEG classification. Therefore, the proposed feature selection and optimization model can improve classification accuracy.

An Improved Azimuth Angle Estimation Method with a Single Acoustic Vector Sensor Based on an Active Sonar Detection System

PubMed Central

Zhao, Anbang; Ma, Lin; Ma, Xuefei; Hui, Juan

2017-01-01

In this paper, an improved azimuth angle estimation method with a single acoustic vector sensor (AVS) is proposed based on matched filtering theory. The proposed method is mainly applied in an active sonar detection system. According to the conventional passive method based on complex acoustic intensity measurement, the mathematical and physical model of this proposed method is described in detail. The computer simulation and lake experiments results indicate that this method can realize the azimuth angle estimation with high precision by using only a single AVS. Compared with the conventional method, the proposed method achieves better estimation performance. Moreover, the proposed method does not require complex operations in frequency-domain and achieves computational complexity reduction. PMID:28230763

An Effective Palmprint Recognition Approach for Visible and Multispectral Sensor Images.

PubMed

Gumaei, Abdu; Sammouda, Rachid; Al-Salman, Abdul Malik; Alsanad, Ahmed

2018-05-15

Among several palmprint feature extraction methods the HOG-based method is attractive and performs well against changes in illumination and shadowing of palmprint images. However, it still lacks the robustness to extract the palmprint features at different rotation angles. To solve this problem, this paper presents a hybrid feature extraction method, named HOG-SGF that combines the histogram of oriented gradients (HOG) with a steerable Gaussian filter (SGF) to develop an effective palmprint recognition approach. The approach starts by processing all palmprint images by David Zhang's method to segment only the region of interests. Next, we extracted palmprint features based on the hybrid HOG-SGF feature extraction method. Then, an optimized auto-encoder (AE) was utilized to reduce the dimensionality of the extracted features. Finally, a fast and robust regularized extreme learning machine (RELM) was applied for the classification task. In the evaluation phase of the proposed approach, a number of experiments were conducted on three publicly available palmprint databases, namely MS-PolyU of multispectral palmprint images and CASIA and Tongji of contactless palmprint images. Experimentally, the results reveal that the proposed approach outperforms the existing state-of-the-art approaches even when a small number of training samples are used.

A multiple maximum scatter difference discriminant criterion for facial feature extraction.

PubMed

Song, Fengxi; Zhang, David; Mei, Dayong; Guo, Zhongwei

2007-12-01

Maximum scatter difference (MSD) discriminant criterion was a recently presented binary discriminant criterion for pattern classification that utilizes the generalized scatter difference rather than the generalized Rayleigh quotient as a class separability measure, thereby avoiding the singularity problem when addressing small-sample-size problems. MSD classifiers based on this criterion have been quite effective on face-recognition tasks, but as they are binary classifiers, they are not as efficient on large-scale classification tasks. To address the problem, this paper generalizes the classification-oriented binary criterion to its multiple counterpart--multiple MSD (MMSD) discriminant criterion for facial feature extraction. The MMSD feature-extraction method, which is based on this novel discriminant criterion, is a new subspace-based feature-extraction method. Unlike most other subspace-based feature-extraction methods, the MMSD computes its discriminant vectors from both the range of the between-class scatter matrix and the null space of the within-class scatter matrix. The MMSD is theoretically elegant and easy to calculate. Extensive experimental studies conducted on the benchmark database, FERET, show that the MMSD out-performs state-of-the-art facial feature-extraction methods such as null space method, direct linear discriminant analysis (LDA), eigenface, Fisherface, and complete LDA.

Analysis of calibrated seafloor backscatter for habitat classification methodology and case study of 158 spots in the Bay of Biscay and Celtic Sea

NASA Astrophysics Data System (ADS)

Fezzani, Ridha; Berger, Laurent

2018-06-01

An automated signal-based method was developed in order to analyse the seafloor backscatter data logged by calibrated multibeam echosounder. The processing consists first in the clustering of each survey sub-area into a small number of homogeneous sediment types, based on the backscatter average level at one or several incidence angles. Second, it uses their local average angular response to extract discriminant descriptors, obtained by fitting the field data to the Generic Seafloor Acoustic Backscatter parametric model. Third, the descriptors are used for seafloor type classification. The method was tested on the multi-year data recorded by a calibrated 90-kHz Simrad ME70 multibeam sonar operated in the Bay of Biscay, France and Celtic Sea, Ireland. It was applied for seafloor-type classification into 12 classes, to a dataset of 158 spots surveyed for demersal and benthic fauna study and monitoring. Qualitative analyses and classified clusters using extracted parameters show a good discriminatory potential, indicating the robustness of this approach.

Large Margin Multi-Modal Multi-Task Feature Extraction for Image Classification.

PubMed

Yong Luo; Yonggang Wen; Dacheng Tao; Jie Gui; Chao Xu

2016-01-01

The features used in many image analysis-based applications are frequently of very high dimension. Feature extraction offers several advantages in high-dimensional cases, and many recent studies have used multi-task feature extraction approaches, which often outperform single-task feature extraction approaches. However, most of these methods are limited in that they only consider data represented by a single type of feature, even though features usually represent images from multiple modalities. We, therefore, propose a novel large margin multi-modal multi-task feature extraction (LM3FE) framework for handling multi-modal features for image classification. In particular, LM3FE simultaneously learns the feature extraction matrix for each modality and the modality combination coefficients. In this way, LM3FE not only handles correlated and noisy features, but also utilizes the complementarity of different modalities to further help reduce feature redundancy in each modality. The large margin principle employed also helps to extract strongly predictive features, so that they are more suitable for prediction (e.g., classification). An alternating algorithm is developed for problem optimization, and each subproblem can be efficiently solved. Experiments on two challenging real-world image data sets demonstrate the effectiveness and superiority of the proposed method.

Behavioral responses by grey seals (Halichoerus grypus) to high frequency sonar.

PubMed

Hastie, Gordon D; Donovan, Carl; Götz, Thomas; Janik, Vincent M

2014-02-15

The use of high frequency sonar is now commonplace in the marine environment. Most marine mammals rely on sound to navigate, and for detecting prey, and there is the potential that the acoustic signals of sonar could cause behavioral responses. To investigate this, we carried out behavioral response tests with grey seals to two sonar systems (200 and 375 kHz systems). Results showed that both systems had significant effects on the seals behavior; when the 200 kHz sonar was active, seals spent significantly more time hauled out and, although seals remained swimming during operation of the 375 kHz sonar, they were distributed further from the sonar. The results show that although peak sonar frequencies may be above marine mammal hearing ranges, high levels of sound can be produced within their hearing ranges that elicit behavioral responses; this has clear implications for the widespread use of sonar in the marine environment. Copyright © 2013 Elsevier Ltd. All rights reserved.

Research of facial feature extraction based on MMC

NASA Astrophysics Data System (ADS)

Xue, Donglin; Zhao, Jiufen; Tang, Qinhong; Shi, Shaokun

2017-07-01

Based on the maximum margin criterion (MMC), a new algorithm of statistically uncorrelated optimal discriminant vectors and a new algorithm of orthogonal optimal discriminant vectors for feature extraction were proposed. The purpose of the maximum margin criterion is to maximize the inter-class scatter while simultaneously minimizing the intra-class scatter after the projection. Compared with original MMC method and principal component analysis (PCA) method, the proposed methods are better in terms of reducing or eliminating the statistically correlation between features and improving recognition rate. The experiment results on Olivetti Research Laboratory (ORL) face database shows that the new feature extraction method of statistically uncorrelated maximum margin criterion (SUMMC) are better in terms of recognition rate and stability. Besides, the relations between maximum margin criterion and Fisher criterion for feature extraction were revealed.

Deep feature extraction and combination for synthetic aperture radar target classification

NASA Astrophysics Data System (ADS)

Amrani, Moussa; Jiang, Feng

2017-10-01

Feature extraction has always been a difficult problem in the classification performance of synthetic aperture radar automatic target recognition (SAR-ATR). It is very important to select discriminative features to train a classifier, which is a prerequisite. Inspired by the great success of convolutional neural network (CNN), we address the problem of SAR target classification by proposing a feature extraction method, which takes advantage of exploiting the extracted deep features from CNNs on SAR images to introduce more powerful discriminative features and robust representation ability for them. First, the pretrained VGG-S net is fine-tuned on moving and stationary target acquisition and recognition (MSTAR) public release database. Second, after a simple preprocessing is performed, the fine-tuned network is used as a fixed feature extractor to extract deep features from the processed SAR images. Third, the extracted deep features are fused by using a traditional concatenation and a discriminant correlation analysis algorithm. Finally, for target classification, K-nearest neighbors algorithm based on LogDet divergence-based metric learning triplet constraints is adopted as a baseline classifier. Experiments on MSTAR are conducted, and the classification accuracy results demonstrate that the proposed method outperforms the state-of-the-art methods.

A Transform-Based Feature Extraction Approach for Motor Imagery Tasks Classification

PubMed Central

Khorshidtalab, Aida; Mesbah, Mostefa; Salami, Momoh J. E.

2015-01-01

In this paper, we present a new motor imagery classification method in the context of electroencephalography (EEG)-based brain–computer interface (BCI). This method uses a signal-dependent orthogonal transform, referred to as linear prediction singular value decomposition (LP-SVD), for feature extraction. The transform defines the mapping as the left singular vectors of the LP coefficient filter impulse response matrix. Using a logistic tree-based model classifier; the extracted features are classified into one of four motor imagery movements. The proposed approach was first benchmarked against two related state-of-the-art feature extraction approaches, namely, discrete cosine transform (DCT) and adaptive autoregressive (AAR)-based methods. By achieving an accuracy of 67.35%, the LP-SVD approach outperformed the other approaches by large margins (25% compared with DCT and 6 % compared with AAR-based methods). To further improve the discriminatory capability of the extracted features and reduce the computational complexity, we enlarged the extracted feature subset by incorporating two extra features, namely, Q- and the Hotelling’s \\documentclass[12pt]{minimal} \\usepackage{amsmath} \\usepackage{wasysym} \\usepackage{amsfonts} \\usepackage{amssymb} \\usepackage{amsbsy} \\usepackage{upgreek} \\usepackage{mathrsfs} \\setlength{\\oddsidemargin}{-69pt} \\begin{document} }{}$T^{2}$ \\end{document} statistics of the transformed EEG and introduced a new EEG channel selection method. The performance of the EEG classification based on the expanded feature set and channel selection method was compared with that of a number of the state-of-the-art classification methods previously reported with the BCI IIIa competition data set. Our method came second with an average accuracy of 81.38%. PMID:27170898

Non-negative matrix factorization in texture feature for classification of dementia with MRI data

NASA Astrophysics Data System (ADS)

Sarwinda, D.; Bustamam, A.; Ardaneswari, G.

2017-07-01

This paper investigates applications of non-negative matrix factorization as feature selection method to select the features from gray level co-occurrence matrix. The proposed approach is used to classify dementia using MRI data. In this study, texture analysis using gray level co-occurrence matrix is done to feature extraction. In the feature extraction process of MRI data, we found seven features from gray level co-occurrence matrix. Non-negative matrix factorization selected three features that influence of all features produced by feature extractions. A Naïve Bayes classifier is adapted to classify dementia, i.e. Alzheimer's disease, Mild Cognitive Impairment (MCI) and normal control. The experimental results show that non-negative factorization as feature selection method able to achieve an accuracy of 96.4% for classification of Alzheimer's and normal control. The proposed method also compared with other features selection methods i.e. Principal Component Analysis (PCA).

Concurrent initialization for Bearing-Only SLAM.

PubMed

Munguía, Rodrigo; Grau, Antoni

2010-01-01

Simultaneous Localization and Mapping (SLAM) is perhaps the most fundamental problem to solve in robotics in order to build truly autonomous mobile robots. The sensors have a large impact on the algorithm used for SLAM. Early SLAM approaches focused on the use of range sensors as sonar rings or lasers. However, cameras have become more and more used, because they yield a lot of information and are well adapted for embedded systems: they are light, cheap and power saving. Unlike range sensors which provide range and angular information, a camera is a projective sensor which measures the bearing of images features. Therefore depth information (range) cannot be obtained in a single step. This fact has propitiated the emergence of a new family of SLAM algorithms: the Bearing-Only SLAM methods, which mainly rely in especial techniques for features system-initialization in order to enable the use of bearing sensors (as cameras) in SLAM systems. In this work a novel and robust method, called Concurrent Initialization, is presented which is inspired by having the complementary advantages of the Undelayed and Delayed methods that represent the most common approaches for addressing the problem. The key is to use concurrently two kinds of feature representations for both undelayed and delayed stages of the estimation. The simulations results show that the proposed method surpasses the performance of previous schemes.

Engagement Assessment Using EEG Signals

NASA Technical Reports Server (NTRS)

Li, Feng; Li, Jiang; McKenzie, Frederic; Zhang, Guangfan; Wang, Wei; Pepe, Aaron; Xu, Roger; Schnell, Thomas; Anderson, Nick; Heitkamp, Dean

2012-01-01

In this paper, we present methods to analyze and improve an EEG-based engagement assessment approach, consisting of data preprocessing, feature extraction and engagement state classification. During data preprocessing, spikes, baseline drift and saturation caused by recording devices in EEG signals are identified and eliminated, and a wavelet based method is utilized to remove ocular and muscular artifacts in the EEG recordings. In feature extraction, power spectrum densities with 1 Hz bin are calculated as features, and these features are analyzed using the Fisher score and the one way ANOVA method. In the classification step, a committee classifier is trained based on the extracted features to assess engagement status. Finally, experiment results showed that there exist significant differences in the extracted features among different subjects, and we have implemented a feature normalization procedure to mitigate the differences and significantly improved the engagement assessment performance.

The optional selection of micro-motion feature based on Support Vector Machine

NASA Astrophysics Data System (ADS)

Li, Bo; Ren, Hongmei; Xiao, Zhi-he; Sheng, Jing

2017-11-01

Micro-motion form of target is multiple, different micro-motion forms are apt to be modulated, which makes it difficult for feature extraction and recognition. Aiming at feature extraction of cone-shaped objects with different micro-motion forms, this paper proposes the best selection method of micro-motion feature based on support vector machine. After the time-frequency distribution of radar echoes, comparing the time-frequency spectrum of objects with different micro-motion forms, features are extracted based on the differences between the instantaneous frequency variations of different micro-motions. According to the methods based on SVM (Support Vector Machine) features are extracted, then the best features are acquired. Finally, the result shows the method proposed in this paper is feasible under the test condition of certain signal-to-noise ratio(SNR).

Wire bonding quality monitoring via refining process of electrical signal from ultrasonic generator

NASA Astrophysics Data System (ADS)

Feng, Wuwei; Meng, Qingfeng; Xie, Youbo; Fan, Hong

2011-04-01

In this paper, a technique for on-line quality detection of ultrasonic wire bonding is developed. The electrical signals from the ultrasonic generator supply, namely, voltage and current, are picked up by a measuring circuit and transformed into digital signals by a data acquisition system. A new feature extraction method is presented to characterize the transient property of the electrical signals and further evaluate the bond quality. The method includes three steps. First, the captured voltage and current are filtered by digital bandpass filter banks to obtain the corresponding subband signals such as fundamental signal, second harmonic, and third harmonic. Second, each subband envelope is obtained using the Hilbert transform for further feature extraction. Third, the subband envelopes are, respectively, separated into three phases, namely, envelope rising, stable, and damping phases, to extract the tiny waveform changes. The different waveform features are extracted from each phase of these subband envelopes. The principal components analysis (PCA) method is used for the feature selection in order to remove the relevant information and reduce the dimension of original feature variables. Using the selected features as inputs, an artificial neural network (ANN) is constructed to identify the complex bond fault pattern. By analyzing experimental data with the proposed feature extraction method and neural network, the results demonstrate the advantages of the proposed feature extraction method and the constructed artificial neural network in detecting and identifying bond quality.

Planetary Gears Feature Extraction and Fault Diagnosis Method Based on VMD and CNN.

PubMed

Liu, Chang; Cheng, Gang; Chen, Xihui; Pang, Yusong

2018-05-11

Given local weak feature information, a novel feature extraction and fault diagnosis method for planetary gears based on variational mode decomposition (VMD), singular value decomposition (SVD), and convolutional neural network (CNN) is proposed. VMD was used to decompose the original vibration signal to mode components. The mode matrix was partitioned into a number of submatrices and local feature information contained in each submatrix was extracted as a singular value vector using SVD. The singular value vector matrix corresponding to the current fault state was constructed according to the location of each submatrix. Finally, by training a CNN using singular value vector matrices as inputs, planetary gear fault state identification and classification was achieved. The experimental results confirm that the proposed method can successfully extract local weak feature information and accurately identify different faults. The singular value vector matrices of different fault states have a distinct difference in element size and waveform. The VMD-based partition extraction method is better than ensemble empirical mode decomposition (EEMD), resulting in a higher CNN total recognition rate of 100% with fewer training times (14 times). Further analysis demonstrated that the method can also be applied to the degradation recognition of planetary gears. Thus, the proposed method is an effective feature extraction and fault diagnosis technique for planetary gears.

Planetary Gears Feature Extraction and Fault Diagnosis Method Based on VMD and CNN

PubMed Central

Cheng, Gang; Chen, Xihui

2018-01-01

Given local weak feature information, a novel feature extraction and fault diagnosis method for planetary gears based on variational mode decomposition (VMD), singular value decomposition (SVD), and convolutional neural network (CNN) is proposed. VMD was used to decompose the original vibration signal to mode components. The mode matrix was partitioned into a number of submatrices and local feature information contained in each submatrix was extracted as a singular value vector using SVD. The singular value vector matrix corresponding to the current fault state was constructed according to the location of each submatrix. Finally, by training a CNN using singular value vector matrices as inputs, planetary gear fault state identification and classification was achieved. The experimental results confirm that the proposed method can successfully extract local weak feature information and accurately identify different faults. The singular value vector matrices of different fault states have a distinct difference in element size and waveform. The VMD-based partition extraction method is better than ensemble empirical mode decomposition (EEMD), resulting in a higher CNN total recognition rate of 100% with fewer training times (14 times). Further analysis demonstrated that the method can also be applied to the degradation recognition of planetary gears. Thus, the proposed method is an effective feature extraction and fault diagnosis technique for planetary gears. PMID:29751671

Research on Optimal Observation Scale for Damaged Buildings after Earthquake Based on Optimal Feature Space

NASA Astrophysics Data System (ADS)

Chen, J.; Chen, W.; Dou, A.; Li, W.; Sun, Y.

2018-04-01

A new information extraction method of damaged buildings rooted in optimal feature space is put forward on the basis of the traditional object-oriented method. In this new method, ESP (estimate of scale parameter) tool is used to optimize the segmentation of image. Then the distance matrix and minimum separation distance of all kinds of surface features are calculated through sample selection to find the optimal feature space, which is finally applied to extract the image of damaged buildings after earthquake. The overall extraction accuracy reaches 83.1 %, the kappa coefficient 0.813. The new information extraction method greatly improves the extraction accuracy and efficiency, compared with the traditional object-oriented method, and owns a good promotional value in the information extraction of damaged buildings. In addition, the new method can be used for the information extraction of different-resolution images of damaged buildings after earthquake, then to seek the optimal observation scale of damaged buildings through accuracy evaluation. It is supposed that the optimal observation scale of damaged buildings is between 1 m and 1.2 m, which provides a reference for future information extraction of damaged buildings.

Integrated feature extraction and selection for neuroimage classification

NASA Astrophysics Data System (ADS)

Fan, Yong; Shen, Dinggang

2009-02-01

Feature extraction and selection are of great importance in neuroimage classification for identifying informative features and reducing feature dimensionality, which are generally implemented as two separate steps. This paper presents an integrated feature extraction and selection algorithm with two iterative steps: constrained subspace learning based feature extraction and support vector machine (SVM) based feature selection. The subspace learning based feature extraction focuses on the brain regions with higher possibility of being affected by the disease under study, while the possibility of brain regions being affected by disease is estimated by the SVM based feature selection, in conjunction with SVM classification. This algorithm can not only take into account the inter-correlation among different brain regions, but also overcome the limitation of traditional subspace learning based feature extraction methods. To achieve robust performance and optimal selection of parameters involved in feature extraction, selection, and classification, a bootstrapping strategy is used to generate multiple versions of training and testing sets for parameter optimization, according to the classification performance measured by the area under the ROC (receiver operating characteristic) curve. The integrated feature extraction and selection method is applied to a structural MR image based Alzheimer's disease (AD) study with 98 non-demented and 100 demented subjects. Cross-validation results indicate that the proposed algorithm can improve performance of the traditional subspace learning based classification.

Action Enhances Acoustic Cues for 3-D Target Localization by Echolocating Bats

PubMed Central

Wohlgemuth, Melville J.

2016-01-01

Under natural conditions, animals encounter a barrage of sensory information from which they must select and interpret biologically relevant signals. Active sensing can facilitate this process by engaging motor systems in the sampling of sensory information. The echolocating bat serves as an excellent model to investigate the coupling between action and sensing because it adaptively controls both the acoustic signals used to probe the environment and movements to receive echoes at the auditory periphery. We report here that the echolocating bat controls the features of its sonar vocalizations in tandem with the positioning of the outer ears to maximize acoustic cues for target detection and localization. The bat’s adaptive control of sonar vocalizations and ear positioning occurs on a millisecond timescale to capture spatial information from arriving echoes, as well as on a longer timescale to track target movement. Our results demonstrate that purposeful control over sonar sound production and reception can serve to improve acoustic cues for localization tasks. This finding also highlights the general importance of movement to sensory processing across animal species. Finally, our discoveries point to important parallels between spatial perception by echolocation and vision. PMID:27608186

Rapid jamming avoidance in biosonar.

PubMed

Gillam, Erin H; Ulanovsky, Nachum; McCracken, Gary F

2007-03-07

The sonar systems of bats and dolphins are in many ways superior to man-made sonar and radar systems, and considerable effort has been devoted to understanding the signal-processing strategies underlying these capabilities. A major feature determining the efficiency of sonar systems is the sensitivity to noise and jamming signals. Previous studies indicated that echolocating bats may adjust their signal structure to avoid jamming ('jamming avoidance response'; JAR). However, these studies relied on behavioural correlations and not controlled experiments. Here, we provide the first experimental evidence for JAR in bats. We presented bats (Tadarida brasiliensis) with 'playback stimuli' consisting of recorded echolocation calls at one of six frequencies. The bats exhibited a JAR by shifting their call frequency away from the presented playback frequency. When the approaching bats were challenged by an abrupt change in the playback stimulus, they responded by shifting their call frequencies upwards, away from the playback. Interestingly, even bats initially calling below the playback's frequency shifted their frequencies upwards, 'jumping' over the playback frequency. These spectral shifts in the bats' calls occurred often within less than 200 ms, in the first echolocation call emitted after the stimulus switch-suggesting that rapid jamming avoidance is important for the bat.

High reliability outdoor sonar prototype based on efficient signal coding.

PubMed

Alvarez, Fernando J; Ureña, Jesús; Mazo, Manuel; Hernández, Alvaro; García, Juan J; de Marziani, Carlos

2006-10-01

Many mobile robots and autonomous vehicles designed for outdoor operation have incorporated ultrasonic sensors in their navigation systems, whose function is mainly to avoid possible collisions with very close obstacles. The use of these systems in more precise tasks requires signal encoding and the incorporation of pulse compression techniques that have already been used with success in the design of high-performance indoor sonars. However, the transmission of ultrasonic encoded signals outdoors entails a new challenge because of the effects of atmospheric turbulence. This phenomenon causes random fluctuations in the phase and amplitude of traveling acoustic waves, a fact that can make the encoded signal completely unrecognizable by its matched receiver. Atmospheric turbulence is investigated in this work, with the aim of determining the conditions under which it is possible to assure the reliable outdoor operation of an ultrasonic pulse compression system. As a result of this analysis, a novel sonar prototype based on complementary sequences coding is developed and experimentally tested. This encoding scheme provides the system with very useful additional features, namely, high robustness to noise, multi-mode operation capability (simultaneous emissions with minimum cross talk interference), and the possibility of applying an efficient detection algorithm that notably decreases the hardware resource requirements.

Sea-floor geology in northeastern Block Island Sound, Rhode Island

USGS Publications Warehouse

McMullen, Kate Y.; Poppe, Lawrence J.; Ackerman, Seth D.; Blackwood, Dann S.; Lewit, P.G.; Parker, Castle E.

2013-01-01

Multibeam-echosounder and sidescan-sonar data collected by the National Oceanic and Atmospheric Administration in northeastern Block Island Sound, combined with sediment samples and bottom photography collected by the U.S. Geological Survey, are used to interpret sea-floor features and sedimentary environments in this 52-square-kilometer-area offshore Rhode Island. Boulders, which are often overgrown with sessile fauna and flora, are mostly in water depths shallower than 20 meters. They are probably part of the southern flank of the Harbor Hill-Roanoke Point-Charlestown-Buzzards Bay moraine, deposited about 18,000 years ago. Scour depressions, areas of the sea floor with a coarser grained, rippled surface lying about 0.5 meter below the finer grained, surrounding sea floor, along with erosional outliers within the depressions are in a band near shore and also offshore in deep parts of the study area. Textural and bathymetric differences between areas of scour depressions and the surrounding sea floor or erosional outliers stand out in the sidescan-sonar imagery with sharp tonal contrasts. Also visible in the sidescan-sonar imagery are broad, low-profile bedforms with coarser grained troughs and finer grained crests.

Enhanced Multistatic Active Sonar via Innovative Signal Processing

DTIC Science & Technology

2015-09-30

3. DATES COVERED (From - To) Oct. 01, 2014-Sept. 30, 2015 4. TITLE AND SUBTITLE Enhanced Multistatic Active Sonar via Innovative Signal...active sonar (CAS) in the presence of strong direct blast is studied for the Doppler-tolerant linear frequency modulation waveform. A receiver design...beamformer variants is examined. 15. SUBJECT TERMS Pulsed active sonar (PAS), continuous active sonar (CAS), strong delay and Doppler-spread direct blast

Mid-Frequency Sonar Interactions with Beaked Whales

DTIC Science & Technology

2010-09-30

1 Mid-Frequency Sonar Interactions with Beaked Whales PI Kenneth G. Foote Woods Hole Oceanographic Institution, 98 Water Street, Woods Hole, MA...modeling and visualization system, called the Virtual Beaked Whale, to enable users to predict mid-frequency sonar -induced acoustic fields inside beaked...nature of sonar interactions with beaked whales, and may prove useful in evaluating alternate sonar transmit signals that retain the required

A two-dimensional matrix image based feature extraction method for classification of sEMG: A comparative analysis based on SVM, KNN and RBF-NN.

PubMed

Wen, Tingxi; Zhang, Zhongnan; Qiu, Ming; Zeng, Ming; Luo, Weizhen

2017-01-01

The computer mouse is an important human-computer interaction device. But patients with physical finger disability are unable to operate this device. Surface EMG (sEMG) can be monitored by electrodes on the skin surface and is a reflection of the neuromuscular activities. Therefore, we can control limbs auxiliary equipment by utilizing sEMG classification in order to help the physically disabled patients to operate the mouse. To develop a new a method to extract sEMG generated by finger motion and apply novel features to classify sEMG. A window-based data acquisition method was presented to extract signal samples from sEMG electordes. Afterwards, a two-dimensional matrix image based feature extraction method, which differs from the classical methods based on time domain or frequency domain, was employed to transform signal samples to feature maps used for classification. In the experiments, sEMG data samples produced by the index and middle fingers at the click of a mouse button were separately acquired. Then, characteristics of the samples were analyzed to generate a feature map for each sample. Finally, the machine learning classification algorithms (SVM, KNN, RBF-NN) were employed to classify these feature maps on a GPU. The study demonstrated that all classifiers can identify and classify sEMG samples effectively. In particular, the accuracy of the SVM classifier reached up to 100%. The signal separation method is a convenient, efficient and quick method, which can effectively extract the sEMG samples produced by fingers. In addition, unlike the classical methods, the new method enables to extract features by enlarging sample signals' energy appropriately. The classical machine learning classifiers all performed well by using these features.

Deep Learning Methods for Underwater Target Feature Extraction and Recognition

PubMed Central

Peng, Yuan; Qiu, Mengran; Shi, Jianfei; Liu, Liangliang

2018-01-01

The classification and recognition technology of underwater acoustic signal were always an important research content in the field of underwater acoustic signal processing. Currently, wavelet transform, Hilbert-Huang transform, and Mel frequency cepstral coefficients are used as a method of underwater acoustic signal feature extraction. In this paper, a method for feature extraction and identification of underwater noise data based on CNN and ELM is proposed. An automatic feature extraction method of underwater acoustic signals is proposed using depth convolution network. An underwater target recognition classifier is based on extreme learning machine. Although convolution neural networks can execute both feature extraction and classification, their function mainly relies on a full connection layer, which is trained by gradient descent-based; the generalization ability is limited and suboptimal, so an extreme learning machine (ELM) was used in classification stage. Firstly, CNN learns deep and robust features, followed by the removing of the fully connected layers. Then ELM fed with the CNN features is used as the classifier to conduct an excellent classification. Experiments on the actual data set of civil ships obtained 93.04% recognition rate; compared to the traditional Mel frequency cepstral coefficients and Hilbert-Huang feature, recognition rate greatly improved. PMID:29780407

Neyman Pearson detection of K-distributed random variables

NASA Astrophysics Data System (ADS)

Tucker, J. Derek; Azimi-Sadjadi, Mahmood R.

2010-04-01

In this paper a new detection method for sonar imagery is developed in K-distributed background clutter. The equation for the log-likelihood is derived and compared to the corresponding counterparts derived for the Gaussian and Rayleigh assumptions. Test results of the proposed method on a data set of synthetic underwater sonar images is also presented. This database contains images with targets of different shapes inserted into backgrounds generated using a correlated K-distributed model. Results illustrating the effectiveness of the K-distributed detector are presented in terms of probability of detection, false alarm, and correct classification rates for various bottom clutter scenarios.

New Finger Biometric Method Using Near Infrared Imaging

PubMed Central

Lee, Eui Chul; Jung, Hyunwoo; Kim, Daeyeoul

2011-01-01

In this paper, we propose a new finger biometric method. Infrared finger images are first captured, and then feature extraction is performed using a modified Gaussian high-pass filter through binarization, local binary pattern (LBP), and local derivative pattern (LDP) methods. Infrared finger images include the multimodal features of finger veins and finger geometries. Instead of extracting each feature using different methods, the modified Gaussian high-pass filter is fully convolved. Therefore, the extracted binary patterns of finger images include the multimodal features of veins and finger geometries. Experimental results show that the proposed method has an error rate of 0.13%. PMID:22163741

Minehunting sonar system research and development

NASA Astrophysics Data System (ADS)

Ferguson, Brian

2002-05-01

Sea mines have the potential to threaten the freedom of the seas by disrupting maritime trade and restricting the freedom of maneuver of navies. The acoustic detection, localization, and classification of sea mines involves a sequence of operations starting with the transmission of a sonar pulse and ending with an operator interpreting the information on a sonar display. A recent improvement to the process stems from the application of neural networks to the computed aided detection of sea mines. The advent of ultrawideband sonar transducers together with pulse compression techniques offers a thousandfold increase in the bandwidth-time product of conventional minehunting sonar transmissions enabling stealth mines to be detected at longer ranges. These wideband signals also enable mines to be imaged at safe standoff distances by applying tomographic image reconstruction techniques. The coupling of wideband transducer technology with synthetic aperture processing enhances the resolution of side scan sonars in both the cross-track and along-track directions. The principles on which conventional and advanced minehunting sonars are based are reviewed and the results of applying novel sonar signal processing algorithms to high-frequency sonar data collected in Australian waters are presented.

Optical character recognition with feature extraction and associative memory matrix

NASA Astrophysics Data System (ADS)

Sasaki, Osami; Shibahara, Akihito; Suzuki, Takamasa

1998-06-01

A method is proposed in which handwritten characters are recognized using feature extraction and an associative memory matrix. In feature extraction, simple processes such as shifting and superimposing patterns are executed. A memory matrix is generated with singular value decomposition and by modifying small singular values. The method is optically implemented with two liquid crystal displays. Experimental results for the recognition of 25 handwritten alphabet characters clearly shows the effectiveness of the method.

A method of vehicle license plate recognition based on PCANet and compressive sensing

NASA Astrophysics Data System (ADS)

Ye, Xianyi; Min, Feng

2018-03-01

The manual feature extraction of the traditional method for vehicle license plates has no good robustness to change in diversity. And the high feature dimension that is extracted with Principal Component Analysis Network (PCANet) leads to low classification efficiency. For solving these problems, a method of vehicle license plate recognition based on PCANet and compressive sensing is proposed. First, PCANet is used to extract the feature from the images of characters. And then, the sparse measurement matrix which is a very sparse matrix and consistent with Restricted Isometry Property (RIP) condition of the compressed sensing is used to reduce the dimensions of extracted features. Finally, the Support Vector Machine (SVM) is used to train and recognize the features whose dimension has been reduced. Experimental results demonstrate that the proposed method has better performance than Convolutional Neural Network (CNN) in the recognition and time. Compared with no compression sensing, the proposed method has lower feature dimension for the increase of efficiency.

Very High Resolution Bathymetric Mapping at the Ridge 2000 Integrated Study Sites: Acquisition and Processing Protocols Developed During Recent Alvin Field Programs to the East Pacific Rise and Juan de Fuca Ridge

NASA Astrophysics Data System (ADS)

Ferrini, V.; Fornari, D. J.; Shank, T.; Tivey, M.; Kelley, D. S.; Glickson, D.; Carbotte, S. M.; Howland, J.; Whitcomb, L. L.; Yoerger, D.

2004-12-01

Recent field programs at the East Pacific Rise and Juan de Fuca Ridge have resulted in the refinement of data processing protocols that enable the rapid creation of high-resolution (meter-scale) bathymetric maps from pencil-beam altimetric sonar data that are routinely collected during DSV Alvin dives. With the development of the appropriate processing tools, the Imagenex sonar, a permanent sensor on Alvin, can be used by a broad range of scientists permitting the analysis of various data sets within the context of high-quality bathymetric maps. The data processing protocol integrates depth data recorded with Alvin's Paroscientific pressure sensor with bathymetric soundings collected with an Imagenex 675 kHz articulating (scanning) sonar system, and high-resolution navigational data acquired with DVLNAV, which includes bottom lock Doppler sonar and long baseline (LBL) navigation. Together these data allow us, for the first time, to visualize portions of Ridge 2000 Integrated Study Sites (ISS) at 1-m vertical and horizontal resolution. These maps resolve morphological details of structures within the summit trough at scales that are relevant to biological communities (e.g. hydrothermal vents, lava pillars, trough walls), thus providing the important geologic context necessary to better understand spatial patterns associated with integrated biological-hydrothermal-geological processes. The Imagenex sonar is also a permanent sensor on the Jason2 ROV, which is also equipped with an SM2000 (200 kHz) near-bottom multibeam sonar. In the future, it is envisioned that near-bottom multibeam sonars will be standard sensors on all National Deep Submergence Facility (NDSF) vehicles. Streamlining data processing protocols makes these datasets more accessible to NDSF users and ensures broad compatibility between data formats among NDSF vehicle systems and allied vehicles (e.g. ABE). Establishing data processing protocols and software suites, routinely calibrating sensors (e.g. Paroscientific depth sensors), and ensuring good navigational benchmarks between various cruises to the Ridge 2000 ISS improves the capability and quality of rapidly produced high-resolution bathymetric maps enabling users to optimize their diving programs. This is especially important within the context of augmenting high-resolution bathymetric data collection in ISS areas (several cruises to the same area over multiple years) and investigating possible changes in seafloor topography, hydrothermal vent features and/or biological communities that are related to tectonic or volcanic events.

Quantifying Fish Backscattering using SONAR Instrument and Kirchhoff Ray Mode (KRM) Model

NASA Astrophysics Data System (ADS)

Manik, Henry M.

2016-08-01

Sonar instrument was used to study backscattering from tuna fish. Extraction of target strength, incidence angle, and frequency dependence of the backscattered signal for individual scatterer was important for biological information. For this purpose, acoustic measurement of fish backscatter was conducted in the laboratory. Characteristics and general trends of the target strength of fish with special reference to tuna fish were investigated by using a Kirchhoff Ray Mode (KRM) model. Backscattering strength were calculated for the KRM having typical morphological and physical parameters of actual fish. Those backscattering amplitudes were shown as frequency, body length, backscattering patterns, the density and sound speed dependences, and orientation dependence. These results were compared with experimentally measured target strength data and good agreement was found. Measurement and model showed the target strength from the fish are depend on the presence of swimbladder. Target Strength increase with increasing the frequency and fish length.

Nonlinear features for classification and pose estimation of machined parts from single views

NASA Astrophysics Data System (ADS)

Talukder, Ashit; Casasent, David P.

1998-10-01

A new nonlinear feature extraction method is presented for classification and pose estimation of objects from single views. The feature extraction method is called the maximum representation and discrimination feature (MRDF) method. The nonlinear MRDF transformations to use are obtained in closed form, and offer significant advantages compared to nonlinear neural network implementations. The features extracted are useful for both object discrimination (classification) and object representation (pose estimation). We consider MRDFs on image data, provide a new 2-stage nonlinear MRDF solution, and show it specializes to well-known linear and nonlinear image processing transforms under certain conditions. We show the use of MRDF in estimating the class and pose of images of rendered solid CAD models of machine parts from single views using a feature-space trajectory neural network classifier. We show new results with better classification and pose estimation accuracy than are achieved by standard principal component analysis and Fukunaga-Koontz feature extraction methods.

Exploration and discovery in Yellowstone Lake: Results from high-resolution sonar imaging, seismic reflection profiling, and submersible studies

USGS Publications Warehouse

Morgan, L.A.; Shanks, Wayne C.; Lovalvo, D.A.; Johnson, S.Y.; Stephenson, W.J.; Pierce, K.L.; Harlan, S.S.; Finn, C.A.; Lee, G.; Webring, M.; Schulze, B.; Duhn, J.; Sweeney, R.; Balistrieri, L.

2003-01-01

Discoveries from multi-beam sonar mapping and seismic reflection surveys of the northern, central, and West Thumb basins of Yellowstone Lake provide new insight into the extent of post-collapse volcanism and active hydrothermal processes occurring in a large lake environment above a large magma chamber. Yellowstone Lake has an irregular bottom covered with dozens of features directly related to hydrothermal, tectonic, volcanic, and sedimentary processes. Detailed bathymetric, seismic reflection, and magnetic evidence reveals that rhyolitic lava flows underlie much of Yellowstone Lake and exert fundamental control on lake bathymetry and localization of hydrothermal activity. Many previously unknown features have been identified and include over 250 hydrothermal vents, several very large (>500 m diameter) hydrothermal explosion craters, many small hydrothermal vent craters (???1-200 m diameter), domed lacustrine sediments related to hydrothermal activity, elongate fissures cutting post-glacial sediments, siliceous hydrothermal spire structures, sublacustrine landslide deposits, submerged former shorelines, and a recently active graben. Sampling and observations with a submersible remotely operated vehicle confirm and extend our understanding of the identified features. Faults, fissures, hydrothermally inflated domal structures, hydrothermal explosion craters, and sublacustrine landslides constitute potentially significant geologic hazards. Toxic elements derived from hydrothermal processes also may significantly affect the Yellowstone ecosystem. Published by Elsevier Science B.V.

Research on oral test modeling based on multi-feature fusion

NASA Astrophysics Data System (ADS)

Shi, Yuliang; Tao, Yiyue; Lei, Jun

2018-04-01

In this paper, the spectrum of speech signal is taken as an input of feature extraction. The advantage of PCNN in image segmentation and other processing is used to process the speech spectrum and extract features. And a new method combining speech signal processing and image processing is explored. At the same time of using the features of the speech map, adding the MFCC to establish the spectral features and integrating them with the features of the spectrogram to further improve the accuracy of the spoken language recognition. Considering that the input features are more complicated and distinguishable, we use Support Vector Machine (SVM) to construct the classifier, and then compare the extracted test voice features with the standard voice features to achieve the spoken standard detection. Experiments show that the method of extracting features from spectrograms using PCNN is feasible, and the fusion of image features and spectral features can improve the detection accuracy.

Analysis on spectra of hydroacoustic field in sonar cavity of the sandwich elastic wall structure

NASA Astrophysics Data System (ADS)

Xuetao, W.; Rui, H.; Weike, W.

2017-09-01

In this paper, the characteristics of the mechanical self - noise in sonar array cavity are studied by using the elastic flatbed - filled rectangular cavity parameterization model. Firstly, the analytic derivation of the vibration differential equation of the single layer, sandwich elastic wall plate structure and internal fluid coupling is carried out, and the modal method is used to solve it. Finally, the spectral characteristics of the acoustic field of rectangular cavity of different elastic wallboard materials are simulated and analyzed, which provides a theoretical reference for the prediction and control of sonar mechanical self-noise. In this paper, the sandwich board as control inside the dome background noise of a potential means were discussed, the dome background noise of qualitative prediction analysis and control has important theoretical significance.

An Effective Palmprint Recognition Approach for Visible and Multispectral Sensor Images

PubMed Central

Sammouda, Rachid; Al-Salman, Abdul Malik; Alsanad, Ahmed

2018-01-01

Among several palmprint feature extraction methods the HOG-based method is attractive and performs well against changes in illumination and shadowing of palmprint images. However, it still lacks the robustness to extract the palmprint features at different rotation angles. To solve this problem, this paper presents a hybrid feature extraction method, named HOG-SGF that combines the histogram of oriented gradients (HOG) with a steerable Gaussian filter (SGF) to develop an effective palmprint recognition approach. The approach starts by processing all palmprint images by David Zhang’s method to segment only the region of interests. Next, we extracted palmprint features based on the hybrid HOG-SGF feature extraction method. Then, an optimized auto-encoder (AE) was utilized to reduce the dimensionality of the extracted features. Finally, a fast and robust regularized extreme learning machine (RELM) was applied for the classification task. In the evaluation phase of the proposed approach, a number of experiments were conducted on three publicly available palmprint databases, namely MS-PolyU of multispectral palmprint images and CASIA and Tongji of contactless palmprint images. Experimentally, the results reveal that the proposed approach outperforms the existing state-of-the-art approaches even when a small number of training samples are used. PMID:29762519

Combining Deep and Handcrafted Image Features for Presentation Attack Detection in Face Recognition Systems Using Visible-Light Camera Sensors

PubMed Central

Nguyen, Dat Tien; Pham, Tuyen Danh; Baek, Na Rae; Park, Kang Ryoung

2018-01-01

Although face recognition systems have wide application, they are vulnerable to presentation attack samples (fake samples). Therefore, a presentation attack detection (PAD) method is required to enhance the security level of face recognition systems. Most of the previously proposed PAD methods for face recognition systems have focused on using handcrafted image features, which are designed by expert knowledge of designers, such as Gabor filter, local binary pattern (LBP), local ternary pattern (LTP), and histogram of oriented gradients (HOG). As a result, the extracted features reflect limited aspects of the problem, yielding a detection accuracy that is low and varies with the characteristics of presentation attack face images. The deep learning method has been developed in the computer vision research community, which is proven to be suitable for automatically training a feature extractor that can be used to enhance the ability of handcrafted features. To overcome the limitations of previously proposed PAD methods, we propose a new PAD method that uses a combination of deep and handcrafted features extracted from the images by visible-light camera sensor. Our proposed method uses the convolutional neural network (CNN) method to extract deep image features and the multi-level local binary pattern (MLBP) method to extract skin detail features from face images to discriminate the real and presentation attack face images. By combining the two types of image features, we form a new type of image features, called hybrid features, which has stronger discrimination ability than single image features. Finally, we use the support vector machine (SVM) method to classify the image features into real or presentation attack class. Our experimental results indicate that our proposed method outperforms previous PAD methods by yielding the smallest error rates on the same image databases. PMID:29495417

Combining Deep and Handcrafted Image Features for Presentation Attack Detection in Face Recognition Systems Using Visible-Light Camera Sensors.

PubMed

Nguyen, Dat Tien; Pham, Tuyen Danh; Baek, Na Rae; Park, Kang Ryoung

2018-02-26

Although face recognition systems have wide application, they are vulnerable to presentation attack samples (fake samples). Therefore, a presentation attack detection (PAD) method is required to enhance the security level of face recognition systems. Most of the previously proposed PAD methods for face recognition systems have focused on using handcrafted image features, which are designed by expert knowledge of designers, such as Gabor filter, local binary pattern (LBP), local ternary pattern (LTP), and histogram of oriented gradients (HOG). As a result, the extracted features reflect limited aspects of the problem, yielding a detection accuracy that is low and varies with the characteristics of presentation attack face images. The deep learning method has been developed in the computer vision research community, which is proven to be suitable for automatically training a feature extractor that can be used to enhance the ability of handcrafted features. To overcome the limitations of previously proposed PAD methods, we propose a new PAD method that uses a combination of deep and handcrafted features extracted from the images by visible-light camera sensor. Our proposed method uses the convolutional neural network (CNN) method to extract deep image features and the multi-level local binary pattern (MLBP) method to extract skin detail features from face images to discriminate the real and presentation attack face images. By combining the two types of image features, we form a new type of image features, called hybrid features, which has stronger discrimination ability than single image features. Finally, we use the support vector machine (SVM) method to classify the image features into real or presentation attack class. Our experimental results indicate that our proposed method outperforms previous PAD methods by yielding the smallest error rates on the same image databases.

Automatic Extraction of Planetary Image Features

NASA Technical Reports Server (NTRS)

Troglio, G.; LeMoigne, J.; Moser, G.; Serpico, S. B.; Benediktsson, J. A.

2009-01-01

With the launch of several Lunar missions such as the Lunar Reconnaissance Orbiter (LRO) and Chandrayaan-1, a large amount of Lunar images will be acquired and will need to be analyzed. Although many automatic feature extraction methods have been proposed and utilized for Earth remote sensing images, these methods are not always applicable to Lunar data that often present low contrast and uneven illumination characteristics. In this paper, we propose a new method for the extraction of Lunar features (that can be generalized to other planetary images), based on the combination of several image processing techniques, a watershed segmentation and the generalized Hough Transform. This feature extraction has many applications, among which image registration.

Dose-response relationships for the onset of avoidance of sonar by free-ranging killer whales.

PubMed

Miller, Patrick J O; Antunes, Ricardo N; Wensveen, Paul J; Samarra, Filipa I P; Alves, Ana Catarina; Tyack, Peter L; Kvadsheim, Petter H; Kleivane, Lars; Lam, Frans-Peter A; Ainslie, Michael A; Thomas, Len

2014-02-01

Eight experimentally controlled exposures to 1-2 kHz or 6-7 kHz sonar signals were conducted with four killer whale groups. The source level and proximity of the source were increased during each exposure in order to reveal response thresholds. Detailed inspection of movements during each exposure session revealed sustained changes in speed and travel direction judged to be avoidance responses during six of eight sessions. Following methods developed for Phase-I clinical trials in human medicine, response thresholds ranging from 94 to 164 dB re 1 μPa received sound pressure level (SPL) were fitted to Bayesian dose-response functions. Thresholds did not consistently differ by sonar frequency or whether a group had previously been exposed, with a mean SPL response threshold of 142 ± 15 dB (mean ± s.d.). High levels of between- and within-individual variability were identified, indicating that thresholds depended upon other undefined contextual variables. The dose-response functions indicate that some killer whales started to avoid sonar at received SPL below thresholds assumed by the U.S. Navy. The predicted extent of habitat over which avoidance reactions occur depends upon whether whales responded to proximity or received SPL of the sonar or both, but was large enough to raise concerns about biological consequences to the whales.

Adaptive echolocation behavior in bats for the analysis of auditory scenes

PubMed Central

Chiu, Chen; Xian, Wei; Moss, Cynthia F.

2009-01-01

Summary Echolocating bats emit sonar pulses and listen to returning echoes to probe their surroundings. Bats adapt their echolocation call design to cope with dynamic changes in the acoustic environment, including habitat change or the presence of nearby conspecifics/heterospecifics. Seven pairs of big brown bats, Eptesicus fuscus, were tested in this study to examine how they adjusted their echolocation calls when flying and competing with a conspecific for food. Results showed that differences in five call parameters, start/end frequencies, duration, bandwidth and sweep rate, significantly increased in the two-bat condition compared with the baseline data. In addition, the magnitude of spectral separation of calls was negatively correlated with the baseline call design differences in individual bats. Bats with small baseline call frequency differences showed larger increases in call frequency separation when paired than those with large baseline call frequency differences, suggesting that bats actively change their sonar call structure if pre-existing differences in call design are small. Call design adjustments were also influenced by physical spacing between two bats. Calls of paired bats exhibited the largest design separations when inter-bat distance was shorter than 0.5 m, and the separation decreased as the spacing increased. All individuals modified at least one baseline call parameter in response to the presence of another conspecific. We propose that dissimilarity between the time–frequency features of sonar calls produced by different bats aids each individual in segregating echoes of its own sonar vocalizations from the acoustic signals of neighboring bats. PMID:19376960

Multibeam Formation with a Parametric Sonar

DTIC Science & Technology

1976-03-05

AD-A022 815 MULTIBEAM FORMATION WITH A PARAMETRIC SONAR Robert L. White Texas University at Austin Prepared for: Office of Naval Research 5 March...PARAMETRIC SONAR Final Report under Contract N00014-70-A-0166, Task 0020 1 February - 31 July 1974 Robe&, L. White OFFICE OF NAVAL RESEARCH Contract N00014...78712 APPROVED FOR PUBLIC RELEASE; DISTRIBUTION UNLIMITED. r-X: ~ ... ABSTRACT Parametric sonar has proven to be an effective concept in sonar

Study on identifying deciduous forest by the method of feature space transformation

NASA Astrophysics Data System (ADS)

Zhang, Xuexia; Wu, Pengfei

2009-10-01

The thematic remotely sensed information extraction is always one of puzzling nuts which the remote sensing science faces, so many remote sensing scientists devotes diligently to this domain research. The methods of thematic information extraction include two kinds of the visual interpretation and the computer interpretation, the developing direction of which is intellectualization and comprehensive modularization. The paper tries to develop the intelligent extraction method of feature space transformation for the deciduous forest thematic information extraction in Changping district of Beijing city. The whole Chinese-Brazil resources satellite images received in 2005 are used to extract the deciduous forest coverage area by feature space transformation method and linear spectral decomposing method, and the result from remote sensing is similar to woodland resource census data by Chinese forestry bureau in 2004.

New feature extraction method for classification of agricultural products from x-ray images

NASA Astrophysics Data System (ADS)

Talukder, Ashit; Casasent, David P.; Lee, Ha-Woon; Keagy, Pamela M.; Schatzki, Thomas F.

1999-01-01

Classification of real-time x-ray images of randomly oriented touching pistachio nuts is discussed. The ultimate objective is the development of a system for automated non- invasive detection of defective product items on a conveyor belt. We discuss the extraction of new features that allow better discrimination between damaged and clean items. This feature extraction and classification stage is the new aspect of this paper; our new maximum representation and discrimination between damaged and clean items. This feature extraction and classification stage is the new aspect of this paper; our new maximum representation and discriminating feature (MRDF) extraction method computes nonlinear features that are used as inputs to a new modified k nearest neighbor classifier. In this work the MRDF is applied to standard features. The MRDF is robust to various probability distributions of the input class and is shown to provide good classification and new ROC data.

Aerial ultrasonic micro Doppler sonar detection range in outdoor environments.

PubMed

Bradley, Marshall; Sabatier, James M

2012-03-01

Current research demonstrates that micro Doppler sonar has the capability to uniquely identify the presence of a moving human, making it an attractive component in surveillance systems for border security applications. Primary environmental factors that limit sonar performance are two-way spreading losses, ultrasonic absorption, and backscattered energy from the ground that appears at zero Doppler shift in the sonar signal processor. Spectral leakage from the backscatter component has a significant effect on sonar performance for slow moving targets. Sonar performance is shown to rapidly decay as the sensor is moved closer to the ground due to increasing surface backscatter levels. © 2012 Acoustical Society of America

Range compensation for backscattering measurements in the difference-frequency nearfield of a parametric sonar.

PubMed

Foote, Kenneth G

2012-05-01

Measurement of acoustic backscattering properties of targets requires removal of the range dependence of echoes. This process is called range compensation. For conventional sonars making measurements in the transducer farfield, the compensation removes effects of geometrical spreading and absorption. For parametric sonars consisting of a parametric acoustic transmitter and a conventional-sonar receiver, two additional range dependences require compensation when making measurements in the nonlinearly generated difference-frequency nearfield: an apparently increasing source level and a changing beamwidth. General expressions are derived for range compensation functions in the difference-frequency nearfield of parametric sonars. These are evaluated numerically for a parametric sonar whose difference-frequency band, effectively 1-6 kHz, is being used to observe Atlantic herring (Clupea harengus) in situ. Range compensation functions for this sonar are compared with corresponding functions for conventional sonars for the cases of single and multiple scatterers. Dependences of these range compensation functions on the parametric sonar transducer shape, size, acoustic power density, and hydrography are investigated. Parametric range compensation functions, when applied with calibration data, will enable difference-frequency echoes to be expressed in physical units of volume backscattering, and backscattering spectra, including fish-swimbladder-resonances, to be analyzed.

Approximation-based common principal component for feature extraction in multi-class brain-computer interfaces.

PubMed

Hoang, Tuan; Tran, Dat; Huang, Xu

2013-01-01

Common Spatial Pattern (CSP) is a state-of-the-art method for feature extraction in Brain-Computer Interface (BCI) systems. However it is designed for 2-class BCI classification problems. Current extensions of this method to multiple classes based on subspace union and covariance matrix similarity do not provide a high performance. This paper presents a new approach to solving multi-class BCI classification problems by forming a subspace resembled from original subspaces and the proposed method for this approach is called Approximation-based Common Principal Component (ACPC). We perform experiments on Dataset 2a used in BCI Competition IV to evaluate the proposed method. This dataset was designed for motor imagery classification with 4 classes. Preliminary experiments show that the proposed ACPC feature extraction method when combining with Support Vector Machines outperforms CSP-based feature extraction methods on the experimental dataset.

Region of interest extraction based on multiscale visual saliency analysis for remote sensing images

NASA Astrophysics Data System (ADS)

Zhang, Yinggang; Zhang, Libao; Yu, Xianchuan

2015-01-01

Region of interest (ROI) extraction is an important component of remote sensing image processing. However, traditional ROI extraction methods are usually prior knowledge-based and depend on classification, segmentation, and a global searching solution, which are time-consuming and computationally complex. We propose a more efficient ROI extraction model for remote sensing images based on multiscale visual saliency analysis (MVS), implemented in the CIE L*a*b* color space, which is similar to visual perception of the human eye. We first extract the intensity, orientation, and color feature of the image using different methods: the visual attention mechanism is used to eliminate the intensity feature using a difference of Gaussian template; the integer wavelet transform is used to extract the orientation feature; and color information content analysis is used to obtain the color feature. Then, a new feature-competition method is proposed that addresses the different contributions of each feature map to calculate the weight of each feature image for combining them into the final saliency map. Qualitative and quantitative experimental results of the MVS model as compared with those of other models show that it is more effective and provides more accurate ROI extraction results with fewer holes inside the ROI.

Textural Analysis and Substrate Classification in the Nearshore Region of Lake Superior Using High-Resolution Multibeam Bathymetry

NASA Astrophysics Data System (ADS)

Dennison, Andrew G.

Classification of the seafloor substrate can be done with a variety of methods. These methods include Visual (dives, drop cameras); mechanical (cores, grab samples); acoustic (statistical analysis of echosounder returns). Acoustic methods offer a more powerful and efficient means of collecting useful information about the bottom type. Due to the nature of an acoustic survey, larger areas can be sampled, and by combining the collected data with visual and mechanical survey methods provide greater confidence in the classification of a mapped region. During a multibeam sonar survey, both bathymetric and backscatter data is collected. It is well documented that the statistical characteristic of a sonar backscatter mosaic is dependent on bottom type. While classifying the bottom-type on the basis on backscatter alone can accurately predict and map bottom-type, i.e a muddy area from a rocky area, it lacks the ability to resolve and capture fine textural details, an important factor in many habitat mapping studies. Statistical processing of high-resolution multibeam data can capture the pertinent details about the bottom-type that are rich in textural information. Further multivariate statistical processing can then isolate characteristic features, and provide the basis for an accurate classification scheme. The development of a new classification method is described here. It is based upon the analysis of textural features in conjunction with ground truth sampling. The processing and classification result of two geologically distinct areas in nearshore regions of Lake Superior; off the Lester River,MN and Amnicon River, WI are presented here, using the Minnesota Supercomputer Institute's Mesabi computing cluster for initial processing. Processed data is then calibrated using ground truth samples to conduct an accuracy assessment of the surveyed areas. From analysis of high-resolution bathymetry data collected at both survey sites is was possible to successfully calculate a series of measures that describe textural information about the lake floor. Further processing suggests that the features calculated capture a significant amount of statistical information about the lake floor terrain as well. Two sources of error, an anomalous heave and refraction error significantly deteriorated the quality of the processed data and resulting validate results. Ground truth samples used to validate the classification methods utilized for both survey sites, however, resulted in accuracy values ranging from 5 -30 percent at the Amnicon River, and between 60-70 percent for the Lester River. The final results suggest that this new processing methodology does adequately capture textural information about the lake floor and does provide an acceptable classification in the absence of significant data quality issues.

50 CFR 216.186 - Requirements for reporting.

Code of Federal Regulations, 2011 CFR

2011-10-01

... Low Frequency Active (SURTASS LFA sonar) Sonar § 216.186 Requirements for reporting. (a) The Holder of... of each vessel during each mission; (2) Information on sonar transmissions during each mission; (3... must contain an unclassified analysis of new passive sonar technologies and an assessment of whether...

50 CFR 218.234 - Mitigation.

Code of Federal Regulations, 2012 CFR

2012-10-01

... Frequency Active (SURTASS LFA) Sonar § 218.234 Mitigation. When conducting operations identified in § 218... monitoring. (b) General Operating Procedures: (1) Prior to SURTASS LFA sonar operations, the Navy will... SURTASS LFA sonar signal at a frequency greater than 500 Hertz (Hz). (c) LFA Sonar Mitigation Zone and 1...

50 CFR 218.234 - Mitigation.

Code of Federal Regulations, 2014 CFR

2014-10-01

... Frequency Active (SURTASS LFA) Sonar § 218.234 Mitigation. When conducting operations identified in § 218... monitoring. (b) General Operating Procedures: (1) Prior to SURTASS LFA sonar operations, the Navy will... SURTASS LFA sonar signal at a frequency greater than 500 Hertz (Hz). (c) LFA Sonar Mitigation Zone and 1...

50 CFR 216.186 - Requirements for reporting.

Code of Federal Regulations, 2010 CFR

2010-10-01

... Low Frequency Active (SURTASS LFA sonar) Sonar § 216.186 Requirements for reporting. (a) The Holder of... of each vessel during each mission; (2) Information on sonar transmissions during each mission; (3... must contain an unclassified analysis of new passive sonar technologies and an assessment of whether...

50 CFR 218.234 - Mitigation.

Code of Federal Regulations, 2013 CFR

2013-10-01

... Frequency Active (SURTASS LFA) Sonar § 218.234 Mitigation. When conducting operations identified in § 218... monitoring. (b) General Operating Procedures: (1) Prior to SURTASS LFA sonar operations, the Navy will... SURTASS LFA sonar signal at a frequency greater than 500 Hertz (Hz). (c) LFA Sonar Mitigation Zone and 1...

Signal analysis by means of time-frequency (Wigner-type) distributions -- Applications to sonar and radar echoes

DOE Office of Scientific and Technical Information (OSTI.GOV)

Gaunaurd, G.; Strifors, H.C.

1996-09-01

Time series data have been traditionally analyzed in either the time or the frequency domains. For signals with a time-varying frequency content, the combined time-frequency (TF) representations, based on the Cohen class of (generalized) Wigner distributions (WD`s) offer a powerful analysis tool. Using them, it is possible to: (1) trace the time-evolution of the resonance features usually present in a standard sonar cross section (SCS), or in a radar cross section (RCS) and (2) extract target information that may be difficult to even notice in an ordinary SCS or RCS. After a brief review of the fundamental properties of themore » WD, the authors discuss ways to reduce or suppress the cross term interference that appears in the WD of multicomponent systems. These points are illustrated with a variety of three-dimensional (3-D) plots of Wigner and pseudo-Wigner distributions (PWD), in which the strength of the distribution is depicted as the height of a Wigner surface with height scales measured by various color shades or pseudocolors. The authors also review studies they have made of the echoes returned by conducting or dielectric targets in the atmosphere, when they are illuminated by broadband radar pings. A TF domain analysis of these impulse radar returns demonstrates their superior informative content. These plots allow the identification of targets in an easier and clearer fashion than by the conventional RCS of narrowband systems. The authors show computed and measured plots of WD and PWD of various types of aircraft to illustrate the classification advantages of the approach at any aspect angle. They also show analogous results for metallic objects buried underground, in dielectric media, at various depths.« less

Feature ranking and rank aggregation for automatic sleep stage classification: a comparative study.

PubMed

Najdi, Shirin; Gharbali, Ali Abdollahi; Fonseca, José Manuel

2017-08-18

Nowadays, sleep quality is one of the most important measures of healthy life, especially considering the huge number of sleep-related disorders. Identifying sleep stages using polysomnographic (PSG) signals is the traditional way of assessing sleep quality. However, the manual process of sleep stage classification is time-consuming, subjective and costly. Therefore, in order to improve the accuracy and efficiency of the sleep stage classification, researchers have been trying to develop automatic classification algorithms. Automatic sleep stage classification mainly consists of three steps: pre-processing, feature extraction and classification. Since classification accuracy is deeply affected by the extracted features, a poor feature vector will adversely affect the classifier and eventually lead to low classification accuracy. Therefore, special attention should be given to the feature extraction and selection process. In this paper the performance of seven feature selection methods, as well as two feature rank aggregation methods, were compared. Pz-Oz EEG, horizontal EOG and submental chin EMG recordings of 22 healthy males and females were used. A comprehensive feature set including 49 features was extracted from these recordings. The extracted features are among the most common and effective features used in sleep stage classification from temporal, spectral, entropy-based and nonlinear categories. The feature selection methods were evaluated and compared using three criteria: classification accuracy, stability, and similarity. Simulation results show that MRMR-MID achieves the highest classification performance while Fisher method provides the most stable ranking. In our simulations, the performance of the aggregation methods was in the average level, although they are known to generate more stable results and better accuracy. The Borda and RRA rank aggregation methods could not outperform significantly the conventional feature ranking methods. Among conventional methods, some of them slightly performed better than others, although the choice of a suitable technique is dependent on the computational complexity and accuracy requirements of the user.

50 CFR 216.189 - Renewal of Letters of Authorization.

Code of Federal Regulations, 2011 CFR

2011-10-01

... Array Sensor System Low Frequency Active (SURTASS LFA sonar) Sonar § 216.189 Renewal of Letters of... each SURTASS LFA sonar operation; (3) Timely receipt of the monitoring reports required under § 216.185... comment on the proposed modification. Amending the areas for upcoming SURTASS LFA sonar operations is not...

50 CFR 216.184 - Mitigation.

Code of Federal Regulations, 2010 CFR

2010-10-01

... Frequency Active (SURTASS LFA sonar) Sonar § 216.184 Mitigation. The activity identified in § 216.180(a....54 nm) buffer zone extending beyond the 180-dB zone), SURTASS LFA sonar transmissions will be... active acoustic monitoring described in § 216.185. (c) The high-frequency marine mammal monitoring sonar...

78 FR 68091 - Certain Marine Sonar Imaging Devices, Products Containing the Same, and Components Thereof...

Federal Register 2010, 2011, 2012, 2013, 2014

2013-11-13

... INTERNATIONAL TRADE COMMISSION [Investigation No. 337-TA-898] Certain Marine Sonar Imaging Devices... importation of certain marine sonar imaging devices, products containing the same, and components thereof by... marine sonar imaging devices, products containing the same, and components thereof by reason of...

50 CFR 216.189 - Renewal of Letters of Authorization.

Code of Federal Regulations, 2010 CFR

2010-10-01

... Array Sensor System Low Frequency Active (SURTASS LFA sonar) Sonar § 216.189 Renewal of Letters of... each SURTASS LFA sonar operation; (3) Timely receipt of the monitoring reports required under § 216.185... comment on the proposed modification. Amending the areas for upcoming SURTASS LFA sonar operations is not...

76 FR 3092 - Taking and Importing Marine Mammals: Taking Marine Mammals Incidental to Navy's Mission...

Federal Register 2010, 2011, 2012, 2013, 2014

2011-01-19

... involve underwater explosive detonation, projectile firing, and sonar testing. Summary of Activity Under..., most of the mid-frequency active sonar (MFAS) and high-frequency active sonar (HFAS) testing events... (Number Authorized vs. Conducted). Number Number Sonar system authorized conducted (hrs) (hrs) AN/SQS-53...

50 CFR 218.235 - Requirements for monitoring.

Code of Federal Regulations, 2012 CFR

2012-10-01

... Low Frequency Active (SURTASS LFA) Sonar § 218.235 Requirements for monitoring. (a) The Holder of a...) during operations that employ SURTASS LFA sonar in the active mode. The SURTASS vessels shall have... frequency passive SURTASS sonar to listen for vocalizing marine mammals; and (3) Use the HF/M3 active sonar...

50 CFR 216.184 - Mitigation.

Code of Federal Regulations, 2011 CFR

2011-10-01

... Frequency Active (SURTASS LFA sonar) Sonar § 216.184 Mitigation. The activity identified in § 216.180(a....54 nm) buffer zone extending beyond the 180-dB zone), SURTASS LFA sonar transmissions will be... active acoustic monitoring described in § 216.185. (c) The high-frequency marine mammal monitoring sonar...

50 CFR 218.235 - Requirements for monitoring.

Code of Federal Regulations, 2014 CFR

2014-10-01

... Low Frequency Active (SURTASS LFA) Sonar § 218.235 Requirements for monitoring. (a) The Holder of a...) during operations that employ SURTASS LFA sonar in the active mode. The SURTASS vessels shall have... frequency passive SURTASS sonar to listen for vocalizing marine mammals; and (3) Use the HF/M3 active sonar...

50 CFR 218.235 - Requirements for monitoring.

Code of Federal Regulations, 2013 CFR

2013-10-01

... Low Frequency Active (SURTASS LFA) Sonar § 218.235 Requirements for monitoring. (a) The Holder of a...) during operations that employ SURTASS LFA sonar in the active mode. The SURTASS vessels shall have... frequency passive SURTASS sonar to listen for vocalizing marine mammals; and (3) Use the HF/M3 active sonar...

Raft cultivation area extraction from high resolution remote sensing imagery by fusing multi-scale region-line primitive association features

NASA Astrophysics Data System (ADS)

Wang, Min; Cui, Qi; Wang, Jie; Ming, Dongping; Lv, Guonian

2017-01-01

In this paper, we first propose several novel concepts for object-based image analysis, which include line-based shape regularity, line density, and scale-based best feature value (SBV), based on the region-line primitive association framework (RLPAF). We then propose a raft cultivation area (RCA) extraction method for high spatial resolution (HSR) remote sensing imagery based on multi-scale feature fusion and spatial rule induction. The proposed method includes the following steps: (1) Multi-scale region primitives (segments) are obtained by image segmentation method HBC-SEG, and line primitives (straight lines) are obtained by phase-based line detection method. (2) Association relationships between regions and lines are built based on RLPAF, and then multi-scale RLPAF features are extracted and SBVs are selected. (3) Several spatial rules are designed to extract RCAs within sea waters after land and water separation. Experiments show that the proposed method can successfully extract different-shaped RCAs from HR images with good performance.

Joint recognition and discrimination in nonlinear feature space

NASA Astrophysics Data System (ADS)

Talukder, Ashit; Casasent, David P.

1997-09-01

A new general method for linear and nonlinear feature extraction is presented. It is novel since it provides both representation and discrimination while most other methods are concerned with only one of these issues. We call this approach the maximum representation and discrimination feature (MRDF) method and show that the Bayes classifier and the Karhunen- Loeve transform are special cases of it. We refer to our nonlinear feature extraction technique as nonlinear eigen- feature extraction. It is new since it has a closed-form solution and produces nonlinear decision surfaces with higher rank than do iterative methods. Results on synthetic databases are shown and compared with results from standard Fukunaga- Koontz transform and Fisher discriminant function methods. The method is also applied to an automated product inspection problem (discrimination) and to the classification and pose estimation of two similar objects (representation and discrimination).

Global Multi-Resolution Topography (GMRT) Synthesis - Version 2.0

NASA Astrophysics Data System (ADS)

Ferrini, V.; Coplan, J.; Carbotte, S. M.; Ryan, W. B.; O'Hara, S.; Morton, J. J.

2010-12-01

The detailed morphology of the global ocean floor is poorly known, with most areas mapped only at low resolution using satellite-based measurements. Ship-based sonars provide data at resolution sufficient to quantify seafloor features related to the active processes of erosion, sediment flow, volcanism, and faulting. To date, these data have been collected in a small fraction of the global ocean (<10%). The Global Multi-Resolution Topography (GMRT) synthesis makes use of sonar data collected by scientists and institutions worldwide, merging them into a single continuously updated compilation of high-resolution seafloor topography. Several applications, including GeoMapApp (http://www.geomapapp.org) and Virtual Ocean (http://www.virtualocean.org), make use of the GMRT Synthesis and provide direct access to images and underlying gridded data. Source multibeam files included in the compilation can also accessed through custom functionality in GeoMapApp. The GMRT Synthesis began in 1992 as the Ridge Multibeam Synthesis. It was subsequently expanded to include bathymetry data from the Southern Ocean, and now includes data from throughout the global oceans. Our design strategy has been to make data available at the full native resolution of shipboard sonar systems, which historically has been ~100 m in the deep sea (Ryan et al., 2009). A new release of the GMRT Synthesis in Fall of 2010 includes several significant improvements over our initial strategy. In addition to increasing the number of cruises included in the compilation by over 25%, we have developed a new protocol for handling multibeam source data, which has improved the overall quality of the compilation. The new tileset also includes a discrete layer of sonar data in the public domain that are gridded to the full resolution of the sonar system, with data gridded 25 m in some areas. This discrete layer of sonar data has been provided to Google for integration into Google’s default ocean base map. NOAA coastal grids and numerous grids contributed by the international science community are also integrated into the GMRT Synthesis. Finally, terrestrial elevation data from NASA’s ASTER (Advanced Spaceborne Thermal Emission and Reflection Radiometer) global DEM, and the USGS National Elevation Dataset have been included in the synthesis, providing resolution of up to 10 m in some areas of the US.

High-Resolution Remote Sensing Image Building Extraction Based on Markov Model

NASA Astrophysics Data System (ADS)

Zhao, W.; Yan, L.; Chang, Y.; Gong, L.

2018-04-01

With the increase of resolution, remote sensing images have the characteristics of increased information load, increased noise, more complex feature geometry and texture information, which makes the extraction of building information more difficult. To solve this problem, this paper designs a high resolution remote sensing image building extraction method based on Markov model. This method introduces Contourlet domain map clustering and Markov model, captures and enhances the contour and texture information of high-resolution remote sensing image features in multiple directions, and further designs the spectral feature index that can characterize "pseudo-buildings" in the building area. Through the multi-scale segmentation and extraction of image features, the fine extraction from the building area to the building is realized. Experiments show that this method can restrain the noise of high-resolution remote sensing images, reduce the interference of non-target ground texture information, and remove the shadow, vegetation and other pseudo-building information, compared with the traditional pixel-level image information extraction, better performance in building extraction precision, accuracy and completeness.

Modeling interface roughness scattering in a layered seabed for normal-incident chirp sonar signals.

PubMed

Tang, Dajun; Hefner, Brian T

2012-04-01

Downward looking sonar, such as the chirp sonar, is widely used as a sediment survey tool in shallow water environments. Inversion of geo-acoustic parameters from such sonar data precedes the availability of forward models. An exact numerical model is developed to initiate the simulation of the acoustic field produced by such a sonar in the presence of multiple rough interfaces. The sediment layers are assumed to be fluid layers with non-intercepting rough interfaces.

Behavioral Responses of Naive Cuvier’s Beaked Whales in the Ligurian Sea to Playback of Anthropogenic and Natural Sounds

DTIC Science & Technology

2012-09-30

cavirostris) to MFA sonar signals. A secondary goal of conducting a killer whale playback that has not been preceded by a sonar playback (as in Tyack et al...2011) was also planned. OBJECTIVES This investigation set out to safely test responses of Ziphius to sonar signals and to determine the...exposure level required to elicit a response in a site where strandings have been associated with sonar exercises and where the whales seldom hear sonar

A new approach for automatic matching of ground control points in urban areas from heterogeneous images

NASA Astrophysics Data System (ADS)

Cong, Chao; Liu, Dingsheng; Zhao, Lingjun

2008-12-01

This paper discusses a new method for the automatic matching of ground control points (GCPs) between satellite remote sensing Image and digital raster graphic (DRG) in urban areas. The key of this method is to automatically extract tie point pairs according to geographic characters from such heterogeneous images. Since there are big differences between such heterogeneous images respect to texture and corner features, more detail analyzations are performed to find similarities and differences between high resolution remote sensing Image and (DRG). Furthermore a new algorithms based on the fuzzy-c means (FCM) method is proposed to extract linear feature in remote sensing Image. Based on linear feature, crossings and corners extracted from these features are chosen as GCPs. On the other hand, similar method was used to find same features from DRGs. Finally, Hausdorff Distance was adopted to pick matching GCPs from above two GCP groups. Experiences shown the method can extract GCPs from such images with a reasonable RMS error.

Morphology-Induced Information Transfer in Bat Sonar

NASA Astrophysics Data System (ADS)

Reijniers, Jonas; Vanderelst, Dieter; Peremans, Herbert

2010-10-01

It has been argued that an important part of understanding bat echolocation comes down to understanding the morphology of the bat sound processing apparatus. In this Letter we present a method based on information theory that allows us to assess target localization performance of bat sonar, without a priori knowledge on the position, size, or shape of the reflecting target. We demonstrate this method using simulated directivity patterns of the frequency-modulated bat Micronycteris microtis. The results of this analysis indicate that the morphology of this bat’s sound processing apparatus has evolved to be a compromise between sensitivity and accuracy with the pinnae and the noseleaf playing different roles.

Detection of goal events in soccer videos

NASA Astrophysics Data System (ADS)

Kim, Hyoung-Gook; Roeber, Steffen; Samour, Amjad; Sikora, Thomas

2005-01-01

In this paper, we present an automatic extraction of goal events in soccer videos by using audio track features alone without relying on expensive-to-compute video track features. The extracted goal events can be used for high-level indexing and selective browsing of soccer videos. The detection of soccer video highlights using audio contents comprises three steps: 1) extraction of audio features from a video sequence, 2) event candidate detection of highlight events based on the information provided by the feature extraction Methods and the Hidden Markov Model (HMM), 3) goal event selection to finally determine the video intervals to be included in the summary. For this purpose we compared the performance of the well known Mel-scale Frequency Cepstral Coefficients (MFCC) feature extraction method vs. MPEG-7 Audio Spectrum Projection feature (ASP) extraction method based on three different decomposition methods namely Principal Component Analysis( PCA), Independent Component Analysis (ICA) and Non-Negative Matrix Factorization (NMF). To evaluate our system we collected five soccer game videos from various sources. In total we have seven hours of soccer games consisting of eight gigabytes of data. One of five soccer games is used as the training data (e.g., announcers' excited speech, audience ambient speech noise, audience clapping, environmental sounds). Our goal event detection results are encouraging.

Digitally enhanced GLORIA images for petroleum exploration

DOE Office of Scientific and Technical Information (OSTI.GOV)

Prindle, R.O.; Lanz, K

1990-05-01

This poster presentation graphically depicts the geological and structural information that can be derived from digitally enhanced Geological Long Range Inclined Asdic (GLORIA) sonar images. This presentation illustrates the advantages of scale enlargement as an interpreter's tool in an offshore area within the Eel River Basin, Northern California. Sonographs were produced from digital tapes originally collected for the exclusive economic zone (EEZ)-SCAN 1984 survey, which was published in the Atlas of the Western Conterminous US at a scale of 1:500,000. This scale is suitable for displaying regional offshore tectonic features but does not have the resolution required for detailed geologicalmore » mapping necessary for petroleum exploration. Applications of digital enhancing techniques which utilize contrast stretching and assign false colors to wide-swath sonar imagery (approximately 40 km) with 50-m resolution enables the acquisition and interpretation of significantly more geological and structural data. This, combined with a scale enlargement to 1:100,000 and high contrast contact prints vs. the offset prints of the atlas, increases the resolution and sharpness of bathymetric features so that many more subtle features may be mapped in detail. A tectonic interpretation of these digitally enhanced GLORIA sonographs from the Eel River basin is presented, displaying anticlines, lineaments, ridge axis, pathways of sediment flow, and subtle doming. Many of these features are not present on published bathymetric maps and have not been derived from seismic data because the plan view spatial resolution is much less than that available from the GLORIA imagery.« less

Main Road Extraction from ZY-3 Grayscale Imagery Based on Directional Mathematical Morphology and VGI Prior Knowledge in Urban Areas

PubMed Central

Liu, Bo; Wu, Huayi; Wang, Yandong; Liu, Wenming

2015-01-01

Main road features extracted from remotely sensed imagery play an important role in many civilian and military applications, such as updating Geographic Information System (GIS) databases, urban structure analysis, spatial data matching and road navigation. Current methods for road feature extraction from high-resolution imagery are typically based on threshold value segmentation. It is difficult however, to completely separate road features from the background. We present a new method for extracting main roads from high-resolution grayscale imagery based on directional mathematical morphology and prior knowledge obtained from the Volunteered Geographic Information found in the OpenStreetMap. The two salient steps in this strategy are: (1) using directional mathematical morphology to enhance the contrast between roads and non-roads; (2) using OpenStreetMap roads as prior knowledge to segment the remotely sensed imagery. Experiments were conducted on two ZiYuan-3 images and one QuickBird high-resolution grayscale image to compare our proposed method to other commonly used techniques for road feature extraction. The results demonstrated the validity and better performance of the proposed method for urban main road feature extraction. PMID:26397832

The sonar aperture and its neural representation in bats.

PubMed

Heinrich, Melina; Warmbold, Alexander; Hoffmann, Susanne; Firzlaff, Uwe; Wiegrebe, Lutz

2011-10-26

As opposed to visual imaging, biosonar imaging of spatial object properties represents a challenge for the auditory system because its sensory epithelium is not arranged along space axes. For echolocating bats, object width is encoded by the amplitude of its echo (echo intensity) but also by the naturally covarying spread of angles of incidence from which the echoes impinge on the bat's ears (sonar aperture). It is unclear whether bats use the echo intensity and/or the sonar aperture to estimate an object's width. We addressed this question in a combined psychophysical and electrophysiological approach. In three virtual-object playback experiments, bats of the species Phyllostomus discolor had to discriminate simple reflections of their own echolocation calls differing in echo intensity, sonar aperture, or both. Discrimination performance for objects with physically correct covariation of sonar aperture and echo intensity ("object width") did not differ from discrimination performances when only the sonar aperture was varied. Thus, the bats were able to detect changes in object width in the absence of intensity cues. The psychophysical results are reflected in the responses of a population of units in the auditory midbrain and cortex that responded strongest to echoes from objects with a specific sonar aperture, regardless of variations in echo intensity. Neurometric functions obtained from cortical units encoding the sonar aperture are sufficient to explain the behavioral performance of the bats. These current data show that the sonar aperture is a behaviorally relevant and reliably encoded cue for object size in bat sonar.

Face-iris multimodal biometric scheme based on feature level fusion

NASA Astrophysics Data System (ADS)

Huo, Guang; Liu, Yuanning; Zhu, Xiaodong; Dong, Hongxing; He, Fei

2015-11-01

Unlike score level fusion, feature level fusion demands all the features extracted from unimodal traits with high distinguishability, as well as homogeneity and compatibility, which is difficult to achieve. Therefore, most multimodal biometric research focuses on score level fusion, whereas few investigate feature level fusion. We propose a face-iris recognition method based on feature level fusion. We build a special two-dimensional-Gabor filter bank to extract local texture features from face and iris images, and then transform them by histogram statistics into an energy-orientation variance histogram feature with lower dimensions and higher distinguishability. Finally, through a fusion-recognition strategy based on principal components analysis and support vector machine (FRSPS), feature level fusion and one-to-n identification are accomplished. The experimental results demonstrate that this method can not only effectively extract face and iris features but also provide higher recognition accuracy. Compared with some state-of-the-art fusion methods, the proposed method has a significant performance advantage.

An Extended Spectral-Spatial Classification Approach for Hyperspectral Data

NASA Astrophysics Data System (ADS)

Akbari, D.

2017-11-01

In this paper an extended classification approach for hyperspectral imagery based on both spectral and spatial information is proposed. The spatial information is obtained by an enhanced marker-based minimum spanning forest (MSF) algorithm. Three different methods of dimension reduction are first used to obtain the subspace of hyperspectral data: (1) unsupervised feature extraction methods including principal component analysis (PCA), independent component analysis (ICA), and minimum noise fraction (MNF); (2) supervised feature extraction including decision boundary feature extraction (DBFE), discriminate analysis feature extraction (DAFE), and nonparametric weighted feature extraction (NWFE); (3) genetic algorithm (GA). The spectral features obtained are then fed into the enhanced marker-based MSF classification algorithm. In the enhanced MSF algorithm, the markers are extracted from the classification maps obtained by both SVM and watershed segmentation algorithm. To evaluate the proposed approach, the Pavia University hyperspectral data is tested. Experimental results show that the proposed approach using GA achieves an approximately 8 % overall accuracy higher than the original MSF-based algorithm.

Machinery running state identification based on discriminant semi-supervised local tangent space alignment for feature fusion and extraction

NASA Astrophysics Data System (ADS)

Su, Zuqiang; Xiao, Hong; Zhang, Yi; Tang, Baoping; Jiang, Yonghua

2017-04-01

Extraction of sensitive features is a challenging but key task in data-driven machinery running state identification. Aimed at solving this problem, a method for machinery running state identification that applies discriminant semi-supervised local tangent space alignment (DSS-LTSA) for feature fusion and extraction is proposed. Firstly, in order to extract more distinct features, the vibration signals are decomposed by wavelet packet decomposition WPD, and a mixed-domain feature set consisted of statistical features, autoregressive (AR) model coefficients, instantaneous amplitude Shannon entropy and WPD energy spectrum is extracted to comprehensively characterize the properties of machinery running state(s). Then, the mixed-dimension feature set is inputted into DSS-LTSA for feature fusion and extraction to eliminate redundant information and interference noise. The proposed DSS-LTSA can extract intrinsic structure information of both labeled and unlabeled state samples, and as a result the over-fitting problem of supervised manifold learning and blindness problem of unsupervised manifold learning are overcome. Simultaneously, class discrimination information is integrated within the dimension reduction process in a semi-supervised manner to improve sensitivity of the extracted fusion features. Lastly, the extracted fusion features are inputted into a pattern recognition algorithm to achieve the running state identification. The effectiveness of the proposed method is verified by a running state identification case in a gearbox, and the results confirm the improved accuracy of the running state identification.

A graph-Laplacian-based feature extraction algorithm for neural spike sorting.

PubMed

Ghanbari, Yasser; Spence, Larry; Papamichalis, Panos

2009-01-01

Analysis of extracellular neural spike recordings is highly dependent upon the accuracy of neural waveform classification, commonly referred to as spike sorting. Feature extraction is an important stage of this process because it can limit the quality of clustering which is performed in the feature space. This paper proposes a new feature extraction method (which we call Graph Laplacian Features, GLF) based on minimizing the graph Laplacian and maximizing the weighted variance. The algorithm is compared with Principal Components Analysis (PCA, the most commonly-used feature extraction method) using simulated neural data. The results show that the proposed algorithm produces more compact and well-separated clusters compared to PCA. As an added benefit, tentative cluster centers are output which can be used to initialize a subsequent clustering stage.

[The research on separating and extracting overlapping spectral feature lines in LIBS using damped least squares method].

PubMed

Wang, Yin; Zhao, Nan-jing; Liu, Wen-qing; Yu, Yang; Fang, Li; Meng, De-shuo; Hu, Li; Zhang, Da-hai; Ma, Min-jun; Xiao, Xue; Wang, Yu; Liu, Jian-guo

2015-02-01

In recent years, the technology of laser induced breakdown spectroscopy has been developed rapidly. As one kind of new material composition detection technology, laser induced breakdown spectroscopy can simultaneously detect multi elements fast and simply without any complex sample preparation and realize field, in-situ material composition detection of the sample to be tested. This kind of technology is very promising in many fields. It is very important to separate, fit and extract spectral feature lines in laser induced breakdown spectroscopy, which is the cornerstone of spectral feature recognition and subsequent elements concentrations inversion research. In order to realize effective separation, fitting and extraction of spectral feature lines in laser induced breakdown spectroscopy, the original parameters for spectral lines fitting before iteration were analyzed and determined. The spectral feature line of' chromium (Cr I : 427.480 nm) in fly ash gathered from a coal-fired power station, which was overlapped with another line(FeI: 427.176 nm), was separated from the other one and extracted by using damped least squares method. Based on Gauss-Newton iteration, damped least squares method adds damping factor to step and adjust step length dynamically according to the feedback information after each iteration, in order to prevent the iteration from diverging and make sure that the iteration could converge fast. Damped least squares method helps to obtain better results of separating, fitting and extracting spectral feature lines and give more accurate intensity values of these spectral feature lines: The spectral feature lines of chromium in samples which contain different concentrations of chromium were separated and extracted. And then, the intensity values of corresponding spectral lines were given by using damped least squares method and least squares method separately. The calibration curves were plotted, which showed the relationship between spectral line intensity values and chromium concentrations in different samples. And then their respective linear correlations were compared. The experimental results showed that the linear correlation of the intensity values of spectral feature lines and the concentrations of chromium in different samples, which was obtained by damped least squares method, was better than that one obtained by least squares method. And therefore, damped least squares method was stable, reliable and suitable for separating, fitting and extracting spectral feature lines in laser induced breakdown spectroscopy.

Chinese License Plates Recognition Method Based on A Robust and Efficient Feature Extraction and BPNN Algorithm

NASA Astrophysics Data System (ADS)

Zhang, Ming; Xie, Fei; Zhao, Jing; Sun, Rui; Zhang, Lei; Zhang, Yue

2018-04-01

The prosperity of license plate recognition technology has made great contribution to the development of Intelligent Transport System (ITS). In this paper, a robust and efficient license plate recognition method is proposed which is based on a combined feature extraction model and BPNN (Back Propagation Neural Network) algorithm. Firstly, the candidate region of the license plate detection and segmentation method is developed. Secondly, a new feature extraction model is designed considering three sets of features combination. Thirdly, the license plates classification and recognition method using the combined feature model and BPNN algorithm is presented. Finally, the experimental results indicate that the license plate segmentation and recognition both can be achieved effectively by the proposed algorithm. Compared with three traditional methods, the recognition accuracy of the proposed method has increased to 95.7% and the consuming time has decreased to 51.4ms.

Sea-floor geology and character offshore of Rocky Point, New York

USGS Publications Warehouse

Poppe, L.J.; McMullen, K.Y.; Ackerman, S.D.; Blackwood, D.S.; Irwin, B.J.; Schaer, J.D.; Lewit, P.G.; Doran, E.F.

2010-01-01

The U.S. Geological Survey (USGS), the Connecticut Department of Environmental Protection, and the National Oceanic and Atmospheric Administration (NOAA) have been working cooperatively to interpret surficial sea-floor geology along the coast of the Northeastern United States. NOAA survey H11445 in eastern Long Island Sound, offshore of Plum Island, New York, covers an area of about 12 square kilometers. Multibeam bathymetry and sidescan-sonar imagery from the survey, as well as sediment and photographic data from 13 stations occupied during a USGS verification cruise are used to delineate sea-floor features and characterize the environment. Bathymetry gradually deepens offshore to over 100 meters in a depression in the northwest part of the study area and reaches 60 meters in Plum Gut, a channel between Plum Island and Orient Point. Sand waves are present on a shoal north of Plum Island and in several smaller areas around the basin. Sand-wave asymmetry indicates that counter-clockwise net sediment transport maintains the shoal. Sand is prevalent where there is low backscatter in the sidescan-sonar imagery. Gravel and boulder areas are submerged lag deposits produced from the Harbor Hill-Orient Point-Fishers Island moraine segment and are found adjacent to the shorelines and just north of Plum Island, where high backscatter is present in the sidescan-sonar imagery.

Electroencephalogram-based decoding cognitive states using convolutional neural network and likelihood ratio based score fusion.

PubMed

Zafar, Raheel; Dass, Sarat C; Malik, Aamir Saeed

2017-01-01

Electroencephalogram (EEG)-based decoding human brain activity is challenging, owing to the low spatial resolution of EEG. However, EEG is an important technique, especially for brain-computer interface applications. In this study, a novel algorithm is proposed to decode brain activity associated with different types of images. In this hybrid algorithm, convolutional neural network is modified for the extraction of features, a t-test is used for the selection of significant features and likelihood ratio-based score fusion is used for the prediction of brain activity. The proposed algorithm takes input data from multichannel EEG time-series, which is also known as multivariate pattern analysis. Comprehensive analysis was conducted using data from 30 participants. The results from the proposed method are compared with current recognized feature extraction and classification/prediction techniques. The wavelet transform-support vector machine method is the most popular currently used feature extraction and prediction method. This method showed an accuracy of 65.7%. However, the proposed method predicts the novel data with improved accuracy of 79.9%. In conclusion, the proposed algorithm outperformed the current feature extraction and prediction method.

50 CFR 216.270 - Specified activity and specified geographical region.

Code of Federal Regulations, 2012 CFR

2012-10-01

... the following mid-frequency active sonar (MFAS) and high frequency active sonar (HFAS) sources, or...) (estimated amounts below): (i) AN/SQS-53 (hull-mounted active sonar)—up to 9885 hours over the course of 5 years (an average of 1977 hours per year) (ii) AN/SQS-56 (hull-mounted active sonar)—up to 2470 hours...

50 CFR 216.270 - Specified activity and specified geographical region.

Code of Federal Regulations, 2013 CFR

2013-10-01

... the following mid-frequency active sonar (MFAS) and high frequency active sonar (HFAS) sources, or...) (estimated amounts below): (i) AN/SQS-53 (hull-mounted active sonar)—up to 9885 hours over the course of 5 years (an average of 1977 hours per year) (ii) AN/SQS-56 (hull-mounted active sonar)—up to 2470 hours...

Highly Directive Array Aperture

DTIC Science & Technology

2013-02-13

generally to sonar arrays with acoustic discontinuities, and, more particularly, to increasing the directivity gain of a sonar array aperture by...sought by sonar designers. [0005] The following patents and publication show various types of acoustic arrays with coatings and discontinuities that...discloses a sonar array uses multiple acoustically transparent layers. One layer is a linear array of acoustic sensors that is substantially

A Directional Dogbone Flextensional Sonar Transducer

DTIC Science & Technology

2010-10-01

A Directional Dogbone Flextensional Sonar Transducer Stephen C. Butler Naval Undersea Warfare Center, Newport, RI 02841 Abstract: In order to...transmit energy in one direction, sonar flextensional transducers are combined into arrays of elements that are spaced a 1/4 wavelength apart. The...electroacoustic performance and compared with an experimental data. Keywords: Transducer, Flextensional, Sonar , Piezoelectric, Directional, Cardioid

Concurrent Initialization for Bearing-Only SLAM

PubMed Central

Munguía, Rodrigo; Grau, Antoni

2010-01-01

Simultaneous Localization and Mapping (SLAM) is perhaps the most fundamental problem to solve in robotics in order to build truly autonomous mobile robots. The sensors have a large impact on the algorithm used for SLAM. Early SLAM approaches focused on the use of range sensors as sonar rings or lasers. However, cameras have become more and more used, because they yield a lot of information and are well adapted for embedded systems: they are light, cheap and power saving. Unlike range sensors which provide range and angular information, a camera is a projective sensor which measures the bearing of images features. Therefore depth information (range) cannot be obtained in a single step. This fact has propitiated the emergence of a new family of SLAM algorithms: the Bearing-Only SLAM methods, which mainly rely in especial techniques for features system-initialization in order to enable the use of bearing sensors (as cameras) in SLAM systems. In this work a novel and robust method, called Concurrent Initialization, is presented which is inspired by having the complementary advantages of the Undelayed and Delayed methods that represent the most common approaches for addressing the problem. The key is to use concurrently two kinds of feature representations for both undelayed and delayed stages of the estimation. The simulations results show that the proposed method surpasses the performance of previous schemes. PMID:22294884

Intelligence, Surveillance, and Reconnaissance Fusion for Coalition Operations

DTIC Science & Technology

2008-07-01

classification of the targets of interest. The MMI features extracted in this manner have two properties that provide a sound justification for...are generalizations of well- known feature extraction methods such as Principal Components Analysis (PCA) and Independent Component Analysis (ICA...augment (without degrading performance) a large class of generic fusion processes. Ontologies Classifications Feature extraction Feature analysis

Mapping coral and sponge habitats on a shelf-depth environment using multibeam sonar and ROV video observations: Learmonth Bank, northern British Columbia, Canada

NASA Astrophysics Data System (ADS)

Neves, Bárbara M.; Du Preez, Cherisse; Edinger, Evan

2014-01-01

Efforts to locate and map deep-water coral and sponge habitats are essential for the effective management and conservation of these vulnerable marine ecosystems. Here we test the applicability of a simple multibeam sonar classification method developed for fjord environments to map the distribution of shelf-depth substrates and gorgonian coral- and sponge-dominated biotopes. The studied area is a shelf-depth feature Learmonth Bank, northern British Columbia, Canada and the method was applied aiming to map primarily non-reef forming coral and sponge biotopes. Aside from producing high-resolution maps (5 m2 raster grid), biotope-substrate associations were also investigated. A multibeam sonar survey yielded bathymetry, acoustic backscatter strength and slope. From benthic video transects recorded by remotely operated vehicles (ROVs) six primary substrate types and twelve biotope categories were identified, defined by the primary sediment and dominant biological structure, respectively. Substrate and biotope maps were produced using a supervised classification mostly based on the inter-quartile range of the acoustic variables for each substrate type and biotope. Twenty-five percent of the video observations were randomly reserved for testing the classification accuracy. The dominant biotope-defining corals were red tree coral Primnoa pacifica and small styasterids, of which Stylaster parageus was common. Demosponges and hexactinellid sponges were frequently observed but no sponge reefs were observed. The substrate classification readily distinguished fine sediment, Sand and Bedrock from the other substrate types, but had greater difficulty distinguishing Bedrock from Boulders and Cobble. The biotope classification accurately identified Gardens (dense aggregations of sponges and corals) and Primnoa-dominated biotopes (67% accuracy), but most other biotopes had lower accuracies. There was a significant correspondence between Learmonth's biotopes and substrate types, with many biotopes strongly associated with only a single substrate type. This strong correspondence allowed substrate types to function as a surrogate for helping to map biotope distributions. Our results add new information on the distribution of corals and sponges at Learmonth Bank, which can be used to guide management at this location.

A Study for Texture Feature Extraction of High-Resolution Satellite Images Based on a Direction Measure and Gray Level Co-Occurrence Matrix Fusion Algorithm

PubMed Central

Zhang, Xin; Cui, Jintian; Wang, Weisheng; Lin, Chao

2017-01-01

To address the problem of image texture feature extraction, a direction measure statistic that is based on the directionality of image texture is constructed, and a new method of texture feature extraction, which is based on the direction measure and a gray level co-occurrence matrix (GLCM) fusion algorithm, is proposed in this paper. This method applies the GLCM to extract the texture feature value of an image and integrates the weight factor that is introduced by the direction measure to obtain the final texture feature of an image. A set of classification experiments for the high-resolution remote sensing images were performed by using support vector machine (SVM) classifier with the direction measure and gray level co-occurrence matrix fusion algorithm. Both qualitative and quantitative approaches were applied to assess the classification results. The experimental results demonstrated that texture feature extraction based on the fusion algorithm achieved a better image recognition, and the accuracy of classification based on this method has been significantly improved. PMID:28640181

Extracting foreground ensemble features to detect abnormal crowd behavior in intelligent video-surveillance systems

NASA Astrophysics Data System (ADS)

Chan, Yi-Tung; Wang, Shuenn-Jyi; Tsai, Chung-Hsien

2017-09-01

Public safety is a matter of national security and people's livelihoods. In recent years, intelligent video-surveillance systems have become important active-protection systems. A surveillance system that provides early detection and threat assessment could protect people from crowd-related disasters and ensure public safety. Image processing is commonly used to extract features, e.g., people, from a surveillance video. However, little research has been conducted on the relationship between foreground detection and feature extraction. Most current video-surveillance research has been developed for restricted environments, in which the extracted features are limited by having information from a single foreground; they do not effectively represent the diversity of crowd behavior. This paper presents a general framework based on extracting ensemble features from the foreground of a surveillance video to analyze a crowd. The proposed method can flexibly integrate different foreground-detection technologies to adapt to various monitored environments. Furthermore, the extractable representative features depend on the heterogeneous foreground data. Finally, a classification algorithm is applied to these features to automatically model crowd behavior and distinguish an abnormal event from normal patterns. The experimental results demonstrate that the proposed method's performance is both comparable to that of state-of-the-art methods and satisfies the requirements of real-time applications.

Building a 3d Reference Model for Canal Tunnel Surveying Using Sonar and Laser Scanning

NASA Astrophysics Data System (ADS)

Moisan, E.; Charbonnier, P.; Foucher, P.; Grussenmeyer, P.; Guillemin, S.; Koehl, M.

2015-04-01

Maintaining canal tunnels is not only a matter of cultural and historical preservation, but also a commercial necessity and a security issue. This contribution adresses the problem of building a full 3D reference model of a canal tunnel by merging SONAR (for underwater data recording) and LASER data (for the above-water parts). Although both scanning devices produce point clouds, their properties are rather different. In particular, SONAR data are very noisy and their processing raises several issues related to the device capacities, the acquisition setup and the tubular shape of the tunnel. The proposed methodology relies on a denoising step by meshing, followed by the registration of SONAR data with the geo-referenced LASER data. Since there is no overlap between point clouds, a 3-step procedure is proposed to robustly estimate the registration parameters. In this paper, we report a first experimental survey, which concerned the entrance of a canal tunnel. The obtained results are promising and the analysis of the method raises several improvement directions that will help obtaining more accurate models, in a more automated fashion, in the limits of the involved technology.

Kinematic analysis of conically scanned environmental properties

NASA Technical Reports Server (NTRS)

Wilkerson, Thomas D. (Inventor); Sanders, Jason A. (Inventor); Andrus, Ionio Q. (Inventor)

2003-01-01

A method for determining the velocity of features such as wind. The method preferably includes producing sensor signals and projecting the sensor signals sequentially along lines lying on the surface of a cone. The sensor signals may be in the form of lidar, radar or sonar for example. As the sensor signals are transmitted, the signals contact objects and are backscattered. The backscattered sensor signals are received to determine the location of objects as they pass through the transmission path. The speed and direction the object is moving may be calculated using the backscattered data. The data may be plotted in a two dimensional array with a scan angle on one axis and a scan time on the other axis. The prominent curves that appear in the plot may be analyzed to determine the speed and direction the object is traveling.

A method for automatic feature points extraction of human vertebrae three-dimensional model

NASA Astrophysics Data System (ADS)

Wu, Zhen; Wu, Junsheng

2017-05-01

A method for automatic extraction of the feature points of the human vertebrae three-dimensional model is presented. Firstly, the statistical model of vertebrae feature points is established based on the results of manual vertebrae feature points extraction. Then anatomical axial analysis of the vertebrae model is performed according to the physiological and morphological characteristics of the vertebrae. Using the axial information obtained from the analysis, a projection relationship between the statistical model and the vertebrae model to be extracted is established. According to the projection relationship, the statistical model is matched with the vertebrae model to get the estimated position of the feature point. Finally, by analyzing the curvature in the spherical neighborhood with the estimated position of feature points, the final position of the feature points is obtained. According to the benchmark result on multiple test models, the mean relative errors of feature point positions are less than 5.98%. At more than half of the positions, the error rate is less than 3% and the minimum mean relative error is 0.19%, which verifies the effectiveness of the method.

[Identification of special quality eggs with NIR spectroscopy technology based on symbol entropy feature extraction method].

PubMed

Zhao, Yong; Hong, Wen-Xue

2011-11-01

Fast, nondestructive and accurate identification of special quality eggs is an urgent problem. The present paper proposed a new feature extraction method based on symbol entropy to identify near infrared spectroscopy of special quality eggs. The authors selected normal eggs, free range eggs, selenium-enriched eggs and zinc-enriched eggs as research objects and measured the near-infrared diffuse reflectance spectra in the range of 12 000-4 000 cm(-1). Raw spectra were symbolically represented with aggregation approximation algorithm and symbolic entropy was extracted as feature vector. An error-correcting output codes multiclass support vector machine classifier was designed to identify the spectrum. Symbolic entropy feature is robust when parameter changed and the highest recognition rate reaches up to 100%. The results show that the identification method of special quality eggs using near-infrared is feasible and the symbol entropy can be used as a new feature extraction method of near-infrared spectra.

Processing of SeaMARC swath sonar imagery

DOE Office of Scientific and Technical Information (OSTI.GOV)

Pratson, L.; Malinverno, A.; Edwards, M.

1990-05-01

Side-scan swath sonar systems have become an increasingly important means of mapping the sea floor. Two such systems are the deep-towed, high-resolution SeaMARC I sonar, which has a variable swath width of up to 5 km, and the shallow-towed, lower-resolution SeaMARC II sonar, which has a swath width of 10 km. The sea-floor imagery of acoustic backscatter output by the SeaMARC sonars is analogous to aerial photographs and airborne side-looking radar images of continental topography. Geologic interpretation of the sea-floor imagery is greatly facilitated by image processing. Image processing of the digital backscatter data involves removal of noise by medianmore » filtering, spatial filtering to remove sonar scans of anomalous intensity, across-track corrections to remove beam patterns caused by nonuniform response of the sonar transducers to changes in incident angle, and contrast enhancement by histogram equalization to maximize the available dynamic range. Correct geologic interpretation requires submarine structural fabrics to be displayed in their proper locations and orientations. Geographic projection of sea-floor imagery is achieved by merging the enhanced imagery with the sonar vehicle navigation and correcting for vehicle attitude. Co-registration of bathymetry with sonar imagery introduces sea-floor relief and permits the imagery to be displayed in three-dimensional perspectives, furthering the ability of the marine geologist to infer the processes shaping formerly hidden subsea terrains.« less

Extraction of Seabed/Subsurface Features in a Potential CO2 Sequestration Site in the Southern Baltic Sea, Using Wavelet Transform of High-resolution Sub-Bottom Profiler Data

NASA Astrophysics Data System (ADS)

Tegowski, J.; Zajfert, G.

2014-12-01

Carbon Capture & Storage (CCS) efficiently prevents the release of anthropogenic CO2 into the atmosphere. We investigate a potential site in the Polish Sector of the Baltic Sea (B3 field site), consisting in a depleted oil and gas reservoir. An area ca. 30 x 8 km was surveyed along 138 acoustic transects, realised from R/V St. Barbara in 2012 and combining multibeam echosounder, sidescan sonar and sub-bottom profiler. Preparation of CCS sites requires accurate knowledge of the subsurface structure of the seafloor, in particular deposit compactness. Gas leaks in the water column were monitored, along with the structure of upper sediment layers. Our analyses show the shallow sub-seabed is layered, and quantified the spatial distribution of gas diffusion chimneys and seabed effusion craters. Remote detection of gas-containing surface sediments can be rather complex if bubbles are not emitted directly into the overlying water and thus detectable acoustically. The heterogeneity of gassy sediments makes conventional bottom sampling methods inefficient. Therefore, we propose a new approach to identification, mapping, and monitoring of potentially gassy surface sediments, based on wavelet analysis of echo signal envelopes of a chirp sub-bottom profiler (EdgeTech SB-0512). Each echo envelope was subjected to wavelet transformation, whose coefficients were used to calculate wavelet energies. The set of echo envelope parameters was input to fuzzy logic and c-means algorithms. The resulting classification highlights seafloor areas with different subsurface morphological features, which can indicate gassy sediments. This work has been conducted under EC FP7-CP-IP project No. 265847: Sub-seabed CO2 Storage: Impact on Marine Ecosystems (ECO2).

Texture feature extraction based on a uniformity estimation method for local brightness and structure in chest CT images.

PubMed

Peng, Shao-Hu; Kim, Deok-Hwan; Lee, Seok-Lyong; Lim, Myung-Kwan

2010-01-01

Texture feature is one of most important feature analysis methods in the computer-aided diagnosis (CAD) systems for disease diagnosis. In this paper, we propose a Uniformity Estimation Method (UEM) for local brightness and structure to detect the pathological change in the chest CT images. Based on the characteristics of the chest CT images, we extract texture features by proposing an extension of rotation invariant LBP (ELBP(riu4)) and the gradient orientation difference so as to represent a uniform pattern of the brightness and structure in the image. The utilization of the ELBP(riu4) and the gradient orientation difference allows us to extract rotation invariant texture features in multiple directions. Beyond this, we propose to employ the integral image technique to speed up the texture feature computation of the spatial gray level dependent method (SGLDM). Copyright © 2010 Elsevier Ltd. All rights reserved.

Acoustic Seabed Characterization of the Porcupine Bank, Irish Margin

NASA Astrophysics Data System (ADS)

O'Toole, Ronan; Monteys, Xavier

2010-05-01

The Porcupine Bank represents a large section of continental shelf situated west of the Irish landmass, located in water depths ranging between 150 and 500m. Under the Irish National Seabed Survey (INSS 1999-2006) this area was comprehensively mapped, generating multiple acoustic datasets including high resolution multibeam echosounder data. The unique nature of the area's datasets in terms of data density, consistency and geographic extent has allowed the development of a large-scale integrated physical characterization of the Porcupine Bank for multidisciplinary applications. Integrated analysis of backscatter and bathymetry data has resulted in a baseline delineation of sediment distribution, seabed geology and geomorphological features on the bank, along with an inclusive set of related database information. The methodology used incorporates a variety of statistical techniques which are necessary in isolating sonar system artefacts and addressing sonar geometry related issues. A number of acoustic backscatter parameters at several angles of incidence have been analysed in order to complement the characterization for both surface and subsurface sediments. Acoustic sub bottom records have also been incorporated in order to investigate the physical characteristics of certain features on the Porcupine Bank. Where available, groundtruthing information in terms of sediment samples, video footage and cores has been applied to add physical descriptors and validation to the characterization. Extensive mapping of different rock outcrops, sediment drifts, seabed features and other geological classes has been achieved using this methodology.

Exploration and discovery in Yellowstone Lake: results from high-resolution sonar imaging, seismic reflection profiling, and submersible studies

NASA Astrophysics Data System (ADS)

Morgan, L. A.; Shanks, W. C.; Lovalvo, D. A.; Johnson, S. Y.; Stephenson, W. J.; Pierce, K. L.; Harlan, S. S.; Finn, C. A.; Lee, G.; Webring, M.; Schulze, B.; Dühn, J.; Sweeney, R.; Balistrieri, L.

2003-04-01

'No portion of the American continent is perhaps so rich in wonders as the Yellow Stone' (F.V. Hayden, September 2, 1874) Discoveries from multi-beam sonar mapping and seismic reflection surveys of the northern, central, and West Thumb basins of Yellowstone Lake provide new insight into the extent of post-collapse volcanism and active hydrothermal processes occurring in a large lake environment above a large magma chamber. Yellowstone Lake has an irregular bottom covered with dozens of features directly related to hydrothermal, tectonic, volcanic, and sedimentary processes. Detailed bathymetric, seismic reflection, and magnetic evidence reveals that rhyolitic lava flows underlie much of Yellowstone Lake and exert fundamental control on lake bathymetry and localization of hydrothermal activity. Many previously unknown features have been identified and include over 250 hydrothermal vents, several very large (>500 m diameter) hydrothermal explosion craters, many small hydrothermal vent craters (˜1-200 m diameter), domed lacustrine sediments related to hydrothermal activity, elongate fissures cutting post-glacial sediments, siliceous hydrothermal spire structures, sublacustrine landslide deposits, submerged former shorelines, and a recently active graben. Sampling and observations with a submersible remotely operated vehicle confirm and extend our understanding of the identified features. Faults, fissures, hydrothermally inflated domal structures, hydrothermal explosion craters, and sublacustrine landslides constitute potentially significant geologic hazards. Toxic elements derived from hydrothermal processes also may significantly affect the Yellowstone ecosystem.

50 CFR 218.110 - Specified activity and specified geographical area.

Code of Federal Regulations, 2012 CFR

2012-10-01

... sonar (MFAS) and high frequency active sonar (HFAS) sources, or similar sources, for Navy training...-53 (hull-mounted active sonar)—up to 215 hours over the course of 5 years (an average of 43 hours per year); (ii) AN/SQS-56 (hull-mounted active sonar)—up to 325 hours over the course of 5 years (an...

50 CFR 218.100 - Specified activity and specified geographical area.

Code of Federal Regulations, 2013 CFR

2013-10-01

... active sonar (MFAS) and high frequency active sonar (HFAS) sources, or similar sources, for Navy training...-53 (hull-mounted active sonar)—up to 10865 hours over the course of 5 years (an average of 2173 hours per year); (ii) AN/SQS-56 (hull-mounted active sonar)-up to 705 hours over the course of 5 years (an...

50 CFR 216.170 - Specified activity and specified geographical region.

Code of Federal Regulations, 2010 CFR

2010-10-01

...-frequency active sonar (MFAS) and high frequency active sonar (HFAS) sources for U.S. Navy anti-submarine warfare (ASW) training in the amounts indicated below (±10 percent): (i) AN/SQS-53 (hull-mounted sonar)—up...-mounted sonar)—up to 1915 hours over the course of 5 years (an average of 383 hours per year) (iii) AN/AQS...

50 CFR 218.100 - Specified activity and specified geographical area.

Code of Federal Regulations, 2014 CFR

2014-10-01

... active sonar (MFAS) and high frequency active sonar (HFAS) sources, or similar sources, for Navy training...-53 (hull-mounted active sonar)—up to 10865 hours over the course of 5 years (an average of 2173 hours per year); (ii) AN/SQS-56 (hull-mounted active sonar)-up to 705 hours over the course of 5 years (an...

50 CFR 218.110 - Specified activity and specified geographical area.

Code of Federal Regulations, 2014 CFR

2014-10-01

... sonar (MFAS) and high frequency active sonar (HFAS) sources, or similar sources, for Navy training...-53 (hull-mounted active sonar)—up to 215 hours over the course of 5 years (an average of 43 hours per year); (ii) AN/SQS-56 (hull-mounted active sonar)—up to 325 hours over the course of 5 years (an...

50 CFR 218.100 - Specified activity and specified geographical area.

Code of Federal Regulations, 2012 CFR

2012-10-01

... active sonar (MFAS) and high frequency active sonar (HFAS) sources, or similar sources, for Navy training...-53 (hull-mounted active sonar)—up to 10865 hours over the course of 5 years (an average of 2173 hours per year); (ii) AN/SQS-56 (hull-mounted active sonar)-up to 705 hours over the course of 5 years (an...

50 CFR 218.120 - Specified activity and geographical area.

Code of Federal Regulations, 2014 CFR

2014-10-01

... the following mid-frequency active sonar (MFAS) sources, high-frequency active sonar (HFAS) sources...-mounted active sonar)—up to 2,890 hours over the course of 5 years (an average of 578 hours per year); (ii) AN/SQS-56 (hull-mounted active sonar)—up to 260 hours over the course of 5 years (an average of 52...

50 CFR 218.100 - Specified activity and specified geographical area.

Code of Federal Regulations, 2011 CFR

2011-10-01

...: (1) The use of the following mid-frequency active sonar (MFAS) and high frequency active sonar (HFAS..., testing and evaluation (RDT&E): (i) AN/SQS-53 (hull-mounted active sonar)—up to 10865 hours over the course of 5 years (an average of 2173 hours per year); (ii) AN/SQS-56 (hull-mounted active sonar)-up to...

50 CFR 218.110 - Specified activity and specified geographical area.

Code of Federal Regulations, 2013 CFR

2013-10-01

... sonar (MFAS) and high frequency active sonar (HFAS) sources, or similar sources, for Navy training...-53 (hull-mounted active sonar)—up to 215 hours over the course of 5 years (an average of 43 hours per year); (ii) AN/SQS-56 (hull-mounted active sonar)—up to 325 hours over the course of 5 years (an...

50 CFR 218.110 - Specified activity and specified geographical area.

Code of Federal Regulations, 2011 CFR

2011-10-01

... the following mid-frequency active sonar (MFAS) sources, high frequency active sonar (HFAS) sources... below: (i) AN/SQS-53 (hull-mounted active sonar)—up to 215 hours over the course of 5 years (an average of 43 hours per year); (ii) AN/SQS-56 (hull-mounted active sonar)—up to 325 hours over the course of...

50 CFR 218.120 - Specified activity and geographical area.

Code of Federal Regulations, 2013 CFR

2013-10-01

... the following mid-frequency active sonar (MFAS) sources, high-frequency active sonar (HFAS) sources...-mounted active sonar)—up to 2,890 hours over the course of 5 years (an average of 578 hours per year); (ii) AN/SQS-56 (hull-mounted active sonar)—up to 260 hours over the course of 5 years (an average of 52...

50 CFR 218.120 - Specified activity and geographical area.

Code of Federal Regulations, 2012 CFR

2012-10-01

... the following mid-frequency active sonar (MFAS) sources, high-frequency active sonar (HFAS) sources...-mounted active sonar)—up to 2,890 hours over the course of 5 years (an average of 578 hours per year); (ii) AN/SQS-56 (hull-mounted active sonar)—up to 260 hours over the course of 5 years (an average of 52...

50 CFR 218.100 - Specified activity and specified geographical area.

Code of Federal Regulations, 2010 CFR

2010-10-01

...: (1) The use of the following mid-frequency active sonar (MFAS) and high frequency active sonar (HFAS..., testing and evaluation (RDT&E): (i) AN/SQS-53 (hull-mounted active sonar)—up to 10865 hours over the course of 5 years (an average of 2173 hours per year); (ii) AN/SQS-56 (hull-mounted active sonar)-up to...

50 CFR 218.120 - Specified activity and geographical area.

Code of Federal Regulations, 2011 CFR

2011-10-01

... the following mid-frequency active sonar (MFAS) sources, high-frequency active sonar (HFAS) sources...-mounted active sonar)—up to 2,890 hours over the course of 5 years (an average of 578 hours per year); (ii) AN/SQS-56 (hull-mounted active sonar)—up to 260 hours over the course of 5 years (an average of 52...

Sonar beam dynamics in leaf-nosed bats

PubMed Central

Linnenschmidt, Meike; Wiegrebe, Lutz

2016-01-01

Ultrasonic emissions of bats are directional and delimit the echo-acoustic space. Directionality is quantified by the aperture of the sonar beam. Recent work has shown that bats often widen their sonar beam when approaching movable prey or sharpen their sonar beam when navigating through cluttered habitats. Here we report how nose-emitting bats, Phyllostomus discolor, adjust their sonar beam to object distance. First, we show that the height and width of the bats sonar beam, as imprinted on a parabolic 45 channel microphone array, varies even within each animal and this variation is unrelated to changes in call level or spectral content. Second, we show that these animals are able to systematically decrease height and width of their sonar beam while focusing on the approaching object. Thus it appears that sonar beam sharpening is a further, facultative means of reducing search volume, likely to be employed by stationary animals when the object position is close and unambiguous. As only half of our individuals sharpened their beam onto the approaching object we suggest that this strategy is facultative, under voluntary control, and that beam formation is likely mediated by muscular control of the acoustic aperture of the bats’ nose leaf. PMID:27384865

Sonar beam dynamics in leaf-nosed bats.

PubMed

Linnenschmidt, Meike; Wiegrebe, Lutz

2016-07-07

Ultrasonic emissions of bats are directional and delimit the echo-acoustic space. Directionality is quantified by the aperture of the sonar beam. Recent work has shown that bats often widen their sonar beam when approaching movable prey or sharpen their sonar beam when navigating through cluttered habitats. Here we report how nose-emitting bats, Phyllostomus discolor, adjust their sonar beam to object distance. First, we show that the height and width of the bats sonar beam, as imprinted on a parabolic 45 channel microphone array, varies even within each animal and this variation is unrelated to changes in call level or spectral content. Second, we show that these animals are able to systematically decrease height and width of their sonar beam while focusing on the approaching object. Thus it appears that sonar beam sharpening is a further, facultative means of reducing search volume, likely to be employed by stationary animals when the object position is close and unambiguous. As only half of our individuals sharpened their beam onto the approaching object we suggest that this strategy is facultative, under voluntary control, and that beam formation is likely mediated by muscular control of the acoustic aperture of the bats' nose leaf.

Bubble structure evaluation method of sponge cake by using image morphology

NASA Astrophysics Data System (ADS)

Kato, Kunihito; Yamamoto, Kazuhiko; Nonaka, Masahiko; Katsuta, Yukiyo; Kasamatsu, Chinatsu

2007-01-01

Nowadays, many evaluation methods for food industry by using image processing are proposed. These methods are becoming new evaluation method besides the sensory test and the solid-state measurement that have been used for the quality evaluation recently. The goal of our research is structure evaluation of sponge cake by using the image processing. In this paper, we propose a feature extraction method of the bobble structure in the sponge cake. Analysis of the bubble structure is one of the important properties to understand characteristics of the cake from the image. In order to take the cake image, first we cut cakes and measured that's surface by using the CIS scanner, because the depth of field of this type scanner is very shallow. Therefore the bubble region of the surface has low gray scale value, and it has a feature that is blur. We extracted bubble regions from the surface images based on these features. The input image is binarized, and the feature of bubble is extracted by the morphology analysis. In order to evaluate the result of feature extraction, we compared correlation with "Size of the bubble" of the sensory test result. From a result, the bubble extraction by using morphology analysis gives good correlation. It is shown that our method is as well as the subjectivity evaluation.

First direct measurements of behavioural responses by Cuvier's beaked whales to mid-frequency active sonar.

PubMed

DeRuiter, Stacy L; Southall, Brandon L; Calambokidis, John; Zimmer, Walter M X; Sadykova, Dinara; Falcone, Erin A; Friedlaender, Ari S; Joseph, John E; Moretti, David; Schorr, Gregory S; Thomas, Len; Tyack, Peter L

2013-08-23

Most marine mammal- strandings coincident with naval sonar exercises have involved Cuvier's beaked whales (Ziphius cavirostris). We recorded animal movement and acoustic data on two tagged Ziphius and obtained the first direct measurements of behavioural responses of this species to mid-frequency active (MFA) sonar signals. Each recording included a 30-min playback (one 1.6-s simulated MFA sonar signal repeated every 25 s); one whale was also incidentally exposed to MFA sonar from distant naval exercises. Whales responded strongly to playbacks at low received levels (RLs; 89-127 dB re 1 µPa): after ceasing normal fluking and echolocation, they swam rapidly, silently away, extending both dive duration and subsequent non-foraging interval. Distant sonar exercises (78-106 dB re 1 µPa) did not elicit such responses, suggesting that context may moderate reactions. The observed responses to playback occurred at RLs well below current regulatory thresholds; equivalent responses to operational sonars could elevate stranding risk and reduce foraging efficiency.

Uniform competency-based local feature extraction for remote sensing images

NASA Astrophysics Data System (ADS)

Sedaghat, Amin; Mohammadi, Nazila

2018-01-01

Local feature detectors are widely used in many photogrammetry and remote sensing applications. The quantity and distribution of the local features play a critical role in the quality of the image matching process, particularly for multi-sensor high resolution remote sensing image registration. However, conventional local feature detectors cannot extract desirable matched features either in terms of the number of correct matches or the spatial and scale distribution in multi-sensor remote sensing images. To address this problem, this paper proposes a novel method for uniform and robust local feature extraction for remote sensing images, which is based on a novel competency criterion and scale and location distribution constraints. The proposed method, called uniform competency (UC) local feature extraction, can be easily applied to any local feature detector for various kinds of applications. The proposed competency criterion is based on a weighted ranking process using three quality measures, including robustness, spatial saliency and scale parameters, which is performed in a multi-layer gridding schema. For evaluation, five state-of-the-art local feature detector approaches, namely, scale-invariant feature transform (SIFT), speeded up robust features (SURF), scale-invariant feature operator (SFOP), maximally stable extremal region (MSER) and hessian-affine, are used. The proposed UC-based feature extraction algorithms were successfully applied to match various synthetic and real satellite image pairs, and the results demonstrate its capability to increase matching performance and to improve the spatial distribution. The code to carry out the UC feature extraction is available from href="https://www.researchgate.net/publication/317956777_UC-Feature_Extraction.

a Framework of Change Detection Based on Combined Morphologica Features and Multi-Index Classification

NASA Astrophysics Data System (ADS)

Li, S.; Zhang, S.; Yang, D.

2017-09-01

Remote sensing images are particularly well suited for analysis of land cover change. In this paper, we present a new framework for detection of changing land cover using satellite imagery. Morphological features and a multi-index are used to extract typical objects from the imagery, including vegetation, water, bare land, buildings, and roads. Our method, based on connected domains, is different from traditional methods; it uses image segmentation to extract morphological features, while the enhanced vegetation index (EVI), the differential water index (NDWI) are used to extract vegetation and water, and a fragmentation index is used to the correct extraction results of water. HSV transformation and threshold segmentation extract and remove the effects of shadows on extraction results. Change detection is performed on these results. One of the advantages of the proposed framework is that semantic information is extracted automatically using low-level morphological features and indexes. Another advantage is that the proposed method detects specific types of change without any training samples. A test on ZY-3 images demonstrates that our framework has a promising capability to detect change.

New sidescan sonar and gravity evidence that the Nova-Canton Trough is a fracture zone

NASA Astrophysics Data System (ADS)

Joseph, Devorah; Taylor, Brian; Shor, Alexander N.

1992-05-01

A 1990 sidescan sonar survey in the eastern region of the Nova-Canton Trough mapped 138°-striking abyssal-hill fabric trending into 70°-striking trough structures. The location and angle of intersection of the abyssal hills with the eastern Nova-Canton Trough effectively disprove a spreading-center origin of this feature. Free-air gravity anomalies derived from satellite altimetry data show continuity, across the Line Islands, of the Nova-Canton Trough with the Clipperton Fracture Zone. The Canton-Clipperton trend is copolar, about a pole at 30°S, 152°W, with other coeval Pacific-Farallon fracture-zone segments, from the Pau to Marquesas fracture zones. This copolarity leads us to postulate a Pacific-Farallon spreading pattern for the magnetic quiet zone region north and east of the Manihiki Plateau, with the Nova-Canton Trough originating as a transform fault in this system.

High-resolution multibeam bathymetry of East and West Flower Gardens and Stetson Banks, Gulf of Mexico

USGS Publications Warehouse

Gardner, J.V.; Mayer, L.A.; Hughes, Clarke J.E.; Kleiner, A.

1998-01-01

The 1990s have seen rapid advances in seafloor mapping technology. Multibeam sonars are now capable of mapping a wide range of water depths with beams as narrow as 1??, and provide up to a 150?? swath. When these multibeam sonars are coupled with an extremely accurate vehicle motion sensor and very precise navigation, they are capable of producing unprecedented images of the seafloor. This technology was used in December 1997 to map the East and West Flower Gardens and Stetson Banks, Gulf of Mexico. The results from this survey provide the most accurate maps of these areas yet produced and reveal features at submeter resolution never mapped in these areas before. The digital data provide a database that should become the fundamental base maps for all subsequent work in this recently established National Marine Sanctuary.

Selecting relevant 3D image features of margin sharpness and texture for lung nodule retrieval.

PubMed

Ferreira, José Raniery; de Azevedo-Marques, Paulo Mazzoncini; Oliveira, Marcelo Costa

2017-03-01

Lung cancer is the leading cause of cancer-related deaths in the world. Its diagnosis is a challenge task to specialists due to several aspects on the classification of lung nodules. Therefore, it is important to integrate content-based image retrieval methods on the lung nodule classification process, since they are capable of retrieving similar cases from databases that were previously diagnosed. However, this mechanism depends on extracting relevant image features in order to obtain high efficiency. The goal of this paper is to perform the selection of 3D image features of margin sharpness and texture that can be relevant on the retrieval of similar cancerous and benign lung nodules. A total of 48 3D image attributes were extracted from the nodule volume. Border sharpness features were extracted from perpendicular lines drawn over the lesion boundary. Second-order texture features were extracted from a cooccurrence matrix. Relevant features were selected by a correlation-based method and a statistical significance analysis. Retrieval performance was assessed according to the nodule's potential malignancy on the 10 most similar cases and by the parameters of precision and recall. Statistical significant features reduced retrieval performance. Correlation-based method selected 2 margin sharpness attributes and 6 texture attributes and obtained higher precision compared to all 48 extracted features on similar nodule retrieval. Feature space dimensionality reduction of 83 % obtained higher retrieval performance and presented to be a computationaly low cost method of retrieving similar nodules for the diagnosis of lung cancer.

A Novel Feature Extraction Method with Feature Selection to Identify Golgi-Resident Protein Types from Imbalanced Data

PubMed Central

Yang, Runtao; Zhang, Chengjin; Gao, Rui; Zhang, Lina

2016-01-01

The Golgi Apparatus (GA) is a major collection and dispatch station for numerous proteins destined for secretion, plasma membranes and lysosomes. The dysfunction of GA proteins can result in neurodegenerative diseases. Therefore, accurate identification of protein subGolgi localizations may assist in drug development and understanding the mechanisms of the GA involved in various cellular processes. In this paper, a new computational method is proposed for identifying cis-Golgi proteins from trans-Golgi proteins. Based on the concept of Common Spatial Patterns (CSP), a novel feature extraction technique is developed to extract evolutionary information from protein sequences. To deal with the imbalanced benchmark dataset, the Synthetic Minority Over-sampling Technique (SMOTE) is adopted. A feature selection method called Random Forest-Recursive Feature Elimination (RF-RFE) is employed to search the optimal features from the CSP based features and g-gap dipeptide composition. Based on the optimal features, a Random Forest (RF) module is used to distinguish cis-Golgi proteins from trans-Golgi proteins. Through the jackknife cross-validation, the proposed method achieves a promising performance with a sensitivity of 0.889, a specificity of 0.880, an accuracy of 0.885, and a Matthew’s Correlation Coefficient (MCC) of 0.765, which remarkably outperforms previous methods. Moreover, when tested on a common independent dataset, our method also achieves a significantly improved performance. These results highlight the promising performance of the proposed method to identify Golgi-resident protein types. Furthermore, the CSP based feature extraction method may provide guidelines for protein function predictions. PMID:26861308

Classification of product inspection items using nonlinear features

NASA Astrophysics Data System (ADS)

Talukder, Ashit; Casasent, David P.; Lee, H.-W.

1998-03-01

Automated processing and classification of real-time x-ray images of randomly oriented touching pistachio nuts is discussed. The ultimate objective is the development of a system for automated non-invasive detection of defective product items on a conveyor belt. This approach involves two main steps: preprocessing and classification. Preprocessing locates individual items and segments ones that touch using a modified watershed algorithm. The second stage involves extraction of features that allow discrimination between damaged and clean items (pistachio nuts). This feature extraction and classification stage is the new aspect of this paper. We use a new nonlinear feature extraction scheme called the maximum representation and discriminating feature (MRDF) extraction method to compute nonlinear features that are used as inputs to a classifier. The MRDF is shown to provide better classification and a better ROC (receiver operating characteristic) curve than other methods.

33 CFR 334.78 - Rhode Island Sound, Atlantic Ocean, approximately 4.0 nautical miles due south of Lands End in...

Code of Federal Regulations, 2012 CFR

2012-07-01

... will consist of six inert drill mines each 16 inches in diameter and 5 feet long and one concrete sonar target 48 inches in diameter and 48 inches high located within the designated area. The sonar target will... sonar. Neither variable depth sonar devices or mechanical minesweeping operations will be utilized in...

Submarine Combat Systems Engineering Project Capstone Project

DTIC Science & Technology

2011-06-06

sonar , imaging, Electronic Surveillance (ES) and communications. These sensors passively detect contacts, which emit... passive sensors is included. A Search Detect Identify Track Decide Engage Assess 3 contact can be sensed by the system as either surface or... Detect Track Avoid Search Detect Identify Track Search Engage Assess Detect Track Avoid Search • SONAR •Imagery •TC • SONAR • SONAR •EW •Imagery •ESM

A method for real-time implementation of HOG feature extraction

NASA Astrophysics Data System (ADS)

Luo, Hai-bo; Yu, Xin-rong; Liu, Hong-mei; Ding, Qing-hai

2011-08-01

Histogram of oriented gradient (HOG) is an efficient feature extraction scheme, and HOG descriptors are feature descriptors which is widely used in computer vision and image processing for the purpose of biometrics, target tracking, automatic target detection(ATD) and automatic target recognition(ATR) etc. However, computation of HOG feature extraction is unsuitable for hardware implementation since it includes complicated operations. In this paper, the optimal design method and theory frame for real-time HOG feature extraction based on FPGA were proposed. The main principle is as follows: firstly, the parallel gradient computing unit circuit based on parallel pipeline structure was designed. Secondly, the calculation of arctangent and square root operation was simplified. Finally, a histogram generator based on parallel pipeline structure was designed to calculate the histogram of each sub-region. Experimental results showed that the HOG extraction can be implemented in a pixel period by these computing units.

Research on the frequency hopping bistatic sonar system

NASA Astrophysics Data System (ADS)

Liang, Guo-long; Zhang, Yao; Zhang, Guang-pu; Liu, Kai

2011-10-01

A new model for bistatic sonar system is established, in which frequency hopping (FH) signals are used for targets detection according to some rules. This model can decrease the time between adjacent signals and obtain more information in a unit time. The receiving system will receive and process the signals of different frequency respectively, according the FH pattern, for detecting and locating targets. This method can helps yield more stable and accurate outputs, using the characteristic of the FH signals, increase the ability of anti-detection and anti partial-band jamming.

Decomposition and extraction: a new framework for visual classification.

PubMed

Fang, Yuqiang; Chen, Qiang; Sun, Lin; Dai, Bin; Yan, Shuicheng

2014-08-01

In this paper, we present a novel framework for visual classification based on hierarchical image decomposition and hybrid midlevel feature extraction. Unlike most midlevel feature learning methods, which focus on the process of coding or pooling, we emphasize that the mechanism of image composition also strongly influences the feature extraction. To effectively explore the image content for the feature extraction, we model a multiplicity feature representation mechanism through meaningful hierarchical image decomposition followed by a fusion step. In particularly, we first propose a new hierarchical image decomposition approach in which each image is decomposed into a series of hierarchical semantical components, i.e, the structure and texture images. Then, different feature extraction schemes can be adopted to match the decomposed structure and texture processes in a dissociative manner. Here, two schemes are explored to produce property related feature representations. One is based on a single-stage network over hand-crafted features and the other is based on a multistage network, which can learn features from raw pixels automatically. Finally, those multiple midlevel features are incorporated by solving a multiple kernel learning task. Extensive experiments are conducted on several challenging data sets for visual classification, and experimental results demonstrate the effectiveness of the proposed method.

Uniform Local Binary Pattern Based Texture-Edge Feature for 3D Human Behavior Recognition.

PubMed

Ming, Yue; Wang, Guangchao; Fan, Chunxiao

2015-01-01

With the rapid development of 3D somatosensory technology, human behavior recognition has become an important research field. Human behavior feature analysis has evolved from traditional 2D features to 3D features. In order to improve the performance of human activity recognition, a human behavior recognition method is proposed, which is based on a hybrid texture-edge local pattern coding feature extraction and integration of RGB and depth videos information. The paper mainly focuses on background subtraction on RGB and depth video sequences of behaviors, extracting and integrating historical images of the behavior outlines, feature extraction and classification. The new method of 3D human behavior recognition has achieved the rapid and efficient recognition of behavior videos. A large number of experiments show that the proposed method has faster speed and higher recognition rate. The recognition method has good robustness for different environmental colors, lightings and other factors. Meanwhile, the feature of mixed texture-edge uniform local binary pattern can be used in most 3D behavior recognition.

Bottom Characterization with High Resolution Sonar Data and Geochemical Analyses of an Uninvestigated Cone in Lagoa das Furnas on São Miguel Island, Azores Archipelago

NASA Astrophysics Data System (ADS)

Andersson, T.

2015-12-01

Lagoa das Furnas is a crater lake located in an area exposed to geohazards from earthquakes and volcanic activity on the island São Miguel in the Azores Archipelago. The Furnas volcanic center has a long history of earthquakes and volcanic activity. The area is relatively well studied except for the lake floor. Therefore, a high resolution geophysical and geological mapping survey was conducted at Lagoa das Furnas. Sidescan sonar was used to map the surface of the lake floor and single beam sonar was used to acquire sub-bottom profiles. In addition to the geophysical mapping, sediment surface sampling and core drilling were carried out followed by geochemical analyses of the retrieved material. The mapped data permitted a characterization of the floor of Lagoa das Furnas and revealed several volcanic features including fumarolic activity and a previously uninvestigated volcanic cone in the southern part of the lake. In order to unravel the origin of this cone several methods were applied, including analyses of tephra and minerals collected from the cone itself and from nearby deposits of two known eruptions, Furnas I and Furnas 1630. Sedimentological, petrological, geochemical and geochronological studies of pyroclastic deposits from the cone suggest a subaqueous eruption linked to the Furnas 1630 eruption. The chemistry of glass and crystal fragments sampled from the cone suggests that it is composed of more evolved magma than that of the main Furnas 1630, implying that the lake cone is likely a product of the last eruptional phase. According to historical records, two of three lakes were lost due the Furnas 1630 eruption. The results of this study show that the remaining lake is most likely Lagoa das Furnas, which consequently must have existed before the 1630 eruption.

Automatic Feature Extraction from Planetary Images

NASA Technical Reports Server (NTRS)

Troglio, Giulia; Le Moigne, Jacqueline; Benediktsson, Jon A.; Moser, Gabriele; Serpico, Sebastiano B.

2010-01-01

With the launch of several planetary missions in the last decade, a large amount of planetary images has already been acquired and much more will be available for analysis in the coming years. The image data need to be analyzed, preferably by automatic processing techniques because of the huge amount of data. Although many automatic feature extraction methods have been proposed and utilized for Earth remote sensing images, these methods are not always applicable to planetary data that often present low contrast and uneven illumination characteristics. Different methods have already been presented for crater extraction from planetary images, but the detection of other types of planetary features has not been addressed yet. Here, we propose a new unsupervised method for the extraction of different features from the surface of the analyzed planet, based on the combination of several image processing techniques, including a watershed segmentation and the generalized Hough Transform. The method has many applications, among which image registration and can be applied to arbitrary planetary images.

Applying different independent component analysis algorithms and support vector regression for IT chain store sales forecasting.

PubMed

Dai, Wensheng; Wu, Jui-Yu; Lu, Chi-Jie

2014-01-01

Sales forecasting is one of the most important issues in managing information technology (IT) chain store sales since an IT chain store has many branches. Integrating feature extraction method and prediction tool, such as support vector regression (SVR), is a useful method for constructing an effective sales forecasting scheme. Independent component analysis (ICA) is a novel feature extraction technique and has been widely applied to deal with various forecasting problems. But, up to now, only the basic ICA method (i.e., temporal ICA model) was applied to sale forecasting problem. In this paper, we utilize three different ICA methods including spatial ICA (sICA), temporal ICA (tICA), and spatiotemporal ICA (stICA) to extract features from the sales data and compare their performance in sales forecasting of IT chain store. Experimental results from a real sales data show that the sales forecasting scheme by integrating stICA and SVR outperforms the comparison models in terms of forecasting error. The stICA is a promising tool for extracting effective features from branch sales data and the extracted features can improve the prediction performance of SVR for sales forecasting.

Applying Different Independent Component Analysis Algorithms and Support Vector Regression for IT Chain Store Sales Forecasting

PubMed Central

Dai, Wensheng

2014-01-01

Sales forecasting is one of the most important issues in managing information technology (IT) chain store sales since an IT chain store has many branches. Integrating feature extraction method and prediction tool, such as support vector regression (SVR), is a useful method for constructing an effective sales forecasting scheme. Independent component analysis (ICA) is a novel feature extraction technique and has been widely applied to deal with various forecasting problems. But, up to now, only the basic ICA method (i.e., temporal ICA model) was applied to sale forecasting problem. In this paper, we utilize three different ICA methods including spatial ICA (sICA), temporal ICA (tICA), and spatiotemporal ICA (stICA) to extract features from the sales data and compare their performance in sales forecasting of IT chain store. Experimental results from a real sales data show that the sales forecasting scheme by integrating stICA and SVR outperforms the comparison models in terms of forecasting error. The stICA is a promising tool for extracting effective features from branch sales data and the extracted features can improve the prediction performance of SVR for sales forecasting. PMID:25165740

SD-MSAEs: Promoter recognition in human genome based on deep feature extraction.

PubMed

Xu, Wenxuan; Zhang, Li; Lu, Yaping

2016-06-01

The prediction and recognition of promoter in human genome play an important role in DNA sequence analysis. Entropy, in Shannon sense, of information theory is a multiple utility in bioinformatic details analysis. The relative entropy estimator methods based on statistical divergence (SD) are used to extract meaningful features to distinguish different regions of DNA sequences. In this paper, we choose context feature and use a set of methods of SD to select the most effective n-mers distinguishing promoter regions from other DNA regions in human genome. Extracted from the total possible combinations of n-mers, we can get four sparse distributions based on promoter and non-promoters training samples. The informative n-mers are selected by optimizing the differentiating extents of these distributions. Specially, we combine the advantage of statistical divergence and multiple sparse auto-encoders (MSAEs) in deep learning to extract deep feature for promoter recognition. And then we apply multiple SVMs and a decision model to construct a human promoter recognition method called SD-MSAEs. Framework is flexible that it can integrate new feature extraction or new classification models freely. Experimental results show that our method has high sensitivity and specificity. Copyright © 2016 Elsevier Inc. All rights reserved.

The algorithm of fast image stitching based on multi-feature extraction

NASA Astrophysics Data System (ADS)

Yang, Chunde; Wu, Ge; Shi, Jing

2018-05-01

This paper proposed an improved image registration method combining Hu-based invariant moment contour information and feature points detection, aiming to solve the problems in traditional image stitching algorithm, such as time-consuming feature points extraction process, redundant invalid information overload and inefficiency. First, use the neighborhood of pixels to extract the contour information, employing the Hu invariant moment as similarity measure to extract SIFT feature points in those similar regions. Then replace the Euclidean distance with Hellinger kernel function to improve the initial matching efficiency and get less mismatching points, further, estimate affine transformation matrix between the images. Finally, local color mapping method is adopted to solve uneven exposure, using the improved multiresolution fusion algorithm to fuse the mosaic images and realize seamless stitching. Experimental results confirm high accuracy and efficiency of method proposed in this paper.

50 CFR 216.187 - Applications for Letters of Authorization.

Code of Federal Regulations, 2010 CFR

2010-10-01

... Array Sensor System Low Frequency Active (SURTASS LFA sonar) Sonar § 216.187 Applications for Letters of... scheduled to begin conducting SURTASS LFA sonar operations or the previous Letter of Authorization is...

50 CFR 216.187 - Applications for Letters of Authorization.

Code of Federal Regulations, 2011 CFR

2011-10-01

... Array Sensor System Low Frequency Active (SURTASS LFA sonar) Sonar § 216.187 Applications for Letters of... scheduled to begin conducting SURTASS LFA sonar operations or the previous Letter of Authorization is...

Bats coordinate sonar and flight behavior as they forage in open and cluttered environments.

PubMed

Falk, Benjamin; Jakobsen, Lasse; Surlykke, Annemarie; Moss, Cynthia F

2014-12-15

Echolocating bats use active sensing as they emit sounds and listen to the returning echoes to probe their environment for navigation, obstacle avoidance and pursuit of prey. The sensing behavior of bats includes the planning of 3D spatial trajectory paths, which are guided by echo information. In this study, we examined the relationship between active sonar sampling and flight motor output as bats changed environments from open space to an artificial forest in a laboratory flight room. Using high-speed video and audio recordings, we reconstructed and analyzed 3D flight trajectories, sonar beam aim and acoustic sonar emission patterns as the bats captured prey. We found that big brown bats adjusted their sonar call structure, temporal patterning and flight speed in response to environmental change. The sonar beam aim of the bats predicted the flight turn rate in both the open room and the forest. However, the relationship between sonar beam aim and turn rate changed in the forest during the final stage of prey pursuit, during which the bat made shallower turns. We found flight stereotypy developed over multiple days in the forest, but did not find evidence for a reduction in active sonar sampling with experience. The temporal patterning of sonar sound groups was related to path planning around obstacles in the forest. Together, these results contribute to our understanding of how bats coordinate echolocation and flight behavior to represent and navigate their environment. © 2014. Published by The Company of Biologists Ltd.

Bats coordinate sonar and flight behavior as they forage in open and cluttered environments

PubMed Central

Falk, Benjamin; Jakobsen, Lasse; Surlykke, Annemarie; Moss, Cynthia F.

2014-01-01

Echolocating bats use active sensing as they emit sounds and listen to the returning echoes to probe their environment for navigation, obstacle avoidance and pursuit of prey. The sensing behavior of bats includes the planning of 3D spatial trajectory paths, which are guided by echo information. In this study, we examined the relationship between active sonar sampling and flight motor output as bats changed environments from open space to an artificial forest in a laboratory flight room. Using high-speed video and audio recordings, we reconstructed and analyzed 3D flight trajectories, sonar beam aim and acoustic sonar emission patterns as the bats captured prey. We found that big brown bats adjusted their sonar call structure, temporal patterning and flight speed in response to environmental change. The sonar beam aim of the bats predicted the flight turn rate in both the open room and the forest. However, the relationship between sonar beam aim and turn rate changed in the forest during the final stage of prey pursuit, during which the bat made shallower turns. We found flight stereotypy developed over multiple days in the forest, but did not find evidence for a reduction in active sonar sampling with experience. The temporal patterning of sonar sound groups was related to path planning around obstacles in the forest. Together, these results contribute to our understanding of how bats coordinate echolocation and flight behavior to represent and navigate their environment. PMID:25394632

A Generic multi-dimensional feature extraction method using multiobjective genetic programming.

PubMed

Zhang, Yang; Rockett, Peter I

2009-01-01

In this paper, we present a generic feature extraction method for pattern classification using multiobjective genetic programming. This not only evolves the (near-)optimal set of mappings from a pattern space to a multi-dimensional decision space, but also simultaneously optimizes the dimensionality of that decision space. The presented framework evolves vector-to-vector feature extractors that maximize class separability. We demonstrate the efficacy of our approach by making statistically-founded comparisons with a wide variety of established classifier paradigms over a range of datasets and find that for most of the pairwise comparisons, our evolutionary method delivers statistically smaller misclassification errors. At very worst, our method displays no statistical difference in a few pairwise comparisons with established classifier/dataset combinations; crucially, none of the misclassification results produced by our method is worse than any comparator classifier. Although principally focused on feature extraction, feature selection is also performed as an implicit side effect; we show that both feature extraction and selection are important to the success of our technique. The presented method has the practical consequence of obviating the need to exhaustively evaluate a large family of conventional classifiers when faced with a new pattern recognition problem in order to attain a good classification accuracy.

Waveform fitting and geometry analysis for full-waveform lidar feature extraction

NASA Astrophysics Data System (ADS)

Tsai, Fuan; Lai, Jhe-Syuan; Cheng, Yi-Hsiu

2016-10-01

This paper presents a systematic approach that integrates spline curve fitting and geometry analysis to extract full-waveform LiDAR features for land-cover classification. The cubic smoothing spline algorithm is used to fit the waveform curve of the received LiDAR signals. After that, the local peak locations of the waveform curve are detected using a second derivative method. According to the detected local peak locations, commonly used full-waveform features such as full width at half maximum (FWHM) and amplitude can then be obtained. In addition, the number of peaks, time difference between the first and last peaks, and the average amplitude are also considered as features of LiDAR waveforms with multiple returns. Based on the waveform geometry, dynamic time-warping (DTW) is applied to measure the waveform similarity. The sum of the absolute amplitude differences that remain after time-warping can be used as a similarity feature in a classification procedure. An airborne full-waveform LiDAR data set was used to test the performance of the developed feature extraction method for land-cover classification. Experimental results indicate that the developed spline curve- fitting algorithm and geometry analysis can extract helpful full-waveform LiDAR features to produce better land-cover classification than conventional LiDAR data and feature extraction methods. In particular, the multiple-return features and the dynamic time-warping index can improve the classification results significantly.

A Frequency-Domain Adaptive Matched Filter for Active Sonar Detection.

PubMed

Zhao, Zhishan; Zhao, Anbang; Hui, Juan; Hou, Baochun; Sotudeh, Reza; Niu, Fang

2017-07-04

The most classical detector of active sonar and radar is the matched filter (MF), which is the optimal processor under ideal conditions. Aiming at the problem of active sonar detection, we propose a frequency-domain adaptive matched filter (FDAMF) with the use of a frequency-domain adaptive line enhancer (ALE). The FDAMF is an improved MF. In the simulations in this paper, the signal to noise ratio (SNR) gain of the FDAMF is about 18.6 dB higher than that of the classical MF when the input SNR is -10 dB. In order to improve the performance of the FDAMF with a low input SNR, we propose a pre-processing method, which is called frequency-domain time reversal convolution and interference suppression (TRC-IS). Compared with the classical MF, the FDAMF combined with the TRC-IS method obtains higher SNR gain, a lower detection threshold, and a better receiver operating characteristic (ROC) in the simulations in this paper. The simulation results show that the FDAMF has higher processing gain and better detection performance than the classical MF under ideal conditions. The experimental results indicate that the FDAMF does improve the performance of the MF, and can adapt to actual interference in a way. In addition, the TRC-IS preprocessing method works well in an actual noisy ocean environment.

Delphinid behavioral responses to incidental mid-frequency active sonar.

PubMed

Henderson, E Elizabeth; Smith, Michael H; Gassmann, Martin; Wiggins, Sean M; Douglas, Annie B; Hildebrand, John A

2014-10-01

Opportunistic observations of behavioral responses by delphinids to incidental mid-frequency active (MFA) sonar were recorded in the Southern California Bight from 2004 through 2008 using visual focal follows, static hydrophones, and autonomous recorders. Sound pressure levels were calculated between 2 and 8 kHz. Surface behavioral responses were observed in 26 groups from at least three species of 46 groups out of five species encountered during MFA sonar incidents. Responses included changes in behavioral state or direction of travel, changes in vocalization rates and call intensity, or a lack of vocalizations while MFA sonar occurred. However, 46% of focal groups not exposed to sonar also changed their behavior, and 43% of focal groups exposed to sonar did not change their behavior. Mean peak sound pressure levels when a behavioral response occurred were around 122 dB re: 1 μPa. Acoustic localizations of dolphin groups exhibiting a response gave insight into nighttime movement patterns and provided evidence that impacts of sonar may be mediated by behavioral state. The lack of response in some cases may indicate a tolerance of or habituation to MFA sonar by local populations; however, the responses that occur at lower received levels may point to some sensitization as well.

Electroencephalogram-based decoding cognitive states using convolutional neural network and likelihood ratio based score fusion

PubMed Central

2017-01-01

Electroencephalogram (EEG)-based decoding human brain activity is challenging, owing to the low spatial resolution of EEG. However, EEG is an important technique, especially for brain–computer interface applications. In this study, a novel algorithm is proposed to decode brain activity associated with different types of images. In this hybrid algorithm, convolutional neural network is modified for the extraction of features, a t-test is used for the selection of significant features and likelihood ratio-based score fusion is used for the prediction of brain activity. The proposed algorithm takes input data from multichannel EEG time-series, which is also known as multivariate pattern analysis. Comprehensive analysis was conducted using data from 30 participants. The results from the proposed method are compared with current recognized feature extraction and classification/prediction techniques. The wavelet transform-support vector machine method is the most popular currently used feature extraction and prediction method. This method showed an accuracy of 65.7%. However, the proposed method predicts the novel data with improved accuracy of 79.9%. In conclusion, the proposed algorithm outperformed the current feature extraction and prediction method. PMID:28558002

Arrhythmia Classification Based on Multi-Domain Feature Extraction for an ECG Recognition System.

PubMed

Li, Hongqiang; Yuan, Danyang; Wang, Youxi; Cui, Dianyin; Cao, Lu

2016-10-20

Automatic recognition of arrhythmias is particularly important in the diagnosis of heart diseases. This study presents an electrocardiogram (ECG) recognition system based on multi-domain feature extraction to classify ECG beats. An improved wavelet threshold method for ECG signal pre-processing is applied to remove noise interference. A novel multi-domain feature extraction method is proposed; this method employs kernel-independent component analysis in nonlinear feature extraction and uses discrete wavelet transform to extract frequency domain features. The proposed system utilises a support vector machine classifier optimized with a genetic algorithm to recognize different types of heartbeats. An ECG acquisition experimental platform, in which ECG beats are collected as ECG data for classification, is constructed to demonstrate the effectiveness of the system in ECG beat classification. The presented system, when applied to the MIT-BIH arrhythmia database, achieves a high classification accuracy of 98.8%. Experimental results based on the ECG acquisition experimental platform show that the system obtains a satisfactory classification accuracy of 97.3% and is able to classify ECG beats efficiently for the automatic identification of cardiac arrhythmias.

Arrhythmia Classification Based on Multi-Domain Feature Extraction for an ECG Recognition System

PubMed Central

Li, Hongqiang; Yuan, Danyang; Wang, Youxi; Cui, Dianyin; Cao, Lu

2016-01-01

Automatic recognition of arrhythmias is particularly important in the diagnosis of heart diseases. This study presents an electrocardiogram (ECG) recognition system based on multi-domain feature extraction to classify ECG beats. An improved wavelet threshold method for ECG signal pre-processing is applied to remove noise interference. A novel multi-domain feature extraction method is proposed; this method employs kernel-independent component analysis in nonlinear feature extraction and uses discrete wavelet transform to extract frequency domain features. The proposed system utilises a support vector machine classifier optimized with a genetic algorithm to recognize different types of heartbeats. An ECG acquisition experimental platform, in which ECG beats are collected as ECG data for classification, is constructed to demonstrate the effectiveness of the system in ECG beat classification. The presented system, when applied to the MIT-BIH arrhythmia database, achieves a high classification accuracy of 98.8%. Experimental results based on the ECG acquisition experimental platform show that the system obtains a satisfactory classification accuracy of 97.3% and is able to classify ECG beats efficiently for the automatic identification of cardiac arrhythmias. PMID:27775596

Assessment of Marine Mammal Impact Zones for Use of Military Sonar in the Baltic Sea.

PubMed

Andersson, Mathias H; Johansson, Torbjörn

2016-01-01

Military sonars are known to have caused cetaceans to strand. Navies in shallow seas use different frequencies and sonar pulses, commonly frequencies between 25 and 100 kHz, compared with most studied NATO sonar systems that have been evaluated for their environmental impact. These frequencies match the frequencies of best hearing in the harbor porpoises and seals resident in the Baltic Sea. This study uses published temporary and permanent threshold shifts, measured behavioral response thresholds, technical specifications of a sonar system, and environmental parameters affecting sound propagation common for the Baltic Sea to estimate the impact zones for harbor porpoises and seals.

Feature Vector Construction Method for IRIS Recognition

NASA Astrophysics Data System (ADS)

Odinokikh, G.; Fartukov, A.; Korobkin, M.; Yoo, J.

2017-05-01

One of the basic stages of iris recognition pipeline is iris feature vector construction procedure. The procedure represents the extraction of iris texture information relevant to its subsequent comparison. Thorough investigation of feature vectors obtained from iris showed that not all the vector elements are equally relevant. There are two characteristics which determine the vector element utility: fragility and discriminability. Conventional iris feature extraction methods consider the concept of fragility as the feature vector instability without respect to the nature of such instability appearance. This work separates sources of the instability into natural and encodinginduced which helps deeply investigate each source of instability independently. According to the separation concept, a novel approach of iris feature vector construction is proposed. The approach consists of two steps: iris feature extraction using Gabor filtering with optimal parameters and quantization with separated preliminary optimized fragility thresholds. The proposed method has been tested on two different datasets of iris images captured under changing environmental conditions. The testing results show that the proposed method surpasses all the methods considered as a prior art by recognition accuracy on both datasets.

50 CFR 216.190 - Modifications to Letters of Authorization.

Code of Federal Regulations, 2011 CFR

2011-10-01

... Array Sensor System Low Frequency Active (SURTASS LFA sonar) Sonar § 216.190 Modifications to Letters of... sonar system from one ship to another, is not considered a substantial modification. (b) If the National...

50 CFR 216.190 - Modifications to Letters of Authorization.

Code of Federal Regulations, 2010 CFR

2010-10-01

... Array Sensor System Low Frequency Active (SURTASS LFA sonar) Sonar § 216.190 Modifications to Letters of... sonar system from one ship to another, is not considered a substantial modification. (b) If the National...

BatSLAM: Simultaneous localization and mapping using biomimetic sonar.

PubMed

Steckel, Jan; Peremans, Herbert

2013-01-01

We propose to combine a biomimetic navigation model which solves a simultaneous localization and mapping task with a biomimetic sonar mounted on a mobile robot to address two related questions. First, can robotic sonar sensing lead to intelligent interactions with complex environments? Second, can we model sonar based spatial orientation and the construction of spatial maps by bats? To address these questions we adapt the mapping module of RatSLAM, a previously published navigation system based on computational models of the rodent hippocampus. We analyze the performance of the proposed robotic implementation operating in the real world. We conclude that the biomimetic navigation model operating on the information from the biomimetic sonar allows an autonomous agent to map unmodified (office) environments efficiently and consistently. Furthermore, these results also show that successful navigation does not require the readings of the biomimetic sonar to be interpreted in terms of individual objects/landmarks in the environment. We argue that the system has applications in robotics as well as in the field of biology as a simple, first order, model for sonar based spatial orientation and map building.

BatSLAM: Simultaneous Localization and Mapping Using Biomimetic Sonar

PubMed Central

Steckel, Jan; Peremans, Herbert

2013-01-01

We propose to combine a biomimetic navigation model which solves a simultaneous localization and mapping task with a biomimetic sonar mounted on a mobile robot to address two related questions. First, can robotic sonar sensing lead to intelligent interactions with complex environments? Second, can we model sonar based spatial orientation and the construction of spatial maps by bats? To address these questions we adapt the mapping module of RatSLAM, a previously published navigation system based on computational models of the rodent hippocampus. We analyze the performance of the proposed robotic implementation operating in the real world. We conclude that the biomimetic navigation model operating on the information from the biomimetic sonar allows an autonomous agent to map unmodified (office) environments efficiently and consistently. Furthermore, these results also show that successful navigation does not require the readings of the biomimetic sonar to be interpreted in terms of individual objects/landmarks in the environment. We argue that the system has applications in robotics as well as in the field of biology as a simple, first order, model for sonar based spatial orientation and map building. PMID:23365647

Near-real-time mosaics from high-resolution side-scan sonar

USGS Publications Warehouse

Danforth, William W.; O'Brien, Thomas F.; Schwab, W.C.

1991-01-01

High-resolution side-scan sonar has proven to be a very effective tool for stuyding and understanding the surficial geology of the seafloor. Since the mid-1970s, the US Geological Survey has used high-resolution side-scan sonar systems for mapping various areas of the continental shelf. However, two problems typically encountered included the short range and the high sampling rate of high-resolution side-scan sonar systems and the acquisition and real-time processing of the enormous volume of sonar data generated by high-resolution suystems. These problems were addressed and overcome in August 1989 when the USGS conducted a side-scan sonar and bottom sampling survey of a 1000-sq-km section of the continental shelf in the Gulf of Farallones located offshore of San Francisco. The primary goal of this survey was to map an area of critical interest for studying continental shelf sediment dynamics. This survey provided an opportunity to test an image processing scheme that enabled production of a side-scan sonar hard-copy mosaic during the cruise in near real-time.

Investigation of measureable parameters that correlate with automatic target recognition performance in synthetic aperture sonar

NASA Astrophysics Data System (ADS)

Gazagnaire, Julia; Cobb, J. T.; Isaacs, Jason

2015-05-01

There is a desire in the Mine Counter Measure community to develop a systematic method to predict and/or estimate the performance of Automatic Target Recognition (ATR) algorithms that are detecting and classifying mine-like objects within sonar data. Ideally, parameters exist that can be measured directly from the sonar data that correlate with ATR performance. In this effort, two metrics were analyzed for their predictive potential using high frequency synthetic aperture sonar (SAS) images. The first parameter is a measure of contrast. It is essentially the variance in pixel intensity over a fixed partition of relatively small size. An analysis was performed to determine the optimum block size for this contrast calculation. These blocks were then overlapped in the horizontal and vertical direction over the entire image. The second parameter is the one-dimensional K-shape parameter. The K-distribution is commonly used to describe sonar backscatter return from range cells that contain a finite number of scatterers. An Ada-Boosted Decision Tree classifier was used to calculate the probability of classification (Pc) and false alarm rate (FAR) for several types of targets in SAS images from three different data sets. ROC curves as a function of the measured parameters were generated and the correlation between the measured parameters in the vicinity of each of the contacts and the ATR performance was investigated. The contrast and K-shape parameters were considered separately. Additionally, the contrast and K-shape parameter were associated with background texture types using previously labeled high frequency SAS images.

Spectral Regression Based Fault Feature Extraction for Bearing Accelerometer Sensor Signals

PubMed Central

Xia, Zhanguo; Xia, Shixiong; Wan, Ling; Cai, Shiyu

2012-01-01

Bearings are not only the most important element but also a common source of failures in rotary machinery. Bearing fault prognosis technology has been receiving more and more attention recently, in particular because it plays an increasingly important role in avoiding the occurrence of accidents. Therein, fault feature extraction (FFE) of bearing accelerometer sensor signals is essential to highlight representative features of bearing conditions for machinery fault diagnosis and prognosis. This paper proposes a spectral regression (SR)-based approach for fault feature extraction from original features including time, frequency and time-frequency domain features of bearing accelerometer sensor signals. SR is a novel regression framework for efficient regularized subspace learning and feature extraction technology, and it uses the least squares method to obtain the best projection direction, rather than computing the density matrix of features, so it also has the advantage in dimensionality reduction. The effectiveness of the SR-based method is validated experimentally by applying the acquired vibration signals data to bearings. The experimental results indicate that SR can reduce the computation cost and preserve more structure information about different bearing faults and severities, and it is demonstrated that the proposed feature extraction scheme has an advantage over other similar approaches. PMID:23202017

Quantitative evaluation method of the bubble structure of sponge cake by using morphology image processing

NASA Astrophysics Data System (ADS)

Tatebe, Hironobu; Kato, Kunihito; Yamamoto, Kazuhiko; Katsuta, Yukio; Nonaka, Masahiko

2005-12-01

Now a day, many evaluation methods for the food industry by using image processing are proposed. These methods are becoming new evaluation method besides the sensory test and the solid-state measurement that are using for the quality evaluation. An advantage of the image processing is to be able to evaluate objectively. The goal of our research is structure evaluation of sponge cake by using image processing. In this paper, we propose a feature extraction method of the bobble structure in the sponge cake. Analysis of the bubble structure is one of the important properties to understand characteristics of the cake from the image. In order to take the cake image, first we cut cakes and measured that's surface by using the CIS scanner. Because the depth of field of this type scanner is very shallow, the bubble region of the surface has low gray scale values, and it has a feature that is blur. We extracted bubble regions from the surface images based on these features. First, input image is binarized, and the feature of bubble is extracted by the morphology analysis. In order to evaluate the result of feature extraction, we compared correlation with "Size of the bubble" of the sensory test result. From a result, the bubble extraction by using morphology analysis gives good correlation. It is shown that our method is as well as the subjectivity evaluation.

3S(expn 2): Behavioral Response Studies of Cetaceans to Navy Sonar Signals in Norwegian Waters

DTIC Science & Technology

2013-09-30

orca), long-finned pilot (Globicephala melas ), and sperm whales (Physeter macrocephalus) to naval sonar. Aquatic Mammals 38: 362-401. 9...of sonar signals by long-finned pilot whales (Globicephala melas ). Marine Mammal sci Aoki K, Sakai M, Miller PJO, Visser F, Sato K (2013) Body...Orcinus orca), long-finned pilot (Globicephala melas ), and sperm whales (Physeter macrocephalus) to naval sonar. Aquatic Mammals 38: 362-401

Real-Time 3D Sonar Modeling And Visualization

DTIC Science & Technology

1998-06-01

looking back towards Manta sonar beam, Manta plus sonar from 1000m off track. 185 NUWC sponsor Erik Chaum Principal investigator Don Brutzman...USN Sonar Officer LT Kevin Byrne USN Intelligence Officer CPT Russell Storms USA Erik Chaum works in NUWC Code 22. He supervised the design and...McGhee, Bob, "The Phoenix Autonomous Underwater Vehicle," chapter 13, AI-BasedMobile Robots, editors David Kortenkamp, Pete Bonasso and Robin Murphy

Sonar Test and Test Instrumentation Support.

DTIC Science & Technology

1979-03-29

AD-AlSO 055 TEXAS UNIV AT AUSTIN APPLIED RESEARCH LABS F/6 17/1 SONAR TEST AND TEST INSTRUMENTATION SUPPORT (U) MAR 79 0 D BAKER N00140-76-C-64a7... SONAR TEST AND TEST INSTRUMENTATION SUPPORT quarterly progress report September - 30 November 197Pj 6. PERFORMING ORG. REPORT NUMBER 7. AUTHOR(e) S...involves technical support with sonar testing, test instrumentation, and documentation. This report describes progress made under the tasks that are

Development of an Autonomous Broadband Acoustic Scattering System for Remote Characterization of Zooplankton

DTIC Science & Technology

2009-01-01

measure of a backscatter at a single narrowband frequency, and some AUVs carry single-frequency sidescan sonars (and this technology has been adapted...monostatic Doppler sonar module. Key personnel for this project include: Andone Lavery as the PI for this project and who has overall responsibility...for the successful development, testing, and calibration of the broadband system. Gene Terray, who developed the original sonar Doppler sonar boards

Sonar Transducer Reliability Improvement Program (STRIP) FY81.

DTIC Science & Technology

1981-10-01

that must be considered when selecting a material for the design of a sonar transducer. In the past decade, plastics have decreased in cost and...required in a sonar transducer system. A recent example of this type of failure has been with a neoprene .tfer formulation which was designed to meet...subject of the first design specification for transducer elastomers. Previous work on this material under the aegis of the Sonar Transduction

Whales and Sonar: Environmental Exemptions for the Navy’s Mid-Frequency Active Sonar Training

DTIC Science & Technology

2008-11-14

Balaenoptera musculus E Finback whale Balaenoptera physalus E Humpback whale Megaptera novaeangliae E Killer Southern whale Resident DPS Orcinus orca...Salmo) mykiss T Steelhead south central CA coast Oncorhynchus (=Salmo) mykiss E Steelhead southern CA coast Oncorhynchus (=Salmo) mykiss E Blue whale ...Order Code RL34403 Whales and Sonar: Environmental Exemptions for the Navy’s Mid-Frequency Active Sonar Training Updated November 14, 2008 Kristina

An improved discriminative filter bank selection approach for motor imagery EEG signal classification using mutual information.

PubMed

Kumar, Shiu; Sharma, Alok; Tsunoda, Tatsuhiko

2017-12-28

Common spatial pattern (CSP) has been an effective technique for feature extraction in electroencephalography (EEG) based brain computer interfaces (BCIs). However, motor imagery EEG signal feature extraction using CSP generally depends on the selection of the frequency bands to a great extent. In this study, we propose a mutual information based frequency band selection approach. The idea of the proposed method is to utilize the information from all the available channels for effectively selecting the most discriminative filter banks. CSP features are extracted from multiple overlapping sub-bands. An additional sub-band has been introduced that cover the wide frequency band (7-30 Hz) and two different types of features are extracted using CSP and common spatio-spectral pattern techniques, respectively. Mutual information is then computed from the extracted features of each of these bands and the top filter banks are selected for further processing. Linear discriminant analysis is applied to the features extracted from each of the filter banks. The scores are fused together, and classification is done using support vector machine. The proposed method is evaluated using BCI Competition III dataset IVa, BCI Competition IV dataset I and BCI Competition IV dataset IIb, and it outperformed all other competing methods achieving the lowest misclassification rate and the highest kappa coefficient on all three datasets. Introducing a wide sub-band and using mutual information for selecting the most discriminative sub-bands, the proposed method shows improvement in motor imagery EEG signal classification.

Feature extraction using first and second derivative extrema (FSDE) for real-time and hardware-efficient spike sorting.

PubMed

Paraskevopoulou, Sivylla E; Barsakcioglu, Deren Y; Saberi, Mohammed R; Eftekhar, Amir; Constandinou, Timothy G

2013-04-30

Next generation neural interfaces aspire to achieve real-time multi-channel systems by integrating spike sorting on chip to overcome limitations in communication channel capacity. The feasibility of this approach relies on developing highly efficient algorithms for feature extraction and clustering with the potential of low-power hardware implementation. We are proposing a feature extraction method, not requiring any calibration, based on first and second derivative features of the spike waveform. The accuracy and computational complexity of the proposed method are quantified and compared against commonly used feature extraction methods, through simulation across four datasets (with different single units) at multiple noise levels (ranging from 5 to 20% of the signal amplitude). The average classification error is shown to be below 7% with a computational complexity of 2N-3, where N is the number of sample points of each spike. Overall, this method presents a good trade-off between accuracy and computational complexity and is thus particularly well-suited for hardware-efficient implementation. Copyright © 2013 Elsevier B.V. All rights reserved.

Combining Feature Extraction Methods to Assist the Diagnosis of Alzheimer's Disease.

PubMed

Segovia, F; Górriz, J M; Ramírez, J; Phillips, C

2016-01-01

Neuroimaging data as (18)F-FDG PET is widely used to assist the diagnosis of Alzheimer's disease (AD). Looking for regions with hypoperfusion/ hypometabolism, clinicians may predict or corroborate the diagnosis of the patients. Modern computer aided diagnosis (CAD) systems based on the statistical analysis of whole neuroimages are more accurate than classical systems based on quantifying the uptake of some predefined regions of interests (ROIs). In addition, these new systems allow determining new ROIs and take advantage of the huge amount of information comprised in neuroimaging data. A major branch of modern CAD systems for AD is based on multivariate techniques, which analyse a neuroimage as a whole, considering not only the voxel intensities but also the relations among them. In order to deal with the vast dimensionality of the data, a number of feature extraction methods have been successfully applied. In this work, we propose a CAD system based on the combination of several feature extraction techniques. First, some commonly used feature extraction methods based on the analysis of the variance (as principal component analysis), on the factorization of the data (as non-negative matrix factorization) and on classical magnitudes (as Haralick features) were simultaneously applied to the original data. These feature sets were then combined by means of two different combination approaches: i) using a single classifier and a multiple kernel learning approach and ii) using an ensemble of classifier and selecting the final decision by majority voting. The proposed approach was evaluated using a labelled neuroimaging database along with a cross validation scheme. As conclusion, the proposed CAD system performed better than approaches using only one feature extraction technique. We also provide a fair comparison (using the same database) of the selected feature extraction methods.

Analysis of aircraft spectrometer data with logarithmic residuals

NASA Technical Reports Server (NTRS)

Green, A. A.; Craig, M. D.

1985-01-01

Spectra from airborne systems must be analyzed in terms of their mineral-related absorption features. Methods for removing backgrounds and extracting these features one at a time from reflectance spectra are discussed. Methods for converting radiance spectra into a form similar to reflectance spectra so that the feature extraction procedures can be implemented on aircraft spectrometer data are also discussed.

Measurement of stream channel habitat using sonar

USGS Publications Warehouse

Flug, Marshall; Seitz, Heather; Scott, John

1998-01-01

An efficient and low cost technique using a sonar system was evaluated for describing channel geometry and quantifying inundated area in a large river. The boat-mounted portable sonar equipment was used to record water depths and river width measurements for direct storage on a laptop computer. The field data collected from repeated traverses at a cross-section were evaluated to determine the precision of the system and field technique. Results from validation at two different sites showed average sample standard deviations (S.D.s) of 0.12 m for these complete cross-sections, with coefficient of variations of 10%. Validation using only the mid-channel river cross-section data yields an average sample S.D. of 0.05 m, with a coefficient of variation below 5%, at a stable and gauged river site using only measurements of water depths greater than 0.6 m. Accuracy of the sonar system was evaluated by comparison to traditionally surveyed transect data from a regularly gauged site. We observed an average mean squared deviation of 46.0 cm2, considering only that portion of the cross-section inundated by more than 0.6 m of water. Our procedure proved to be a reliable, accurate, safe, quick, and economic method to record river depths, discharges, bed conditions, and substratum composition necessary for stream habitat studies.

Fluorescence Intrinsic Characterization of Excitation-Emission Matrix Using Multi-Dimensional Ensemble Empirical Mode Decomposition

PubMed Central

Chang, Chi-Ying; Chang, Chia-Chi; Hsiao, Tzu-Chien

2013-01-01

Excitation-emission matrix (EEM) fluorescence spectroscopy is a noninvasive method for tissue diagnosis and has become important in clinical use. However, the intrinsic characterization of EEM fluorescence remains unclear. Photobleaching and the complexity of the chemical compounds make it difficult to distinguish individual compounds due to overlapping features. Conventional studies use principal component analysis (PCA) for EEM fluorescence analysis, and the relationship between the EEM features extracted by PCA and diseases has been examined. The spectral features of different tissue constituents are not fully separable or clearly defined. Recently, a non-stationary method called multi-dimensional ensemble empirical mode decomposition (MEEMD) was introduced; this method can extract the intrinsic oscillations on multiple spatial scales without loss of information. The aim of this study was to propose a fluorescence spectroscopy system for EEM measurements and to describe a method for extracting the intrinsic characteristics of EEM by MEEMD. The results indicate that, although PCA provides the principal factor for the spectral features associated with chemical compounds, MEEMD can provide additional intrinsic features with more reliable mapping of the chemical compounds. MEEMD has the potential to extract intrinsic fluorescence features and improve the detection of biochemical changes. PMID:24240806

Glioma grading using cell nuclei morphologic features in digital pathology images

NASA Astrophysics Data System (ADS)

Reza, Syed M. S.; Iftekharuddin, Khan M.

2016-03-01

This work proposes a computationally efficient cell nuclei morphologic feature analysis technique to characterize the brain gliomas in tissue slide images. In this work, our contributions are two-fold: 1) obtain an optimized cell nuclei segmentation method based on the pros and cons of the existing techniques in literature, 2) extract representative features by k-mean clustering of nuclei morphologic features to include area, perimeter, eccentricity, and major axis length. This clustering based representative feature extraction avoids shortcomings of extensive tile [1] [2] and nuclear score [3] based methods for brain glioma grading in pathology images. Multilayer perceptron (MLP) is used to classify extracted features into two tumor types: glioblastoma multiforme (GBM) and low grade glioma (LGG). Quantitative scores such as precision, recall, and accuracy are obtained using 66 clinical patients' images from The Cancer Genome Atlas (TCGA) [4] dataset. On an average ~94% accuracy from 10 fold crossvalidation confirms the efficacy of the proposed method.

Hierarchical Feature Extraction With Local Neural Response for Image Recognition.

PubMed

Li, Hong; Wei, Yantao; Li, Luoqing; Chen, C L P

2013-04-01

In this paper, a hierarchical feature extraction method is proposed for image recognition. The key idea of the proposed method is to extract an effective feature, called local neural response (LNR), of the input image with nontrivial discrimination and invariance properties by alternating between local coding and maximum pooling operation. The local coding, which is carried out on the locally linear manifold, can extract the salient feature of image patches and leads to a sparse measure matrix on which maximum pooling is carried out. The maximum pooling operation builds the translation invariance into the model. We also show that other invariant properties, such as rotation and scaling, can be induced by the proposed model. In addition, a template selection algorithm is presented to reduce computational complexity and to improve the discrimination ability of the LNR. Experimental results show that our method is robust to local distortion and clutter compared with state-of-the-art algorithms.

50 CFR 218.84 - Mitigation.

Code of Federal Regulations, 2014 CFR

2014-10-01

... aircraft conducting high-frequency or non-hull-mounted mid-frequency active sonar activities associated... or aircraft conducting high-frequency active sonar activities associated with anti-submarine warfare...). (2) High-frequency and non-hull mounted mid-frequency active sonar (except helicopter dipping). (3...

Acoustic analysis of oropharyngeal swallowing using Sonar Doppler.

PubMed

Soria, Franciele Savaris; Silva, Roberta Gonçalves da; Furkim, Ana Maria

2016-01-01

During the aging process, one of the functions that changes is swallowing. These alterations in oropharyngeal swallowing may be diagnosed by methods that allow both the diagnosis and biofeedback monitoring by the patient. One of the methods recently described in the literature for the evaluation of swallowing is the Sonar Doppler. To compare the acoustic parameters of oropharyngeal swallowing between different age groups. This was a field, quantitative, study. Examination with Sonar Doppler was performed in 75 elderly and 72 non-elderly adult subjects. The following acoustic parameters were established: initial frequency, first peak frequency, second peak frequency; initial intensity, final intensity; and time for the swallowing of saliva, liquid, nectar, honey, and pudding, with 5- and 10-mL free drinks. Objective, measurable data were obtained; most acoustic parameters studied between adult and elderly groups with respect to consistency and volume were significant. When comparing elderly with non-elderly adult subjects, there is a modification of the acoustic pattern of swallowing, regarding both consistency and food bolus volume. Copyright © 2015 Associação Brasileira de Otorrinolaringologia e Cirurgia Cérvico-Facial. Published by Elsevier Editora Ltda. All rights reserved.

Interferometric side scan sonar and data fusion

NASA Astrophysics Data System (ADS)

Sintes, Christophe R.; Solaiman, Basel

2000-04-01

This paper concerns the possibilities of sea bottom imaging and altitude determining of each imaged point. The performances of new side scan sonars which are able to image the sea bottom with a high definition and are able to evaluate the relief with the same definition derive from an interferometric multisensor system. The drawbacks concern the precision of the numerical altitude model. One way to improve the measurements precision is to merge all the information issued from the multi-sensors system. This leads to increase the Signal to Noise Ratio (SNR) and the robustness of the used method. The aim of this paper is to clearly demonstrate the ability to derive benefits of all information issued from the three arrays side scan sonar by merging: (1) the three phase signals obtained at the output of the sensors, (2) this same set of data after the application of different processing methods, and (3) the a priori relief contextual information. The key idea the proposed fusion technique is to exploit the strength and the weaknesses of each data element in the fusion of process so that the global SNR will be improved as well as the robustness to hostile noisy environments.

Color image definition evaluation method based on deep learning method

NASA Astrophysics Data System (ADS)

Liu, Di; Li, YingChun

2018-01-01

In order to evaluate different blurring levels of color image and improve the method of image definition evaluation, this paper proposed a method based on the depth learning framework and BP neural network classification model, and presents a non-reference color image clarity evaluation method. Firstly, using VGG16 net as the feature extractor to extract 4,096 dimensions features of the images, then the extracted features and labeled images are employed in BP neural network to train. And finally achieve the color image definition evaluation. The method in this paper are experimented by using images from the CSIQ database. The images are blurred at different levels. There are 4,000 images after the processing. Dividing the 4,000 images into three categories, each category represents a blur level. 300 out of 400 high-dimensional features are trained in VGG16 net and BP neural network, and the rest of 100 samples are tested. The experimental results show that the method can take full advantage of the learning and characterization capability of deep learning. Referring to the current shortcomings of the major existing image clarity evaluation methods, which manually design and extract features. The method in this paper can extract the images features automatically, and has got excellent image quality classification accuracy for the test data set. The accuracy rate is 96%. Moreover, the predicted quality levels of original color images are similar to the perception of the human visual system.

Research of infrared laser based pavement imaging and crack detection

NASA Astrophysics Data System (ADS)

Hong, Hanyu; Wang, Shu; Zhang, Xiuhua; Jing, Genqiang

2013-08-01

Road crack detection is seriously affected by many factors in actual applications, such as some shadows, road signs, oil stains, high frequency noise and so on. Due to these factors, the current crack detection methods can not distinguish the cracks in complex scenes. In order to solve this problem, a novel method based on infrared laser pavement imaging is proposed. Firstly, single sensor laser pavement imaging system is adopted to obtain pavement images, high power laser line projector is well used to resist various shadows. Secondly, the crack extraction algorithm which has merged multiple features intelligently is proposed to extract crack information. In this step, the non-negative feature and contrast feature are used to extract the basic crack information, and circular projection based on linearity feature is applied to enhance the crack area and eliminate noise. A series of experiments have been performed to test the proposed method, which shows that the proposed automatic extraction method is effective and advanced.

[A Feature Extraction Method for Brain Computer Interface Based on Multivariate Empirical Mode Decomposition].

PubMed

Wang, Jinjia; Liu, Yuan

2015-04-01

This paper presents a feature extraction method based on multivariate empirical mode decomposition (MEMD) combining with the power spectrum feature, and the method aims at the non-stationary electroencephalogram (EEG) or magnetoencephalogram (MEG) signal in brain-computer interface (BCI) system. Firstly, we utilized MEMD algorithm to decompose multichannel brain signals into a series of multiple intrinsic mode function (IMF), which was proximate stationary and with multi-scale. Then we extracted and reduced the power characteristic from each IMF to a lower dimensions using principal component analysis (PCA). Finally, we classified the motor imagery tasks by linear discriminant analysis classifier. The experimental verification showed that the correct recognition rates of the two-class and four-class tasks of the BCI competition III and competition IV reached 92.0% and 46.2%, respectively, which were superior to the winner of the BCI competition. The experimental proved that the proposed method was reasonably effective and stable and it would provide a new way for feature extraction.

Surficial geology of the sea floor in Long Island Sound offshore of Plum Island, New York

USGS Publications Warehouse

McMullen, K.Y.; Poppe, L.J.; Danforth, W.W.; Blackwood, D.S.; Schaer, J.D.; Ostapenko, A.J.; Glomb, K.A.; Doran, E.F.

2010-01-01

The U.S. Geological Survey (USGS), the Connecticut Department of Environmental Protection, and the National Oceanic and Atmospheric Administration (NOAA) have been working cooperatively to interpret surficial sea-floor geology along the coast of the Northeastern United States. NOAA survey H11445 in eastern Long Island Sound, offshore of Plum Island, New York, covers an area of about 12 square kilometers. Multibeam bathymetry and sidescan-sonar imagery from the survey, as well as sediment and photographic data from 13 stations occupied during a USGS verification cruise are used to delineate sea-floor features and characterize the environment. Bathymetry gradually deepens offshore to over 100 meters in a depression in the northwest part of the study area and reaches 60 meters in Plum Gut, a channel between Plum Island and Orient Point. Sand waves are present on a shoal north of Plum Island and in several smaller areas around the basin. Sand-wave asymmetry indicates that counter-clockwise net sediment transport maintains the shoal. Sand is prevalent where there is low backscatter in the sidescan-sonar imagery. Gravel and boulder areas are submerged lag deposits produced from the Harbor Hill-Orient Point-Fishers Island moraine segment and are found adjacent to the shorelines and just north of Plum Island, where high backscatter is present in the sidescan-sonar imagery.

Object Tracking Using Adaptive Covariance Descriptor and Clustering-Based Model Updating for Visual Surveillance

PubMed Central

Qin, Lei; Snoussi, Hichem; Abdallah, Fahed

2014-01-01

We propose a novel approach for tracking an arbitrary object in video sequences for visual surveillance. The first contribution of this work is an automatic feature extraction method that is able to extract compact discriminative features from a feature pool before computing the region covariance descriptor. As the feature extraction method is adaptive to a specific object of interest, we refer to the region covariance descriptor computed using the extracted features as the adaptive covariance descriptor. The second contribution is to propose a weakly supervised method for updating the object appearance model during tracking. The method performs a mean-shift clustering procedure among the tracking result samples accumulated during a period of time and selects a group of reliable samples for updating the object appearance model. As such, the object appearance model is kept up-to-date and is prevented from contamination even in case of tracking mistakes. We conducted comparing experiments on real-world video sequences, which confirmed the effectiveness of the proposed approaches. The tracking system that integrates the adaptive covariance descriptor and the clustering-based model updating method accomplished stable object tracking on challenging video sequences. PMID:24865883

Novel Features for Brain-Computer Interfaces

PubMed Central

Woon, W. L.; Cichocki, A.

2007-01-01

While conventional approaches of BCI feature extraction are based on the power spectrum, we have tried using nonlinear features for classifying BCI data. In this paper, we report our test results and findings, which indicate that the proposed method is a potentially useful addition to current feature extraction techniques. PMID:18364991

Biomarkers to Assess Possible Biological Effects on Reproductive Potential, Immune Function, and Energetic Fitness of Bottlenose Dolphins Exposed to Sounds Consistent with Naval Sonars

DTIC Science & Technology

2012-09-30

Reproductive Potential, Immune Function, and Energetic Fitness of Bottlenose Dolphins Exposed to Sounds Consistent with Naval Sonars Dana L. Wetzel...biomarkers to examine whether significant sublethal responses to sonar-type sounds occur in bottlenose dolphins exposed to such sounds. The...investigate samples collected from trained dolphins before exposure to simulated mid-frequency sonar signals, immediately after exposure, and one week post

75 FR 5575 - Taking and Importing Marine Mammals; Navy Training Activities Conducted in the Gulf of Alaska

Federal Register 2010, 2011, 2012, 2013, 2014

2010-02-03

... present in the area to sound from various active tactical sonar sources or to pressure from underwater... utilizing mid- and high frequency active sonar sources and explosive detonations. These sonar and explosive...

Image feature detection and extraction techniques performance evaluation for development of panorama under different light conditions

NASA Astrophysics Data System (ADS)

Patil, Venkat P.; Gohatre, Umakant B.

2018-04-01

The technique of obtaining a wider field-of-view of an image to get high resolution integrated image is normally required for development of panorama of a photographic images or scene from a sequence of part of multiple views. There are various image stitching methods developed recently. For image stitching five basic steps are adopted stitching which are Feature detection and extraction, Image registration, computing homography, image warping and Blending. This paper provides review of some of the existing available image feature detection and extraction techniques and image stitching algorithms by categorizing them into several methods. For each category, the basic concepts are first described and later on the necessary modifications made to the fundamental concepts by different researchers are elaborated. This paper also highlights about the some of the fundamental techniques for the process of photographic image feature detection and extraction methods under various illumination conditions. The Importance of Image stitching is applicable in the various fields such as medical imaging, astrophotography and computer vision. For comparing performance evaluation of the techniques used for image features detection three methods are considered i.e. ORB, SURF, HESSIAN and time required for input images feature detection is measured. Results obtained finally concludes that for daylight condition, ORB algorithm found better due to the fact that less tome is required for more features extracted where as for images under night light condition it shows that SURF detector performs better than ORB/HESSIAN detectors.

A method of evolving novel feature extraction algorithms for detecting buried objects in FLIR imagery using genetic programming

NASA Astrophysics Data System (ADS)

Paino, A.; Keller, J.; Popescu, M.; Stone, K.

2014-06-01

In this paper we present an approach that uses Genetic Programming (GP) to evolve novel feature extraction algorithms for greyscale images. Our motivation is to create an automated method of building new feature extraction algorithms for images that are competitive with commonly used human-engineered features, such as Local Binary Pattern (LBP) and Histogram of Oriented Gradients (HOG). The evolved feature extraction algorithms are functions defined over the image space, and each produces a real-valued feature vector of variable length. Each evolved feature extractor breaks up the given image into a set of cells centered on every pixel, performs evolved operations on each cell, and then combines the results of those operations for every cell using an evolved operator. Using this method, the algorithm is flexible enough to reproduce both LBP and HOG features. The dataset we use to train and test our approach consists of a large number of pre-segmented image "chips" taken from a Forward Looking Infrared Imagery (FLIR) camera mounted on the hood of a moving vehicle. The goal is to classify each image chip as either containing or not containing a buried object. To this end, we define the fitness of a candidate solution as the cross-fold validation accuracy of the features generated by said candidate solution when used in conjunction with a Support Vector Machine (SVM) classifier. In order to validate our approach, we compare the classification accuracy of an SVM trained using our evolved features with the accuracy of an SVM trained using mainstream feature extraction algorithms, including LBP and HOG.

Identification of DNA-Binding Proteins Using Mixed Feature Representation Methods.

PubMed

Qu, Kaiyang; Han, Ke; Wu, Song; Wang, Guohua; Wei, Leyi

2017-09-22

DNA-binding proteins play vital roles in cellular processes, such as DNA packaging, replication, transcription, regulation, and other DNA-associated activities. The current main prediction method is based on machine learning, and its accuracy mainly depends on the features extraction method. Therefore, using an efficient feature representation method is important to enhance the classification accuracy. However, existing feature representation methods cannot efficiently distinguish DNA-binding proteins from non-DNA-binding proteins. In this paper, a multi-feature representation method, which combines three feature representation methods, namely, K-Skip-N-Grams, Information theory, and Sequential and structural features (SSF), is used to represent the protein sequences and improve feature representation ability. In addition, the classifier is a support vector machine. The mixed-feature representation method is evaluated using 10-fold cross-validation and a test set. Feature vectors, which are obtained from a combination of three feature extractions, show the best performance in 10-fold cross-validation both under non-dimensional reduction and dimensional reduction by max-relevance-max-distance. Moreover, the reduced mixed feature method performs better than the non-reduced mixed feature technique. The feature vectors, which are a combination of SSF and K-Skip-N-Grams, show the best performance in the test set. Among these methods, mixed features exhibit superiority over the single features.

Comparison of ANN and SVM for classification of eye movements in EOG signals

NASA Astrophysics Data System (ADS)

Qi, Lim Jia; Alias, Norma

2018-03-01

Nowadays, electrooculogram is regarded as one of the most important biomedical signal in measuring and analyzing eye movement patterns. Thus, it is helpful in designing EOG-based Human Computer Interface (HCI). In this research, electrooculography (EOG) data was obtained from five volunteers. The (EOG) data was then preprocessed before feature extraction methods were employed to further reduce the dimensionality of data. Three feature extraction approaches were put forward, namely statistical parameters, autoregressive (AR) coefficients using Burg method, and power spectral density (PSD) using Yule-Walker method. These features would then become input to both artificial neural network (ANN) and support vector machine (SVM). The performance of the combination of different feature extraction methods and classifiers was presented and analyzed. It was found that statistical parameters + SVM achieved the highest classification accuracy of 69.75%.

Extraction of linear features on SAR imagery

NASA Astrophysics Data System (ADS)

Liu, Junyi; Li, Deren; Mei, Xin

2006-10-01

Linear features are usually extracted from SAR imagery by a few edge detectors derived from the contrast ratio edge detector with a constant probability of false alarm. On the other hand, the Hough Transform is an elegant way of extracting global features like curve segments from binary edge images. Randomized Hough Transform can reduce the computation time and memory usage of the HT drastically. While Randomized Hough Transform will bring about a great deal of cells invalid during the randomized sample. In this paper, we propose a new approach to extract linear features on SAR imagery, which is an almost automatic algorithm based on edge detection and Randomized Hough Transform. The presented improved method makes full use of the directional information of each edge candidate points so as to solve invalid cumulate problems. Applied result is in good agreement with the theoretical study, and the main linear features on SAR imagery have been extracted automatically. The method saves storage space and computational time, which shows its effectiveness and applicability.

Behavioral Responses of Naive Cuvier’s Beaked Whales in the Ligurian Sea to Playback of Anthropogenic and Natural Sounds

DTIC Science & Technology

2013-09-30

cavirostris) to MFA sonar signals. Secondary goals included conducting a killer whale playback that has not been preceded by a sonar playback (as in Tyack...et al. 2011) and collecting more baseline data on Ziphius. OBJECTIVES This investigation set out to safely test responses of Ziphius to sonar ...signals and to determine the exposure level required to elicit a response in a site where strandings have been associated with sonar exercises and

Technology Infusion of CodeSonar into the Space Network Ground Segment

NASA Technical Reports Server (NTRS)

Benson, Markland J.

2009-01-01

This slide presentation reviews the applicability of CodeSonar to the Space Network software. CodeSonar is a commercial off the shelf system that analyzes programs written in C, C++ or Ada for defects in the code. Software engineers use CodeSonar results as an input to the existing source code inspection process. The study is focused on large scale software developed using formal processes. The systems studied are mission critical in nature but some use commodity computer systems.

Feature Extraction and Selection Strategies for Automated Target Recognition

NASA Technical Reports Server (NTRS)

Greene, W. Nicholas; Zhang, Yuhan; Lu, Thomas T.; Chao, Tien-Hsin

2010-01-01

Several feature extraction and selection methods for an existing automatic target recognition (ATR) system using JPLs Grayscale Optical Correlator (GOC) and Optimal Trade-Off Maximum Average Correlation Height (OT-MACH) filter were tested using MATLAB. The ATR system is composed of three stages: a cursory region of-interest (ROI) search using the GOC and OT-MACH filter, a feature extraction and selection stage, and a final classification stage. Feature extraction and selection concerns transforming potential target data into more useful forms as well as selecting important subsets of that data which may aide in detection and classification. The strategies tested were built around two popular extraction methods: Principal Component Analysis (PCA) and Independent Component Analysis (ICA). Performance was measured based on the classification accuracy and free-response receiver operating characteristic (FROC) output of a support vector machine(SVM) and a neural net (NN) classifier.

Feature extraction and selection strategies for automated target recognition

NASA Astrophysics Data System (ADS)

Greene, W. Nicholas; Zhang, Yuhan; Lu, Thomas T.; Chao, Tien-Hsin

2010-04-01

Several feature extraction and selection methods for an existing automatic target recognition (ATR) system using JPLs Grayscale Optical Correlator (GOC) and Optimal Trade-Off Maximum Average Correlation Height (OT-MACH) filter were tested using MATLAB. The ATR system is composed of three stages: a cursory regionof- interest (ROI) search using the GOC and OT-MACH filter, a feature extraction and selection stage, and a final classification stage. Feature extraction and selection concerns transforming potential target data into more useful forms as well as selecting important subsets of that data which may aide in detection and classification. The strategies tested were built around two popular extraction methods: Principal Component Analysis (PCA) and Independent Component Analysis (ICA). Performance was measured based on the classification accuracy and free-response receiver operating characteristic (FROC) output of a support vector machine(SVM) and a neural net (NN) classifier.

Geomorphology of the Southern Gulf of California Seafloor

NASA Astrophysics Data System (ADS)

Eakins, B. W.; Lonsdale, P. F.; Fletcher, J. M.; Ledesma, J. V.

2004-12-01

A Spring 2004 multibeam sonar survey defined the seafloor geomorphology of the southern part of Gulf of California and the intersection of the East Pacific Rise with the North American continent. Survey goals included mapping structural patterns formed during the rifting that opened the Gulf and identifying the spatial transition from continental rifting to seafloor spreading. Multibeam sonar imagery, augmented with archival data and a subaerial DEM of Mexico, illuminates the principal features of this boundary zone between obliquely diverging plates: (i) active and inactive oceanic risecrests within young oceanic basins that are rich in evidence for off-axis magmatic eruption and intrusion; (ii) transforms with pull-apart basins and transpressive ridges along shearing continental margins and within oceanic crust; (iii) orphaned blocks of continental crust detached from sheared and rifted continental margins; and (iv) young, still-extending continental margins dissected by submarine canyons that in many cases are deeply drowned river valleys. Many of the canyons are conduits for turbidity currents that feed deep-sea fans on oceanic and subsided continental crust, and channel sediment to spreading axes, thereby modifying the crustal accretion process. We present a series of detailed bathymetric and seafloor reflectivity maps of this MARGINS Rupturing Continental Lithosphere focus site illustrating geomorphologic features of the southern part of the Gulf, from Guaymas Basin to the Maria Magdalena Rise.

Achieving Accurate Automatic Sleep Staging on Manually Pre-processed EEG Data Through Synchronization Feature Extraction and Graph Metrics.

PubMed

Chriskos, Panteleimon; Frantzidis, Christos A; Gkivogkli, Polyxeni T; Bamidis, Panagiotis D; Kourtidou-Papadeli, Chrysoula

2018-01-01

Sleep staging, the process of assigning labels to epochs of sleep, depending on the stage of sleep they belong, is an arduous, time consuming and error prone process as the initial recordings are quite often polluted by noise from different sources. To properly analyze such data and extract clinical knowledge, noise components must be removed or alleviated. In this paper a pre-processing and subsequent sleep staging pipeline for the sleep analysis of electroencephalographic signals is described. Two novel methods of functional connectivity estimation (Synchronization Likelihood/SL and Relative Wavelet Entropy/RWE) are comparatively investigated for automatic sleep staging through manually pre-processed electroencephalographic recordings. A multi-step process that renders signals suitable for further analysis is initially described. Then, two methods that rely on extracting synchronization features from electroencephalographic recordings to achieve computerized sleep staging are proposed, based on bivariate features which provide a functional overview of the brain network, contrary to most proposed methods that rely on extracting univariate time and frequency features. Annotation of sleep epochs is achieved through the presented feature extraction methods by training classifiers, which are in turn able to accurately classify new epochs. Analysis of data from sleep experiments on a randomized, controlled bed-rest study, which was organized by the European Space Agency and was conducted in the "ENVIHAB" facility of the Institute of Aerospace Medicine at the German Aerospace Center (DLR) in Cologne, Germany attains high accuracy rates, over 90% based on ground truth that resulted from manual sleep staging by two experienced sleep experts. Therefore, it can be concluded that the above feature extraction methods are suitable for semi-automatic sleep staging.

Achieving Accurate Automatic Sleep Staging on Manually Pre-processed EEG Data Through Synchronization Feature Extraction and Graph Metrics

PubMed Central

Chriskos, Panteleimon; Frantzidis, Christos A.; Gkivogkli, Polyxeni T.; Bamidis, Panagiotis D.; Kourtidou-Papadeli, Chrysoula

2018-01-01

Sleep staging, the process of assigning labels to epochs of sleep, depending on the stage of sleep they belong, is an arduous, time consuming and error prone process as the initial recordings are quite often polluted by noise from different sources. To properly analyze such data and extract clinical knowledge, noise components must be removed or alleviated. In this paper a pre-processing and subsequent sleep staging pipeline for the sleep analysis of electroencephalographic signals is described. Two novel methods of functional connectivity estimation (Synchronization Likelihood/SL and Relative Wavelet Entropy/RWE) are comparatively investigated for automatic sleep staging through manually pre-processed electroencephalographic recordings. A multi-step process that renders signals suitable for further analysis is initially described. Then, two methods that rely on extracting synchronization features from electroencephalographic recordings to achieve computerized sleep staging are proposed, based on bivariate features which provide a functional overview of the brain network, contrary to most proposed methods that rely on extracting univariate time and frequency features. Annotation of sleep epochs is achieved through the presented feature extraction methods by training classifiers, which are in turn able to accurately classify new epochs. Analysis of data from sleep experiments on a randomized, controlled bed-rest study, which was organized by the European Space Agency and was conducted in the “ENVIHAB” facility of the Institute of Aerospace Medicine at the German Aerospace Center (DLR) in Cologne, Germany attains high accuracy rates, over 90% based on ground truth that resulted from manual sleep staging by two experienced sleep experts. Therefore, it can be concluded that the above feature extraction methods are suitable for semi-automatic sleep staging. PMID:29628883

Subject-based feature extraction by using fisher WPD-CSP in brain-computer interfaces.

PubMed

Yang, Banghua; Li, Huarong; Wang, Qian; Zhang, Yunyuan

2016-06-01

Feature extraction of electroencephalogram (EEG) plays a vital role in brain-computer interfaces (BCIs). In recent years, common spatial pattern (CSP) has been proven to be an effective feature extraction method. However, the traditional CSP has disadvantages of requiring a lot of input channels and the lack of frequency information. In order to remedy the defects of CSP, wavelet packet decomposition (WPD) and CSP are combined to extract effective features. But WPD-CSP method considers less about extracting specific features that are fitted for the specific subject. So a subject-based feature extraction method using fisher WPD-CSP is proposed in this paper. The idea of proposed method is to adapt fisher WPD-CSP to each subject separately. It mainly includes the following six steps: (1) original EEG signals from all channels are decomposed into a series of sub-bands using WPD; (2) average power values of obtained sub-bands are computed; (3) the specified sub-bands with larger values of fisher distance according to average power are selected for that particular subject; (4) each selected sub-band is reconstructed to be regarded as a new EEG channel; (5) all new EEG channels are used as input of the CSP and a six-dimensional feature vector is obtained by the CSP. The subject-based feature extraction model is so formed; (6) the probabilistic neural network (PNN) is used as the classifier and the classification accuracy is obtained. Data from six subjects are processed by the subject-based fisher WPD-CSP, the non-subject-based fisher WPD-CSP and WPD-CSP, respectively. Compared with non-subject-based fisher WPD-CSP and WPD-CSP, the results show that the proposed method yields better performance (sensitivity: 88.7±0.9%, and specificity: 91±1%) and the classification accuracy from subject-based fisher WPD-CSP is increased by 6-12% and 14%, respectively. The proposed subject-based fisher WPD-CSP method can not only remedy disadvantages of CSP by WPD but also discriminate helpless sub-bands for each subject and make remaining fewer sub-bands keep better separability by fisher distance, which leads to a higher classification accuracy than WPD-CSP method. Copyright © 2016 Elsevier Ireland Ltd. All rights reserved.

Sonar-induced temporary hearing loss in dolphins

PubMed Central

Mooney, T. Aran; Nachtigall, Paul E.; Vlachos, Stephanie

2009-01-01

There is increasing concern that human-produced ocean noise is adversely affecting marine mammals, as several recent cetacean mass strandings may have been caused by animals' interactions with naval ‘mid-frequency’ sonar. However, it has yet to be empirically demonstrated how sonar could induce these strandings or cause physiological effects. In controlled experimental studies, we show that mid-frequency sonar can induce temporary hearing loss in a bottlenose dolphin (Tursiops truncatus). Mild-behavioural alterations were also associated with the exposures. The auditory effects were induced only by repeated exposures to intense sonar pings with total sound exposure levels of 214 dB re: 1 μPa2 s. Data support an increasing energy model to predict temporary noise-induced hearing loss and indicate that odontocete noise exposure effects bear trends similar to terrestrial mammals. Thus, sonar can induce physiological and behavioural effects in at least one species of odontocete; however, exposures must be of prolonged, high sound exposures levels to generate these effects. PMID:19364712

Mapping surface disturbance of energy-related infrastructure in southwest Wyoming--An assessment of methods

USGS Publications Warehouse

Germaine, Stephen S.; O'Donnell, Michael S.; Aldridge, Cameron L.; Baer, Lori; Fancher, Tammy; McBeth, Jamie; McDougal, Robert R.; Waltermire, Robert; Bowen, Zachary H.; Diffendorfer, James; Garman, Steven; Hanson, Leanne

2012-01-01

We evaluated how well three leading information-extraction software programs (eCognition, Feature Analyst, Feature Extraction) and manual hand digitization interpreted information from remotely sensed imagery of a visually complex gas field in Wyoming. Specifically, we compared how each mapped the area of and classified the disturbance features present on each of three remotely sensed images, including 30-meter-resolution Landsat, 10-meter-resolution SPOT (Satellite Pour l'Observation de la Terre), and 0.6-meter resolution pan-sharpened QuickBird scenes. Feature Extraction mapped the spatial area of disturbance features most accurately on the Landsat and QuickBird imagery, while hand digitization was most accurate on the SPOT imagery. Footprint non-overlap error was smallest on the Feature Analyst map of the Landsat imagery, the hand digitization map of the SPOT imagery, and the Feature Extraction map of the QuickBird imagery. When evaluating feature classification success against a set of ground-truthed control points, Feature Analyst, Feature Extraction, and hand digitization classified features with similar success on the QuickBird and SPOT imagery, while eCognition classified features poorly relative to the other methods. All maps derived from Landsat imagery classified disturbance features poorly. Using the hand digitized QuickBird data as a reference and making pixel-by-pixel comparisons, Feature Extraction classified features best overall on the QuickBird imagery, and Feature Analyst classified features best overall on the SPOT and Landsat imagery. Based on the entire suite of tasks we evaluated, Feature Extraction performed best overall on the Landsat and QuickBird imagery, while hand digitization performed best overall on the SPOT imagery, and eCognition performed worst overall on all three images. Error rates for both area measurements and feature classification were prohibitively high on Landsat imagery, while QuickBird was time and cost prohibitive for mapping large spatial extents. The SPOT imagery produced map products that were far more accurate than Landsat and did so at a far lower cost than QuickBird imagery. Consideration of degree of map accuracy required, costs associated with image acquisition, software, operator and computation time, and tradeoffs in the form of spatial extent versus resolution should all be considered when evaluating which combination of imagery and information-extraction method might best serve any given land use mapping project. When resources permit, attaining imagery that supports the highest classification and measurement accuracy possible is recommended.

Improving the performance of univariate control charts for abnormal detection and classification

NASA Astrophysics Data System (ADS)

Yiakopoulos, Christos; Koutsoudaki, Maria; Gryllias, Konstantinos; Antoniadis, Ioannis

2017-03-01

Bearing failures in rotating machinery can cause machine breakdown and economical loss, if no effective actions are taken on time. Therefore, it is of prime importance to detect accurately the presence of faults, especially at their early stage, to prevent sequent damage and reduce costly downtime. The machinery fault diagnosis follows a roadmap of data acquisition, feature extraction and diagnostic decision making, in which mechanical vibration fault feature extraction is the foundation and the key to obtain an accurate diagnostic result. A challenge in this area is the selection of the most sensitive features for various types of fault, especially when the characteristics of failures are difficult to be extracted. Thus, a plethora of complex data-driven fault diagnosis methods are fed by prominent features, which are extracted and reduced through traditional or modern algorithms. Since most of the available datasets are captured during normal operating conditions, the last decade a number of novelty detection methods, able to work when only normal data are available, have been developed. In this study, a hybrid method combining univariate control charts and a feature extraction scheme is introduced focusing towards an abnormal change detection and classification, under the assumption that measurements under normal operating conditions of the machinery are available. The feature extraction method integrates the morphological operators and the Morlet wavelets. The effectiveness of the proposed methodology is validated on two different experimental cases with bearing faults, demonstrating that the proposed approach can improve the fault detection and classification performance of conventional control charts.

An ensemble method for extracting adverse drug events from social media.

PubMed

Liu, Jing; Zhao, Songzheng; Zhang, Xiaodi

2016-06-01

Because adverse drug events (ADEs) are a serious health problem and a leading cause of death, it is of vital importance to identify them correctly and in a timely manner. With the development of Web 2.0, social media has become a large data source for information on ADEs. The objective of this study is to develop a relation extraction system that uses natural language processing techniques to effectively distinguish between ADEs and non-ADEs in informal text on social media. We develop a feature-based approach that utilizes various lexical, syntactic, and semantic features. Information-gain-based feature selection is performed to address high-dimensional features. Then, we evaluate the effectiveness of four well-known kernel-based approaches (i.e., subset tree kernel, tree kernel, shortest dependency path kernel, and all-paths graph kernel) and several ensembles that are generated by adopting different combination methods (i.e., majority voting, weighted averaging, and stacked generalization). All of the approaches are tested using three data sets: two health-related discussion forums and one general social networking site (i.e., Twitter). When investigating the contribution of each feature subset, the feature-based approach attains the best area under the receiver operating characteristics curve (AUC) values, which are 78.6%, 72.2%, and 79.2% on the three data sets. When individual methods are used, we attain the best AUC values of 82.1%, 73.2%, and 77.0% using the subset tree kernel, shortest dependency path kernel, and feature-based approach on the three data sets, respectively. When using classifier ensembles, we achieve the best AUC values of 84.5%, 77.3%, and 84.5% on the three data sets, outperforming the baselines. Our experimental results indicate that ADE extraction from social media can benefit from feature selection. With respect to the effectiveness of different feature subsets, lexical features and semantic features can enhance the ADE extraction capability. Kernel-based approaches, which can stay away from the feature sparsity issue, are qualified to address the ADE extraction problem. Combining different individual classifiers using suitable combination methods can further enhance the ADE extraction effectiveness. Copyright © 2016 Elsevier B.V. All rights reserved.

An approach to predict Sudden Cardiac Death (SCD) using time domain and bispectrum features from HRV signal.

PubMed

Houshyarifar, Vahid; Chehel Amirani, Mehdi

2016-08-12

In this paper we present a method to predict Sudden Cardiac Arrest (SCA) with higher order spectral (HOS) and linear (Time) features extracted from heart rate variability (HRV) signal. Predicting the occurrence of SCA is important in order to avoid the probability of Sudden Cardiac Death (SCD). This work is a challenge to predict five minutes before SCA onset. The method consists of four steps: pre-processing, feature extraction, feature reduction, and classification. In the first step, the QRS complexes are detected from the electrocardiogram (ECG) signal and then the HRV signal is extracted. In second step, bispectrum features of HRV signal and time-domain features are obtained. Six features are extracted from bispectrum and two features from time-domain. In the next step, these features are reduced to one feature by the linear discriminant analysis (LDA) technique. Finally, KNN and support vector machine-based classifiers are used to classify the HRV signals. We used two database named, MIT/BIH Sudden Cardiac Death (SCD) Database and Physiobank Normal Sinus Rhythm (NSR). In this work we achieved prediction of SCD occurrence for six minutes before the SCA with the accuracy over 91%.

50 CFR 218.236 - Requirements for reporting.

Code of Federal Regulations, 2012 CFR

2012-10-01

... Low Frequency Active (SURTASS LFA) Sonar § 218.236 Requirements for reporting. (a) The Holder of the..., and location of each vessel during each mission; (2) Information on sonar transmissions during each..., this report must contain an unclassified analysis of new passive sonar technologies and an assessment...

50 CFR 218.236 - Requirements for reporting.

Code of Federal Regulations, 2013 CFR

2013-10-01

... Low Frequency Active (SURTASS LFA) Sonar § 218.236 Requirements for reporting. (a) The Holder of the..., and location of each vessel during each mission; (2) Information on sonar transmissions during each..., this report must contain an unclassified analysis of new passive sonar technologies and an assessment...

50 CFR 218.236 - Requirements for reporting.

Code of Federal Regulations, 2014 CFR

2014-10-01

... Low Frequency Active (SURTASS LFA) Sonar § 218.236 Requirements for reporting. (a) The Holder of the..., and location of each vessel during each mission; (2) Information on sonar transmissions during each..., this report must contain an unclassified analysis of new passive sonar technologies and an assessment...

Automated Recognition of 3D Features in GPIR Images

NASA Technical Reports Server (NTRS)

Park, Han; Stough, Timothy; Fijany, Amir

2007-01-01

A method of automated recognition of three-dimensional (3D) features in images generated by ground-penetrating imaging radar (GPIR) is undergoing development. GPIR 3D images can be analyzed to detect and identify such subsurface features as pipes and other utility conduits. Until now, much of the analysis of GPIR images has been performed manually by expert operators who must visually identify and track each feature. The present method is intended to satisfy a need for more efficient and accurate analysis by means of algorithms that can automatically identify and track subsurface features, with minimal supervision by human operators. In this method, data from multiple sources (for example, data on different features extracted by different algorithms) are fused together for identifying subsurface objects. The algorithms of this method can be classified in several different ways. In one classification, the algorithms fall into three classes: (1) image-processing algorithms, (2) feature- extraction algorithms, and (3) a multiaxis data-fusion/pattern-recognition algorithm that includes a combination of machine-learning, pattern-recognition, and object-linking algorithms. The image-processing class includes preprocessing algorithms for reducing noise and enhancing target features for pattern recognition. The feature-extraction algorithms operate on preprocessed data to extract such specific features in images as two-dimensional (2D) slices of a pipe. Then the multiaxis data-fusion/ pattern-recognition algorithm identifies, classifies, and reconstructs 3D objects from the extracted features. In this process, multiple 2D features extracted by use of different algorithms and representing views along different directions are used to identify and reconstruct 3D objects. In object linking, which is an essential part of this process, features identified in successive 2D slices and located within a threshold radius of identical features in adjacent slices are linked in a directed-graph data structure. Relative to past approaches, this multiaxis approach offers the advantages of more reliable detections, better discrimination of objects, and provision of redundant information, which can be helpful in filling gaps in feature recognition by one of the component algorithms. The image-processing class also includes postprocessing algorithms that enhance identified features to prepare them for further scrutiny by human analysts (see figure). Enhancement of images as a postprocessing step is a significant departure from traditional practice, in which enhancement of images is a preprocessing step.

Submarine glacial landforms on the Bay of Fundy–northern Gulf of Maine continental shelf

USGS Publications Warehouse

Todd, B.J.; Shaw, J.; Valentine, Page C.

2016-01-01

The Bay of Fundy–northern Gulf of Maine region surrounds the southern part of Nova Scotia, encompassing, from west to east, the Bay of Fundy, Grand Manan Basin, German Bank, Browns Bank, Northeast Channel and northeastern Georges Bank (Fig. 1a, b). During the last glacial maximum (c. 24–20 14C ka BP), the SE margin of the Laurentide Ice Sheet (LIS) occupied the study area, the rest of the Gulf of Maine and the continental Scotian Shelf off Atlantic Canada (see Dyke et al. 2002, fig. 1; Shaw et al. 2006, fig. 8; Hundert & Piper 2008, fig. 16). Early mapping of the glaciated region on the Scotian Shelf using side-scan sonar imagery and seismic-reflection profiles revealed topographic features interpreted to be recessional moraines indicative of retreat of the LIS (King et al. 1972; King 1996). Subsequently, multibeam sonar seafloor mapping of local-scale glacial landforms on the inner Scotian Shelf off Halifax, Nova Scotia (Fig. 1b) provided further information on the dynamics of the advance and retreat of the ice sheet (Loncarevic et al.1994). Interpretation of seismic-reflection profiles across Georges Bank revealed that the surficial sediment is a veneer of glacial debris transported to Georges Bank by the LIS during the late Pleistocene from continental areas to the north (Shepard et al. 1934; Knott & Hoskins 1968; Schlee 1973; Twichell et al. 1987; Fader et al. 1988). Recent high-resolution multibeam sonar surveys of German Bank and the Bay of Fundy mapped a complex of ice-advance and ice-retreat features attributed to the activity of the LIS (Todd et al. 2007; Todd & Shaw 2012).

Pockmarks, fluid flow, and sediments outboard of the deformation front at the Cascadia Subduction Zone from analysis of multi-channel seismic and multi-beam sonar data

NASA Astrophysics Data System (ADS)

Gibson, J. C.; Carbotte, S. M.; Han, S.; Carton, H. D.; Canales, P.; Nedimovic, M. R.

2013-12-01

Evidence of active fluid flow and the nature of the sediment section near the Cascadia deformation front are explored using multi-channel (MCS) seismic and multi-beam sonar data collected in summer 2012 using the R/V Marcus G. Langseth during the Juan de Fuca Ridge to Trench Survey. The MCS data were collected along two full plate transects (the 'Oregon' and 'Washington' transects) and one trench parallel line using a 6600 cubic inch source, and an 8 km streamer with 636 channels (12.5 m spacing). The MCS data pre-stack processing sequence includes geometry definition, trace editing, F-K filter, and deconvolution. Velocity analysis is performed via semblance and constant velocity stacks in order to create a velocity model of the sediments and upper oceanic crust. The traces are then stacked, and post-stack time migrated. The sonar data were collected using the R/V Langseth's Kongsberg EM122 1°x1° multi-beam sonar with 288 beams and 432 total soundings across track. Using MB-system the sonar data are cleaned, and the bathymetry data are then gridded at 35 m, while the backscatter data are gridded at 15 m. From the high-resolution mapping data 48 pockmarks varying in diameter from 50 m - 1 km are identified within 60 km outboard of the deformation front. The surface expression of these large features in an area of heavy sedimentation is likely indicative of active fluid flow. In order to gain sub-seafloor perspective on these features the MCS data are draped below the bathymetry/backscatter grids using QPS Fledermaus. From this perspective, specific locations for detailed velocity and attribute analysis of the sediment section are chosen. Sediment velocity and attribute analysis also provide insight into apparent differences in the sediment section and décollement formation along the Oregon and Washington plate transects. While both lines intersect areas of dense pockmark concentration, the area around the Oregon transect has been shown to contain a continuous positive polarity sedimentary layer that is capping fluid expulsion above a reverse polarity protodécollement (e.g. Cochrane et al., 1994, JGR, 99, pp. 7033-7043). A décollement within the sediment section is not present along the Washington line (e.g. Gutscher et al., 2001, Geology, 29, pp. 379-382). However, this line does intersect the 'Bare' outcrops to the west, which have been shown to provide fluid recharge and discharge pathways for convective cooling of the crust (e.g. Fisher et al., 2003, Nature, 421, pp. 618-621). Detailed velocity models constructed from the MCS data will be used to investigate these regional differences. The location of the pockmarks and corresponding sediment properties will also be explored relative to regional variations in the structure of the deformation front and location of intraplate and interplate faulting.

Biometric sample extraction using Mahalanobis distance in Cardioid based graph using electrocardiogram signals.

PubMed

Sidek, Khairul; Khali, Ibrahim

2012-01-01

In this paper, a person identification mechanism implemented with Cardioid based graph using electrocardiogram (ECG) is presented. Cardioid based graph has given a reasonably good classification accuracy in terms of differentiating between individuals. However, the current feature extraction method using Euclidean distance could be further improved by using Mahalanobis distance measurement producing extracted coefficients which takes into account the correlations of the data set. Identification is then done by applying these extracted features to Radial Basis Function Network. A total of 30 ECG data from MITBIH Normal Sinus Rhythm database (NSRDB) and MITBIH Arrhythmia database (MITDB) were used for development and evaluation purposes. Our experimentation results suggest that the proposed feature extraction method has significantly increased the classification performance of subjects in both databases with accuracy from 97.50% to 99.80% in NSRDB and 96.50% to 99.40% in MITDB. High sensitivity, specificity and positive predictive value of 99.17%, 99.91% and 99.23% for NSRDB and 99.30%, 99.90% and 99.40% for MITDB also validates the proposed method. This result also indicates that the right feature extraction technique plays a vital role in determining the persistency of the classification accuracy for Cardioid based person identification mechanism.

A Fault Alarm and Diagnosis Method Based on Sensitive Parameters and Support Vector Machine

NASA Astrophysics Data System (ADS)

Zhang, Jinjie; Yao, Ziyun; Lv, Zhiquan; Zhu, Qunxiong; Xu, Fengtian; Jiang, Zhinong

2015-08-01

Study on the extraction of fault feature and the diagnostic technique of reciprocating compressor is one of the hot research topics in the field of reciprocating machinery fault diagnosis at present. A large number of feature extraction and classification methods have been widely applied in the related research, but the practical fault alarm and the accuracy of diagnosis have not been effectively improved. Developing feature extraction and classification methods to meet the requirements of typical fault alarm and automatic diagnosis in practical engineering is urgent task. The typical mechanical faults of reciprocating compressor are presented in the paper, and the existing data of online monitoring system is used to extract fault feature parameters within 15 types in total; the inner sensitive connection between faults and the feature parameters has been made clear by using the distance evaluation technique, also sensitive characteristic parameters of different faults have been obtained. On this basis, a method based on fault feature parameters and support vector machine (SVM) is developed, which will be applied to practical fault diagnosis. A better ability of early fault warning has been proved by the experiment and the practical fault cases. Automatic classification by using the SVM to the data of fault alarm has obtained better diagnostic accuracy.

Target detection method by airborne and spaceborne images fusion based on past images

NASA Astrophysics Data System (ADS)

Chen, Shanjing; Kang, Qing; Wang, Zhenggang; Shen, ZhiQiang; Pu, Huan; Han, Hao; Gu, Zhongzheng

2017-11-01

To solve the problem that remote sensing target detection method has low utilization rate of past remote sensing data on target area, and can not recognize camouflage target accurately, a target detection method by airborne and spaceborne images fusion based on past images is proposed in this paper. The target area's past of space remote sensing image is taken as background. The airborne and spaceborne remote sensing data is fused and target feature is extracted by the means of airborne and spaceborne images registration, target change feature extraction, background noise suppression and artificial target feature extraction based on real-time aerial optical remote sensing image. Finally, the support vector machine is used to detect and recognize the target on feature fusion data. The experimental results have established that the proposed method combines the target area change feature of airborne and spaceborne remote sensing images with target detection algorithm, and obtains fine detection and recognition effect on camouflage and non-camouflage targets.

The method of micro-motion cycle feature extraction based on confidence coefficient evaluation criteria

NASA Astrophysics Data System (ADS)

Tang, Chuanzi; Ren, Hongmei; Bo, Li; Jing, Huang

2017-11-01

In radar target recognition, the micro motion characteristics of target is one of the characteristics that researchers pay attention to at home and abroad, in which the characteristics of target precession cycle is one of the important characteristics of target movement characteristics. Periodic feature extraction methods have been studied for years, the complex shape of the target and the scattering center stack lead to random fluctuations of the RCS. These random fluctuations also exist certain periodicity, which has a great influence on the target recognition result. In order to solve the problem, this paper proposes a extraction method of micro-motion cycle feature based on confidence coefficient evaluation criteria.

Feature Extraction of Electronic Nose Signals Using QPSO-Based Multiple KFDA Signal Processing

PubMed Central

Wen, Tailai; Huang, Daoyu; Lu, Kun; Deng, Changjian; Zeng, Tanyue; Yu, Song; He, Zhiyi

2018-01-01

The aim of this research was to enhance the classification accuracy of an electronic nose (E-nose) in different detecting applications. During the learning process of the E-nose to predict the types of different odors, the prediction accuracy was not quite satisfying because the raw features extracted from sensors’ responses were regarded as the input of a classifier without any feature extraction processing. Therefore, in order to obtain more useful information and improve the E-nose’s classification accuracy, in this paper, a Weighted Kernels Fisher Discriminant Analysis (WKFDA) combined with Quantum-behaved Particle Swarm Optimization (QPSO), i.e., QWKFDA, was presented to reprocess the original feature matrix. In addition, we have also compared the proposed method with quite a few previously existing ones including Principal Component Analysis (PCA), Locality Preserving Projections (LPP), Fisher Discriminant Analysis (FDA) and Kernels Fisher Discriminant Analysis (KFDA). Experimental results proved that QWKFDA is an effective feature extraction method for E-nose in predicting the types of wound infection and inflammable gases, which shared much higher classification accuracy than those of the contrast methods. PMID:29382146

Feature Extraction of Electronic Nose Signals Using QPSO-Based Multiple KFDA Signal Processing.

PubMed

Wen, Tailai; Yan, Jia; Huang, Daoyu; Lu, Kun; Deng, Changjian; Zeng, Tanyue; Yu, Song; He, Zhiyi

2018-01-29

The aim of this research was to enhance the classification accuracy of an electronic nose (E-nose) in different detecting applications. During the learning process of the E-nose to predict the types of different odors, the prediction accuracy was not quite satisfying because the raw features extracted from sensors' responses were regarded as the input of a classifier without any feature extraction processing. Therefore, in order to obtain more useful information and improve the E-nose's classification accuracy, in this paper, a Weighted Kernels Fisher Discriminant Analysis (WKFDA) combined with Quantum-behaved Particle Swarm Optimization (QPSO), i.e., QWKFDA, was presented to reprocess the original feature matrix. In addition, we have also compared the proposed method with quite a few previously existing ones including Principal Component Analysis (PCA), Locality Preserving Projections (LPP), Fisher Discriminant Analysis (FDA) and Kernels Fisher Discriminant Analysis (KFDA). Experimental results proved that QWKFDA is an effective feature extraction method for E-nose in predicting the types of wound infection and inflammable gases, which shared much higher classification accuracy than those of the contrast methods.

50 CFR 216.170 - Specified activity and specified geographical region.

Code of Federal Regulations, 2013 CFR

2013-10-01

... incidental to the following activities: (1) The use of the following mid-frequency active sonar (MFAS) and high frequency active sonar (HFAS) sources, or similar sources, for Navy training activities (estimated amounts below): (1) The use of the following mid-frequency active sonar (MFAS) and high frequency active...

50 CFR 216.240 - Specified activity and specified geographical region.

Code of Federal Regulations, 2012 CFR

2012-10-01

... Active Sonar Training (AFAST) § 216.240 Specified activity and specified geographical region. (a... Navy is only authorized if it occurs incidental to the use of the following mid-frequency active sonar (MFAS) sources, high frequency active sonar (HFAS) sources, explosive sonobuoys, or similar sources, for...

50 CFR 216.240 - Specified activity and specified geographical region.

Code of Federal Regulations, 2013 CFR

2013-10-01

... Active Sonar Training (AFAST) § 216.240 Specified activity and specified geographical region. (a... Navy is only authorized if it occurs incidental to the use of the following mid-frequency active sonar (MFAS) sources, high frequency active sonar (HFAS) sources, explosive sonobuoys, or similar sources, for...

50 CFR 216.170 - Specified activity and specified geographical region.

Code of Federal Regulations, 2012 CFR

2012-10-01

... incidental to the following activities: (1) The use of the following mid-frequency active sonar (MFAS) and high frequency active sonar (HFAS) sources, or similar sources, for Navy training activities (estimated amounts below): (1) The use of the following mid-frequency active sonar (MFAS) and high frequency active...

Investigation of the Impact of Sonar Transmission on Fisheries and Habitat in the U.S. Navy’s USWTR: Summary of Stakeholder Concerns and Appropriate Research Areas

DTIC Science & Technology

2007-09-01

sonar transmission on fisheries and habitat in the U.S. Navy’s USWTR: Summary of stakeholder concerns and appropriate research areas by Dr...SUBTITLE: Title (Mix case letters) Investigation of the impact of sonar transmission on fisheries and habitat in the U.S. Navy’s USWTR: Summary of...table of specific public comments is included. 15. NUMBER OF PAGES 30 14. SUBJECT TERMS sonar, USWTR, Navy, fish, fishery , fisherman, behavior

Sonar Test and Test Instrumentation Support.

DTIC Science & Technology

1976-11-10

AD-AI0 � TEXAS UNIV AT AUSTIN APPLIED RESEARCH LARS F/6 17/1 SONAR TEST AND TEST INSTRUMENTATION SUPPDRT.1U) NoV 76 0 0 BAKER N00140-76-C-&687...UNCLASSIFIED_ NL i 0 00 THE UNIVERSITY OF TEXAS AT AUSTIN 10 November 1976 Copy No. 3 SONAR TEST AND TEST INSTRUMENTATION SUPPORT Quarterly Progress...8217 mi a - I TABLE OF CONTENTS A pag. I. INTRODUCTION 1 II. AN/FQM-IO(V) SONAR TEST SET FIELD SUPPORT 3 A. Introduction 3 B. Visit to NAVSHIPYD PEARL 3 C

Potential Population Consequences of Active Sonar Disturbance in Atlantic Herring: Estimating the Maximum Risk.

PubMed

Sivle, Lise Doksæter; Kvadsheim, Petter Helgevold; Ainslie, Michael

2016-01-01

Effects of noise on fish populations may be predicted by the population consequence of acoustic disturbance (PCAD) model. We have predicted the potential risk of population disturbance when the highest sound exposure level (SEL) at which adult herring do not respond to naval sonar (SEL(0)) is exceeded. When the population density is low (feeding), the risk is low even at high sonar source levels and long-duration exercises (>24 h). With densely packed populations (overwintering), a sonar exercise might expose the entire population to levels >SEL(0) within a 24-h exercise period. However, the disturbance will be short and the response threshold used here is highly conservative. It is therefore unlikely that naval sonar will significantly impact the herring population.

Feature extraction based on extended multi-attribute profiles and sparse autoencoder for remote sensing image classification

NASA Astrophysics Data System (ADS)

Teffahi, Hanane; Yao, Hongxun; Belabid, Nasreddine; Chaib, Souleyman

2018-02-01

The satellite images with very high spatial resolution have been recently widely used in image classification topic as it has become challenging task in remote sensing field. Due to a number of limitations such as the redundancy of features and the high dimensionality of the data, different classification methods have been proposed for remote sensing images classification particularly the methods using feature extraction techniques. This paper propose a simple efficient method exploiting the capability of extended multi-attribute profiles (EMAP) with sparse autoencoder (SAE) for remote sensing image classification. The proposed method is used to classify various remote sensing datasets including hyperspectral and multispectral images by extracting spatial and spectral features based on the combination of EMAP and SAE by linking them to kernel support vector machine (SVM) for classification. Experiments on new hyperspectral image "Huston data" and multispectral image "Washington DC data" shows that this new scheme can achieve better performance of feature learning than the primitive features, traditional classifiers and ordinary autoencoder and has huge potential to achieve higher accuracy for classification in short running time.

Motor Fault Diagnosis Based on Short-time Fourier Transform and Convolutional Neural Network

NASA Astrophysics Data System (ADS)

Wang, Li-Hua; Zhao, Xiao-Ping; Wu, Jia-Xin; Xie, Yang-Yang; Zhang, Yong-Hong

2017-11-01

With the rapid development of mechanical equipment, the mechanical health monitoring field has entered the era of big data. However, the method of manual feature extraction has the disadvantages of low efficiency and poor accuracy, when handling big data. In this study, the research object was the asynchronous motor in the drivetrain diagnostics simulator system. The vibration signals of different fault motors were collected. The raw signal was pretreated using short time Fourier transform (STFT) to obtain the corresponding time-frequency map. Then, the feature of the time-frequency map was adaptively extracted by using a convolutional neural network (CNN). The effects of the pretreatment method, and the hyper parameters of network diagnostic accuracy, were investigated experimentally. The experimental results showed that the influence of the preprocessing method is small, and that the batch-size is the main factor affecting accuracy and training efficiency. By investigating feature visualization, it was shown that, in the case of big data, the extracted CNN features can represent complex mapping relationships between signal and health status, and can also overcome the prior knowledge and engineering experience requirement for feature extraction, which is used by traditional diagnosis methods. This paper proposes a new method, based on STFT and CNN, which can complete motor fault diagnosis tasks more intelligently and accurately.

Multiple feature extraction by using simultaneous wavelet transforms

NASA Astrophysics Data System (ADS)

Mazzaferri, Javier; Ledesma, Silvia; Iemmi, Claudio

2003-07-01

We propose here a method to optically perform multiple feature extraction by using wavelet transforms. The method is based on obtaining the optical correlation by means of a Vander Lugt architecture, where the scene and the filter are displayed on spatial light modulators (SLMs). Multiple phase filters containing the information about the features that we are interested in extracting are designed and then displayed on an SLM working in phase mostly mode. We have designed filters to simultaneously detect edges and corners or different characteristic frequencies contained in the input scene. Simulated and experimental results are shown.

Method for shaping and aiming narrow beams. [sonar mapping and target identification

NASA Technical Reports Server (NTRS)

Heyser, R. C. (Inventor)

1981-01-01

A sonar method and apparatus is discribed which utilizes a linear frequency chirp in a transmitter/receiver having a correlator to synthesize a narrow beamwidth pattern from otherwise broadbeam transducers when there is relative velocity between the transmitter/receiver and the target. The chirp is so produced in a generator in bandwidth, B, and time, T, as to produce a time bandwidth product, TB, that is increased for a narrower angle. A replica of the chirp produced in a generator is time delayed and Doppler shifted for use as a reference in the receiver for correlation of received chirps from targets. This reference is Doppler shifted to select targets preferentially, thereby to not only synthesize a narrow beam but also aim the beam in azimuth and elevation.

Comparing phase-sensitive and phase-insensitive echolocation target images using a monaural audible sonar.

PubMed

Kuc, Roman

2018-04-01

This paper describes phase-sensitive and phase-insensitive processing of monaural echolocation waveforms to generate target maps. Composite waveforms containing both the emission and echoes are processed to estimate the target impulse response using an audible sonar. Phase-sensitive processing yields the composite signal envelope, while phase-insensitive processing that starts with the composite waveform power spectrum yields the envelope of the autocorrelation function. Analysis and experimental verification show that multiple echoes form an autocorrelation function that produces near-range phantom-reflector artifacts. These artifacts interfere with true target echoes when the first true echo occurs at a time that is less than the total duration of the target echoes. Initial comparison of phase-sensitive and phase-insensitive maps indicates that both display important target features, indicating that phase is not vital. A closer comparison illustrates the improved resolution of phase-sensitive processing, the near-range phantom-reflectors produced by phase-insensitive processing, and echo interference and multiple reflection artifacts that were independent of the processing.

Recommendations for improved and coherent acquisition and processing of backscatter data from seafloor-mapping sonars

NASA Astrophysics Data System (ADS)

Lamarche, Geoffroy; Lurton, Xavier

2018-06-01

Multibeam echosounders are becoming widespread for the purposes of seafloor bathymetry mapping, but the acquisition and the use of seafloor backscatter measurements, acquired simultaneously with the bathymetric data, are still insufficiently understood, controlled and standardized. This presents an obstacle to well-accepted, standardized analysis and application by end users. The Marine Geological and Biological Habitat Mapping group (Geohab.org) has long recognized the need for better coherence and common agreement on acquisition, processing and interpretation of seafloor backscatter data, and established the Backscatter Working Group (BSWG) in May 2013. This paper presents an overview of this initiative, the mandate, structure and program of the working group, and a synopsis of the BSWG Guidelines and Recommendations to date. The paper includes (1) an overview of the current status in sensors and techniques available in seafloor backscatter data from multibeam sonars; (2) the presentation of the BSWG structure and results; (3) recommendations to operators, end-users, sonar manufacturers, and software developers using sonar backscatter for seafloor-mapping applications, for best practice methods and approaches for data acquisition and processing; and (4) a discussion on the development needs for future systems and data processing. We propose for the first time a nomenclature of backscatter processing levels that affords a means to accurately and efficiently describe the data processing status, and to facilitate comparisons of final products from various origins.

Mobile sailing robot for automatic estimation of fish density and monitoring water quality

PubMed Central

2013-01-01

Introduction The paper presents the methodology and the algorithm developed to analyze sonar images focused on fish detection in small water bodies and measurement of their parameters: volume, depth and the GPS location. The final results are stored in a table and can be exported to any numerical environment for further analysis. Material and method The measurement method for estimating the number of fish using the automatic robot is based on a sequential calculation of the number of occurrences of fish on the set trajectory. The data analysis from the sonar concerned automatic recognition of fish using the methods of image analysis and processing. Results Image analysis algorithm, a mobile robot together with its control in the 2.4 GHz band and full cryptographic communication with the data archiving station was developed as part of this study. For the three model fish ponds where verification of fish catches was carried out (548, 171 and 226 individuals), the measurement error for the described method was not exceeded 8%. Summary Created robot together with the developed software has features for remote work also in the variety of harsh weather and environmental conditions, is fully automated and can be remotely controlled using Internet. Designed system enables fish spatial location (GPS coordinates and the depth). The purpose of the robot is a non-invasive measurement of the number of fish in water reservoirs and a measurement of the quality of drinking water consumed by humans, especially in situations where local sources of pollution could have a significant impact on the quality of water collected for water treatment for people and when getting to these places is difficult. The systematically used robot equipped with the appropriate sensors, can be part of early warning system against the pollution of water used by humans (drinking water, natural swimming pools) which can be dangerous for their health. PMID:23815984

Competitive region orientation code for palmprint verification and identification

NASA Astrophysics Data System (ADS)

Tang, Wenliang

2015-11-01

Orientation features of the palmprint have been widely investigated in coding-based palmprint-recognition methods. Conventional orientation-based coding methods usually used discrete filters to extract the orientation feature of palmprint. However, in real operations, the orientations of the filter usually are not consistent with the lines of the palmprint. We thus propose a competitive region orientation-based coding method. Furthermore, an effective weighted balance scheme is proposed to improve the accuracy of the extracted region orientation. Compared with conventional methods, the region orientation of the palmprint extracted using the proposed method can precisely and robustly describe the orientation feature of the palmprint. Extensive experiments on the baseline PolyU and multispectral palmprint databases are performed and the results show that the proposed method achieves a promising performance in comparison to conventional state-of-the-art orientation-based coding methods in both palmprint verification and identification.

[Lithology feature extraction of CASI hyperspectral data based on fractal signal algorithm].

PubMed

Tang, Chao; Chen, Jian-Ping; Cui, Jing; Wen, Bo-Tao

2014-05-01

Hyperspectral data is characterized by combination of image and spectrum and large data volume dimension reduction is the main research direction. Band selection and feature extraction is the primary method used for this objective. In the present article, the authors tested methods applied for the lithology feature extraction from hyperspectral data. Based on the self-similarity of hyperspectral data, the authors explored the application of fractal algorithm to lithology feature extraction from CASI hyperspectral data. The "carpet method" was corrected and then applied to calculate the fractal value of every pixel in the hyperspectral data. The results show that fractal information highlights the exposed bedrock lithology better than the original hyperspectral data The fractal signal and characterized scale are influenced by the spectral curve shape, the initial scale selection and iteration step. At present, research on the fractal signal of spectral curve is rare, implying the necessity of further quantitative analysis and investigation of its physical implications.

n-SIFT: n-dimensional scale invariant feature transform.

PubMed

Cheung, Warren; Hamarneh, Ghassan

2009-09-01

We propose the n-dimensional scale invariant feature transform (n-SIFT) method for extracting and matching salient features from scalar images of arbitrary dimensionality, and compare this method's performance to other related features. The proposed features extend the concepts used for 2-D scalar images in the computer vision SIFT technique for extracting and matching distinctive scale invariant features. We apply the features to images of arbitrary dimensionality through the use of hyperspherical coordinates for gradients and multidimensional histograms to create the feature vectors. We analyze the performance of a fully automated multimodal medical image matching technique based on these features, and successfully apply the technique to determine accurate feature point correspondence between pairs of 3-D MRI images and dynamic 3D + time CT data.

Improving Geologic Mapping of Mid-ocean Ridges by Integrating sonar and Visual Observations through Seafloor Classification by Machine-learning Systems

NASA Astrophysics Data System (ADS)

White, S. M.; McClinton, J. T.

2011-12-01

Beyond the ability of modern near-bottom sonar systems to deliver air-photo-like images of the seafloor to help guide fieldwork, there is a tremendous amount of information hidden within sonar data that is rarely exploited for geologic mapping. Seafloor texture, backscatter amplitude, seafloor slope and roughness data can provide clues about seafloor geology but not straightforward to interpret. We present techniques for seafloor classification in volcanic terrains that integrate the capability of high-resolution, near-bottom sonar instruments to cover extensive areas of seafloor with the ability of visual mapping to discriminate differences in volcanic terrain. These techniques are adapted from the standard practices of terrestrial remote-sensing for use in the deep seafloor volcanic environment. A combination of sonar backscatter and bathymetry is used to supplement the direct seafloor visual observations by geologists to make quasi-geologic thematic maps that are consistent, objective, and most importantly spatially complete. We have taken two approaches to producing thematic maps of the seafloor for the accurate mapping of fine-scale lava morphology (e.g. pillow, lobate and sheet lava) and for the differentiation of distinct seafloor terrain types on a larger scale (e.g. hummocky or smooth). Mapping lava morphology is most accurate using fuzzy logic capable of making inferences between similar morphotypes (e.g. pillow and lobate) and where high-resolution side-scan and bathymetry data coexist. We present examples of lava morphology maps from the Galápagos Spreading Center that show the results from several analyses using different types of input data. Lava morphology is an important source of information on volcanic emplacement and eruptive dynamics. Terrain modeling can be accomplished at any resolution level, depending on the desired use of the model. For volcanic processes, input data needs to be at the appropriate scale to resolve individual volcanic features on the seafloor (e.g. small haystacks and lava channels). We present examples from the East Pacific Rise, which shows that the number of volcanic terrains differs from the tectonic provinces defined by following the spreading axis. Our terrain modeling suggests that differences in ocean crust construction and evolution can be meaningfully identified and explored without a priori assumptions about the geologic processes in a given region.

Lack of behavioural responses of humpback whales (Megaptera novaeangliae) indicate limited effectiveness of sonar mitigation

PubMed Central

Kvadsheim, Petter H.; Lam, Frans-Peter A.; von Benda-Beckmann, Alexander M.; Sivle, Lise D.; Visser, Fleur; Curé, Charlotte; Tyack, Peter L.; Miller, Patrick J. O.

2017-01-01

ABSTRACT Exposure to underwater sound can cause permanent hearing loss and other physiological effects in marine animals. To reduce this risk, naval sonars are sometimes gradually increased in intensity at the start of transmission (‘ramp-up’). Here, we conducted experiments in which tagged humpback whales were approached with a ship to test whether a sonar operation preceded by ramp-up reduced three risk indicators – maximum sound pressure level (SPLmax), cumulative sound exposure level (SELcum) and minimum source–whale range (Rmin) – compared with a sonar operation not preceded by ramp-up. Whales were subject to one no-sonar control session and either two successive ramp-up sessions (RampUp1, RampUp2) or a ramp-up session (RampUp1) and a full-power session (FullPower). Full-power sessions were conducted only twice; for other whales we used acoustic modelling that assumed transmission of the full-power sequence during their no-sonar control. Averaged over all whales, risk indicators in RampUp1 (n=11) differed significantly from those in FullPower (n=12) by −3.0 dB (SPLmax), −2.0 dB (SELcum) and +168 m (Rmin), but not significantly from those in RampUp2 (n=9). Only five whales in RampUp1, four whales in RampUp2 and none in FullPower or control sessions avoided the sound source. For RampUp1, we found statistically significant differences in risk indicators between whales that avoided the sonar and whales that did not: −4.7 dB (SPLmax), −3.4 dB (SELcum) and +291 m (Rmin). In contrast, for RampUp2, these differences were smaller and not significant. This study suggests that sonar ramp-up has a positive but limited mitigative effect for humpback whales overall, but that ramp-up can reduce the risk of harm more effectively in situations when animals are more responsive and likely to avoid the sonar, e.g. owing to novelty of the stimulus, when they are in the path of an approaching sonar ship. PMID:29141878

Automatic building extraction from LiDAR data fusion of point and grid-based features

NASA Astrophysics Data System (ADS)

Du, Shouji; Zhang, Yunsheng; Zou, Zhengrong; Xu, Shenghua; He, Xue; Chen, Siyang

2017-08-01

This paper proposes a method for extracting buildings from LiDAR point cloud data by combining point-based and grid-based features. To accurately discriminate buildings from vegetation, a point feature based on the variance of normal vectors is proposed. For a robust building extraction, a graph cuts algorithm is employed to combine the used features and consider the neighbor contexture information. As grid feature computing and a graph cuts algorithm are performed on a grid structure, a feature-retained DSM interpolation method is proposed in this paper. The proposed method is validated by the benchmark ISPRS Test Project on Urban Classification and 3D Building Reconstruction and compared to the state-art-of-the methods. The evaluation shows that the proposed method can obtain a promising result both at area-level and at object-level. The method is further applied to the entire ISPRS dataset and to a real dataset of the Wuhan City. The results show a completeness of 94.9% and a correctness of 92.2% at the per-area level for the former dataset and a completeness of 94.4% and a correctness of 95.8% for the latter one. The proposed method has a good potential for large-size LiDAR data.

Texture feature extraction based on wavelet transform and gray-level co-occurrence matrices applied to osteosarcoma diagnosis.

PubMed

Hu, Shan; Xu, Chao; Guan, Weiqiao; Tang, Yong; Liu, Yana

2014-01-01

Osteosarcoma is the most common malignant bone tumor among children and adolescents. In this study, image texture analysis was made to extract texture features from bone CR images to evaluate the recognition rate of osteosarcoma. To obtain the optimal set of features, Sym4 and Db4 wavelet transforms and gray-level co-occurrence matrices were applied to the image, with statistical methods being used to maximize the feature selection. To evaluate the performance of these methods, a support vector machine algorithm was used. The experimental results demonstrated that the Sym4 wavelet had a higher classification accuracy (93.44%) than the Db4 wavelet with respect to osteosarcoma occurrence in the epiphysis, whereas the Db4 wavelet had a higher classification accuracy (96.25%) for osteosarcoma occurrence in the diaphysis. Results including accuracy, sensitivity, specificity and ROC curves obtained using the wavelets were all higher than those obtained using the features derived from the GLCM method. It is concluded that, a set of texture features can be extracted from the wavelets and used in computer-aided osteosarcoma diagnosis systems. In addition, this study also confirms that multi-resolution analysis is a useful tool for texture feature extraction during bone CR image processing.

Sound localization by echolocating bats

NASA Astrophysics Data System (ADS)

Aytekin, Murat

Echolocating bats emit ultrasonic vocalizations and listen to echoes reflected back from objects in the path of the sound beam to build a spatial representation of their surroundings. Important to understanding the representation of space through echolocation are detailed studies of the cues used for localization, the sonar emission patterns and how this information is assembled. This thesis includes three studies, one on the directional properties of the sonar receiver, one on the directional properties of the sonar transmitter, and a model that demonstrates the role of action in building a representation of auditory space. The general importance of this work to a broader understanding of spatial localization is discussed. Investigations of the directional properties of the sonar receiver reveal that interaural level difference and monaural spectral notch cues are both dependent on sound source azimuth and elevation. This redundancy allows flexibility that an echolocating bat may need when coping with complex computational demands for sound localization. Using a novel method to measure bat sonar emission patterns from freely behaving bats, I show that the sonar beam shape varies between vocalizations. Consequently, the auditory system of a bat may need to adapt its computations to accurately localize objects using changing acoustic inputs. Extra-auditory signals that carry information about pinna position and beam shape are required for auditory localization of sound sources. The auditory system must learn associations between extra-auditory signals and acoustic spatial cues. Furthermore, the auditory system must adapt to changes in acoustic input that occur with changes in pinna position and vocalization parameters. These demands on the nervous system suggest that sound localization is achieved through the interaction of behavioral control and acoustic inputs. A sensorimotor model demonstrates how an organism can learn space through auditory-motor contingencies. The model also reveals how different aspects of sound localization, such as experience-dependent acquisition, adaptation, and extra-auditory influences, can be brought together under a comprehensive framework. This thesis presents a foundation for understanding the representation of auditory space that builds upon acoustic cues, motor control, and learning dynamic associations between action and auditory inputs.

A Unified Analysis of Structured Sonar-terrain Data using Bayesian Functional Mixed Models.

PubMed

Zhu, Hongxiao; Caspers, Philip; Morris, Jeffrey S; Wu, Xiaowei; Müller, Rolf

2018-01-01

Sonar emits pulses of sound and uses the reflected echoes to gain information about target objects. It offers a low cost, complementary sensing modality for small robotic platforms. While existing analytical approaches often assume independence across echoes, real sonar data can have more complicated structures due to device setup or experimental design. In this paper, we consider sonar echo data collected from multiple terrain substrates with a dual-channel sonar head. Our goals are to identify the differential sonar responses to terrains and study the effectiveness of this dual-channel design in discriminating targets. We describe a unified analytical framework that achieves these goals rigorously, simultaneously, and automatically. The analysis was done by treating the echo envelope signals as functional responses and the terrain/channel information as covariates in a functional regression setting. We adopt functional mixed models that facilitate the estimation of terrain and channel effects while capturing the complex hierarchical structure in data. This unified analytical framework incorporates both Gaussian models and robust models. We fit the models using a full Bayesian approach, which enables us to perform multiple inferential tasks under the same modeling framework, including selecting models, estimating the effects of interest, identifying significant local regions, discriminating terrain types, and describing the discriminatory power of local regions. Our analysis of the sonar-terrain data identifies time regions that reflect differential sonar responses to terrains. The discriminant analysis suggests that a multi- or dual-channel design achieves target identification performance comparable with or better than a single-channel design.

SONAR: A High-Throughput Pipeline for Inferring Antibody Ontogenies from Longitudinal Sequencing of B Cell Transcripts.

PubMed

Schramm, Chaim A; Sheng, Zizhang; Zhang, Zhenhai; Mascola, John R; Kwong, Peter D; Shapiro, Lawrence

2016-01-01

The rapid advance of massively parallel or next-generation sequencing technologies has made possible the characterization of B cell receptor repertoires in ever greater detail, and these developments have triggered a proliferation of software tools for processing and annotating these data. Of especial interest, however, is the capability to track the development of specific antibody lineages across time, which remains beyond the scope of most current programs. We have previously reported on the use of techniques such as inter- and intradonor analysis and CDR3 tracing to identify transcripts related to an antibody of interest. Here, we present Software for the Ontogenic aNalysis of Antibody Repertoires (SONAR), capable of automating both general repertoire analysis and specialized techniques for investigating specific lineages. SONAR annotates next-generation sequencing data, identifies transcripts in a lineage of interest, and tracks lineage development across multiple time points. SONAR also generates figures, such as identity-divergence plots and longitudinal phylogenetic "birthday" trees, and provides interfaces to other programs such as DNAML and BEAST. SONAR can be downloaded as a ready-to-run Docker image or manually installed on a local machine. In the latter case, it can also be configured to take advantage of a high-performance computing cluster for the most computationally intensive steps, if available. In summary, this software provides a useful new tool for the processing of large next-generation sequencing datasets and the ontogenic analysis of neutralizing antibody lineages. SONAR can be found at https://github.com/scharch/SONAR, and the Docker image can be obtained from https://hub.docker.com/r/scharch/sonar/.

A Unified Analysis of Structured Sonar-terrain Data using Bayesian Functional Mixed Models

PubMed Central

Zhu, Hongxiao; Caspers, Philip; Morris, Jeffrey S.; Wu, Xiaowei; Müller, Rolf

2017-01-01

Sonar emits pulses of sound and uses the reflected echoes to gain information about target objects. It offers a low cost, complementary sensing modality for small robotic platforms. While existing analytical approaches often assume independence across echoes, real sonar data can have more complicated structures due to device setup or experimental design. In this paper, we consider sonar echo data collected from multiple terrain substrates with a dual-channel sonar head. Our goals are to identify the differential sonar responses to terrains and study the effectiveness of this dual-channel design in discriminating targets. We describe a unified analytical framework that achieves these goals rigorously, simultaneously, and automatically. The analysis was done by treating the echo envelope signals as functional responses and the terrain/channel information as covariates in a functional regression setting. We adopt functional mixed models that facilitate the estimation of terrain and channel effects while capturing the complex hierarchical structure in data. This unified analytical framework incorporates both Gaussian models and robust models. We fit the models using a full Bayesian approach, which enables us to perform multiple inferential tasks under the same modeling framework, including selecting models, estimating the effects of interest, identifying significant local regions, discriminating terrain types, and describing the discriminatory power of local regions. Our analysis of the sonar-terrain data identifies time regions that reflect differential sonar responses to terrains. The discriminant analysis suggests that a multi- or dual-channel design achieves target identification performance comparable with or better than a single-channel design. PMID:29749977

Biometric recognition via texture features of eye movement trajectories in a visual searching task.

PubMed

Li, Chunyong; Xue, Jiguo; Quan, Cheng; Yue, Jingwei; Zhang, Chenggang

2018-01-01

Biometric recognition technology based on eye-movement dynamics has been in development for more than ten years. Different visual tasks, feature extraction and feature recognition methods are proposed to improve the performance of eye movement biometric system. However, the correct identification and verification rates, especially in long-term experiments, as well as the effects of visual tasks and eye trackers' temporal and spatial resolution are still the foremost considerations in eye movement biometrics. With a focus on these issues, we proposed a new visual searching task for eye movement data collection and a new class of eye movement features for biometric recognition. In order to demonstrate the improvement of this visual searching task being used in eye movement biometrics, three other eye movement feature extraction methods were also tested on our eye movement datasets. Compared with the original results, all three methods yielded better results as expected. In addition, the biometric performance of these four feature extraction methods was also compared using the equal error rate (EER) and Rank-1 identification rate (Rank-1 IR), and the texture features introduced in this paper were ultimately shown to offer some advantages with regard to long-term stability and robustness over time and spatial precision. Finally, the results of different combinations of these methods with a score-level fusion method indicated that multi-biometric methods perform better in most cases.

Biometric recognition via texture features of eye movement trajectories in a visual searching task

PubMed Central

Li, Chunyong; Xue, Jiguo; Quan, Cheng; Yue, Jingwei

2018-01-01

Biometric recognition technology based on eye-movement dynamics has been in development for more than ten years. Different visual tasks, feature extraction and feature recognition methods are proposed to improve the performance of eye movement biometric system. However, the correct identification and verification rates, especially in long-term experiments, as well as the effects of visual tasks and eye trackers’ temporal and spatial resolution are still the foremost considerations in eye movement biometrics. With a focus on these issues, we proposed a new visual searching task for eye movement data collection and a new class of eye movement features for biometric recognition. In order to demonstrate the improvement of this visual searching task being used in eye movement biometrics, three other eye movement feature extraction methods were also tested on our eye movement datasets. Compared with the original results, all three methods yielded better results as expected. In addition, the biometric performance of these four feature extraction methods was also compared using the equal error rate (EER) and Rank-1 identification rate (Rank-1 IR), and the texture features introduced in this paper were ultimately shown to offer some advantages with regard to long-term stability and robustness over time and spatial precision. Finally, the results of different combinations of these methods with a score-level fusion method indicated that multi-biometric methods perform better in most cases. PMID:29617383

77 FR 52317 - Record of Decision for Surveillance Towed Array Sensor System Low Frequency Active Sonar

Federal Register 2010, 2011, 2012, 2013, 2014

2012-08-29

... DEPARTMENT OF DEFENSE Department of the Navy Record of Decision for Surveillance Towed Array Sensor System Low Frequency Active Sonar AGENCY: Department of the Navy, DoD. ACTION: Notice of decision... to employ up to four Surveillance Towed Array Sensor System Low Frequency Active (SURTASS LFA) sonar...

50 CFR 216.170 - Specified activity and specified geographical region.

Code of Federal Regulations, 2011 CFR

2011-10-01

... incidental to the following activities: (1) The use of the following mid-frequency active sonar (MFAS) and high frequency active sonar (HFAS) sources, or similar sources, for Navy training activities (estimated amounts below): (i) AN/SQS-53 (hull-mounted sonar)—up to 6420 hours over the course of 5 years (an average...

Mechanical Systems Development and Integration for a Second Generation Robot Submarine.

DTIC Science & Technology

1980-05-01

for various scientific ii endeavors. As such, there will be times when the sub- marine must be disassembled for maintenance. This chapter is intended...STBD Side Scan Array 2 Port Side Scan Array 3 Comunications Sonar 4 Pinger 5 Bottom Finding Sonar 6 Collision Avoidance Sonar 7 Gel Cell Battery 8

Sonar equations for planetary exploration.

PubMed

Ainslie, Michael A; Leighton, Timothy G

2016-08-01

The set of formulations commonly known as "the sonar equations" have for many decades been used to quantify the performance of sonar systems in terms of their ability to detect and localize objects submerged in seawater. The efficacy of the sonar equations, with individual terms evaluated in decibels, is well established in Earth's oceans. The sonar equations have been used in the past for missions to other planets and moons in the solar system, for which they are shown to be less suitable. While it would be preferable to undertake high-fidelity acoustical calculations to support planning, execution, and interpretation of acoustic data from planetary probes, to avoid possible errors for planned missions to such extraterrestrial bodies in future, doing so requires awareness of the pitfalls pointed out in this paper. There is a need to reexamine the assumptions, practices, and calibrations that work well for Earth to ensure that the sonar equations can be accurately applied in combination with the decibel to extraterrestrial scenarios. Examples are given for icy oceans such as exist on Europa and Ganymede, Titan's hydrocarbon lakes, and for the gaseous atmospheres of (for example) Jupiter and Venus.

Enhanced Sidescan-Sonar Imagery, North-Central Long Island Sound

USGS Publications Warehouse

McMullen, K.Y.; Poppe, L.J.; Schattgen, P.T.; Doran, E.F.

2008-01-01

The U.S. Geological Survey, National Oceanic and Atmospheric Administration (NOAA), and Connecticut Department of Environmental Protection have been working cooperatively to map the sea-floor geology within Long Island Sound. Sidescan-sonar imagery collected during three NOAA hydrographic surveys (H11043, H11044, and H11045) was used to interpret the surficial-sediment distribution and sedimentary environments within the Sound. The original sidescan-sonar imagery generated by NOAA was used to evaluate hazards to navigation, which does not require consistent tonal matching throughout the survey. In order to fully utilize these data for geologic interpretation, artifacts within the imagery, primarily due to sidescan-system settings (for example, gain changes), processing techniques (for example, lack of across-track normalization) and environmental noise (for example, sea state), need to be minimized. Sidescan-sonar imagery from surveys H11043, H11044, and H11045 in north-central Long Island Sound was enhanced by matching the grayscale tones between adjacent sidescan-sonar lines to decrease the patchwork effect caused by numerous artifacts and to provide a more coherent sidescan-sonar image for use in geologic interpretation.

User-oriented summary extraction for soccer video based on multimodal analysis

NASA Astrophysics Data System (ADS)

Liu, Huayong; Jiang, Shanshan; He, Tingting

2011-11-01

An advanced user-oriented summary extraction method for soccer video is proposed in this work. Firstly, an algorithm of user-oriented summary extraction for soccer video is introduced. A novel approach that integrates multimodal analysis, such as extraction and analysis of the stadium features, moving object features, audio features and text features is introduced. By these features the semantic of the soccer video and the highlight mode are obtained. Then we can find the highlight position and put them together by highlight degrees to obtain the video summary. The experimental results for sports video of world cup soccer games indicate that multimodal analysis is effective for soccer video browsing and retrieval.

Chinese character recognition based on Gabor feature extraction and CNN

NASA Astrophysics Data System (ADS)

Xiong, Yudian; Lu, Tongwei; Jiang, Yongyuan

2018-03-01

As an important application in the field of text line recognition and office automation, Chinese character recognition has become an important subject of pattern recognition. However, due to the large number of Chinese characters and the complexity of its structure, there is a great difficulty in the Chinese character recognition. In order to solve this problem, this paper proposes a method of printed Chinese character recognition based on Gabor feature extraction and Convolution Neural Network(CNN). The main steps are preprocessing, feature extraction, training classification. First, the gray-scale Chinese character image is binarized and normalized to reduce the redundancy of the image data. Second, each image is convoluted with Gabor filter with different orientations, and the feature map of the eight orientations of Chinese characters is extracted. Third, the feature map through Gabor filters and the original image are convoluted with learning kernels, and the results of the convolution is the input of pooling layer. Finally, the feature vector is used to classify and recognition. In addition, the generalization capacity of the network is improved by Dropout technology. The experimental results show that this method can effectively extract the characteristics of Chinese characters and recognize Chinese characters.

Automated prediction of protein function and detection of functional sites from structure.

PubMed

Pazos, Florencio; Sternberg, Michael J E

2004-10-12

Current structural genomics projects are yielding structures for proteins whose functions are unknown. Accordingly, there is a pressing requirement for computational methods for function prediction. Here we present PHUNCTIONER, an automatic method for structure-based function prediction using automatically extracted functional sites (residues associated to functions). The method relates proteins with the same function through structural alignments and extracts 3D profiles of conserved residues. Functional features to train the method are extracted from the Gene Ontology (GO) database. The method extracts these features from the entire GO hierarchy and hence is applicable across the whole range of function specificity. 3D profiles associated with 121 GO annotations were extracted. We tested the power of the method both for the prediction of function and for the extraction of functional sites. The success of function prediction by our method was compared with the standard homology-based method. In the zone of low sequence similarity (approximately 15%), our method assigns the correct GO annotation in 90% of the protein structures considered, approximately 20% higher than inheritance of function from the closest homologue.

A risk function for behavioral disruption of Blainville's beaked whales (Mesoplodon densirostris) from mid-frequency active sonar.

PubMed

Moretti, David; Thomas, Len; Marques, Tiago; Harwood, John; Dilley, Ashley; Neales, Bert; Shaffer, Jessica; McCarthy, Elena; New, Leslie; Jarvis, Susan; Morrissey, Ronald

2014-01-01

There is increasing concern about the potential effects of noise pollution on marine life in the world's oceans. For marine mammals, anthropogenic sounds may cause behavioral disruption, and this can be quantified using a risk function that relates sound exposure to a measured behavioral response. Beaked whales are a taxon of deep diving whales that may be particularly susceptible to naval sonar as the species has been associated with sonar-related mass stranding events. Here we derive the first empirical risk function for Blainville's beaked whales (Mesoplodon densirostris) by combining in situ data from passive acoustic monitoring of animal vocalizations and navy sonar operations with precise ship tracks and sound field modeling. The hydrophone array at the Atlantic Undersea Test and Evaluation Center, Bahamas, was used to locate vocalizing groups of Blainville's beaked whales and identify sonar transmissions before, during, and after Mid-Frequency Active (MFA) sonar operations. Sonar transmission times and source levels were combined with ship tracks using a sound propagation model to estimate the received level (RL) at each hydrophone. A generalized additive model was fitted to data to model the presence or absence of the start of foraging dives in 30-minute periods as a function of the corresponding sonar RL at the hydrophone closest to the center of each group. This model was then used to construct a risk function that can be used to estimate the probability of a behavioral change (cessation of foraging) the individual members of a Blainville's beaked whale population might experience as a function of sonar RL. The function predicts a 0.5 probability of disturbance at a RL of 150 dBrms re µPa (CI: 144 to 155) This is 15dB lower than the level used historically by the US Navy in their risk assessments but 10 dB higher than the current 140 dB step-function.

A Risk Function for Behavioral Disruption of Blainville’s Beaked Whales (Mesoplodon densirostris) from Mid-Frequency Active Sonar

PubMed Central

Moretti, David; Thomas, Len; Marques, Tiago; Harwood, John; Dilley, Ashley; Neales, Bert; Shaffer, Jessica; McCarthy, Elena; New, Leslie; Jarvis, Susan; Morrissey, Ronald

2014-01-01

There is increasing concern about the potential effects of noise pollution on marine life in the world’s oceans. For marine mammals, anthropogenic sounds may cause behavioral disruption, and this can be quantified using a risk function that relates sound exposure to a measured behavioral response. Beaked whales are a taxon of deep diving whales that may be particularly susceptible to naval sonar as the species has been associated with sonar-related mass stranding events. Here we derive the first empirical risk function for Blainville’s beaked whales (Mesoplodon densirostris) by combining in situ data from passive acoustic monitoring of animal vocalizations and navy sonar operations with precise ship tracks and sound field modeling. The hydrophone array at the Atlantic Undersea Test and Evaluation Center, Bahamas, was used to locate vocalizing groups of Blainville’s beaked whales and identify sonar transmissions before, during, and after Mid-Frequency Active (MFA) sonar operations. Sonar transmission times and source levels were combined with ship tracks using a sound propagation model to estimate the received level (RL) at each hydrophone. A generalized additive model was fitted to data to model the presence or absence of the start of foraging dives in 30-minute periods as a function of the corresponding sonar RL at the hydrophone closest to the center of each group. This model was then used to construct a risk function that can be used to estimate the probability of a behavioral change (cessation of foraging) the individual members of a Blainville’s beaked whale population might experience as a function of sonar RL. The function predicts a 0.5 probability of disturbance at a RL of 150dBrms re µPa (CI: 144 to 155) This is 15dB lower than the level used historically by the US Navy in their risk assessments but 10 dB higher than the current 140 dB step-function. PMID:24465477

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.

Automatic quantification of morphological features for hepatic trabeculae analysis in stained liver specimens

PubMed Central

Ishikawa, Masahiro; Murakami, Yuri; Ahi, Sercan Taha; Yamaguchi, Masahiro; Kobayashi, Naoki; Kiyuna, Tomoharu; Yamashita, Yoshiko; Saito, Akira; Abe, Tokiya; Hashiguchi, Akinori; Sakamoto, Michiie

2016-01-01

Abstract. This paper proposes a digital image analysis method to support quantitative pathology by automatically segmenting the hepatocyte structure and quantifying its morphological features. To structurally analyze histopathological hepatic images, we isolate the trabeculae by extracting the sinusoids, fat droplets, and stromata. We then measure the morphological features of the extracted trabeculae, divide the image into cords, and calculate the feature values of the local cords. We propose a method of calculating the nuclear–cytoplasmic ratio, nuclear density, and number of layers using the local cords. Furthermore, we evaluate the effectiveness of the proposed method using surgical specimens. The proposed method was found to be an effective method for the quantification of the Edmondson grade. PMID:27335894

Interference fringes on GLORIA side-scan sonar images from the Bering Sea and their implications

USGS Publications Warehouse

Huggett, Q.J.; Cooper, A. K.; Somers, M.L.; Stubbs, A.R.

1992-01-01

GLORIA side-scan sonographs from the Bering Sea Basin show a complex pattern of interference fringes sub-parallel to the ship's track. Surveys along the same trackline made in 1986 and 1987 show nearly identical patterns. It is concluded from this that the interference patterns are caused by features in the shallow subsurface rather than in the water column. The fringes are interpreted as a thin-layer interference effect that occurs when some of the sound reaching the seafloor passes through it and is reflected off a subsurface layer. The backscattered sound interferes (constructively or desctructively) with the reflected sound. Constructive/destructive interference occurs when the difference in the length of the two soundpaths is a whole/half multiple of GLORIA's 25 cm wavelength. Thus as range from the ship increases, sound moves in and out of phase causing bands of greater and lesser intensity on the GLORIA sonograph. Fluctuations (or 'wiggles') of the fringes on the GLORIA sonographs relate to changes in layer thickness. In principle, a simple three dimensional image of the subsurface layer may be obtained using GLORIA and bathymetric data from adjacent (parallel) ship's tracks. These patterns have also been identified in images from two other systems; SeaMARC II (12 kHz) long-range sonar, and TOBI (30 kHz) deep-towed sonar. In these, and other cases world-wide, the fringes do not appear with the same persistence as those seen in the Bering Sea. ?? 1992 Kluwer Academic Publishers.

Driver drowsiness classification using fuzzy wavelet-packet-based feature-extraction algorithm.

PubMed

Khushaba, Rami N; Kodagoda, Sarath; Lal, Sara; Dissanayake, Gamini

2011-01-01

Driver drowsiness and loss of vigilance are a major cause of road accidents. Monitoring physiological signals while driving provides the possibility of detecting and warning of drowsiness and fatigue. The aim of this paper is to maximize the amount of drowsiness-related information extracted from a set of electroencephalogram (EEG), electrooculogram (EOG), and electrocardiogram (ECG) signals during a simulation driving test. Specifically, we develop an efficient fuzzy mutual-information (MI)- based wavelet packet transform (FMIWPT) feature-extraction method for classifying the driver drowsiness state into one of predefined drowsiness levels. The proposed method estimates the required MI using a novel approach based on fuzzy memberships providing an accurate-information content-estimation measure. The quality of the extracted features was assessed on datasets collected from 31 drivers on a simulation test. The experimental results proved the significance of FMIWPT in extracting features that highly correlate with the different drowsiness levels achieving a classification accuracy of 95%-- 97% on an average across all subjects.

Fabric defect detection based on visual saliency using deep feature and low-rank recovery

NASA Astrophysics Data System (ADS)

Liu, Zhoufeng; Wang, Baorui; Li, Chunlei; Li, Bicao; Dong, Yan

2018-04-01

Fabric defect detection plays an important role in improving the quality of fabric product. In this paper, a novel fabric defect detection method based on visual saliency using deep feature and low-rank recovery was proposed. First, unsupervised training is carried out by the initial network parameters based on MNIST large datasets. The supervised fine-tuning of fabric image library based on Convolutional Neural Networks (CNNs) is implemented, and then more accurate deep neural network model is generated. Second, the fabric images are uniformly divided into the image block with the same size, then we extract their multi-layer deep features using the trained deep network. Thereafter, all the extracted features are concentrated into a feature matrix. Third, low-rank matrix recovery is adopted to divide the feature matrix into the low-rank matrix which indicates the background and the sparse matrix which indicates the salient defect. In the end, the iterative optimal threshold segmentation algorithm is utilized to segment the saliency maps generated by the sparse matrix to locate the fabric defect area. Experimental results demonstrate that the feature extracted by CNN is more suitable for characterizing the fabric texture than the traditional LBP, HOG and other hand-crafted features extraction method, and the proposed method can accurately detect the defect regions of various fabric defects, even for the image with complex texture.

Some Computed Effects of Dome Skin and Temperature Differential on Operation of the AN/SQS-26 Sonar Equipment

DTIC Science & Technology

1963-10-04

Tolerances of Transducer Elements and Preamplifiers on Beam Formation and SSI Performance in the AN/SQS-26 Sonar Equipment (U)", TRACOR Document Number 63...SQS-26 SONAR EQUIPMENT (U) Prepared for GROLP - 4 DOWNGRADED AT% YEAR INTERVALS: l LJ.I The Bureau of Ships DECLASSIFIED A ER 12 YEARS. r . Code 688E t...ON.PERATION OF THEP ,,, Ts 4a nAinS-26 SONAR pul i~ ~ ~ ~ ~ ~ ~ ~~%,i forre o teSFXPora aaeet Prepared for Bull by: DSS11TIAVAILAIIL CODES The Bureau of Ships

Morphological Feature Extraction for Automatic Registration of Multispectral Images

NASA Technical Reports Server (NTRS)

Plaza, Antonio; LeMoigne, Jacqueline; Netanyahu, Nathan S.

2007-01-01

The task of image registration can be divided into two major components, i.e., the extraction of control points or features from images, and the search among the extracted features for the matching pairs that represent the same feature in the images to be matched. Manual extraction of control features can be subjective and extremely time consuming, and often results in few usable points. On the other hand, automated feature extraction allows using invariant target features such as edges, corners, and line intersections as relevant landmarks for registration purposes. In this paper, we present an extension of a recently developed morphological approach for automatic extraction of landmark chips and corresponding windows in a fully unsupervised manner for the registration of multispectral images. Once a set of chip-window pairs is obtained, a (hierarchical) robust feature matching procedure, based on a multiresolution overcomplete wavelet decomposition scheme, is used for registration purposes. The proposed method is validated on a pair of remotely sensed scenes acquired by the Advanced Land Imager (ALI) multispectral instrument and the Hyperion hyperspectral instrument aboard NASA's Earth Observing-1 satellite.

Study on Hybrid Image Search Technology Based on Texts and Contents

NASA Astrophysics Data System (ADS)

Wang, H. T.; Ma, F. L.; Yan, C.; Pan, H.

2018-05-01

Image search was studied first here based on texts and contents, respectively. The text-based image feature extraction was put forward by integrating the statistical and topic features in view of the limitation of extraction of keywords only by means of statistical features of words. On the other hand, a search-by-image method was put forward based on multi-feature fusion in view of the imprecision of the content-based image search by means of a single feature. The layered-searching method depended on primarily the text-based image search method and additionally the content-based image search was then put forward in view of differences between the text-based and content-based methods and their difficult direct fusion. The feasibility and effectiveness of the hybrid search algorithm were experimentally verified.

Using Mobile Laser Scanning Data for Features Extraction of High Accuracy Driving Maps

NASA Astrophysics Data System (ADS)

Yang, Bisheng; Liu, Yuan; Liang, Fuxun; Dong, Zhen

2016-06-01

High Accuracy Driving Maps (HADMs) are the core component of Intelligent Drive Assistant Systems (IDAS), which can effectively reduce the traffic accidents due to human error and provide more comfortable driving experiences. Vehicle-based mobile laser scanning (MLS) systems provide an efficient solution to rapidly capture three-dimensional (3D) point clouds of road environments with high flexibility and precision. This paper proposes a novel method to extract road features (e.g., road surfaces, road boundaries, road markings, buildings, guardrails, street lamps, traffic signs, roadside-trees, power lines, vehicles and so on) for HADMs in highway environment. Quantitative evaluations show that the proposed algorithm attains an average precision and recall in terms of 90.6% and 91.2% in extracting road features. Results demonstrate the efficiencies and feasibilities of the proposed method for extraction of road features for HADMs.

A Review of Feature Extraction Software for Microarray Gene Expression Data

PubMed Central

Tan, Ching Siang; Ting, Wai Soon; Mohamad, Mohd Saberi; Chan, Weng Howe; Deris, Safaai; Ali Shah, Zuraini

2014-01-01

When gene expression data are too large to be processed, they are transformed into a reduced representation set of genes. Transforming large-scale gene expression data into a set of genes is called feature extraction. If the genes extracted are carefully chosen, this gene set can extract the relevant information from the large-scale gene expression data, allowing further analysis by using this reduced representation instead of the full size data. In this paper, we review numerous software applications that can be used for feature extraction. The software reviewed is mainly for Principal Component Analysis (PCA), Independent Component Analysis (ICA), Partial Least Squares (PLS), and Local Linear Embedding (LLE). A summary and sources of the software are provided in the last section for each feature extraction method. PMID:25250315

Introduction to Sonar, Naval Education and Training Command. Revised Edition.

ERIC Educational Resources Information Center

Naval Education and Training Command, Pensacola, FL.

This Rate Training Manual (RTM) and Nonresident Career Course form a self-study package for those U.S. Navy personnel who are seeking advancement in the Sonar Technician Rating. Among the requirements of the rating are the abilities to obtain and interpret underwater data, operate and maintain upkeep of sonar equipment, and interpret target and…

Ceteacean Social Behavioral Response to Sonar

DTIC Science & Technology

2011-09-30

behavior data of humpback whales and minke whales was recorded during 5 and 1 CEEs respectively (including tagging, baseline, sonar exposure and...during fieldwork efforts in 2012 and 2013. Figure 1. Example of humpback whale group behavior sampling...cetacean behavioral responses to sonar signals and other stimuli (tagging effort, killer whale playbacks) as well as baseline behavior, are studied

ATR Performance Estimation Seed Program

DTIC Science & Technology

2015-09-28

to produce simulated MCM sonar data and demonstrate the impact of system, environmental, and target scattering effects on ATR detection...settings and achieving better understanding the relative impact of the factors influencing ATR performance. sonar, mine countermeasures, MCM , automatic...simulated MCM sonar data and demonstrate the impact of system, environmental, and target scattering effects on ATR detection/classification performance. The

Quantitative 3-D Imaging, Segmentation and Feature Extraction of the Respiratory System in Small Mammals for Computational Biophysics Simulations

DOE Office of Scientific and Technical Information (OSTI.GOV)

Trease, Lynn L.; Trease, Harold E.; Fowler, John

2007-03-15

One of the critical steps toward performing computational biology simulations, using mesh based integration methods, is in using topologically faithful geometry derived from experimental digital image data as the basis for generating the computational meshes. Digital image data representations contain both the topology of the geometric features and experimental field data distributions. The geometric features that need to be captured from the digital image data are three-dimensional, therefore the process and tools we have developed work with volumetric image data represented as data-cubes. This allows us to take advantage of 2D curvature information during the segmentation and feature extraction process.more » The process is basically: 1) segmenting to isolate and enhance the contrast of the features that we wish to extract and reconstruct, 2) extracting the geometry of the features in an isosurfacing technique, and 3) building the computational mesh using the extracted feature geometry. “Quantitative” image reconstruction and feature extraction is done for the purpose of generating computational meshes, not just for producing graphics "screen" quality images. For example, the surface geometry that we extract must represent a closed water-tight surface.« less

An Efficient Method for Automatic Road Extraction Based on Multiple Features from LiDAR Data

NASA Astrophysics Data System (ADS)

Li, Y.; Hu, X.; Guan, H.; Liu, P.

2016-06-01

The road extraction in urban areas is difficult task due to the complicated patterns and many contextual objects. LiDAR data directly provides three dimensional (3D) points with less occlusions and smaller shadows. The elevation information and surface roughness are distinguishing features to separate roads. However, LiDAR data has some disadvantages are not beneficial to object extraction, such as the irregular distribution of point clouds and lack of clear edges of roads. For these problems, this paper proposes an automatic road centerlines extraction method which has three major steps: (1) road center point detection based on multiple feature spatial clustering for separating road points from ground points, (2) local principal component analysis with least squares fitting for extracting the primitives of road centerlines, and (3) hierarchical grouping for connecting primitives into complete roads network. Compared with MTH (consist of Mean shift algorithm, Tensor voting, and Hough transform) proposed in our previous article, this method greatly reduced the computational cost. To evaluate the proposed method, the Vaihingen data set, a benchmark testing data provided by ISPRS for "Urban Classification and 3D Building Reconstruction" project, was selected. The experimental results show that our method achieve the same performance by less time in road extraction using LiDAR data.

SONAR: A High-Throughput Pipeline for Inferring Antibody Ontogenies from Longitudinal Sequencing of B Cell Transcripts

PubMed Central

Schramm, Chaim A.; Sheng, Zizhang; Zhang, Zhenhai; Mascola, John R.; Kwong, Peter D.; Shapiro, Lawrence

2016-01-01

The rapid advance of massively parallel or next-generation sequencing technologies has made possible the characterization of B cell receptor repertoires in ever greater detail, and these developments have triggered a proliferation of software tools for processing and annotating these data. Of especial interest, however, is the capability to track the development of specific antibody lineages across time, which remains beyond the scope of most current programs. We have previously reported on the use of techniques such as inter- and intradonor analysis and CDR3 tracing to identify transcripts related to an antibody of interest. Here, we present Software for the Ontogenic aNalysis of Antibody Repertoires (SONAR), capable of automating both general repertoire analysis and specialized techniques for investigating specific lineages. SONAR annotates next-generation sequencing data, identifies transcripts in a lineage of interest, and tracks lineage development across multiple time points. SONAR also generates figures, such as identity–divergence plots and longitudinal phylogenetic “birthday” trees, and provides interfaces to other programs such as DNAML and BEAST. SONAR can be downloaded as a ready-to-run Docker image or manually installed on a local machine. In the latter case, it can also be configured to take advantage of a high-performance computing cluster for the most computationally intensive steps, if available. In summary, this software provides a useful new tool for the processing of large next-generation sequencing datasets and the ontogenic analysis of neutralizing antibody lineages. SONAR can be found at https://github.com/scharch/SONAR, and the Docker image can be obtained from https://hub.docker.com/r/scharch/sonar/. PMID:27708645

A comparison of the role of beamwidth in biological and engineered sonar.

PubMed

Todd, Bryan D; Müller, Rolf

2017-12-28

Sonar is an important sensory modality for engineers as well as in nature. In engineering, sonar is the dominating modality for underwater sensing. In nature, biosonar is likely to have been a central factor behind the unprecedented evolutionary success of bats, a highly diverse group that accounts for over 20% of all mammal species. However, it remains unclear to what extent engineered and biosonar follow similar design and operational principles. In the current work, the key sonar design characteristic of beamwidth is examined in technical and biosonar. To this end, beamwidth data has been obtained for 23 engineered sonar systems and from numerical beampattern predictions for 151 emission and reception elements (noseleaves and ears) representing bat biosonar. Beamwidth data from these sources is compared to the beamwidth of a planar ellipsoidal transducer as a reference. The results show that engineered and biological both obey the basic physical limit on beamwidth as a function of the ratio of aperture size and wavelength. However, beyond that, the beamwidth data revealed very different behaviors between the engineered and the biological sonar systems. Whereas the beamwidths of the technical sonar systems were very close to the planar transducer limit, the biological samples showed a very wide scatter away from this limit. This scatter was as large, if not wider, than what was seen in a small reference data set obtained with random aluminum cones. A possible interpretation of these differences in the variability could be that whereas sonar engineers try to minimize beamwidth subject to constraints on device size, the evolutionary optimization of bat biosonar beampatterns has been directed at other factors that have left beamwidth as a byproduct. Alternatively, the biosonar systems may require beamwidth values that are larger than the physical limit and differ between species and their sensory ecological niches.

Prominent feature extraction for review analysis: an empirical study

NASA Astrophysics Data System (ADS)

Agarwal, Basant; Mittal, Namita

2016-05-01

Sentiment analysis (SA) research has increased tremendously in recent times. SA aims to determine the sentiment orientation of a given text into positive or negative polarity. Motivation for SA research is the need for the industry to know the opinion of the users about their product from online portals, blogs, discussion boards and reviews and so on. Efficient features need to be extracted for machine-learning algorithm for better sentiment classification. In this paper, initially various features are extracted such as unigrams, bi-grams and dependency features from the text. In addition, new bi-tagged features are also extracted that conform to predefined part-of-speech patterns. Furthermore, various composite features are created using these features. Information gain (IG) and minimum redundancy maximum relevancy (mRMR) feature selection methods are used to eliminate the noisy and irrelevant features from the feature vector. Finally, machine-learning algorithms are used for classifying the review document into positive or negative class. Effects of different categories of features are investigated on four standard data-sets, namely, movie review and product (book, DVD and electronics) review data-sets. Experimental results show that composite features created from prominent features of unigram and bi-tagged features perform better than other features for sentiment classification. mRMR is a better feature selection method as compared with IG for sentiment classification. Boolean Multinomial Naïve Bayes) algorithm performs better than support vector machine classifier for SA in terms of accuracy and execution time.

A multiple distributed representation method based on neural network for biomedical event extraction.

PubMed

Wang, Anran; Wang, Jian; Lin, Hongfei; Zhang, Jianhai; Yang, Zhihao; Xu, Kan

2017-12-20

Biomedical event extraction is one of the most frontier domains in biomedical research. The two main subtasks of biomedical event extraction are trigger identification and arguments detection which can both be considered as classification problems. However, traditional state-of-the-art methods are based on support vector machine (SVM) with massive manually designed one-hot represented features, which require enormous work but lack semantic relation among words. In this paper, we propose a multiple distributed representation method for biomedical event extraction. The method combines context consisting of dependency-based word embedding, and task-based features represented in a distributed way as the input of deep learning models to train deep learning models. Finally, we used softmax classifier to label the example candidates. The experimental results on Multi-Level Event Extraction (MLEE) corpus show higher F-scores of 77.97% in trigger identification and 58.31% in overall compared to the state-of-the-art SVM method. Our distributed representation method for biomedical event extraction avoids the problems of semantic gap and dimension disaster from traditional one-hot representation methods. The promising results demonstrate that our proposed method is effective for biomedical event extraction.

Coherent and Noncoherent Joint Processing of Sonar for Detection of Small Targets in Shallow Water.

PubMed

Pan, Xiang; Jiang, Jingning; Li, Si; Ding, Zhenping; Pan, Chen; Gong, Xianyi

2018-04-10

A coherent-noncoherent joint processing framework is proposed for active sonar to combine diversity gain and beamforming gain for detection of a small target in shallow water environments. Sonar utilizes widely-spaced arrays to sense environments and illuminate a target of interest from multiple angles. Meanwhile, it exploits spatial diversity for time-reversal focusing to suppress reverberation, mainly strong bottom reverberation. For enhancement of robustness of time-reversal focusing, an adaptive iterative strategy is utilized in the processing framework. A probing signal is firstly transmitted and echoes of a likely target are utilized as steering vectors for the second transmission. With spatial diversity, target bearing and range are estimated using a broadband signal model. Numerical simulations show that the novel sonar outperforms the traditional phased-array sonar due to benefits of spatial diversity. The effectiveness of the proposed framework has been validated by localization of a small target in at-lake experiments.

Sonar sound groups and increased terminal buzz duration reflect task complexity in hunting bats.

PubMed

Hulgard, Katrine; Ratcliffe, John M

2016-02-09

More difficult tasks are generally regarded as such because they demand greater attention. Echolocators provide rare insight into this relationship because biosonar signals can be monitored. Here we show that bats produce longer terminal buzzes and more sonar sound groups during their approach to prey under presumably more difficult conditions. Specifically, we found Daubenton's bats, Myotis daubentonii, produced longer buzzes when aerial-hawking versus water-trawling prey, but that bats taking revolving air- and water-borne prey produced more sonar sound groups than did the bats when taking stationary prey. Buzz duration and sonar sound groups have been suggested to be independent means by which bats attend to would-be targets and other objects of interest. We suggest that for attacking bats both should be considered as indicators of task difficulty and that the buzz is, essentially, an extended sonar sound group.

Single-trial laser-evoked potentials feature extraction for prediction of pain perception.

PubMed

Huang, Gan; Xiao, Ping; Hu, Li; Hung, Yeung Sam; Zhang, Zhiguo

2013-01-01

Pain is a highly subjective experience, and the availability of an objective assessment of pain perception would be of great importance for both basic and clinical applications. The objective of the present study is to develop a novel approach to extract pain-related features from single-trial laser-evoked potentials (LEPs) for classification of pain perception. The single-trial LEP feature extraction approach combines a spatial filtering using common spatial pattern (CSP) and a multiple linear regression (MLR). The CSP method is effective in separating laser-evoked EEG response from ongoing EEG activity, while MLR is capable of automatically estimating the amplitudes and latencies of N2 and P2 from single-trial LEP waveforms. The extracted single-trial LEP features are used in a Naïve Bayes classifier to classify different levels of pain perceived by the subjects. The experimental results show that the proposed single-trial LEP feature extraction approach can effectively extract pain-related LEP features for achieving high classification accuracy.

Extraction of ECG signal with adaptive filter for hearth abnormalities detection

NASA Astrophysics Data System (ADS)

Turnip, Mardi; Saragih, Rijois. I. E.; Dharma, Abdi; Esti Kusumandari, Dwi; Turnip, Arjon; Sitanggang, Delima; Aisyah, Siti

2018-04-01

This paper demonstrates an adaptive filter method for extraction ofelectrocardiogram (ECG) feature in hearth abnormalities detection. In particular, electrocardiogram (ECG) is a recording of the heart's electrical activity by capturing a tracingof cardiac electrical impulse as it moves from the atrium to the ventricles. The applied algorithm is to evaluate and analyze ECG signals for abnormalities detection based on P, Q, R and S peaks. In the first phase, the real-time ECG data is acquired and pre-processed. In the second phase, the procured ECG signal is subjected to feature extraction process. The extracted features detect abnormal peaks present in the waveform. Thus the normal and abnormal ECG signal could be differentiated based on the features extracted.

Bearing performance degradation assessment based on time-frequency code features and SOM network

NASA Astrophysics Data System (ADS)

Zhang, Yan; Tang, Baoping; Han, Yan; Deng, Lei

2017-04-01

Bearing performance degradation assessment and prognostics are extremely important in supporting maintenance decision and guaranteeing the system’s reliability. To achieve this goal, this paper proposes a novel feature extraction method for the degradation assessment and prognostics of bearings. Features of time-frequency codes (TFCs) are extracted from the time-frequency distribution using a hybrid procedure based on short-time Fourier transform (STFT) and non-negative matrix factorization (NMF) theory. An alternative way to design the health indicator is investigated by quantifying the similarity between feature vectors using a self-organizing map (SOM) network. On the basis of this idea, a new health indicator called time-frequency code quantification error (TFCQE) is proposed to assess the performance degradation of the bearing. This indicator is constructed based on the bearing real-time behavior and the SOM model that is previously trained with only the TFC vectors under the normal condition. Vibration signals collected from the bearing run-to-failure tests are used to validate the developed method. The comparison results demonstrate the superiority of the proposed TFCQE indicator over many other traditional features in terms of feature quality metrics, incipient degradation identification and achieving accurate prediction. Highlights • Time-frequency codes are extracted to reflect the signals’ characteristics. • SOM network served as a tool to quantify the similarity between feature vectors. • A new health indicator is proposed to demonstrate the whole stage of degradation development. • The method is useful for extracting the degradation features and detecting the incipient degradation. • The superiority of the proposed method is verified using experimental data.

Spectral-Spatial Scale Invariant Feature Transform for Hyperspectral Images.

PubMed

Al-Khafaji, Suhad Lateef; Jun Zhou; Zia, Ali; Liew, Alan Wee-Chung

2018-02-01

Spectral-spatial feature extraction is an important task in hyperspectral image processing. In this paper we propose a novel method to extract distinctive invariant features from hyperspectral images for registration of hyperspectral images with different spectral conditions. Spectral condition means images are captured with different incident lights, viewing angles, or using different hyperspectral cameras. In addition, spectral condition includes images of objects with the same shape but different materials. This method, which is named spectral-spatial scale invariant feature transform (SS-SIFT), explores both spectral and spatial dimensions simultaneously to extract spectral and geometric transformation invariant features. Similar to the classic SIFT algorithm, SS-SIFT consists of keypoint detection and descriptor construction steps. Keypoints are extracted from spectral-spatial scale space and are detected from extrema after 3D difference of Gaussian is applied to the data cube. Two descriptors are proposed for each keypoint by exploring the distribution of spectral-spatial gradient magnitude in its local 3D neighborhood. The effectiveness of the SS-SIFT approach is validated on images collected in different light conditions, different geometric projections, and using two hyperspectral cameras with different spectral wavelength ranges and resolutions. The experimental results show that our method generates robust invariant features for spectral-spatial image matching.

DOE Office of Scientific and Technical Information (OSTI.GOV)

Jing, Yaqi; Meng, Qinghao, E-mail: qh-meng@tju.edu.cn; Qi, Peifeng

An electronic nose (e-nose) was designed to classify Chinese liquors of the same aroma style. A new method of feature reduction which combined feature selection with feature extraction was proposed. Feature selection method used 8 feature-selection algorithms based on information theory and reduced the dimension of the feature space to 41. Kernel entropy component analysis was introduced into the e-nose system as a feature extraction method and the dimension of feature space was reduced to 12. Classification of Chinese liquors was performed by using back propagation artificial neural network (BP-ANN), linear discrimination analysis (LDA), and a multi-linear classifier. The classificationmore » rate of the multi-linear classifier was 97.22%, which was higher than LDA and BP-ANN. Finally the classification of Chinese liquors according to their raw materials and geographical origins was performed using the proposed multi-linear classifier and classification rate was 98.75% and 100%, respectively.« less

The pressure ridge distribution in the Arctic Ocean from submarine sonar data

NASA Astrophysics Data System (ADS)

Rodrigues, Joao; Wadhams, Peter

2010-05-01

The profiling of the underside of the sea ice with upward-looking sonars fitted to submarines is the best method of studying the large scale distribution of morphological features such as pressure ridges and leads. We present the statistical analysis of the distributions of pressure ridge spacings and heights, and lead spacings and widths observed during two Arctic cruises by the Royal Navy submarine HMS Tireless in the winters of 2004 and 2007 in which more than 10000km of sea ice draft data were collected. We briefly describe the main characteristics of the full ice draft distribution in the several regions of the Arctic Ocean visited by the submarine and discuss the most significant differences between 2004 and 2007. In the area of heavily ridged ice north of Greenland and Ellesmere Island we found an increase in ridge density (number of ridges per unit track length) accompanied by a decrease in modal ice draft, leaving the mean ice thickness essentially unchanged, between 2004 and 2007. This area is likely to be the only one in the Arctic Ocean where the sea ice thickness may not be in decline. We investigate the causes of this invariance in the context of an Arctic Ocean in transition from a multi-year to a first-year ice cover and discuss its relation with the strengthening of the transpolar drift and consequent accumulation of ice north of Greenland and increase in ice export through Fram Strait. Our analysis shows that the number of deep ridges per km is well described by a Poisson distribution while the corresponding distribution for shallow ridges is more complicated. The tail of the distribution of the pressure ridge heights is approximately a negative exponential, in agreement with similar observations made in previous cruises. We pay special attention to the uncertainties and biases in the measurement of the ice draft. Specifically, we discuss the effects of the finite beamwidth of the single-beam sonars traditionally used in British submarines on the determination of sea ice draft, which may have been underestimated in previous work.

Recent development of feature extraction and classification multispectral/hyperspectral images: a systematic literature review

NASA Astrophysics Data System (ADS)

Setiyoko, A.; Dharma, I. G. W. S.; Haryanto, T.

2017-01-01

Multispectral data and hyperspectral data acquired from satellite sensor have the ability in detecting various objects on the earth ranging from low scale to high scale modeling. These data are increasingly being used to produce geospatial information for rapid analysis by running feature extraction or classification process. Applying the most suited model for this data mining is still challenging because there are issues regarding accuracy and computational cost. This research aim is to develop a better understanding regarding object feature extraction and classification applied for satellite image by systematically reviewing related recent research projects. A method used in this research is based on PRISMA statement. After deriving important points from trusted sources, pixel based and texture-based feature extraction techniques are promising technique to be analyzed more in recent development of feature extraction and classification.

50 CFR 218.232 - Permissible methods of taking.

Code of Federal Regulations, 2013 CFR

2013-10-01

... 50 Wildlife and Fisheries 10 2013-10-01 2013-10-01 false Permissible methods of taking. 218.232... Low Frequency Active (SURTASS LFA) Sonar § 218.232 Permissible methods of taking. (a) Under Letters of... species listed in § 218.230(b) by the method of take indicated in paragraphs (c)(2) through (5) of this...

High resolution mapping and classification of oyster habitats in nearshore Louisiana using sidescan sonar

USGS Publications Warehouse

Allen, Y.C.; Wilson, C.A.; Roberts, H.H.; Supan, J.

2005-01-01

Sidescan sonar holds great promise as a tool to quantitatively depict the distribution and extent of benthic habitats in Louisiana's turbid estuaries. In this study, we describe an effective protocol for acoustic sampling in this environment. We also compared three methods of classification in detail: mean-based thresholding, supervised, and unsupervised techniques to classify sidescan imagery into categories of mud and shell. Classification results were compared to ground truth results using quadrat and dredge sampling. Supervised classification gave the best overall result (kappa = 75%) when compared to quadrat results. Classification accuracy was less robust when compared to all dredge samples (kappa = 21-56%), but increased greatly (90-100%) when only dredge samples taken from acoustically homogeneous areas were considered. Sidescan sonar when combined with ground truth sampling at an appropriate scale can be effectively used to establish an accurate substrate base map for both research applications and shellfish management. The sidescan imagery presented here also provides, for the first time, a detailed presentation of oyster habitat patchiness and scale in a productive oyster growing area.

77 FR 6084 - Taking and Importing Marine Mammals; U.S. Navy Training in the Southern California Range Complex

Federal Register 2010, 2011, 2012, 2013, 2014

2012-02-07

... the Navy in SOCAL, the Hawaii Range Complex, and the Atlantic Fleet Active Sonar Training Study Area... estimated usage of two sonar systems, they remain well within the authorized 5-year source amounts and the... exercise report indicates that the Navy exceeded the average annual amount of two sonar systems during this...

50 CFR 218.80 - Specified activity and specified geographical region.

Code of Federal Regulations, 2014 CFR

2014-10-01

... Study Area also includes U.S. Navy pierside locations where sonar maintenance and testing occurs within...): (A) M3—an average of 461 hours per year. (B) [Reserved] (vii) Swimmer Detection Sonar (SD): (A) SD1 and SD2—an average of 230 hours per year. (B) [Reserved] (viii) Forward Looking Sonar (FLS): (A) FLS2...

Multi-Layer Tiled Array.

DTIC Science & Technology

1996-12-16

the Invention 13 The present invention relates to planar sonar arrays. More 14 particularly, the invention relates to the arrangement of 15...transducer elements in planar sonar arrays. 16 (2) Description of the Prior Art 17 Conventional planar sonar array designs typically comprise 18 ceramic...signal 5 conditioners ( preamplifiers )/as short as possible. However, this 6 requirement complicates fabrication and provides little space to 7

Patterns of Occurrence and Marine Mammal Acoustic Behavior in Relation to Navy Sonar Activity Off Jacksonville, Florida.

PubMed

Oswald, Julie N; Norris, Thomas F; Yack, Tina M; Ferguson, Elizabeth L; Kumar, Anurag; Nissen, Jene; Bell, Joel

2016-01-01

Passive acoustic data collected from marine autonomous recording units deployed off Jacksonville, FL (from 13 September to 8 October 2009 and 3 December 2009 to 8 January 2010), were analyzed for detection of cetaceans and Navy sonar. Cetaceans detected included Balaenoptera acutorostrata, Eubalaena glacialis, B. borealis, Physeter macrocephalus, blackfish, and delphinids. E. glacialis were detected at shallow and, somewhat unexpectedly, deep sites. P. macrocephalus were characterized by a strong diel pattern. B. acutorostrata showed the strongest relationship between sonar activity and vocal behavior. These results provide a preliminary assessment of cetacean occurrence off Jacksonville and new insights on vocal responses to sonar.

Diver-based integrated navigation/sonar sensor

NASA Astrophysics Data System (ADS)

Lent, Keith H.

1999-07-01

Two diver based systems, the Small Object Locating Sonar (SOLS) and the Integrated Navigation and Sonar Sensor (INSS) have been developed at Applied Research Laboratories, the University of Texas at Austin (ARL:UT). They are small and easy to use systems that allow a diver to: detect, classify, and identify underwater objects; render large sector visual images; and track, map and reacquire diver location, diver path, and target locations. The INSS hardware consists of a unique, simple, single beam high resolution sonar, an acoustic navigation systems, an electronic depth gauge, compass, and GPS and RF interfaces, all integrated with a standard 486 based PC. These diver sonars have been evaluated by the very shallow water mine countermeasure detachment since spring 1997. Results are very positive, showing significantly greater capabilities than current diver held systems. For example, the detection ranges are increased over existing systems, and the system allows the divers to classify mines at a significant stand off range. As a result, the INSS design has been chosen for acquisition as the next generation diver navigation and sonar system. The EDMs for this system will be designed and built by ARL:UT during 1998 and 1999 with production planned in 2000.

Bathymetry mapping using a GPS-sonar equipped remote control boat: Application in waste stabilisation ponds

NASA Astrophysics Data System (ADS)

Coggins, Liah; Ghadouani, Anas; Ghisalberti, Marco

2014-05-01

Traditionally, bathymetry mapping of ponds, lakes and rivers have used techniques which are low in spatial resolution, sometimes subjective in terms of precision and accuracy, labour intensive, and that require a high level of safety precautions. In waste stabilisation ponds (WSP) in particular, sludge heights, and thus sludge volume, are commonly measured using a sludge judge (a clear plastic pipe with length markings). A remote control boat fitted with a GPS-equipped sonar unit can improve the resolution of depth measurements, and reduce safety and labour requirements. Sonar devices equipped with GPS technology, also known as fish finders, are readily available and widely used by people in boating. Through the use of GPS technology in conjunction with sonar, the location and depth can be recorded electronically onto a memory card. However, despite its high applicability to the field, this technology has so far been underutilised. In the case of WSP, the sonar can measure the water depth to the top of the sludge layer, which can then be used to develop contour maps of sludge distribution and to determine sludge volume. The coupling of sonar technology with a remotely operative vehicle has several advantages of traditional measurement techniques, particularly in removing human subjectivity of readings, and the sonar being able to collect more data points in a shorter period of time, and continuously, with a much higher spatial resolution. The GPS-sonar equipped remote control boat has been tested on in excess of 50 WSP within Western Australia, and has shown a very strong correlation (R2 = 0.98) between spot readings taken with the sonar compared to a sludge judge. This has shown that the remote control boat with GPS-sonar device is capable of providing sludge bathymetry with greatly increased spatial resolution, while greatly reducing profiling time. Remotely operated vehicles, such as the one built in this study, are useful for not only determining sludge distribution, but also in calculating sludge accumulation rates, and in evaluating pond hydraulic efficiency (e.g., as input bathymetry for computational fluid dynamics models). This technology is not limited to application for wastewater management, and could potentially have a wider application in the monitoring of other small to medium water bodies, including reservoirs, channels, recreational water bodies, river beds, mine tailings dams and commercial ports.

Lack of behavioural responses of humpback whales (Megaptera novaeangliae) indicate limited effectiveness of sonar mitigation.

PubMed

Wensveen, Paul J; Kvadsheim, Petter H; Lam, Frans-Peter A; von Benda-Beckmann, Alexander M; Sivle, Lise D; Visser, Fleur; Curé, Charlotte; Tyack, Peter L; Miller, Patrick J O

2017-11-15

Exposure to underwater sound can cause permanent hearing loss and other physiological effects in marine animals. To reduce this risk, naval sonars are sometimes gradually increased in intensity at the start of transmission ('ramp-up'). Here, we conducted experiments in which tagged humpback whales were approached with a ship to test whether a sonar operation preceded by ramp-up reduced three risk indicators - maximum sound pressure level (SPL max ), cumulative sound exposure level (SEL cum ) and minimum source-whale range ( R min ) - compared with a sonar operation not preceded by ramp-up. Whales were subject to one no-sonar control session and either two successive ramp-up sessions (RampUp1, RampUp2) or a ramp-up session (RampUp1) and a full-power session (FullPower). Full-power sessions were conducted only twice; for other whales we used acoustic modelling that assumed transmission of the full-power sequence during their no-sonar control. Averaged over all whales, risk indicators in RampUp1 ( n =11) differed significantly from those in FullPower ( n =12) by -3.0 dB (SPL max ), -2.0 dB (SEL cum ) and +168 m ( R min ), but not significantly from those in RampUp2 ( n =9). Only five whales in RampUp1, four whales in RampUp2 and none in FullPower or control sessions avoided the sound source. For RampUp1, we found statistically significant differences in risk indicators between whales that avoided the sonar and whales that did not: -4.7 dB (SPL max ), -3.4 dB (SEL cum ) and +291 m ( R min ). In contrast, for RampUp2, these differences were smaller and not significant. This study suggests that sonar ramp-up has a positive but limited mitigative effect for humpback whales overall, but that ramp-up can reduce the risk of harm more effectively in situations when animals are more responsive and likely to avoid the sonar, e.g. owing to novelty of the stimulus, when they are in the path of an approaching sonar ship. © 2017. Published by The Company of Biologists Ltd.

Finger-vein and fingerprint recognition based on a feature-level fusion method

NASA Astrophysics Data System (ADS)

Yang, Jinfeng; Hong, Bofeng

2013-07-01

Multimodal biometrics based on the finger identification is a hot topic in recent years. In this paper, a novel fingerprint-vein based biometric method is proposed to improve the reliability and accuracy of the finger recognition system. First, the second order steerable filters are used here to enhance and extract the minutiae features of the fingerprint (FP) and finger-vein (FV). Second, the texture features of fingerprint and finger-vein are extracted by a bank of Gabor filter. Third, a new triangle-region fusion method is proposed to integrate all the fingerprint and finger-vein features in feature-level. Thus, the fusion features contain both the finger texture-information and the minutiae triangular geometry structure. Finally, experimental results performed on the self-constructed finger-vein and fingerprint databases are shown that the proposed method is reliable and precise in personal identification.

HMMBinder: DNA-Binding Protein Prediction Using HMM Profile Based Features.

PubMed

Zaman, Rianon; Chowdhury, Shahana Yasmin; Rashid, Mahmood A; Sharma, Alok; Dehzangi, Abdollah; Shatabda, Swakkhar

2017-01-01

DNA-binding proteins often play important role in various processes within the cell. Over the last decade, a wide range of classification algorithms and feature extraction techniques have been used to solve this problem. In this paper, we propose a novel DNA-binding protein prediction method called HMMBinder. HMMBinder uses monogram and bigram features extracted from the HMM profiles of the protein sequences. To the best of our knowledge, this is the first application of HMM profile based features for the DNA-binding protein prediction problem. We applied Support Vector Machines (SVM) as a classification technique in HMMBinder. Our method was tested on standard benchmark datasets. We experimentally show that our method outperforms the state-of-the-art methods found in the literature.

Thermography based diagnosis of ruptured anterior cruciate ligament (ACL) in canines

NASA Astrophysics Data System (ADS)

Lama, Norsang; Umbaugh, Scott E.; Mishra, Deependra; Dahal, Rohini; Marino, Dominic J.; Sackman, Joseph

2016-09-01

Anterior cruciate ligament (ACL) rupture in canines is a common orthopedic injury in veterinary medicine. Veterinarians use both imaging and non-imaging methods to diagnose the disease. Common imaging methods such as radiography, computed tomography (CT scan) and magnetic resonance imaging (MRI) have some disadvantages: expensive setup, high dose of radiation, and time-consuming. In this paper, we present an alternative diagnostic method based on feature extraction and pattern classification (FEPC) to diagnose abnormal patterns in ACL thermograms. The proposed method was experimented with a total of 30 thermograms for each camera view (anterior, lateral and posterior) including 14 disease and 16 non-disease cases provided from Long Island Veterinary Specialists. The normal and abnormal patterns in thermograms are analyzed in two steps: feature extraction and pattern classification. Texture features based on gray level co-occurrence matrices (GLCM), histogram features and spectral features are extracted from the color normalized thermograms and the computed feature vectors are applied to Nearest Neighbor (NN) classifier, K-Nearest Neighbor (KNN) classifier and Support Vector Machine (SVM) classifier with leave-one-out validation method. The algorithm gives the best classification success rate of 86.67% with a sensitivity of 85.71% and a specificity of 87.5% in ACL rupture detection using NN classifier for the lateral view and Norm-RGB-Lum color normalization method. Our results show that the proposed method has the potential to detect ACL rupture in canines.

Classification of epileptic EEG signals based on simple random sampling and sequential feature selection.

PubMed

Ghayab, Hadi Ratham Al; Li, Yan; Abdulla, Shahab; Diykh, Mohammed; Wan, Xiangkui

2016-06-01

Electroencephalogram (EEG) signals are used broadly in the medical fields. The main applications of EEG signals are the diagnosis and treatment of diseases such as epilepsy, Alzheimer, sleep problems and so on. This paper presents a new method which extracts and selects features from multi-channel EEG signals. This research focuses on three main points. Firstly, simple random sampling (SRS) technique is used to extract features from the time domain of EEG signals. Secondly, the sequential feature selection (SFS) algorithm is applied to select the key features and to reduce the dimensionality of the data. Finally, the selected features are forwarded to a least square support vector machine (LS_SVM) classifier to classify the EEG signals. The LS_SVM classifier classified the features which are extracted and selected from the SRS and the SFS. The experimental results show that the method achieves 99.90, 99.80 and 100 % for classification accuracy, sensitivity and specificity, respectively.

Classification of speech dysfluencies using LPC based parameterization techniques.

PubMed

Hariharan, M; Chee, Lim Sin; Ai, Ooi Chia; Yaacob, Sazali

2012-06-01

The goal of this paper is to discuss and compare three feature extraction methods: Linear Predictive Coefficients (LPC), Linear Prediction Cepstral Coefficients (LPCC) and Weighted Linear Prediction Cepstral Coefficients (WLPCC) for recognizing the stuttered events. Speech samples from the University College London Archive of Stuttered Speech (UCLASS) were used for our analysis. The stuttered events were identified through manual segmentation and were used for feature extraction. Two simple classifiers namely, k-nearest neighbour (kNN) and Linear Discriminant Analysis (LDA) were employed for speech dysfluencies classification. Conventional validation method was used for testing the reliability of the classifier results. The study on the effect of different frame length, percentage of overlapping, value of ã in a first order pre-emphasizer and different order p were discussed. The speech dysfluencies classification accuracy was found to be improved by applying statistical normalization before feature extraction. The experimental investigation elucidated LPC, LPCC and WLPCC features can be used for identifying the stuttered events and WLPCC features slightly outperforms LPCC features and LPC features.

Sensor Phenomenology and Feature Development for Improved Sonar-based Detection & Classification of Underwater UXO

DTIC Science & Technology

2009-12-01

the validity of approximating poroelastic media with acoustic or acoustic /elastic models , and to characterize how scattering physics will differ for...elastic buried object (yellow rectangle in the figure) in three types of environments: • (1) Model 1: acoustic layer on top of a poroelastic medium with a...porosity gradient and no viscous damping. • (2) Model 2: acoustic layer on top of a poroelastic medium with a porosity gradient and viscous damping

Multi-Sensory Features for Personnel Detection at Border Crossings

DTIC Science & Technology

2011-07-08

challenging problem. Video sensors consume high amounts of power and require a large volume for storage. Hence, it is preferable to use non- imaging sensors...temporal distribution of gait beats [5]. At border crossings, animals such as mules, horses, or donkeys are often known to carry loads. Animal hoof...field, passive ultrasonic, sonar, and both infrared and visi- ble video sensors. Each sensor suite is placed along the path with a spacing of 40 to

Study on the traditional pattern retrieval method of minorities in Gansu province

NASA Astrophysics Data System (ADS)

Zheng, Gang; Wang, Beizhan; Sun, Yuchun; Xu, Jin

2018-03-01

The traditional patterns of ethnic minorities in gansu province are ethnic arts with strong ethnic characteristics. It is the crystallization of the hard work and wisdom of minority nationalities in gansu province. Unique traditional patterns of ethnic minorities in Gansu province with rich ethnic folk arts, is the crystallization of geographical environment in Gansu minority diligence and wisdom. By using the Surf feature point identification algorithm, the feature point extractor in OpenCV is used to extract the feature points. And the feature points are applied to compare the pattern features to find patterns similar to the artistic features. The application of this method can quickly or efficiently extract pattern information in a database.

No-reference image quality assessment based on natural scene statistics and gradient magnitude similarity

NASA Astrophysics Data System (ADS)

Jia, Huizhen; Sun, Quansen; Ji, Zexuan; Wang, Tonghan; Chen, Qiang

2014-11-01

The goal of no-reference/blind image quality assessment (NR-IQA) is to devise a perceptual model that can accurately predict the quality of a distorted image as human opinions, in which feature extraction is an important issue. However, the features used in the state-of-the-art "general purpose" NR-IQA algorithms are usually natural scene statistics (NSS) based or are perceptually relevant; therefore, the performance of these models is limited. To further improve the performance of NR-IQA, we propose a general purpose NR-IQA algorithm which combines NSS-based features with perceptually relevant features. The new method extracts features in both the spatial and gradient domains. In the spatial domain, we extract the point-wise statistics for single pixel values which are characterized by a generalized Gaussian distribution model to form the underlying features. In the gradient domain, statistical features based on neighboring gradient magnitude similarity are extracted. Then a mapping is learned to predict quality scores using a support vector regression. The experimental results on the benchmark image databases demonstrate that the proposed algorithm correlates highly with human judgments of quality and leads to significant performance improvements over state-of-the-art methods.

Generalized Feature Extraction for Wrist Pulse Analysis: From 1-D Time Series to 2-D Matrix.

PubMed

Dimin Wang; Zhang, David; Guangming Lu

2017-07-01

Traditional Chinese pulse diagnosis, known as an empirical science, depends on the subjective experience. Inconsistent diagnostic results may be obtained among different practitioners. A scientific way of studying the pulse should be to analyze the objectified wrist pulse waveforms. In recent years, many pulse acquisition platforms have been developed with the advances in sensor and computer technology. And the pulse diagnosis using pattern recognition theories is also increasingly attracting attentions. Though many literatures on pulse feature extraction have been published, they just handle the pulse signals as simple 1-D time series and ignore the information within the class. This paper presents a generalized method of pulse feature extraction, extending the feature dimension from 1-D time series to 2-D matrix. The conventional wrist pulse features correspond to a particular case of the generalized models. The proposed method is validated through pattern classification on actual pulse records. Both quantitative and qualitative results relative to the 1-D pulse features are given through diabetes diagnosis. The experimental results show that the generalized 2-D matrix feature is effective in extracting both the periodic and nonperiodic information. And it is practical for wrist pulse analysis.

Research on feature extraction techniques of Hainan Li brocade pattern

NASA Astrophysics Data System (ADS)

Zhou, Yuping; Chen, Fuqiang; Zhou, Yuhua

2016-03-01

Hainan Li brocade skills has been listed as world non-material cultural heritage preservation, therefore, the research on Hainan Li brocade patterns plays an important role in Li brocade culture inheritance. The meaning of Li brocade patterns was analyzed and the shape feature extraction techniques to original Li brocade patterns were advanced in this paper, based on the contour tracking algorithm. First, edge detection was made on the design patterns, and then the morphological closing operation was used to smooth the image, and finally contour tracking was used to extract the outer contours of Li brocade patterns. The extracted contour features were processed by means of morphology, and digital characteristics of contours are obtained by invariant moments. At last, different patterns of Li brocade design are briefly analyzed according to the digital characteristics. The results showed that the pattern extraction method to Li brocade pattern shapes is feasible and effective according to above method.

Fault Diagnosis for Rotating Machinery: A Method based on Image Processing

PubMed Central

Lu, Chen; Wang, Yang; Ragulskis, Minvydas; Cheng, Yujie

2016-01-01

Rotating machinery is one of the most typical types of mechanical equipment and plays a significant role in industrial applications. Condition monitoring and fault diagnosis of rotating machinery has gained wide attention for its significance in preventing catastrophic accident and guaranteeing sufficient maintenance. With the development of science and technology, fault diagnosis methods based on multi-disciplines are becoming the focus in the field of fault diagnosis of rotating machinery. This paper presents a multi-discipline method based on image-processing for fault diagnosis of rotating machinery. Different from traditional analysis method in one-dimensional space, this study employs computing method in the field of image processing to realize automatic feature extraction and fault diagnosis in a two-dimensional space. The proposed method mainly includes the following steps. First, the vibration signal is transformed into a bi-spectrum contour map utilizing bi-spectrum technology, which provides a basis for the following image-based feature extraction. Then, an emerging approach in the field of image processing for feature extraction, speeded-up robust features, is employed to automatically exact fault features from the transformed bi-spectrum contour map and finally form a high-dimensional feature vector. To reduce the dimensionality of the feature vector, thus highlighting main fault features and reducing subsequent computing resources, t-Distributed Stochastic Neighbor Embedding is adopt to reduce the dimensionality of the feature vector. At last, probabilistic neural network is introduced for fault identification. Two typical rotating machinery, axial piston hydraulic pump and self-priming centrifugal pumps, are selected to demonstrate the effectiveness of the proposed method. Results show that the proposed method based on image-processing achieves a high accuracy, thus providing a highly effective means to fault diagnosis for rotating machinery. PMID:27711246

Fault Diagnosis for Rotating Machinery: A Method based on Image Processing.

PubMed

Lu, Chen; Wang, Yang; Ragulskis, Minvydas; Cheng, Yujie

2016-01-01

Rotating machinery is one of the most typical types of mechanical equipment and plays a significant role in industrial applications. Condition monitoring and fault diagnosis of rotating machinery has gained wide attention for its significance in preventing catastrophic accident and guaranteeing sufficient maintenance. With the development of science and technology, fault diagnosis methods based on multi-disciplines are becoming the focus in the field of fault diagnosis of rotating machinery. This paper presents a multi-discipline method based on image-processing for fault diagnosis of rotating machinery. Different from traditional analysis method in one-dimensional space, this study employs computing method in the field of image processing to realize automatic feature extraction and fault diagnosis in a two-dimensional space. The proposed method mainly includes the following steps. First, the vibration signal is transformed into a bi-spectrum contour map utilizing bi-spectrum technology, which provides a basis for the following image-based feature extraction. Then, an emerging approach in the field of image processing for feature extraction, speeded-up robust features, is employed to automatically exact fault features from the transformed bi-spectrum contour map and finally form a high-dimensional feature vector. To reduce the dimensionality of the feature vector, thus highlighting main fault features and reducing subsequent computing resources, t-Distributed Stochastic Neighbor Embedding is adopt to reduce the dimensionality of the feature vector. At last, probabilistic neural network is introduced for fault identification. Two typical rotating machinery, axial piston hydraulic pump and self-priming centrifugal pumps, are selected to demonstrate the effectiveness of the proposed method. Results show that the proposed method based on image-processing achieves a high accuracy, thus providing a highly effective means to fault diagnosis for rotating machinery.

A combination of feature extraction methods with an ensemble of different classifiers for protein structural class prediction problem.

PubMed

Dehzangi, Abdollah; Paliwal, Kuldip; Sharma, Alok; Dehzangi, Omid; Sattar, Abdul

2013-01-01

Better understanding of structural class of a given protein reveals important information about its overall folding type and its domain. It can also be directly used to provide critical information on general tertiary structure of a protein which has a profound impact on protein function determination and drug design. Despite tremendous enhancements made by pattern recognition-based approaches to solve this problem, it still remains as an unsolved issue for bioinformatics that demands more attention and exploration. In this study, we propose a novel feature extraction model that incorporates physicochemical and evolutionary-based information simultaneously. We also propose overlapped segmented distribution and autocorrelation-based feature extraction methods to provide more local and global discriminatory information. The proposed feature extraction methods are explored for 15 most promising attributes that are selected from a wide range of physicochemical-based attributes. Finally, by applying an ensemble of different classifiers namely, Adaboost.M1, LogitBoost, naive Bayes, multilayer perceptron (MLP), and support vector machine (SVM) we show enhancement of the protein structural class prediction accuracy for four popular benchmarks.

Method for indexing and retrieving manufacturing-specific digital imagery based on image content

DOEpatents

Ferrell, Regina K.; Karnowski, Thomas P.; Tobin, Jr., Kenneth W.

2004-06-15

A method for indexing and retrieving manufacturing-specific digital images based on image content comprises three steps. First, at least one feature vector can be extracted from a manufacturing-specific digital image stored in an image database. In particular, each extracted feature vector corresponds to a particular characteristic of the manufacturing-specific digital image, for instance, a digital image modality and overall characteristic, a substrate/background characteristic, and an anomaly/defect characteristic. Notably, the extracting step includes generating a defect mask using a detection process. Second, using an unsupervised clustering method, each extracted feature vector can be indexed in a hierarchical search tree. Third, a manufacturing-specific digital image associated with a feature vector stored in the hierarchicial search tree can be retrieved, wherein the manufacturing-specific digital image has image content comparably related to the image content of the query image. More particularly, can include two data reductions, the first performed based upon a query vector extracted from a query image. Subsequently, a user can select relevant images resulting from the first data reduction. From the selection, a prototype vector can be calculated, from which a second-level data reduction can be performed. The second-level data reduction can result in a subset of feature vectors comparable to the prototype vector, and further comparable to the query vector. An additional fourth step can include managing the hierarchical search tree by substituting a vector average for several redundant feature vectors encapsulated by nodes in the hierarchical search tree.

Classification of clinically useful sentences in clinical evidence resources.

PubMed

Morid, Mohammad Amin; Fiszman, Marcelo; Raja, Kalpana; Jonnalagadda, Siddhartha R; Del Fiol, Guilherme

2016-04-01

Most patient care questions raised by clinicians can be answered by online clinical knowledge resources. However, important barriers still challenge the use of these resources at the point of care. To design and assess a method for extracting clinically useful sentences from synthesized online clinical resources that represent the most clinically useful information for directly answering clinicians' information needs. We developed a Kernel-based Bayesian Network classification model based on different domain-specific feature types extracted from sentences in a gold standard composed of 18 UpToDate documents. These features included UMLS concepts and their semantic groups, semantic predications extracted by SemRep, patient population identified by a pattern-based natural language processing (NLP) algorithm, and cue words extracted by a feature selection technique. Algorithm performance was measured in terms of precision, recall, and F-measure. The feature-rich approach yielded an F-measure of 74% versus 37% for a feature co-occurrence method (p<0.001). Excluding predication, population, semantic concept or text-based features reduced the F-measure to 62%, 66%, 58% and 69% respectively (p<0.01). The classifier applied to Medline sentences reached an F-measure of 73%, which is equivalent to the performance of the classifier on UpToDate sentences (p=0.62). The feature-rich approach significantly outperformed general baseline methods. This approach significantly outperformed classifiers based on a single type of feature. Different types of semantic features provided a unique contribution to overall classification performance. The classifier's model and features used for UpToDate generalized well to Medline abstracts. Copyright © 2016 Elsevier Inc. All rights reserved.

Changes in dive behavior during naval sonar exposure in killer whales, long-finned pilot whales, and sperm whales.

PubMed

Sivle, L D; Kvadsheim, P H; Fahlman, A; Lam, F P A; Tyack, P L; Miller, P J O

2012-01-01

Anthropogenic underwater sound in the environment might potentially affect the behavior of marine mammals enough to have an impact on their reproduction and survival. Diving behavior of four killer whales (Orcinus orca), seven long-finned pilot whales (Globicephala melas), and four sperm whales (Physeter macrocephalus) was studied during controlled exposures to naval sonar [low frequency active sonar (LFAS): 1-2 kHz and mid frequency active sonar (MFAS): 6-7 kHz] during three field seasons (2006-2009). Diving behavior was monitored before, during and after sonar exposure using an archival tag placed on the animal with suction cups. The tag recorded the animal's vertical movement, and additional data on horizontal movement and vocalizations were used to determine behavioral modes. Killer whales that were conducting deep dives at sonar onset changed abruptly to shallow diving (ShD) during LFAS, while killer whales conducting deep dives at the onset of MFAS did not alter dive mode. When in ShD mode at sonar onset, killer whales did not change their diving behavior. Pilot and sperm whales performed normal deep dives (NDD) during MFAS exposure. During LFAS exposures, long-finned pilot whales mostly performed fewer deep dives and some sperm whales performed shallower and shorter dives. Acoustic recording data presented previously indicates that deep diving (DD) is associated with feeding. Therefore, the observed changes in dive behavior of the three species could potentially reduce the foraging efficiency of the affected animals.

Changes in dive behavior during naval sonar exposure in killer whales, long-finned pilot whales, and sperm whales

PubMed Central

Sivle, L. D.; Kvadsheim, P. H.; Fahlman, A.; Lam, F. P. A.; Tyack, P. L.; Miller, P. J. O.

2012-01-01

Anthropogenic underwater sound in the environment might potentially affect the behavior of marine mammals enough to have an impact on their reproduction and survival. Diving behavior of four killer whales (Orcinus orca), seven long-finned pilot whales (Globicephala melas), and four sperm whales (Physeter macrocephalus) was studied during controlled exposures to naval sonar [low frequency active sonar (LFAS): 1–2 kHz and mid frequency active sonar (MFAS): 6–7 kHz] during three field seasons (2006–2009). Diving behavior was monitored before, during and after sonar exposure using an archival tag placed on the animal with suction cups. The tag recorded the animal's vertical movement, and additional data on horizontal movement and vocalizations were used to determine behavioral modes. Killer whales that were conducting deep dives at sonar onset changed abruptly to shallow diving (ShD) during LFAS, while killer whales conducting deep dives at the onset of MFAS did not alter dive mode. When in ShD mode at sonar onset, killer whales did not change their diving behavior. Pilot and sperm whales performed normal deep dives (NDD) during MFAS exposure. During LFAS exposures, long-finned pilot whales mostly performed fewer deep dives and some sperm whales performed shallower and shorter dives. Acoustic recording data presented previously indicates that deep diving (DD) is associated with feeding. Therefore, the observed changes in dive behavior of the three species could potentially reduce the foraging efficiency of the affected animals. PMID:23087648

a Landmark Extraction Method Associated with Geometric Features and Location Distribution

NASA Astrophysics Data System (ADS)

Zhang, W.; Li, J.; Wang, Y.; Xiao, Y.; Liu, P.; Zhang, S.

2018-04-01

Landmark plays an important role in spatial cognition and spatial knowledge organization. Significance measuring model is the main method of landmark extraction. It is difficult to take account of the spatial distribution pattern of landmarks because that the significance of landmark is built in one-dimensional space. In this paper, we start with the geometric features of the ground object, an extraction method based on the target height, target gap and field of view is proposed. According to the influence region of Voronoi Diagram, the description of target gap is established to the geometric representation of the distribution of adjacent targets. Then, segmentation process of the visual domain of Voronoi K order adjacent is given to set up target view under the multi view; finally, through three kinds of weighted geometric features, the landmarks are identified. Comparative experiments show that this method has a certain coincidence degree with the results of traditional significance measuring model, which verifies the effectiveness and reliability of the method and reduces the complexity of landmark extraction process without losing the reference value of landmark.

A new method for recognizing hand configurations of Brazilian gesture language.

PubMed

Costa Filho, C F F; Dos Santos, B L; de Souza, R S; Dos Santos, J R; Costa, M G F

2016-08-01

This paper describes a new method for recognizing hand configurations of the Brazilian Gesture Language - LIBRAS - using depth maps obtained with a Kinect® camera. The proposed method comprised three phases: hand segmentation, feature extraction, and classification. The segmentation phase is independent from the background and depends only on pixel depth information. Using geometric operations and numerical normalization, the feature extraction process was done independent from rotation and translation. The features are extracted employing two techniques: (2D)2LDA and (2D)2PCA. The classification is made with a novelty classifier. A robust database was constructed for classifier evaluation, with 12,200 images of LIBRAS and 200 gestures of each hand configuration. The best accuracy obtained was 95.41%, which was greater than previous values obtained in the literature.

Vessel extraction in retinal images using automatic thresholding and Gabor Wavelet.

PubMed

Ali, Aziah; Hussain, Aini; Wan Zaki, Wan Mimi Diyana

2017-07-01

Retinal image analysis has been widely used for early detection and diagnosis of multiple systemic diseases. Accurate vessel extraction in retinal image is a crucial step towards a fully automated diagnosis system. This work affords an efficient unsupervised method for extracting blood vessels from retinal images by combining existing Gabor Wavelet (GW) method with automatic thresholding. Green channel image is extracted from color retinal image and used to produce Gabor feature image using GW. Both green channel image and Gabor feature image undergo vessel-enhancement step in order to highlight blood vessels. Next, the two vessel-enhanced images are transformed to binary images using automatic thresholding before combined to produce the final vessel output. Combining the images results in significant improvement of blood vessel extraction performance compared to using individual image. Effectiveness of the proposed method was proven via comparative analysis with existing methods validated using publicly available database, DRIVE.

Heuristic algorithm for optical character recognition of Arabic script

NASA Astrophysics Data System (ADS)

Yarman-Vural, Fatos T.; Atici, A.

1996-02-01

In this paper, a heuristic method is developed for segmentation, feature extraction and recognition of the Arabic script. The study is part of a large project for the transcription of the documents in Ottoman Archives. A geometrical and topological feature analysis method is developed for segmentation and feature extraction stages. Chain code transformation is applied to main strokes of the characters which are then classified by the hidden Markov model (HMM) in the recognition stage. Experimental results indicate that the performance of the proposed method is impressive, provided that the thinning process does not yield spurious branches.

Finger-Vein Verification Based on Multi-Features Fusion

PubMed Central

Qin, Huafeng; Qin, Lan; Xue, Lian; He, Xiping; Yu, Chengbo; Liang, Xinyuan

2013-01-01

This paper presents a new scheme to improve the performance of finger-vein identification systems. Firstly, a vein pattern extraction method to extract the finger-vein shape and orientation features is proposed. Secondly, to accommodate the potential local and global variations at the same time, a region-based matching scheme is investigated by employing the Scale Invariant Feature Transform (SIFT) matching method. Finally, the finger-vein shape, orientation and SIFT features are combined to further enhance the performance. The experimental results on databases of 426 and 170 fingers demonstrate the consistent superiority of the proposed approach. PMID:24196433

Mobile sailing robot for automatic estimation of fish density and monitoring water quality.

PubMed

Koprowski, Robert; Wróbel, Zygmunt; Kleszcz, Agnieszka; Wilczyński, Sławomir; Woźnica, Andrzej; Łozowski, Bartosz; Pilarczyk, Maciej; Karczewski, Jerzy; Migula, Paweł

2013-07-01

The paper presents the methodology and the algorithm developed to analyze sonar images focused on fish detection in small water bodies and measurement of their parameters: volume, depth and the GPS location. The final results are stored in a table and can be exported to any numerical environment for further analysis. The measurement method for estimating the number of fish using the automatic robot is based on a sequential calculation of the number of occurrences of fish on the set trajectory. The data analysis from the sonar concerned automatic recognition of fish using the methods of image analysis and processing. Image analysis algorithm, a mobile robot together with its control in the 2.4 GHz band and full cryptographic communication with the data archiving station was developed as part of this study. For the three model fish ponds where verification of fish catches was carried out (548, 171 and 226 individuals), the measurement error for the described method was not exceeded 8%. Created robot together with the developed software has features for remote work also in the variety of harsh weather and environmental conditions, is fully automated and can be remotely controlled using Internet. Designed system enables fish spatial location (GPS coordinates and the depth). The purpose of the robot is a non-invasive measurement of the number of fish in water reservoirs and a measurement of the quality of drinking water consumed by humans, especially in situations where local sources of pollution could have a significant impact on the quality of water collected for water treatment for people and when getting to these places is difficult. The systematically used robot equipped with the appropriate sensors, can be part of early warning system against the pollution of water used by humans (drinking water, natural swimming pools) which can be dangerous for their health.

Capability of geometric features to classify ships in SAR imagery

NASA Astrophysics Data System (ADS)

Lang, Haitao; Wu, Siwen; Lai, Quan; Ma, Li

2016-10-01

Ship classification in synthetic aperture radar (SAR) imagery has become a new hotspot in remote sensing community for its valuable potential in many maritime applications. Several kinds of ship features, such as geometric features, polarimetric features, and scattering features have been widely applied on ship classification tasks. Compared with polarimetric features and scattering features, which are subject to SAR parameters (e.g., sensor type, incidence angle, polarization, etc.) and environment factors (e.g., sea state, wind, wave, current, etc.), geometric features are relatively independent of SAR and environment factors, and easy to be extracted stably from SAR imagery. In this paper, the capability of geometric features to classify ships in SAR imagery with various resolution has been investigated. Firstly, the relationship between the geometric feature extraction accuracy and the SAR imagery resolution is analyzed. It shows that the minimum bounding rectangle (MBR) of ship can be extracted exactly in terms of absolute precision by the proposed automatic ship-sea segmentation method. Next, six simple but effective geometric features are extracted to build a ship representation for the subsequent classification task. These six geometric features are composed of length (f1), width (f2), area (f3), perimeter (f4), elongatedness (f5) and compactness (f6). Among them, two basic features, length (f1) and width (f2), are directly extracted based on the MBR of ship, the other four are derived from those two basic features. The capability of the utilized geometric features to classify ships are validated on two data set with different image resolutions. The results show that the performance of ship classification solely by geometric features is close to that obtained by the state-of-the-art methods, which obtained by a combination of multiple kinds of features, including scattering features and geometric features after a complex feature selection process.