Science.gov

Sample records for feature detection systems

  1. Hydrogen Fire Detection System Features Sharp Discrimination

    NASA Technical Reports Server (NTRS)

    Bright, C. S.

    1966-01-01

    Hydrogen fire detection system discovers fires by detecting the flickering ultraviolet radiation emitted by the OH molecule, a short-lived intermediate combustion product found in hydrogen-air flames. In a space application, the system discriminates against false signals from sunlight and rocket engine exhaust plume radiation.

  2. Feature Detection Systems Enhance Satellite Imagery

    NASA Technical Reports Server (NTRS)

    2009-01-01

    -resolution satellites, which provide the benefit of images detailed enough to reveal large features like highways while still broad enough for global coverage, continue to scan the entirety of the Earth s surface. In 2012, NASA plans to launch the Landsat Data Continuity Mission (LDCM), or Landsat 8, to extend the Landsat program s contributions to cartography, water management, natural disaster relief planning, and more.

  3. Autonomous rendezvous and feature detection system using TV imagery

    NASA Technical Reports Server (NTRS)

    Rice, R. B., Jr.

    1977-01-01

    Algorithms and equations are used for conversion of standard television imaging system information into directly usable spatial and dimensional information. System allows utilization of spacecraft imagery system as sensor in application to operations such as deriving spacecraft steering signal, tracking, autonomous rendezvous and docking and ranging.

  4. Modeling Network Intrusion Detection System Using Feature Selection and Parameters Optimization

    NASA Astrophysics Data System (ADS)

    Kim, Dong Seong; Park, Jong Sou

    Previous approaches for modeling Intrusion Detection System (IDS) have been on twofold: improving detection model(s) in terms of (i) feature selection of audit data through wrapper and filter methods and (ii) parameters optimization of detection model design, based on classification, clustering algorithms, etc. In this paper, we present three approaches to model IDS in the context of feature selection and parameters optimization: First, we present Fusion of Genetic Algorithm (GA) and Support Vector Machines (SVM) (FuGAS), which employs combinations of GA and SVM through genetic operation and it is capable of building an optimal detection model with only selected important features and optimal parameters value. Second, we present Correlation-based Hybrid Feature Selection (CoHyFS), which utilizes a filter method in conjunction of GA for feature selection in order to reduce long training time. Third, we present Simultaneous Intrinsic Model Identification (SIMI), which adopts Random Forest (RF) and shows better intrusion detection rates and feature selection results, along with no additional computational overheads. We show the experimental results and analysis of three approaches on KDD 1999 intrusion detection datasets.

  5. An on-board pedestrian detection and warning system with features of side pedestrian

    NASA Astrophysics Data System (ADS)

    Cheng, Ruzhong; Zhao, Yong; Wong, ChupChung; Chan, KwokPo; Xu, Jiayao; Wang, Xin'an

    2012-01-01

    Automotive Active Safety(AAS) is the main branch of intelligence automobile study and pedestrian detection is the key problem of AAS, because it is related with the casualties of most vehicle accidents. For on-board pedestrian detection algorithms, the main problem is to balance efficiency and accuracy to make the on-board system available in real scenes, so an on-board pedestrian detection and warning system with the algorithm considered the features of side pedestrian is proposed. The system includes two modules, pedestrian detecting and warning module. Haar feature and a cascade of stage classifiers trained by Adaboost are first applied, and then HOG feature and SVM classifier are used to refine false positives. To make these time-consuming algorithms available in real-time use, a divide-window method together with operator context scanning(OCS) method are applied to increase efficiency. To merge the velocity information of the automotive, the distance of the detected pedestrian is also obtained, so the system could judge if there is a potential danger for the pedestrian in the front. With a new dataset captured in urban environment with side pedestrians on zebra, the embedded system and its algorithm perform an on-board available result on side pedestrian detection.

  6. Modeling and Detecting Feature Interactions among Integrated Services of Home Network Systems

    NASA Astrophysics Data System (ADS)

    Igaki, Hiroshi; Nakamura, Masahide

    This paper presents a framework for formalizing and detecting feature interactions (FIs) in the emerging smart home domain. We first establish a model of home network system (HNS), where every networked appliance (or the HNS environment) is characterized as an object consisting of properties and methods. Then, every HNS service is defined as a sequence of method invocations of the appliances. Within the model, we next formalize two kinds of FIs: (a) appliance interactions and (b) environment interactions. An appliance interaction occurs when two method invocations conflict on the same appliance, whereas an environment interaction arises when two method invocations conflict indirectly via the environment. Finally, we propose offline and online methods that detect FIs before service deployment and during execution, respectively. Through a case study with seven practical services, it is shown that the proposed framework is generic enough to capture feature interactions in HNS integrated services. We also discuss several FI resolution schemes within the proposed framework.

  7. A two-view ultrasound CAD system for spina bifida detection using Zernike features

    NASA Astrophysics Data System (ADS)

    Konur, Umut; Gürgen, Fikret; Varol, Füsun

    2011-03-01

    In this work, we address a very specific CAD (Computer Aided Detection/Diagnosis) problem and try to detect one of the relatively common birth defects - spina bifida, in the prenatal period. To do this, fetal ultrasound images are used as the input imaging modality, which is the most convenient so far. Our approach is to decide using two particular types of views of the fetal neural tube. Transcerebellar head (i.e. brain) and transverse (axial) spine images are processed to extract features which are then used to classify healthy (normal), suspicious (probably defective) and non-decidable cases. Decisions raised by two independent classifiers may be individually treated, or if desired and data related to both modalities are available, those decisions can be combined to keep matters more secure. Even more security can be attained by using more than two modalities and base the final decision on all those potential classifiers. Our current system relies on feature extraction from images for cases (for particular patients). The first step is image preprocessing and segmentation to get rid of useless image pixels and represent the input in a more compact domain, which is hopefully more representative for good classification performance. Next, a particular type of feature extraction, which uses Zernike moments computed on either B/W or gray-scale image segments, is performed. The aim here is to obtain values for indicative markers that signal the presence of spina bifida. Markers differ depending on the image modality being used. Either shape or texture information captured by moments may propose useful features. Finally, SVM is used to train classifiers to be used as decision makers. Our experimental results show that a promising CAD system can be actualized for the specific purpose. On the other hand, the performance of such a system would highly depend on the qualities of image preprocessing, segmentation, feature extraction and comprehensiveness of image data.

  8. Performance Analysis of Grey-World-based Feature Detection and Matching for Mobile Positioning Systems

    NASA Astrophysics Data System (ADS)

    Bejuri, Wan Mohd Yaakob Wan; Mohamad, Mohd Murtadha

    2014-11-01

    This paper introduces a new grey-world-based feature detection and matching algorithm, intended for use with mobile positioning systems. This approach uses a combination of a wireless local area network (WLAN) and a mobile phone camera to determine positioning in an illumination environment using a practical and pervasive approach. The signal combination is based on retrieved signal strength from the WLAN access point and the image processing information from the building hallways. The results show our method can handle information better than Harlan Hile's method relative to the illumination environment, producing lower illumination error in five (5) different environments.

  9. Non-invasive health status detection system using Gabor filters based on facial block texture features.

    PubMed

    Shu, Ting; Zhang, Bob

    2015-04-01

    Blood tests allow doctors to check for certain diseases and conditions. However, using a syringe to extract the blood can be deemed invasive, slightly painful, and its analysis time consuming. In this paper, we propose a new non-invasive system to detect the health status (Healthy or Diseased) of an individual based on facial block texture features extracted using the Gabor filter. Our system first uses a non-invasive capture device to collect facial images. Next, four facial blocks are located on these images to represent them. Afterwards, each facial block is convolved with a Gabor filter bank to calculate its texture value. Classification is finally performed using K-Nearest Neighbor and Support Vector Machines via a Library for Support Vector Machines (with four kernel functions). The system was tested on a dataset consisting of 100 Healthy and 100 Diseased (with 13 forms of illnesses) samples. Experimental results show that the proposed system can detect the health status with an accuracy of 93 %, a sensitivity of 94 %, a specificity of 92 %, using a combination of the Gabor filters and facial blocks. PMID:25722202

  10. Combined optimization of image-gathering and image-processing systems for scene feature detection

    NASA Technical Reports Server (NTRS)

    Halyo, Nesim; Arduini, Robert F.; Samms, Richard W.

    1987-01-01

    The relationship between the image gathering and image processing systems for minimum mean squared error estimation of scene characteristics is investigated. A stochastic optimization problem is formulated where the objective is to determine a spatial characteristic of the scene rather than a feature of the already blurred, sampled and noisy image data. An analytical solution for the optimal characteristic image processor is developed. The Wiener filter for the sampled image case is obtained as a special case, where the desired characteristic is scene restoration. Optimal edge detection is investigated using the Laplacian operator x G as the desired characteristic, where G is a two dimensional Gaussian distribution function. It is shown that the optimal edge detector compensates for the blurring introduced by the image gathering optics, and notably, that it is not circularly symmetric. The lack of circular symmetry is largely due to the geometric effects of the sampling lattice used in image acquisition. The optimal image gathering optical transfer function is also investigated and the results of a sensitivity analysis are shown.

  11. An ultra low power feature extraction and classification system for wearable seizure detection.

    PubMed

    Page, Adam; Pramod Tim Oates, Siddharth; Mohsenin, Tinoosh

    2015-08-01

    In this paper we explore the use of a variety of machine learning algorithms for designing a reliable and low-power, multi-channel EEG feature extractor and classifier for predicting seizures from electroencephalographic data (scalp EEG). Different machine learning classifiers including k-nearest neighbor, support vector machines, naïve Bayes, logistic regression, and neural networks are explored with the goal of maximizing detection accuracy while minimizing power, area, and latency. The input to each machine learning classifier is a 198 feature vector containing 9 features for each of the 22 EEG channels obtained over 1-second windows. All classifiers were able to obtain F1 scores over 80% and onset sensitivity of 100% when tested on 10 patients. Among five different classifiers that were explored, logistic regression (LR) proved to have minimum hardware complexity while providing average F-1 score of 91%. Both ASIC and FPGA implementations of logistic regression are presented and show the smallest area, power consumption, and the lowest latency when compared to the previous work. PMID:26737931

  12. Monocular precrash vehicle detection: features and classifiers.

    PubMed

    Sun, Zehang; Bebis, George; Miller, Ronald

    2006-07-01

    Robust and reliable vehicle detection from images acquired by a moving vehicle (i.e., on-road vehicle detection) is an important problem with applications to driver assistance systems and autonomous, self-guided vehicles. The focus of this work is on the issues of feature extraction and classification for rear-view vehicle detection. Specifically, by treating the problem of vehicle detection as a two-class classification problem, we have investigated several different feature extraction methods such as principal component analysis, wavelets, and Gabor filters. To evaluate the extracted features, we have experimented with two popular classifiers, neural networks and support vector machines (SVMs). Based on our evaluation results, we have developed an on-board real-time monocular vehicle detection system that is capable of acquiring grey-scale images, using Ford's proprietary low-light camera, achieving an average detection rate of 10 Hz. Our vehicle detection algorithm consists of two main steps: a multiscale driven hypothesis generation step and an appearance-based hypothesis verification step. During the hypothesis generation step, image locations where vehicles might be present are extracted. This step uses multiscale techniques not only to speed up detection, but also to improve system robustness. The appearance-based hypothesis verification step verifies the hypotheses using Gabor features and SVMs. The system has been tested in Ford's concept vehicle under different traffic conditions (e.g., structured highway, complex urban streets, and varying weather conditions), illustrating good performance. PMID:16830921

  13. Sensor feature fusion for detecting buried objects

    SciTech Connect

    Clark, G.A.; Sengupta, S.K.; Sherwood, R.J.; Hernandez, J.E.; Buhl, M.R.; Schaich, P.C.; Kane, R.J.; Barth, M.J.; DelGrande, N.K.

    1993-04-01

    Given multiple registered images of the earth`s surface from dual-band sensors, our system fuses information from the sensors to reduce the effects of clutter and improve the ability to detect buried or surface target sites. The sensor suite currently includes two sensors (5 micron and 10 micron wavelengths) and one ground penetrating radar (GPR) of the wide-band pulsed synthetic aperture type. We use a supervised teaming pattern recognition approach to detect metal and plastic land mines buried in soil. The overall process consists of four main parts: Preprocessing, feature extraction, feature selection, and classification. These parts are used in a two step process to classify a subimage. Thee first step, referred to as feature selection, determines the features of sub-images which result in the greatest separability among the classes. The second step, image labeling, uses the selected features and the decisions from a pattern classifier to label the regions in the image which are likely to correspond to buried mines. We extract features from the images, and use feature selection algorithms to select only the most important features according to their contribution to correct detections. This allows us to save computational complexity and determine which of the sensors add value to the detection system. The most important features from the various sensors are fused using supervised teaming pattern classifiers (including neural networks). We present results of experiments to detect buried land mines from real data, and evaluate the usefulness of fusing feature information from multiple sensor types, including dual-band infrared and ground penetrating radar. The novelty of the work lies mostly in the combination of the algorithms and their application to the very important and currently unsolved operational problem of detecting buried land mines from an airborne standoff platform.

  14. Investigation of atmospheric insect wing-beat frequencies and iridescence features using a multispectral kHz remote detection system

    NASA Astrophysics Data System (ADS)

    Gebru, Alem; Rohwer, Erich; Neethling, Pieter; Brydegaard, Mikkel

    2014-01-01

    Quantitative investigation of insect activity in their natural habitat is a challenging task for entomologists. It is difficult to address questions such as flight direction, predation strength, and overall activities using the current techniques such as traps and sweep nets. A multispectral kHz remote detection system using sunlight as an illumination source is presented. We explore the possibilities of remote optical classification of insects based on their wing-beat frequencies and iridescence features. It is shown that the wing-beat frequency of the fast insect events can be resolved by implementing high-sampling frequency. The iridescence features generated from the change of color in two channels (visible and near-infrared) during wing-beat cycle are presented. We show that the shape of the wing-beat trajectory is different for different insects. The flight direction of an atmospheric insect is also determined using a silicon quadrant detector.

  15. Fall Detection Using Smartphone Audio Features.

    PubMed

    Cheffena, Michael

    2016-07-01

    An automated fall detection system based on smartphone audio features is developed. The spectrogram, mel frequency cepstral coefficents (MFCCs), linear predictive coding (LPC), and matching pursuit (MP) features of different fall and no-fall sound events are extracted from experimental data. Based on the extracted audio features, four different machine learning classifiers: k-nearest neighbor classifier (k-NN), support vector machine (SVM), least squares method (LSM), and artificial neural network (ANN) are investigated for distinguishing between fall and no-fall events. For each audio feature, the performance of each classifier in terms of sensitivity, specificity, accuracy, and computational complexity is evaluated. The best performance is achieved using spectrogram features with ANN classifier with sensitivity, specificity, and accuracy all above 98%. The classifier also has acceptable computational requirement for training and testing. The system is applicable in home environments where the phone is placed in the vicinity of the user. PMID:25915965

  16. Testing of Haar-Like Feature in Region of Interest Detection for Automated Target Recognition (ATR) System

    NASA Technical Reports Server (NTRS)

    Zhang, Yuhan; Lu, Dr. Thomas

    2010-01-01

    The objectives of this project were to develop a ROI (Region of Interest) detector using Haar-like feature similar to the face detection in Intel's OpenCV library, implement it in Matlab code, and test the performance of the new ROI detector against the existing ROI detector that uses Optimal Trade-off Maximum Average Correlation Height filter (OTMACH). The ROI detector included 3 parts: 1, Automated Haar-like feature selection in finding a small set of the most relevant Haar-like features for detecting ROIs that contained a target. 2, Having the small set of Haar-like features from the last step, a neural network needed to be trained to recognize ROIs with targets by taking the Haar-like features as inputs. 3, using the trained neural network from the last step, a filtering method needed to be developed to process the neural network responses into a small set of regions of interests. This needed to be coded in Matlab. All the 3 parts needed to be coded in Matlab. The parameters in the detector needed to be trained by machine learning and tested with specific datasets. Since OpenCV library and Haar-like feature were not available in Matlab, the Haar-like feature calculation needed to be implemented in Matlab. The codes for Adaptive Boosting and max/min filters in Matlab could to be found from the Internet but needed to be integrated to serve the purpose of this project. The performance of the new detector was tested by comparing the accuracy and the speed of the new detector against the existing OTMACH detector. The speed was referred as the average speed to find the regions of interests in an image. The accuracy was measured by the number of false positives (false alarms) at the same detection rate between the two detectors.

  17. Far-Infrared Based Pedestrian Detection for Driver-Assistance Systems Based on Candidate Filters, Gradient-Based Feature and Multi-Frame Approval Matching

    PubMed Central

    Wang, Guohua; Liu, Qiong

    2015-01-01

    Far-infrared pedestrian detection approaches for advanced driver-assistance systems based on high-dimensional features fail to simultaneously achieve robust and real-time detection. We propose a robust and real-time pedestrian detection system characterized by novel candidate filters, novel pedestrian features and multi-frame approval matching in a coarse-to-fine fashion. Firstly, we design two filters based on the pedestrians’ head and the road to select the candidates after applying a pedestrian segmentation algorithm to reduce false alarms. Secondly, we propose a novel feature encapsulating both the relationship of oriented gradient distribution and the code of oriented gradient to deal with the enormous variance in pedestrians’ size and appearance. Thirdly, we introduce a multi-frame approval matching approach utilizing the spatiotemporal continuity of pedestrians to increase the detection rate. Large-scale experiments indicate that the system works in real time and the accuracy has improved about 9% compared with approaches based on high-dimensional features only. PMID:26703611

  18. Elderly fall detection using SIFT hybrid features

    NASA Astrophysics Data System (ADS)

    Wang, Xiaoxiao; Gao, Chao; Guo, Yongcai

    2015-10-01

    With the tendency of aging society, countries all over the world are dealing with the demographic change. Fall had been proven to be of the highest fatality rate among the elderly. To realize the elderly fall detection, the proposed algorithm used the hybrid feature. Based on the rate of centroid change, the algorithm adopted VEI to offer the posture feature, this combined motion feature with posture feature. The algorithm also took advantage of SIFT descriptor of VEI(V-SIFT) to show more details of behaviors with occlusion. An improved motion detection method was proposed to improve the accuracy of front-view motion detection. The experimental results on CASIA database and self-built database showed that the proposed approach has high efficiency and strong robustness which effectively improved the accuracy of fall detection.

  19. Toward Automated Feature Detection in UAVSAR Images

    NASA Astrophysics Data System (ADS)

    Parker, J. W.; Donnellan, A.; Glasscoe, M. T.

    2014-12-01

    Edge detection identifies seismic or aseismic fault motion, as demonstrated in repeat-pass inteferograms obtained by the Uninhabited Aerial Vehicle Synthetic Aperture Radar (UAVSAR) program. But this identification is not robust at present: it requires a flattened background image, interpolation into missing data (holes) and outliers, and background noise that is either sufficiently small or roughly white Gaussian. Identification and mitigation of nongaussian background image noise is essential to creating a robust, automated system to search for such features. Clearly a robust method is needed for machine scanning of the thousands of UAVSAR repeat-pass interferograms for evidence of fault slip, landslides, and other local features.Empirical examination of detrended noise based on 20 km east-west profiles through desert terrain with little tectonic deformation for a suite of flight interferograms shows nongaussian characteristics. Statistical measurement of curvature with varying length scale (Allan variance) shows nearly white behavior (Allan variance slope with spatial distance from roughly -1.76 to -2) from 25 to 400 meters, deviations from -2 suggesting short-range differences (such as used in detecting edges) are often freer of noise than longer-range differences. At distances longer than 400 m the Allan variance flattens out without consistency from one interferogram to another. We attribute this additional noise afflicting difference estimates at longer distances to atmospheric water vapor and uncompensated aircraft motion.Paradoxically, California interferograms made with increasing time intervals before and after the El Mayor Cucapah earthquake (2008, M7.2, Mexico) show visually stronger and more interesting edges, but edge detection methods developed for the first year do not produce reliable results over the first two years, because longer time spans suffer reduced coherence in the interferogram. The changes over time are reflecting fault slip and block

  20. LIC for Surface Flow Feature Detection

    NASA Technical Reports Server (NTRS)

    Kao, David L.; Bryson, Steve (Technical Monitor)

    1999-01-01

    The Line Integral Convolution (LIC) algorithm has received a lot of attention and interest. Yet, only a few of the current LIC related algorithms deal specifically with color textures for automatic detection of flow features. This paper provides an overview of research in this area.

  1. Satellite mapping and automated feature extraction: Geographic information system-based change detection of the Antarctic coast

    NASA Astrophysics Data System (ADS)

    Kim, Kee-Tae

    Declassified Intelligence Satellite Photograph (DISP) data are important resources for measuring the geometry of the coastline of Antarctica. By using the state-of-art digital imaging technology, bundle block triangulation based on tie points and control points derived from a RADARSAT-1 Synthetic Aperture Radar (SAR) image mosaic and Ohio State University (OSU) Antarctic digital elevation model (DEM), the individual DISP images were accurately assembled into a map quality mosaic of Antarctica as it appeared in 1963. The new map is one of important benchmarks for gauging the response of the Antarctic coastline to changing climate. Automated coastline extraction algorithm design is the second theme of this dissertation. At the pre-processing stage, an adaptive neighborhood filtering was used to remove the film-grain noise while preserving edge features. At the segmentation stage, an adaptive Bayesian approach to image segmentation was used to split the DISP imagery into its homogenous regions, in which the fuzzy c-means clustering (FCM) technique and Gibbs random field (GRF) model were introduced to estimate the conditional and prior probability density functions. A Gaussian mixture model was used to estimate the reliable initial values for the FCM technique. At the post-processing stage, image object formation and labeling, removal of noisy image objects, and vectorization algorithms were sequentially applied to segmented images for extracting a vector representation of coastlines. Results were presented that demonstrate the effectiveness of the algorithm in segmenting the DISP data. In the cases of cloud cover and little contrast scenes, manual editing was carried out based on intermediate image processing and visual inspection in comparison of old paper maps. Through a geographic information system (GIS), the derived DISP coastline data were integrated with earlier and later data to assess continental scale changes in the Antarctic coast. Computing the area of

  2. Investigation of atmospheric insect wing-beat frequencies and iridescence features using a multi-spectral kHz remote detection system

    NASA Astrophysics Data System (ADS)

    Gebru, Alem; Rohwer, Erich; Neethling, Pieter; Brydegaard, Mikkel

    2014-10-01

    Quantitative investigation of insect activity in their natural habitat is a challenging task for entomologist. It is difficult to address questions such as flight direction, predation strength and overall activities using the current techniques such as traps and sweep nets. A multi-spectral kHz remote detection system using sunlight as an illumination source is presented. We explore possibilities of remote optical classification of insects based on their wing-beat frequencies and iridescence features. It is shown that the wing-beat frequency of the fast insect events can be resolved by implementing high sampling frequency. The iridescence features generated from the change of color in two channels (visible and near infrared) during wing-beat cycle is presented. We show that the shape of the wing-beat trajectory is different for different insects. The flight direction of atmospheric insect is also determined using silicon quadrant detector.

  3. Picture Detection in RSVP: Features or Identity?

    PubMed Central

    Potter, Mary C.; Wyble, Brad; Pandav, Rijuta; Olejarczyk, Jennifer

    2010-01-01

    A pictured object can be readily detected in an RSVP sequence when the target is specified by a superordinate category name such as animal or vehicle. Are category features the initial basis for detection, with identification of the specific object occurring in a second stage (Evans & Treisman, 2005), or is identification of the object the basis for detection? When two targets in the same superordinate category are presented successively (lag 1), only the identification-first hypothesis predicts lag 1 sparing of the second target. The results of two experiments with novel pictures and a wide range of categories supported the identification-first hypothesis and a transient-attention model of lag 1 sparing and the attentional blink (Wyble, Bowman, & Potter, 2009). PMID:20695696

  4. Statistics over features for internal carotid arterial disorders detection.

    PubMed

    Ubeyli, Elif Derya

    2008-03-01

    The objective of the present study is to extract the representative features of the internal carotid arterial (ICA) Doppler ultrasound signals and to present the accurate classification model. This paper presented the usage of statistics over the set of the extracted features (Lyapunov exponents and the power levels of the power spectral density estimates obtained by the eigenvector methods) in order to reduce the dimensionality of the extracted feature vectors. Since classification is more accurate when the pattern is simplified through representation by important features, feature extraction and selection play an important role in classifying systems such as neural networks. Mixture of experts (ME) and modified mixture of experts (MME) architectures were formulated and used as basis for detection of arterial disorders. Three types of ICA Doppler signals (Doppler signals recorded from healthy subjects, subjects having stenosis, and subjects having occlusion) were classified. The classification results confirmed that the proposed ME and MME has potential in detecting the arterial disorders. PMID:18179791

  5. Morphological feature detection for cervical cancer screening

    NASA Astrophysics Data System (ADS)

    Narayanswamy, Ramkumar; Sharpe, John P.; Duke, Heather J.; Stewart, Rosemary J.; Johnson, Kristina M.

    1995-03-01

    An optoelectronic system has been designed to pre-screen pap-smear slides and detect the suspicious cells using the hit/miss transform. Computer simulation of the algorithm tested on 184 pap-smear images detected 95% of the suspicious region as suspect while tagging just 5% of the normal regions as suspect. An optoelectronic implementation of the hit/miss transform using a 4f Vander-Lugt correlator architecture is proposed and demonstrated with experimental results.

  6. Statistical feature selection for enhanced detection of brain tumor

    NASA Astrophysics Data System (ADS)

    Chaddad, Ahmad; Colen, Rivka R.

    2014-09-01

    Feature-based methods are widely used in the brain tumor recognition system. Robust of early cancer detection is one of the most powerful image processing tools. Specifically, statistical features, such as geometric mean, harmonic mean, mean excluding outliers, median, percentiles, skewness and kurtosis, have been extracted from brain tumor glioma to aid in discriminating two levels namely, Level I and Level II using fluid attenuated inversion recovery (FLAIR) sequence in the diagnosis of brain tumor. Statistical feature describes the major characteristics of each level from glioma which is an important step to evaluate heterogeneity of cancer area pixels. In this paper, we address the task of feature selection to identify the relevant subset of features in the statistical domain, while discarding those that are either redundant or confusing, thereby improving the performance of feature-based scheme to distinguish between Level I and Level II. We apply a Decision Structure algorithm to find the optimal combination of nonhomogeneity based statistical features for the problem at hand. We employ a Naïve Bayes classifier to evaluate the performance of the optimal statistical feature based scheme in terms of its glioma Level I and Level II discrimination capability and use real-data collected from 17 patients have a glioblastoma multiforme (GBM). Dataset provided from 3 Tesla MR imaging system by MD Anderson Cancer Center. For the specific data analyzed, it is shown that the identified dominant features yield higher classification accuracy, with lower number of false alarms and missed detections, compared to the full statistical based feature set. This work has been proposed and analyzed specific GBM types which Level I and Level II and the dominant features were considered as feature aid to prognostic indicators. These features were selected automatically to be better able to determine prognosis from classical imaging studies.

  7. Robust feature detection using sonar sensors for mobile robots

    NASA Astrophysics Data System (ADS)

    Choi, Jinwoo; Ahn, Sunghwan; Chung, Wan Kyun

    2005-12-01

    Sonar sensor is an attractive tool for the SLAM of mobile robot because of their economic aspects. This cheap sensor gives relatively accurate range readings if disregarding angular uncertainty and specular reflections. However, these defects make feature detection difficult for the most part of the SLAM. This paper proposed a robust sonar feature detection algorithm. This algorithm gives feature detection methods for both point features and line features. The point feature detection method was based on the TBF scheme. Moreover, three additional processes improved the performance of feature detection as follows; 1) stable intersections, 2) efficient sliding window update and 3) removal of the false point features on the wall. The line feature detection method was based on the basic property of adjacent sonar sensors. Along the line feature, three adjacent sonar sensors gave similar range readings. Using this sensor property, it proposed a novel algorithm for line feature detection, which is simple and the feature can be obtained by using only current sensor data. The proposed feature detection algorithm gives a good solution for the SLAM of mobile robots because it gives accurate feature information for both the point and line features even with sensor errors. Furthermore, a sufficient number of features are available to correct mobile robot pose. Experimental results for point feature and line feature detection demonstrate the performance of the proposed algorithm in a home-like environment.

  8. Wildfire smoke detection using temporospatial features and random forest classifiers

    NASA Astrophysics Data System (ADS)

    Ko, Byoungchul; Kwak, Joon-Young; Nam, Jae-Yeal

    2012-01-01

    We propose a wildfire smoke detection algorithm that uses temporospatial visual features and an ensemble of decision trees and random forest classifiers. In general, wildfire smoke detection is particularly important for early warning systems because smoke is usually generated before flames; in addition, smoke can be detected from a long distance owing to its diffusion characteristics. In order to detect wildfire smoke using a video camera, temporospatial characteristics such as color, wavelet coefficients, motion orientation, and a histogram of oriented gradients are extracted from the preceding 100 corresponding frames and the current keyframe. Two RFs are then trained using independent temporal and spatial feature vectors. Finally, a candidate block is declared as a smoke block if the average probability of two RFs in a smoke class is maximum. The proposed algorithm was successfully applied to various wildfire-smoke and smoke-colored videos and performed better than other related algorithms.

  9. A prototype feature system for feature retrieval using relationships

    USGS Publications Warehouse

    Choi, J.; Usery, E.L.

    2009-01-01

    Using a feature data model, geographic phenomena can be represented effectively by integrating space, theme, and time. This paper extends and implements a feature data model that supports query and visualization of geographic features using their non-spatial and temporal relationships. A prototype feature-oriented geographic information system (FOGIS) is then developed and storage of features named Feature Database is designed. Buildings from the U.S. Marine Corps Base, Camp Lejeune, North Carolina and subways in Chicago, Illinois are used to test the developed system. The results of the applications show the strength of the feature data model and the developed system 'FOGIS' when they utilize non-spatial and temporal relationships in order to retrieve and visualize individual features.

  10. Lean histogram of oriented gradients features for effective eye detection

    NASA Astrophysics Data System (ADS)

    Sharma, Riti; Savakis, Andreas

    2015-11-01

    Reliable object detection is very important in computer vision and robotics applications. The histogram of oriented gradients (HOG) is established as one of the most popular hand-crafted features, which along with support vector machine (SVM) classification provides excellent performance for object recognition. We investigate dimensionality deduction on HOG features in combination with SVM classifiers to obtain efficient feature representation and improved classification performance. In addition to lean HOG features, we explore descriptors resulting from dimensionality reduction on histograms of binary descriptors. We consider three-dimensionality reduction techniques: standard principal component analysis, random projections, a computationally efficient linear mapping that is data independent, and locality preserving projections (LPP), which learns the manifold structure of the data. Our methods focus on the application of eye detection and were tested on an eye database created using the BioID and FERET face databases. Our results indicate that manifold learning is beneficial to classification utilizing HOG features. To demonstrate the broader usefulness of lean HOG features for object class recognition, we evaluated our system's classification performance on the CalTech-101 dataset with favorable outcomes.

  11. Multispectral face liveness detection method based on gradient features

    NASA Astrophysics Data System (ADS)

    Hou, Ya-Li; Hao, Xiaoli; Wang, Yueyang; Guo, Changqing

    2013-11-01

    Face liveness detection aims to distinguish genuine faces from disguised faces. Most previous works under visible light focus on classification of genuine faces and planar photos or videos. To handle the three-dimensional (3-D) disguised faces, liveness detection based on multispectral images has been shown to be an effective choice. In this paper, a gradient-based multispectral method has been proposed for face liveness detection. Three feature vectors are developed to reduce the influence of varying illuminations. The reflectance-based feature achieves the best performance, which has a true positive rate of 98.3% and a true negative rate of 98.7%. The developed methods are also tested on individual bands to provide a clue for band selection in the imaging system. Preliminary results on different face orientations are also shown. The contributions of this paper are threefold. First, a gradient-based multispectral method has been proposed for liveness detection, which considers the reflectance properties of all the distinctive regions in a face. Second, three illumination-robust features are studied based on a dataset with two-dimensional planar photos, 3-D mannequins, and masks. Finally, the performance of the method on different spectral bands and face orientations is also shown in the evaluations.

  12. P300 Detection Based on EEG Shape Features.

    PubMed

    Alvarado-González, Montserrat; Garduño, Edgar; Bribiesca, Ernesto; Yáñez-Suárez, Oscar; Medina-Bañuelos, Verónica

    2016-01-01

    We present a novel approach to describe a P300 by a shape-feature vector, which offers several advantages over the feature vector used by the BCI2000 system. Additionally, we present a calibration algorithm that reduces the dimensionality of the shape-feature vector, the number of trials, and the electrodes needed by a Brain Computer Interface to accurately detect P300s; we also define a method to find a template that best represents, for a given electrode, the subject's P300 based on his/her own acquired signals. Our experiments with 21 subjects showed that the SWLDA's performance using our shape-feature vector was 93%, that is, 10% higher than the one obtained with BCI2000-feature's vector. The shape-feature vector is 34-dimensional for every electrode; however, it is possible to significantly reduce its dimensionality while keeping a high sensitivity. The validation of the calibration algorithm showed an averaged area under the ROC (AUROC) curve of 0.88. Also, most of the subjects needed less than 15 trials to have an AUROC superior to 0.8. Finally, we found that the electrode C4 also leads to better classification. PMID:26881010

  13. P300 Detection Based on EEG Shape Features

    PubMed Central

    Alvarado-González, Montserrat; Garduño, Edgar; Bribiesca, Ernesto; Yáñez-Suárez, Oscar; Medina-Bañuelos, Verónica

    2016-01-01

    We present a novel approach to describe a P300 by a shape-feature vector, which offers several advantages over the feature vector used by the BCI2000 system. Additionally, we present a calibration algorithm that reduces the dimensionality of the shape-feature vector, the number of trials, and the electrodes needed by a Brain Computer Interface to accurately detect P300s; we also define a method to find a template that best represents, for a given electrode, the subject's P300 based on his/her own acquired signals. Our experiments with 21 subjects showed that the SWLDA's performance using our shape-feature vector was 93%, that is, 10% higher than the one obtained with BCI2000-feature's vector. The shape-feature vector is 34-dimensional for every electrode; however, it is possible to significantly reduce its dimensionality while keeping a high sensitivity. The validation of the calibration algorithm showed an averaged area under the ROC (AUROC) curve of 0.88. Also, most of the subjects needed less than 15 trials to have an AUROC superior to 0.8. Finally, we found that the electrode C4 also leads to better classification. PMID:26881010

  14. Feature Selection and Pedestrian Detection Based on Sparse Representation.

    PubMed

    Yao, Shihong; Wang, Tao; Shen, Weiming; Pan, Shaoming; Chong, Yanwen; Ding, Fei

    2015-01-01

    Pedestrian detection have been currently devoted to the extraction of effective pedestrian features, which has become one of the obstacles in pedestrian detection application according to the variety of pedestrian features and their large dimension. Based on the theoretical analysis of six frequently-used features, SIFT, SURF, Haar, HOG, LBP and LSS, and their comparison with experimental results, this paper screens out the sparse feature subsets via sparse representation to investigate whether the sparse subsets have the same description abilities and the most stable features. When any two of the six features are fused, the fusion feature is sparsely represented to obtain its important components. Sparse subsets of the fusion features can be rapidly generated by avoiding calculation of the corresponding index of dimension numbers of these feature descriptors; thus, the calculation speed of the feature dimension reduction is improved and the pedestrian detection time is reduced. Experimental results show that sparse feature subsets are capable of keeping the important components of these six feature descriptors. The sparse features of HOG and LSS possess the same description ability and consume less time compared with their full features. The ratios of the sparse feature subsets of HOG and LSS to their full sets are the highest among the six, and thus these two features can be used to best describe the characteristics of the pedestrian and the sparse feature subsets of the combination of HOG-LSS show better distinguishing ability and parsimony. PMID:26295480

  15. Feature Selection and Pedestrian Detection Based on Sparse Representation

    PubMed Central

    Yao, Shihong; Wang, Tao; Shen, Weiming; Pan, Shaoming; Chong, Yanwen; Ding, Fei

    2015-01-01

    Pedestrian detection have been currently devoted to the extraction of effective pedestrian features, which has become one of the obstacles in pedestrian detection application according to the variety of pedestrian features and their large dimension. Based on the theoretical analysis of six frequently-used features, SIFT, SURF, Haar, HOG, LBP and LSS, and their comparison with experimental results, this paper screens out the sparse feature subsets via sparse representation to investigate whether the sparse subsets have the same description abilities and the most stable features. When any two of the six features are fused, the fusion feature is sparsely represented to obtain its important components. Sparse subsets of the fusion features can be rapidly generated by avoiding calculation of the corresponding index of dimension numbers of these feature descriptors; thus, the calculation speed of the feature dimension reduction is improved and the pedestrian detection time is reduced. Experimental results show that sparse feature subsets are capable of keeping the important components of these six feature descriptors. The sparse features of HOG and LSS possess the same description ability and consume less time compared with their full features. The ratios of the sparse feature subsets of HOG and LSS to their full sets are the highest among the six, and thus these two features can be used to best describe the characteristics of the pedestrian and the sparse feature subsets of the combination of HOG-LSS show better distinguishing ability and parsimony. PMID:26295480

  16. RESIDENTIAL RADON RESISTANT CONSTRUCTION FEATURE SELECTION SYSTEM

    EPA Science Inventory

    The report describes a proposed residential radon resistant construction feature selection system. The features consist of engineered barriers to reduce radon entry and accumulation indoors. The proposed Florida standards require radon resistant features in proportion to regional...

  17. Cooperative spectral and spatial feature fusion for camouflaged target detection

    NASA Astrophysics Data System (ADS)

    Kim, Sungho; Shim, Min-Sheob

    2015-05-01

    This paper presents a novel camouflaged target detection method using spectral and spatial feature fusion. Conventional unsupervised learning methods using spectral information only can be feasible solutions. Such approaches, however, sometimes produce incorrect detection results because spatial information is not considered. This paper proposes a novel band feature selection method by considering both the spectral distance and spatial statistics after spectral normalization for illumination invariance. The statistical distance metric can generate candidate feature bands and further analysis of the spatial grouping property can trim the useless feature bands. Camouflaged targets can be detected better with less computational complexity by the spectral-spatial feature fusion.

  18. Operational multi-angle hyperspectral remote sensing for feature detection

    NASA Astrophysics Data System (ADS)

    Bostater, Charles R.; Brooks, Donald K.

    2013-10-01

    Remote sensing results of land and water surfaces from airborne and satellite platforms are dependent upon the illumination geometry and the sensor viewing geometry. Correction of pushbroom hyperspectral imagery can be achieved using bidirectional reflectance factors (BRF's) image features based upon their multi-angle hyperspectral signatures. Ground validation of features and targets utilize non-imaging sensors such as hemispherical goniometers. In this paper, a new linear translation based hyperspectral imaging goniometer system is described. Imagery and hyperspectral signatures obtained from a rotation stage platform and the new linear non-hemispherical goniometer system shows applications and a multi-angle correction approach for multi-angle hyperspectral pushbroom imagery corrections. Results are presented in a manner in order to describe how ground, vessel and airborne based multi-angle hyperspectral signatures can be applied to operational hyperspectral image acquisition by the calculation of hyperspectral anisotropic signature imagery. The results demonstrate the analysis framework from the systems to water and coastal vegetation for exploitation of surface and subsurface feature or target detection based using the multi-angle radiative transfer based BRF's. The hyperspectral pushbroom multi-angle analysis methodology forms a basis for future multi-sensor based multi-angle change detection algorithms.

  19. Multispectral image feature fusion for detecting land mines

    SciTech Connect

    Clark, G.A.; Fields, D.J.; Sherwood, R.J.

    1994-11-15

    Our system fuses information contained in registered images from multiple sensors to reduce the effect of clutter and improve the the ability to detect surface and buried land mines. The sensor suite currently consists if a camera that acquires images in sixible wavelength bands, du, dual-band infrared (5 micron and 10 micron) and ground penetrating radar. Past research has shown that it is extremely difficult to distinguish land mines from background clutter in images obtained from a single sensor. It is hypothesized, however, that information fused from a suite of various sensors is likely to provide better detection reliability, because the suite of sensors detects a variety of physical properties that are more separate in feature space. The materials surrounding the mines can include natural materials (soil, rocks, foliage, water, holes made by animals and natural processes, etc.) and some artifacts.

  20. Computer detection of features in biomedical images

    SciTech Connect

    Not Available

    1993-05-01

    Two projects under way at LLNL require the detection of spots in biomedical images: physical mapping of DNA in chromosomes, for the Human Genome Project, and finding microcalcifications, which may be an early sign of breast cancer, in mammograms. We have developed several computational algorithms to analyze these two kinds of images. The two detection methods described here use morphological imaging techniques to obtain size, shape, texture, and other information inherent in am image without trying to fit the data to a rigid mathematical model. The spot-finding algorithm has been incorporated into a DNA mapping tool for chromosomes in the metaphase of cell division; it is heavily used by researchers at the University of California, San Francisco, and may soon be distributed to other universities. Our computerized mammography work is in progress; when completed, we plan to transfer the technology to a medical imaging company.

  1. Improved Facial-Feature Detection for AVSP via Unsupervised Clustering and Discriminant Analysis

    NASA Astrophysics Data System (ADS)

    Lucey, Simon; Sridharan, Sridha; Chandran, Vinod

    2003-12-01

    An integral part of any audio-visual speech processing (AVSP) system is the front-end visual system that detects facial-features (e.g., eyes and mouth) pertinent to the task of visual speech processing. The ability of this front-end system to not only locate, but also give a confidence measure that the facial-feature is present in the image, directly affects the ability of any subsequent post-processing task such as speech or speaker recognition. With these issues in mind, this paper presents a framework for a facial-feature detection system suitable for use in an AVSP system, but whose basic framework is useful for any application requiring frontal facial-feature detection. A novel approach for facial-feature detection is presented, based on an appearance paradigm. This approach, based on intraclass unsupervised clustering and discriminant analysis, displays improved detection performance over conventional techniques.

  2. Facial Features Detection Using Texture Hough Transform

    NASA Astrophysics Data System (ADS)

    Gorbatsevich, V. S.

    2015-05-01

    The paper presents an original method for object detection. The "texture" Hough transform is used as the main tool in the search. Unlike classical generalized Hough transform, this variation uses texture LBP descriptor as a primitive for voting. The voting weight of each primitive is assumed by learning at a training set. This paper gives an overview of an original method for weights learning, and a number of ways to get the maximum searching algorithm speed on practice.

  3. Deep PDF parsing to extract features for detecting embedded malware.

    SciTech Connect

    Munson, Miles Arthur; Cross, Jesse S.

    2011-09-01

    The number of PDF files with embedded malicious code has risen significantly in the past few years. This is due to the portability of the file format, the ways Adobe Reader recovers from corrupt PDF files, the addition of many multimedia and scripting extensions to the file format, and many format properties the malware author may use to disguise the presence of malware. Current research focuses on executable, MS Office, and HTML formats. In this paper, several features and properties of PDF Files are identified. Features are extracted using an instrumented open source PDF viewer. The feature descriptions of benign and malicious PDFs can be used to construct a machine learning model for detecting possible malware in future PDF files. The detection rate of PDF malware by current antivirus software is very low. A PDF file is easy to edit and manipulate because it is a text format, providing a low barrier to malware authors. Analyzing PDF files for malware is nonetheless difficult because of (a) the complexity of the formatting language, (b) the parsing idiosyncrasies in Adobe Reader, and (c) undocumented correction techniques employed in Adobe Reader. In May 2011, Esparza demonstrated that PDF malware could be hidden from 42 of 43 antivirus packages by combining multiple obfuscation techniques [4]. One reason current antivirus software fails is the ease of varying byte sequences in PDF malware, thereby rendering conventional signature-based virus detection useless. The compression and encryption functions produce sequences of bytes that are each functions of multiple input bytes. As a result, padding the malware payload with some whitespace before compression/encryption can change many of the bytes in the final payload. In this study we analyzed a corpus of 2591 benign and 87 malicious PDF files. While this corpus is admittedly small, it allowed us to test a system for collecting indicators of embedded PDF malware. We will call these indicators features throughout

  4. Feature detection on 3D images of dental imprints

    NASA Astrophysics Data System (ADS)

    Mokhtari, Marielle; Laurendeau, Denis

    1994-09-01

    A computer vision approach for the extraction of feature points on 3D images of dental imprints is presented. The position of feature points are needed for the measurement of a set of parameters for automatic diagnosis of malocclusion problems in orthodontics. The system for the acquisition of the 3D profile of the imprint, the procedure for the detection of the interstices between teeth, and the approach for the identification of the type of tooth are described, as well as the algorithm for the reconstruction of the surface of each type of tooth. A new approach for the detection of feature points, called the watershed algorithm, is described in detail. The algorithm is a two-stage procedure which tracks the position of local minima at four different scales and produces a final map of the position of the minima. Experimental results of the application of the watershed algorithm on actual 3D images of dental imprints are presented for molars, premolars and canines. The segmentation approach for the analysis of the shape of incisors is also described in detail.

  5. Neonatal Jaundice Detection System.

    PubMed

    Aydın, Mustafa; Hardalaç, Fırat; Ural, Berkan; Karap, Serhat

    2016-07-01

    Neonatal jaundice is a common condition that occurs in newborn infants in the first week of life. Today, techniques used for detection are required blood samples and other clinical testing with special equipment. The aim of this study is creating a non-invasive system to control and to detect the jaundice periodically and helping doctors for early diagnosis. In this work, first, a patient group which is consisted from jaundiced babies and a control group which is consisted from healthy babies are prepared, then between 24 and 48 h after birth, 40 jaundiced and 40 healthy newborns are chosen. Second, advanced image processing techniques are used on the images which are taken with a standard smartphone and the color calibration card. Segmentation, pixel similarity and white balancing methods are used as image processing techniques and RGB values and pixels' important information are obtained exactly. Third, during feature extraction stage, with using colormap transformations and feature calculation, comparisons are done in RGB plane between color change values and the 8-color calibration card which is specially designed. Finally, in the bilirubin level estimation stage, kNN and SVR machine learning regressions are used on the dataset which are obtained from feature extraction. At the end of the process, when the control group is based on for comparisons, jaundice is succesfully detected for 40 jaundiced infants and the success rate is 85 %. Obtained bilirubin estimation results are consisted with bilirubin results which are obtained from the standard blood test and the compliance rate is 85 %. PMID:27229489

  6. Land cover change detection using a GIS-guided, feature-based classification of Landsat thematic mapper data. [Geographic Information System

    NASA Technical Reports Server (NTRS)

    Enslin, William R.; Ton, Jezching; Jain, Anil

    1987-01-01

    Landsat TM data were combined with land cover and planimetric data layers contained in the State of Michigan's geographic information system (GIS) to identify changes in forestlands, specifically new oil/gas wells. A GIS-guided feature-based classification method was developed. The regions extracted by the best image band/operator combination were studied using a set of rules based on the characteristics of the GIS oil/gas pads.

  7. Detection and analysis of diamond fingerprinting feature and its application

    NASA Astrophysics Data System (ADS)

    Li, Xin; Huang, Guoliang; Li, Qiang; Chen, Shengyi

    2011-01-01

    Before becoming a jewelry diamonds need to be carved artistically with some special geometric features as the structure of the polyhedron. There are subtle differences in the structure of this polyhedron in each diamond. With the spatial frequency spectrum analysis of diamond surface structure, we can obtain the diamond fingerprint information which represents the "Diamond ID" and has good specificity. Based on the optical Fourier Transform spatial spectrum analysis, the fingerprinting identification of surface structure of diamond in spatial frequency domain was studied in this paper. We constructed both the completely coherent diamond fingerprinting detection system illuminated by laser and the partially coherent diamond fingerprinting detection system illuminated by led, and analyzed the effect of the coherence of light source to the diamond fingerprinting feature. We studied rotation invariance and translation invariance of the diamond fingerprinting and verified the feasibility of real-time and accurate identification of diamond fingerprint. With the profit of this work, we can provide customs, jewelers and consumers with a real-time and reliable diamonds identification instrument, which will curb diamond smuggling, theft and other crimes, and ensure the healthy development of the diamond industry.

  8. Aberration features in directional dark matter detection

    SciTech Connect

    Bozorgnia, Nassim; Gelmini, Graciela B.; Gondolo, Paolo E-mail: gelmini@physics.ucla.edu

    2012-08-01

    The motion of the Earth around the Sun causes an annual change in the magnitude and direction of the arrival velocity of dark matter particles on Earth, in a way analogous to aberration of stellar light. In directional detectors, aberration of weakly interacting massive particles (WIMPs) modulates the pattern of nuclear recoil directions in a way that depends on the orbital velocity of the Earth and the local galactic distribution of WIMP velocities. Knowing the former, WIMP aberration can give information on the latter, besides being a curious way of confirming the revolution of the Earth and the extraterrestrial provenance of WIMPs. While observing the full aberration pattern requires extremely large exposures, we claim that the annual variation of the mean recoil direction or of the event counts over specific solid angles may be detectable with moderately large exposures. For example, integrated counts over Galactic hemispheres separated by planes perpendicular to Earth's orbit would modulate annually, resulting in Galactic Hemisphere Annual Modulations (GHAM) with amplitudes larger than the usual non-directional annual modulation.

  9. Automated feature detection and identification in digital point-ordered signals

    DOEpatents

    Oppenlander, Jane E.; Loomis, Kent C.; Brudnoy, David M.; Levy, Arthur J.

    1998-01-01

    A computer-based automated method to detect and identify features in digital point-ordered signals. The method is used for processing of non-destructive test signals, such as eddy current signals obtained from calibration standards. The signals are first automatically processed to remove noise and to determine a baseline. Next, features are detected in the signals using mathematical morphology filters. Finally, verification of the features is made using an expert system of pattern recognition methods and geometric criteria. The method has the advantage that standard features can be, located without prior knowledge of the number or sequence of the features. Further advantages are that standard features can be differentiated from irrelevant signal features such as noise, and detected features are automatically verified by parameters extracted from the signals. The method proceeds fully automatically without initial operator set-up and without subjective operator feature judgement.

  10. Stereo vision-based pedestrian detection using multiple features for automotive application

    NASA Astrophysics Data System (ADS)

    Lee, Chung-Hee; Kim, Dongyoung

    2015-12-01

    In this paper, we propose a stereo vision-based pedestrian detection using multiple features for automotive application. The disparity map from stereo vision system and multiple features are utilized to enhance the pedestrian detection performance. Because the disparity map offers us 3D information, which enable to detect obstacles easily and reduce the overall detection time by removing unnecessary backgrounds. The road feature is extracted from the v-disparity map calculated by the disparity map. The road feature is a decision criterion to determine the presence or absence of obstacles on the road. The obstacle detection is performed by comparing the road feature with all columns in the disparity. The result of obstacle detection is segmented by the bird's-eye-view mapping to separate the obstacle area which has multiple objects into single obstacle area. The histogram-based clustering is performed in the bird's-eye-view map. Each segmented result is verified by the classifier with the training model. To enhance the pedestrian recognition performance, multiple features such as HOG, CSS, symmetry features are utilized. In particular, the symmetry feature is proper to represent the pedestrian standing or walking. The block-based symmetry feature is utilized to minimize the type of image and the best feature among the three symmetry features of H-S-V image is selected as the symmetry feature in each pixel. ETH database is utilized to verify our pedestrian detection algorithm.

  11. Portable modular detection system

    DOEpatents

    Brennan, James S.; Singh, Anup; Throckmorton, Daniel J.; Stamps, James F.

    2009-10-13

    Disclosed herein are portable and modular detection devices and systems for detecting electromagnetic radiation, such as fluorescence, from an analyte which comprises at least one optical element removably attached to at least one alignment rail. Also disclosed are modular detection devices and systems having an integrated lock-in amplifier and spatial filter and assay methods using the portable and modular detection devices.

  12. Automatic detection of clustered microcalcifications in digital mammograms based on wavelet features and neural network classification

    NASA Astrophysics Data System (ADS)

    Yu, Songyang; Guan, Ling; Brown, Stephen

    1998-06-01

    The appearance of clustered microcalcifications in mammogram films is one of the important early signs of breast cancer. This paper presents a new image processing system for the automatic detection of clustered microcalcifications in digitized mammogram films. The detection method uses wavelet features and feed forward neural network to find possible microcalcifications pixels and a set of features to locate individual microcalcifications.

  13. PCA-HOG symmetrical feature based diseased cell detection

    NASA Astrophysics Data System (ADS)

    Wan, Min-jie

    2016-04-01

    A histogram of oriented gradient (HOG) feature is applied to the field of diseased cell detection, which can detect diseased cells in high resolution tissue images rapidly, accurately and efficiently. Firstly, motivated by symmetrical cellular forms, a new HOG symmetrical feature based on the traditional HOG feature is proposed to meet the condition of cell detection. Secondly, considering the high feature dimension of traditional HOG feature leads to plenty of memory resources and long runtime in practical applications, a classical dimension reduction method called principal component analysis (PCA) is used to reduce the dimension of high-dimensional HOG descriptor. Because of that, computational speed is increased greatly, and the accuracy of detection can be controlled in a proper range at the same time. Thirdly, support vector machine (SVM) classifier is trained with PCA-HOG symmetrical features proposed above. At last, practical tissue images is detected and analyzed by SVM classifier. In order to verify the effectiveness of this new algorithm, it is practically applied to conduct diseased cell detection which takes 200 pieces of H&E (hematoxylin & eosin) high resolution staining histopathological images collected from 20 breast cancer patients as a sample. The experiment shows that the average processing rate can be 25 frames per second and the detection accuracy can be 92.1%.

  14. Detection of gratings and small features in speckle imagery.

    PubMed

    Korwar, V N; Pierce, J R

    1981-01-15

    The extent of picture degradation of speckle, in particular in synthetic aperture radar pictures, has been investigated in the cases where an observer has to detect (a) a small feature immersed in a darker background, and (b) a square wave grating. In each case, a theoretical model is developed for the observer's detection mechanism, and the probability of correct decision is related to relevant picture parameters such as contrast, looks per pixel, and size. These calculations are verified by psychophysical experiments using computer-simulated pictures. Detectability of gratings as a criterion for characterizing picture quality is shown to be far inferior to feature detectability. PMID:20309108

  15. Detecting Image Splicing Using Merged Features in Chroma Space

    PubMed Central

    Liu, Guangjie; Dai, Yuewei

    2014-01-01

    Image splicing is an image editing method to copy a part of an image and paste it onto another image, and it is commonly followed by postprocessing such as local/global blurring, compression, and resizing. To detect this kind of forgery, the image rich models, a feature set successfully used in the steganalysis is evaluated on the splicing image dataset at first, and the dominant submodel is selected as the first kind of feature. The selected feature and the DCT Markov features are used together to detect splicing forgery in the chroma channel, which is convinced effective in splicing detection. The experimental results indicate that the proposed method can detect splicing forgeries with lower error rate compared to the previous literature. PMID:24574877

  16. Feature Integration Theory Revisited: Dissociating Feature Detection and Attentional Guidance in Visual Search

    ERIC Educational Resources Information Center

    Chan, Louis K. H.; Hayward, William G.

    2009-01-01

    In feature integration theory (FIT; A. Treisman & S. Sato, 1990), feature detection is driven by independent dimensional modules, and other searches are driven by a master map of locations that integrates dimensional information into salience signals. Although recent theoretical models have largely abandoned this distinction, some observed results…

  17. System Complexity Reduction via Feature Selection

    ERIC Educational Resources Information Center

    Deng, Houtao

    2011-01-01

    This dissertation transforms a set of system complexity reduction problems to feature selection problems. Three systems are considered: classification based on association rules, network structure learning, and time series classification. Furthermore, two variable importance measures are proposed to reduce the feature selection bias in tree…

  18. Interior intrusion detection systems

    SciTech Connect

    Rodriguez, J.R.; Matter, J.C. ); Dry, B. )

    1991-10-01

    The purpose of this NUREG is to present technical information that should be useful to NRC licensees in designing interior intrusion detection systems. Interior intrusion sensors are discussed according to their primary application: boundary-penetration detection, volumetric detection, and point protection. Information necessary for implementation of an effective interior intrusion detection system is presented, including principles of operation, performance characteristics and guidelines for design, procurement, installation, testing, and maintenance. A glossary of sensor data terms is included. 36 figs., 6 tabs.

  19. Voice activity detection for speaker verification systems

    NASA Astrophysics Data System (ADS)

    Borowski, Filip

    2008-01-01

    Complex algorithm for speech activity detection was presented in this article. It is based on speech enhancement, features extraction and final detection algorithm. The first one was published in ETSI standard as a module of "Advanced front-end feature extraction algorithm" in distributed speech recognition system. It consists of two main parts, noise estimatiom and Wiener filtering. For the final detection modified linear prediction coefficients and spectral entropy features are extracted form denoised signal.

  20. Systemic features of rotavirus infection.

    PubMed

    Rivero-Calle, Irene; Gómez-Rial, José; Martinón-Torres, Federico

    2016-07-01

    A growing body of evidence warrants a revision of the received/conventional wisdom of rotavirus infection as synonymous with acute gastroenteritis. Rotavirus vaccines have boosted our interest and knowledge of this virus, but also importantly, they may have changed the landscape of the disease. Extraintestinal spread of rotavirus is well documented, and the clinical spectrum of the disease is widening. Furthermore, the positive impact of current rotavirus vaccines in reducing seizure hospitalization rates should prompt a reassessment of the actual burden of extraintestinal manifestations of rotavirus diseases. This article discusses current knowledge of the systemic extraintestinal manifestations of rotavirus infection and their underlying mechanisms, and aims to pave the way for future clinical, public health and research questions. PMID:27181101

  1. Buried target detection in FLIR images using Shearlet features

    NASA Astrophysics Data System (ADS)

    Tuomanen, Brian; Stone, Kevin; Madison, Timothy; Popescu, Mihail; Keller, James

    2013-06-01

    In this paper we investigate a new approach for representing objects in FLIR images based on shearlets. Similar to wavelets, shearlets represent an affine system for image representation obtained by scaling and translation of a generating function called mother shearlet. Unlike wavelets, the mother shearlet has an extra parameter called shear that allows the shearlet transform to be anisotropic. Anisotropic property of the shearlet transform could allow for a better representation of objects with irregular shape. We test our representation methodology on Froward looking long wave infrared (LWIR) images obtained from an IR camera installed on a moving vehicle. Objects of interest (spots) are detected in each frame using a prescreener presented in our previous work. Each spot is then represented using its shearlet features and assigned a confidence coming from a support vector machine classifier. We compare shearlets to various traditional features such as local binary patterns (LPB) and histogram of gradients (HOG). The comparison is performed on a large dataset that consists of 16 runs at a US Army test site.

  2. Evaluation of Selected Features for CAR Detection in Aerial Images

    NASA Astrophysics Data System (ADS)

    Tuermer, S.; Leitloff, J.; Reinartz, P.; Stilla, U.

    2011-09-01

    The extraction of vehicles from aerial images provides a wide area traffic situation within a short time. Applications for the gathered data are various and reach from smart routing in the case of congestions to usability validation of roads in the case of disasters. The challenge of the vehicle detection task is finding adequate features which are capable to separate cars from other objects; especially those that look similar. We present an experiment where selected features show their ability of car detection. Precisely, Haar-like and HoG features are utilized and passed to the AdaBoost algorithm for calculating the final detector. Afterwards the classifying power of the features is accurately analyzed and evaluated. The tests a carried out on aerial data from the inner city of Munich, Germany and include small inner city roads with rooftops close by which raise the complexity factor.

  3. Advanced Querying Features for Disease Surveillance Systems

    PubMed Central

    Hashemian, Mohammad R.

    2010-01-01

    Most automated disease surveillance systems notify users of increases in the prevalence of reports in syndrome categories and allow users to view patient level data related to those increases. Occasionally, a more dynamic level of control is required to properly detect an emerging disease in a community. Dynamic querying features are invaluable when using existing surveillance systems to investigate outbreaks of newly emergent diseases or to identify cases of reportable diseases within data being captured for surveillance. The objective of the Advance Querying Tool (AQT) is to build a more flexible query interface for most web-based disease surveillance systems. This interface allows users to define and build their query as if they were writing a logical expression for a mathematical computation. The AQT allows users to develop, investigate, save, and share complex case definitions. It provides a flexible interface that accommodates both advanced and novice users, checks the validity of the expression as it is built, and marks errors for users. PMID:23569575

  4. Multiscale differential fractal feature with application to target detection

    NASA Astrophysics Data System (ADS)

    Shi, Zelin; Wei, Ying; Huang, Shabai

    2004-07-01

    A multiscale differential fractal feature of an image is proposed and a small target detection method from complex nature clutter is presented. Considering the speciality that the fractal features of man-made objects change much more violently than that of nature's when the scale is varied, fractal features at multiple scales used for distinguishing man-made target from nature clutter should have more advantages over standard fractal dimensions. Multiscale differential fractal dimensions are deduced from typical fractal model and standard covering-blanket method is improved and used to estimate multiscale fractal dimensions. A multiscale differential fractal feature is defined as the variation of fractal dimensions between two scales at a rational scale range. It can stand out the fractal feature of man-made object from natural clutters much better than the fractal dimension by standard covering-blanket method. Meanwhile, the calculation and the storage amount are reduced greatly, they are 4/M and 2/M that of the standard covering-blanket method respectively (M is scale). In the image of multiscale differential fractal feature, local gray histogram statistical method is used for target detection. Experiment results indicate that this method is suitable for both kinds background of land and sea. It also can be appropriate in both kinds of infrared and TV images, and can detect small targets from a single frame correctly. This method is with high speed and is easy to be implemented.

  5. Features for voice activity detection: a comparative analysis

    NASA Astrophysics Data System (ADS)

    Graf, Simon; Herbig, Tobias; Buck, Markus; Schmidt, Gerhard

    2015-12-01

    In many speech signal processing applications, voice activity detection (VAD) plays an essential role for separating an audio stream into time intervals that contain speech activity and time intervals where speech is absent. Many features that reflect the presence of speech were introduced in literature. However, to our knowledge, no extensive comparison has been provided yet. In this article, we therefore present a structured overview of several established VAD features that target at different properties of speech. We categorize the features with respect to properties that are exploited, such as power, harmonicity, or modulation, and evaluate the performance of some dedicated features. The importance of temporal context is discussed in relation to latency restrictions imposed by different applications. Our analyses allow for selecting promising VAD features and finding a reasonable trade-off between performance and complexity.

  6. Hemorrhage detection in MRI brain images using images features

    NASA Astrophysics Data System (ADS)

    Moraru, Luminita; Moldovanu, Simona; Bibicu, Dorin; Stratulat (Visan), Mirela

    2013-11-01

    The abnormalities appear frequently on Magnetic Resonance Images (MRI) of brain in elderly patients presenting either stroke or cognitive impairment. Detection of brain hemorrhage lesions in MRI is an important but very time-consuming task. This research aims to develop a method to extract brain tissue features from T2-weighted MR images of the brain using a selection of the most valuable texture features in order to discriminate between normal and affected areas of the brain. Due to textural similarity between normal and affected areas in brain MR images these operation are very challenging. A trauma may cause microstructural changes, which are not necessarily perceptible by visual inspection, but they could be detected by using a texture analysis. The proposed analysis is developed in five steps: i) in the pre-processing step: the de-noising operation is performed using the Daubechies wavelets; ii) the original images were transformed in image features using the first order descriptors; iii) the regions of interest (ROIs) were cropped from images feature following up the axial symmetry properties with respect to the mid - sagittal plan; iv) the variation in the measurement of features was quantified using the two descriptors of the co-occurrence matrix, namely energy and homogeneity; v) finally, the meaningful of the image features is analyzed by using the t-test method. P-value has been applied to the pair of features in order to measure they efficacy.

  7. Unified Saliency Detection Model Using Color and Texture Features

    PubMed Central

    Luo, Tiejian

    2016-01-01

    Saliency detection attracted attention of many researchers and had become a very active area of research. Recently, many saliency detection models have been proposed and achieved excellent performance in various fields. However, most of these models only consider low-level features. This paper proposes a novel saliency detection model using both color and texture features and incorporating higher-level priors. The SLIC superpixel algorithm is applied to form an over-segmentation of the image. Color saliency map and texture saliency map are calculated based on the region contrast method and adaptive weight. Higher-level priors including location prior and color prior are incorporated into the model to achieve a better performance and full resolution saliency map is obtained by using the up-sampling method. Experimental results on three datasets demonstrate that the proposed saliency detection model outperforms the state-of-the-art models. PMID:26889826

  8. Feature-based eye corner detection from static images

    NASA Astrophysics Data System (ADS)

    Xia, Haiying; Yan, Guoping; You, Chao

    2009-10-01

    Eye corner detection is important for eye extraction, face normalization, other facial landmark extraction and so on. We present a feature-based method for eye corner detection from static images in this paper. This method is capable of locating eye corners automatically. The process of eye corner detection is divided into two stages: classifier training and classifier application. For training, two classifiers trained by AdaBoost with Haar-like features, are skillfully designed to detect inner eye corners and outer eye corners. Then, two classifiers are applied to input images to search targets. Eye corners are finally located according to two eye models from targets. Experimental results tested on BioID face database and our own database demonstrate that our method obtains a high accuracy under clutter conditions.

  9. Accurate feature detection and estimation using nonlinear and multiresolution analysis

    NASA Astrophysics Data System (ADS)

    Rudin, Leonid; Osher, Stanley

    1994-11-01

    A program for feature detection and estimation using nonlinear and multiscale analysis was completed. The state-of-the-art edge detection was combined with multiscale restoration (as suggested by the first author) and robust results in the presence of noise were obtained. Successful applications to numerous images of interest to DOD were made. Also, a new market in the criminal justice field was developed, based in part, on this work.

  10. Adaptive skin segmentation via feature-based face detection

    NASA Astrophysics Data System (ADS)

    Taylor, Michael J.; Morris, Tim

    2014-05-01

    Variations in illumination can have significant effects on the apparent colour of skin, which can be damaging to the efficacy of any colour-based segmentation approach. We attempt to overcome this issue by presenting a new adaptive approach, capable of generating skin colour models at run-time. Our approach adopts a Viola-Jones feature-based face detector, in a moderate-recall, high-precision configuration, to sample faces within an image, with an emphasis on avoiding potentially detrimental false positives. From these samples, we extract a set of pixels that are likely to be from skin regions, filter them according to their relative luma values in an attempt to eliminate typical non-skin facial features (eyes, mouths, nostrils, etc.), and hence establish a set of pixels that we can be confident represent skin. Using this representative set, we train a unimodal Gaussian function to model the skin colour in the given image in the normalised rg colour space - a combination of modelling approach and colour space that benefits us in a number of ways. A generated function can subsequently be applied to every pixel in the given image, and, hence, the probability that any given pixel represents skin can be determined. Segmentation of the skin, therefore, can be as simple as applying a binary threshold to the calculated probabilities. In this paper, we touch upon a number of existing approaches, describe the methods behind our new system, present the results of its application to arbitrary images of people with detectable faces, which we have found to be extremely encouraging, and investigate its potential to be used as part of real-time systems.

  11. Joint Analysis of Dependent Features within Compound Spectra Can Improve Detection of Differential Features

    PubMed Central

    Trutschel, Diana; Schmidt, Stephan; Grosse, Ivo; Neumann, Steffen

    2015-01-01

    Mass spectrometry is an important analytical technology in metabolomics. After the initial feature detection and alignment steps, the raw data processing results in a high-dimensional data matrix of mass spectral features, which is then subjected to further statistical analysis. Univariate tests like Student’s t-test and Analysis of Variances (ANOVA) are hypothesis tests, which aim to detect differences between two or more sample classes, e.g., wildtype-mutant or between different doses of treatments. In both cases, one of the underlying assumptions is the independence between metabolic features. However, in mass spectrometry, a single metabolite usually gives rise to several mass spectral features, which are observed together and show a common behavior. This paper suggests to group the related features of metabolites with CAMERA into compound spectra, and then to use a multivariate statistical method to test whether a compound spectrum (and thus the actual metabolite) is differential between two sample classes. The multivariate method is first demonstrated with an analysis between wild-type and an over-expression line of the model plant Arabidopsis thaliana. For a quantitative evaluation data sets with a simulated known effect between two sample classes were analyzed. The spectra-wise analysis showed better detection results for all simulated effects. PMID:26442246

  12. Detection of fungal damaged popcorn using image property covariance features

    Technology Transfer Automated Retrieval System (TEKTRAN)

    Covariance-matrix-based features were applied to the detection of popcorn infected by a fungus that cause a symptom called “blue-eye.” This infection of popcorn kernels causes economic losses because of their poor appearance and the frequently disagreeable flavor of the popped kernels. Images of ker...

  13. Computer-aided detection of lung nodules using outer surface features.

    PubMed

    Demir, Önder; Yılmaz Çamurcu, Ali

    2015-01-01

    In this study, a computer-aided detection (CAD) system was developed for the detection of lung nodules in computed tomography images. The CAD system consists of four phases, including two-dimensional and three-dimensional preprocessing phases. In the feature extraction phase, four different groups of features are extracted from volume of interests: morphological features, statistical and histogram features, statistical and histogram features of outer surface, and texture features of outer surface. The support vector machine algorithm is optimized using particle swarm optimization for classification. The CAD system provides 97.37% sensitivity, 86.38% selectivity, 88.97% accuracy and 2.7 false positive per scan using three groups of classification features. After the inclusion of outer surface texture features, classification results of the CAD system reaches 98.03% sensitivity, 87.71% selectivity, 90.12% accuracy and 2.45 false positive per scan. Experimental results demonstrate that outer surface texture features of nodule candidates are useful to increase sensitivity and decrease the number of false positives in the detection of lung nodules in computed tomography images. PMID:26405880

  14. Idaho Explosive Detection System

    SciTech Connect

    Klinger, Jeff

    2011-01-01

    Learn how INL researchers are making the world safer by developing an explosives detection system that can inspect cargo. For more information about INL security research, visit http://www.facebook.com/idahonationallaboratory

  15. Idaho Explosive Detection System

    ScienceCinema

    Klinger, Jeff

    2013-05-28

    Learn how INL researchers are making the world safer by developing an explosives detection system that can inspect cargo. For more information about INL security research, visit http://www.facebook.com/idahonationallaboratory

  16. Breast cancer detection in rotational thermography images using texture features

    NASA Astrophysics Data System (ADS)

    Francis, Sheeja V.; Sasikala, M.; Bhavani Bharathi, G.; Jaipurkar, Sandeep D.

    2014-11-01

    Breast cancer is a major cause of mortality in young women in the developing countries. Early diagnosis is the key to improve survival rate in cancer patients. Breast thermography is a diagnostic procedure that non-invasively images the infrared emissions from breast surface to aid in the early detection of breast cancer. Due to limitations in imaging protocol, abnormality detection by conventional breast thermography, is often a challenging task. Rotational thermography is a novel technique developed in order to overcome the limitations of conventional breast thermography. This paper evaluates this technique's potential for automatic detection of breast abnormality, from the perspective of cold challenge. Texture features are extracted in the spatial domain, from rotational thermogram series, prior to and post the application of cold challenge. These features are fed to a support vector machine for automatic classification of normal and malignant breasts, resulting in a classification accuracy of 83.3%. Feature reduction has been performed by principal component analysis. As a novel attempt, the ability of this technique to locate the abnormality has been studied. The results of the study indicate that rotational thermography holds great potential as a screening tool for breast cancer detection.

  17. Underwater laser detection system

    NASA Astrophysics Data System (ADS)

    Gomaa, Walid; El-Sherif, Ashraf F.; El-Sharkawy, Yasser H.

    2015-02-01

    The conventional method used to detect an underwater target is by sending and receiving some form of acoustic energy. But the acoustic systems have limitations in the range resolution and accuracy; while, the potential benefits of a laserbased underwater target detection include high directionality, high response, and high range accuracy. Lasers operating in the blue-green region of the light spectrum(420 : 570nm)have a several applications in the area of detection and ranging of submersible targets due to minimum attenuation through water ( less than 0.1 m-1) and maximum laser reflection from estimated target (like mines or submarines) to provide a long range of detection. In this paper laser attenuation in water was measured experimentally by new simple method by using high resolution spectrometer. The laser echoes from different targets (metal, plastic, wood, and rubber) were detected using high resolution CCD camera; the position of detection camera was optimized to provide a high reflection laser from target and low backscattering noise from the water medium, digital image processing techniques were applied to detect and discriminate the echoes from the metal target and subtract the echoes from other objects. Extraction the image of target from the scattering noise is done by background subtraction and edge detection techniques. As a conclusion, we present a high response laser imaging system to detect and discriminate small size, like-mine underwater targets.

  18. Bro Intrusion Detection System

    SciTech Connect

    Paxson, Vern; Campbell, Scott; leres, Craig; Lee, Jason

    2006-01-25

    Bro is a Unix-based Network Intrusion Detection System (IDS). Bro monitors network traffic and detects intrusion attempts based on the traffic characteristics and content. Bro detects intrusions by comparing network traffic against rules describing events that are deemed troublesome. These rules might describe activities (e.g., certain hosts connecting to certain services), what activities are worth alerting (e.g., attempts to a given number of different hosts constitutes a "scan"), or signatures describing known attacks or access to known vulnerabilities. If Bro detects something of interest, it can be instructed to either issue a log entry or initiate the execution of an operating system command. Bro targets high-speed (Gbps), high-volume intrusion detection. By judiciously leveraging packet filtering techniques, Bro is able to achieve the performance necessary to do so while running on commercially available PC hardware, and thus can serve as a cost effective means of monitoring a site’s Internet connection.

  19. Pedestrian Detection with Spatially Pooled Features and Structured Ensemble Learning.

    PubMed

    Paisitkriangkrai, Sakrapee; Shen, Chunhua; Hengel, Anton van den

    2016-06-01

    Many typical applications of object detection operate within a prescribed false-positive range. In this situation the performance of a detector should be assessed on the basis of the area under the ROC curve over that range, rather than over the full curve, as the performance outside the prescribed range is irrelevant. This measure is labelled as the partial area under the ROC curve (pAUC). We propose a novel ensemble learning method which achieves a maximal detection rate at a user-defined range of false positive rates by directly optimizing the partial AUC using structured learning. In addition, in order to achieve high object detection performance, we propose a new approach to extracting low-level visual features based on spatial pooling. Incorporating spatial pooling improves the translational invariance and thus the robustness of the detection process. Experimental results on both synthetic and real-world data sets demonstrate the effectiveness of our approach, and we show that it is possible to train state-of-the-art pedestrian detectors using the proposed structured ensemble learning method with spatially pooled features. The result is the current best reported performance on the Caltech-USA pedestrian detection dataset. PMID:26336118

  20. Image enhancement techniques applied to solar feature detection

    NASA Astrophysics Data System (ADS)

    Kowalski, Artur J.

    This dissertation presents the development of automatic image enhancement techniques for solar feature detection. The new method allows for detection and tracking of the evolution of filaments in solar images. Series of H-alpha full-disk images are taken in regular time intervals to observe the changes of the solar disk features. In each picture, the solar chromosphere filaments are identified for further evolution examination. The initial preprocessing step involves local thresholding to convert grayscale images into black-and-white pictures with chromosphere granularity enhanced. An alternative preprocessing method, based on image normalization and global thresholding is presented. The next step employs morphological closing operations with multi-directional linear structuring elements to extract elongated shapes in the image. After logical union of directional filtering results, the remaining noise is removed from the final outcome using morphological dilation and erosion with a circular structuring element. Experimental results show that the developed techniques can achieve excellent results in detecting large filaments and good detection rates for small filaments. The final chapter discusses proposed directions of the future research and applications to other areas of solar image processing, in particular to detection of solar flares, plages and sunspots.

  1. Feature Extraction and Selection From the Perspective of Explosive Detection

    SciTech Connect

    Sengupta, S K

    2009-09-01

    Features are extractable measurements from a sample image summarizing the information content in an image and in the process providing an essential tool in image understanding. In particular, they are useful for image classification into pre-defined classes or grouping a set of image samples (also called clustering) into clusters with similar within-cluster characteristics as defined by such features. At the lowest level, features may be the intensity levels of a pixel in an image. The intensity levels of the pixels in an image may be derived from a variety of sources. For example, it can be the temperature measurement (using an infra-red camera) of the area representing the pixel or the X-ray attenuation in a given volume element of a 3-d image or it may even represent the dielectric differential in a given volume element obtained from an MIR image. At a higher level, geometric descriptors of objects of interest in a scene may also be considered as features in the image. Examples of such features are: area, perimeter, aspect ratio and other shape features, or topological features like the number of connected components, the Euler number (the number of connected components less the number of 'holes'), etc. Occupying an intermediate level in the feature hierarchy are texture features which are typically derived from a group of pixels often in a suitably defined neighborhood of a pixel. These texture features are useful not only in classification but also in the segmentation of an image into different objects/regions of interest. At the present state of our investigation, we are engaged in the task of finding a set of features associated with an object under inspection ( typically a piece of luggage or a brief case) that will enable us to detect and characterize an explosive inside, when present. Our tool of inspection is an X-Ray device with provisions for computed tomography (CT) that generate one or more (depending on the number of energy levels used) digitized 3

  2. Feature detection in satellite images using neural network technology

    NASA Technical Reports Server (NTRS)

    Augusteijn, Marijke F.; Dimalanta, Arturo S.

    1992-01-01

    A feasibility study of automated classification of satellite images is described. Satellite images were characterized by the textures they contain. In particular, the detection of cloud textures was investigated. The method of second-order gray level statistics, using co-occurrence matrices, was applied to extract feature vectors from image segments. Neural network technology was employed to classify these feature vectors. The cascade-correlation architecture was successfully used as a classifier. The use of a Kohonen network was also investigated but this architecture could not reliably classify the feature vectors due to the complicated structure of the classification problem. The best results were obtained when data from different spectral bands were fused.

  3. An auditory feature detection circuit for sound pattern recognition

    PubMed Central

    Schöneich, Stefan; Kostarakos, Konstantinos; Hedwig, Berthold

    2015-01-01

    From human language to birdsong and the chirps of insects, acoustic communication is based on amplitude and frequency modulation of sound signals. Whereas frequency processing starts at the level of the hearing organs, temporal features of the sound amplitude such as rhythms or pulse rates require processing by central auditory neurons. Besides several theoretical concepts, brain circuits that detect temporal features of a sound signal are poorly understood. We focused on acoustically communicating field crickets and show how five neurons in the brain of females form an auditory feature detector circuit for the pulse pattern of the male calling song. The processing is based on a coincidence detector mechanism that selectively responds when a direct neural response and an intrinsically delayed response to the sound pulses coincide. This circuit provides the basis for auditory mate recognition in field crickets and reveals a principal mechanism of sensory processing underlying the perception of temporal patterns. PMID:26601259

  4. Pectoral muscle detection in mammograms using local statistical features.

    PubMed

    Liu, Li; Liu, Qian; Lu, Wei

    2014-10-01

    Mammography is a primary imaging method for breast cancer diagnosis. It is an important issue to accurately identify and separate pectoral muscles (PM) from breast tissues. Hough-transform-based methods are commonly adopted for PM detection. But their performances are susceptible when PM edges cannot be depicted by straight lines. In this study, we present a new pectoral muscle identification algorithm which utilizes statistical features of pixel responses. First, the Anderson-Darling goodness-of-fit test is used to extract a feature image by assuming non-Gaussianity for PM boundaries. Second, a global weighting scheme based on the location of PM was applied onto the feature image to suppress non-PM regions. From the weighted image, a preliminary set of pectoral muscles boundary components is detected via row-wise peak detection. An iterative procedure based on the edge continuity and orientation is used to determine the final PM boundary. Our results on a public mammogram database were assessed using four performance metrics: the false positive rate, the false negative rate, the Hausdorff distance, and the average distance. Compared to previous studies, our method demonstrates the state-of-art performance in terms of four measures. PMID:24482043

  5. Cluster-based differential features to improve detection accuracy of focal cortical dysplasia

    NASA Astrophysics Data System (ADS)

    Yang, Chin-Ann; Kaveh, Mostafa; Erickson, Bradley

    2012-03-01

    In this paper, a computer aided diagnosis (CAD) system for automatic detection of focal cortical dysplasia (FCD) on T1-weighted MRI is proposed. We introduce a new set of differential cluster-wise features comparing local differences of the candidate lesional area with its surroundings and other GM/WM boundaries. The local differences are measured in a distributional sense using χ2 distances. Finally, a Support Vector Machine (SVM) classifier is used to classify the clusters. Experimental results show an 88% lesion detection rate with only 1.67 false positive clusters per subject. Also, the results show that using additional differential features clearly outperforms the result using only absolute features.

  6. Portable pathogen detection system

    DOEpatents

    Colston, Billy W.; Everett, Matthew; Milanovich, Fred P.; Brown, Steve B.; Vendateswaran, Kodumudi; Simon, Jonathan N.

    2005-06-14

    A portable pathogen detection system that accomplishes on-site multiplex detection of targets in biological samples. The system includes: microbead specific reagents, incubation/mixing chambers, a disposable microbead capture substrate, and an optical measurement and decoding arrangement. The basis of this system is a highly flexible Liquid Array that utilizes optically encoded microbeads as the templates for biological assays. Target biological samples are optically labeled and captured on the microbeads, which are in turn captured on an ordered array or disordered array disposable capture substrate and then optically read.

  7. Solar system fault detection

    DOEpatents

    Farrington, R.B.; Pruett, J.C. Jr.

    1984-05-14

    A fault detecting apparatus and method are provided for use with an active solar system. The apparatus provides an indication as to whether one or more predetermined faults have occurred in the solar system. The apparatus includes a plurality of sensors, each sensor being used in determining whether a predetermined condition is present. The outputs of the sensors are combined in a pre-established manner in accordance with the kind of predetermined faults to be detected. Indicators communicate with the outputs generated by combining the sensor outputs to give the user of the solar system and the apparatus an indication as to whether a predetermined fault has occurred. Upon detection and indication of any predetermined fault, the user can take appropriate corrective action so that the overall reliability and efficiency of the active solar system are increased.

  8. Solar system fault detection

    DOEpatents

    Farrington, Robert B.; Pruett, Jr., James C.

    1986-01-01

    A fault detecting apparatus and method are provided for use with an active solar system. The apparatus provides an indication as to whether one or more predetermined faults have occurred in the solar system. The apparatus includes a plurality of sensors, each sensor being used in determining whether a predetermined condition is present. The outputs of the sensors are combined in a pre-established manner in accordance with the kind of predetermined faults to be detected. Indicators communicate with the outputs generated by combining the sensor outputs to give the user of the solar system and the apparatus an indication as to whether a predetermined fault has occurred. Upon detection and indication of any predetermined fault, the user can take appropriate corrective action so that the overall reliability and efficiency of the active solar system are increased.

  9. Multimodal spectroscopy detects features of vulnerable atherosclerotic plaque

    PubMed Central

    Šćepanović, Obrad R.; Fitzmaurice, Maryann; Miller, Arnold; Kong, Chae-Ryon; Volynskaya, Zoya; Dasari, Ramachandra R.; Kramer, John R.; Feld, Michael S.

    2011-01-01

    Early detection and treatment of rupture-prone vulnerable atherosclerotic plaques is critical to reducing patient mortality associated with cardiovascular disease. The combination of reflectance, fluorescence, and Raman spectroscopy—termed multimodal spectroscopy (MMS)—provides detailed biochemical information about tissue and can detect vulnerable plaque features: thin fibrous cap (TFC), necrotic core (NC), superficial foam cells (SFC), and thrombus. Ex vivo MMS spectra are collected from 12 patients that underwent carotid endarterectomy or femoral bypass surgery. Data are collected by means of a unitary MMS optical fiber probe and a portable clinical instrument. Blinded histopathological analysis is used to assess the vulnerability of each spectrally evaluated artery lesion. Modeling of the ex vivo MMS spectra produce objective parameters that correlate with the presence of vulnerable plaque features: TFC with fluorescence parameters indicative of collagen presence; NC∕SFC with a combination of diffuse reflectance β-carotene∕ceroid absorption and the Raman spectral signature of lipids; and thrombus with its Raman signature. Using these parameters, suspected vulnerable plaques can be detected with a sensitivity of 96% and specificity of 72%. These encouraging results warrant the continued development of MMS as a catheter-based clinical diagnostic technique for early detection of vulnerable plaques. PMID:21280896

  10. Multimodal spectroscopy detects features of vulnerable atherosclerotic plaque

    NASA Astrophysics Data System (ADS)

    Šćepanović, Obrad R.; Fitzmaurice, Maryann; Miller, Arnold; Kong, Chae-Ryon; Volynskaya, Zoya; Dasari, Ramachandra R.; Kramer, John R.; Feld, Michael S.

    2011-01-01

    Early detection and treatment of rupture-prone vulnerable atherosclerotic plaques is critical to reducing patient mortality associated with cardiovascular disease. The combination of reflectance, fluorescence, and Raman spectroscopy-termed multimodal spectroscopy (MMS)-provides detailed biochemical information about tissue and can detect vulnerable plaque features: thin fibrous cap (TFC), necrotic core (NC), superficial foam cells (SFC), and thrombus. Ex vivo MMS spectra are collected from 12 patients that underwent carotid endarterectomy or femoral bypass surgery. Data are collected by means of a unitary MMS optical fiber probe and a portable clinical instrument. Blinded histopathological analysis is used to assess the vulnerability of each spectrally evaluated artery lesion. Modeling of the ex vivo MMS spectra produce objective parameters that correlate with the presence of vulnerable plaque features: TFC with fluorescence parameters indicative of collagen presence; NC/SFC with a combination of diffuse reflectance β-carotene/ceroid absorption and the Raman spectral signature of lipids; and thrombus with its Raman signature. Using these parameters, suspected vulnerable plaques can be detected with a sensitivity of 96% and specificity of 72%. These encouraging results warrant the continued development of MMS as a catheter-based clinical diagnostic technique for early detection of vulnerable plaques.

  11. Idaho Explosives Detection System

    SciTech Connect

    Edward L. Reber; Larry G. Blackwood; Andrew J. Edwards; J. Keith Jewell; Kenneth W. Rohde; Edward H. Seabury; Jeffery B. Klinger

    2005-12-01

    The Idaho Explosives Detection System was developed at the Idaho National Laboratory (INL) to respond to threats imposed by delivery trucks potentially carrying explosives into military bases. A full-scale prototype system has been built and is currently undergoing testing. The system consists of two racks, one on each side of a subject vehicle. Each rack includes a neutron generator and an array of NaI detectors. The two neutron generators are pulsed and synchronized. A laptop computer controls the entire system. The control software is easily operable by minimally trained staff. The system was developed to detect explosives in a medium size truck within a 5-min measurement time. System performance was successfully demonstrated with explosives at the INL in June 2004 and at Andrews Air Force Base in July 2004.

  12. Non-contact feature detection using ultrasonic Lamb waves

    DOEpatents

    Sinha, Dipen N.

    2011-06-28

    Apparatus and method for non-contact ultrasonic detection of features on or within the walls of hollow pipes are described. An air-coupled, high-power ultrasonic transducer for generating guided waves in the pipe wall, and a high-sensitivity, air-coupled transducer for detecting these waves, are disposed at a distance apart and at chosen angle with respect to the surface of the pipe, either inside of or outside of the pipe. Measurements may be made in reflection or transmission modes depending on the relative position of the transducers and the pipe. Data are taken by sweeping the frequency of the incident ultrasonic waves, using a tracking narrow-band filter to reduce detected noise, and transforming the frequency domain data into the time domain using fast Fourier transformation, if required.

  13. Integration of Image-Derived and Pos-Derived Features for Image Blur Detection

    NASA Astrophysics Data System (ADS)

    Teo, Tee-Ann; Zhan, Kai-Zhi

    2016-06-01

    The image quality plays an important role for Unmanned Aerial Vehicle (UAV)'s applications. The small fixed wings UAV is suffering from the image blur due to the crosswind and the turbulence. Position and Orientation System (POS), which provides the position and orientation information, is installed onto an UAV to enable acquisition of UAV trajectory. It can be used to calculate the positional and angular velocities when the camera shutter is open. This study proposes a POS-assisted method to detect the blur image. The major steps include feature extraction, blur image detection and verification. In feature extraction, this study extracts different features from images and POS. The image-derived features include mean and standard deviation of image gradient. For POS-derived features, we modify the traditional degree-of-linear-blur (blinear) method to degree-of-motion-blur (bmotion) based on the collinear condition equations and POS parameters. Besides, POS parameters such as positional and angular velocities are also adopted as POS-derived features. In blur detection, this study uses Support Vector Machines (SVM) classifier and extracted features (i.e. image information, POS data, blinear and bmotion) to separate blur and sharp UAV images. The experiment utilizes SenseFly eBee UAV system. The number of image is 129. In blur image detection, we use the proposed degree-of-motion-blur and other image features to classify the blur image and sharp images. The classification result shows that the overall accuracy using image features is only 56%. The integration of image-derived and POS-derived features have improved the overall accuracy from 56% to 76% in blur detection. Besides, this study indicates that the performance of the proposed degree-of-motion-blur is better than the traditional degree-of-linear-blur.

  14. Full-Featured Web Conferencing Systems

    ERIC Educational Resources Information Center

    Foreman, Joel; Jenkins, Roy

    2005-01-01

    In order to match the customary strengths of the still dominant face-to-face instructional mode, a high-performance online learning system must employ synchronous as well as asynchronous communications; buttress graphics, animation, and text with live audio and video; and provide many of the features and processes associated with course management…

  15. Feature Detection, Characterization and Confirmation Methodology: Final Report

    SciTech Connect

    Karasaki, Kenzi; Apps, John; Doughty, Christine; Gwatney, Hope; Onishi, Celia Tiemi; Trautz, Robert; Tsang, Chin-Fu

    2007-03-01

    This is the final report of the NUMO-LBNL collaborative project: Feature Detection, Characterization and Confirmation Methodology under NUMO-DOE/LBNL collaboration agreement, the task description of which can be found in the Appendix. We examine site characterization projects from several sites in the world. The list includes Yucca Mountain in the USA, Tono and Horonobe in Japan, AECL in Canada, sites in Sweden, and Olkiluoto in Finland. We identify important geologic features and parameters common to most (or all) sites to provide useful information for future repository siting activity. At first glance, one could question whether there was any commonality among the sites, which are in different rock types at different locations. For example, the planned Yucca Mountain site is a dry repository in unsaturated tuff, whereas the Swedish sites are situated in saturated granite. However, the study concludes that indeed there are a number of important common features and parameters among all the sites--namely, (1) fault properties, (2) fracture-matrix interaction (3) groundwater flux, (4) boundary conditions, and (5) the permeability and porosity of the materials. We list the lessons learned from the Yucca Mountain Project and other site characterization programs. Most programs have by and large been quite successful. Nonetheless, there are definitely 'should-haves' and 'could-haves', or lessons to be learned, in all these programs. Although each site characterization program has some unique aspects, we believe that these crosscutting lessons can be very useful for future site investigations to be conducted in Japan. One of the most common lessons learned is that a repository program should allow for flexibility, in both schedule and approach. We examine field investigation technologies used to collect site characterization data in the field. An extensive list of existing field technologies is presented, with some discussion on usage and limitations. Many of the

  16. Detecting circular and rectangular particles based on geometric feature detection in electron micrographs.

    PubMed

    Yu, Zeyun; Bajaj, Chandrajit

    2004-01-01

    Accurate and automatic particle detection from cryo-electron microscopy (cryo-EM images) is very important for high-resolution reconstruction of large macromolecular structures. In this paper, we present a method for particle picking based on shape feature detection. Two fundamental concepts of computational geometry, namely, the distance transform and the Voronoi diagram, are used for detection of critical features as well as for accurate location of particles from the images or micrographs. Unlike the conventional template-matching methods, our approach detects the particles based on their boundary features instead of intensities. The geometric features derived from the boundaries provide an efficient way for locating particles quickly and accurately, which avoids a brute-force searching for the best position/orientation. Our approach is fully automatic and has been successfully applied to detect particles with approximately circular or rectangular shapes (e.g., KLH particles). Particle detection can be enhanced by multiple sets of parameters used in edge detection and/or by anisotropic filtering. We also discuss the extension of this approach to other types of particles with certain geometric features. PMID:15065684

  17. Road marking features extraction using the VIAPIX® system

    NASA Astrophysics Data System (ADS)

    Kaddah, W.; Ouerhani, Y.; Alfalou, A.; Desthieux, M.; Brosseau, C.; Gutierrez, C.

    2016-07-01

    Precise extraction of road marking features is a critical task for autonomous urban driving, augmented driver assistance, and robotics technologies. In this study, we consider an autonomous system allowing us lane detection for marked urban roads and analysis of their features. The task is to relate the georeferencing of road markings from images obtained using the VIAPIX® system. Based on inverse perspective mapping and color segmentation to detect all white objects existing on this road, the present algorithm enables us to examine these images automatically and rapidly and also to get information on road marks, their surface conditions, and their georeferencing. This algorithm allows detecting all road markings and identifying some of them by making use of a phase-only correlation filter (POF). We illustrate this algorithm and its robustness by applying it to a variety of relevant scenarios.

  18. Robustness of time frequency distribution based features for automated neonatal EEG seizure detection.

    PubMed

    Nagaraj, S B; Stevenson, N J; Marnane, W P; Boylan, G B; Lightbody, G

    2014-01-01

    In this paper we examined the robustness of a feature-set based on time-frequency distributions (TFDs) for neonatal EEG seizure detection. This feature-set was originally proposed in literature for neonatal seizure detection using a support vector machine (SVM). We tested the performance of this feature-set with a smoothed Wigner-Ville distribution and modified B distribution as the underlying TFDs. The seizure detection system using time-frequency signal and image processing features from the TFD of the EEG signal using modified B distribution was able to achieve a median receiver operator characteristic area of 0.96 (IQR 0.91-0.98) tested on a large clinical dataset of 826 h of EEG data from 18 full-term newborns with 1389 seizures. The mean AUC was 0.93. PMID:25570580

  19. Contrast sensitivity and the detection of moving patterns and features.

    PubMed

    O'Carroll, David C; Wiederman, Steven D

    2014-01-01

    Theories based on optimal sampling by the retina have been widely applied to visual ecology at the level of the optics of the eye, supported by visual behaviour. This leads to speculation about the additional processing that must lie in between-in the brain itself. But fewer studies have adopted a quantitative approach to evaluating the detectability of specific features in these neural pathways. We briefly review this approach with a focus on contrast sensitivity of two parallel pathways for motion processing in insects, one used for analysis of wide-field optic flow, the other for detection of small features. We further use a combination of optical modelling of image blur and physiological recording from both photoreceptors and higher-order small target motion detector neurons sensitive to small targets to show that such neurons operate right at the limits imposed by the optics of the eye and the noise level of single photoreceptors. Despite this, and the limitation of only being able to use information from adjacent receptors to detect target motion, they achieve a contrast sensitivity that rivals that of wide-field motion sensitive pathways in either insects or vertebrates-among the highest in absolute terms seen in any animal. PMID:24395970

  20. Contrast sensitivity and the detection of moving patterns and features

    PubMed Central

    O'Carroll, David C.; Wiederman, Steven D.

    2014-01-01

    Theories based on optimal sampling by the retina have been widely applied to visual ecology at the level of the optics of the eye, supported by visual behaviour. This leads to speculation about the additional processing that must lie in between—in the brain itself. But fewer studies have adopted a quantitative approach to evaluating the detectability of specific features in these neural pathways. We briefly review this approach with a focus on contrast sensitivity of two parallel pathways for motion processing in insects, one used for analysis of wide-field optic flow, the other for detection of small features. We further use a combination of optical modelling of image blur and physiological recording from both photoreceptors and higher-order small target motion detector neurons sensitive to small targets to show that such neurons operate right at the limits imposed by the optics of the eye and the noise level of single photoreceptors. Despite this, and the limitation of only being able to use information from adjacent receptors to detect target motion, they achieve a contrast sensitivity that rivals that of wide-field motion sensitive pathways in either insects or vertebrates—among the highest in absolute terms seen in any animal. PMID:24395970

  1. Water system virus detection

    NASA Technical Reports Server (NTRS)

    Fraser, A. S.; Wells, A. F.; Tenoso, H. J.

    1975-01-01

    A monitoring system developed to test the capability of a water recovery system to reject the passage of viruses into the recovered water is described. A nonpathogenic marker virus, bacteriophage F2, is fed into the process stream before the recovery unit and the reclaimed water is assayed for its presence. Detection of the marker virus consists of two major components, concentration and isolation of the marker virus, and detection of the marker virus. The concentration system involves adsorption of virus to cellulose acetate filters in the presence of trivalent cations and low pH with subsequent desorption of the virus using volumes of high pH buffer. The detection of the virus is performed by a passive immune agglutination test utilizing specially prepared polystyrene particles. An engineering preliminary design was performed as a parallel effort to the laboratory development of the marker virus test system. Engineering schematics and drawings of a fully functional laboratory prototype capable of zero-G operation are presented. The instrument consists of reagent pump/metering system, reagent storage containers, a filter concentrator, an incubation/detector system, and an electronic readout and control system.

  2. Cepstrum based feature extraction method for fungus detection

    NASA Astrophysics Data System (ADS)

    Yorulmaz, Onur; Pearson, Tom C.; Çetin, A. Enis

    2011-06-01

    In this paper, a method for detection of popcorn kernels infected by a fungus is developed using image processing. The method is based on two dimensional (2D) mel and Mellin-cepstrum computation from popcorn kernel images. Cepstral features that were extracted from popcorn images are classified using Support Vector Machines (SVM). Experimental results show that high recognition rates of up to 93.93% can be achieved for both damaged and healthy popcorn kernels using 2D mel-cepstrum. The success rate for healthy popcorn kernels was found to be 97.41% and the recognition rate for damaged kernels was found to be 89.43%.

  3. Unsupervised detection of abnormalities in medical images using salient features

    NASA Astrophysics Data System (ADS)

    Alpert, Sharon; Kisilev, Pavel

    2014-03-01

    In this paper we propose a new method for abnormality detection in medical images which is based on the notion of medical saliency. The proposed method is general and is suitable for a variety of tasks related to detection of: 1) lesions and microcalcifications (MCC) in mammographic images, 2) stenoses in angiographic images, 3) lesions found in magnetic resonance (MRI) images of brain. The main idea of our approach is that abnormalities manifest as rare events, that is, as salient areas compared to normal tissues. We define the notion of medical saliency by combining local patch information from the lightness channel with geometric shape local descriptors. We demonstrate the efficacy of the proposed method by applying it to various modalities, and to various abnormality detection problems. Promising results are demonstrated for detection of MCC and of masses in mammographic images, detection of stenoses in angiography images, and detection of lesions in brain MRI. We also demonstrate how the proposed automatic abnormality detection method can be combined with a system that performs supervised classification of mammogram images into benign or malignant/premalignant MCC's. We use a well known DDSM mammogram database for the experiment on MCC classification, and obtain 80% accuracy in classifying images containing premalignant MCC versus benign ones. In contrast to supervised detection methods, the proposed approach does not rely on ground truth markings, and, as such, is very attractive and applicable for big corpus image data processing.

  4. A multilevel approach to sequential detection of pictorial features

    NASA Technical Reports Server (NTRS)

    Ramapriyan, H. K.

    1976-01-01

    The problem of detecting the local similarity between templates in a given class and a given image using a hierarchically ordered sequential decision rule is examined. It is proposed that the set of templates be partitioned and a 'representative template' be defined for each of the partitions. Several levels of partitioning are defined. Elimination of mismatching locations and termination of computation can take place at each level of detection. Each level of testing is over a more restrictive subset of the template class than the previous level. Criteria are given for selecting representative templates, the ordering of components of a template vector for error evaluation, and the threshold sequences to be used in deciding about a 'match'. Suboptimal solutions are given satisfying these criteria. Examples showing recognition of linear features in test patterns and photographs obtained by aerial and spaceborne sensors are provided.

  5. A ROC-based feature selection method for computer-aided detection and diagnosis

    NASA Astrophysics Data System (ADS)

    Wang, Songyuan; Zhang, Guopeng; Liao, Qimei; Zhang, Junying; Jiao, Chun; Lu, Hongbing

    2014-03-01

    Image-based computer-aided detection and diagnosis (CAD) has been a very active research topic aiming to assist physicians to detect lesions and distinguish them from benign to malignant. However, the datasets fed into a classifier usually suffer from small number of samples, as well as significantly less samples available in one class (have a disease) than the other, resulting in the classifier's suboptimal performance. How to identifying the most characterizing features of the observed data for lesion detection is critical to improve the sensitivity and minimize false positives of a CAD system. In this study, we propose a novel feature selection method mR-FAST that combines the minimal-redundancymaximal relevance (mRMR) framework with a selection metric FAST (feature assessment by sliding thresholds) based on the area under a ROC curve (AUC) generated on optimal simple linear discriminants. With three feature datasets extracted from CAD systems for colon polyps and bladder cancer, we show that the space of candidate features selected by mR-FAST is more characterizing for lesion detection with higher AUC, enabling to find a compact subset of superior features at low cost.

  6. Bro Intrusion Detection System

    Energy Science and Technology Software Center (ESTSC)

    2006-01-25

    Bro is a Unix-based Network Intrusion Detection System (IDS). Bro monitors network traffic and detects intrusion attempts based on the traffic characteristics and content. Bro detects intrusions by comparing network traffic against rules describing events that are deemed troublesome. These rules might describe activities (e.g., certain hosts connecting to certain services), what activities are worth alerting (e.g., attempts to a given number of different hosts constitutes a "scan"), or signatures describing known attacks or accessmore » to known vulnerabilities. If Bro detects something of interest, it can be instructed to either issue a log entry or initiate the execution of an operating system command. Bro targets high-speed (Gbps), high-volume intrusion detection. By judiciously leveraging packet filtering techniques, Bro is able to achieve the performance necessary to do so while running on commercially available PC hardware, and thus can serve as a cost effective means of monitoring a site’s Internet connection.« less

  7. Radiation detection system

    DOEpatents

    Nelson, Melvin A.; Davies, Terence J.; Morton, III, John R.

    1976-01-01

    A radiation detection system which utilizes the generation of Cerenkov light in and the transmission of that light longitudinally through fiber optic wave guides in order to transmit intelligence relating to the radiation to a remote location. The wave guides are aligned with respect to charged particle radiation so that the Cerenkov light, which is generated at an angle to the radiation, is accepted by the fiber for transmission therethrough. The Cerenkov radiation is detected, recorded, and analyzed at the other end of the fiber.

  8. Camouflaged target detection based on polarized spectral features

    NASA Astrophysics Data System (ADS)

    Tan, Jian; Zhang, Junping; Zou, Bin

    2016-05-01

    The polarized hyperspectral images (PHSI) include polarization, spectral, spatial and radiant features, which provide more information about objects and scenes than traditional intensity or spectrum ones. And polarization can suppress the background and highlight the object, leading to the high potential to improve camouflaged target detection. So polarized hyperspectral imaging technique has aroused extensive concern in the last few years. Nowadays, the detection methods are still not very mature, most of which are rooted in the detection of hyperspectral image. And before using these algorithms, Stokes vector is used to process the original four-dimensional polarized hyperspectral data firstly. However, when the data is large and complex, the amount of calculation and error will increase. In this paper, tensor is applied to reconstruct the original four-dimensional data into new three-dimensional data, then, the constraint energy minimization (CEM) is used to process the new data, which adds the polarization information to construct the polarized spectral filter operator and takes full advantages of spectral and polarized information. This way deals with the original data without extracting the Stokes vector, so as to reduce the computation and error greatly. The experimental results also show that the proposed method in this paper is more suitable for the target detection of the PHSI.

  9. Special feature on imaging systems and techniques

    NASA Astrophysics Data System (ADS)

    Yang, Wuqiang; Giakos, George

    2013-07-01

    The IEEE International Conference on Imaging Systems and Techniques (IST'2012) was held in Manchester, UK, on 16-17 July 2012. The participants came from 26 countries or regions: Austria, Brazil, Canada, China, Denmark, France, Germany, Greece, India, Iran, Iraq, Italy, Japan, Korea, Latvia, Malaysia, Norway, Poland, Portugal, Sweden, Switzerland, Taiwan, Tunisia, UAE, UK and USA. The technical program of the conference consisted of a series of scientific and technical sessions, exploring physical principles, engineering and applications of new imaging systems and techniques, as reflected by the diversity of the submitted papers. Following a rigorous review process, a total of 123 papers were accepted, and they were organized into 30 oral presentation sessions and a poster session. In addition, six invited keynotes were arranged. The conference not only provided the participants with a unique opportunity to exchange ideas and disseminate research outcomes but also paved a way to establish global collaboration. Following the IST'2012, a total of 55 papers, which were technically extended substantially from their versions in the conference proceeding, were submitted as regular papers to this special feature of Measurement Science and Technology . Following a rigorous reviewing process, 25 papers have been finally accepted for publication in this special feature and they are organized into three categories: (1) industrial tomography, (2) imaging systems and techniques and (3) image processing. These papers not only present the latest developments in the field of imaging systems and techniques but also offer potential solutions to existing problems. We hope that this special feature provides a good reference for researchers who are active in the field and will serve as a catalyst to trigger further research. It has been our great pleasure to be the guest editors of this special feature. We would like to thank the authors for their contributions, without which it would

  10. Tape Cassette Bacteria Detection System

    NASA Technical Reports Server (NTRS)

    1973-01-01

    The design, fabrication, and testing of an automatic bacteria detection system with a zero-g capability and based on the filter-capsule approach is described. This system is intended for monitoring the sterility of regenerated water in a spacecraft. The principle of detection is based on measuring the increase in chemiluminescence produced by the action of bacterial porphyrins (i.e., catalase, cytochromes, etc.) on a luminol-hydrogen peroxide mixture. Since viable as well as nonviable organisms initiate this luminescence, viable organisms are detected by comparing the signal of an incubated water sample with an unincubated control. Higher signals for the former indicate the presence of viable organisms. System features include disposable sealed sterile capsules, each containing a filter membrane, for processing discrete water samples and a tape transport for moving these capsules through a processing sequence which involves sample concentration, nutrient addition, incubation, a 4 Molar Urea wash and reaction with luminol-hydrogen peroxide in front of a photomultiplier tube. Liquids are introduced by means of a syringe needle which pierces a rubber septum contained in the wall of the capsule. Detection thresholds obtained with this unit towards E. coli and S. marcescens assuming a 400 ml water sample are indicated.

  11. Pulsed helium ionization detection system

    DOEpatents

    Ramsey, Roswitha S.; Todd, Richard A.

    1987-01-01

    A helium ionization detection system is provided which produces stable operation of a conventional helium ionization detector while providing improved sensitivity and linearity. Stability is improved by applying pulsed dc supply voltage across the ionization detector, thereby modifying the sampling of the detectors output current. A unique pulse generator is used to supply pulsed dc to the detector which has variable width and interval adjust features that allows up to 500 V to be applied in pulse widths ranging from about 150 nsec to about dc conditions.

  12. Pulsed helium ionization detection system

    DOEpatents

    Ramsey, R.S.; Todd, R.A.

    1985-04-09

    A helium ionization detection system is provided which produces stable operation of a conventional helium ionization detector while providing improved sensitivity and linearity. Stability is improved by applying pulsed dc supply voltage across the ionization detector, thereby modifying the sampling of the detectors output current. A unique pulse generator is used to supply pulsed dc to the detector which has variable width and interval adjust features that allows up to 500 V to be applied in pulse widths ranging from about 150 nsec to about dc conditions.

  13. New features for detecting cervical precancer using hyperspectral diagnostic imaging

    NASA Astrophysics Data System (ADS)

    Okimoto, Gordon S.; Parker, Mary F.; Mooradian, Gregory C.; Saggese, Steven J.; Grisanti, Ames A.; O'Connor, Dennis M.; Miyazawa, Kunio

    2001-05-01

    Principal component analysis (PCA) in the wavelet domain provides powerful new features for the non-invasive detection of cervical intraepithelial neoplasia (CIN) using fluorescence imaging spectroscopy. These features are known as principal wavelet components (PWCs). The multiscale structure of the fluorescence spectrum for each pixel of the hyperspectral data cube is extracted using the continuous wavelet transform. PCA is then used to compress and denoise the wavelet representation for presentation to a feed- forward neural network for tissue classification. Using PWC features as inputs to a 5-class NN resulted in average correct classification rates of 95% over five cervical tissue classes corresponding to low-grade dysplasia, squamous, columnar, metaplasia plus a fifth class for other unspecified tissue types, blood and mucus. A 2-class NN was also trained to discriminate between CIN1 and normal tissue with sensitivity and specificity of 98% and 99%, respectively. All performance assessments were based on test data from a set of patients not seen during NN training. Trained neural classifiers were used to `compress' and transform 3D hyperspectral data cubes into 2D color-coded images that accurately mapped the spatial distribution of both normal and dysplastic tissue over the surface of the entire cervix.

  14. Textural feature selection for enhanced detection of stationary humans in through-the-wall radar imagery

    NASA Astrophysics Data System (ADS)

    Chaddad, A.; Ahmad, F.; Amin, M. G.; Sevigny, P.; DiFilippo, D.

    2014-05-01

    Feature-based methods have been recently considered in the literature for detection of stationary human targets in through-the-wall radar imagery. Specifically, textural features, such as contrast, correlation, energy, entropy, and homogeneity, have been extracted from gray-level co-occurrence matrices (GLCMs) to aid in discriminating the true targets from multipath ghosts and clutter that closely mimic the target in size and intensity. In this paper, we address the task of feature selection to identify the relevant subset of features in the GLCM domain, while discarding those that are either redundant or confusing, thereby improving the performance of feature-based scheme to distinguish between targets and ghosts/clutter. We apply a Decision Tree algorithm to find the optimal combination of co-occurrence based textural features for the problem at hand. We employ a K-Nearest Neighbor classifier to evaluate the performance of the optimal textural feature based scheme in terms of its target and ghost/clutter discrimination capability and use real-data collected with the vehicle-borne multi-channel through-the-wall radar imaging system by Defence Research and Development Canada. For the specific data analyzed, it is shown that the identified dominant features yield a higher classification accuracy, with lower number of false alarms and missed detections, compared to the full GLCM based feature set.

  15. Feature selection of seismic waveforms for long period event detection at Cotopaxi Volcano

    NASA Astrophysics Data System (ADS)

    Lara-Cueva, R. A.; Benítez, D. S.; Carrera, E. V.; Ruiz, M.; Rojo-Álvarez, J. L.

    2016-04-01

    Volcano Early Warning Systems (VEWS) have become a research topic in order to preserve human lives and material losses. In this setting, event detection criteria based on classification using machine learning techniques have proven useful, and a number of systems have been proposed in the literature. However, to the best of our knowledge, no comprehensive and principled study has been conducted to compare the influence of the many different sets of possible features that have been used as input spaces in previous works. We present an automatic recognition system of volcano seismicity, by considering feature extraction, event classification, and subsequent event detection, in order to reduce the processing time as a first step towards a high reliability automatic detection system in real-time. We compiled and extracted a comprehensive set of temporal, moving average, spectral, and scale-domain features, for separating long period seismic events from background noise. We benchmarked two usual kinds of feature selection techniques, namely, filter (mutual information and statistical dependence) and embedded (cross-validation and pruning), each of them by using suitable and appropriate classification algorithms such as k Nearest Neighbors (k-NN) and Decision Trees (DT). We applied this approach to the seismicity presented at Cotopaxi Volcano in Ecuador during 2009 and 2010. The best results were obtained by using a 15 s segmentation window, feature matrix in the frequency domain, and DT classifier, yielding 99% of detection accuracy and sensitivity. Selected features and their interpretation were consistent among different input spaces, in simple terms of amplitude and spectral content. Our study provides the framework for an event detection system with high accuracy and reduced computational requirements.

  16. Chromatic Information and Feature Detection in Fast Visual Analysis

    PubMed Central

    Del Viva, Maria M.; Punzi, Giovanni; Shevell, Steven K.

    2016-01-01

    The visual system is able to recognize a scene based on a sketch made of very simple features. This ability is likely crucial for survival, when fast image recognition is necessary, and it is believed that a primal sketch is extracted very early in the visual processing. Such highly simplified representations can be sufficient for accurate object discrimination, but an open question is the role played by color in this process. Rich color information is available in natural scenes, yet artist's sketches are usually monochromatic; and, black-and-white movies provide compelling representations of real world scenes. Also, the contrast sensitivity of color is low at fine spatial scales. We approach the question from the perspective of optimal information processing by a system endowed with limited computational resources. We show that when such limitations are taken into account, the intrinsic statistical properties of natural scenes imply that the most effective strategy is to ignore fine-scale color features and devote most of the bandwidth to gray-scale information. We find confirmation of these information-based predictions from psychophysics measurements of fast-viewing discrimination of natural scenes. We conclude that the lack of colored features in our visual representation, and our overall low sensitivity to high-frequency color components, are a consequence of an adaptation process, optimizing the size and power consumption of our brain for the visual world we live in. PMID:27478891

  17. Chromatic Information and Feature Detection in Fast Visual Analysis.

    PubMed

    Del Viva, Maria M; Punzi, Giovanni; Shevell, Steven K

    2016-01-01

    The visual system is able to recognize a scene based on a sketch made of very simple features. This ability is likely crucial for survival, when fast image recognition is necessary, and it is believed that a primal sketch is extracted very early in the visual processing. Such highly simplified representations can be sufficient for accurate object discrimination, but an open question is the role played by color in this process. Rich color information is available in natural scenes, yet artist's sketches are usually monochromatic; and, black-and-white movies provide compelling representations of real world scenes. Also, the contrast sensitivity of color is low at fine spatial scales. We approach the question from the perspective of optimal information processing by a system endowed with limited computational resources. We show that when such limitations are taken into account, the intrinsic statistical properties of natural scenes imply that the most effective strategy is to ignore fine-scale color features and devote most of the bandwidth to gray-scale information. We find confirmation of these information-based predictions from psychophysics measurements of fast-viewing discrimination of natural scenes. We conclude that the lack of colored features in our visual representation, and our overall low sensitivity to high-frequency color components, are a consequence of an adaptation process, optimizing the size and power consumption of our brain for the visual world we live in. PMID:27478891

  18. The Use of Linear Feature Detection to Investigate Thematic Mapper Data Performance and Processing

    NASA Technical Reports Server (NTRS)

    Gurney, C. M.

    1985-01-01

    The geometric and radiometric characteristics of thematic mapper data through analysis of linear features in the data are investigated. The particular aspects considered are: (1) thematic mapper ground IFUV; (2) radiometric contrast between linear features and background; (3) precision of system geometric correction; (4) band-to-band registration; and (5) potential utility of TM data for linear feature detection especially as compared to MSS data. It is shown that TM data may be used to estimate TM pixel size illustrate band: band mis-registration.

  19. Ultrasonic Leak Detection System

    NASA Technical Reports Server (NTRS)

    Youngquist, Robert C. (Inventor); Moerk, J. Steven (Inventor)

    1998-01-01

    A system for detecting ultrasonic vibrations. such as those generated by a small leak in a pressurized container. vessel. pipe. or the like. comprises an ultrasonic transducer assembly and a processing circuit for converting transducer signals into an audio frequency range signal. The audio frequency range signal can be used to drive a pair of headphones worn by an operator. A diode rectifier based mixing circuit provides a simple, inexpensive way to mix the transducer signal with a square wave signal generated by an oscillator, and thereby generate the audio frequency signal. The sensitivity of the system is greatly increased through proper selection and matching of the system components. and the use of noise rejection filters and elements. In addition, a parabolic collecting horn is preferably employed which is mounted on the transducer assembly housing. The collecting horn increases sensitivity of the system by amplifying the received signals. and provides directionality which facilitates easier location of an ultrasonic vibration source.

  20. Water system virus detection

    NASA Technical Reports Server (NTRS)

    Fraser, A. S.; Wells, A. F.; Tenoso, H. J. (Inventor)

    1978-01-01

    The performance of a waste water reclamation system is monitored by introducing a non-pathogenic marker virus, bacteriophage F2, into the waste-water prior to treatment and, thereafter, testing the reclaimed water for the presence of the marker virus. A test sample is first concentrated by absorbing any marker virus onto a cellulose acetate filter in the presence of a trivalent cation at low pH and then flushing the filter with a limited quantity of a glycine buffer solution to desorb any marker virus present on the filter. Photo-optical detection of indirect passive immune agglutination by polystyrene beads indicates the performance of the water reclamation system in removing the marker virus. A closed system provides for concentrating any marker virus, initiating and monitoring the passive immune agglutination reaction, and then flushing the system to prepare for another sample.

  1. Gas Flow Detection System

    NASA Technical Reports Server (NTRS)

    Moss, Thomas; Ihlefeld, Curtis; Slack, Barry

    2010-01-01

    This system provides a portable means to detect gas flow through a thin-walled tube without breaking into the tubing system. The flow detection system was specifically designed to detect flow through two parallel branches of a manifold with only one inlet and outlet, and is a means for verifying a space shuttle program requirement that saves time and reduces the risk of flight hardware damage compared to the current means of requirement verification. The prototype Purge Vent and Drain Window Cavity Conditioning System (PVD WCCS) Flow Detection System consists of a heater and a temperature-sensing thermistor attached to a piece of Velcro to be attached to each branch of a WCCS manifold for the duration of the requirement verification test. The heaters and thermistors are connected to a shielded cable and then to an electronics enclosure, which contains the power supplies, relays, and circuit board to provide power, signal conditioning, and control. The electronics enclosure is then connected to a commercial data acquisition box to provide analog to digital conversion as well as digital control. This data acquisition box is then connected to a commercial laptop running a custom application created using National Instruments LabVIEW. The operation of the PVD WCCS Flow Detection System consists of first attaching a heater/thermistor assembly to each of the two branches of one manifold while there is no flow through the manifold. Next, the software application running on the laptop is used to turn on the heaters and to monitor the manifold branch temperatures. When the system has reached thermal equilibrium, the software application s graphical user interface (GUI) will indicate that the branch temperatures are stable. The operator can then physically open the flow control valve to initiate the test flow of gaseous nitrogen (GN2) through the manifold. Next, the software user interface will be monitored for stable temperature indications when the system is again at

  2. Improving the detection of wind fields from LIDAR aerosol backscatter using feature extraction

    NASA Astrophysics Data System (ADS)

    Bickel, Brady R.; Rotthoff, Eric R.; Walters, Gage S.; Kane, Timothy J.; Mayor, Shane D.

    2016-04-01

    The tracking of winds and atmospheric features has many applications, from predicting and analyzing weather patterns in the upper and lower atmosphere to monitoring air movement from pig and chicken farms. Doppler LIDAR systems exist to quantify the underlying wind speeds, but cost of these systems can sometimes be relatively high, and processing limitations exist. The alternative is using an incoherent LIDAR system to analyze aerosol backscatter. Improving the detection and analysis of wind information from aerosol backscatter LIDAR systems will allow for the adoption of these relatively low cost instruments in environments where the size, complexity, and cost of other options are prohibitive. Using data from a simple aerosol backscatter LIDAR system, we attempt to extend the processing capabilities by calculating wind vectors through image correlation techniques to improve the detection of wind features.

  3. Developments in multisensor ocean feature monitoring: the ocean fronts feature analysis system

    NASA Astrophysics Data System (ADS)

    Brownsword, Chris; Jory, Ian

    1994-12-01

    Traditionally oceanographic features such as fronts and eddies have been monitored using satellites equipped with optical sensors such as the Advanced Very High Resolution Radiometer (AVHRR). The disadvantage of using AVHRR, however, is that it requires cloud free conditions to view the Earth's surface. Synthetic Aperture Radar (SAR) provides all weather, day/night image acquisition capabilities. Currently little is known about the effects of sea surface temperature variations on the returned SAR signal, though it is known that some variation in surface roughness is present where oceanographic features occur. Research into these phenomena is being undertaken at the Defence Research Agency (DRA) in Farnborough, U.K.. To investigate the capabilities of SAR, for ocean front detection, the Ocean Fronts Feature Analysis System (OFFAS) has been developed by Earth Observation Sciences Limited under contract to DRA. Using OFFAS, AVHRR data is used to verify and validate the SAR responses to oceanographic features. This paper is an update to that presented at Oceanology International '94, in Brighton which discussed the research and development work into the use of SAR data from the ERS-1 satellite to identify oceanographic features. Since then the modifications to the software, mentioned in that paper, have been made. OFFAS II now provides the capability to geometrically rectify the images to a regular map projection prior to the simultaneous display and manipulation of both image types. Thus allowing faster location of features of interest identified within a reference image (e.g. AVHRR), delineation of them using a tracing procedure, then the automatic redrawing of the trace on the corresponding target image (e.g. SAR). In addition the geometrically rectified SAR images can be mosaiced together, this is particularly useful where large oceanographic features span more than one image. To date, trials performed using OFFAS have indicated its value in aiding interpretation

  4. Arc fault detection system

    DOEpatents

    Jha, K.N.

    1999-05-18

    An arc fault detection system for use on ungrounded or high-resistance-grounded power distribution systems is provided which can be retrofitted outside electrical switchboard circuits having limited space constraints. The system includes a differential current relay that senses a current differential between current flowing from secondary windings located in a current transformer coupled to a power supply side of a switchboard, and a total current induced in secondary windings coupled to a load side of the switchboard. When such a current differential is experienced, a current travels through a operating coil of the differential current relay, which in turn opens an upstream circuit breaker located between the switchboard and a power supply to remove the supply of power to the switchboard. 1 fig.

  5. Arc fault detection system

    DOEpatents

    Jha, Kamal N.

    1999-01-01

    An arc fault detection system for use on ungrounded or high-resistance-grounded power distribution systems is provided which can be retrofitted outside electrical switchboard circuits having limited space constraints. The system includes a differential current relay that senses a current differential between current flowing from secondary windings located in a current transformer coupled to a power supply side of a switchboard, and a total current induced in secondary windings coupled to a load side of the switchboard. When such a current differential is experienced, a current travels through a operating coil of the differential current relay, which in turn opens an upstream circuit breaker located between the switchboard and a power supply to remove the supply of power to the switchboard.

  6. The use of linear feature detection to investigate thematic mapper data performance and processing

    NASA Technical Reports Server (NTRS)

    Gurney, C. M.

    1983-01-01

    The geometric and radiometric characteristics of thematic mapper data through analysis of linear features in the data are investigated. The particular aspects considered are: (1) thematic mapper ground IFOV; (2) radiometric contrast between linear features and background; (3) precision of system geometric correction; (4) band-to-band registration; and (5) potential utility of TM data for linear feature detection especially as compared to MSS data. It is shown that TM data may be used to estimate TM pixel size and to illustrate band-band mis-registration. Further, the geometry and radiometry of the data are sufficiently precise to allow accurate estimation of the widths of linear features. In optimum conditions features one quarter of a pixel in width may be accurately measured. These results have considerable potential for applications for hydrological and topographic mapping.

  7. A biomimetic algorithm for the improved detection of microarray features

    NASA Astrophysics Data System (ADS)

    Nicolau, Dan V., Jr.; Nicolau, Dan V.; Maini, Philip K.

    2007-02-01

    One the major difficulties of microarray technology relate to the processing of large and - importantly - error-loaded images of the dots on the chip surface. Whatever the source of these errors, those obtained in the first stage of data acquisition - segmentation - are passed down to the subsequent processes, with deleterious results. As it has been demonstrated recently that biological systems have evolved algorithms that are mathematically efficient, this contribution attempts to test an algorithm that mimics a bacterial-"patented" algorithm for the search of available space and nutrients to find, "zero-in" and eventually delimitate the features existent on the microarray surface.

  8. Features, Events, and Processes: system Level

    SciTech Connect

    D. McGregor

    2004-10-15

    The purpose of this analysis report is to evaluate and document the inclusion or exclusion of the system-level features, events, and processes (FEPs) with respect to modeling used to support the total system performance assessment for the license application (TSPA-LA). A screening decision, either Included or Excluded, is given for each FEP along with the technical basis for screening decisions. This information is required by the U.S. Nuclear Regulatory Commission (NRC) at 10 CFR 63.113 (d, e, and f) (DIRS 156605). The system-level FEPs addressed in this report typically are overarching in nature, rather than being focused on a particular process or subsystem. As a result, they are best dealt with at the system level rather than addressed within supporting process-level or subsystem-level analyses and models reports. The system-level FEPs also tend to be directly addressed by regulations, guidance documents, or assumptions listed in the regulations; or are addressed in background information used in development of the regulations. For included FEPs, this analysis summarizes the implementation of the FEP in the TSPA-LA (i.e., how the FEP is included). For excluded FEPs, this analysis provides the technical basis for exclusion from the TSPA-LA (i.e., why the FEP is excluded). The initial version of this report (Revision 00) was developed to support the total system performance assessment for site recommendation (TSPA-SR). This revision addresses the license application (LA) FEP List (DIRS 170760).

  9. Persistent topological features of dynamical systems

    NASA Astrophysics Data System (ADS)

    Maletić, Slobodan; Zhao, Yi; Rajković, Milan

    2016-05-01

    Inspired by an early work of Muldoon et al., Physica D 65, 1-16 (1993), we present a general method for constructing simplicial complex from observed time series of dynamical systems based on the delay coordinate reconstruction procedure. The obtained simplicial complex preserves all pertinent topological features of the reconstructed phase space, and it may be analyzed from topological, combinatorial, and algebraic aspects. In focus of this study is the computation of homology of the invariant set of some well known dynamical systems that display chaotic behavior. Persistent homology of simplicial complex and its relationship with the embedding dimensions are examined by studying the lifetime of topological features and topological noise. The consistency of topological properties for different dynamic regimes and embedding dimensions is examined. The obtained results shed new light on the topological properties of the reconstructed phase space and open up new possibilities for application of advanced topological methods. The method presented here may be used as a generic method for constructing simplicial complex from a scalar time series that has a number of advantages compared to the mapping of the same time series to a complex network.

  10. Persistent topological features of dynamical systems.

    PubMed

    Maletić, Slobodan; Zhao, Yi; Rajković, Milan

    2016-05-01

    Inspired by an early work of Muldoon et al., Physica D 65, 1-16 (1993), we present a general method for constructing simplicial complex from observed time series of dynamical systems based on the delay coordinate reconstruction procedure. The obtained simplicial complex preserves all pertinent topological features of the reconstructed phase space, and it may be analyzed from topological, combinatorial, and algebraic aspects. In focus of this study is the computation of homology of the invariant set of some well known dynamical systems that display chaotic behavior. Persistent homology of simplicial complex and its relationship with the embedding dimensions are examined by studying the lifetime of topological features and topological noise. The consistency of topological properties for different dynamic regimes and embedding dimensions is examined. The obtained results shed new light on the topological properties of the reconstructed phase space and open up new possibilities for application of advanced topological methods. The method presented here may be used as a generic method for constructing simplicial complex from a scalar time series that has a number of advantages compared to the mapping of the same time series to a complex network. PMID:27249945

  11. Our Solar System Features Eight Planets

    NASA Technical Reports Server (NTRS)

    2009-01-01

    Our solar system features eight planets, seen in this artist's diagram. Although there is some debate within the science community as to whether Pluto should be classified as a Planet or a dwarf planet, the International Astronomical Union has decided on the term plutoid as a name for dwarf planets like Pluto.

    This representation is intentionally fanciful, as the planets are depicted far closer together than they really are. Similarly, the bodies' relative sizes are inaccurate. This is done for the purpose of being able to depict the solar system and still represent the bodies with some detail. (Otherwise the Sun would be a mere speck, and the planets even the majestic Jupiter would be far too small to be seen.)

  12. Neurocognitive disorder detection based on feature vectors extracted from VBM analysis of structural MRI.

    PubMed

    Savio, A; García-Sebastián, M T; Chyzyk, D; Hernandez, C; Graña, M; Sistiaga, A; López de Munain, A; Villanúa, J

    2011-08-01

    Dementia is a growing concern due to the aging process of the western societies. Non-invasive detection is therefore a high priority research endeavor. In this paper we report results of classification systems applied to the feature vectors obtained by a feature extraction method computed on structural magnetic resonance imaging (sMRI) volumes for the detection of two neurological disorders with cognitive impairment: myotonic dystrophy of type 1 (MD1) and Alzheimer disease (AD). The feature extraction process is based on the voxel clusters detected by voxel-based morphometry (VBM) analysis of sMRI upon a set of patient and control subjects. This feature extraction process is specific for each kind of disease and is grounded on the findings obtained by medical experts. The 10-fold cross-validation results of several statistical and neural network based classification algorithms trained and tested on these features show high specificity and moderate sensitivity of the classifiers, suggesting that the approach is better suited for rejecting than for detecting early stages of the diseases. PMID:21621760

  13. Automated retrieval of cloud and aerosol properties from the ARM Raman lidar, part 1: feature detection

    SciTech Connect

    Thorsen, Tyler J.; Fu, Qiang; Newsom, Rob K.; Turner, David D.; Comstock, Jennifer M.

    2015-11-01

    A Feature detection and EXtinction retrieval (FEX) algorithm for the Atmospheric Radiation Measurement (ARM) program’s Raman lidar (RL) has been developed. Presented here is part 1 of the FEX algorithm: the detection of features including both clouds and aerosols. The approach of FEX is to use multiple quantities— scattering ratios derived using elastic and nitro-gen channel signals from two fields of view, the scattering ratio derived using only the elastic channel, and the total volume depolarization ratio— to identify features using range-dependent detection thresholds. FEX is designed to be context-sensitive with thresholds determined for each profile by calculating the expected clear-sky signal and noise. The use of multiple quantities pro-vides complementary depictions of cloud and aerosol locations and allows for consistency checks to improve the accuracy of the feature mask. The depolarization ratio is shown to be particularly effective at detecting optically-thin features containing non-spherical particles such as cirrus clouds. Improve-ments over the existing ARM RL cloud mask are shown. The performance of FEX is validated against a collocated micropulse lidar and observations from the Cloud-Aerosol Lidar and Infrared Pathfinder Satellite Observations (CALIPSO) satellite over the ARM Darwin, Australia site. While we focus on a specific lidar system, the FEX framework presented here is suitable for other Raman or high spectral resolution lidars.

  14. Automatic detection of suspicious behavior of pickpockets with track-based features in a shopping mall

    NASA Astrophysics Data System (ADS)

    Bouma, Henri; Baan, Jan; Burghouts, Gertjan J.; Eendebak, Pieter T.; van Huis, Jasper R.; Dijk, Judith; van Rest, Jeroen H. C.

    2014-10-01

    Proactive detection of incidents is required to decrease the cost of security incidents. This paper focusses on the automatic early detection of suspicious behavior of pickpockets with track-based features in a crowded shopping mall. Our method consists of several steps: pedestrian tracking, feature computation and pickpocket recognition. This is challenging because the environment is crowded, people move freely through areas which cannot be covered by a single camera, because the actual snatch is a subtle action, and because collaboration is complex social behavior. We carried out an experiment with more than 20 validated pickpocket incidents. We used a top-down approach to translate expert knowledge in features and rules, and a bottom-up approach to learn discriminating patterns with a classifier. The classifier was used to separate the pickpockets from normal passers-by who are shopping in the mall. We performed a cross validation to train and evaluate our system. In this paper, we describe our method, identify the most valuable features, and analyze the results that were obtained in the experiment. We estimate the quality of these features and the performance of automatic detection of (collaborating) pickpockets. The results show that many of the pickpockets can be detected at a low false alarm rate.

  15. Safety features of subcritical fluid fueled systems

    SciTech Connect

    Bell, Charles R.

    1995-09-15

    Accelerator-driven transmutation technology has been under study at Los Alamos for several years for application to nuclear waste treatment, tritium production, energy generation, and recently, to the disposition of excess weapons plutonium. Studies and evaluations performed to date at Los Alamos have led to a current focus on a fluid-fuel, fission system operating in a neutron source-supported subcritical mode, using molten salt reactor technology and accelerator-driven proton-neutron spallation. In this paper, the safety features and characteristics of such systems are explored from the perspective of the fundamental nuclear safety objectives that any reactor-type system should address. This exploration is qualitative in nature and uses current vintage solid-fueled reactors as a baseline for comparison. Based on the safety perspectives presented, such systems should be capable of meeting the fundamental nuclear safety objectives. In addition, they should be able to provide the safety robustness desired for advanced reactors. However, the manner in which safety objectives and robustness are achieved is very different from that associated with conventional reactors. Also, there are a number of safety design and operational challenges that will have to be addressed for the safety potential of such systems to be credible.

  16. Safety features of subcritical fluid fueled systems

    SciTech Connect

    Bell, C.R.

    1994-09-01

    Accelerator-driven transmutation technology has been under study at Los Alamos for several years for application to nuclear waste treatment, tritium production, energy generation, and recently, to the disposition of excess weapons plutonium. Studies and evaluations performed to date at Los Alamos have led to a current focus on a fluid-fuel, fission system operating in a neutron source-supported subcritical mode, using molten salt reactor technology and accelerator-driven proton-neutron spallation. In this paper, the safety features and characteristics of such systems are explored from the perspective of the fundamental nuclear safety objectives that any reactor-type system should address. This exploration is qualitative in nature and uses current vintage solid-fueled reactors as a baseline for comparison. Based on the safety perspectives presented, such systems should be capable of meeting the fundamental nuclear safety objectives. In addition, they should be able to provide the safety robustness desired for advanced reactors. However, the manner in which safety objectives and robustness are achieved in very different from that associated with conventional reactors. Also, there are a number of safety design and operational challenges that will have to be addressed for the safety potential of such systems to be credible.

  17. ENGINEERED BARRIER SYSTEM FEATURES, EVENTS AND PROCESSES

    SciTech Connect

    Jaros, W.

    2005-08-30

    The purpose of this report is to evaluate and document the inclusion or exclusion of engineered barrier system (EBS) features, events, and processes (FEPs) with respect to models and analyses used to support the total system performance assessment for the license application (TSPA-LA). A screening decision, either Included or Excluded, is given for each FEP along with the technical basis for exclusion screening decisions. This information is required by the U.S. Nuclear Regulatory Commission (NRC) at 10 CFR 63.114 (d, e, and f) [DIRS 173273]. The FEPs addressed in this report deal with those features, events, and processes relevant to the EBS focusing mainly on those components and conditions exterior to the waste package and within the rock mass surrounding emplacement drifts. The components of the EBS are the drip shield, waste package, waste form, cladding, emplacement pallet, emplacement drift excavated opening (also referred to as drift opening in this report), and invert. FEPs specific to the waste package, cladding, and drip shield are addressed in separate FEP reports: for example, ''Screening of Features, Events, and Processes in Drip Shield and Waste Package Degradation'' (BSC 2005 [DIRS 174995]), ''Clad Degradation--FEPs Screening Arguments (BSC 2004 [DIRS 170019]), and Waste-Form Features, Events, and Processes'' (BSC 2004 [DIRS 170020]). For included FEPs, this report summarizes the implementation of the FEP in the TSPA-LA (i.e., how the FEP is included). For excluded FEPs, this analysis provides the technical basis for exclusion from TSPA-LA (i.e., why the FEP is excluded). This report also documents changes to the EBS FEPs list that have occurred since the previous versions of this report. These changes have resulted due to a reevaluation of the FEPs for TSPA-LA as identified in Section 1.2 of this report and described in more detail in Section 6.1.1. This revision addresses updates in Yucca Mountain Project (YMP) administrative procedures as they

  18. Vision-based in-line fabric defect detection using yarn-specific shape features

    NASA Astrophysics Data System (ADS)

    Schneider, Dorian; Aach, Til

    2012-01-01

    We develop a methodology for automatic in-line flaw detection in industrial woven fabrics. Where state of the art detection algorithms apply texture analysis methods to operate on low-resolved ({200 ppi) image data, we describe here a process flow to segment single yarns in high-resolved ({1000 ppi) textile images. Four yarn shape features are extracted, allowing a precise detection and measurement of defects. The degree of precision reached allows a classification of detected defects according to their nature, providing an innovation in the field of automatic fabric flaw detection. The design has been carried out to meet real time requirements and face adverse conditions caused by loom vibrations and dirt. The entire process flow is discussed followed by an evaluation using a database with real-life industrial fabric images. This work pertains to the construction of an on-loom defect detection system to be used in manufacturing practice.

  19. Detecting submerged features in water: modeling, sensors, and measurements

    NASA Astrophysics Data System (ADS)

    Bostater, Charles R., Jr.; Bassetti, Luce

    2004-11-01

    It is becoming more important to understand the remote sensing systems and associated autonomous or semi-autonomous methodologies (robotic & mechatronics) that may be utilized in freshwater and marine aquatic environments. This need comes from several issues related not only to advances in our scientific understanding and technological capabilities, but also from the desire to insure that the risk associated with UXO (unexploded ordnance), related submerged mines, as well as submerged targets (such as submerged aquatic vegetation) and debris left from previous human activities are remotely sensed and identified followed by reduced risks through detection and removal. This paper will describe (a) remote sensing systems, (b) platforms (fixed and mobile, as well as to demonstrate (c) the value of thinking in terms of scalability as well as modularity in the design and application of new systems now being constructed within our laboratory and other laboratories, as well as future systems. New remote sensing systems - moving or fixed sensing systems, as well as autonomous or semi-autonomous robotic and mechatronic systems will be essential to secure domestic preparedness for humanitarian reasons. These remote sensing systems hold tremendous value, if thoughtfully designed for other applications which include environmental monitoring in ambient environments.

  20. Using Activity-Related Behavioural Features towards More Effective Automatic Stress Detection

    PubMed Central

    Giakoumis, Dimitris; Drosou, Anastasios; Cipresso, Pietro; Tzovaras, Dimitrios; Hassapis, George; Gaggioli, Andrea; Riva, Giuseppe

    2012-01-01

    This paper introduces activity-related behavioural features that can be automatically extracted from a computer system, with the aim to increase the effectiveness of automatic stress detection. The proposed features are based on processing of appropriate video and accelerometer recordings taken from the monitored subjects. For the purposes of the present study, an experiment was conducted that utilized a stress-induction protocol based on the stroop colour word test. Video, accelerometer and biosignal (Electrocardiogram and Galvanic Skin Response) recordings were collected from nineteen participants. Then, an explorative study was conducted by following a methodology mainly based on spatiotemporal descriptors (Motion History Images) that are extracted from video sequences. A large set of activity-related behavioural features, potentially useful for automatic stress detection, were proposed and examined. Experimental evaluation showed that several of these behavioural features significantly correlate to self-reported stress. Moreover, it was found that the use of the proposed features can significantly enhance the performance of typical automatic stress detection systems, commonly based on biosignal processing. PMID:23028461

  1. Unified Mars detection system. [life detection

    NASA Technical Reports Server (NTRS)

    Martin, J. P.; Kok, B.; Radmer, R.; Johnson, R. D.

    1976-01-01

    A life-detection system is described which is designed to detect and characterize possible Martian biota and to gather information about the chemical environment of Mars, especially the water and amino acid contents of the soil. The system is organized around a central mass spectrometer that can sensitively analyze trace gases from a variety of different experiments. Some biological assays and soil-chemistry tests that have been performed in the laboratory as typical experiment candidates for the system are discussed, including tests for soil-organism metabolism, measurements of soil carbon contents, and determinations of primary aliphatic amines (amino acids and protein) in soils. Two possible test strategies are outlined, and the operational concept of the detection system is illustrated. Detailed descriptions are given for the mass spectrometer, gas inlet, incubation box, test cell modules, seal drive mechanism, soil distribution assembly, and electronic control system.

  2. Systematic comparison of automated geological feature detection methods for impact craters

    NASA Astrophysics Data System (ADS)

    Vinogradova, T.; Mjolsness, E.

    2001-12-01

    Accurate, automated crater counts will be essential in extrapolating from existing Mars crater catalogs to much larger catalogs of impact craters in high-resolution orbital imagery for use in relative dating of surfaces in such imagery. Once validated, automatic methods for performing crater counts could be integrated into tools such as the Planetary Image Atlas, which is designed to be a convenient interface through which a user can search for, display, and download images and other ancillary data for planetary Missions, and the Diamond Eye image mining system. Here we report on preliminary computational experiments in using a trainable feature detection algorithm [Burl et al. 2001] to detect craters in real and simulated Mars orbital imagery, and to derive approximate impact crater counts for geological use. In these experiments, we consider two uses of the trainable feature detector: first, directly as a crater detector, and second, as two detectors for sunlit and shadowed inner walls of craters which can then be assembled into a single crater detection based on multiple pieces of evidence. For both of these methods, we consider two data sources: one consisting of real Viking Orbiter imagery of Mars with human expert-supplied ground truth labels, and the other consisting of computer generated renderings of simplified, synthetic cratered terrain with 100% accurate ground truth labels and known, controllable crater density. Each detector reports out a numeric detection ``likelihood'' for every candidate crater. This likelihood must then be thresholded to produce a detection decision. For each combination of two data sources (one natural and one synthetic) and two crater detection methods (whole-crater and parts-model), we vary image complexity and finally measure detection accuracy. Detection accuracy is measured by a Receiver Operator Characteristic (ROC) curve in which detection efficiency (the fraction of true craters detected) and purity (the fraction of

  3. Textural feature based target detection in through-the-wall radar imagery

    NASA Astrophysics Data System (ADS)

    Sengur, A.; Amin, M.; Ahmad, F.; Sévigny, P.; DiFilippo, D.

    2013-05-01

    Stationary target detection in through-the-wall radar imaging (TWRI) using image segmentation techniques has recently been considered in the literature. Specifically, histogram thresholding methods have been used to aid in removing the clutter, resulting in `clean' radar images with target regions only. In this paper, we show that histogram thresholding schemes are effective only against clutter regions, which are distinct from target regions. Target detection using these methods becomes challenging, if not impossible, in the presence of multipath ghosts and clutter that closely mimics the target in size and intensity. Because of the small variations between the target regions and such clutter and multipath ghosts, we propose a textural feature based classifier for through-the-wall target detection. The feature based scheme is applied as a follow-on step after application of histogram thresholding techniques. The training set consists of feature vectors based on gray level co-occurrence matrices corresponding to the target and ghost/clutter image regions. Feature vectors are then used in training a minimum distance classifier based on Mahalanobis distance metric. Performance of the proposed scheme is evaluated using real-data collected with Defence Research and Development Canada's vehicle-borne TWRI system. The results show that the proposed textural feature based method yields much improved results compared to histogram thresholding based segmentation methods for the considered cases.

  4. Automated colon cancer detection using hybrid of novel geometric features and some traditional features.

    PubMed

    Rathore, Saima; Hussain, Mutawarra; Khan, Asifullah

    2015-10-01

    Automatic classification of colon into normal and malignant classes is complex due to numerous factors including similar colors in different biological constituents of histopathological imagery. Therefore, such techniques, which exploit the textural and geometric properties of constituents of colon tissues, are desired. In this paper, a novel feature extraction strategy that mathematically models the geometric characteristics of constituents of colon tissues is proposed. In this study, we also show that the hybrid feature space encompassing diverse knowledge about the tissues׳ characteristics is quite promising for classification of colon biopsy images. This paper thus presents a hybrid feature space based colon classification (HFS-CC) technique, which utilizes hybrid features for differentiating normal and malignant colon samples. The hybrid feature space is formed to provide the classifier different types of discriminative features such as features having rich information about geometric structure and image texture. Along with the proposed geometric features, a few conventional features such as morphological, texture, scale invariant feature transform (SIFT), and elliptic Fourier descriptors (EFDs) are also used to develop a hybrid feature set. The SIFT features are reduced using minimum redundancy and maximum relevancy (mRMR). Various kernels of support vector machines (SVM) are employed as classifiers, and their performance is analyzed on 174 colon biopsy images. The proposed geometric features have achieved an accuracy of 92.62%, thereby showing their effectiveness. Moreover, the proposed HFS-CC technique achieves 98.07% testing and 99.18% training accuracy. The better performance of HFS-CC is largely due to the discerning ability of the proposed geometric features and the developed hybrid feature space. PMID:25819060

  5. Cascaded ensemble of convolutional neural networks and handcrafted features for mitosis detection

    NASA Astrophysics Data System (ADS)

    Wang, Haibo; Cruz-Roa, Angel; Basavanhally, Ajay; Gilmore, Hannah; Shih, Natalie; Feldman, Mike; Tomaszewski, John; Gonzalez, Fabio; Madabhushi, Anant

    2014-03-01

    Breast cancer (BCa) grading plays an important role in predicting disease aggressiveness and patient outcome. A key component of BCa grade is mitotic count, which involves quantifying the number of cells in the process of dividing (i.e. undergoing mitosis) at a specific point in time. Currently mitosis counting is done manually by a pathologist looking at multiple high power fields on a glass slide under a microscope, an extremely laborious and time consuming process. The development of computerized systems for automated detection of mitotic nuclei, while highly desirable, is confounded by the highly variable shape and appearance of mitoses. Existing methods use either handcrafted features that capture certain morphological, statistical or textural attributes of mitoses or features learned with convolutional neural networks (CNN). While handcrafted features are inspired by the domain and the particular application, the data-driven CNN models tend to be domain agnostic and attempt to learn additional feature bases that cannot be represented through any of the handcrafted features. On the other hand, CNN is computationally more complex and needs a large number of labeled training instances. Since handcrafted features attempt to model domain pertinent attributes and CNN approaches are largely unsupervised feature generation methods, there is an appeal to attempting to combine these two distinct classes of feature generation strategies to create an integrated set of attributes that can potentially outperform either class of feature extraction strategies individually. In this paper, we present a cascaded approach for mitosis detection that intelligently combines a CNN model and handcrafted features (morphology, color and texture features). By employing a light CNN model, the proposed approach is far less demanding computationally, and the cascaded strategy of combining handcrafted features and CNN-derived features enables the possibility of maximizing performance by

  6. Mitosis detection in breast cancer pathology images by combining handcrafted and convolutional neural network features.

    PubMed

    Wang, Haibo; Cruz-Roa, Angel; Basavanhally, Ajay; Gilmore, Hannah; Shih, Natalie; Feldman, Mike; Tomaszewski, John; Gonzalez, Fabio; Madabhushi, Anant

    2014-10-01

    Breast cancer (BCa) grading plays an important role in predicting disease aggressiveness and patient outcome. A key component of BCa grade is the mitotic count, which involves quantifying the number of cells in the process of dividing (i.e., undergoing mitosis) at a specific point in time. Currently, mitosis counting is done manually by a pathologist looking at multiple high power fields (HPFs) on a glass slide under a microscope, an extremely laborious and time consuming process. The development of computerized systems for automated detection of mitotic nuclei, while highly desirable, is confounded by the highly variable shape and appearance of mitoses. Existing methods use either handcrafted features that capture certain morphological, statistical, or textural attributes of mitoses or features learned with convolutional neural networks (CNN). Although handcrafted features are inspired by the domain and the particular application, the data-driven CNN models tend to be domain agnostic and attempt to learn additional feature bases that cannot be represented through any of the handcrafted features. On the other hand, CNN is computationally more complex and needs a large number of labeled training instances. Since handcrafted features attempt to model domain pertinent attributes and CNN approaches are largely supervised feature generation methods, there is an appeal in attempting to combine these two distinct classes of feature generation strategies to create an integrated set of attributes that can potentially outperform either class of feature extraction strategies individually. We present a cascaded approach for mitosis detection that intelligently combines a CNN model and handcrafted features (morphology, color, and texture features). By employing a light CNN model, the proposed approach is far less demanding computationally, and the cascaded strategy of combining handcrafted features and CNN-derived features enables the possibility of maximizing the performance

  7. Mitosis detection in breast cancer pathology images by combining handcrafted and convolutional neural network features

    PubMed Central

    Wang, Haibo; Cruz-Roa, Angel; Basavanhally, Ajay; Gilmore, Hannah; Shih, Natalie; Feldman, Mike; Tomaszewski, John; Gonzalez, Fabio; Madabhushi, Anant

    2014-01-01

    Abstract. Breast cancer (BCa) grading plays an important role in predicting disease aggressiveness and patient outcome. A key component of BCa grade is the mitotic count, which involves quantifying the number of cells in the process of dividing (i.e., undergoing mitosis) at a specific point in time. Currently, mitosis counting is done manually by a pathologist looking at multiple high power fields (HPFs) on a glass slide under a microscope, an extremely laborious and time consuming process. The development of computerized systems for automated detection of mitotic nuclei, while highly desirable, is confounded by the highly variable shape and appearance of mitoses. Existing methods use either handcrafted features that capture certain morphological, statistical, or textural attributes of mitoses or features learned with convolutional neural networks (CNN). Although handcrafted features are inspired by the domain and the particular application, the data-driven CNN models tend to be domain agnostic and attempt to learn additional feature bases that cannot be represented through any of the handcrafted features. On the other hand, CNN is computationally more complex and needs a large number of labeled training instances. Since handcrafted features attempt to model domain pertinent attributes and CNN approaches are largely supervised feature generation methods, there is an appeal in attempting to combine these two distinct classes of feature generation strategies to create an integrated set of attributes that can potentially outperform either class of feature extraction strategies individually. We present a cascaded approach for mitosis detection that intelligently combines a CNN model and handcrafted features (morphology, color, and texture features). By employing a light CNN model, the proposed approach is far less demanding computationally, and the cascaded strategy of combining handcrafted features and CNN-derived features enables the possibility of maximizing the

  8. Intelligent Leak Detection System

    Energy Science and Technology Software Center (ESTSC)

    2014-10-27

    apability of underground carbon dioxide storage to confine and sustain injected CO2 for a very long time is the main concern for geologic CO2 sequestration. If a leakage from a geological CO2 sequestration site occurs, it is crucial to find the approximate amount and the location of the leak in order to implement proper remediation activity. An overwhelming majority of research and development for storage site monitoring has been concentrated on atmospheric, surface or nearmore » surface monitoring of the sequestered CO2. This study aims to monitor the integrity of CO2 storage at the reservoir level. This work proposes developing in-situ CO2 Monitoring and Verification technology based on the implementation of Permanent Down-hole Gauges (PDG) or “Smart Wells” along with Artificial Intelligence and Data Mining (AI&DM). The technology attempts to identify the characteristics of the CO2 leakage by de-convolving the pressure signals collected from Permanent Down-hole Gauges (PDG). Citronelle field, a saline aquifer reservoir, located in the U.S. was considered for this study. A reservoir simulation model for CO2 sequestration in the Citronelle field was developed and history matched. The presence of the PDGs were considered in the reservoir model at the injection well and an observation well. High frequency pressure data from sensors were collected based on different synthetic CO2 leakage scenarios in the model. Due to complexity of the pressure signal behaviors, a Machine Learning-based technology was introduced to build an Intelligent Leakage Detection System (ILDS). The ILDS was able to detect leakage characteristics in a short period of time (less than a day) demonstrating the capability of the system in quantifying leakage characteristics subject to complex rate behaviors. The performance of ILDS was examined under different conditions such as multiple well leakages, cap rock leakage, availability of an additional monitoring well, presence of pressure drift

  9. Intelligent Leak Detection System

    SciTech Connect

    Mohaghegh, Shahab D.

    2014-10-27

    apability of underground carbon dioxide storage to confine and sustain injected CO2 for a very long time is the main concern for geologic CO2 sequestration. If a leakage from a geological CO2 sequestration site occurs, it is crucial to find the approximate amount and the location of the leak in order to implement proper remediation activity. An overwhelming majority of research and development for storage site monitoring has been concentrated on atmospheric, surface or near surface monitoring of the sequestered CO2. This study aims to monitor the integrity of CO2 storage at the reservoir level. This work proposes developing in-situ CO2 Monitoring and Verification technology based on the implementation of Permanent Down-hole Gauges (PDG) or “Smart Wells” along with Artificial Intelligence and Data Mining (AI&DM). The technology attempts to identify the characteristics of the CO2 leakage by de-convolving the pressure signals collected from Permanent Down-hole Gauges (PDG). Citronelle field, a saline aquifer reservoir, located in the U.S. was considered for this study. A reservoir simulation model for CO2 sequestration in the Citronelle field was developed and history matched. The presence of the PDGs were considered in the reservoir model at the injection well and an observation well. High frequency pressure data from sensors were collected based on different synthetic CO2 leakage scenarios in the model. Due to complexity of the pressure signal behaviors, a Machine Learning-based technology was introduced to build an Intelligent Leakage Detection System (ILDS). The ILDS was able to detect leakage characteristics in a short period of time (less than a day) demonstrating the capability of the system in quantifying leakage characteristics subject to complex rate behaviors. The performance of ILDS was examined under different conditions such as multiple well leakages, cap rock leakage, availability of an additional monitoring well, presence of pressure drift and noise

  10. Cross-Examination for Angle-Closure Glaucoma Feature Detection.

    PubMed

    Niwas, Swamidoss Issac; Lin, Weisi; Kwoh, Chee Keong; Kuo, C-C Jay; Sng, Chelvin C; Aquino, Maria Cecilia; Chew, Paul T K

    2016-01-01

    Effective feature selection plays a vital role in anterior segment imaging for determining the mechanism involved in angle-closure glaucoma (ACG) diagnosis. This research focuses on the use of redundant features for complex disease diagnosis such as ACG using anterior segment optical coherence tomography images. Both supervised [minimum redundancy maximum relevance (MRMR)] and unsupervised [Laplacian score (L-score)] feature selection algorithms have been cross-examined with different ACG mechanisms. An AdaBoost machine learning classifier is then used for classifying the five various classes of ACG mechanism such as iris roll, lens, pupil block, plateau iris, and no mechanism using both feature selection methods. The overall accuracy has shown that the usefulness of redundant features by L-score method in improved ACG diagnosis compared to minimum redundant features by MRMR method. PMID:25561599

  11. Automatic Feature Detection, Description and Matching from Mobile Laser Scanning Data and Aerial Imagery

    NASA Astrophysics Data System (ADS)

    Hussnain, Zille; Oude Elberink, Sander; Vosselman, George

    2016-06-01

    In mobile laser scanning systems, the platform's position is measured by GNSS and IMU, which is often not reliable in urban areas. Consequently, derived Mobile Laser Scanning Point Cloud (MLSPC) lacks expected positioning reliability and accuracy. Many of the current solutions are either semi-automatic or unable to achieve pixel level accuracy. We propose an automatic feature extraction method which involves utilizing corresponding aerial images as a reference data set. The proposed method comprise three steps; image feature detection, description and matching between corresponding patches of nadir aerial and MLSPC ortho images. In the data pre-processing step the MLSPC is patch-wise cropped and converted to ortho images. Furthermore, each aerial image patch covering the area of the corresponding MLSPC patch is also cropped from the aerial image. For feature detection, we implemented an adaptive variant of Harris-operator to automatically detect corner feature points on the vertices of road markings. In feature description phase, we used the LATCH binary descriptor, which is robust to data from different sensors. For descriptor matching, we developed an outlier filtering technique, which exploits the arrangements of relative Euclidean-distances and angles between corresponding sets of feature points. We found that the positioning accuracy of the computed correspondence has achieved the pixel level accuracy, where the image resolution is 12cm. Furthermore, the developed approach is reliable when enough road markings are available in the data sets. We conclude that, in urban areas, the developed approach can reliably extract features necessary to improve the MLSPC accuracy to pixel level.

  12. Incipient fire detection system

    DOEpatents

    Brooks, Jr., William K.

    1999-01-01

    A method and apparatus for an incipient fire detection system that receives gaseous samples and measures the light absorption spectrum of the mixture of gases evolving from heated combustibles includes a detector for receiving gaseous samples and subjecting the samples to spectroscopy and determining wavelengths of absorption of the gaseous samples. The wavelengths of absorption of the gaseous samples are compared to predetermined absorption wavelengths. A warning signal is generated whenever the wavelengths of absorption of the gaseous samples correspond to the predetermined absorption wavelengths. The method includes receiving gaseous samples, subjecting the samples to light spectroscopy, determining wavelengths of absorption of the gaseous samples, comparing the wavelengths of absorption of the gaseous samples to predetermined absorption wavelengths and generating a warning signal whenever the wavelengths of absorption of the gaseous samples correspond to the predetermined absorption wavelengths. In an alternate embodiment, the apparatus includes a series of channels fluidically connected to a plurality of remote locations. A pump is connected to the channels for drawing gaseous samples into the channels. A detector is connected to the channels for receiving the drawn gaseous samples and subjecting the samples to spectroscopy. The wavelengths of absorption are determined and compared to predetermined absorption wavelengths is provided. A warning signal is generated whenever the wavelengths correspond.

  13. Oil Spill Detection by SAR Images: Dark Formation Detection, Feature Extraction and Classification Algorithms

    PubMed Central

    Topouzelis, Konstantinos N.

    2008-01-01

    This paper provides a comprehensive review of the use of Synthetic Aperture Radar images (SAR) for detection of illegal discharges from ships. It summarizes the current state of the art, covering operational and research aspects of the application. Oil spills are seriously affecting the marine ecosystem and cause political and scientific concern since they seriously effect fragile marine and coastal ecosystem. The amount of pollutant discharges and associated effects on the marine environment are important parameters in evaluating sea water quality. Satellite images can improve the possibilities for the detection of oil spills as they cover large areas and offer an economical and easier way of continuous coast areas patrolling. SAR images have been widely used for oil spill detection. The present paper gives an overview of the methodologies used to detect oil spills on the radar images. In particular we concentrate on the use of the manual and automatic approaches to distinguish oil spills from other natural phenomena. We discuss the most common techniques to detect dark formations on the SAR images, the features which are extracted from the detected dark formations and the most used classifiers. Finally we conclude with discussion of suggestions for further research. The references throughout the review can serve as starting point for more intensive studies on the subject.

  14. Biosensor method and system based on feature vector extraction

    DOEpatents

    Greenbaum, Elias; Rodriguez, Jr., Miguel; Qi, Hairong; Wang, Xiaoling

    2013-07-02

    A system for biosensor-based detection of toxins includes providing at least one time-dependent control signal generated by a biosensor in a gas or liquid medium, and obtaining a time-dependent biosensor signal from the biosensor in the gas or liquid medium to be monitored or analyzed for the presence of one or more toxins selected from chemical, biological or radiological agents. The time-dependent biosensor signal is processed to obtain a plurality of feature vectors using at least one of amplitude statistics and a time-frequency analysis. At least one parameter relating to toxicity of the gas or liquid medium is then determined from the feature vectors based on reference to the control signal.

  15. Detection of braking intention in diverse situations during simulated driving based on EEG feature combination

    NASA Astrophysics Data System (ADS)

    Kim, Il-Hwa; Kim, Jeong-Woo; Haufe, Stefan; Lee, Seong-Whan

    2015-02-01

    Objective. We developed a simulated driving environment for studying neural correlates of emergency braking in diversified driving situations. We further investigated to what extent these neural correlates can be used to detect a participant's braking intention prior to the behavioral response. Approach. We measured electroencephalographic (EEG) and electromyographic signals during simulated driving. Fifteen participants drove a virtual vehicle and were exposed to several kinds of traffic situations in a simulator system, while EEG signals were measured. After that, we extracted characteristic features to categorize whether the driver intended to brake or not. Main results. Our system shows excellent detection performance in a broad range of possible emergency situations. In particular, we were able to distinguish three different kinds of emergency situations (sudden stop of a preceding vehicle, sudden cutting-in of a vehicle from the side and unexpected appearance of a pedestrian) from non-emergency (soft) braking situations, as well as from situations in which no braking was required, but the sensory stimulation was similar to stimulations inducing an emergency situation (e.g., the sudden stop of a vehicle on a neighboring lane). Significance. We proposed a novel feature combination comprising movement-related potentials such as the readiness potential, event-related desynchronization features besides the event-related potentials (ERP) features used in a previous study. The performance of predicting braking intention based on our proposed feature combination was superior compared to using only ERP features. Our study suggests that emergency situations are characterized by specific neural patterns of sensory perception and processing, as well as motor preparation and execution, which can be utilized by neurotechnology based braking assistance systems.

  16. A Portable Infrasonic Detection System

    NASA Technical Reports Server (NTRS)

    Shams, Qamar A.; Burkett, Cecil G.; Zuckerwar, Allan J.; Lawrenson, Christopher C.; Masterman, Michael

    2008-01-01

    During last couple of years, NASA Langley has designed and developed a portable infrasonic detection system which can be used to make useful infrasound measurements at a location where it was not possible previously. The system comprises an electret condenser microphone, having a 3-inch membrane diameter, and a small, compact windscreen. Electret-based technology offers the lowest possible background noise, because Johnson noise generated in the supporting electronics (preamplifier) is minimized. The microphone features a high membrane compliance with a large backchamber volume, a prepolarized backplane and a high impedance preamplifier located inside the backchamber. The windscreen, based on the high transmission coefficient of infrasound through matter, is made of a material having a low acoustic impedance and sufficiently thick wall to insure structural stability. Close-cell polyurethane foam has been found to serve the purpose well. In the proposed test, test parameters will be sensitivity, background noise, signal fidelity (harmonic distortion), and temporal stability. The design and results of the compact system, based upon laboratory and field experiments, will be presented.

  17. Splat feature classification with application to retinal hemorrhage detection in fundus images.

    PubMed

    Tang, Li; Niemeijer, Meindert; Reinhardt, Joseph M; Garvin, Mona K; Abràmoff, Michael D

    2013-02-01

    A novel splat feature classification method is presented with application to retinal hemorrhage detection in fundus images. Reliable detection of retinal hemorrhages is important in the development of automated screening systems which can be translated into practice. Under our supervised approach, retinal color images are partitioned into nonoverlapping segments covering the entire image. Each segment, i.e., splat, contains pixels with similar color and spatial location. A set of features is extracted from each splat to describe its characteristics relative to its surroundings, employing responses from a variety of filter bank, interactions with neighboring splats, and shape and texture information. An optimal subset of splat features is selected by a filter approach followed by a wrapper approach. A classifier is trained with splat-based expert annotations and evaluated on the publicly available Messidor dataset. An area under the receiver operating characteristic curve of 0.96 is achieved at the splat level and 0.87 at the image level. While we are focused on retinal hemorrhage detection, our approach has potential to be applied to other object detection tasks. PMID:23193310

  18. Karst features detection and mapping using airphotos, DSMs and GIS techniques

    NASA Astrophysics Data System (ADS)

    Kakavas, M. P.; Nikolakopoulos, K. G.; Zagana, E.

    2015-10-01

    The aim of this work is to detect and qualify natural karst depressions in the Aitoloakarnania Prefecture, Western Greece, using remote sensing data in conjunction with the Geographical Information Systems - GIS. The study area is a part of the Ionian geotectonic zone, and its geological background consists of the Triassic Evaporates. The Triassic carbonate breccias where formed as a result of the tectonic and orogenetic setting of the external Hellenides and the diaper phenomena of the Triassic Evaporates. The landscape characterized by exokarst features closed depressions in the Triassic carbonate breccias. At the threshold of this study, an in situ observation was performed in order to identify dolines and swallow holes. The creation of sinkholes, in general, is based on the collapse of the surface layer due to chemical dissolution of carbonate rocks. In the current study airphotos stereopairs, DSMs and GIS were combined in order to detect and map the karst features. Thirty seven airphotos were imported in Leica Photogrammetry Suite and a stereo model of the study area was created. Then in 3D view possible karst features were detected and digitized. Those sites were verified during the in situ survey. ASTER GDEM, SRTM DEM, high resolution airphoto DSM created from the Greek Cadastral and a DEM from digitized contours from the 1/50,000 topographic were also evaluated in GIS environment for the automatic detection of the karst depressions. The results are presented in this study.

  19. Red Lesion Detection Using Dynamic Shape Features for Diabetic Retinopathy Screening.

    PubMed

    Seoud, Lama; Hurtut, Thomas; Chelbi, Jihed; Cheriet, Farida; Langlois, J M Pierre

    2016-04-01

    The development of an automatic telemedicine system for computer-aided screening and grading of diabetic retinopathy depends on reliable detection of retinal lesions in fundus images. In this paper, a novel method for automatic detection of both microaneurysms and hemorrhages in color fundus images is described and validated. The main contribution is a new set of shape features, called Dynamic Shape Features, that do not require precise segmentation of the regions to be classified. These features represent the evolution of the shape during image flooding and allow to discriminate between lesions and vessel segments. The method is validated per-lesion and per-image using six databases, four of which are publicly available. It proves to be robust with respect to variability in image resolution, quality and acquisition system. On the Retinopathy Online Challenge's database, the method achieves a FROC score of 0.420 which ranks it fourth. On the Messidor database, when detecting images with diabetic retinopathy, the proposed method achieves an area under the ROC curve of 0.899, comparable to the score of human experts, and it outperforms state-of-the-art approaches. PMID:26701180

  20. Feature points detection and tracking based on SIFT combining with KLT method

    NASA Astrophysics Data System (ADS)

    Wang, Hongbing; Peng, Zhenming; Liu, Jie; Zheng, Youwang; Liao, Baobing; Wang, Yue

    2009-11-01

    For feature point detection with variable scale, rotation, variable illumination and variable 3D view port, a feature point detection and tracking method combining scale invariant feature transform (SIFT) and KLT (Kanade-Lucas-Tomasi) is proposed in this paper. SIFT feature point detection method is improved and it is used to detect feature points of image, and then KLT method is used to track the feature points continuously. In order to verify the feasibility of the proposed method, simulation experiments are carried out in real scene image sequences with different complexity using this method, better results of detection and tracking are obtained and the obtained feature point is more stable than conventional method.

  1. Force protection demining system (FPDS) detection subsystem

    NASA Astrophysics Data System (ADS)

    Zachery, Karen N.; Schultz, Gregory M.; Collins, Leslie M.

    2005-06-01

    This study describes the U.S. Army Force Protection Demining System (FPDS); a remotely-operated, multisensor platform developed for reliable detection and neutralization of both anti-tank and anti-personnel landmines. The ongoing development of the prototype multisensor detection subsystem is presented, which integrates an advanced electromagnetic pulsed-induction array and ground penetrating synthetic aperture radar array on a single standoff platform. The FPDS detection subsystem is mounted on a robotic rubber-tracked vehicle and incorporates an accurate and precise navigation/positioning module making it well suited for operation in varied and irregular terrains. Detection sensors are optimally configured to minimize interference without loss in sensitivity or performance. Mine lane test data acquired from the prototype sensors are processed to extract signal- and image-based features for automatic target recognition. Preliminary results using optimal feature and classifier selection indicate the potential of the system to achieve high probabilities of detection while minimizing false alarms. The FPDS detection software system also exploits modern multi-sensor data fusion algorithms to provide real-time detection and discrimination information to the user.

  2. Advanced signal processing method for ground penetrating radar feature detection and enhancement

    NASA Astrophysics Data System (ADS)

    Zhang, Yu; Venkatachalam, Anbu Selvam; Huston, Dryver; Xia, Tian

    2014-03-01

    This paper focuses on new signal processing algorithms customized for an air coupled Ultra-Wideband (UWB) Ground Penetrating Radar (GPR) system targeting highway pavements and bridge deck inspections. The GPR hardware consists of a high-voltage pulse generator, a high speed 8 GSps real time data acquisition unit, and a customized field-programmable gate array (FPGA) control element. In comparison to most existing GPR system with low survey speeds, this system can survey at normal highway speed (60 mph) with a high horizontal resolution of up to 10 scans per centimeter. Due to the complexity and uncertainty of subsurface media, the GPR signal processing is important but challenging. In this GPR system, an adaptive GPR signal processing algorithm using Curvelet Transform, 2D high pass filtering and exponential scaling is proposed to alleviate noise and clutter while the subsurface features are preserved and enhanced. First, Curvelet Transform is used to remove the environmental and systematic noises while maintain the range resolution of the B-Scan image. Then, mathematical models for cylinder-shaped object and clutter are built. A two-dimension (2D) filter based on these models removes clutter and enhances the hyperbola feature in a B-Scan image. Finally, an exponential scaling method is applied to compensate the signal attenuation in subsurface materials and to improve the desired signal feature. For performance test and validation, rebar detection experiments and subsurface feature inspection in laboratory and field configurations are performed.

  3. ENGINEERED BARRIER SYSTEM FEATURES, EVENTS, AND PROCESSES

    SciTech Connect

    na

    2005-05-30

    This analysis report is one of the technical reports containing documentation of the Environmental Radiation Model for Yucca Mountain, Nevada (ERMYN), a biosphere model supporting the total system performance assessment (TSPA) for the license application (LA) for the Yucca Mountain repository. This analysis report describes the development of biosphere dose conversion factors (BDCFs) for the volcanic ash exposure scenario, and the development of dose factors for calculating inhalation dose during volcanic eruption. A graphical representation of the documentation hierarchy for the ERMYN is presented in Figure 1 - 1. This figure shows the interrelationships among the products (i.e., analysis and model reports) developed for biosphere modeling and provides an understanding of how this analysis report contributes to biosphere modeling. This report is one of two reports that develop biosphere BDCFs, which are input parameters for the TSPA model. The ''Biosphere Model Report'' (BSC 2004 [DIRS 169460]) describes in detail the ERMYN conceptual model and mathematical model. The input parameter reports, shown to the right of the Biosphere Model Report in Figure 1-1, contain detailed descriptions of the model input parameters, their development and the relationship between the parameters and specific features, events and processes (FEPs). This report describes biosphere model calculations and their output, the BDCFs, for the volcanic ash exposure scenario. This analysis receives direct input from the outputs of the ''Biosphere Model Report'' (BSC 2004 [DIRS 169460]) and from the five analyses that develop parameter values for the biosphere model (BSC 2005 [DIRS 172827]; BSC 2004 [DIRS 169672]; BSC 2004 [DIRS 169673]; BSC 2004 [DIRS 169458]; and BSC 2004 [DIRS 169459]). The results of this report are further analyzed in the ''Biosphere Dose Conversion Factor Importance and Sensitivity Analysis'' (Figure 1 - 1). The objective of this analysis was to develop the BDCFs for the

  4. Hand held explosives detection system

    DOEpatents

    Conrad, Frank J.

    1992-01-01

    The present invention is directed to a sensitive hand-held explosives detection device capable of detecting the presence of extremely low quantities of high explosives molecules, and which is applicable to sampling vapors from personnel, baggage, cargo, etc., as part of an explosives detection system.

  5. Research on Copy-Move Image Forgery Detection Using Features of Discrete Polar Complex Exponential Transform

    NASA Astrophysics Data System (ADS)

    Gan, Yanfen; Zhong, Junliu

    2015-12-01

    With the aid of sophisticated photo-editing software, such as Photoshop, copy-move image forgery operation has been widely applied and has become a major concern in the field of information security in the modern society. A lot of work on detecting this kind of forgery has gained great achievements, but the detection results of geometrical transformations of copy-move regions are not so satisfactory. In this paper, a new method based on the Polar Complex Exponential Transform is proposed. This method addresses issues in image geometric moment, focusing on constructing rotation invariant moment and extracting features of the rotation invariant moment. In order to reduce rounding errors of the transform from the Polar coordinate system to the Cartesian coordinate system, a new transformation method is presented and discussed in detail at the same time. The new method constructs a 9 × 9 shrunk template to transform the Cartesian coordinate system back to the Polar coordinate system. It can reduce transform errors to a much greater degree. Forgery detection, such as copy-move image forgery detection, is a difficult procedure, but experiments prove our method is a great improvement in detecting and identifying forgery images affected by the rotated transform.

  6. Thermography based breast cancer detection using texture features and minimum variance quantization

    PubMed Central

    Milosevic, Marina; Jankovic, Dragan; Peulic, Aleksandar

    2014-01-01

    In this paper, we present a system based on feature extraction techniques and image segmentation techniques for detecting and diagnosing abnormal patterns in breast thermograms. The proposed system consists of three major steps: feature extraction, classification into normal and abnormal pattern and segmentation of abnormal pattern. Computed features based on gray-level co-occurrence matrices are used to evaluate the effectiveness of textural information possessed by mass regions. A total of 20 GLCM features are extracted from thermograms. The ability of feature set in differentiating abnormal from normal tissue is investigated using a Support Vector Machine classifier, Naive Bayes classifier and K-Nearest Neighbor classifier. To evaluate the classification performance, five-fold cross validation method and Receiver operating characteristic analysis was performed. The verification results show that the proposed algorithm gives the best classification results using K-Nearest Neighbor classifier and a accuracy of 92.5%. Image segmentation techniques can play an important role to segment and extract suspected hot regions of interests in the breast infrared images. Three image segmentation techniques: minimum variance quantization, dilation of image and erosion of image are discussed. The hottest regions of thermal breast images are extracted and compared to the original images. According to the results, the proposed method has potential to extract almost exact shape of tumors. PMID:26417334

  7. Pavement crack detection combining non-negative feature with fast LoG in complex scene

    NASA Astrophysics Data System (ADS)

    Wang, Wanli; Zhang, Xiuhua; Hong, Hanyu

    2015-12-01

    Pavement crack detection is affected by much interference in the realistic situation, such as the shadow, road sign, oil stain, salt and pepper noise etc. Due to these unfavorable factors, the exist crack detection methods are difficult to distinguish the crack from background correctly. How to extract crack information effectively is the key problem to the road crack detection system. To solve this problem, a novel method for pavement crack detection based on combining non-negative feature with fast LoG is proposed. The two key novelties and benefits of this new approach are that 1) using image pixel gray value compensation to acquisit uniform image, and 2) combining non-negative feature with fast LoG to extract crack information. The image preprocessing results demonstrate that the method is indeed able to homogenize the crack image with more accurately compared to existing methods. A large number of experimental results demonstrate the proposed approach can detect the crack regions more correctly compared with traditional methods.

  8. Unsupervised Spectral-Spatial Feature Selection-Based Camouflaged Object Detection Using VNIR Hyperspectral Camera

    PubMed Central

    2015-01-01

    The detection of camouflaged objects is important for industrial inspection, medical diagnoses, and military applications. Conventional supervised learning methods for hyperspectral images can be a feasible solution. Such approaches, however, require a priori information of a camouflaged object and background. This letter proposes a fully autonomous feature selection and camouflaged object detection method based on the online analysis of spectral and spatial features. The statistical distance metric can generate candidate feature bands and further analysis of the entropy-based spatial grouping property can trim the useless feature bands. Camouflaged objects can be detected better with less computational complexity by optical spectral-spatial feature analysis. PMID:25879073

  9. Antigen detection systems

    Technology Transfer Automated Retrieval System (TEKTRAN)

    Infectious agents or their constituent parts (antigens or nucleic acids) can be detected in fresh, frozen, or fixed tissue using a variety of direct or indirect assays. The assays can be modified to yield the greatest sensitivity and specificity but in most cases a particular methodology is chosen ...

  10. Antigen detection systems

    Technology Transfer Automated Retrieval System (TEKTRAN)

    Infectious agents or their constituent parts (antigens or nucleic acids) can be detected in fresh, frozen, or fixed tissues or other specimens, using a variety of direct or indirect assays. The assays can be modified to yield the greatest sensitivity and specificity but in most cases a particular m...

  11. Automated detection of broadband clicks of freshwater fish using spectro-temporal features.

    PubMed

    Kottege, Navinda; Jurdak, Raja; Kroon, Frederieke; Jones, Dean

    2015-05-01

    Large scale networks of embedded wireless sensor nodes can passively capture sound for species detection. However, the acoustic recordings result in large amounts of data requiring in-network classification for such systems to be feasible. The current state of the art in the area of in-network bioacoustics classification targets narrowband or long-duration signals, which render it unsuitable for detecting species that emit impulsive broadband signals. In this study, impulsive broadband signals were classified using a small set of spectral and temporal features to aid in their automatic detection and classification. A prototype system is presented along with an experimental evaluation of automated classification methods. The sound used was recorded from a freshwater invasive fish in Australia, the spotted tilapia (Tilapia mariae). Results show a high degree of accuracy after evaluating the proposed detection and classification method for T. mariae sounds and comparing its performance against the state of the art. Moreover, performance slightly improves when the original signal was down-sampled from 44.1 to 16 kHz. This indicates that the proposed method is well-suited for detection and classification on embedded devices, which can be deployed to implement a large scale wireless sensor network for automated species detection. PMID:25994683

  12. North energy system risk analysis features

    NASA Astrophysics Data System (ADS)

    Prokhorov, V. A.; Prokhorov, D. V.

    2015-12-01

    Risk indicator analysis for a decentralized energy system of the North was carried out. Based on analysis of damages caused by accidents at energy systems, their structure is selected, and a North energy system risk determination method was proposed.

  13. Feature Analysis of Generalized Data Base Management Systems.

    ERIC Educational Resources Information Center

    Conference on Data Systems Languages, Monroeville, PA. Systems Committee.

    A more complete definition of the features offered in present day generalized data base management systems is provided by this second technical report of the CODASYL Systems Committee. In a tutorial format, each feature description is followed by either narrative information covering ten systems or by a table for all systems. The ten systems…

  14. Detection and Tracking of Subtle Cloud Features on Uranus

    NASA Astrophysics Data System (ADS)

    Fry, P. M.; Sromovsky, L. A.; de Pater, I.; Hammel, H. B.; Rages, K. A.

    2012-06-01

    The recently updated Uranus zonal wind profile (Sromovsky et al.) samples latitudes from 71° S to 73° N. But many latitudes remain grossly undersampled (outside 20°-45° S and 20°-50° N) due to a lack of trackable cloud features. Offering some hope of filling these gaps is our recent discovery of low-contrast cloud that can be revealed by imaging at much higher signal-to-noise ratios (S/Ns) than previously obtained. This is demonstrated using an average of 2007 Keck II NIRC2 near-IR observations. Eleven one-minute H-band exposures, acquired over a 1.6 hr time span, were rectilinearly remapped and zonally shifted to account for planetary rotation. This increased the S/N by about a factor of 3.3. A new fine structure in latitude bands appeared, small previously unobservable cloud tracers became discernible, and some faint cloud features became prominent. While we could produce one such high-quality average, we could not produce enough to actually track the newly revealed features. This requires a specially designed observational effort. We have designed recent Hubble Space Telescope WFC3 F845M observations to allow application of the technique. We measured eight zonal winds by tracking features in these images and found that several fall off of the current zonal wind profile of Sromovsky et al., and are consistent with a partial reversal of their hemispherically asymmetric profile.

  15. Detection of Abnormal Events via Optical Flow Feature Analysis

    PubMed Central

    Wang, Tian; Snoussi, Hichem

    2015-01-01

    In this paper, a novel algorithm is proposed to detect abnormal events in video streams. The algorithm is based on the histogram of the optical flow orientation descriptor and the classification method. The details of the histogram of the optical flow orientation descriptor are illustrated for describing movement information of the global video frame or foreground frame. By combining one-class support vector machine and kernel principal component analysis methods, the abnormal events in the current frame can be detected after a learning period characterizing normal behaviors. The difference abnormal detection results are analyzed and explained. The proposed detection method is tested on benchmark datasets, then the experimental results show the effectiveness of the algorithm. PMID:25811227

  16. Obscenity detection using haar-like features and Gentle Adaboost classifier.

    PubMed

    Mustafa, Rashed; Min, Yang; Zhu, Dingju

    2014-01-01

    Large exposure of skin area of an image is considered obscene. This only fact may lead to many false images having skin-like objects and may not detect those images which have partially exposed skin area but have exposed erotogenic human body parts. This paper presents a novel method for detecting nipples from pornographic image contents. Nipple is considered as an erotogenic organ to identify pornographic contents from images. In this research Gentle Adaboost (GAB) haar-cascade classifier and haar-like features used for ensuring detection accuracy. Skin filter prior to detection made the system more robust. The experiment showed that, considering accuracy, haar-cascade classifier performs well, but in order to satisfy detection time, train-cascade classifier is suitable. To validate the results, we used 1198 positive samples containing nipple objects and 1995 negative images. The detection rates for haar-cascade and train-cascade classifiers are 0.9875 and 0.8429, respectively. The detection time for haar-cascade is 0.162 seconds and is 0.127 seconds for train-cascade classifier. PMID:25003153

  17. Obscenity Detection Using Haar-Like Features and Gentle Adaboost Classifier

    PubMed Central

    Min, Yang; Zhu, Dingju

    2014-01-01

    Large exposure of skin area of an image is considered obscene. This only fact may lead to many false images having skin-like objects and may not detect those images which have partially exposed skin area but have exposed erotogenic human body parts. This paper presents a novel method for detecting nipples from pornographic image contents. Nipple is considered as an erotogenic organ to identify pornographic contents from images. In this research Gentle Adaboost (GAB) haar-cascade classifier and haar-like features used for ensuring detection accuracy. Skin filter prior to detection made the system more robust. The experiment showed that, considering accuracy, haar-cascade classifier performs well, but in order to satisfy detection time, train-cascade classifier is suitable. To validate the results, we used 1198 positive samples containing nipple objects and 1995 negative images. The detection rates for haar-cascade and train-cascade classifiers are 0.9875 and 0.8429, respectively. The detection time for haar-cascade is 0.162 seconds and is 0.127 seconds for train-cascade classifier. PMID:25003153

  18. Protein detection system

    DOEpatents

    Fruetel, Julie A.; Fiechtner, Gregory J.; Kliner, Dahv A. V.; McIlroy, Andrew

    2009-05-05

    The present embodiment describes a miniature, microfluidic, absorption-based sensor to detect proteins at sensitivities comparable to LIF but without the need for tagging. This instrument utilizes fiber-based evanescent-field cavity-ringdown spectroscopy, in combination with faceted prism microchannels. The combination of these techniques will increase the effective absorption path length by a factor of 10.sup.3 to 10.sup.4 (to .about.1-m), thereby providing unprecedented sensitivity using direct absorption. The coupling of high-sensitivity absorption with high-performance microfluidic separation will enable real-time sensing of biological agents in aqueous samples (including aerosol collector fluids) and will provide a general method with spectral fingerprint capability for detecting specific bio-agents.

  19. Eigenvalue-weighting and feature selection for computer-aided polyp detection in CT colonography

    NASA Astrophysics Data System (ADS)

    Zhu, Hongbin; Wang, Su; Fan, Yi; Lu, Hongbing; Liang, Zhengrong

    2010-03-01

    With the development of computer-aided polyp detection towards virtual colonoscopy screening, the trade-off between detection sensitivity and specificity has gained increasing attention. An optimum detection, with least number of false positives and highest true positive rate, is desirable and involves interdisciplinary knowledge, such as feature extraction, feature selection as well as machine learning. Toward that goal, various geometrical and textural features, associated with each suspicious polyp candidate, have been individually extracted and stacked together as a feature vector. However, directly inputting these high-dimensional feature vectors into a learning machine, e.g., neural network, for polyp detection may introduce redundant information due to feature correlation and induce the curse of dimensionality. In this paper, we explored an indispensable building block of computer-aided polyp detection, i.e., principal component analysis (PCA)-weighted feature selection for neural network classifier of true and false positives. The major concepts proposed in this paper include (1) the use of PCA to reduce the feature correlation, (2) the scheme of adaptively weighting each principal component (PC) by the associated eigenvalue, and (3) the selection of feature combinations via the genetic algorithm. As such, the eigenvalue is also taken as part of the characterizing feature, and the necessary number of features can be exposed to mitigate the curse of dimensionality. Learned and tested by radial basis neural network, the proposed computer-aided polyp detection has achieved 95% sensitivity at a cost of average 2.99 false positives per polyp.

  20. Detection of dynamic background due to swaying movements from motion features.

    PubMed

    Pham, Duc-Son; Arandjelović, Ognjen; Venkatesh, Svetha

    2015-01-01

    Dynamically changing background (dynamic background) still presents a great challenge to many motion-based video surveillance systems. In the context of event detection, it is a major source of false alarms. There is a strong need from the security industry either to detect and suppress these false alarms, or dampen the effects of background changes, so as to increase the sensitivity to meaningful events of interest. In this paper, we restrict our focus to one of the most common causes of dynamic background changes: 1) that of swaying tree branches and 2) their shadows under windy conditions. Considering the ultimate goal in a video analytics pipeline, we formulate a new dynamic background detection problem as a signal processing alternative to the previously described but unreliable computer vision-based approaches. Within this new framework, we directly reduce the number of false alarms by testing if the detected events are due to characteristic background motions. In addition, we introduce a new data set suitable for the evaluation of dynamic background detection. It consists of real-world events detected by a commercial surveillance system from two static surveillance cameras. The research question we address is whether dynamic background can be detected reliably and efficiently using simple motion features and in the presence of similar but meaningful events, such as loitering. Inspired by the tree aerodynamics theory, we propose a novel method named local variation persistence (LVP), that captures the key characteristics of swaying motions. The method is posed as a convex optimization problem, whose variable is the local variation. We derive a computationally efficient algorithm for solving the optimization problem, the solution of which is then used to form a powerful detection statistic. On our newly collected data set, we demonstrate that the proposed LVP achieves excellent detection results and outperforms the best alternative adapted from existing art in

  1. Feature optimization and creation of a real time pattern matching system

    NASA Astrophysics Data System (ADS)

    Wildling, E.; Sidla, O.; Rosner, M.

    2006-10-01

    State of the art algorithms for people or vehicle detection should not only be accurate in terms of detection performance and low false alarm rate, but also fast enough for real time applications. Accurate algorithms are usually very complex and tend to have a lot of calculated features to be used or parameters available for adjustments. So one big goal is to decrease the amount of necessary features used for object detection while increasing the speed of the algorithm and overall performance by finding an optimum set of classifier variables. In this paper we describe algorithms for feature selection, parameter optimisation and pattern matching especially for the task of pedestrian detection based on Histograms of Oriented Gradients and Support Vector Machine classifiers. Shape features were derived with the Histogram of Oriented Gradients algorithm which resulted in a feature vector of 6318 elements. To decrease computation time to an acceptable limit for real-time detection we reduced the full feature vector to sizes of 1000, 500, 300, 200, and 160 elements with a genetic feature selection method. With the remaining features a Support Vector Machine classifier was build and its classification parameters further optimized to result in less support vectors for further improvements in processing speed. This paper compares the classification performance, of the different SVM's on real videos (some sample images), visualizes the chosen features (which histogram bins on which location in the image search feature) and analyses the performance of the final system with respect to execution time and frame rate.

  2. Data assimilation in systems with strong signal features

    NASA Astrophysics Data System (ADS)

    Rosenthal, W. Steven

    Filtering problems in high dimensional geophysical applications often require spatially continuous models to interpolate spatially and temporally sparse data. Many applications in numerical weather and ocean state prediction are concerned with tracking and assessing the uncertainty in the position of large scale vorticity features, such as storm fronts, jets streams, and hurricanes. Quantifying the amplitude variance in these features is complicated by the fact that both height and lateral perturbations in the feature geometry are represented in the same covariance estimate. However, when there are sufficient observations to detect feature information like spatial gradients, the positions of these features can be used to further constrain the filter, as long as the statistical model (cost function) has provisions for both height perturbations and lateral displacements. Several authors since the 1990s have proposed various formalisms for the simultaneous modeling of position and amplitude errors, and the typical approaches to computing the generalized solutions in these applications are variational or direct optimization. The ensemble Kalman filter is often employed in large scale nonlinear filtering problems, but its predication on Gaussian statistics causes its estimators suffer from analysis deflation or collapse, as well as the usual curse of dimensionality in high dimensional Monte Carlo simulations. Moreover, there is no theoretical guarantee of the performance of the ensemble Kalman filter with nonlinear models. Particle filters which employ importance sampling to focus attention on the important regions of the likelihood have shown promise in recent studies on the control of particle size. Consider an ensemble forecast of a system with prominent feature information. The correction of displacements in these features, by pushing them into better aggrement with observations, is an application of importance sampling, and Monte Carlo methods, including particle

  3. Quench detection system for twin coils HTS SMES

    NASA Astrophysics Data System (ADS)

    Badel, A.; Tixador, P.; Simiand, G.; Exchaw, O.

    2010-10-01

    The quench detection and protection system is a critical element in superconducting magnets. After a short summary of the quench detection and protection issues in HTS magnets, an original detection system is presented. The main feature of this system is an active protection of the detection electronics during the discharges, making it possible to use standard electronics even if the discharge voltage is very high. The design of the detection system is therefore easier and it can be made very sensitive. An implementation example is presented for a twin coil HTS SMES prototype, showing the improvements when compared to classical detection systems during operation.

  4. Image Recognition and Feature Detection in Solar Physics

    NASA Astrophysics Data System (ADS)

    Martens, Petrus C.

    2012-05-01

    The Solar Dynamics Observatory (SDO) data repository will dwarf the archives of all previous solar physics missions put together. NASA recognized early on that the traditional methods of analyzing the data -- solar scientists and grad students in particular analyzing the images by hand -- would simply not work and tasked our Feature Finding Team (FFT) with developing automated feature recognition modules for solar events and phenomena likely to be observed by SDO. Having these metadata available on-line will enable solar scientist to conduct statistical studies involving large sets of events that would be impossible now with traditional means. We have followed a two-track approach in our project: we have been developing some existing task-specific solar feature finding modules to be "pipe-line" ready for the stream of SDO data, plus we are designing a few new modules. Secondly, we took it upon us to develop an entirely new "trainable" module that would be capable of identifying different types of solar phenomena starting from a limited number of user-provided examples. Both approaches are now reaching fruition, and I will show examples and movies with results from several of our feature finding modules. In the second part of my presentation I will focus on our “trainable” module, which is the most innovative in character. First, there is the strong similarity between solar and medical X-ray images with regard to their texture, which has allowed us to apply some advances made in medical image recognition. Second, we have found that there is a strong similarity between the way our trainable module works and the way our brain recognizes images. The brain can quickly recognize similar images from key characteristics, just as our code does. We conclude from that that our approach represents the beginning of a more human-like procedure for computer image recognition.

  5. DETECTION AND TRACKING OF SUBTLE CLOUD FEATURES ON URANUS

    SciTech Connect

    Fry, P. M.; Sromovsky, L. A.; De Pater, I.; Hammel, H. B.; Rages, K. A.

    2012-06-15

    The recently updated Uranus zonal wind profile (Sromovsky et al.) samples latitudes from 71 Degree-Sign S to 73 Degree-Sign N. But many latitudes remain grossly undersampled (outside 20 Degree-Sign -45 Degree-Sign S and 20 Degree-Sign -50 Degree-Sign N) due to a lack of trackable cloud features. Offering some hope of filling these gaps is our recent discovery of low-contrast cloud that can be revealed by imaging at much higher signal-to-noise ratios (S/Ns) than previously obtained. This is demonstrated using an average of 2007 Keck II NIRC2 near-IR observations. Eleven one-minute H-band exposures, acquired over a 1.6 hr time span, were rectilinearly remapped and zonally shifted to account for planetary rotation. This increased the S/N by about a factor of 3.3. A new fine structure in latitude bands appeared, small previously unobservable cloud tracers became discernible, and some faint cloud features became prominent. While we could produce one such high-quality average, we could not produce enough to actually track the newly revealed features. This requires a specially designed observational effort. We have designed recent Hubble Space Telescope WFC3 F845M observations to allow application of the technique. We measured eight zonal winds by tracking features in these images and found that several fall off of the current zonal wind profile of Sromovsky et al., and are consistent with a partial reversal of their hemispherically asymmetric profile.

  6. Feature optimization in chemometric algorithms for explosives detection

    NASA Astrophysics Data System (ADS)

    Pinkham, Daniel W.; Bonick, James R.; Woodka, Marc D.

    2012-06-01

    This paper details the use of a genetic algorithm (GA) as a method to preselect spectral feature variables for chemometric algorithms, using spectroscopic data gathered on explosive threat targets. The GA was applied to laserinduced breakdown spectroscopy (LIBS) and ultraviolet Raman spectroscopy (UVRS) data, in which the spectra consisted of approximately 10000 and 1000 distinct spectral values, respectively. The GA-selected variables were examined using two chemometric techniques: multi-class linear discriminant analysis (LDA) and support vector machines (SVM), and the performance from LDA and SVM was fed back to the GA through a fitness function evaluation. In each case, an optimal selection of features was achieved within 20 generations of the GA, with few improvements thereafter. The GA selected chemically significant signatures, such as oxygen and hydron peaks from LIBS spectra and characteristic Raman shifts for AN, TNT, and PETN. Successes documented herein suggest that this GA approach could be useful in analyzing spectroscopic data in complex environments, where the discriminating features of desired targets are not yet fully understood.

  7. Learning Slowness in a Sparse Model of Invariant Feature Detection.

    PubMed

    Chandrapala, Thusitha N; Shi, Bertram E

    2015-07-01

    Primary visual cortical complex cells are thought to serve as invariant feature detectors and to provide input to higher cortical areas. We propose a single model for learning the connectivity required by complex cells that integrates two factors that have been hypothesized to play a role in the development of invariant feature detectors: temporal slowness and sparsity. This model, the generative adaptive subspace self-organizing map (GASSOM), extends Kohonen's adaptive subspace self-organizing map (ASSOM) with a generative model of the input. Each observation is assumed to be generated by one among many nodes in the network, each being associated with a different subspace in the space of all observations. The generating nodes evolve according to a first-order Markov chain and generate inputs that lie close to the associated subspace. This model differs from prior approaches in that temporal slowness is not an externally imposed criterion to be maximized during learning but, rather, an emergent property of the model structure as it seeks a good model of the input statistics. Unlike the ASSOM, the GASSOM does not require an explicit segmentation of the input training vectors into separate episodes. This enables us to apply this model to an unlabeled naturalistic image sequence generated by a realistic eye movement model. We show that the emergence of temporal slowness within the model improves the invariance of feature detectors trained on this input. PMID:25973550

  8. Ring-like features in directional dark matter detection

    SciTech Connect

    Bozorgnia, Nassim; Gelmini, Graciela B.; Gondolo, Paolo E-mail: gelmini@physics.ucla.edu

    2012-06-01

    We discuss a novel dark matter signature relevant for directional detection of Weakly Interacting Massive Particles (WIMPs). For heavy enough WIMPs and low enough recoil energies, the maximum of the recoil rate is not in the direction of the average WIMP arrival direction but in a ring around it at an angular radius that increases with the WIMP mass and can approach 90° at very low energies. The ring is easier to detect for smaller WIMP velocity dispersion and larger average WIMP velocities relative to the detector. In principle the ring could be used as an additional indication of the WIMP mass range.

  9. Particle detection systems and methods

    DOEpatents

    Morris, Christopher L.; Makela, Mark F.

    2010-05-11

    Techniques, apparatus and systems for detecting particles such as muons and neutrons. In one implementation, a particle detection system employs a plurality of drift cells, which can be for example sealed gas-filled drift tubes, arranged on sides of a volume to be scanned to track incoming and outgoing charged particles, such as cosmic ray-produced muons. The drift cells can include a neutron sensitive medium to enable concurrent counting of neutrons. The system can selectively detect devices or materials, such as iron, lead, gold, uranium, plutonium, and/or tungsten, occupying the volume from multiple scattering of the charged particles passing through the volume and can concurrently detect any unshielded neutron sources occupying the volume from neutrons emitted therefrom. If necessary, the drift cells can be used to also detect gamma rays. The system can be employed to inspect occupied vehicles at border crossings for nuclear threat objects.

  10. Detection of mammographic masses using sector features with a multiple-circular-path neural network

    NASA Astrophysics Data System (ADS)

    Lo, Shih-Chung B.; Li, Huai; Hasegawa, Akira; Wang, Yue J.; Freedman, Matthew T.; Mun, Seong K.

    1998-06-01

    In the clinical course of detecting masses, mammographers usually evaluate the surrounding background of a radiodense when breast cancer is suspected. In this study, we adapted this fundamental concept and computed features of the suspicious region in radial sections. These features were then arranged by circular convolution processes within a neural network, which led to an improvement in detecting mammographic masses.

  11. Pair normalized channel feature and statistics-based learning for high-performance pedestrian detection

    NASA Astrophysics Data System (ADS)

    Zeng, Bobo; Wang, Guijin; Ruan, Zhiwei; Lin, Xinggang; Meng, Long

    2012-07-01

    High-performance pedestrian detection with good accuracy and fast speed is an important yet challenging task in computer vision. We design a novel feature named pair normalized channel feature (PNCF), which simultaneously combines and normalizes two channel features in image channels, achieving a highly discriminative power and computational efficiency. PNCF applies to both gradient channels and color channels so that shape and appearance information are described and integrated in the same feature. To efficiently explore the formidably large PNCF feature space, we propose a statistics-based feature learning method to select a small number of potentially discriminative candidate features, which are fed into the boosting algorithm. In addition, channel compression and a hybrid pyramid are employed to speed up the multiscale detection. Experiments illustrate the effectiveness of PNCF and its learning method. Our proposed detector outperforms the state-of-the-art on several benchmark datasets in both detection accuracy and efficiency.

  12. Feature extraction using Hough transform for solid waste bin level detection and classification.

    PubMed

    Hannan, M A; Zaila, W A; Arebey, M; Begum, R A; Basri, H

    2014-09-01

    This paper deals with the solid waste image detection and classification to detect and classify the solid waste bin level. To do so, Hough transform techniques is used for feature extraction to identify the line detection based on image's gradient field. The feedforward neural network (FFNN) model is used to classify the level content of solid waste based on learning concept. Numbers of training have been performed using FFNN to learn and match the targets of the testing images to compute the sum squared error with the performance goal met. The images for each class are used as input samples for classification. Result from the neural network and the rules decision are used to build the receiver operating characteristic (ROC) graph. Decision graph shows the performance of the system waste system based on area under curve (AUC), WS-class reached 0.9875 for excellent result and WS-grade reached 0.8293 for good result. The system has been successfully designated with the motivation of solid waste bin monitoring system that can applied to a wide variety of local municipal authorities system. PMID:24829160

  13. Features versus Context: An approach for precise and detailed detection and delineation of faces and facial features

    PubMed Central

    Ding, Liya; Martinez, Aleix M.

    2013-01-01

    The appearance-based approach to face detection has seen great advances in the last several years. In this approach, we learn the image statistics describing the texture pattern (appearance) of the object class we want to detect, e.g., the face. However, this approach has had a limited success in providing an accurate and detailed description of the internal facial features, i.e., eyes, brows, nose and mouth. In general, this is due to the limited information carried by the learned statistical model. While the face template is relatively rich in texture, facial features (e.g., eyes, nose and mouth) do not carry enough discriminative information to tell them apart from all possible background images. We resolve this problem by adding the context information of each facial feature in the design of the statistical model. In the proposed approach, the context information defines the image statistics most correlated with the surroundings of each facial component. This means that when we search for a face or facial feature we look for those locations which most resemble the feature yet are most dissimilar to its context. This dissimilarity with the context features forces the detector to gravitate toward an accurate estimate of the position of the facial feature. Learning to discriminate between feature and context templates is difficult however, because the context and the texture of the facial features vary widely under changing expression, pose and illumination, and may even resemble one another. We address this problem with the use of subclass divisions. We derive two algorithms to automatically divide the training samples of each facial feature into a set of subclasses, each representing a distinct construction of the same facial component (e.g., closed versus open eyes) or its context (e.g., different hairstyles). The first algorithm is based on a discriminant analysis formulation. The second algorithm is an extension of the AdaBoost approach. We provide extensive

  14. Portable weighing system with alignment features

    DOEpatents

    Abercrombie, Robert Knox; Richardson, Gregory David; Scudiere, Matthew Bligh; Sheldon, Frederick T.

    2012-11-06

    A system for weighing a load is disclosed. The weighing system includes a pad having at least one transducer for weighing a load disposed on the pad. In some embodiments the pad has a plurality of foot members and the weighing system may include a plate that disposed underneath the pad for receiving the plurality of foot member and for aligning the foot members when the weighing system is installed. The weighing system may include a spacer disposed adjacent the pad and in some embodiments, a spacer anchor operatively secures the spacer to a support surface, such as a plate, a railway bed, or a roadway. In some embodiments the spacer anchor operatively secures both the spacer and the pad to a roadway.

  15. Thermal neutron detection system

    DOEpatents

    Peurrung, Anthony J.; Stromswold, David C.

    2000-01-01

    According to the present invention, a system for measuring a thermal neutron emission from a neutron source, has a reflector/moderator proximate the neutron source that reflects and moderates neutrons from the neutron source. The reflector/moderator further directs thermal neutrons toward an unmoderated thermal neutron detector.

  16. Power line detection system

    DOEpatents

    Latorre, Victor R.; Watwood, Donald B.

    1994-01-01

    A short-range, radio frequency (RF) transmitting-receiving system that provides both visual and audio warnings to the pilot of a helicopter or light aircraft of an up-coming power transmission line complex. Small, milliwatt-level narrowband transmitters, powered by the transmission line itself, are installed on top of selected transmission line support towers or within existing warning balls, and provide a continuous RF signal to approaching aircraft. The on-board receiver can be either a separate unit or a portion of the existing avionics, and can also share an existing antenna with another airborne system. Upon receipt of a warning signal, the receiver will trigger a visual and an audio alarm to alert the pilot to the potential power line hazard.

  17. Power line detection system

    DOEpatents

    Latorre, V.R.; Watwood, D.B.

    1994-09-27

    A short-range, radio frequency (RF) transmitting-receiving system that provides both visual and audio warnings to the pilot of a helicopter or light aircraft of an up-coming power transmission line complex. Small, milliwatt-level narrowband transmitters, powered by the transmission line itself, are installed on top of selected transmission line support towers or within existing warning balls, and provide a continuous RF signal to approaching aircraft. The on-board receiver can be either a separate unit or a portion of the existing avionics, and can also share an existing antenna with another airborne system. Upon receipt of a warning signal, the receiver will trigger a visual and an audio alarm to alert the pilot to the potential power line hazard. 4 figs.

  18. Centrifugal unbalance detection system

    DOEpatents

    Cordaro, Joseph V.; Reeves, George; Mets, Michael

    2002-01-01

    A system consisting of an accelerometer sensor attached to a centrifuge enclosure for sensing vibrations and outputting a signal in the form of a sine wave with an amplitude and frequency that is passed through a pre-amp to convert it to a voltage signal, a low pass filter for removing extraneous noise, an A/D converter and a processor and algorithm for operating on the signal, whereby the algorithm interprets the amplitude and frequency associated with the signal and once an amplitude threshold has been exceeded the algorithm begins to count cycles during a predetermined time period and if a given number of complete cycles exceeds the frequency threshold during the predetermined time period, the system shuts down the centrifuge.

  19. Radiation detection system

    DOEpatents

    Whited, R.C.

    A system for obtaining improved resolution in relatively thick semiconductor radiation detectors, such as HgI/sub 2/, which exhibit significant hole trapping. Two amplifiers are used: the first measures the charge collected and the second the contribution of the electrons to the charge collected. The outputs of the two amplifiers are utilized to unfold the total charge generated within the detector in response to a radiation event.

  20. QRS complex detection based on simple robust 2-D pictorial-geometrical feature.

    PubMed

    Hoseini Sabzevari, S A; Moavenian, Majid

    2014-01-01

    In this paper a heuristic method aimed for detecting of QRS complexes without any pre-process was developed. All the methods developed in previous studies were used pre-process, the most novelty of this study was suggesting a simple method which did not need any pre-process. Toward this objective, a new simple 2-D geometrical feature space was extracted from the original electrocardiogram (ECG) signal. In this method, a sliding window was moved sample-by-sample on the pre-processed ECG signal. During each forward slide of the analysis window an artificial image was generated from the excerpted segment allocated in the window. Then, a geometrical feature extraction technique based on curve-length and angle of highest point was applied to each image for establishment of an appropriate feature space. Afterwards the K-Nearest Neighbors (KNN), Artificial Neural Network (ANN) and Adaptive Network Fuzzy Inference Systems (ANFIS) were designed and implemented to the ECG signal. The proposed methods were applied to DAY general hospital high resolution holter data. For detection of QRS complex the average values of sensitivity Se = 99.93% and positive predictivity P+ = 99.92% were obtained. PMID:24144188

  1. Face detection on distorted images using perceptual quality-aware features

    NASA Astrophysics Data System (ADS)

    Gunasekar, Suriya; Ghosh, Joydeep; Bovik, Alan C.

    2014-02-01

    We quantify the degradation in performance of a popular and effective face detector when human-perceived image quality is degraded by distortions due to additive white gaussian noise, gaussian blur or JPEG compression. It is observed that, within a certain range of perceived image quality, a modest increase in image quality can drastically improve face detection performance. These results can be used to guide resource or bandwidth allocation in a communication/delivery system that is associated with face detection tasks. A new face detector based on QualHOG features is also proposed that augments face-indicative HOG features with perceptual quality-aware spatial Natural Scene Statistics (NSS) features, yielding improved tolerance against image distortions. The new detector provides statistically significant improvements over a strong baseline on a large database of face images representing a wide range of distortions. To facilitate this study, we created a new Distorted Face Database, containing face and non-face patches from images impaired by a variety of common distortion types and levels. This new dataset is available for download and further experimentation at www.ideal.ece.utexas.edu/˜suriya/DFD/.

  2. Breast cancer mitosis detection in histopathological images with spatial feature extraction

    NASA Astrophysics Data System (ADS)

    Albayrak, Abdülkadir; Bilgin, Gökhan

    2013-12-01

    In this work, cellular mitosis detection in histopathological images has been investigated. Mitosis detection is very expensive and time consuming process. Development of digital imaging in pathology has enabled reasonable and effective solution to this problem. Segmentation of digital images provides easier analysis of cell structures in histopathological data. To differentiate normal and mitotic cells in histopathological images, feature extraction step is very crucial step for the system accuracy. A mitotic cell has more distinctive textural dissimilarities than the other normal cells. Hence, it is important to incorporate spatial information in feature extraction or in post-processing steps. As a main part of this study, Haralick texture descriptor has been proposed with different spatial window sizes in RGB and La*b* color spaces. So, spatial dependencies of normal and mitotic cellular pixels can be evaluated within different pixel neighborhoods. Extracted features are compared with various sample sizes by Support Vector Machines using k-fold cross validation method. According to the represented results, it has been shown that separation accuracy on mitotic and non-mitotic cellular pixels gets better with the increasing size of spatial window.

  3. APDS: Autonomous Pathogen Detection System

    SciTech Connect

    Langlois, R G; Brown, S; Burris, L; Colston, B; Jones, L; Makarewicz, T; Mariella, R; Masquelier, D; McBride, M; Milanovich, F; Masarabadi, S; Venkateswaran, K; Marshall, G; Olson, D; Wolcott, D

    2002-02-14

    An early warning system to counter bioterrorism, the Autonomous Pathogen Detection System (APDS) continuously monitors the environment for the presence of biological pathogens (e.g., anthrax) and once detected, it sounds an alarm much like a smoke detector warns of a fire. Long before September 11, 2001, this system was being developed to protect domestic venues and events including performing arts centers, mass transit systems, major sporting and entertainment events, and other high profile situations in which the public is at risk of becoming a target of bioterrorist attacks. Customizing off-the-shelf components and developing new components, a multidisciplinary team developed APDS, a stand-alone system for rapid, continuous monitoring of multiple airborne biological threat agents in the environment. The completely automated APDS samples the air, prepares fluid samples in-line, and performs two orthogonal tests: immunoassay and nucleic acid detection. When compared to competing technologies, APDS is unprecedented in terms of flexibility and system performance.

  4. Diversified transmission multichannel detection system

    SciTech Connect

    Tournois, P.; Engelhard, P.

    1984-07-03

    A detection system for imaging by sonar or radar signals. The system associates diversified transmissions with an interferometric base. This base provides an angular channel formation means and each signal formed in this way is processed by matched filtering in a circuit containing copy signals characterizing the space coloring obtained by the diversified transmission means. The invention is particularly applicable to side or front looking detection sonars.

  5. Comparing features extractors in EEG-based cognitive fatigue detection of demanding computer tasks.

    PubMed

    Rifai Chai; Smith, Mitchell R; Nguyen, Tuan N; Sai Ho Ling; Coutts, Aaron J; Nguyen, Hung T

    2015-08-01

    An electroencephalography (EEG)-based classification system could be used as a tool for detecting cognitive fatigue from demanding computer tasks. The most widely used feature extractor in EEG-based fatigue classification is power spectral density (PSD). This paper investigates PSD and three alternative feature extraction methods, in order to find the best feature extractor for the classification of cognitive fatigue during cognitively demanding tasks. These compared methods are power spectral entropy (PSE), wavelet, and autoregressive (AR). Bayesian neural network was selected as the classifier in this study. The results showed that the use of PSD and PSE methods provide an average accuracy of 60% for each computer task. This finding is slightly improved using the wavelet method which has an average accuracy of 61%. The AR method is the best feature extractor compared with the PSD, PSE and wavelet in this study with accuracy of 75.95% in AX-continuous performance test (AX-CPT), 75.23% in psychomotor vigilance test (PVT) and 76.02% in Stroop task (p-value <; 0.05). PMID:26738050

  6. Singular Value Decomposition Based Features for Automatic Tumor Detection in Wireless Capsule Endoscopy Images

    PubMed Central

    Karimian Khosroshahi, Ghader; Zolfy Lighvan, Mina

    2016-01-01

    Wireless capsule endoscopy (WCE) is a new noninvasive instrument which allows direct observation of the gastrointestinal tract to diagnose its relative diseases. Because of the large number of images obtained from the capsule endoscopy per patient, doctors need too much time to investigate all of them. So, it would be worthwhile to design a system for detecting diseases automatically. In this paper, a new method is presented for automatic detection of tumors in the WCE images. This method will utilize the advantages of the discrete wavelet transform (DWT) and singular value decomposition (SVD) algorithms to extract features from different color channels of the WCE images. Therefore, the extracted features are invariant to rotation and can describe multiresolution characteristics of the WCE images. In order to classify the WCE images, the support vector machine (SVM) method is applied to a data set which includes 400 normal and 400 tumor WCE images. The experimental results show proper performance of the proposed algorithm for detection and isolation of the tumor images which, in the best way, shows 94%, 93%, and 93.5% of sensitivity, specificity, and accuracy in the RGB color space, respectively. PMID:27478364

  7. Singular Value Decomposition Based Features for Automatic Tumor Detection in Wireless Capsule Endoscopy Images.

    PubMed

    Faghih Dinevari, Vahid; Karimian Khosroshahi, Ghader; Zolfy Lighvan, Mina

    2016-01-01

    Wireless capsule endoscopy (WCE) is a new noninvasive instrument which allows direct observation of the gastrointestinal tract to diagnose its relative diseases. Because of the large number of images obtained from the capsule endoscopy per patient, doctors need too much time to investigate all of them. So, it would be worthwhile to design a system for detecting diseases automatically. In this paper, a new method is presented for automatic detection of tumors in the WCE images. This method will utilize the advantages of the discrete wavelet transform (DWT) and singular value decomposition (SVD) algorithms to extract features from different color channels of the WCE images. Therefore, the extracted features are invariant to rotation and can describe multiresolution characteristics of the WCE images. In order to classify the WCE images, the support vector machine (SVM) method is applied to a data set which includes 400 normal and 400 tumor WCE images. The experimental results show proper performance of the proposed algorithm for detection and isolation of the tumor images which, in the best way, shows 94%, 93%, and 93.5% of sensitivity, specificity, and accuracy in the RGB color space, respectively. PMID:27478364

  8. Effective method for detecting regions of given colors and the features of the region surfaces

    NASA Astrophysics Data System (ADS)

    Gong, Yihong; Zhang, HongJiang

    1994-03-01

    Color can be used as a very important cue for image recognition. In industrial and commercial areas, color is widely used as a trademark or identifying feature in objects, such as packaged goods, advertising signs, etc. In image database systems, one may retrieve an image of interest by specifying prominent colors and their locations in the image (image retrieval by contents). These facts enable us to detect or identify a target object using colors. However, this task depends mainly on how effectively we can identify a color and detect regions of the given color under possibly non-uniform illumination conditions such as shade, highlight, and strong contrast. In this paper, we present an effective method to detect regions matching given colors, along with the features of the region surfaces. We adopt the HVC color coordinates in the method because of its ability of completely separating the luminant and chromatic components of colors. Three basis functions functionally serving as the low-pass, high-pass, and band-pass filters, respectively, are introduced.

  9. Magnetic mirror fusion systems: Characteristics and distinctive features

    SciTech Connect

    Post, R.F.

    1987-08-10

    A tutorial account is given of the main characteristics and distinctive features of conceptual magnetic fusion systems employing the magnetic mirror principle. These features are related to the potential advantages that mirror-based fusion systems may exhibit for the generation of economic fusion power.

  10. Road detection in arid environments using uniformly distributed random based features

    NASA Astrophysics Data System (ADS)

    Plodpradista, P.; Keller, J. M.; Popescu, M.

    2016-05-01

    The capability of detecting an unpaved road in arid environments can greatly enhance an explosive hazard detection system. One approach is to segment out the off-road area and the area above the horizon, which is considered to be irrelevant for the task in hand. Segmenting out irrelevant areas, such as the region above the horizon, allows the explosive hazard detection system to process a smaller region in a scene, enabling a more computationally complex approach. In this paper, we propose a novel approach for speeding up the detection algorithms based on random projection and random selection. Both methods have a low computational cost and reduce the dimensionality of the data while approximately preserving, with a certain probability, the pair-wise point distances. Dimensionality reduction allows any classifier employed in our proposed algorithm to consume fewer computational resources. Furthermore, by applying the random projections directly to image intensity patches, there is no feature extraction needed. The data used in our proposed algorithms are obtained from sensors on board a U.S. Army countermine vehicle. We tested our proposed algorithms on data obtained from several runs on an arid climate road. In our experiments we compare our algorithms based on random projection and random selection to Principal Component Analysis (PCA), a popular dimensionality reduction method.

  11. Rotation-invariant features for multi-oriented text detection in natural images.

    PubMed

    Yao, Cong; Zhang, Xin; Bai, Xiang; Liu, Wenyu; Ma, Yi; Tu, Zhuowen

    2013-01-01

    Texts in natural scenes carry rich semantic information, which can be used to assist a wide range of applications, such as object recognition, image/video retrieval, mapping/navigation, and human computer interaction. However, most existing systems are designed to detect and recognize horizontal (or near-horizontal) texts. Due to the increasing popularity of mobile-computing devices and applications, detecting texts of varying orientations from natural images under less controlled conditions has become an important but challenging task. In this paper, we propose a new algorithm to detect texts of varying orientations. Our algorithm is based on a two-level classification scheme and two sets of features specially designed for capturing the intrinsic characteristics of texts. To better evaluate the proposed method and compare it with the competing algorithms, we generate a comprehensive dataset with various types of texts in diverse real-world scenes. We also propose a new evaluation protocol, which is more suitable for benchmarking algorithms for detecting texts in varying orientations. Experiments on benchmark datasets demonstrate that our system compares favorably with the state-of-the-art algorithms when handling horizontal texts and achieves significantly enhanced performance on variant texts in complex natural scenes. PMID:23940544

  12. A Widely Applicable Silver Sol for TLC Detection with Rich and Stable SERS Features

    NASA Astrophysics Data System (ADS)

    Zhu, Qingxia; Li, Hao; Lu, Feng; Chai, Yifeng; Yuan, Yongfang

    2016-04-01

    Thin-layer chromatography (TLC) coupled with surface-enhanced Raman spectroscopy (SERS) has gained tremendous popularity in the study of various complex systems. However, the detection of hydrophobic analytes is difficult, and the specificity still needs to be improved. In this study, a SERS-active non-aqueous silver sol which could activate the analytes to produce rich and stable spectral features was rapidly synthesized. Then, the optimized silver nanoparticles (AgNPs)-DMF sol was employed for TLC-SERS detection of hydrophobic (and also hydrophilic) analytes. SERS performance of this sol was superior to that of traditional Lee-Meisel AgNPs due to its high specificity, acceptable stability, and wide applicability. The non-aqueous AgNPs would be suitable for the TLC-SERS method, which shows great promise for applications in food safety assurance, environmental monitoring, medical diagnoses, and many other fields.

  13. A Widely Applicable Silver Sol for TLC Detection with Rich and Stable SERS Features.

    PubMed

    Zhu, Qingxia; Li, Hao; Lu, Feng; Chai, Yifeng; Yuan, Yongfang

    2016-12-01

    Thin-layer chromatography (TLC) coupled with surface-enhanced Raman spectroscopy (SERS) has gained tremendous popularity in the study of various complex systems. However, the detection of hydrophobic analytes is difficult, and the specificity still needs to be improved. In this study, a SERS-active non-aqueous silver sol which could activate the analytes to produce rich and stable spectral features was rapidly synthesized. Then, the optimized silver nanoparticles (AgNPs)-DMF sol was employed for TLC-SERS detection of hydrophobic (and also hydrophilic) analytes. SERS performance of this sol was superior to that of traditional Lee-Meisel AgNPs due to its high specificity, acceptable stability, and wide applicability. The non-aqueous AgNPs would be suitable for the TLC-SERS method, which shows great promise for applications in food safety assurance, environmental monitoring, medical diagnoses, and many other fields. PMID:27107773

  14. Regression-Based Approach For Feature Selection In Classification Issues. Application To Breast Cancer Detection And Recurrence

    NASA Astrophysics Data System (ADS)

    Belciug, Smaranda; Serbanescu, Mircea-Sebastian

    2015-09-01

    Feature selection is considered a key factor in classifications/decision problems. It is currently used in designing intelligent decision systems to choose the best features which allow the best performance. This paper proposes a regression-based approach to select the most important predictors to significantly increase the classification performance. Application to breast cancer detection and recurrence using publically available datasets proved the efficiency of this technique.

  15. Hyperspectral Feature Detection Onboard the Earth Observing One Spacecraft using Superpixel Segmentation and Endmember Extraction

    NASA Technical Reports Server (NTRS)

    Thompson, David R.; Bornstein, Benjamin; Bue, Brian D.; Tran, Daniel Q.; Chien, Steve A.; Castano, Rebecca

    2012-01-01

    We present a demonstration of onboard hyperspectral image processing with the potential to reduce mission downlink requirements. The system detects spectral endmembers and then uses them to map units of surface material. This summarizes the content of the scene, reveals spectral anomalies warranting fast response, and reduces data volume by two orders of magnitude. We have integrated this system into the Autonomous Science craft Experiment for operational use onboard the Earth Observing One (EO-1) Spacecraft. The system does not require prior knowledge about spectra of interest. We report on a series of trial overflights in which identical spacecraft commands are effective for autonomous spectral discovery and mapping for varied target features, scenes and imaging conditions.

  16. A Web Based Cardiovascular Disease Detection System.

    PubMed

    Alshraideh, Hussam; Otoom, Mwaffaq; Al-Araida, Aseel; Bawaneh, Haneen; Bravo, José

    2015-10-01

    Cardiovascular Disease (CVD) is one of the most catastrophic and life threatening health issue nowadays. Early detection of CVD is an important solution to reduce its devastating effects on health. In this paper, an efficient CVD detection algorithm is identified. The algorithm uses patient demographic data as inputs, along with several ECG signal features extracted automatically through signal processing techniques. Cross-validation results show a 98.29 % accuracy for the decision tree classification algorithm. The algorithm has been integrated into a web based system that can be used at anytime by patients to check their heart health status. At one end of the system is the ECG sensor attached to the patient's body, while at the other end is the detection algorithm. Communication between the two ends is done through an Android application. PMID:26293754

  17. Change detection on a hunch: pre-attentive vision allows "sensing" of unique feature changes.

    PubMed

    Ball, Felix; Busch, Niko A

    2015-11-01

    Studies on change detection and change blindness have investigated the nature of visual representations by testing the conditions under which observers are able to detect when an object in a complex scene changes from one moment to the next. Several authors have proposed that change detection can occur without identification of the changing object, but the perceptual processes underlying this phenomenon are currently unknown. We hypothesized that change detection without localization or identification occurs when the change happens outside the focus of attention. Such changes would usually go entirely unnoticed, unless the change brings about a modification of one of the feature maps representing the scene. Thus, the appearance or disappearance of a unique feature might be registered even in the absence of focused attention and without feature binding, allowing for change detection, but not localization or identification. We tested this hypothesis in three experiments, in which changes either involved colors that were already present elsewhere in the display or entirely unique colors. Observers detected whether any change had occurred and then localized or identified the change. Change detection without localization occurred almost exclusively when changes involved a unique color. Moreover, change detection without localization for unique feature changes was independent of the number of objects in the display and independent of change identification. These findings suggest that pre-attentive registration of a change on a feature map can give rise to a conscious experience even when feature binding has failed: that something has changed without knowing what or where. PMID:26353860

  18. Offline signature verification and skilled forgery detection using HMM and sum graph features with ANN and knowledge based classifier

    NASA Astrophysics Data System (ADS)

    Mehta, Mohit; Choudhary, Vijay; Das, Rupam; Khan, Ilyas

    2010-02-01

    Signature verification is one of the most widely researched areas in document analysis and signature biometric. Various methodologies have been proposed in this area for accurate signature verification and forgery detection. In this paper we propose a unique two stage model of detecting skilled forgery in the signature by combining two feature types namely Sum graph and HMM model for signature generation and classify them with knowledge based classifier and probability neural network. We proposed a unique technique of using HMM as feature rather than a classifier as being widely proposed by most of the authors in signature recognition. Results show a higher false rejection than false acceptance rate. The system detects forgeries with an accuracy of 80% and can detect the signatures with 91% accuracy. The two stage model can be used in realistic signature biometric applications like the banking applications where there is a need to detect the authenticity of the signature before processing documents like checks.

  19. Thermal stability of soils and detectability of intrinsic soil features

    NASA Astrophysics Data System (ADS)

    Siewert, Christian; Kucerik, Jiri

    2014-05-01

    applicability of thermogravimetry for soil property determination. Despite of the extreme diversity of individual substances in soils, the thermal decay can be predicted with simple mathematical models. For example, the sum of mass losses in the large temperature interval from 100 °C to 550 °C (known from organic matter determination by ignition mass loss in past) can be predicted using TML in two small temperature intervals: 130 - 140 °C and 320 - 330 °C. In this case, the coefficient of determination between measured and calculated results reached an R2 above 0.97. Further, we found close autocorrelations between thermal mass losses in different temperature intervals. They refer to interrelations between evaporation of bound water and thermal decay of organo-mineral complexes in soils less affected by human influence. In contrast, deviations from such interrelations were found under extreme environmental conditions and in soils under human use. Those results confirm current knowledge about influence of clay on both water binding and organic matter accumulation during natural soil formation. Therefore, these interrelations between soil components are discussed as intrinsic features of soils which open the opportunity for experimental distinction of natural soils from organic and inorganic materials which do not have pedogenetic origin.

  20. Investigation of context, soft spatial, and spatial frequency domain features for buried explosive hazard detection in FL-LWIR

    NASA Astrophysics Data System (ADS)

    Price, Stanton R.; Anderson, Derek T.; Stone, Kevin; Keller, James M.

    2014-05-01

    It is well-known that a pattern recognition system is only as good as the features it is built upon. In the fields of image processing and computer vision, we have numerous spatial domain and spatial-frequency domain features to extract characteristics of imagery according to its color, shape and texture. However, these approaches extract information across a local neighborhood, or region of interest, which for target detection contains both object(s) of interest and background (surrounding context). A goal of this research is to filter out as much task irrelevant information as possible, e.g., tire tracks, surface texture, etc., to allow a system to place more emphasis on image features in spatial regions that likely belong to the object(s) of interest. Herein, we outline a procedure coined soft feature extraction to refine the focus of spatial domain features. This idea is demonstrated in the context of an explosive hazards detection system using forward looking infrared imagery. We also investigate different ways to spatially contextualize and calculate mathematical features from shearlet filtered candidate image chips. Furthermore, we investigate localization strategies in relation to different ways of grouping image features to reduce the false alarm rate. Performance is explored in the context of receiver operating characteristic curves on data from a U.S. Army test site that contains multiple target and clutter types, burial depths, and times of day.

  1. Integrated multisensor perimeter detection systems

    NASA Astrophysics Data System (ADS)

    Kent, P. J.; Fretwell, P.; Barrett, D. J.; Faulkner, D. A.

    2007-10-01

    The report describes the results of a multi-year programme of research aimed at the development of an integrated multi-sensor perimeter detection system capable of being deployed at an operational site. The research was driven by end user requirements in protective security, particularly in threat detection and assessment, where effective capability was either not available or prohibitively expensive. Novel video analytics have been designed to provide robust detection of pedestrians in clutter while new radar detection and tracking algorithms provide wide area day/night surveillance. A modular integrated architecture based on commercially available components has been developed. A graphical user interface allows intuitive interaction and visualisation with the sensors. The fusion of video, radar and other sensor data provides the basis of a threat detection capability for real life conditions. The system was designed to be modular and extendable in order to accommodate future and legacy surveillance sensors. The current sensor mix includes stereoscopic video cameras, mmWave ground movement radar, CCTV and a commercially available perimeter detection cable. The paper outlines the development of the system and describes the lessons learnt after deployment in a pilot trial.

  2. Sea Turtle Navigation and the Detection of Geomagnetic Field Features

    NASA Astrophysics Data System (ADS)

    Lohmann, Kenneth J.; Lohmann, Catherine M. F.

    The lives of sea turtles consist of a continuous series of migrations. As hatchlings, the turtles swim from their natal beaches into the open sea, often taking refuge in circular current systems (gyres) that serve as moving, open-ocean nursery grounds. The juveniles of many populations subsequently take up residence in coastal feeding areas that are located hundreds or thousands of kilometres from the beaches on which the turtles hatched; some juveniles also migrate between summer and winter habitats. As adults, turtles periodically leave their feeding grounds and migrate to breeding and nesting regions, after which many return to their own specific feeding sites. The itinerant lifestyle characteristic of most sea turtle species is thus inextricably linked to an ability to orient and navigate accurately across large expanses of seemingly featureless ocean.In some sea turtle populations, migratory performance reaches extremes. The total distances certain green turtles (Chelonia mydas) and loggerheads (Caretta caretta) traverse over the span of their lifetimes exceed tens of thousands of kilometres, several times the diameter of the turtle's home ocean basin. Adult migrations between feeding and nesting habitats can require continuous swimming for periods of several weeks. In addition, the paths of migrating turtles often lead almost straight across the open ocean and directly to the destination, leaving little doubt that turtles can navigate to distant target sites with remarkable efficiency.

  3. Detection of linear features using a localized radon transform with a wavelet filter

    SciTech Connect

    Warrick, A L; Delaney, P A

    1999-12-13

    One problem of interest to the oceanic engineering community is the detection and enhancement of internal wakes in open water synthetic aperture radar (SAR) images. Internal wakes, which occur when a ship travels in a stratified medium, have a V shape extending from the ship, and a chirp-like feature across each arm. The Radon transform has been applied to the detection and the enhancement problems in internal wake images to account for the linear features while the wavelet transform has been applied to the enhancement problem in internal wake images to account for the chirp-like features. In this paper, a new transform, a localized Radon transform with a wavelet filter (LRTWF), is developed which accounts for both the linear and the chirp-like features of the internal wake. This transform is then incorporated into optimal and sub-optimal detection schemes for images (with these features) which are contaminated by additive Gaussian noise.

  4. Synthetic aperture radar target detection, feature extraction, and image formation techniques

    NASA Technical Reports Server (NTRS)

    Li, Jian

    1994-01-01

    This report presents new algorithms for target detection, feature extraction, and image formation with the synthetic aperture radar (SAR) technology. For target detection, we consider target detection with SAR and coherent subtraction. We also study how the image false alarm rates are related to the target template false alarm rates when target templates are used for target detection. For feature extraction from SAR images, we present a computationally efficient eigenstructure-based 2D-MODE algorithm for two-dimensional frequency estimation. For SAR image formation, we present a robust parametric data model for estimating high resolution range signatures of radar targets and for forming high resolution SAR images.

  5. Application of Geologic Mapping Techniques and Autonomous Feature Detection to Future Exploration of Europa

    NASA Astrophysics Data System (ADS)

    Bunte, M. K.; Tanaka, K. L.; Doggett, T.; Figueredo, P. H.; Lin, Y.; Greeley, R.; Saripalli, S.; Bell, J. F.

    2013-12-01

    disrupted surface morphologies. Areas of high interest include lineaments and chaos margins. The limitations on detecting activity at these locations are approximated by studying similar observed conditions on other bodies. By adapting machine learning and data mining techniques to signatures of plumes and morphology, I have demonstrated autonomous rule-based detection of known features using edge-detection and supervised classification methods. These methods successfully detect ≤94% of known volcanic plumes or jets at Io, Enceladus, and comets. They also allow recognition of multiple feature types. Applying these results to conditions expected for Europa enables a prediction of the potential for detection of similar features and enables recommendations for mission concepts to increase the science return and efficiency of future missions to observe Europa. This post-Galileo view of Europa provides a synthesis of the overall history of this unique icy satellite and will be a useful frame of reference for future exploration of the jovian system and other potentially active outer solar system bodies.

  6. A pattern recognition system for JPEG steganography detection

    NASA Astrophysics Data System (ADS)

    Chen, C. L. Philip; Chen, Mei-Ching; Agaian, Sos; Zhou, Yicong; Roy, Anuradha; Rodriguez, Benjamin M.

    2012-10-01

    This paper builds up a pattern recognition system to detect anomalies in JPEG images, especially steganographic content. The system consists of feature generation, feature ranking and selection, feature extraction, and pattern classification. These processes tend to capture image characteristics, reduce the problem dimensionality, eliminate the noise inferences between features, and further improve classification accuracies on clean and steganography JPEG images. Based on the discussion and analysis of six popular JPEG steganography methods, the entire recognition system results in higher classification accuracies between clean and steganography classes compared to merely using individual feature subset for JPEG steganography detection. The strength of feature combination and preprocessing has been integrated even when a small amount of information is embedded. The work demonstrated in this paper is extensible and can be improved by integrating various new and current techniques.

  7. Fuzzy Logic-Supported Detection of Complex Geospatial Features in a Web Service Environment

    NASA Astrophysics Data System (ADS)

    He, L. L.; Di, L. P.; Yue, P.; Zhang, M. D.

    2013-10-01

    Spatial relations among simple features can be used to characterize complex geospatial features. These spatial relations are often represented using linguistic terms such as near, which have inherent vagueness and imprecision. Fuzzy logic can be used to modeling fuzziness of the terms. Once simple features are extracted from remote sensing imagery, degree of satisfaction of spatial relations among these simple features can be derived to detect complex features. The derivation process can be performed in a distributed service environment, which benefits Earth science society in the last decade. Workflow-based service can provide ondemand uncertainty-aware discovery of complex features in a distributed environment. A use case on the complex facility detection illustrates the applicability of the fuzzy logic-supported service-oriented approach.

  8. A new approach for EEG feature extraction in P300-based lie detection.

    PubMed

    Abootalebi, Vahid; Moradi, Mohammad Hassan; Khalilzadeh, Mohammad Ali

    2009-04-01

    P300-based Guilty Knowledge Test (GKT) has been suggested as an alternative approach for conventional polygraphy. The purpose of this study was to extend a previously introduced pattern recognition method for the ERP assessment in this application. This extension was done by the further extending the feature set and also the employing a method for the selection of optimal features. For the evaluation of the method, several subjects went through the designed GKT paradigm and their respective brain signals were recorded. Next, a P300 detection approach based on some features and a statistical classifier was implemented. The optimal feature set was selected using a genetic algorithm from a primary feature set including some morphological, frequency and wavelet features and was used for the classification of the data. The rates of correct detection in guilty and innocent subjects were 86%, which was better than other previously used methods. PMID:19041154

  9. Computing network-based features from physiological time series: application to sepsis detection.

    PubMed

    Santaniello, Sabato; Granite, Stephen J; Sarma, Sridevi V; Winslow, Raimond L

    2014-01-01

    Sepsis is a systemic deleterious host response to infection. It is a major healthcare problem that affects millions of patients every year in the intensive care units (ICUs) worldwide. Despite the fact that ICU patients are heavily instrumented with physiological sensors, early sepsis detection remains challenging, perhaps because clinicians identify sepsis by using static scores derived from bed-side measurements individually, i.e., without systematically accounting for potential interactions between these signals and their dynamics. In this study, we apply network-based data analysis to take into account interactions between bed-side physiological time series (PTS) data collected in ICU patients, and we investigate features to distinguish between sepsis and non-sepsis conditions. We treated each PTS source as a node on a graph and we retrieved the graph connectivity matrix over time by tracking the correlation between each pair of sources' signals over consecutive time windows. Then, for each connectivity matrix, we computed the eigenvalue decomposition. We found that, even though raw PTS measurements may have indistinguishable distributions in non-sepsis and early sepsis states, the median /I of the eigenvalues computed from the same data is statistically different (p <; 0.001) in the two states and the evolution of /I may reflect the disease progression. Although preliminary, these findings suggest that network-based features computed from continuous PTS data may be useful for early sepsis detection. PMID:25570825

  10. Joint Spatial-Spectral Feature Space Clustering for Speech Activity Detection from ECoG Signals

    PubMed Central

    Kanas, Vasileios G.; Mporas, Iosif; Benz, Heather L.; Sgarbas, Kyriakos N.; Bezerianos, Anastasios; Crone, Nathan E.

    2014-01-01

    Brain machine interfaces for speech restoration have been extensively studied for more than two decades. The success of such a system will depend in part on selecting the best brain recording sites and signal features corresponding to speech production. The purpose of this study was to detect speech activity automatically from electrocorticographic signals based on joint spatial-frequency clustering of the ECoG feature space. For this study, the ECoG signals were recorded while a subject performed two different syllable repetition tasks. We found that the optimal frequency resolution to detect speech activity from ECoG signals was 8 Hz, achieving 98.8% accuracy by employing support vector machines (SVM) as a classifier. We also defined the cortical areas that held the most information about the discrimination of speech and non-speech time intervals. Additionally, the results shed light on the distinct cortical areas associated with the two syllable repetition tasks and may contribute to the development of portable ECoG-based communication. PMID:24658248

  11. Spinal focal lesion detection in multiple myeloma using multimodal image features

    NASA Astrophysics Data System (ADS)

    Fränzle, Andrea; Hillengass, Jens; Bendl, Rolf

    2015-03-01

    Multiple myeloma is a tumor disease in the bone marrow that affects the skeleton systemically, i.e. multiple lesions can occur in different sites in the skeleton. To quantify overall tumor mass for determining degree of disease and for analysis of therapy response, volumetry of all lesions is needed. Since the large amount of lesions in one patient impedes manual segmentation of all lesions, quantification of overall tumor volume is not possible until now. Therefore development of automatic lesion detection and segmentation methods is necessary. Since focal tumors in multiple myeloma show different characteristics in different modalities (changes in bone structure in CT images, hypointensity in T1 weighted MR images and hyperintensity in T2 weighted MR images), multimodal image analysis is necessary for the detection of focal tumors. In this paper a pattern recognition approach is presented that identifies focal lesions in lumbar vertebrae based on features from T1 and T2 weighted MR images. Image voxels within bone are classified using random forests based on plain intensities and intensity value derived features (maximum, minimum, mean, median) in a 5 x 5 neighborhood around a voxel from both T1 and T2 weighted MR images. A test data sample of lesions in 8 lumbar vertebrae from 4 multiple myeloma patients can be classified at an accuracy of 95% (using a leave-one-patient-out test). The approach provides a reasonable delineation of the example lesions. This is an important step towards automatic tumor volume quantification in multiple myeloma.

  12. Affective video retrieval: violence detection in Hollywood movies by large-scale segmental feature extraction.

    PubMed

    Eyben, Florian; Weninger, Felix; Lehment, Nicolas; Schuller, Björn; Rigoll, Gerhard

    2013-01-01

    Without doubt general video and sound, as found in large multimedia archives, carry emotional information. Thus, audio and video retrieval by certain emotional categories or dimensions could play a central role for tomorrow's intelligent systems, enabling search for movies with a particular mood, computer aided scene and sound design in order to elicit certain emotions in the audience, etc. Yet, the lion's share of research in affective computing is exclusively focusing on signals conveyed by humans, such as affective speech. Uniting the fields of multimedia retrieval and affective computing is believed to lend to a multiplicity of interesting retrieval applications, and at the same time to benefit affective computing research, by moving its methodology "out of the lab" to real-world, diverse data. In this contribution, we address the problem of finding "disturbing" scenes in movies, a scenario that is highly relevant for computer-aided parental guidance. We apply large-scale segmental feature extraction combined with audio-visual classification to the particular task of detecting violence. Our system performs fully data-driven analysis including automatic segmentation. We evaluate the system in terms of mean average precision (MAP) on the official data set of the MediaEval 2012 evaluation campaign's Affect Task, which consists of 18 original Hollywood movies, achieving up to .398 MAP on unseen test data in full realism. An in-depth analysis of the worth of individual features with respect to the target class and the system errors is carried out and reveals the importance of peak-related audio feature extraction and low-level histogram-based video analysis. PMID:24391704

  13. Affective Video Retrieval: Violence Detection in Hollywood Movies by Large-Scale Segmental Feature Extraction

    PubMed Central

    Eyben, Florian; Weninger, Felix; Lehment, Nicolas; Schuller, Björn; Rigoll, Gerhard

    2013-01-01

    Without doubt general video and sound, as found in large multimedia archives, carry emotional information. Thus, audio and video retrieval by certain emotional categories or dimensions could play a central role for tomorrow's intelligent systems, enabling search for movies with a particular mood, computer aided scene and sound design in order to elicit certain emotions in the audience, etc. Yet, the lion's share of research in affective computing is exclusively focusing on signals conveyed by humans, such as affective speech. Uniting the fields of multimedia retrieval and affective computing is believed to lend to a multiplicity of interesting retrieval applications, and at the same time to benefit affective computing research, by moving its methodology “out of the lab” to real-world, diverse data. In this contribution, we address the problem of finding “disturbing” scenes in movies, a scenario that is highly relevant for computer-aided parental guidance. We apply large-scale segmental feature extraction combined with audio-visual classification to the particular task of detecting violence. Our system performs fully data-driven analysis including automatic segmentation. We evaluate the system in terms of mean average precision (MAP) on the official data set of the MediaEval 2012 evaluation campaign's Affect Task, which consists of 18 original Hollywood movies, achieving up to .398 MAP on unseen test data in full realism. An in-depth analysis of the worth of individual features with respect to the target class and the system errors is carried out and reveals the importance of peak-related audio feature extraction and low-level histogram-based video analysis. PMID:24391704

  14. Feature extraction for ultrasonic sensor based defect detection in ceramic components

    NASA Astrophysics Data System (ADS)

    Kesharaju, Manasa; Nagarajah, Romesh

    2014-02-01

    High density silicon carbide materials are commonly used as the ceramic element of hard armour inserts used in traditional body armour systems to reduce their weight, while providing improved hardness, strength and elastic response to stress. Currently, armour ceramic tiles are inspected visually offline using an X-ray technique that is time consuming and very expensive. In addition, from X-rays multiple defects are also misinterpreted as single defects. Therefore, to address these problems the ultrasonic non-destructive approach is being investigated. Ultrasound based inspection would be far more cost effective and reliable as the methodology is applicable for on-line quality control including implementation of accept/reject criteria. This paper describes a recently developed methodology to detect, locate and classify various manufacturing defects in ceramic tiles using sub band coding of ultrasonic test signals. The wavelet transform is applied to the ultrasonic signal and wavelet coefficients in the different frequency bands are extracted and used as input features to an artificial neural network (ANN) for purposes of signal classification. Two different classifiers, using artificial neural networks (supervised) and clustering (un-supervised) are supplied with features selected using Principal Component Analysis(PCA) and their classification performance compared. This investigation establishes experimentally that Principal Component Analysis(PCA) can be effectively used as a feature selection method that provides superior results for classifying various defects in the context of ultrasonic inspection in comparison with the X-ray technique.

  15. Efficient Markov feature extraction method for image splicing detection using maximization and threshold expansion

    NASA Astrophysics Data System (ADS)

    Han, Jong Goo; Park, Tae Hee; Moon, Yong Ho; Eom, Il Kyu

    2016-03-01

    We propose an efficient Markov feature extraction method for color image splicing detection. The maximum value among the various directional difference values in the discrete cosine transform domain of three color channels is used to choose the Markov features. We show that the discriminability for slicing detection is increased through the maximization process from the point of view of the Kullback-Leibler divergence. In addition, we present a threshold expansion and Markov state decomposition algorithm. Threshold expansion reduces the information loss caused by the coefficient thresholding that is used to restrict the number of Markov features. To compensate the increased number of features due to the threshold expansion, we propose an even-odd Markov state decomposition algorithm. A fixed number of features, regardless of the difference directions, color channels and test datasets, are used in the proposed algorithm. We introduce three kinds of Markov feature vectors. The number of Markov features for splicing detection used in this paper is relatively small compared to the conventional methods, and our method does not require additional feature reduction algorithms. Through experimental simulations, we demonstrate that the proposed method achieves high performance in splicing detection.

  16. Earth analysis methods, subsurface feature detection methods, earth analysis devices, and articles of manufacture

    DOEpatents

    West, Phillip B.; Novascone, Stephen R.; Wright, Jerry P.

    2011-09-27

    Earth analysis methods, subsurface feature detection methods, earth analysis devices, and articles of manufacture are described. According to one embodiment, an earth analysis method includes engaging a device with the earth, analyzing the earth in a single substantially lineal direction using the device during the engaging, and providing information regarding a subsurface feature of the earth using the analysis.

  17. Earth analysis methods, subsurface feature detection methods, earth analysis devices, and articles of manufacture

    DOEpatents

    West, Phillip B.; Novascone, Stephen R.; Wright, Jerry P.

    2012-05-29

    Earth analysis methods, subsurface feature detection methods, earth analysis devices, and articles of manufacture are described. According to one embodiment, an earth analysis method includes engaging a device with the earth, analyzing the earth in a single substantially lineal direction using the device during the engaging, and providing information regarding a subsurface feature of the earth using the analysis.

  18. Medical image retrieval system using multiple features from 3D ROIs

    NASA Astrophysics Data System (ADS)

    Lu, Hongbing; Wang, Weiwei; Liao, Qimei; Zhang, Guopeng; Zhou, Zhiming

    2012-02-01

    Compared to a retrieval using global image features, features extracted from regions of interest (ROIs) that reflect distribution patterns of abnormalities would benefit more for content-based medical image retrieval (CBMIR) systems. Currently, most CBMIR systems have been designed for 2D ROIs, which cannot reflect 3D anatomical features and region distribution of lesions comprehensively. To further improve the accuracy of image retrieval, we proposed a retrieval method with 3D features including both geometric features such as Shape Index (SI) and Curvedness (CV) and texture features derived from 3D Gray Level Co-occurrence Matrix, which were extracted from 3D ROIs, based on our previous 2D medical images retrieval system. The system was evaluated with 20 volume CT datasets for colon polyp detection. Preliminary experiments indicated that the integration of morphological features with texture features could improve retrieval performance greatly. The retrieval result using features extracted from 3D ROIs accorded better with the diagnosis from optical colonoscopy than that based on features from 2D ROIs. With the test database of images, the average accuracy rate for 3D retrieval method was 76.6%, indicating its potential value in clinical application.

  19. 3D reconstruction for sinusoidal motion based on different feature detection algorithms

    NASA Astrophysics Data System (ADS)

    Zhang, Peng; Zhang, Jin; Deng, Huaxia; Yu, Liandong

    2015-02-01

    The dynamic testing of structures and components is an important area of research. Extensive researches on the methods of using sensors for vibration parameters have been studied for years. With the rapid development of industrial high-speed camera and computer hardware, the method of using stereo vision for dynamic testing has been the focus of the research since the advantages of non-contact, full-field, high resolution and high accuracy. But in the country there is not much research about the dynamic testing based on stereo vision, and yet few people publish articles about the three-dimensional (3D) reconstruction of feature points in the case of dynamic. It is essential to the following analysis whether it can obtain accurate movement of target objects. In this paper, an object with sinusoidal motion is detected by stereo vision and the accuracy with different feature detection algorithms is investigated. Three different marks including dot, square and circle are stuck on the object and the object is doing sinusoidal motion by vibration table. Then use feature detection algorithm speed-up robust feature (SURF) to detect point, detect square corners by Harris and position the center by Hough transform. After obtaining the pixel coordinate values of the feature point, the stereo calibration parameters are used to achieve three-dimensional reconstruction through triangulation principle. The trajectories of the specific direction according to the vibration frequency and the frequency camera acquisition are obtained. At last, the reconstruction accuracy of different feature detection algorithms is compared.

  20. Integration of local and global features for anatomical object detection in ultrasound.

    PubMed

    Rahmatullah, Bahbibi; Papageorghiou, Aris T; Noble, J Alison

    2012-01-01

    The use of classifier-based object detection has found to be a promising approach in medical anatomy detection. In ultrasound images, the detection task is very challenging due to speckle, shadows and low contrast characteristic features. Typical detection algorithms that use purely intensity-based image features with an exhaustive scan of the image (sliding window approach) tend not to perform very well and incur a very high computational cost. The proposed approach in this paper achieves a significant improvement in detection rates while avoiding exhaustive scanning, thereby gaining a large increase in speed. Our approach uses the combination of local features from an intensity image and global features derived from a local phase-based image known as feature symmetry. The proposed approach has been applied to 2384 two-dimensional (2D) fetal ultrasound abdominal images for the detection of the stomach and the umbilical vein. The results presented show that it outperforms prior related work that uses only local or only global features. PMID:23286156

  1. Feature extraction from Doppler ultrasound signals for automated diagnostic systems.

    PubMed

    Ubeyli, Elif Derya; Güler, Inan

    2005-11-01

    This paper presented the assessment of feature extraction methods used in automated diagnosis of arterial diseases. Since classification is more accurate when the pattern is simplified through representation by important features, feature extraction and selection play an important role in classifying systems such as neural networks. Different feature extraction methods were used to obtain feature vectors from ophthalmic and internal carotid arterial Doppler signals. In addition to this, the problem of selecting relevant features among the features available for the purpose of classification of Doppler signals was dealt with. Multilayer perceptron neural networks (MLPNNs) with different inputs (feature vectors) were used for diagnosis of ophthalmic and internal carotid arterial diseases. The assessment of feature extraction methods was performed by taking into consideration of performances of the MLPNNs. The performances of the MLPNNs were evaluated by the convergence rates (number of training epochs) and the total classification accuracies. Finally, some conclusions were drawn concerning the efficiency of discrete wavelet transform as a feature extraction method used for the diagnosis of ophthalmic and internal carotid arterial diseases. PMID:16278106

  2. Hearing aid malfunction detection system

    NASA Technical Reports Server (NTRS)

    Kessinger, R. L. (Inventor)

    1977-01-01

    A malfunction detection system for detecting malfunctions in electrical signal processing circuits is disclosed. Malfunctions of a hearing aid in the form of frequency distortion and/or inadequate amplification by the hearing aid amplifier, as well as weakening of the hearing aid power supply are detectable. A test signal is generated and a timed switching circuit periodically applies the test signal to the input of the hearing aid amplifier in place of the input signal from the microphone. The resulting amplifier output is compared with the input test signal used as a reference signal. The hearing aid battery voltage is also periodically compared to a reference voltage. Deviations from the references beyond preset limits cause a warning system to operate.

  3. Robust Ground Target Detection by SAR and IR Sensor Fusion Using Adaboost-Based Feature Selection.

    PubMed

    Kim, Sungho; Song, Woo-Jin; Kim, So-Hyun

    2016-01-01

    Long-range ground targets are difficult to detect in a noisy cluttered environment using either synthetic aperture radar (SAR) images or infrared (IR) images. SAR-based detectors can provide a high detection rate with a high false alarm rate to background scatter noise. IR-based approaches can detect hot targets but are affected strongly by the weather conditions. This paper proposes a novel target detection method by decision-level SAR and IR fusion using an Adaboost-based machine learning scheme to achieve a high detection rate and low false alarm rate. The proposed method consists of individual detection, registration, and fusion architecture. This paper presents a single framework of a SAR and IR target detection method using modified Boolean map visual theory (modBMVT) and feature-selection based fusion. Previous methods applied different algorithms to detect SAR and IR targets because of the different physical image characteristics. One method that is optimized for IR target detection produces unsuccessful results in SAR target detection. This study examined the image characteristics and proposed a unified SAR and IR target detection method by inserting a median local average filter (MLAF, pre-filter) and an asymmetric morphological closing filter (AMCF, post-filter) into the BMVT. The original BMVT was optimized to detect small infrared targets. The proposed modBMVT can remove the thermal and scatter noise by the MLAF and detect extended targets by attaching the AMCF after the BMVT. Heterogeneous SAR and IR images were registered automatically using the proposed RANdom SAmple Region Consensus (RANSARC)-based homography optimization after a brute-force correspondence search using the detected target centers and regions. The final targets were detected by feature-selection based sensor fusion using Adaboost. The proposed method showed good SAR and IR target detection performance through feature selection-based decision fusion on a synthetic database generated

  4. Robust Ground Target Detection by SAR and IR Sensor Fusion Using Adaboost-Based Feature Selection

    PubMed Central

    Kim, Sungho; Song, Woo-Jin; Kim, So-Hyun

    2016-01-01

    Long-range ground targets are difficult to detect in a noisy cluttered environment using either synthetic aperture radar (SAR) images or infrared (IR) images. SAR-based detectors can provide a high detection rate with a high false alarm rate to background scatter noise. IR-based approaches can detect hot targets but are affected strongly by the weather conditions. This paper proposes a novel target detection method by decision-level SAR and IR fusion using an Adaboost-based machine learning scheme to achieve a high detection rate and low false alarm rate. The proposed method consists of individual detection, registration, and fusion architecture. This paper presents a single framework of a SAR and IR target detection method using modified Boolean map visual theory (modBMVT) and feature-selection based fusion. Previous methods applied different algorithms to detect SAR and IR targets because of the different physical image characteristics. One method that is optimized for IR target detection produces unsuccessful results in SAR target detection. This study examined the image characteristics and proposed a unified SAR and IR target detection method by inserting a median local average filter (MLAF, pre-filter) and an asymmetric morphological closing filter (AMCF, post-filter) into the BMVT. The original BMVT was optimized to detect small infrared targets. The proposed modBMVT can remove the thermal and scatter noise by the MLAF and detect extended targets by attaching the AMCF after the BMVT. Heterogeneous SAR and IR images were registered automatically using the proposed RANdom SAmple Region Consensus (RANSARC)-based homography optimization after a brute-force correspondence search using the detected target centers and regions. The final targets were detected by feature-selection based sensor fusion using Adaboost. The proposed method showed good SAR and IR target detection performance through feature selection-based decision fusion on a synthetic database generated

  5. LC-IMS-MS Feature Finder. Detecting Multidimensional Liquid Chromatography, Ion Mobility, and Mass Spectrometry Features in Complex Datasets

    SciTech Connect

    Crowell, Kevin L.; Slysz, Gordon W.; Baker, Erin Shammel; Lamarche, Brian L.; Monroe, Matthew E.; Ibrahim, Yehia M.; Payne, Samuel H.; Anderson, Gordon A.; Smith, Richard D.

    2013-09-05

    We introduce a command line software application LC-IMS-MS Feature Finder that searches for molecular ion signatures in multidimensional liquid chromatography-ion mobility spectrometry-mass spectrometry (LC-IMS-MS) data by clustering deisotoped peaks with similar monoisotopic mass, charge state, LC elution time, and ion mobility drift time values. The software application includes an algorithm for detecting and quantifying co-eluting chemical species, including species that exist in multiple conformations that may have been separated in the IMS dimension.

  6. Interactive volume exploration for feature detection and quantification in industrial CT data.

    PubMed

    Hadwiger, Markus; Laura, Fritz; Rezk-Salama, Christof; Höllt, Thomas; Geier, Georg; Pabel, Thomas

    2008-01-01

    This paper presents a novel method for interactive exploration of industrial CT volumes such as cast metal parts, with the goal of interactively detecting, classifying, and quantifying features using a visualization-driven approach. The standard approach for defect detection builds on region growing, which requires manually tuning parameters such as target ranges for density and size, variance, as well as the specification of seed points. If the results are not satisfactory, region growing must be performed again with different parameters. In contrast, our method allows interactive exploration of the parameter space, completely separated from region growing in an unattended pre-processing stage. The pre-computed feature volume tracks a feature size curve for each voxel over time, which is identified with the main region growing parameter such as variance. A novel 3D transfer function domain over (density, feature size, time) allows for interactive exploration of feature classes. Features and feature size curves can also be explored individually, which helps with transfer function specification and allows coloring individual features and disabling features resulting from CT artifacts. Based on the classification obtained through exploration, the classified features can be quantified immediately. PMID:18989003

  7. Semi autonomous mine detection system

    SciTech Connect

    Douglas Few; Roelof Versteeg; Herman Herman

    2010-04-01

    CMMAD is a risk reduction effort for the AMDS program. As part of CMMAD, multiple instances of semi autonomous robotic mine detection systems were created. Each instance consists of a robotic vehicle equipped with sensors required for navigation and marking, a countermine sensors and a number of integrated software packages which provide for real time processing of the countermine sensor data as well as integrated control of the robotic vehicle, the sensor actuator and the sensor. These systems were used to investigate critical interest functions (CIF) related to countermine robotic systems. To address the autonomy CIF, the INL developed RIK was extended to allow for interaction with a mine sensor processing code (MSPC). In limited field testing this system performed well in detecting, marking and avoiding both AT and AP mines. Based on the results of the CMMAD investigation we conclude that autonomous robotic mine detection is feasible. In addition, CMMAD contributed critical technical advances with regard to sensing, data processing and sensor manipulation, which will advance the performance of future fieldable systems. As a result, no substantial technical barriers exist which preclude – from an autonomous robotic perspective – the rapid development and deployment of fieldable systems.

  8. Portable Microleak-Detection System

    NASA Technical Reports Server (NTRS)

    Rivers, H. Kevin; Sikora, Joseph G.; Sankaran, Sankara N.

    2007-01-01

    The figure schematically depicts a portable microleak-detection system that has been built especially for use in testing hydrogen tanks made of polymer-matrix composite materials. (As used here, microleak signifies a leak that is too small to be detectable by the simple soap-bubble technique.) The system can also be used to test for microleaks in tanks that are made of other materials and that contain gases other than hydrogen. Results of calibration tests have shown that measurement errors are less than 10 percent for leak rates ranging from 0.3 to 200 cm3/min. Like some other microleak-detection systems, this system includes a vacuum pump and associated plumbing for sampling the leaking gas, and a mass spectrometer for analyzing the molecular constituents of the gas. The system includes a flexible vacuum chamber that can be attached to the outer surface of a tank or other object of interest that is to be tested for leakage (hereafter denoted, simply, the test object). The gas used in a test can be the gas or vapor (e.g., hydrogen in the original application) to be contained by the test object. Alternatively, following common practice in leak testing, helium can be used as a test gas. In either case, the mass spectrometer can be used to verify that the gas measured by the system is the test gas rather than a different gas and, hence, that the leak is indeed from the test object.

  9. NASA scatterometer data processing system: Features for validation

    NASA Technical Reports Server (NTRS)

    Callahan, Philip S.; Benada, J. R.

    1986-01-01

    The design of the N-ROSS scatterometer data system and the development of key processing algorithms are described. The data products and parts of the data system to be directly validated are listed. The main features of the Data Management Subsystem, which delivers data to science users and supports system validation are outlined.

  10. Features and Historical Aspects of the Philippines Educational System

    ERIC Educational Resources Information Center

    Musa, Sajid; Ziatdinov, Rushan

    2012-01-01

    This article deals with the features of the Philippine educational system. Additionally, brief and concise information will be given on how the educational system came into existence, the organization and the structure of the system itself. This paper also tackles the obstacles and problems observed in the past and up to the present, and gives…

  11. Real-Time Sensor Validation, Signal Reconstruction, and Feature Detection for an RLV Propulsion Testbed

    NASA Technical Reports Server (NTRS)

    Jankovsky, Amy L.; Fulton, Christopher E.; Binder, Michael P.; Maul, William A., III; Meyer, Claudia M.

    1998-01-01

    A real-time system for validating sensor health has been developed in support of the reusable launch vehicle program. This system was designed for use in a propulsion testbed as part of an overall effort to improve the safety, diagnostic capability, and cost of operation of the testbed. The sensor validation system was designed and developed at the NASA Lewis Research Center and integrated into a propulsion checkout and control system as part of an industry-NASA partnership, led by Rockwell International for the Marshall Space Flight Center. The system includes modules for sensor validation, signal reconstruction, and feature detection and was designed to maximize portability to other applications. Review of test data from initial integration testing verified real-time operation and showed the system to perform correctly on both hard and soft sensor failure test cases. This paper discusses the design of the sensor validation and supporting modules developed at LeRC and reviews results obtained from initial test cases.

  12. Automated cervical precancerous cells screening system based on Fourier transform infrared spectroscopy features

    NASA Astrophysics Data System (ADS)

    Jusman, Yessi; Mat Isa, Nor Ashidi; Ng, Siew-Cheok; Hasikin, Khairunnisa; Abu Osman, Noor Azuan

    2016-07-01

    Fourier transform infrared (FTIR) spectroscopy technique can detect the abnormality of a cervical cell that occurs before the morphological change could be observed under the light microscope as employed in conventional techniques. This paper presents developed features extraction for an automated screening system for cervical precancerous cell based on the FTIR spectroscopy as a second opinion to pathologists. The automated system generally consists of the developed features extraction and classification stages. Signal processing techniques are used in the features extraction stage. Then, discriminant analysis and principal component analysis are employed to select dominant features for the classification process. The datasets of the cervical precancerous cells obtained from the feature selection process are classified using a hybrid multilayered perceptron network. The proposed system achieved 92% accuracy.

  13. Detection of Emotional Faces: Salient Physical Features Guide Effective Visual Search

    ERIC Educational Resources Information Center

    Calvo, Manuel G.; Nummenmaa, Lauri

    2008-01-01

    In this study, the authors investigated how salient visual features capture attention and facilitate detection of emotional facial expressions. In a visual search task, a target emotional face (happy, disgusted, fearful, angry, sad, or surprised) was presented in an array of neutral faces. Faster detection of happy and, to a lesser extent,…

  14. Face detection and feature points location and tracking in video sequence

    NASA Astrophysics Data System (ADS)

    Zhang, Xiaowe; Zhang, Wenjun

    2013-07-01

    In the basic study and analysis of the human face detection and feature point location and tracking algorithm in video sequence, this paper proposes a method that first to determine the like-face area in the video frame with local SMQT characteristics; then positioning the detected human face feature point with the modified ASM, which is improved by changing the 1D texture model which is easier to fall into minimum to 2D texture model; finally, grouping feature points based on their characteristics, tracking them by using optical flow method, elastic graph matching, and binary respectively. This method was tested to show good positioning of facial features based on fast detection, and gain well tracking results.

  15. A Hybrid Swarm Intelligence Algorithm for Intrusion Detection Using Significant Features

    PubMed Central

    Amudha, P.; Karthik, S.; Sivakumari, S.

    2015-01-01

    Intrusion detection has become a main part of network security due to the huge number of attacks which affects the computers. This is due to the extensive growth of internet connectivity and accessibility to information systems worldwide. To deal with this problem, in this paper a hybrid algorithm is proposed to integrate Modified Artificial Bee Colony (MABC) with Enhanced Particle Swarm Optimization (EPSO) to predict the intrusion detection problem. The algorithms are combined together to find out better optimization results and the classification accuracies are obtained by 10-fold cross-validation method. The purpose of this paper is to select the most relevant features that can represent the pattern of the network traffic and test its effect on the success of the proposed hybrid classification algorithm. To investigate the performance of the proposed method, intrusion detection KDDCup'99 benchmark dataset from the UCI Machine Learning repository is used. The performance of the proposed method is compared with the other machine learning algorithms and found to be significantly different. PMID:26221625

  16. A Hybrid Swarm Intelligence Algorithm for Intrusion Detection Using Significant Features.

    PubMed

    Amudha, P; Karthik, S; Sivakumari, S

    2015-01-01

    Intrusion detection has become a main part of network security due to the huge number of attacks which affects the computers. This is due to the extensive growth of internet connectivity and accessibility to information systems worldwide. To deal with this problem, in this paper a hybrid algorithm is proposed to integrate Modified Artificial Bee Colony (MABC) with Enhanced Particle Swarm Optimization (EPSO) to predict the intrusion detection problem. The algorithms are combined together to find out better optimization results and the classification accuracies are obtained by 10-fold cross-validation method. The purpose of this paper is to select the most relevant features that can represent the pattern of the network traffic and test its effect on the success of the proposed hybrid classification algorithm. To investigate the performance of the proposed method, intrusion detection KDDCup'99 benchmark dataset from the UCI Machine Learning repository is used. The performance of the proposed method is compared with the other machine learning algorithms and found to be significantly different. PMID:26221625

  17. Enhancement of the Feature Extraction Capability in Global Damage Detection Using Wavelet Theory

    NASA Technical Reports Server (NTRS)

    Saleeb, Atef F.; Ponnaluru, Gopi Krishna

    2006-01-01

    The main objective of this study is to assess the specific capabilities of the defect energy parameter technique for global damage detection developed by Saleeb and coworkers. The feature extraction is the most important capability in any damage-detection technique. Features are any parameters extracted from the processed measurement data in order to enhance damage detection. The damage feature extraction capability was studied extensively by analyzing various simulation results. The practical significance in structural health monitoring is that the detection at early stages of small-size defects is always desirable. The amount of changes in the structure's response due to these small defects was determined to show the needed level of accuracy in the experimental methods. The arrangement of fine/extensive sensor network to measure required data for the detection is an "unlimited" ability, but there is a difficulty to place extensive number of sensors on a structure. Therefore, an investigation was conducted using the measurements of coarse sensor network. The white and the pink noises, which cover most of the frequency ranges that are typically encountered in the many measuring devices used (e.g., accelerometers, strain gauges, etc.) are added to the displacements to investigate the effect of noisy measurements in the detection technique. The noisy displacements and the noisy damage parameter values are used to study the signal feature reconstruction using wavelets. The enhancement of the feature extraction capability was successfully achieved by the wavelet theory.

  18. Drogue detection for vision-based autonomous aerial refueling via low rank and sparse decomposition with multiple features

    NASA Astrophysics Data System (ADS)

    Gao, Shibo; Cheng, Yongmei; Song, Chunhua

    2013-09-01

    The technology of vision-based probe-and-drogue autonomous aerial refueling is an amazing task in modern aviation for both manned and unmanned aircraft. A key issue is to determine the relative orientation and position of the drogue and the probe accurately for relative navigation system during the approach phase, which requires locating the drogue precisely. Drogue detection is a challenging task due to disorderly motion of drogue caused by both the tanker wake vortex and atmospheric turbulence. In this paper, the problem of drogue detection is considered as a problem of moving object detection. A drogue detection algorithm based on low rank and sparse decomposition with local multiple features is proposed. The global and local information of drogue is introduced into the detection model in a unified way. The experimental results on real autonomous aerial refueling videos show that the proposed drogue detection algorithm is effective.

  19. The relationship study between image features and detection probability based on psychology experiments

    NASA Astrophysics Data System (ADS)

    Lin, Wei; Chen, Yu-hua; Wang, Ji-yuan; Gao, Hong-sheng; Wang, Ji-jun; Su, Rong-hua; Mao, Wei

    2011-04-01

    Detection probability is an important index to represent and estimate target viability, which provides basis for target recognition and decision-making. But it will expend a mass of time and manpower to obtain detection probability in reality. At the same time, due to the different interpretation of personnel practice knowledge and experience, a great difference will often exist in the datum obtained. By means of studying the relationship between image features and perception quantity based on psychology experiments, the probability model has been established, in which the process is as following.Firstly, four image features have been extracted and quantified, which affect directly detection. Four feature similarity degrees between target and background were defined. Secondly, the relationship between single image feature similarity degree and perception quantity was set up based on psychological principle, and psychological experiments of target interpretation were designed which includes about five hundred people for interpretation and two hundred images. In order to reduce image features correlativity, a lot of artificial synthesis images have been made which include images with single brightness feature difference, images with single chromaticity feature difference, images with single texture feature difference and images with single shape feature difference. By analyzing and fitting a mass of experiments datum, the model quantitys have been determined. Finally, by applying statistical decision theory and experimental results, the relationship between perception quantity with target detection probability has been found. With the verification of a great deal of target interpretation in practice, the target detection probability can be obtained by the model quickly and objectively.

  20. Spectral feature variations in x-ray diffraction imaging systems

    NASA Astrophysics Data System (ADS)

    Wolter, Scott D.; Greenberg, Joel A.

    2016-05-01

    Materials with different atomic or molecular structures give rise to unique scatter spectra when measured by X-ray diffraction. The details of these spectra, though, can vary based on both intrinsic (e.g., degree of crystallinity or doping) and extrinsic (e.g., pressure or temperature) conditions. While this sensitivity is useful for detailed characterizations of the material properties, these dependences make it difficult to perform more general classification tasks, such as explosives threat detection in aviation security. A number of challenges, therefore, currently exist for reliable substance detection including the similarity in spectral features among some categories of materials combined with spectral feature variations from materials processing and environmental factors. These factors complicate the creation of a material dictionary and the implementation of conventional classification and detection algorithms. Herein, we report on two prominent factors that lead to variations in spectral features: crystalline texture and temperature variations. Spectral feature comparisons between materials categories will be described for solid metallic sheet, aqueous liquids, polymer sheet, and metallic, organic, and inorganic powder specimens. While liquids are largely immune to texture effects, they are susceptible to temperature changes that can modify their density or produce phase changes. We will describe in situ temperature-dependent measurement of aqueous-based commercial goods in the temperature range of -20°C to 35°C.

  1. Vehicle detection from high-resolution aerial images based on superpixel and color name features

    NASA Astrophysics Data System (ADS)

    Chen, Ziyi; Cao, Liujuan; Yu, Zang; Chen, Yiping; Wang, Cheng; Li, Jonathan

    2016-03-01

    Automatic vehicle detection from aerial images is emerging due to the strong demand of large-area traffic monitoring. In this paper, we present a novel framework for automatic vehicle detection from the aerial images. Through superpixel segmentation, we first segment the aerial images into homogeneous patches, which consist of the basic units during the detection to improve efficiency. By introducing the sparse representation into our method, powerful classification ability is achieved after the dictionary training. To effectively describe a patch, the Histogram of Oriented Gradient (HOG) is used. We further propose to integrate color information to enrich the feature representation by using the color name feature. The final feature consists of both HOG and color name based histogram, by which we get a strong descriptor of a patch. Experimental results demonstrate the effectiveness and robust performance of the proposed algorithm for vehicle detection from aerial images.

  2. Evaluation of various feature extraction methods for landmine detection using hidden Markov models

    NASA Astrophysics Data System (ADS)

    Hamdi, Anis; Frigui, Hichem

    2012-06-01

    Hidden Markov Models (HMM) have proved to be eective for detecting buried land mines using data collected by a moving-vehicle-mounted ground penetrating radar (GPR). The general framework for a HMM-based landmine detector consists of building a HMM model for mine signatures and a HMM model for clutter signatures. A test alarm is assigned a condence proportional to the probability of that alarm being generated by the mine model and inversely proportional to its probability in the clutter model. The HMM models are built based on features extracted from GPR training signatures. These features are expected to capture the salient properties of the 3-dimensional alarms in a compact representation. The baseline HMM framework for landmine detection is based on gradient features. It models the time varying behavior of GPR signals, encoded using edge direction information, to compute the likelihood that a sequence of measurements is consistent with a buried landmine. In particular, the HMM mine models learns the hyperbolic shape associated with the signature of a buried mine by three states that correspond to the succession of an increasing edge, a at edge, and a decreasing edge. Recently, for the same application, other features have been used with dierent classiers. In particular, the Edge Histogram Descriptor (EHD) has been used within a K-nearest neighbor classier. Another descriptor is based on Gabor features and has been used within a discrete HMM classier. A third feature, that is closely related to the EHD, is the Bar histogram feature. This feature has been used within a Neural Networks classier for handwritten word recognition. In this paper, we propose an evaluation of the HMM based landmine detection framework with several feature extraction techniques. We adapt and evaluate the EHD, Gabor, Bar, and baseline gradient feature extraction methods. We compare the performance of these features using a large and diverse GPR data collection.

  3. Classification method, spectral diversity, band combination and accuracy assessment evaluation for urban feature detection

    NASA Astrophysics Data System (ADS)

    Erener, A.

    2013-04-01

    Automatic extraction of urban features from high resolution satellite images is one of the main applications in remote sensing. It is useful for wide scale applications, namely: urban planning, urban mapping, disaster management, GIS (geographic information systems) updating, and military target detection. One common approach to detecting urban features from high resolution images is to use automatic classification methods. This paper has four main objectives with respect to detecting buildings. The first objective is to compare the performance of the most notable supervised classification algorithms, including the maximum likelihood classifier (MLC) and the support vector machine (SVM). In this experiment the primary consideration is the impact of kernel configuration on the performance of the SVM. The second objective of the study is to explore the suitability of integrating additional bands, namely first principal component (1st PC) and the intensity image, for original data for multi classification approaches. The performance evaluation of classification results is done using two different accuracy assessment methods: pixel based and object based approaches, which reflect the third aim of the study. The objective here is to demonstrate the differences in the evaluation of accuracies of classification methods. Considering consistency, the same set of ground truth data which is produced by labeling the building boundaries in the GIS environment is used for accuracy assessment. Lastly, the fourth aim is to experimentally evaluate variation in the accuracy of classifiers for six different real situations in order to identify the impact of spatial and spectral diversity on results. The method is applied to Quickbird images for various urban complexity levels, extending from simple to complex urban patterns. The simple surface type includes a regular urban area with low density and systematic buildings with brick rooftops. The complex surface type involves almost all

  4. Fiber optic hydrogen detection system

    NASA Astrophysics Data System (ADS)

    Kazemi, Alex A.; Larson, David B.; Wuestling, Mark D.

    1999-12-01

    Commercial and military launch vehicles are designed to use hydrogen as the main propellant, which is very volatile, extremely flammable, and highly explosive. Current detection systems uses Teflon transfer tubes at a large number of vehicle locations through which gas samples are drawn and the stream analyzed by a mass spectrometer. A concern with this approach is the high cost of the system. Also, the current system does not provide leak location and is not in real-time. This system is very complex and cumbersome for production and ground support measurement personnel. The fiber optic micromirror sensor under development for cryogenic environment relies on a reversible chemical interaction causing a change in reflectivity of a thin film of coated Palladium. The magnitude of the reflectivity change is correlated to hydrogen concentration. The sensor uses only a tiny light beam, with no electricity whatsoever at the sensor, leading to devices that is intrinsically safe from explosive ignition. The sensor, extremely small in size and weight detects, hydrogen concentration using a passive element consisting of chemically reactive microcoatings deposited on the surface of a glass microlens, which is then bonded to an optical fiber. The system uses a multiplexing technique with a fiber optic driver-receiver consisting of a modulated LED source that is launched into the sensor, and a photodiode detector that synchronously measures the reflected signal. The system incorporates a microprocessor (or PC) to perform the data analysis and storage, as well as trending and set alarm function. As it is a low cost system with a fast response, many more detection sensors can be used that will be extremely helpful in determining leak location for safety of crew and vehicles during launch operations.

  5. Human Detection Using Random Color Similarity Feature and Random Ferns Classifier.

    PubMed

    Zhang, Miaohui; Xin, Ming

    2016-01-01

    We explore a novel approach for human detection based on random color similarity feature (RCS) and random ferns classifier which is also known as semi-naive Bayesian classifier. In contrast to other existing features employed by human detection, color-based features are rarely used in vision-based human detection because of large intra-class variations. In this paper, we propose a novel color-based feature, RCS feature, which is yielded by simple color similarity computation between image cells randomly picked in still images, and can effectively characterize human appearances. In addition, a histogram of oriented gradient based local binary feature (HOG-LBF) is also introduced to enrich the human descriptor set. Furthermore, random ferns classifier is used in the proposed approach because of its faster speed in training and testing than traditional classifiers such as Support Vector Machine (SVM) classifier, without a loss in performance. Finally, the proposed method is conducted in public datasets and achieves competitive detection results. PMID:27611217

  6. Application of feature descriptors to low-pixel-count persistent surveillance tracking systems

    NASA Astrophysics Data System (ADS)

    Edelberg, Jason; Miller, Christopher; Wilson, Michael

    2013-05-01

    The largest challenge with all persistent surveillance systems is they require a trade between area coverage and ground object resolution. This trade typically results in provision of imagery where objects desired to be tracked have a small total number of pixels (often less than a few hundred total). With such low pixel counts, traditional target recognition methods become difficult. For this reason, most persistent surveillance tracking systems are based on detection and tracking of image changes. These change-detection tracking systems, however, struggle to maintain tracks through quick maneuvers, stops, obscurations, and dense traffic. Feature descriptors, including template matching, histogram of oriented gradients (HOG), and local binary patterns (LBP) are evaluated for use in the special case of very low pixel count target detection and track maintenance. These dynamic feature-based detection models are incorporated into a change-detection based tracking system. The resulting composite tracking system will be described as applied to EO and MWIR wide area data collected under a variety of conditions. Resulting tracking system improvements and tradeoffs between feature descriptors are presented.

  7. Landmine detection with Bayesian cross-categorization on point-wise, contextual and spatial features

    NASA Astrophysics Data System (ADS)

    Léveillé, Jasmin; Yu, Ssu-Hsin; Gandhe, Avinash

    2016-05-01

    Recently developed feature extraction methods proposed in the explosive hazard detection community have yielded many features that potentially provide complementary information for explosive detection. Finding the right combination of features that is most effective in distinguishing targets from clutter, on the other hand, is extremely challenging due to a large number of potential features to explore. Furthermore, sensors employed for mine and buried explosive hazard detection are typically sensitive to environmental conditions such as soil properties and weather as well as other operating parameters. In this work, we applied Bayesian cross-categorization (CrossCat) to a heterogeneous set of features derived from electromagnetic induction (EMI) sensor time-series for purposes of buried explosive hazard detection. The set of features used here includes simple, point-wise measurements such as the overall magnitude of the EMI response, contextual information such as soil type, and a new feature consisting of spatially aggregated Discrete Spectra of Relaxation Frequencies (DSRFs). Previous work showed that the DSRF characterizes target properties with some invariance to orientation and position. We have developed a novel approach to aggregate point-wise DSRF estimates. The spatial aggregation is based on the Bag-of-Words (BoW) model found in the machine learning and computer vision literatures and aims to enhance the invariance properties of point-wise DSRF estimates. We considered various refinements to the BoW model for purpose of buried explosive hazard detection and tested their usefulness as part of a Bayesian cross-categorization framework on data collected from two different sites. The results show improved performance over classifiers using only point-wise features.

  8. Feature selection and classifier parameters estimation for EEG signals peak detection using particle swarm optimization.

    PubMed

    Adam, Asrul; Shapiai, Mohd Ibrahim; Tumari, Mohd Zaidi Mohd; Mohamad, Mohd Saberi; Mubin, Marizan

    2014-01-01

    Electroencephalogram (EEG) signal peak detection is widely used in clinical applications. The peak point can be detected using several approaches, including time, frequency, time-frequency, and nonlinear domains depending on various peak features from several models. However, there is no study that provides the importance of every peak feature in contributing to a good and generalized model. In this study, feature selection and classifier parameters estimation based on particle swarm optimization (PSO) are proposed as a framework for peak detection on EEG signals in time domain analysis. Two versions of PSO are used in the study: (1) standard PSO and (2) random asynchronous particle swarm optimization (RA-PSO). The proposed framework tries to find the best combination of all the available features that offers good peak detection and a high classification rate from the results in the conducted experiments. The evaluation results indicate that the accuracy of the peak detection can be improved up to 99.90% and 98.59% for training and testing, respectively, as compared to the framework without feature selection adaptation. Additionally, the proposed framework based on RA-PSO offers a better and reliable classification rate as compared to standard PSO as it produces low variance model. PMID:25243236

  9. Feature Selection and Classifier Parameters Estimation for EEG Signals Peak Detection Using Particle Swarm Optimization

    PubMed Central

    Adam, Asrul; Mohd Tumari, Mohd Zaidi; Mohamad, Mohd Saberi

    2014-01-01

    Electroencephalogram (EEG) signal peak detection is widely used in clinical applications. The peak point can be detected using several approaches, including time, frequency, time-frequency, and nonlinear domains depending on various peak features from several models. However, there is no study that provides the importance of every peak feature in contributing to a good and generalized model. In this study, feature selection and classifier parameters estimation based on particle swarm optimization (PSO) are proposed as a framework for peak detection on EEG signals in time domain analysis. Two versions of PSO are used in the study: (1) standard PSO and (2) random asynchronous particle swarm optimization (RA-PSO). The proposed framework tries to find the best combination of all the available features that offers good peak detection and a high classification rate from the results in the conducted experiments. The evaluation results indicate that the accuracy of the peak detection can be improved up to 99.90% and 98.59% for training and testing, respectively, as compared to the framework without feature selection adaptation. Additionally, the proposed framework based on RA-PSO offers a better and reliable classification rate as compared to standard PSO as it produces low variance model. PMID:25243236

  10. A Feature Analysis of Interactive Retrieval Systems. Final Report.

    ERIC Educational Resources Information Center

    Martin, Thomas H.

    The command language features of 11 on-line information retrieval systems are presented in terms of the functional needs of a searcher sitting at a terminal. Functional areas considered are: becoming familiar with the system, receiving help when in trouble, regulating usage, selecting a data base, formulating simple queries, expressing single…

  11. Detection of Sharp Symmetric Features in the Circumbinary Disk around AK Sco

    NASA Astrophysics Data System (ADS)

    Janson, Markus; Thalmann, Christian; Boccaletti, Anthony; Maire, Anne-Lise; Zurlo, Alice; Marzari, Francesco; Meyer, Michael R.; Carson, Joseph C.; Augereau, Jean-Charles; Garufi, Antonio; Henning, Thomas; Desidera, Silvano; Asensio-Torres, Ruben; Pohl, Adriana

    2016-01-01

    The Search for Planets Orbiting Two Stars survey aims to study the formation and distribution of planets in binary systems by detecting and characterizing circumbinary planets and their formation environments through direct imaging. With the SPHERE Extreme Adaptive Optics instrument, a good contrast can be achieved even at small (<300 mas) separations from bright stars, which enables studies of planets and disks in a separation range that was previously inaccessible. Here, we report the discovery of resolved scattered light emission from the circumbinary disk around the well-studied young double star AK Sco, at projected separations in the ∼13–40 AU range. The sharp morphology of the imaged feature is surprising, given the smooth appearance of the disk in its spectral energy distribution. We show that the observed morphology can be represented either as a highly eccentric ring around AK Sco, or as two separate spiral arms in the disk, wound in opposite directions. The relative merits of these interpretations are discussed, as well as whether these features may have been caused by one or several circumbinary planets interacting with the disk. Based on observations collected at the European Southern Observatory, Chile, under observing program 095.C-0346(B).

  12. The Autonomous Pathogen Detection System

    SciTech Connect

    Dzenitis, J M; Makarewicz, A J

    2009-01-13

    We developed, tested, and now operate a civilian biological defense capability that continuously monitors the air for biological threat agents. The Autonomous Pathogen Detection System (APDS) collects, prepares, reads, analyzes, and reports results of multiplexed immunoassays and multiplexed PCR assays using Luminex{copyright} xMAP technology and flow cytometer. The mission we conduct is particularly demanding: continuous monitoring, multiple threat agents, high sensitivity, challenging environments, and ultimately extremely low false positive rates. Here, we introduce the mission requirements and metrics, show the system engineering and analysis framework, and describe the progress to date including early development and current status.

  13. Detection and Classification of Cancer from Microscopic Biopsy Images Using Clinically Significant and Biologically Interpretable Features

    PubMed Central

    Kumar, Rajesh; Srivastava, Subodh

    2015-01-01

    A framework for automated detection and classification of cancer from microscopic biopsy images using clinically significant and biologically interpretable features is proposed and examined. The various stages involved in the proposed methodology include enhancement of microscopic images, segmentation of background cells, features extraction, and finally the classification. An appropriate and efficient method is employed in each of the design steps of the proposed framework after making a comparative analysis of commonly used method in each category. For highlighting the details of the tissue and structures, the contrast limited adaptive histogram equalization approach is used. For the segmentation of background cells, k-means segmentation algorithm is used because it performs better in comparison to other commonly used segmentation methods. In feature extraction phase, it is proposed to extract various biologically interpretable and clinically significant shapes as well as morphology based features from the segmented images. These include gray level texture features, color based features, color gray level texture features, Law's Texture Energy based features, Tamura's features, and wavelet features. Finally, the K-nearest neighborhood method is used for classification of images into normal and cancerous categories because it is performing better in comparison to other commonly used methods for this application. The performance of the proposed framework is evaluated using well-known parameters for four fundamental tissues (connective, epithelial, muscular, and nervous) of randomly selected 1000 microscopic biopsy images. PMID:27006938

  14. Efficient feature selection using a hybrid algorithm for the task of epileptic seizure detection

    NASA Astrophysics Data System (ADS)

    Lai, Kee Huong; Zainuddin, Zarita; Ong, Pauline

    2014-07-01

    Feature selection is a very important aspect in the field of machine learning. It entails the search of an optimal subset from a very large data set with high dimensional feature space. Apart from eliminating redundant features and reducing computational cost, a good selection of feature also leads to higher prediction and classification accuracy. In this paper, an efficient feature selection technique is introduced in the task of epileptic seizure detection. The raw data are electroencephalography (EEG) signals. Using discrete wavelet transform, the biomedical signals were decomposed into several sets of wavelet coefficients. To reduce the dimension of these wavelet coefficients, a feature selection method that combines the strength of both filter and wrapper methods is proposed. Principal component analysis (PCA) is used as part of the filter method. As for wrapper method, the evolutionary harmony search (HS) algorithm is employed. This metaheuristic method aims at finding the best discriminating set of features from the original data. The obtained features were then used as input for an automated classifier, namely wavelet neural networks (WNNs). The WNNs model was trained to perform a binary classification task, that is, to determine whether a given EEG signal was normal or epileptic. For comparison purposes, different sets of features were also used as input. Simulation results showed that the WNNs that used the features chosen by the hybrid algorithm achieved the highest overall classification accuracy.

  15. 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.

  16. Detectability of the infrared surface features of the wake behind a moving underwater body

    NASA Astrophysics Data System (ADS)

    Shi, Zhi-guang; Li, Ji-cheng

    2015-10-01

    In this paper, by using the theoretical analysis and computer simulation method, the lower boundary requirements of the infrared imaging sensor are analyzed when detecting the thermal surface features of the wake behind a moving underwater body. Firstly, the computer simulation model of the underwater body wake's surface temperature field and the corresponding surface infrared features are established. Secondly, the measures of the sensor's detection performance and the computation method of these measures are described. Thirdly, by using the infrared features' simulation model and the performance measures, we have done simulation tests to analyze the given infrared imaging sensor's detection ability for detecting underwater body wake, and the requirements of infrared sensor to detect the wake's infrared surface feature under some given working states are also investigated. Lastly, the simulation results and the conclusions of the paper are given. The problem-solving flow chart and the simulation results on underwater object wake's detectability given in this paper may be useful for the designment and performance evaluation of the infrared imaging sensors.

  17. GridMass: a fast two-dimensional feature detection method for LC/MS.

    PubMed

    Treviño, Victor; Yañez-Garza, Irma-Luz; Rodriguez-López, Carlos E; Urrea-López, Rafael; Garza-Rodriguez, Maria-Lourdes; Barrera-Saldaña, Hugo-Alberto; Tamez-Peña, José G; Winkler, Robert; Díaz de-la-Garza, Rocío-Isabel

    2015-01-01

    One of the initial and critical procedures for the analysis of metabolomics data using liquid chromatography and mass spectrometry is feature detection. Feature detection is the process to detect boundaries of the mass surface from raw data. It consists of detected abundances arranged in a two-dimensional (2D) matrix of mass/charge and elution time. MZmine 2 is one of the leading software environments that provide a full analysis pipeline for these data. However, the feature detection algorithms provided in MZmine 2 are based mainly on the analysis of one-dimension at a time. We propose GridMass, an efficient algorithm for 2D feature detection. The algorithm is based on landing probes across the chromatographic space that are moved to find local maxima providing accurate boundary estimations. We tested GridMass on a controlled marker experiment, on plasma samples, on plant fruits, and in a proteome sample. Compared with other algorithms, GridMass is faster and may achieve comparable or better sensitivity and specificity. As a proof of concept, GridMass has been implemented in Java under the MZmine 2 environment and is available at http://www.bioinformatica.mty.itesm.mx/GridMass and MASSyPup. It has also been submitted to the MZmine 2 developing community. PMID:25601689

  18. Detection of Harbours from High Resolution Remote Sensing Imagery via Saliency Analysis and Feature Learning

    NASA Astrophysics Data System (ADS)

    Wang, Yetianjian; Pan, Li; Wang, Dagang; Kang, Yifei

    2016-06-01

    Harbours are very important objects in civil and military fields. To detect them from high resolution remote sensing imagery is important in various fields and also a challenging task. Traditional methods of detecting harbours mainly focus on the segmentation of water and land and the manual selection of knowledge. They do not make enough use of other features of remote sensing imagery and often fail to describe the harbours completely. In order to improve the detection, a new method is proposed. First, the image is transformed to Hue, Saturation, Value (HSV) colour space and saliency analysis is processed via the generation and enhancement of the co-occurrence histogram to help detect and locate the regions of interest (ROIs) that is salient and may be parts of the harbour. Next, SIFT features are extracted and feature learning is processed to help represent the ROIs. Then, by using classified feature of the harbour, a classifier is trained and used to check the ROIs to find whether they belong to the harbour. Finally, if the ROIs belong to the harbour, a minimum bounding rectangle is formed to include all the harbour ROIs and detect and locate the harbour. The experiment on high resolution remote sensing imagery shows that the proposed method performs better than other methods in precision of classifying ROIs and accuracy of completely detecting and locating harbours.

  19. Detecting transition in agricultural systems

    NASA Technical Reports Server (NTRS)

    Neary, P. J.; Coiner, J. C.

    1979-01-01

    Remote sensing of agricultural phenomena has been largely concentrated on analysis of agriculture at the field level. Concern has been to identify crop status, crop condition, and crop distribution, all of which are spatially analyzed on a field-by-field basis. A more general level of abstraction is the agricultural system, or the complex of crops and other land cover that differentiate various agricultural economies. The paper reports on a methodology to assist in the analysis of the landscape elements of agricultural systems with Landsat digital data. The methodology involves tracing periods of photosynthetic activity for a fixed area. Change from one agricultural system to another is detected through shifts in the intensity and periodicity of photosynthetic activity as recorded in the radiometric return to Landsat. The Landsat-derived radiometric indicator of photosynthetic activity appears to provide the ability to differentiate agricultural systems from each other as well as from conterminous natural vegetation.

  20. Nucleic acid detection system and method for detecting influenza

    SciTech Connect

    Cai, Hong; Song, Jian

    2015-03-17

    The invention provides a rapid, sensitive and specific nucleic acid detection system which utilizes isothermal nucleic acid amplification in combination with a lateral flow chromatographic device, or DNA dipstick, for DNA-hybridization detection. The system of the invention requires no complex instrumentation or electronic hardware, and provides a low cost nucleic acid detection system suitable for highly sensitive pathogen detection. Hybridization to single-stranded DNA amplification products using the system of the invention provides a sensitive and specific means by which assays can be multiplexed for the detection of multiple target sequences.

  1. Detection of obstacles on runway using Ego-Motion compensation and tracking of significant features

    NASA Technical Reports Server (NTRS)

    Kasturi, Rangachar (Principal Investigator); Camps, Octavia (Principal Investigator); Gandhi, Tarak; Devadiga, Sadashiva

    1996-01-01

    This report describes a method for obstacle detection on a runway for autonomous navigation and landing of an aircraft. Detection is done in the presence of extraneous features such as tiremarks. Suitable features are extracted from the image and warping using approximately known camera and plane parameters is performed in order to compensate ego-motion as far as possible. Residual disparity after warping is estimated using an optical flow algorithm. Features are tracked from frame to frame so as to obtain more reliable estimates of their motion. Corrections are made to motion parameters with the residual disparities using a robust method, and features having large residual disparities are signaled as obstacles. Sensitivity analysis of the procedure is also studied. Nelson's optical flow constraint is proposed to separate moving obstacles from stationary ones. A Bayesian framework is used at every stage so that the confidence in the estimates can be determined.

  2. A General Purpose Feature Extractor for Light Detection and Ranging Data

    PubMed Central

    Li, Yangming; Olson, Edwin B.

    2010-01-01

    Feature extraction is a central step of processing Light Detection and Ranging (LIDAR) data. Existing detectors tend to exploit characteristics of specific environments: corners and lines from indoor (rectilinear) environments, and trees from outdoor environments. While these detectors work well in their intended environments, their performance in different environments can be poor. We describe a general purpose feature detector for both 2D and 3D LIDAR data that is applicable to virtually any environment. Our method adapts classic feature detection methods from the image processing literature, specifically the multi-scale Kanade-Tomasi corner detector. The resulting method is capable of identifying highly stable and repeatable features at a variety of spatial scales without knowledge of environment, and produces principled uncertainty estimates and corner descriptors at same time. We present results on both software simulation and standard datasets, including the 2D Victoria Park and Intel Research Center datasets, and the 3D MIT DARPA Urban Challenge dataset. PMID:22163474

  3. A general purpose feature extractor for light detection and ranging data.

    PubMed

    Li, Yangming; Olson, Edwin B

    2010-01-01

    Feature extraction is a central step of processing Light Detection and Ranging (LIDAR) data. Existing detectors tend to exploit characteristics of specific environments: corners and lines from indoor (rectilinear) environments, and trees from outdoor environments. While these detectors work well in their intended environments, their performance in different environments can be poor. We describe a general purpose feature detector for both 2D and 3D LIDAR data that is applicable to virtually any environment. Our method adapts classic feature detection methods from the image processing literature, specifically the multi-scale Kanade-Tomasi corner detector. The resulting method is capable of identifying highly stable and repeatable features at a variety of spatial scales without knowledge of environment, and produces principled uncertainty estimates and corner descriptors at same time. We present results on both software simulation and standard datasets, including the 2D Victoria Park and Intel Research Center datasets, and the 3D MIT DARPA Urban Challenge dataset. PMID:22163474

  4. A new and fast image feature selection method for developing an optimal mammographic mass detection scheme

    PubMed Central

    Tan, Maxine; Pu, Jiantao; Zheng, Bin

    2014-01-01

    Purpose: Selecting optimal features from a large image feature pool remains a major challenge in developing computer-aided detection (CAD) schemes of medical images. The objective of this study is to investigate a new approach to significantly improve efficacy of image feature selection and classifier optimization in developing a CAD scheme of mammographic masses. Methods: An image dataset including 1600 regions of interest (ROIs) in which 800 are positive (depicting malignant masses) and 800 are negative (depicting CAD-generated false positive regions) was used in this study. After segmentation of each suspicious lesion by a multilayer topographic region growth algorithm, 271 features were computed in different feature categories including shape, texture, contrast, isodensity, spiculation, local topological features, as well as the features related to the presence and location of fat and calcifications. Besides computing features from the original images, the authors also computed new texture features from the dilated lesion segments. In order to select optimal features from this initial feature pool and build a highly performing classifier, the authors examined and compared four feature selection methods to optimize an artificial neural network (ANN) based classifier, namely: (1) Phased Searching with NEAT in a Time-Scaled Framework, (2) A sequential floating forward selection (SFFS) method, (3) A genetic algorithm (GA), and (4) A sequential forward selection (SFS) method. Performances of the four approaches were assessed using a tenfold cross validation method. Results: Among these four methods, SFFS has highest efficacy, which takes 3%–5% of computational time as compared to GA approach, and yields the highest performance level with the area under a receiver operating characteristic curve (AUC) = 0.864 ± 0.034. The results also demonstrated that except using GA, including the new texture features computed from the dilated mass segments improved the AUC

  5. Compensated intruder-detection systems

    DOEpatents

    McNeilly, David R.; Miller, William R.

    1984-01-01

    Intruder-detection systems in which intruder-induced signals are transmitted through a medium also receive spurious signals induced by changes in a climatic condition affecting the medium. To combat this, signals received from the detection medium are converted to a first signal. The system also provides a reference signal proportional to climate-induced changes in the medium. The first signal and the reference signal are combined for generating therefrom an output signal which is insensitive to the climatic changes in the medium. An alarm is energized if the output signal exceeds a preselected value. In one embodiment, an acoustic cable is coupled to a fence to generate a first electrical signal proportional to movements thereof. False alarms resulting from wind-induced movements of the fence (detection medium) are eliminated by providing an anemometer-driven voltage generator to provide a reference voltage proportional to the velocity of wind incident on the fence. An analog divider receives the first electrical signal and the reference signal as its numerator and denominator inputs, respectively, and generates therefrom an output signal which is insensitive to the wind-induced movements in the fence.

  6. Capillary Electrophoresis - Optical Detection Systems

    SciTech Connect

    Sepaniak, M. J.

    2001-08-06

    Molecular recognition systems are developed via molecular modeling and synthesis to enhance separation performance in capillary electrophoresis and optical detection methods for capillary electrophoresis. The underpinning theme of our work is the rational design and development of molecular recognition systems in chemical separations and analysis. There have been, however, some subtle and exciting shifts in our research paradigm during this period. Specifically, we have moved from mostly separations research to a good balance between separations and spectroscopic detection for separations. This shift is based on our perception that the pressing research challenges and needs in capillary electrophoresis and electrokinetic chromatography relate to the persistent detection and flow rate reproducibility limitations of these techniques (see page 1 of the accompanying Renewal Application for further discussion). In most of our work molecular recognition reagents are employed to provide selectivity and enhance performance. Also, an emerging trend is the use of these reagents with specially-prepared nano-scale materials. Although not part of our DOE BES-supported work, the modeling and synthesis of new receptors has indirectly supported the development of novel microcantilevers-based MEMS for the sensing of vapor and liquid phase analytes. This fortuitous overlap is briefly covered in this report. Several of the more significant publications that have resulted from our work are appended. To facilitate brevity we refer to these publications liberally in this progress report. Reference is also made to very recent work in the Background and Preliminary Studies Section of the Renewal Application.

  7. Computer-aided diagnosis in CT colonography: detection of polyps based on geometric and texture features

    NASA Astrophysics Data System (ADS)

    Yoshida, Hiroyuki; Naeppi, Janne J.; Frimmel, Hans; Dachman, Abraham H.

    2002-05-01

    A computer-aided diagnosis scheme for the detection of colonic polyps in CT colonography has been developed, and its performance has been assessed based on clinical cases with colonoscopy-confirmed polyps. In the scheme, the colon was automatically segmented by use of knowledge-guided segmentation from 3-dimensional isotropic volumes reconstructed from axial CT slices in CT colonography. Polyp candidates are detected by first computing of 3-dimensional geometric features that characterize polyps, and then segmenting of connected components corresponding to suspicious regions by hysteresis thresholding and fuzzy clustering based on these geometric features. False-positive detections are reduced by computation of 3-dimensional texture features characterizing the internal structures of the polyp candidates, followed by application of discriminant analysis to the feature space generated by the geometric and texture features. We applied our scheme to 43 CT colonographic cases with cleansed colon, including 12 polyps larger than 5 mm. In a by-dataset analysis, the CAD scheme yielded a sensitivity of 95% with 1.2 false positives per data set. The false negative was one of the two polyps in a single patient. Consequently, in by-patient analysis, our method yielded 100% sensitivity with 2.0 false positives per patient. The results indicate that our CAD scheme has the potential to detect clinically important polyp cases with a high sensitivity and a relatively low false-positive rate.

  8. Digital mammography: Mixed feature neural network with spectral entropy decision for detection of microcalcifications

    SciTech Connect

    Zheng, B. |; Qian, W.; Clarke, L.P.

    1996-10-01

    A computationally efficient mixed feature based neural network (MFNN) is proposed for the detection of microcalcification clusters (MCC`s) in digitized mammograms. The MFNN employs features computed in both the spatial and spectral domain and uses spectral entropy as a decision parameter. Backpropagation with Kalman Filtering (KF) is employed to allow more efficient network training as required for evaluation of different features, input images, and related error analysis. A previously reported, wavelet-based image-enhancement method is also employed to enhance microcalcification clusters for improved detection. The relative performance of the MFNN for both the raw and enhanced images is evaluated using a common image database of 30 digitized mammograms, with 20 images containing 21 biopsy proven MCC`s and ten normal cases. The computed sensitivity (true positive (TP) detection rate) was 90.1% with an average low false positive (FP) detection of 0.71 MCCs/image for the enhanced images using a modified k-fold validation error estimation technique. The corresponding computed sensitivity for the raw images was reduced to 81.4% and with 0.59 FP`s MCCs/image. A relative comparison to an earlier neural network (NN) design, using only spatially related features, suggests the importance of the addition of spectral domain features when the raw image data are analyzed.

  9. Object-Based Analysis of LIDAR Geometric Features for Vegetation Detection in Shaded Areas

    NASA Astrophysics Data System (ADS)

    Lin, Yu-Ching; Lin, ChinSu; Tsai, Ming-Da; Lin, Chun-Lin

    2016-06-01

    The extraction of land cover information from remote sensing data is a complex process. Spectral information has been widely utilized in classifying remote sensing images. However, shadows limit the use of multispectral images because they result in loss of spectral radiometric information. In addition, true reflectance may be underestimated in shaded areas. In land cover classification, shaded areas are often left unclassified or simply assigned as a shadow class. Vegetation indices from remote sensing measurement are radiation-based measurements computed through spectral combination. They indicate vegetation properties and play an important role in remote sensing of forests. Airborne light detection and ranging (LiDAR) technology is an active remote sensing technique that produces a true orthophoto at a single wavelength. This study investigated three types of geometric lidar features where NDVI values fail to represent meaningful forest information. The three features include echo width, normalized eigenvalue, and standard deviation of the unit weight observation of the plane adjustment, and they can be derived from waveform data and discrete point clouds. Various feature combinations were carried out to evaluate the compensation of the three lidar features to vegetation detection in shaded areas. Echo width was found to outperform the other two features. Furthermore, surface characteristics estimated by echo width were similar to that by normalized eigenvalues. Compared to the combination of only NDVI and mean height difference, those including one of the three features had a positive effect on the detection of vegetation class.

  10. Feature Line Based Building Detection and Reconstruction from Oblique Airborne Imagery

    NASA Astrophysics Data System (ADS)

    Zhu, Q.; Jiang, W.; Zhang, J.

    2015-05-01

    In this paper, a feature line based method for building detection and reconstruction from oblique airborne imagery is presented. With the development of Multi-View Stereo technology, increasing photogrammetric softwares are provided to generate textured meshes from oblique airborne imagery. However, errors in image matching and mesh segmentation lead to the low geometrical accuracy of building models, especially at building boundaries. To simplify massive meshes and construct accurate 3D building models, we integrate multi-view images and meshes by using feature lines, in which contour lines are used for building detection and straight skeleton for building reconstruction. Firstly, through the contour clustering method, buildings can be quickly and robustly detected from meshes. Then, a feature preserving mesh segmentation method is applied to accurately extract 3D straight skeleton from meshes. Finally, straight feature lines derived from multi-view images are used to rectify inaccurate parts of 3D straight skeleton of buildings. Therefore, low quality model can be refined by the accuracy improvement of mesh feature lines and rectification with feature lines of multi-view images. The test dataset in Zürich is provided by ISPRS/EuroSDR initiative Benchmark on High Density Image Matching for DSM Computation. The experiments reveal that the proposed method can obtain convincing and high quality 3D building models from oblique airborne imagery.

  11. Object detection and tracking with active camera on motion vectors of feature points and particle filter.

    PubMed

    Chen, Yong; Zhang, Rong-Hua; Shang, Lei; Hu, Eric

    2013-06-01

    A method based on motion vectors of feature points and particle filter has been proposed and developed for an active∕moving camera for object detection and tracking purposes. The object is detected by histogram of motion vectors first, and then, on the basis of particle filter algorithm, the weighing factors are obtained via color information. In addition, re-sampling strategy and surf feature points are used to remedy the drawback of particle degeneration. Experimental results demonstrate the practicability and accuracy of the new method and are presented in the paper. PMID:23822380

  12. Systems and processes for identifying features and determining feature associations in groups of documents

    DOEpatents

    Rose, Stuart J.; Cowley, Wendy E.; Crow, Vernon L.

    2016-01-12

    Systems and computer-implemented processes for identification of features and determination of feature associations in a group of documents can involve providing a plurality of keywords identified among the terms of at least some of the documents. A value measure can be calculated for each keyword. High-value keywords are defined as those keywords having value measures that exceed a threshold. For each high-value keyword, term-document associations (TDA) are accessed. The TDA characterize measures of association between each term and at least some documents in the group. A processor quantifies similarities between unique pairs of high-value keywords based on the TDA for each respective high-value keyword and generates a similarity matrix that indicates one or more sets that each comprise highly associated high-value keywords.

  13. A new feature constituting approach to detection of vocal fold pathology

    NASA Astrophysics Data System (ADS)

    Hariharan, M.; Polat, Kemal; Yaacob, Sazali

    2014-08-01

    In the last two decades, non-invasive methods through acoustic analysis of voice signal have been proved to be excellent and reliable tool to diagnose vocal fold pathologies. This paper proposes a new feature vector based on the wavelet packet transform and singular value decomposition for the detection of vocal fold pathology. k-means clustering based feature weighting is proposed to increase the distinguishing performance of the proposed features. In this work, two databases Massachusetts Eye and Ear Infirmary (MEEI) voice disorders database and MAPACI speech pathology database are used. Four different supervised classifiers such as k-nearest neighbour (k-NN), least-square support vector machine, probabilistic neural network and general regression neural network are employed for testing the proposed features. The experimental results uncover that the proposed features give very promising classification accuracy of 100% for both MEEI database and MAPACI speech pathology database.

  14. Fish detection and classification system

    NASA Astrophysics Data System (ADS)

    Tidd, Richard A.; Wilder, Joseph

    2001-01-01

    Marine biologists traditionally determine the presence and quantities of different types of fish by dragging nets across the bottom, and examining their contents. This method, although accurate, kills the collected fish, damages their habitat, and consumes large quantities of resources. This paper presents an alternative, a machine vision system capable of determining the presence of fish species. Illumination presents a unique problem in this environment, and the design of an effective illumination system is discussed. The related issues of object orientation and measurement are also discussed and resolved. Capturing images of fish in murky water also presents challenges. An adaptive thresholding technique is required to appropriately segment the fish from the background in these images. Mode detection, and histogram analysis are useful tools in determining these localized thresholds. It is anticipated that this system, created in conjunction with the Rutgers Institute for Marine and Coastal Science, will effectively classify fish in the estuarine environment.

  15. Ionization detection system for aerosols

    DOEpatents

    Jacobs, Martin E.

    1977-01-01

    This invention relates to an improved smoke-detection system of the ionization-chamber type. In the preferred embodiment, the system utilizes a conventional detector head comprising a measuring ionization chamber, a reference ionization chamber, and a normally non-conductive gas triode for discharging when a threshold concentration of airborne particulates is present in the measuring chamber. The improved system utilizes a measuring ionization chamber which is modified to minimize false alarms and reductions in sensitivity resulting from changes in ambient temperature. In the preferred form of the modification, an annular radiation shield is mounted about the usual radiation source provided to effect ionization in the measuring chamber. The shield is supported by a bimetallic strip which flexes in response to changes in ambient temperature, moving the shield relative to the source so as to vary the radiative area of the source in a manner offsetting temperature-induced variations in the sensitivity of the chamber.

  16. Change Detection in Uav Video Mosaics Combining a Feature Based Approach and Extended Image Differencing

    NASA Astrophysics Data System (ADS)

    Saur, Günter; Krüger, Wolfgang

    2016-06-01

    Change detection is an important task when using unmanned aerial vehicles (UAV) for video surveillance. We address changes of short time scale using observations in time distances of a few hours. Each observation (previous and current) is a short video sequence acquired by UAV in near-Nadir view. Relevant changes are, e.g., recently parked or moved vehicles. Examples for non-relevant changes are parallaxes caused by 3D structures of the scene, shadow and illumination changes, and compression or transmission artifacts. In this paper we present (1) a new feature based approach to change detection, (2) a combination with extended image differencing (Saur et al., 2014), and (3) the application to video sequences using temporal filtering. In the feature based approach, information about local image features, e.g., corners, is extracted in both images. The label "new object" is generated at image points, where features occur in the current image and no or weaker features are present in the previous image. The label "vanished object" corresponds to missing or weaker features in the current image and present features in the previous image. This leads to two "directed" change masks and differs from image differencing where only one "undirected" change mask is extracted which combines both label types to the single label "changed object". The combination of both algorithms is performed by merging the change masks of both approaches. A color mask showing the different contributions is used for visual inspection by a human image interpreter.

  17. Computer-aided detection of renal calculi from noncontrast CT images using TV-flow and MSER features

    SciTech Connect

    Liu, Jianfei; Wang, Shijun; Turkbey, Evrim B.; Yao, Jianhua; Summers, Ronald M.; Linguraru, Marius George

    2015-01-15

    Purpose: Renal calculi are common extracolonic incidental findings on computed tomographic colonography (CTC). This work aims to develop a fully automated computer-aided diagnosis system to accurately detect renal calculi on CTC images. Methods: The authors developed a total variation (TV) flow method to reduce image noise within the kidneys while maintaining the characteristic appearance of renal calculi. Maximally stable extremal region (MSER) features were then calculated to robustly identify calculi candidates. Finally, the authors computed texture and shape features that were imported to support vector machines for calculus classification. The method was validated on a dataset of 192 patients and compared to a baseline approach that detects calculi by thresholding. The authors also compared their method with the detection approaches using anisotropic diffusion and nonsmoothing. Results: At a false positive rate of 8 per patient, the sensitivities of the new method and the baseline thresholding approach were 69% and 35% (p < 1e − 3) on all calculi from 1 to 433 mm{sup 3} in the testing dataset. The sensitivities of the detection methods using anisotropic diffusion and nonsmoothing were 36% and 0%, respectively. The sensitivity of the new method increased to 90% if only larger and more clinically relevant calculi were considered. Conclusions: Experimental results demonstrated that TV-flow and MSER features are efficient means to robustly and accurately detect renal calculi on low-dose, high noise CTC images. Thus, the proposed method can potentially improve diagnosis.

  18. Computer-aided detection of renal calculi from noncontrast CT images using TV-flow and MSER features

    PubMed Central

    Liu, Jianfei; Wang, Shijun; Turkbey, Evrim B.; Linguraru, Marius George; Yao, Jianhua; Summers, Ronald M.

    2015-01-01

    Purpose: Renal calculi are common extracolonic incidental findings on computed tomographic colonography (CTC). This work aims to develop a fully automated computer-aided diagnosis system to accurately detect renal calculi on CTC images. Methods: The authors developed a total variation (TV) flow method to reduce image noise within the kidneys while maintaining the characteristic appearance of renal calculi. Maximally stable extremal region (MSER) features were then calculated to robustly identify calculi candidates. Finally, the authors computed texture and shape features that were imported to support vector machines for calculus classification. The method was validated on a dataset of 192 patients and compared to a baseline approach that detects calculi by thresholding. The authors also compared their method with the detection approaches using anisotropic diffusion and nonsmoothing. Results: At a false positive rate of 8 per patient, the sensitivities of the new method and the baseline thresholding approach were 69% and 35% (p < 1e − 3) on all calculi from 1 to 433 mm3 in the testing dataset. The sensitivities of the detection methods using anisotropic diffusion and nonsmoothing were 36% and 0%, respectively. The sensitivity of the new method increased to 90% if only larger and more clinically relevant calculi were considered. Conclusions: Experimental results demonstrated that TV-flow and MSER features are efficient means to robustly and accurately detect renal calculi on low-dose, high noise CTC images. Thus, the proposed method can potentially improve diagnosis. PMID:25563255

  19. Probing the terrestrial regions of planetary systems: warm debris disks with emission features

    SciTech Connect

    Ballering, Nicholas P.; Rieke, George H.; Gáspár, András

    2014-09-20

    Observations of debris disks allow for the study of planetary systems, even where planets have not been detected. However, debris disks are often only characterized by unresolved infrared excesses that resemble featureless blackbodies, and the location of the emitting dust is uncertain due to a degeneracy with the dust grain properties. Here, we characterize the Spitzer Infrared Spectrograph spectra of 22 debris disks exhibiting 10 μm silicate emission features. Such features arise from small warm dust grains, and their presence can significantly constrain the orbital location of the emitting debris. We find that these features can be explained by the presence of an additional dust component in the terrestrial zones of the planetary systems, i.e., an exozodiacal belt. Aside from possessing exozodiacal dust, these debris disks are not particularly unique; their minimum grain sizes are consistent with the blowout sizes of their systems, and their brightnesses are comparable to those of featureless warm debris disks. These disks are in systems of a range of ages, though the older systems with features are found only around A-type stars. The features in young systems may be signatures of terrestrial planet formation. Analyzing the spectra of unresolved debris disks with emission features may be one of the simplest and most accessible ways to study the terrestrial regions of planetary systems.

  20. Pattern Recognition Methods and Features Selection for Speech Emotion Recognition System

    PubMed Central

    Partila, Pavol; Voznak, Miroslav; Tovarek, Jaromir

    2015-01-01

    The impact of the classification method and features selection for the speech emotion recognition accuracy is discussed in this paper. Selecting the correct parameters in combination with the classifier is an important part of reducing the complexity of system computing. This step is necessary especially for systems that will be deployed in real-time applications. The reason for the development and improvement of speech emotion recognition systems is wide usability in nowadays automatic voice controlled systems. Berlin database of emotional recordings was used in this experiment. Classification accuracy of artificial neural networks, k-nearest neighbours, and Gaussian mixture model is measured considering the selection of prosodic, spectral, and voice quality features. The purpose was to find an optimal combination of methods and group of features for stress detection in human speech. The research contribution lies in the design of the speech emotion recognition system due to its accuracy and efficiency. PMID:26346654

  1. Feature Transformation Detection Method with Best Spectral Band Selection Process for Hyper-spectral Imaging

    NASA Astrophysics Data System (ADS)

    Chen, Hai-Wen; McGurr, Mike; Brickhouse, Mark

    2015-11-01

    We present a newly developed feature transformation (FT) detection method for hyper-spectral imagery (HSI) sensors. In essence, the FT method, by transforming the original features (spectral bands) to a different feature domain, may considerably increase the statistical separation between the target and background probability density functions, and thus may significantly improve the target detection and identification performance, as evidenced by the test results in this paper. We show that by differentiating the original spectral, one can completely separate targets from the background using a single spectral band, leading to perfect detection results. In addition, we have proposed an automated best spectral band selection process with a double-threshold scheme that can rank the available spectral bands from the best to the worst for target detection. Finally, we have also proposed an automated cross-spectrum fusion process to further improve the detection performance in lower spectral range (<1000 nm) by selecting the best spectral band pair with multivariate analysis. Promising detection performance has been achieved using a small background material signature library for concept-proving, and has then been further evaluated and verified using a real background HSI scene collected by a HYDICE sensor.

  2. On the Putative Detection of Z>0 X-Ray Absorption Features in the Spectrum of Mrk 421

    SciTech Connect

    Rasmussen, Andrew P.; Kahn, Steven M.; Paerels, Frits; Herder, Jan Willem den; Kaastra, Jelle; de Vries, Cor; /SRON, Utrecht

    2006-04-28

    In a series of papers, Nicastro et al. have claimed the detection of z > 0 O VII absorption features in the spectrum of Mrk 421 obtained with the Chandra Low Energy Transmission Grating Spectrometer (LETGS). We evaluate those claims in the context of a high quality spectrum of the same source obtained with the Reflection Grating Spectrometer (RGS) on XMM-Newton. The data comprise over 955 ksec of usable exposure time and more than 2.6 x 10{sup 4} counts per 50 m{angstrom} at 21.6 {angstrom}. We concentrate on the spectrally clean region (21.3 < {lambda} < 22.5 {angstrom}) where sharp features due to the astrophysically abundant O VII may reveal an intervening, warm-hot intergalactic medium (WHIM). In spite of the fact that the sensitivity of the RGS data is higher than that of the original LETGS data presented by Nicastro et al., we do not confirm detection of any of the intervening systems claimed to date. Rather, we detect only three unsurprising, astrophysically expected features down to the log (N{sub i}) {approx} 14.6 (3{sigma}) sensitivity level. Each of the two purported WHIM features is rejected with a statistical confidence that exceeds that reported for its initial detection. While we can not rule out the existence of fainter, WHIM related features in these spectra, we suggest that previous discovery claims were premature. A more recent paper by Williams et al. claims to have demonstrated that the RGS data we analyze here do not have the resolution or statistical quality required to confirm or deny the LETGS detections. We show that the Williams et al. reduction of the RGS data was highly flawed, leading to an artificial and spurious degradation of the instrument response. We carefully highlight the differences between our analysis presented here and those published by Williams et al.

  3. FRaC: a feature-modeling approach for semi-supervised and unsupervised anomaly detection

    PubMed Central

    Brodley, Carla; Slonim, Donna

    2011-01-01

    Anomaly detection involves identifying rare data instances (anomalies) that come from a different class or distribution than the majority (which are simply called “normal” instances). Given a training set of only normal data, the semi-supervised anomaly detection task is to identify anomalies in the future. Good solutions to this task have applications in fraud and intrusion detection. The unsupervised anomaly detection task is different: Given unlabeled, mostly-normal data, identify the anomalies among them. Many real-world machine learning tasks, including many fraud and intrusion detection tasks, are unsupervised because it is impractical (or impossible) to verify all of the training data. We recently presented FRaC, a new approach for semi-supervised anomaly detection. FRaC is based on using normal instances to build an ensemble of feature models, and then identifying instances that disagree with those models as anomalous. In this paper, we investigate the behavior of FRaC experimentally and explain why FRaC is so successful. We also show that FRaC is a superior approach for the unsupervised as well as the semi-supervised anomaly detection task, compared to well-known state-of-the-art anomaly detection methods, LOF and one-class support vector machines, and to an existing feature-modeling approach. PMID:22639542

  4. Infrared trace element detection system

    DOEpatents

    Bien, Fritz; Bernstein, Lawrence S.; Matthew, Michael W.

    1988-01-01

    An infrared trace element detection system including an optical cell into which the sample fluid to be examined is introduced and removed. Also introduced into the optical cell is a sample beam of infrared radiation in a first wavelength band which is significantly absorbed by the trace element and a second wavelength band which is not significantly absorbed by the trace element for passage through the optical cell through the sample fluid. The output intensities of the sample beam of radiation are selectively detected in the first and second wavelength bands. The intensities of a reference beam of the radiation are similarly detected in the first and second wavelength bands. The sensed output intensity of the sample beam in one of the first and second wavelength bands is normalized with respect to the other and similarly, the intensity of the reference beam of radiation in one of the first and second wavelength bands is normalized with respect to the other. The normalized sample beam intensity and normalized reference beam intensity are then compared to provide a signal from which the amount of trace element in the sample fluid can be determined.

  5. Infrared trace element detection system

    DOEpatents

    Bien, F.; Bernstein, L.S.; Matthew, M.W.

    1988-11-15

    An infrared trace element detection system includes an optical cell into which the sample fluid to be examined is introduced and removed. Also introduced into the optical cell is a sample beam of infrared radiation in a first wavelength band which is significantly absorbed by the trace element and a second wavelength band which is not significantly absorbed by the trace element for passage through the optical cell through the sample fluid. The output intensities of the sample beam of radiation are selectively detected in the first and second wavelength bands. The intensities of a reference beam of the radiation are similarly detected in the first and second wavelength bands. The sensed output intensity of the sample beam in one of the first and second wavelength bands is normalized with respect to the other and similarly, the intensity of the reference beam of radiation in one of the first and second wavelength bands is normalized with respect to the other. The normalized sample beam intensity and normalized reference beam intensity are then compared to provide a signal from which the amount of trace element in the sample fluid can be determined. 11 figs.

  6. Genetic algorithm based feature selection combined with dual classification for the automated detection of proliferative diabetic retinopathy.

    PubMed

    Welikala, R A; Fraz, M M; Dehmeshki, J; Hoppe, A; Tah, V; Mann, S; Williamson, T H; Barman, S A

    2015-07-01

    Proliferative diabetic retinopathy (PDR) is a condition that carries a high risk of severe visual impairment. The hallmark of PDR is the growth of abnormal new vessels. In this paper, an automated method for the detection of new vessels from retinal images is presented. This method is based on a dual classification approach. Two vessel segmentation approaches are applied to create two separate binary vessel map which each hold vital information. Local morphology features are measured from each binary vessel map to produce two separate 4-D feature vectors. Independent classification is performed for each feature vector using a support vector machine (SVM) classifier. The system then combines these individual outcomes to produce a final decision. This is followed by the creation of additional features to generate 21-D feature vectors, which feed into a genetic algorithm based feature selection approach with the objective of finding feature subsets that improve the performance of the classification. Sensitivity and specificity results using a dataset of 60 images are 0.9138 and 0.9600, respectively, on a per patch basis and 1.000 and 0.975, respectively, on a per image basis. PMID:25841182

  7. Detection and clustering of features in aerial images by neuron network-based algorithm

    NASA Astrophysics Data System (ADS)

    Vozenilek, Vit

    2015-12-01

    The paper presents the algorithm for detection and clustering of feature in aerial photographs based on artificial neural networks. The presented approach is not focused on the detection of specific topographic features, but on the combination of general features analysis and their use for clustering and backward projection of clusters to aerial image. The basis of the algorithm is a calculation of the total error of the network and a change of weights of the network to minimize the error. A classic bipolar sigmoid was used for the activation function of the neurons and the basic method of backpropagation was used for learning. To verify that a set of features is able to represent the image content from the user's perspective, the web application was compiled (ASP.NET on the Microsoft .NET platform). The main achievements include the knowledge that man-made objects in aerial images can be successfully identified by detection of shapes and anomalies. It was also found that the appropriate combination of comprehensive features that describe the colors and selected shapes of individual areas can be useful for image analysis.

  8. Explosives detection system and method

    DOEpatents

    Reber, Edward L.; Jewell, James K.; Rohde, Kenneth W.; Seabury, Edward H.; Blackwood, Larry G.; Edwards, Andrew J.; Derr, Kurt W.

    2007-12-11

    A method of detecting explosives in a vehicle includes providing a first rack on one side of the vehicle, the rack including a neutron generator and a plurality of gamma ray detectors; providing a second rack on another side of the vehicle, the second rack including a neutron generator and a plurality of gamma ray detectors; providing a control system, remote from the first and second racks, coupled to the neutron generators and gamma ray detectors; using the control system, causing the neutron generators to generate neutrons; and performing gamma ray spectroscopy on spectra read by the gamma ray detectors to look for a signature indicative of presence of an explosive. Various apparatus and other methods are also provided.

  9. Evaluation of Intrusion Detection Systems

    PubMed Central

    Ulvila, Jacob W.; Gaffney, John E.

    2003-01-01

    This paper presents a comprehensive method for evaluating intrusion detection systems (IDSs). It integrates and extends ROC (receiver operating characteristic) and cost analysis methods to provide an expected cost metric. Results are given for determining the optimal operation of an IDS based on this expected cost metric. Results are given for the operation of a single IDS and for a combination of two IDSs. The method is illustrated for: 1) determining the best operating point for a single and double IDS based on the costs of mistakes and the hostility of the operating environment as represented in the prior probability of intrusion and 2) evaluating single and double IDSs on the basis of expected cost. A method is also described for representing a compound IDS as an equivalent single IDS. Results are presented from the point of view of a system administrator, but they apply equally to designers of IDSs.

  10. Detecting Small-Scale Topographic Changes and Relict Geomorphic Features on Barrier Islands Using SAR

    NASA Technical Reports Server (NTRS)

    Gibeaut, James C.; Crawford, Melba M.; Gutierrez, Roberto; Slatton, K. Clint; Neuenschwander, Amy L.; Ricard, Michael R.

    1997-01-01

    The shapes and elevations of barrier islands may change dramatically over a short period of time during a storm. Coastal scientists and engineers, however, are currently unable to measure these changes occurring over an entire barrier island at once. This three-year project, which is funded by NASA and jointly conducted by the Bureau of Economic Geology and the Center for Space Research at The University of Texas at Austin, is designed to overcome this problem by developing the use of interferometry from airborne synthetic aperture radar (AIRSAR) to measure coastal topography and to detect storm-induced changes in topography. Surrogate measures of topography observed in multiband, fully polarimetric AIRSAR (This type of data are now referred to as POLSAR data.) are also being investigated. Digital elevation models (DEM) of Galveston Island and Bolivar Peninsula, Texas obtained with Topographic SAR (TOPSAR) are compared with measurements by Global Positioning System (GPS) ground surveys and electronic total station surveys. In addition to topographic mapping, this project is evaluating the use of POLSAR to detect old features such as storm scarps, storm channels, former tidal inlets, and beach ridges that have been obscured by vegetation, erosion, deposition, and artificial filling. We have also expanded the work from the original proposal to include the mapping of coastal wetland vegetation and depositional environments. Methods developed during this project will provide coastal geologists with an unprecedented tool for monitoring and understanding barrier island systems. This understanding will improve overall coastal management policies and will help reduce the effects of natural and man-induced coastal hazards. This report summarizes our accomplishments during the second year of the study. Also included is a discussion of our planned activities for year 3 and a revised budget.

  11. Detection of the 3.3-micron feature in two starburst galaxies

    NASA Technical Reports Server (NTRS)

    Dennefeld, M.; Desert, F. X.

    1990-01-01

    This paper reports the detection of the 3.3-micron emission feature in the center of two external galaxies: IC 694 (interacting with NGC 3690) and NGC 4194 (a merger). This feature has been previously detected in various galactic and extragalactic objects and is thought to be due to very small grains or large molecules that probably belong to the Polycyclic Aromatic Hydrocarbon (PAH) family. Its presence, as well as the IRAS colors, strongly suggest that these galaxies are dominated by starbursts rather than active nuclei. From publishing data and the present observations, the brightness of the feature in different galaxies is studied. A simple model of radiative transfer shows that the 3.3-micron feature brightness of a given galaxy allows the determination of the unreddened surface brightness of the galaxy stellar content. In galaxies with relatively large extinction, the 3.3-micron feature (and the other PAH related features) is therefore a useful spatial indicator of star-formation activity in their centers.

  12. Hybrid image representation learning model with invariant features for basal cell carcinoma detection

    NASA Astrophysics Data System (ADS)

    Arevalo, John; Cruz-Roa, Angel; González, Fabio A.

    2013-11-01

    This paper presents a novel method for basal-cell carcinoma detection, which combines state-of-the-art methods for unsupervised feature learning (UFL) and bag of features (BOF) representation. BOF, which is a form of representation learning, has shown a good performance in automatic histopathology image classi cation. In BOF, patches are usually represented using descriptors such as SIFT and DCT. We propose to use UFL to learn the patch representation itself. This is accomplished by applying a topographic UFL method (T-RICA), which automatically learns visual invariance properties of color, scale and rotation from an image collection. These learned features also reveals these visual properties associated to cancerous and healthy tissues and improves carcinoma detection results by 7% with respect to traditional autoencoders, and 6% with respect to standard DCT representations obtaining in average 92% in terms of F-score and 93% of balanced accuracy.

  13. On the use of log-gabor features for subsurface object detection using ground penetrating radar

    NASA Astrophysics Data System (ADS)

    Harris, Samuel; Ho, K. C.; Zare, Alina

    2016-05-01

    regions with significant amount of metal debris. The challenge for the handheld GPR is to reduce the false alarm rate and limit the undesirable human operator effect. This paper proposes the use of log-Gabor features to improve the detection performance. In particular, we apply 36 log-Gabor filters to the B-scan of the GPR data in the time domain for the purpose to extract the edge behaviors of a prescreener alarm. The 36 log-Gabor filters cover the entire frequency plane with different bandwidths and orientations. The energy of each filter output forms an element of the feature vector and an SVM is trained to perform target vs non-target classification. Experimental results using the experimental hand held demonstrator data collected at a government site supports the increase in detection performance by using the log-Gabor features.

  14. Detection of explosive hazards using spectrum features from forward-looking ground penetrating radar imagery

    NASA Astrophysics Data System (ADS)

    Farrell, Justin; Havens, Timothy C.; Ho, K. C.; Keller, James M.; Ton, Tuan T.; Wong, David C.; Soumekh, Mehrdad

    2011-06-01

    Buried explosives have proven to be a challenging problem for which ground penetrating radar (GPR) has shown to be effective. This paper discusses an explosive hazard detection algorithm for forward looking GPR (FLGPR). The proposed algorithm uses the fast Fourier transform (FFT) to obtain spectral features of anomalies in the FLGPR imagery. Results show that the spectral characteristics of explosive hazards differ from that of background clutter and are useful for rejecting false alarms (FAs). A genetic algorithm (GA) is developed in order to select a subset of spectral features to produce a more generalized classifier. Furthermore, a GA-based K-Nearest Neighbor probability density estimator is employed in which targets and false alarms are used as training data to produce a two-class classifier. The experimental results of this paper use data collected by the US Army and show the effectiveness of spectrum based features in the detection of explosive hazards.

  15. Biosensor method and system based on feature vector extraction

    DOEpatents

    Greenbaum, Elias; Rodriguez, Jr., Miguel; Qi, Hairong; Wang, Xiaoling

    2012-04-17

    A method of biosensor-based detection of toxins comprises the steps of providing at least one time-dependent control signal generated by a biosensor in a gas or liquid medium, and obtaining a time-dependent biosensor signal from the biosensor in the gas or liquid medium to be monitored or analyzed for the presence of one or more toxins selected from chemical, biological or radiological agents. The time-dependent biosensor signal is processed to obtain a plurality of feature vectors using at least one of amplitude statistics and a time-frequency analysis. At least one parameter relating to toxicity of the gas or liquid medium is then determined from the feature vectors based on reference to the control signal.

  16. Comparison of spatial frequency domain features for the detection of side attack explosive ballistics in synthetic aperture acoustics

    NASA Astrophysics Data System (ADS)

    Dowdy, Josh; Anderson, Derek T.; Luke, Robert H.; Ball, John E.; Keller, James M.; Havens, Timothy C.

    2016-05-01

    Explosive hazards in current and former conflict zones are a threat to both military and civilian personnel. As a result, much effort has been dedicated to identifying automated algorithms and systems to detect these threats. However, robust detection is complicated due to factors like the varied composition and anatomy of such hazards. In order to solve this challenge, a number of platforms (vehicle-based, handheld, etc.) and sensors (infrared, ground penetrating radar, acoustics, etc.) are being explored. In this article, we investigate the detection of side attack explosive ballistics via a vehicle-mounted acoustic sensor. In particular, we explore three acoustic features, one in the time domain and two on synthetic aperture acoustic (SAA) beamformed imagery. The idea is to exploit the varying acoustic frequency profile of a target due to its unique geometry and material composition with respect to different viewing angles. The first two features build their angle specific frequency information using a highly constrained subset of the signal data and the last feature builds its frequency profile using all available signal data for a given region of interest (centered on the candidate target location). Performance is assessed in the context of receiver operating characteristic (ROC) curves on cross-validation experiments for data collected at a U.S. Army test site on different days with multiple target types and clutter. Our preliminary results are encouraging and indicate that the top performing feature is the unrolled two dimensional discrete Fourier transform (DFT) of SAA beamformed imagery.

  17. Recent advances in microfluidic detection systems

    PubMed Central

    Baker, Christopher A; Duong, Cindy T; Grimley, Alix; Roper, Michael G

    2009-01-01

    There are numerous detection methods available for methods are being put to use for detection on these miniaturized systems, with the analyte of interest driving the choice of detection method. In this article, we summarize microfluidic 2 years. More focus is given to unconventional approaches to detection routes and novel strategies for performing high-sensitivity detection. PMID:20414455

  18. Detection of Variable Gaseous Absorption Features in the Debris Disks Around Young A-type Stars

    NASA Astrophysics Data System (ADS)

    Montgomery, Sharon L.; Welsh, Barry Y.

    2012-10-01

    We present medium resolution (R = 60,000) absorption measurements of the interstellar Ca II K line observed towards five nearby A-type stars (49 Ceti, 5 Vul, ι Cyg, 2 And, and HD 223884) suspected of possessing circumstellar gas debris disks. The stars were observed on a nightly basis during a six night observing run on the 2.1-meter Otto Struve telescope at the McDonald Observatory, Texas. We have detected nightly changes in the absorption strength of the Ca II K line observed near the stellar radial velocity in three of the stars (49 Ceti, i Cyg and HD 223884). Such changes in absorption suggest the presence of a circumstellar (atomic) gas disk around these stars. In addition to the absorption changes in the main Ca II K line profile, we have also observed weak transient absorption features that randomly appear at redshifted velocities in the spectra of 49 Ceti, 5 Vul, and 2 And. These absorption features are most probably associated with the presence of falling evaporated bodies (exo-comets) that liberate evaporating gas on their approach to the central star. This now brings the total number of systems in which exocomet activity has been observed at Ca II or Na I wavelengths on a nightly basis to seven (β Pic, HR 10, HD 85905, β Car, 49 Ceti, 5 Vul, and 2 And), with 2 And exhibiting weaker and less frequent changes. All of the disk systems presently known to exhibit either type of short-term variability in Ca II K line absorption are rapidly rotating A-type stars (V sin i > 120 km s-1). Most exhibit mid-IR excesses, and many of them are very young (< 20 Myr), thus supporting the argument that many of them are transitional objects between Herbig Ae and "Vega-like" A-type stars with more tenuous circumstellar disks. No mid-IR excess (due to the presence of a dust disk) has yet been detected around either 2 And or HD 223884, both of which have been classified as λ Boötis-type stars. This may indicate that the observed changes in gas absorption for these two

  19. Aggressiveness features and outcomes of true interval cancers: comparison between screen-detected and symptom-detected cancers.

    PubMed

    Domingo, Laia; Blanch, Jordi; Servitja, Sònia; Corominas, Josep Maria; Murta-Nascimento, Cristiane; Rueda, Antonio; Redondo, Maximino; Castells, Xavier; Sala, Maria

    2013-01-01

    The question of whether screen detection confers an additional survival benefit in breast cancer is unclear and subject to several biases. Our aim was to examine the role of the diagnostic method (screen-detected, symptom-detected, and true interval cancers) and the clinical-pathological features in relapse-free survival and overall survival in breast cancer patients. We included 228 invasive breast cancers diagnosed in Barcelona from 1996 to 2008 among women aged 50-69 years. Ninety-seven patients were screen detected within the screening, 34 truly arose between 2-year screening mammograms (true interval cancers), and 97 were symptom detected outside the screening. The clinical-pathological features at diagnosis were compared. The overall and disease-free survival probabilities were computed using the Kaplan-Meier method. Cox proportional hazard models were applied, with adjustment by clinical-pathological variables. At diagnosis, symptom-detected and true interval cancers were in more advanced stages and were less differentiated. The highest proportion of triple-negative cancers was detected among true interval cancers (P=0.002). At 5 years of follow-up, the disease-free survival rates for screen-detected, true interval, and symptom-detected cancers were 87.5% (95% confidence interval, 80.5-95.2%), 64.1% (46.4-88.5%), and 79.4% (71.0-88.8%), respectively, and the overall survival rates were 94.5% (89.3-99.9%), 65.5% (47.1-91.2%), and 85.6% (78.3-93.6%), respectively. True interval cancers had the highest hazard ratio for relapse prediction (1.89; 0.67-5.31) and a hazard ratio of death of 5.55 (1.61-19.15) after adjustment for tumor-node-metastasis stage and phenotype. Clinically detected tumors, especially true interval cancers, more frequently showed biological features related to worse prognosis and were associated with poorer survival even after adjustment for clinical-pathological characteristics. PMID:22584215

  20. Computerized detection of unruptured aneurysms in MRA images: reduction of false positives using anatomical location features

    NASA Astrophysics Data System (ADS)

    Uchiyama, Yoshikazu; Gao, Xin; Hara, Takeshi; Fujita, Hiroshi; Ando, Hiromichi; Yamakawa, Hiroyasu; Asano, Takahiko; Kato, Hiroki; Iwama, Toru; Kanematsu, Masayuki; Hoshi, Hiroaki

    2008-03-01

    The detection of unruptured aneurysms is a major subject in magnetic resonance angiography (MRA). However, their accurate detection is often difficult because of the overlapping between the aneurysm and the adjacent vessels on maximum intensity projection images. The purpose of this study is to develop a computerized method for the detection of unruptured aneurysms in order to assist radiologists in image interpretation. The vessel regions were first segmented using gray-level thresholding and a region growing technique. The gradient concentration (GC) filter was then employed for the enhancement of the aneurysms. The initial candidates were identified in the GC image using a gray-level threshold. For the elimination of false positives (FPs), we determined shape features and an anatomical location feature. Finally, rule-based schemes and quadratic discriminant analysis were employed along with these features for distinguishing between the aneurysms and the FPs. The sensitivity for the detection of unruptured aneurysms was 90.0% with 1.52 FPs per patient. Our computerized scheme can be useful in assisting the radiologists in the detection of unruptured aneurysms in MRA images.

  1. A stereo vision-based obstacle detection system in vehicles

    NASA Astrophysics Data System (ADS)

    Huh, Kunsoo; Park, Jaehak; Hwang, Junyeon; Hong, Daegun

    2008-02-01

    Obstacle detection is a crucial issue for driver assistance systems as well as for autonomous vehicle guidance function and it has to be performed with high reliability to avoid any potential collision with the front vehicle. The vision-based obstacle detection systems are regarded promising for this purpose because they require little infrastructure on a highway. However, the feasibility of these systems in passenger car requires accurate and robust sensing performance. In this paper, an obstacle detection system using stereo vision sensors is developed. This system utilizes feature matching, epipoplar constraint and feature aggregation in order to robustly detect the initial corresponding pairs. After the initial detection, the system executes the tracking algorithm for the obstacles. The proposed system can detect a front obstacle, a leading vehicle and a vehicle cutting into the lane. Then, the position parameters of the obstacles and leading vehicles can be obtained. The proposed obstacle detection system is implemented on a passenger car and its performance is verified experimentally.

  2. An FPGA-based rapid wheezing detection system.

    PubMed

    Lin, Bor-Shing; Yen, Tian-Shiue

    2014-02-01

    Wheezing is often treated as a crucial indicator in the diagnosis of obstructive pulmonary diseases. A rapid wheezing detection system may help physicians to monitor patients over the long-term. In this study, a portable wheezing detection system based on a field-programmable gate array (FPGA) is proposed. This system accelerates wheezing detection, and can be used as either a single-process system, or as an integrated part of another biomedical signal detection system. The system segments sound signals into 2-second units. A short-time Fourier transform was used to determine the relationship between the time and frequency components of wheezing sound data. A spectrogram was processed using 2D bilateral filtering, edge detection, multithreshold image segmentation, morphological image processing, and image labeling, to extract wheezing features according to computerized respiratory sound analysis (CORSA) standards. These features were then used to train the support vector machine (SVM) and build the classification models. The trained model was used to analyze sound data to detect wheezing. The system runs on a Xilinx Virtex-6 FPGA ML605 platform. The experimental results revealed that the system offered excellent wheezing recognition performance (0.912). The detection process can be used with a clock frequency of 51.97 MHz, and is able to perform rapid wheezing classification. PMID:24481034

  3. Study of features based on nonlinear dynamical modeling in ECG arrhythmia detection and classification.

    PubMed

    Owis, Mohamed I; Abou-Zied, Ahmed H; Youssef, Abou-Bakr M; Kadah, Yasser M

    2002-07-01

    We present a study of the nonlinear dynamics of electrocardiogram (ECG) signals for arrhythmia characterization. The correlation dimension and largest Lyapunov exponent are used to model the chaotic nature of five different classes of ECG signals. The model parameters are evaluated for a large number of real ECG signals within each class and the results are reported. The presented algorithms allow automatic calculation of the features. The statistical analysis of the calculated features indicates that they differ significantly between normal heart rhythm and the different arrhythmia types and, hence, can be rather useful in ECG arrhythmia detection. On the other hand, the results indicate that the discrimination between different arrhythmia types is difficult using such features. The results of this work are supported by statistical analysis that provides a clear outline for the potential uses and limitations of these features. PMID:12083309

  4. Automatic detection of anatomical features on 3D ear impressions for canonical representation.

    PubMed

    Baloch, Sajjad; Melkisetoglu, Rupen; Flöry, Simon; Azernikov, Sergei; Slabaugh, Greg; Zouhar, Alexander; Fang, Tong

    2010-01-01

    We propose a shape descriptor for 3D ear impressions, derived from a comprehensive set of anatomical features. Motivated by hearing aid (HA) manufacturing, the selection of the anatomical features is carried out according to their uniqueness and importance in HA design. This leads to a canonical ear signature that is highly distinctive and potentially well suited for classification. First, the anatomical features are characterized into generic topological and geometric features, namely concavities, elbows, ridges, peaks, and bumps on the surface of the ear. Fast and robust algorithms are then developed for their detection. This indirect approach ensures the generality of the algorithms with potential applications in biomedicine, biometrics, and reverse engineering. PMID:20879444

  5. Complex Features in Lotka-Volterra Systems with Behavioral Adaptation

    NASA Astrophysics Data System (ADS)

    Tebaldi, Claudio; Lacitignola, Deborah

    Lotka-Volterra systems have played a fundamental role for mathematical modelling in many branches of theoretical biology and proved to describe, at least qualitatively, the essential features of many phenomena, see for example Murray [Murray 2002]. Furthermore models of that kind have been considered successfully also in quite different and less mathematically formalized context: Goodwin' s model of economic growth cycles [Goodwin 1967] and urban dynamics [Dendrinos 1992] are only two of a number of examples. Such systems can certainly be defined as complex ones and in fact the aim of modelling was essentially to clarify mechanims rather than to provide actual precise simulations and predictions. With regards to complex systems, we recall that one of their main feature, no matter of the specific definition one has in mind, is adaptation, i. e. the ability to adjust.

  6. Blind Detection of Region Duplication Forgery Using Fractal Coding and Feature Matching.

    PubMed

    Jenadeleh, Mohsen; Ebrahimi Moghaddam, Mohsen

    2016-05-01

    Digital image forgery detection is important because of its wide use in applications such as medical diagnosis, legal investigations, and entertainment. Copy-move forgery is one of the famous techniques, which is used in region duplication. Many of the existing copy-move detection algorithms cannot effectively blind detect duplicated regions that are made by powerful image manipulation software like Photoshop. In this study, a new method is proposed for blind detecting manipulations in digital images based on modified fractal coding and feature vector matching. The proposed method not only detects typical copy-move forgery, but also finds multiple copied forgery regions for images that are subjected to rotation, scaling, reflection, and a mixture of these postprocessing operations. The proposed method is robust against tampered images undergoing attacks such as Gaussian blurring, contrast scaling, and brightness adjustment. The experimental results demonstrated the validity and efficiency of the method. PMID:27122398

  7. Registration using natural features for augmented reality systems.

    PubMed

    Yuan, M L; Ong, S K; Nee, A Y C

    2006-01-01

    Registration is one of the most difficult problems in augmented reality (AR) systems. In this paper, a simple registration method using natural features based on the projective reconstruction technique is proposed. This method consists of two steps: embedding and rendering. Embedding involves specifying four points to build the world coordinate system on which a virtual object will be superimposed. In rendering, the Kanade-Lucas-Tomasi (KLT) feature tracker is used to track the natural feature correspondences in the live video. The natural features that have been tracked are used to estimate the corresponding projective matrix in the image sequence. Next, the projective reconstruction technique is used to transfer the four specified points to compute the registration matrix for augmentation. This paper also proposes a robust method for estimating the projective matrix, where the natural features that have been tracked are normalized (translation and scaling) and used as the input data. The estimated projective matrix will be used as an initial estimate for a nonlinear optimization method that minimizes the actual residual errors based on the Levenberg-Marquardt (LM) minimization method, thus making the results more robust and stable. The proposed registration method has three major advantages: 1) It is simple, as no predefined fiducials or markers are used for registration for either indoor and outdoor AR applications. 2) It is robust, because it remains effective as long as at least six natural features are tracked during the entire augmentation, and the existence of the corresponding projective matrices in the live video is guaranteed. Meanwhile, the robust method to estimate the projective matrix can obtain stable results even when there are some outliers during the tracking process. 3) Virtual objects can still be superimposed on the specified areas, even if some parts of the areas are occluded during the entire process. Some indoor and outdoor experiments have

  8. No evidence for feature binding by pigeons in a change detection task.

    PubMed

    Lazareva, Olga F; Wasserman, Edward A

    2016-02-01

    We trained pigeons to respond to one key when two consecutive displays were the same as one another (no-change trial) and to respond to another key when the two displays were different from one another (change trial; change detection task). Change-trial displays were distinguished by a change in all three features (color, orientation, and location) of all four items presented in the display. Pigeons learned this change-no change discrimination to high levels of accuracy. In Experiments 1 and 2, we compared replace trials in which one or two features were replaced by novel features to switch trials in which the features were exchanged among the objects. Pigeons reported both replace and switch trials as "no-change" trials. In contrast, adult humans in Experiment 3 reported both types of trials as "change" trials and showed robust evidence for feature binding. In Experiment 4, we manipulated the total number of objects in the display and the number of objects that underwent change. Unlike people, pigeons showed strong control by the number of feature changes in the second display; pigeons' failure to exhibit feature binding may therefore be attributed to their failure to attend to items in the displays as integral objects. PMID:26394018

  9. Bathroom watching using a breath detection system

    NASA Astrophysics Data System (ADS)

    Nishiura, Tomofumi; Nakajima, Masato

    2004-10-01

    Recently, domestic accidents have been increasing in Japan. These kinds of accidents occur in private areas such as bedrooms, toilets and bathrooms, and tend to be found too late. Accidents, particularly those occurring in the bathroom, can often result in death. Many systems which have been proposed or which are in use are designed to detect body motion in the bathroom, and determine that a bather has suddenly taken ill when movement ceases. However, the relaxed posture of a person bathing is actually very similar to that of a person who has passed out. It is therefore very difficult to differentiate between the two postures. We have developed a watching system for bathrooms. The new feature of this system lies in its ability to detect a person"s breathing by using an FG vision sensor. From the experiment, it was found that the false alarm rate is expected to reach less than 0.0001% when waiting time is set to 36.8 seconds.

  10. Fault detection and feature analysis in interferometric fringe patterns by the application of wavelet filters in convolution processors

    NASA Astrophysics Data System (ADS)

    Krueger, Sven; Wernicke, Guenther K.; Osten, Wolfgang; Kayser, Daniel; Demoli, Nazif; Gruber, Hartmut

    2001-01-01

    The detection and classification of faults is a major task for optical nondestructive testing in industrial quality control. Interferometric fringes, obtained by real-time optical measurement methods, contain a large amount of image data with information about possible defect features. This mass of data must be reduced for further evaluation. One possible way is the filtering of these images by applying the adaptive wavelet transform, which has been proved to be a capable tool in the detection of structures with definite spatial resolution. In this paper we show the extraction and classification of disturbances in interferometric fringe patterns, the application of several wavelet functions with different parameters for the detection of faults, and the combination of wavelet filters for fault classification. Examples of fringe patterns of known and varying vault parameters are processed showing the trend of the extracted features in order to draw conclusions concerning the relation between the feature, the filter parameter, and the fault attributes. Real-time processing was achieved by importing video sequences in a hybrid optoelectronic system with digital image processing and an optical correlation module. The optical correlator system is based on liquid- crystal spatial light modulators, which are addressed with image and filter data. Results of digital simulation and optical realization are compared.

  11. A road sign detection and recognition system for mobile devices

    NASA Astrophysics Data System (ADS)

    Xiong, Bo; Izmirli, Ozgur

    2012-01-01

    We present an automatic road sign detection and recognition service system for mobile devices. The system is based on a client-server architecture which allows mobile users to take pictures of road signs and request detection and recognition service from a centralized server for processing. The preprocessing, detection and recognition take place at the server end and consequently, the result is sent back to the mobile device. For road sign detection, we use particular color features calculated from the input image. Recognition is implemented using a neural network based on normalized color histogram features. We report on the effects of various parameters on recognition accuracy. Our results demonstrate that the system can provide an efficient framework for locale-dependent road sign recognition with multilingual support.

  12. [The Argentine Health System: organization and financial features].

    PubMed

    Arce, Hugo E

    2012-01-01

    The Argentine health system is defined by the following features: a) federal country organization; b) coexistence of public and private services with either outpatients or inpatients; c) fragmented entities of social security, most of these originated outside of the state organization. Components of the system are described and weighed; making decisions strength between national and provincial health authorities is analyzed and the Argentine system is compared with that of other countries. Statistical data on distribution of health expenditures and coverage of health services are presented as well as financial flow among diverse funding sources, insurers, providers and users of each sector. PMID:23089118

  13. Dynamical Systems Analysis of Fully 3D Ocean Features

    NASA Astrophysics Data System (ADS)

    Pratt, L. J.

    2011-12-01

    Dynamical systems analysis of transport and stirring processes has been developed most thoroughly for 2D flow fields. The calculation of manifolds, turnstile lobes, transport barriers, etc. based on observations of the ocean is most often conducted near the sea surface, whereas analyses at depth, usually carried out with model output, is normally confined to constant-z surfaces. At the meoscale and larger, ocean flows are quasi 2D, but smaller scale (submesoscale) motions, including mixed layer phenomena with significant vertical velocity, may be predominantly 3D. The zoology of hyperbolic trajectories becomes richer in such cases and their attendant manifolds are much more difficult to calculate. I will describe some of the basic geometrical features and corresponding Lagrangian Coherent Features expected to arise in upper ocean fronts, eddies, and Langmuir circulations. Traditional GFD models such as the rotating can flow may capture the important generic features. The dynamical systems approach is most helpful when these features are coherent and persistent and the implications and difficulties for this requirement in fully 3D flows will also be discussed.

  14. Photoelectric detection system. [manufacturing automation

    NASA Technical Reports Server (NTRS)

    Currie, J. R.; Schansman, R. R. (Inventor)

    1982-01-01

    A photoelectric beam system for the detection of the arrival of an object at a discrete station wherein artificial light, natural light, or no light may be present is described. A signal generator turns on and off a signal light at a selected frequency. When the object in question arrives on station, ambient light is blocked by the object, and the light from the signal light is reflected onto a photoelectric sensor which has a delayed electrical output but is of the frequency of the signal light. Outputs from both the signal source and the photoelectric sensor are fed to inputs of an exclusively OR detector which provides as an output the difference between them. The difference signal is a small width pulse occurring at the frequency of the signal source. By filter means, this signal is distinguished from those responsive to sunlight, darkness, or 120 Hz artificial light. In this fashion, the presence of an object is positively established.

  15. Planetary system detection by POINTS

    NASA Technical Reports Server (NTRS)

    Reasenberg, Robert D.

    1993-01-01

    The final report and semiannual reports 1, 2, and 3 in response to the study of 'Planetary System Detection by POINTS' is presented. The grant covered the period from 15 Jun. 1988 through 31 Dec. 1989. The work during that period comprised the further development and refinement of the POINTS concept. The status of the POINTS development at the end of the Grant period was described by Reasenberg in a paper given at the JPL Workshop on Space Interferometry, 12-13 Mar. 1990, and distributed as CfA Preprint 3138. That paper, 'POINTS: a Small Astrometric Interferometer,' follows as Appendix-A. Our proposal P2276-7-09, dated July 1990, included a more detailed description of the state of the development of POINTS at the end of the tenure of Grant NAGW-1355. That proposal, which resulted in Grant NAGW-2497, is included by reference.

  16. EEG error potentials detection and classification using time-frequency features for robot reinforcement learning.

    PubMed

    Boubchir, Larbi; Touati, Youcef; Daachi, Boubaker; Chérif, Arab Ali

    2015-08-01

    In thought-based steering of robots, error potentials (ErrP) can appear when the action resulting from the brain-machine interface (BMI) classifier/controller does not correspond to the user's thought. Using the Steady State Visual Evoked Potentials (SSVEP) techniques, ErrP, which appear when a classification error occurs, are not easily recognizable by only examining the temporal or frequency characteristics of EEG signals. A supplementary classification process is therefore needed to identify them in order to stop the course of the action and back up to a recovery state. This paper presents a set of time-frequency (t-f) features for the detection and classification of EEG ErrP in extra-brain activities due to misclassification observed by a user exploiting non-invasive BMI and robot control in the task space. The proposed features are able to characterize and detect ErrP activities in the t-f domain. These features are derived from the information embedded in the t-f representation of EEG signals, and include the Instantaneous Frequency (IF), t-f information complexity, SVD information, energy concentration and sub-bands' energies. The experiment results on real EEG data show that the use of the proposed t-f features for detecting and classifying EEG ErrP achieved an overall classification accuracy up to 97% for 50 EEG segments using 2-class SVM classifier. PMID:26736619

  17. Feature-Based Change Detection Reveals Inconsistent Individual Differences in Visual Working Memory Capacity.

    PubMed

    Ambrose, Joseph P; Wijeakumar, Sobanawartiny; Buss, Aaron T; Spencer, John P

    2016-01-01

    Visual working memory (VWM) is a key cognitive system that enables people to hold visual information in mind after a stimulus has been removed and compare past and present to detect changes that have occurred. VWM is severely capacity limited to around 3-4 items, although there are robust individual differences in this limit. Importantly, these individual differences are evident in neural measures of VWM capacity. Here, we capitalized on recent work showing that capacity is lower for more complex stimulus dimension. In particular, we asked whether individual differences in capacity remain consistent if capacity is shifted by a more demanding task, and, further, whether the correspondence between behavioral and neural measures holds across a shift in VWM capacity. Participants completed a change detection (CD) task with simple colors and complex shapes in an fMRI experiment. As expected, capacity was significantly lower for the shape dimension. Moreover, there were robust individual differences in behavioral estimates of VWM capacity across dimensions. Similarly, participants with a stronger BOLD response for color also showed a strong neural response for shape within the lateral occipital cortex, intraparietal sulcus (IPS), and superior IPS. Although there were robust individual differences in the behavioral and neural measures, we found little evidence of systematic brain-behavior correlations across feature dimensions. This suggests that behavioral and neural measures of capacity provide different views onto the processes that underlie VWM and CD. Recent theoretical approaches that attempt to bridge between behavioral and neural measures are well positioned to address these findings in future work. PMID:27147986

  18. Feature-Based Change Detection Reveals Inconsistent Individual Differences in Visual Working Memory Capacity

    PubMed Central

    Ambrose, Joseph P.; Wijeakumar, Sobanawartiny; Buss, Aaron T.; Spencer, John P.

    2016-01-01

    Visual working memory (VWM) is a key cognitive system that enables people to hold visual information in mind after a stimulus has been removed and compare past and present to detect changes that have occurred. VWM is severely capacity limited to around 3–4 items, although there are robust individual differences in this limit. Importantly, these individual differences are evident in neural measures of VWM capacity. Here, we capitalized on recent work showing that capacity is lower for more complex stimulus dimension. In particular, we asked whether individual differences in capacity remain consistent if capacity is shifted by a more demanding task, and, further, whether the correspondence between behavioral and neural measures holds across a shift in VWM capacity. Participants completed a change detection (CD) task with simple colors and complex shapes in an fMRI experiment. As expected, capacity was significantly lower for the shape dimension. Moreover, there were robust individual differences in behavioral estimates of VWM capacity across dimensions. Similarly, participants with a stronger BOLD response for color also showed a strong neural response for shape within the lateral occipital cortex, intraparietal sulcus (IPS), and superior IPS. Although there were robust individual differences in the behavioral and neural measures, we found little evidence of systematic brain-behavior correlations across feature dimensions. This suggests that behavioral and neural measures of capacity provide different views onto the processes that underlie VWM and CD. Recent theoretical approaches that attempt to bridge between behavioral and neural measures are well positioned to address these findings in future work. PMID:27147986

  19. Detection of Cardiac Abnormalities from Multilead ECG using Multiscale Phase Alternation Features.

    PubMed

    Tripathy, R K; Dandapat, S

    2016-06-01

    The cardiac activities such as the depolarization and the relaxation of atria and ventricles are observed in electrocardiogram (ECG). The changes in the morphological features of ECG are the symptoms of particular heart pathology. It is a cumbersome task for medical experts to visually identify any subtle changes in the morphological features during 24 hours of ECG recording. Therefore, the automated analysis of ECG signal is a need for accurate detection of cardiac abnormalities. In this paper, a novel method for automated detection of cardiac abnormalities from multilead ECG is proposed. The method uses multiscale phase alternation (PA) features of multilead ECG and two classifiers, k-nearest neighbor (KNN) and fuzzy KNN for classification of bundle branch block (BBB), myocardial infarction (MI), heart muscle defect (HMD) and healthy control (HC). The dual tree complex wavelet transform (DTCWT) is used to decompose the ECG signal of each lead into complex wavelet coefficients at different scales. The phase of the complex wavelet coefficients is computed and the PA values at each wavelet scale are used as features for detection and classification of cardiac abnormalities. A publicly available multilead ECG database (PTB database) is used for testing of the proposed method. The experimental results show that, the proposed multiscale PA features and the fuzzy KNN classifier have better performance for detection of cardiac abnormalities with sensitivity values of 78.12 %, 80.90 % and 94.31 % for BBB, HMD and MI classes. The sensitivity value of proposed method for MI class is compared with the state-of-art techniques from multilead ECG. PMID:27118009

  20. Detection of Important Atmospheric and Surface Features by Employing Principal Component Image Transformation of GOES Imagery.

    NASA Astrophysics Data System (ADS)

    Hillger, Donald W.; Ellrod, Gary P.

    2003-05-01

    The detection of dust, fire hot spots, and smoke from the Geostationary Operational Environmental Satellite (GOES) is made easier by employing the principal component image (PCI) technique. PCIs are created by an eigenvector transformation of spectral band images from the five-band GOES Imager. The transformation is a powerful tool that provides a new set of images that are linear combinations of the original spectral band images. This facilitates viewing the explained variance or signal in the available imagery, allowing both gross and more subtle features in the imagery to be seen. Whereas this multispectral technique is normally applied to high-spatial-resolution land remote sensing imagery, the application is herein made to lower-spatial-resolution weather satellite imagery for the purpose of feature detection and enhancement. Features used as examples include atmospheric dust as well as forest and range fire hot spots and their resulting smoke plumes. The applications of PCIs to GOES utilized the three infrared window images (bands 2, 4, and 5) in dust situations as well as the visible image (band 1) in smoke situations. Two conclusions of this study are 1) atmospheric and surface features are more easily identified in multiband PCIs than in the enhanced single-band images or even in some two-band difference images and 2) the elimination of certain bands can be made either directly by inspection of the PCIs, discarding bands that do not to contribute to the PCIs showing the desired features, or by including all available bands and letting the transformation process indicate the bands that are useful for detecting the desired features. This technique will be increasingly useful with the introduction of new and increased numbers of spectral bands with current and future satellite instrumentation.

  1. Military personnel recognition system using texture, colour, and SURF features

    NASA Astrophysics Data System (ADS)

    Irhebhude, Martins E.; Edirisinghe, Eran A.

    2014-06-01

    This paper presents an automatic, machine vision based, military personnel identification and classification system. Classification is done using a Support Vector Machine (SVM) on sets of Army, Air Force and Navy camouflage uniform personnel datasets. In the proposed system, the arm of service of personnel is recognised by the camouflage of a persons uniform, type of cap and the type of badge/logo. The detailed analysis done include; camouflage cap and plain cap differentiation using gray level co-occurrence matrix (GLCM) texture feature; classification on Army, Air Force and Navy camouflaged uniforms using GLCM texture and colour histogram bin features; plain cap badge classification into Army, Air Force and Navy using Speed Up Robust Feature (SURF). The proposed method recognised camouflage personnel arm of service on sets of data retrieved from google images and selected military websites. Correlation-based Feature Selection (CFS) was used to improve recognition and reduce dimensionality, thereby speeding the classification process. With this method success rates recorded during the analysis include 93.8% for camouflage appearance category, 100%, 90% and 100% rates of plain cap and camouflage cap categories for Army, Air Force and Navy categories, respectively. Accurate recognition was recorded using SURF for the plain cap badge category. Substantial analysis has been carried out and results prove that the proposed method can correctly classify military personnel into various arms of service. We show that the proposed method can be integrated into a face recognition system, which will recognise personnel in addition to determining the arm of service which the personnel belong. Such a system can be used to enhance the security of a military base or facility.

  2. Detection, tracking and event localization of interesting features in 4-D atmospheric data

    NASA Astrophysics Data System (ADS)

    Limbach, S.; Schömer, E.; Wernli, H.

    2011-11-01

    We introduce a novel algorithm for the efficient detection and tracking of interesting features in spatial-temporal atmospheric data, as well as for the precise localization of the occurring genesis, lysis, merging and splitting events. The algorithm is based on the well-known region growing segmentation method. We extended the basic idea towards the analysis of the complete 4-D dataset, identifying segments representing the spatial features and their development over time. Each segment consists of one set of distinct 3-D features per time step. The algorithm keeps track of the successors of each 3-D feature, constructing the so-called event graph of each segment. The precise localization of the splitting events is based on a search for all grid points inside the initial 3-D feature which have a similar distance to all successive 3-D features of the next time step. The merging event is localized analogously considering inverted direction of time. We tested the implementation on a four-dimensional field of wind speed data from European Centre for Medium-Range Weather Forecasts (ECMWF) analyses and computed a climatology of upper-tropospheric jet streams and their events. We compare our results with a previous climatology, investigate the statistical distribution of the merging and splitting events, and illustrate the meteorological significance of the jet splitting events with a case study. A brief outlook is given on additional potential applications of the 4-D data segmentation technique.

  3. Generalized Local-to-Global Shape Feature Detection Based on Graph Wavelets.

    PubMed

    Li, Nannan; Wang, Shengfa; Zhong, Ming; Su, Zhixun; Qin, Hong

    2016-09-01

    Informative and discriminative feature descriptors are vital in qualitative and quantitative shape analysis for a large variety of graphics applications. Conventional feature descriptors primarily concentrate on discontinuity of certain differential attributes at different orders that naturally give rise to their discriminative power in depicting point, line, small patch features, etc. This paper seeks novel strategies to define generalized, user-specified features anywhere on shapes. Our new region-based feature descriptors are constructed primarily with the powerful spectral graph wavelets (SGWs) that are both multi-scale and multi-level in nature, incorporating both local (differential) and global (integral) information. To our best knowledge, this is the first attempt to organize SGWs in a hierarchical way and unite them with the bi-harmonic diffusion field towards quantitative region-based shape analysis. Furthermore, we develop a local-to-global shape feature detection framework to facilitate a host of graphics applications, including partial matching without point-wise correspondence, coarse-to-fine recognition, model recognition, etc. Through the extensive experiments and comprehensive comparisons with the state-of-the-art, our framework has exhibited many attractive advantages such as being geometry-aware, robust, discriminative, isometry-invariant, etc. PMID:26561459

  4. Gaussian Process Regression-Based Video Anomaly Detection and Localization With Hierarchical Feature Representation.

    PubMed

    Cheng, Kai-Wen; Chen, Yie-Tarng; Fang, Wen-Hsien

    2015-12-01

    This paper presents a hierarchical framework for detecting local and global anomalies via hierarchical feature representation and Gaussian process regression (GPR) which is fully non-parametric and robust to the noisy training data, and supports sparse features. While most research on anomaly detection has focused more on detecting local anomalies, we are more interested in global anomalies that involve multiple normal events interacting in an unusual manner, such as car accidents. To simultaneously detect local and global anomalies, we cast the extraction of normal interactions from the training videos as a problem of finding the frequent geometric relations of the nearby sparse spatio-temporal interest points (STIPs). A codebook of interaction templates is then constructed and modeled using the GPR, based on which a novel inference method for computing the likelihood of an observed interaction is also developed. Thereafter, these local likelihood scores are integrated into globally consistent anomaly masks, from which anomalies can be succinctly identified. To the best of our knowledge, it is the first time GPR is employed to model the relationship of the nearby STIPs for anomaly detection. Simulations based on four widespread datasets show that the new method outperforms the main state-of-the-art methods with lower computational burden. PMID:26394423

  5. Aircraft Detection from VHR Images Based on Circle-Frequency Filter and Multilevel Features

    PubMed Central

    Gao, Feng; Li, Bo

    2013-01-01

    Aircraft automatic detection from very high-resolution (VHR) images plays an important role in a wide variety of applications. This paper proposes a novel detector for aircraft detection from very high-resolution (VHR) remote sensing images. To accurately distinguish aircrafts from background, a circle-frequency filter (CF-filter) is used to extract the candidate locations of aircrafts from a large size image. A multi-level feature model is then employed to represent both local appearance and spatial layout of aircrafts by means of Robust Hue Descriptor and Histogram of Oriented Gradients. The experimental results demonstrate the superior performance of the proposed method. PMID:24163637

  6. JHUF-5 Steganalyzer: Huffman Based Steganalytic Features for Reliable Detection of YASS in JPEG Images

    NASA Astrophysics Data System (ADS)

    Bhat, Veena H.; Krishna, S.; Shenoy, P. Deepa; Venugopal, K. R.; Patnaik, L. M.

    Yet Another Steganographic Scheme (YASS) is one of the recent steganographic schemes that embeds data at randomized locations in a JPEG image, to avert blind steganalysis. In this paper we present JHUF-5, a statistical steganalyzer wherein J stands for JPEG, HU represents Huffman based statistics, F denotes FR Index (ratio of file size to resolution) and 5 - the number of features used as predictors for classification. The contribution of this paper is twofold; first the ability of the proposed blind steganalyzer to detect YASS reliably with a consistent performance for several settings. Second, the algorithm is based on only five uncalibrated features for efficient prediction as against other techniques, some of which employs several hundreds of predictors. The detection accuracy of the proposed method is found to be superior to existing blind steganalysis techniques.

  7. Digital Image Forgery Detection Using JPEG Features and Local Noise Discrepancies

    PubMed Central

    Liu, Bo; Pun, Chi-Man; Yuan, Xiao-Chen

    2014-01-01

    Wide availability of image processing software makes counterfeiting become an easy and low-cost way to distort or conceal facts. Driven by great needs for valid forensic technique, many methods have been proposed to expose such forgeries. In this paper, we proposed an integrated algorithm which was able to detect two commonly used fraud practices: copy-move and splicing forgery in digital picture. To achieve this target, a special descriptor for each block was created combining the feature from JPEG block artificial grid with that from noise estimation. And forehand image quality assessment procedure reconciled these different features by setting proper weights. Experimental results showed that, compared to existing algorithms, our proposed method is effective on detecting both copy-move and splicing forgery regardless of JPEG compression ratio of the input image. PMID:24955389

  8. A Speech Endpoint Detection Algorithm Based on BP Neural Network and Multiple Features

    NASA Astrophysics Data System (ADS)

    Shi, Yong-Qiang; Li, Ru-Wei; Zhang, Shuang; Wang, Shuai; Yi, Xiao-Qun

    Focusing on a sharp decline in the performance of endpoint detection algorithm in a complicated noise environment, a new speech endpoint detection method based on BPNN (back propagation neural network) and multiple features is presented. Firstly, maximum of short-time autocorrelation function and spectrum variance of speech signals are extracted respectively. Secondly, these feature vectors as the input of BP neural network are trained and modeled and then the Genetic Algorithm is used to optimize the BP Neural Network. Finally, the signal's type is determined according to the output of Neural Network. The experiments show that the correct rate of this proposed algorithm is improved, because this method has better robustness and adaptability than algorithm based on maximum of short-time autocorrelation function or spectrum variance.

  9. Non-invasive detection of liver fibrosis: MR imaging features vs. MR elastography

    PubMed Central

    Venkatesh, Sudhakar K.; Yin, Meng; Takahashi, Naoki; Glockner, James F.; Talwalkar, Jayant A.; Ehman, Richard L.

    2016-01-01

    Purpose To compare accuracy of morphological features of liver on MRI and liver stiffness with MR elastography (MRE) for detection of significant liver fibrosis and cirrhosis. Materials and Methods In this retrospective study, we evaluated 62 patients who underwent liver MRI with MRE and histological confirmation of liver fibrosis within 6 months. Two radiologists, blinded to histology results, independently evaluated liver parenchyma texture, surface nodularity, signs of volumetric changes and portal hypertension for presence of significant fibrosis and cirrhosis. Two more readers independently calculated mean liver stiffness values with MRE. Interobserver agreement was evaluated with kappa and intra-class correlation coefficient (ICC) analysis. Diagnostic accuracy was assessed with area under receiver operating characteristic (AUROC) analysis. Comparison of AUROCs of MRI and MRE was performed. Results Liver fibrosis was present in 37 patients. The interobserver agreement was poor to good (kappa= 0.12 - 0.74) for MRI features and excellent for MRE (ICC, 0.97, 95% CI, 0.95-0.98). MRI features had 48.5-87.9%sensitivity, 55.2%-100%specificity and 71.5-81.6% accuracy //for detection of significant fibrosis. MRE performed better with 100% sensitivity, 96.5% specificity and 98.9% accuracy .For the detection of cirrhosis, MRE performed better than MRI features with 88.2% sensitivity (vs.41.2-82.3%), 91.1% specificity (vs. 64.4-95.6%) and 93.5% accuracy (vs. 60.6%-80.5%) Among the MRI features, surface nodularity and overall impression had the best accuracies of 80.3% and 81.6% for detection of significant fibrosis respectively. For cirrhosis, parenchyma texture and overall impression had the best accuracies of 80.5% and 79.7% respectively . Overall, MRE had significantly greater AUROC than MRI features for detection of both significant fibrosis (0.98.9 vs 0.71-0.82, p<0.001) and cirrhosis (0.93.5-vs. 0.61 -0.80.5, p<0.01). Conclusion MRE is superior to MRI for the non

  10. Detection, tracking and event localization of jet stream features in 4-D atmospheric data

    NASA Astrophysics Data System (ADS)

    Limbach, S.; Schömer, E.; Wernli, H.

    2012-04-01

    We introduce a novel algorithm for the efficient detection and tracking of features in spatiotemporal atmospheric data, as well as for the precise localization of the occurring genesis, lysis, merging and splitting events. The algorithm works on data given on a four-dimensional structured grid. Feature selection and clustering are based on adjustable local and global criteria, feature tracking is predominantly based on spatial overlaps of the feature's full volumes. The resulting 3-D features and the identified correspondences between features of consecutive time steps are represented as the nodes and edges of a directed acyclic graph, the event graph. Merging and splitting events appear in the event graph as nodes with multiple incoming or outgoing edges, respectively. The precise localization of the splitting events is based on a search for all grid points inside the initial 3-D feature that have a similar distance to two successive 3-D features of the next time step. The merging event is localized analogously, operating backward in time. As a first application of our method we present a climatology of upper-tropospheric jet streams and their events, based on four-dimensional wind speed data from European Centre for Medium-Range Weather Forecasts (ECMWF) analyses. We compare our results with a climatology from a previous study, investigate the statistical distribution of the merging and splitting events, and illustrate the meteorological significance of the jet splitting events with a case study. A brief outlook is given on additional potential applications of the 4-D data segmentation technique.

  11. Automated detection of prostate cancer using wavelet transform features of ultrasound RF time series

    NASA Astrophysics Data System (ADS)

    Aboofazeli, Mohammad; Abolmaesumi, Purang; Moradi, Mehdi; Sauerbrei, Eric; Siemens, Robert; Boag, Alexander; Mousavi, Parvin

    2009-02-01

    The aim of this research was to investigate the performance of wavelet transform based features of ultrasound radiofrequency (RF) time series for automated detection of prostate cancer tumors in transrectal ultrasound images. Sequential frames of RF echo signals from 35 extracted prostate specimens were recorded in parallel planes, while the ultrasound probe and the tissue were fixed in position in each imaging plane. The sequence of RF echo signal samples corresponding to a particular spot in tissue imaging plane constitutes one RF time series. Each region of interest (ROI) of ultrasound image was represented by three groups of features of its time series, namely, wavelet, spectral and fractal features. Wavelet transform approximation and detail sequences of each ROI were averaged and used as wavelet features. The average value of the normalized spectrum in four quarters of the frequency range along with the intercept and slope of a regression line fitted to the values of the spectrum versus normalized frequency plot formed six spectral features. Fractal dimension (FD) of the RF time series were computed based on the Higuchi's approach. A support vector machine (SVM) classifier was used to classify the ROIs. The results indicate that combining wavelet coefficient based features with previously proposed spectral and fractal features of RF time series data would increase the area under ROC curve from 93.1% to 95.0%, respectively. Furthermore, the accuracy, sensitivity, and specificity increases to 91.7%, 86.6%, and 94.7%, from 85.7%, 85.2%, and 86.1%, respectively, using only spectral and fractal features.

  12. Spatial-temporal features of thermal images for Carpal Tunnel Syndrome detection

    NASA Astrophysics Data System (ADS)

    Estupinan Roldan, Kevin; Ortega Piedrahita, Marco A.; Benitez, Hernan D.

    2014-02-01

    Disorders associated with repeated trauma account for about 60% of all occupational illnesses, Carpal Tunnel Syndrome (CTS) being the most consulted today. Infrared Thermography (IT) has come to play an important role in the field of medicine. IT is non-invasive and detects diseases based on measuring temperature variations. IT represents a possible alternative to prevalent methods for diagnosis of CTS (i.e. nerve conduction studies and electromiography). This work presents a set of spatial-temporal features extracted from thermal images taken in healthy and ill patients. Support Vector Machine (SVM) classifiers test this feature space with Leave One Out (LOO) validation error. The results of the proposed approach show linear separability and lower validation errors when compared to features used in previous works that do not account for temperature spatial variability.

  13. First Science Results from Solar Data Mining Using Automated Feature Detection

    NASA Astrophysics Data System (ADS)

    Martens, P. C.

    2014-12-01

    The SDO Feature Finding Team (FFT) has produced 16 automated feature tracking modules for data from SDO, LASCO, and ground-based H-alpha observatories. The metadata produced by those modules and others are available from the Heliophysics Events Knowledgebase (HEK) and the Virtual Solar Observatory (VSO). Having metadata available for large amounts of events and phenomena, obtained with consistent detection criteria unlike catalogs produced by human observers, allows researchers to effectively search solar data for patterns. I will show a number of science results obtained recently. Not surprisingly several of the patterns are well known (e.g. flares occur mostly in active regions), but some really surprising new trends have been discovered as well, in at least one case upending scientific consensus. These results show the power and promise that systematic feature recognition and data mining holds for solar physics.

  14. PlanetPack software tool for exoplanets detection: coming new features

    NASA Astrophysics Data System (ADS)

    Baluev, Roman V.

    2014-07-01

    We briefly overview the new features of PlanetPack2, the forthcoming update of PlanetPack, which is a software tool for exoplanets detection and characterization from Doppler radial velocity data. Among other things, this major update brings parallelized computing, new advanced models of the Doppler noise, handling of the so-called Keplerian periodogram, and routines for transits fitting and transit timing variation analysis.

  15. Automated detection of anesthetic depth levels using chaotic features with artificial neural networks.

    PubMed

    Lalitha, V; Eswaran, C

    2007-12-01

    Monitoring the depth of anesthesia (DOA) during surgery is very important in order to avoid patients' interoperative awareness. Since the traditional methods of assessing DOA which involve monitoring the heart rate, pupil size, sweating etc, may vary from patient to patient depending on the type of surgery and the type of drug administered, modern methods based on electroencephalogram (EEG) are preferred. EEG being a nonlinear signal, it is appropriate to use nonlinear chaotic parameters to identify the anesthetic depth levels. This paper discusses an automated detection method of anesthetic depth levels based on EEG recordings using non-linear chaotic features and neural network classifiers. Three nonlinear parameters, namely, correlation dimension (CD), Lyapunov exponent (LE) and Hurst exponent (HE) are used as features and two neural network models, namely, multi-layer perceptron network (feed forward model) and Elman network (feedback model) are used for classification. The neural network models are trained and tested with single and multiple features derived from chaotic parameters and the performances are evaluated in terms of sensitivity, specificity and overall accuracy. It is found from the experimental results that the Lyapunov exponent feature with Elman network yields an overall accuracy of 99% in detecting the anesthetic depth levels. PMID:18041276

  16. Detection of sea mines in sonar imagery using higher-order spectral features

    NASA Astrophysics Data System (ADS)

    Chandran, Vinod; Elgar, Steve L.

    1999-08-01

    A new approach to detection of sea-mines in sonar imagery that improves the detection density ACF method is presented. The steps are: 1) background normalization, 2) spatially adaptive Wiener filtering, 3) convolution with a 2D FIR filter matched to the target signature, 4) adaptive thresholding to reduce noise, 5) extraction of higher-order spectral features to capture the spatial correlations, 6) extraction of size, strength, and density features, 7) optimal feature selection, and 8) classification. An adaptive Wiener filter is applied to remove noise without destroying the structural information in the mine shapes. The FIR filter is designed to suppress noise and clutter, while enhancing the target signature. A double peak pattern is revealed as the filter passes over highlight and shadow regions. The location, size, and orientation of this pattern can vary. Higher-order spectral features capture the spatial correlations in this pattern and provide invariance to translation and scaling. The approach has been tested on the CSS Sonar 3 database of 60 images with about 84 percent classification accuracy and 11 percent probability of false alarm.

  17. Sequential Filtering Processes Shape Feature Detection in Crickets: A Framework for Song Pattern Recognition

    PubMed Central

    Hedwig, Berthold G.

    2016-01-01

    Intraspecific acoustic communication requires filtering processes and feature detectors in the auditory pathway of the receiver for the recognition of species-specific signals. Insects like acoustically communicating crickets allow describing and analysing the mechanisms underlying auditory processing at the behavioral and neural level. Female crickets approach male calling song, their phonotactic behavior is tuned to the characteristic features of the song, such as the carrier frequency and the temporal pattern of sound pulses. Data from behavioral experiments and from neural recordings at different stages of processing in the auditory pathway lead to a concept of serially arranged filtering mechanisms. These encompass a filter for the carrier frequency at the level of the hearing organ, and the pulse duration through phasic onset responses of afferents and reciprocal inhibition of thoracic interneurons. Further, processing by a delay line and coincidence detector circuit in the brain leads to feature detecting neurons that specifically respond to the species-specific pulse rate, and match the characteristics of the phonotactic response. This same circuit may also control the response to the species-specific chirp pattern. Based on these serial filters and the feature detecting mechanism, female phonotactic behavior is shaped and tuned to the characteristic properties of male calling song. PMID:26941647

  18. Sequential Filtering Processes Shape Feature Detection in Crickets: A Framework for Song Pattern Recognition.

    PubMed

    Hedwig, Berthold G

    2016-01-01

    Intraspecific acoustic communication requires filtering processes and feature detectors in the auditory pathway of the receiver for the recognition of species-specific signals. Insects like acoustically communicating crickets allow describing and analysing the mechanisms underlying auditory processing at the behavioral and neural level. Female crickets approach male calling song, their phonotactic behavior is tuned to the characteristic features of the song, such as the carrier frequency and the temporal pattern of sound pulses. Data from behavioral experiments and from neural recordings at different stages of processing in the auditory pathway lead to a concept of serially arranged filtering mechanisms. These encompass a filter for the carrier frequency at the level of the hearing organ, and the pulse duration through phasic onset responses of afferents and reciprocal inhibition of thoracic interneurons. Further, processing by a delay line and coincidence detector circuit in the brain leads to feature detecting neurons that specifically respond to the species-specific pulse rate, and match the characteristics of the phonotactic response. This same circuit may also control the response to the species-specific chirp pattern. Based on these serial filters and the feature detecting mechanism, female phonotactic behavior is shaped and tuned to the characteristic properties of male calling song. PMID:26941647

  19. Systemic connective tissue features in women with fibromuscular dysplasia.

    PubMed

    O'Connor, Sarah; Kim, Esther Sh; Brinza, Ellen; Moran, Rocio; Fendrikova-Mahlay, Natalia; Wolski, Kathy; Gornik, Heather L

    2015-10-01

    Fibromuscular dysplasia (FMD) is a non-atherosclerotic disease associated with hypertension, headache, dissection, stroke, and aneurysm. The etiology is unknown but hypothesized to involve genetic and environmental components. Previous studies suggest a possible overlap of FMD with other connective tissue diseases that present with dissections and aneurysms. The aim of this study was to investigate the prevalence of connective tissue physical features in FMD. A total of 142 FMD patients were consecutively enrolled at a single referral center (97.9% female, 92.1% of whom had multifocal FMD). Data are reported for 139 female patients. Moderately severe myopia (29.1%), high palate (33.1%), dental crowding (29.7%), and early-onset arthritis (15.6%) were prevalent features. Classic connective features such as hypertelorism, cleft palate, and hypermobility were uncommon. The frequency of systemic connective tissue features was compared between FMD patients with a high vascular risk profile (having had ⩾1 dissection and/or ⩾2 aneurysms) and those with a standard vascular risk profile. A history of spontaneous pneumothorax (5.9% high risk vs 0% standard risk) and atrophic scarring (17.6% high risk vs 6.8% standard risk) were significantly more prevalent in the high risk group, p<0.05. High palate was observed in 43.1% of the high risk group versus 27.3% in the standard risk group, p=0.055. In conclusion, in a cohort of women with FMD, there was a prevalence of moderately severe myopia, high palate, dental crowding, and early-onset osteoarthritis. However, a characteristic phenotype was not discovered. Several connective tissue features such as high palate and pneumothorax were more prominent among FMD patients with a high vascular risk profile. PMID:26156071

  20. Development of an algorithm for heartbeats detection and classification in Holter records based on temporal and morphological features

    NASA Astrophysics Data System (ADS)

    García, A.; Romano, H.; Laciar, E.; Correa, R.

    2011-12-01

    In this work a detection and classification algorithm for heartbeats analysis in Holter records was developed. First, a QRS complexes detector was implemented and their temporal and morphological characteristics were extracted. A vector was built with these features; this vector is the input of the classification module, based on discriminant analysis. The beats were classified in three groups: Premature Ventricular Contraction beat (PVC), Atrial Premature Contraction beat (APC) and Normal Beat (NB). These beat categories represent the most important groups of commercial Holter systems. The developed algorithms were evaluated in 76 ECG records of two validated open-access databases "arrhythmias MIT BIH database" and "MIT BIH supraventricular arrhythmias database". A total of 166343 beats were detected and analyzed, where the QRS detection algorithm provides a sensitivity of 99.69 % and a positive predictive value of 99.84 %. The classification stage gives sensitivities of 97.17% for NB, 97.67% for PCV and 92.78% for APC.

  1. Spike detection, characterization, and discrimination using feature analysis software written in LabVIEW.

    PubMed

    Stewart, C M; Newlands, S D; Perachio, A A

    2004-12-01

    Rapid and accurate discrimination of single units from extracellular recordings is a fundamental process for the analysis and interpretation of electrophysiological recordings. We present an algorithm that performs detection, characterization, discrimination, and analysis of action potentials from extracellular recording sessions. The program was entirely written in LabVIEW (National Instruments), and requires no external hardware devices or a priori information about action potential shapes. Waveform events are detected by scanning the digital record for voltages that exceed a user-adjustable trigger. Detected events are characterized to determine nine different time and voltage levels for each event. Various algebraic combinations of these waveform features are used as axis choices for 2-D Cartesian plots of events. The user selects axis choices that generate distinct clusters. Multiple clusters may be defined as action potentials by manually generating boundaries of arbitrary shape. Events defined as action potentials are validated by visual inspection of overlain waveforms. Stimulus-response relationships may be identified by selecting any recorded channel for comparison to continuous and average cycle histograms of binned unit data. The algorithm includes novel aspects of feature analysis and acquisition, including higher acquisition rates for electrophysiological data compared to other channels. The program confirms that electrophysiological data may be discriminated with high-speed and efficiency using algebraic combinations of waveform features derived from high-speed digital records. PMID:15501510

  2. Spectrum and Image Texture Features Analysis for Early Blight Disease Detection on Eggplant Leaves

    PubMed Central

    Xie, Chuanqi; He, Yong

    2016-01-01

    This study investigated both spectrum and texture features for detecting early blight disease on eggplant leaves. Hyperspectral images for healthy and diseased samples were acquired covering the wavelengths from 380 to 1023 nm. Four gray images were identified according to the effective wavelengths (408, 535, 624 and 703 nm). Hyperspectral images were then converted into RGB, HSV and HLS images. Finally, eight texture features (mean, variance, homogeneity, contrast, dissimilarity, entropy, second moment and correlation) based on gray level co-occurrence matrix (GLCM) were extracted from gray images, RGB, HSV and HLS images, respectively. The dependent variables for healthy and diseased samples were set as 0 and 1. K-Nearest Neighbor (KNN) and AdaBoost classification models were established for detecting healthy and infected samples. All models obtained good results with the classification rates (CRs) over 88.46% in the testing sets. The results demonstrated that spectrum and texture features were effective for early blight disease detection on eggplant leaves. PMID:27187387

  3. Spectrum and Image Texture Features Analysis for Early Blight Disease Detection on Eggplant Leaves.

    PubMed

    Xie, Chuanqi; He, Yong

    2016-01-01

    This study investigated both spectrum and texture features for detecting early blight disease on eggplant leaves. Hyperspectral images for healthy and diseased samples were acquired covering the wavelengths from 380 to 1023 nm. Four gray images were identified according to the effective wavelengths (408, 535, 624 and 703 nm). Hyperspectral images were then converted into RGB, HSV and HLS images. Finally, eight texture features (mean, variance, homogeneity, contrast, dissimilarity, entropy, second moment and correlation) based on gray level co-occurrence matrix (GLCM) were extracted from gray images, RGB, HSV and HLS images, respectively. The dependent variables for healthy and diseased samples were set as 0 and 1. K-Nearest Neighbor (KNN) and AdaBoost classification models were established for detecting healthy and infected samples. All models obtained good results with the classification rates (CRs) over 88.46% in the testing sets. The results demonstrated that spectrum and texture features were effective for early blight disease detection on eggplant leaves. PMID:27187387

  4. Robust detection of premature ventricular contractions using sparse signal decomposition and temporal features.

    PubMed

    Manikandan, M Sabarimalai; Ramkumar, Barathram; Deshpande, Pranav S; Choudhary, Tilendra

    2015-12-01

    An automated noise-robust premature ventricular contraction (PVC) detection method is proposed based on the sparse signal decomposition, temporal features, and decision rules. In this Letter, the authors exploit sparse expansion of electrocardiogram (ECG) signals on mixed dictionaries for simultaneously enhancing the QRS complex and reducing the influence of tall P and T waves, baseline wanders, and muscle artefacts. They further investigate a set of ten generalised temporal features combined with decision-rule-based detection algorithm for discriminating PVC beats from non-PVC beats. The accuracy and robustness of the proposed method is evaluated using 47 ECG recordings from the MIT/BIH arrhythmia database. Evaluation results show that the proposed method achieves an average sensitivity of 89.69%, and specificity 99.63%. Results further show that the proposed decision-rule-based algorithm with ten generalised features can accurately detect different patterns of PVC beats (uniform and multiform, couplets, triplets, and ventricular tachycardia) in presence of other normal and abnormal heartbeats. PMID:26713158

  5. Unique Features and Spacecraft Applications of Dynamic Isotope Power Systems

    SciTech Connect

    Raab, B.

    1982-01-01

    The dynamic isotope power system represents the most recent attempt to develop a heat-engine generator for space electric power. A major objective in this most recent effort was to increase the power and to reduce the cost of nuclear space power systems to the point where the unique features of this power source could be brought to bear for Earth-orbit missions which could benefit therefrom. This objective was largely achieved; both weight and cost of the dynamic isotope systems are comparable to solar power systems. The dynamic isotope power system, designed for spacecraft requiring prime power in the 500-2000 W range, has been successfully built and ground tested. A number of studies, summarized herein, have demonstrated the advantages of using such a power system instead of the conventional solar system for a variety of Earth-orbit missions. These advantages stem from the unique nature of the dynamic isotope system, different in kind from solar power systems. As a result, in many cases, the spacecraft design can be significantly simplified and more closely harmonized with mission requirements. This overall advantage can be crucial in missions which have stringent pointing, stability, viewing, and/or positioning requirements.

  6. Reduction of False Positives by Internal Features for Polyp Detection in CT-Based Virtual Colonoscopy

    PubMed Central

    Wang, Zigang; Liang, Zhengrong; Li, Lihong; Li, Xiang; Li, Bin; Anderson, Joseph; Harrington, Donald

    2005-01-01

    In this paper, we present a computer-aided detection (CAD) method to extract and use internal features to reduce false positive (FP) rate generated by surface-based measures on the inner colon wall in computed tomographic (CT) colonography. Firstly, a new shape description global curvature, which can provide an overall shape description of the colon wall, is introduced to improve the detection of suspicious patches on the colon wall whose geometrical features are similar to that of the colonic polyps. By a ray-driven edge finder, the volume of each detected patch is extracted as a fitted ellipsoid model. Within the ellipsoid model, CT image density distribution is analyzed. Three types of (geometrical, morphological and textural) internal features are extracted and applied to eliminate the FPs from the detected patches. The presented CAD method was tested by a total of 153 patient datasets in which 45 patients were found with 61 polyps of sizes 4–30 mm by optical colonoscopy. For a 100% detection sensitivity (on polyps), the presented CAD method had an average FPs of 2.68 per patient dataset and eliminated 93.1% of FPs generated by the surface-based measures. The presented CAD method was also evaluated by different polyp sizes. For polyp sizes of 10–30 mm, the method achieved mean number of FPs per dataset of 2.0 with 100% sensitivity. For polyp sizes of 4–10 mm, the method achieved 3.44 FP per dataset with 100% sensitivity. PMID:16475759

  7. Network Anomaly Detection System with Optimized DS Evidence Theory

    PubMed Central

    Liu, Yuan; Wang, Xiaofeng; Liu, Kaiyu

    2014-01-01

    Network anomaly detection has been focused on by more people with the fast development of computer network. Some researchers utilized fusion method and DS evidence theory to do network anomaly detection but with low performance, and they did not consider features of network—complicated and varied. To achieve high detection rate, we present a novel network anomaly detection system with optimized Dempster-Shafer evidence theory (ODS) and regression basic probability assignment (RBPA) function. In this model, we add weights for each senor to optimize DS evidence theory according to its previous predict accuracy. And RBPA employs sensor's regression ability to address complex network. By four kinds of experiments, we find that our novel network anomaly detection model has a better detection rate, and RBPA as well as ODS optimization methods can improve system performance significantly. PMID:25254258

  8. Network anomaly detection system with optimized DS evidence theory.

    PubMed

    Liu, Yuan; Wang, Xiaofeng; Liu, Kaiyu

    2014-01-01

    Network anomaly detection has been focused on by more people with the fast development of computer network. Some researchers utilized fusion method and DS evidence theory to do network anomaly detection but with low performance, and they did not consider features of network-complicated and varied. To achieve high detection rate, we present a novel network anomaly detection system with optimized Dempster-Shafer evidence theory (ODS) and regression basic probability assignment (RBPA) function. In this model, we add weights for each sensor to optimize DS evidence theory according to its previous predict accuracy. And RBPA employs sensor's regression ability to address complex network. By four kinds of experiments, we find that our novel network anomaly detection model has a better detection rate, and RBPA as well as ODS optimization methods can improve system performance significantly. PMID:25254258

  9. Ear feature region detection based on a combined image segmentation algorithm-KRM

    NASA Astrophysics Data System (ADS)

    Jiang, Jingying; Zhang, Hao; Zhang, Qi; Lu, Junsheng; Ma, Zhenhe; Xu, Kexin

    2014-02-01

    Scale Invariant Feature Transform SIFT algorithm is widely used for ear feature matching and recognition. However, the application of the algorithm is usually interfered by the non-target areas within the whole image, and the interference would then affect the matching and recognition of ear features. To solve this problem, a combined image segmentation algorithm i.e. KRM was introduced in this paper, As the human ear recognition pretreatment method. Firstly, the target areas of ears were extracted by the KRM algorithm and then SIFT algorithm could be applied to the detection and matching of features. The present KRM algorithm follows three steps: (1)the image was preliminarily segmented into foreground target area and background area by using K-means clustering algorithm; (2)Region growing method was used to merge the over-segmented areas; (3)Morphology erosion filtering method was applied to obtain the final segmented regions. The experiment results showed that the KRM method could effectively improve the accuracy and robustness of ear feature matching and recognition based on SIFT algorithm.

  10. A fast seed detection using local geometrical feature for automatic tracking of coronary arteries in CTA.

    PubMed

    Han, Dongjin; Doan, Nam-Thai; Shim, Hackjoon; Jeon, Byunghwan; Lee, Hyunna; Hong, Youngtaek; Chang, Hyuk-Jae

    2014-11-01

    We propose a fast seed detection for automatic tracking of coronary arteries in coronary computed tomographic angiography (CCTA). To detect vessel regions, Hessian-based filtering is combined with a new local geometric feature that is based on the similarity of the consecutive cross-sections perpendicular to the vessel direction. It is in turn founded on the prior knowledge that a vessel segment is shaped like a cylinder in axial slices. To improve computational efficiency, an axial slice, which contains part of three main coronary arteries, is selected and regions of interest (ROIs) are extracted in the slice. Only for the voxels belonging to the ROIs, the proposed geometric feature is calculated. With the seed points, which are the centroids of the detected vessel regions, and their vessel directions, vessel tracking method can be used for artery extraction. Here a particle filtering-based tracking algorithm is tested. Using 19 clinical CCTA datasets, it is demonstrated that the proposed method detects seed points and can be used for full automatic coronary artery extraction. ROC (receiver operating characteristic) curve analysis shows the advantages of the proposed method. PMID:25106730

  11. False-alarm mitigation and feature-based discrimination for airborne mine detection

    NASA Astrophysics Data System (ADS)

    Menon, Deepak; Agarwal, Sanjeev; Ganju, Ritesh; Swonger, C. W.

    2004-09-01

    The aim of an anomaly detector is to locate spatial target locations that show significantly different spectral/spatial characteristics as compared to the background. Typical anomaly detectors can achieve a high probability of detection, however at the cost of significantly high false alarm rates. For successful minefield detection there is a need for a further processing step to identify mine-like targets and/or reject non-mine targets in order to improve the mine detection to false alarm ratio. In this paper, we discuss a number of false alarm mitigation (FAM) modalities for MWIR imagery. In particular, we investigate measures based on circularity, gray scale shape profile and reflection symmetry. The performance of these modalities is evaluated for false alarm mitigation using real airborne MWIR data at different times of the day and for different spectral bands. We also motivate a feature based clustering and discrimination scheme based on these modalities to classify similar targets. While false alarm mitigation is primarily used to reject non-mine like targets, feature based clustering can be used to select similar-looking mine-like targets. Minefield detection can subsequently proceed on each localized cluster of similar looking targets.

  12. Research of detecting details and features of infrared polarization imaging experiment

    NASA Astrophysics Data System (ADS)

    Yang, Fan; Liu, Xiao-cheng; Wang, Ji-zhong

    2013-09-01

    Along with modern infrared camouflage technique developed, it is hard to distinguish target and background by using traditional infrared intensity imaging in general because infrared feature of target and background are tending to consistent. To address this issue, a thought that utilizes infrared polarization imaging technique to detect target is proposed in this paper based on analyzing of the principle of infrared polarization imaging. The experiments are carried out for detecting of infrared low-contrast target imaging. Comparing with the infrared intensity images, the average gradient of the infrared polarization image has been improved 155% and the contrast of target and background has been improved 120% in infrared polarization images. The effective experimental data and imaging law between infrared polarization images and infrared intensity images are obtained that, the technology of infrared polarization imaging can detect details of infrared target more clearly than the infrared intensity imaging, and it can obviously increase the contrast between target and background. Therefore, it is more helpful to detecting details and features of target.

  13. Random feature subspace ensemble based Extreme Learning Machine for liver tumor detection and segmentation.

    PubMed

    Huang, Weimin; Yang, Yongzhong; Lin, Zhiping; Huang, Guang-Bin; Zhou, Jiayin; Duan, Yuping; Xiong, Wei

    2014-01-01

    This paper presents a new approach to detect and segment liver tumors. The detection and segmentation of liver tumors can be formulized as novelty detection or two-class classification problem. Each voxel is characterized by a rich feature vector, and a classifier using random feature subspace ensemble is trained to classify the voxels. Since Extreme Learning Machine (ELM) has advantages of very fast learning speed and good generalization ability, it is chosen to be the base classifier in the ensemble. Besides, majority voting is incorporated for fusion of classification results from the ensemble of base classifiers. In order to further increase testing accuracy, ELM autoencoder is implemented as a pre-training step. In automatic liver tumor detection, ELM is trained as a one-class classifier with only healthy liver samples, and the performance is compared with two-class ELM. In liver tumor segmentation, a semi-automatic approach is adopted by selecting samples in 3D space to train the classifier. The proposed method is tested and evaluated on a group of patients' CT data and experiment show promising results. PMID:25571035

  14. Origins and features of oil slicks in the Bohai Sea detected from satellite SAR images.

    PubMed

    Ding, Yi; Cao, Conghua; Huang, Juan; Song, Yan; Liu, Guiyan; Wu, Lingjuan; Wan, Zhenwen

    2016-05-15

    Oil slicks were detected using satellite Synthetic Aperture Radar (SAR) images in 2011. We investigated potential origins and regional and seasonal features of oil slick in the Bohai Sea. Distance between oil slicks and potential origins (ships, seaports, and oil exploitation platforms) and the angle at which oil slicks move relative to potential driving forces were evaluated. Most oil slicks were detected along main ship routes rather than around seaports and oil exploitation platforms. Few oil slicks were detected within 20km of seaports. Directions of oil slicks movement were much more strongly correlated with directions of ship routes than with directions of winds and currents. These findings support the premise that oil slicks in the Bohai Sea most likely originate from illegal disposal of oil-polluted wastes from ships. Seasonal variation of oil slicks followed an annual cycle, with a peak in August and a trough in December. PMID:26988390

  15. Adaptive road crack detection system by pavement classification.

    PubMed

    Gavilán, Miguel; Balcones, David; Marcos, Oscar; Llorca, David F; Sotelo, Miguel A; Parra, Ignacio; Ocaña, Manuel; Aliseda, Pedro; Yarza, Pedro; Amírola, Alejandro

    2011-01-01

    This paper presents a road distress detection system involving the phases needed to properly deal with fully automatic road distress assessment. A vehicle equipped with line scan cameras, laser illumination and acquisition HW-SW is used to storage the digital images that will be further processed to identify road cracks. Pre-processing is firstly carried out to both smooth the texture and enhance the linear features. Non-crack features detection is then applied to mask areas of the images with joints, sealed cracks and white painting, that usually generate false positive cracking. A seed-based approach is proposed to deal with road crack detection, combining Multiple Directional Non-Minimum Suppression (MDNMS) with a symmetry check. Seeds are linked by computing the paths with the lowest cost that meet the symmetry restrictions. The whole detection process involves the use of several parameters. A correct setting becomes essential to get optimal results without manual intervention. A fully automatic approach by means of a linear SVM-based classifier ensemble able to distinguish between up to 10 different types of pavement that appear in the Spanish roads is proposed. The optimal feature vector includes different texture-based features. The parameters are then tuned depending on the output provided by the classifier. Regarding non-crack features detection, results show that the introduction of such module reduces the impact of false positives due to non-crack features up to a factor of 2. In addition, the observed performance of the crack detection system is significantly boosted by adapting the parameters to the type of pavement. PMID:22163717

  16. Adaptive Road Crack Detection System by Pavement Classification

    PubMed Central

    Gavilán, Miguel; Balcones, David; Marcos, Oscar; Llorca, David F.; Sotelo, Miguel A.; Parra, Ignacio; Ocaña, Manuel; Aliseda, Pedro; Yarza, Pedro; Amírola, Alejandro

    2011-01-01

    This paper presents a road distress detection system involving the phases needed to properly deal with fully automatic road distress assessment. A vehicle equipped with line scan cameras, laser illumination and acquisition HW-SW is used to storage the digital images that will be further processed to identify road cracks. Pre-processing is firstly carried out to both smooth the texture and enhance the linear features. Non-crack features detection is then applied to mask areas of the images with joints, sealed cracks and white painting, that usually generate false positive cracking. A seed-based approach is proposed to deal with road crack detection, combining Multiple Directional Non-Minimum Suppression (MDNMS) with a symmetry check. Seeds are linked by computing the paths with the lowest cost that meet the symmetry restrictions. The whole detection process involves the use of several parameters. A correct setting becomes essential to get optimal results without manual intervention. A fully automatic approach by means of a linear SVM-based classifier ensemble able to distinguish between up to 10 different types of pavement that appear in the Spanish roads is proposed. The optimal feature vector includes different texture-based features. The parameters are then tuned depending on the output provided by the classifier. Regarding non-crack features detection, results show that the introduction of such module reduces the impact of false positives due to non-crack features up to a factor of 2. In addition, the observed performance of the crack detection system is significantly boosted by adapting the parameters to the type of pavement. PMID:22163717

  17. Radiological features of uncommon aneurysms of the cardiovascular system

    PubMed Central

    Kalisz, Kevin; Rajiah, Prabhakar

    2016-01-01

    Although aortic aneurysms are the most common type encountered clinically, they do not span the entire spectrum of possible aneurysms of the cardiovascular system. As cross sectional imaging techniques with cardiac computed tomography and cardiac magnetic resonance imaging continue to improve and becomes more commonplace, once rare cardiovascular aneurysms are being encountered at higher rates. In this review, a series of uncommon, yet clinically important, cardiovascular aneurysms will be presented with review of epidemiology, clinical presentation and complications, imaging features and relevant differential diagnoses, and aneurysm management. PMID:27247710

  18. Improving mass detection using combined feature representations from projection views and reconstructed volume of DBT and boosting based classification with feature selection

    NASA Astrophysics Data System (ADS)

    Kim, Dae Hoe; Kim, Seong Tae; Ro, Yong Man

    2015-11-01

    In digital breast tomosynthesis (DBT), image characteristics of projection views and reconstructed volume are different and both have the advantage of detecting breast masses, e.g. reconstructed volume mitigates a tissue overlap, while projection views have less reconstruction blur artifacts. In this paper, an improved mass detection is proposed by using combined feature representations from projection views and reconstructed volume in the DBT. To take advantage of complementary effects on different image characteristics of both data, combined feature representations are extracted from both projection views and reconstructed volume concurrently. An indirect region-of-interest segmentation in projection views, which projects volume-of-interest in reconstructed volume into the corresponding projection views, is proposed to extract combined feature representations. In addition, a boosting based classification with feature selection has been employed for selecting effective feature representations among a large number of combined feature representations, and for reducing false positives. Experiments have been conducted on a clinical data set that contains malignant masses. Experimental results demonstrate that the proposed mass detection can achieve high sensitivity with a small number of false positives. In addition, the experimental results demonstrate that the selected feature representations for classifying masses complementarily come from both projection views and reconstructed volume.

  19. Improving mass detection using combined feature representations from projection views and reconstructed volume of DBT and boosting based classification with feature selection.

    PubMed

    Kim, Dae Hoe; Kim, Seong Tae; Ro, Yong Man

    2015-11-21

    In digital breast tomosynthesis (DBT), image characteristics of projection views and reconstructed volume are different and both have the advantage of detecting breast masses, e.g. reconstructed volume mitigates a tissue overlap, while projection views have less reconstruction blur artifacts. In this paper, an improved mass detection is proposed by using combined feature representations from projection views and reconstructed volume in the DBT. To take advantage of complementary effects on different image characteristics of both data, combined feature representations are extracted from both projection views and reconstructed volume concurrently. An indirect region-of-interest segmentation in projection views, which projects volume-of-interest in reconstructed volume into the corresponding projection views, is proposed to extract combined feature representations. In addition, a boosting based classification with feature selection has been employed for selecting effective feature representations among a large number of combined feature representations, and for reducing false positives. Experiments have been conducted on a clinical data set that contains malignant masses. Experimental results demonstrate that the proposed mass detection can achieve high sensitivity with a small number of false positives. In addition, the experimental results demonstrate that the selected feature representations for classifying masses complementarily come from both projection views and reconstructed volume. PMID:26529080

  20. Object detection via feature synthesis using MDL-based genetic programming.

    PubMed

    Lin, Yingqiang; Bhanu, Bir

    2005-06-01

    In this paper, we use genetic programming (GP) to synthesize composite operators and composite features from combinations of primitive operations and primitive features for object detection. The motivation for using GP is to overcome the human experts' limitations of focusing only on conventional combinations of primitive image processing operations in the feature synthesis. GP attempts many unconventional combinations that in some cases yield exceptionally good results. To improve the efficiency of GP and prevent its well-known code bloat problem without imposing severe restriction on the GP search, we design a new fitness function based on minimum description length principle to incorporate both the pixel labeling error and the size of a composite operator into the fitness evaluation process. To further improve the efficiency of GP, smart crossover, smart mutation and a public library ideas are incorporated to identify and keep the effective components of composite operators. Our experiments, which are performed on selected training regions of a training image to reduce the training time, show that compared to normal GP, our GP algorithm finds effective composite operators more quickly and the learned composite operators can be applied to the whole training image and other similar testing images. Also, compared to a traditional region-of-interest extraction algorithm, the composite operators learned by GP are more effective and efficient for object detection. PMID:15971921

  1. Enhanced flyby science with onboard computer vision: Tracking and surface feature detection at small bodies

    NASA Astrophysics Data System (ADS)

    Fuchs, Thomas J.; Thompson, David R.; Bue, Brian D.; Castillo-Rogez, Julie; Chien, Steve A.; Gharibian, Dero; Wagstaff, Kiri L.

    2015-10-01

    Spacecraft autonomy is crucial to increase the science return of optical remote sensing observations at distant primitive bodies. To date, most small bodies exploration has involved short timescale flybys that execute prescripted data collection sequences. Light time delay means that the spacecraft must operate completely autonomously without direct control from the ground, but in most cases the physical properties and morphologies of prospective targets are unknown before the flyby. Surface features of interest are highly localized, and successful observations must account for geometry and illumination constraints. Under these circumstances onboard computer vision can improve science yield by responding immediately to collected imagery. It can reacquire bad data or identify features of opportunity for additional targeted measurements. We present a comprehensive framework for onboard computer vision for flyby missions at small bodies. We introduce novel algorithms for target tracking, target segmentation, surface feature detection, and anomaly detection. The performance and generalization power are evaluated in detail using expert annotations on data sets from previous encounters with primitive bodies.

  2. Feature Extraction using Wavelet Transform for Multi-class Fault Detection of Induction Motor

    NASA Astrophysics Data System (ADS)

    Chattopadhyay, P.; Konar, P.

    2014-01-01

    In this paper the theoretical aspects and feature extraction capabilities of continuous wavelet transform (CWT) and discrete wavelet transform (DWT) are experimentally verified from the point of view of fault diagnosis of induction motors. Vertical frame vibration signal is analyzed to develop a wavelet based multi-class fault detection scheme. The redundant and high dimensionality information of CWT makes it computationally in-efficient. Using greedy-search feature selection technique (Greedy-CWT) the redundancy is eliminated to a great extent and found much superior to the widely used DWT technique, even in presence of high level of noise. The results are verified using MLP, SVM, RBF classifiers. The feature selection technique has enabled determination of the most relevant CWT scales and corresponding coefficients. Thus, the inherent limitations of CWT like proper selection of scales and redundant information are eliminated. In the present investigation `db8' is found as the best mother wavelet, due to its long period and higher number of vanishing moments, for detection of motor faults.

  3. A new feature detection mechanism and its application in secured ECG transmission with noise masking.

    PubMed

    Sufi, Fahim; Khalil, Ibrahim

    2009-04-01

    With cardiovascular disease as the number one killer of modern era, Electrocardiogram (ECG) is collected, stored and transmitted in greater frequency than ever before. However, in reality, ECG is rarely transmitted and stored in a secured manner. Recent research shows that eavesdropper can reveal the identity and cardiovascular condition from an intercepted ECG. Therefore, ECG data must be anonymized before transmission over the network and also stored as such in medical repositories. To achieve this, first of all, this paper presents a new ECG feature detection mechanism, which was compared against existing cross correlation (CC) based template matching algorithms. Two types of CC methods were used for comparison. Compared to the CC based approaches, which had 40% and 53% misclassification rates, the proposed detection algorithm did not perform any single misclassification. Secondly, a new ECG obfuscation method was designed and implemented on 15 subjects using added noises corresponding to each of the ECG features. This obfuscated ECG can be freely distributed over the internet without the necessity of encryption, since the original features needed to identify personal information of the patient remain concealed. Only authorized personnel possessing a secret key will be able to reconstruct the original ECG from the obfuscated ECG. Distribution of the would appear as regular ECG without encryption. Therefore, traditional decryption techniques including powerful brute force attack are useless against this obfuscation. PMID:19397097

  4. Wood Texture Features Extraction by Using GLCM Combined With Various Edge Detection Methods

    NASA Astrophysics Data System (ADS)

    Fahrurozi, A.; Madenda, S.; Ernastuti; Kerami, D.

    2016-06-01

    An image forming specific texture can be distinguished manually through the eye. However, sometimes it is difficult to do if the texture owned quite similar. Wood is a natural material that forms a unique texture. Experts can distinguish the quality of wood based texture observed in certain parts of the wood. In this study, it has been extracted texture features of the wood image that can be used to identify the characteristics of wood digitally by computer. Feature extraction carried out using Gray Level Co-occurrence Matrices (GLCM) built on an image from several edge detection methods applied to wood image. Edge detection methods used include Roberts, Sobel, Prewitt, Canny and Laplacian of Gaussian. The image of wood taken in LE2i laboratory, Universite de Bourgogne from the wood sample in France that grouped by their quality by experts and divided into four types of quality. Obtained a statistic that illustrates the distribution of texture features values of each wood type which compared according to the edge operator that is used and selection of specified GLCM parameters.

  5. CAXSS: an intelligent threat-detection system

    NASA Astrophysics Data System (ADS)

    Feather, Thomas; Guan, Ling; Lee-Kwen, Adrian; Paranjape, Raman B.

    1993-04-01

    Array Systems Computing Inc. (ASC) is developing a prototype Computer Assisted X-ray Screening System (CAXSS) which uses state-of-the-art image processing and computer vision technology to detect threats seen in x-ray images of passenger carry-on luggage at national and international airports. This system is successful in detecting weapons including guns, knives, grenades, aerosol cans, etc. Currently, bomb detection is also being implemented; preliminary results using this bomb detector are promising.

  6. Topographic attributes as a guide for automated detection or highlighting of geological features

    NASA Astrophysics Data System (ADS)

    Viseur, Sophie; Le Men, Thibaud; Guglielmi, Yves

    2015-04-01

    Photogrammetry or LIDAR technology combined with photography allow geoscientists to obtain 3D high-resolution numerical representations of outcrops, generally termed as Digital Outcrop Models (DOM). For over a decade, these 3D numerical outcrops serve as support for precise and accurate interpretations of geological features such as fracture traces or plans, strata, facies mapping, etc. These interpretations have the benefit to be directly georeferenced and embedded into the 3D space. They are then easily integrated into GIS or geomodeler softwares for modelling in 3D the subsurface geological structures. However, numerical outcrops generally represent huge data sets that are heavy to manipulate and hence to interpret. This may be particularly tedious as soon as several scales of geological features must be investigated or as geological features are very dense and imbricated. Automated tools for interpreting geological features from DOMs would be then a significant help to process these kinds of data. Such technologies are commonly used for interpreting seismic or medical data. However, it may be noticed that even if many efforts have been devoted to easily and accurately acquire 3D topographic point clouds and photos and to visualize accurate 3D textured DOMs, few attentions have been paid to the development of algorithms for automated detection of the geological structures from DOMs. The automatic detection of objects on numerical data generally assumes that signals or attributes computed from this data allows the recognition of the targeted object boundaries. The first step consists then in defining attributes that highlight the objects or their boundaries. For DOM interpretations, some authors proposed to use differential operators computed on the surface such as normal or curvatures. These methods generally extract polylines corresponding to fracture traces or bed limits. Other approaches rely on the PCA technology to segregate different topographic plans

  7. FEATURES, EVENTS, AND PROCESSES: SYSTEM-LEVEL AND CRITICALITY

    SciTech Connect

    D.L. McGregor

    2000-12-20

    The primary purpose of this Analysis/Model Report (AMR) is to identify and document the screening analyses for the features, events, and processes (FEPs) that do not easily fit into the existing Process Model Report (PMR) structure. These FEPs include the 3 1 FEPs designated as System-Level Primary FEPs and the 22 FEPs designated as Criticality Primary FEPs. A list of these FEPs is provided in Section 1.1. This AMR (AN-WIS-MD-000019) documents the Screening Decision and Regulatory Basis, Screening Argument, and Total System Performance Assessment (TSPA) Disposition for each of the subject Primary FEPs. This AMR provides screening information and decisions for the TSPA-SR report and provides the same information for incorporation into a project-specific FEPs database. This AMR may also assist reviewers during the licensing-review process.

  8. Multi-object Feature Detection and Error Correction for NIF Automatic Optical Alignment

    SciTech Connect

    Awwal, A S

    2006-07-17

    Fiducials imprinted on laser beams are used to perform video image based alignment of the beams in the National Ignition Facility (NIF) of Lawrence Livermore National Laboratory. In any laser beam alignment operation, a beam needs to be aligned to a reference location. Generally, the beam and reference fiducials are composed of separate beams, as a result only a single feature of each beam needs to be identified for determining the position of the beam or reference. However, it is possible to have the same beam image contain both the beam and reference fiducials. In such instances, it is essential to separately identify these features. In the absence of wavefront correction or when image quality is poor, the features of such beams may get distorted making it difficult to distinguish between different fiducials. Error checking and correction mechanism must be implemented to avoid misidentification of one type of feature as the other. This work presents the algorithm for multi-object detection and error correction implemented for such a beam line image in the NIF facility. Additionally, we show how when the original algorithm fails a secondary algorithm takes over and provides required location outputs.

  9. Pulmonary embolism detection using localized vessel-based features in dual energy CT

    NASA Astrophysics Data System (ADS)

    Dicente Cid, Yashin; Depeursinge, Adrien; Foncubierta Rodríguez, Antonio; Platon, Alexandra; Poletti, Pierre-Alexandre; Müller, Henning

    2015-03-01

    Pulmonary embolism (PE) affects up to 600,000 patients and contributes to at least 100,000 deaths every year in the United States alone. Diagnosis of PE can be difficult as most symptoms are unspecific and early diagnosis is essential for successful treatment. Computed Tomography (CT) images can show morphological anomalies that suggest the existence of PE. Various image-based procedures have been proposed for improving computer-aided diagnosis of PE. We propose a novel method for detecting PE based on localized vessel-based features computed in Dual Energy CT (DECT) images. DECT provides 4D data indexed by the three spatial coordinates and the energy level. The proposed features encode the variation of the Hounsfield Units across the different levels and the CT attenuation related to the amount of iodine contrast in each vessel. A local classification of the vessels is obtained through the classification of these features. Moreover, the localization of the vessel in the lung provides better comparison between patients. Results show that the simple features designed are able to classify pulmonary embolism patients with an AUC (area under the receiver operating curve) of 0.71 on a lobe basis. Prior segmentation of the lung lobes is not necessary because an automatic atlas-based segmentation obtains similar AUC levels (0.65) for the same dataset. The automatic atlas reaches 0.80 AUC in a larger dataset with more control cases.

  10. Land Cover Change Detection Based on Genetically Feature Aelection and Image Algebra Using Hyperion Hyperspectral Imagery

    NASA Astrophysics Data System (ADS)

    Seydi, S. T.; Hasanlou, M.

    2015-12-01

    The Earth has always been under the influence of population growth and human activities. This process causes the changes in land use. Thus, for optimal management of the use of resources, it is necessary to be aware of these changes. Satellite remote sensing has several advantages for monitoring land use/cover resources, especially for large geographic areas. Change detection and attribution of cultivation area over time present additional challenges for correctly analyzing remote sensing imagery. In this regards, for better identifying change in multi temporal images we use hyperspectral images. Hyperspectral images due to high spectral resolution created special placed in many of field. Nevertheless, selecting suitable and adequate features/bands from this data is crucial for any analysis and especially for the change detection algorithms. This research aims to automatically feature selection for detect land use changes are introduced. In this study, the optimal band images using hyperspectral sensor using Hyperion hyperspectral images by using genetic algorithms and Ratio bands, we select the optimal band. In addition, the results reveal the superiority of the implemented method to extract change map with overall accuracy by a margin of nearly 79% using multi temporal hyperspectral imagery.

  11. Load-differential features for automated detection of fatigue cracks using guided waves

    NASA Astrophysics Data System (ADS)

    Chen, Xin; Lee, Sang Jun; Michaels, Jennifer E.; Michaels, Thomas E.

    2012-05-01

    Guided wave structural health monitoring (SHM) is being considered to assess the integrity of plate-like structures for many applications. Prior research has investigated how guided wave propagation is affected by applied loads, which induce anisotropic changes in both dimensions and phase velocity. In addition, it is well-known that applied tensile loads open fatigue cracks and thus enhance their detectability using ultrasonic methods. Here we describe load-differential methods in which signals recorded from different loads at the same damage state are compared without using previously obtained damage-free data. Changes in delay-and-sum images are considered as a function of differential loads and damage state. Load-differential features are extracted from these images that capture the effects of loading as fatigue cracks are opened. Damage detection thresholds are adaptively set based upon the load-differential behavior of the various features, which enables implementation of an automated fatigue crack detection process. The efficacy of the proposed approach is examined using data from a fatigue test performed on an aluminum plate specimen that is instrumented with a sparse array of surface-mounted ultrasonic guided wave transducers.

  12. A cyber-physical system for senior collapse detection

    NASA Astrophysics Data System (ADS)

    Grewe, Lynne; Magaña-Zook, Steven

    2014-06-01

    Senior Collapse Detection (SCD) is a system that uses cyber-physical techniques to create a "smart home" system to predict and detect the falling of senior/geriatric participants in home environments. This software application addresses the needs of millions of senior citizens who live at home by themselves and can find themselves in situations where they have fallen and need assistance. We discuss how SCD uses imagery, depth and audio to fuse and interact in a system that does not require the senior to wear any devices allowing them to be more autonomous. The Microsoft Kinect Sensor is used to collect imagery, depth and audio. We will begin by discussing the physical attributes of the "collapse detection problem". Next, we will discuss the task of feature extraction resulting in skeleton and joint tracking. Improvements in error detection of joint tracking will be highlighted. Next, we discuss the main module of "fall detection" using our mid-level skeleton features. Attributes including acceleration, position and room environment factor into the SCD fall detection decision. Finally, how a detected fall and the resultant emergency response are handled will be presented. Results in a home environment will be given.

  13. Bright Retinal Lesions Detection using Colour Fundus Images Containing Reflective Features

    SciTech Connect

    Giancardo, Luca; Karnowski, Thomas Paul; Chaum, Edward; Meriaudeau, Fabrice; Tobin Jr, Kenneth William; Li, Yaquin

    2009-01-01

    In the last years the research community has developed many techniques to detect and diagnose diabetic retinopathy with retinal fundus images. This is a necessary step for the implementation of a large scale screening effort in rural areas where ophthalmologists are not available. In the United States of America, the incidence of diabetes is worryingly increasing among the young population. Retina fundus images of patients younger than 20 years old present a high amount of reflection due to the Nerve Fibre Layer (NFL), the younger the patient the more these reflections are visible. To our knowledge we are not aware of algorithms able to explicitly deal with this type of reflection artefact. This paper presents a technique to detect bright lesions also in patients with a high degree of reflective NFL. First, the candidate bright lesions are detected using image equalization and relatively simple histogram analysis. Then, a classifier is trained using texture descriptor (Multi-scale Local Binary Patterns) and other features in order to remove the false positives in the lesion detection. Finally, the area of the lesions is used to diagnose diabetic retinopathy. Our database consists of 33 images from a telemedicine network currently developed. When determining moderate to high diabetic retinopathy using the bright lesions detected the algorithm achieves a sensitivity of 100% at a specificity of 100% using hold-one-out testing.

  14. Truncated feature representation for automatic target detection using transformed data-based decomposition

    NASA Astrophysics Data System (ADS)

    Riasati, Vahid R.

    2016-05-01

    In this work, the data covariance matrix is diagonalized to provide an orthogonal bases set using the eigen vectors of the data. The eigen-vector decomposition of the data is transformed and filtered in the transform domain to truncate the data for robust features related to a specified set of targets. These truncated eigen features are then combined and reconstructed to utilize in a composite filter and consequently utilized for the automatic target detection of the same class of targets. The results associated with the testing of the current technique are evaluated using the peak-correlation and peak-correlation energy metrics and are presented in this work. The inverse transformed eigen-bases of the current technique may be thought of as an injected sparsity to minimize data in representing the skeletal data structure information associated with the set of targets under consideration.

  15. Sleep apnoea detection in children using PPG envelope-based dynamic features.

    PubMed

    Sepúlveda-Cano, L M; Gil, E; Laguna, P; Castellanos-Dominguez, G

    2011-01-01

    Photopletysmography signal has been developed for monitoring of Obstructive Sleep Apnoea, in particular, whenever an apneic episode occurs, that is reflected by decreases in the photopletysmography signal amplitude fluctuation. However, other physiological events such as artifacts and deep inspiratory gasp produce sympathetic activation, being unrelated to apnea. Thus, its high sensitivity can produce misdetections and overestimate apneic episodes. In this regard, a methodology for selecting a set of relevant non-stationary features to increase the specificity of the obstructive sleep apnea detector is discussed. A time-evolving version of the standard linear multivariate decomposition is discussed to perform stochastic dimensionality reduction. As a result, performed outcomes of accuracy bring enough evidence that if using a subset of cepstral-based dynamic features, then patient classification accuracy is 83.3%. Therefore, photoplethysmography--based detection provides an adequate scheme for obstructive sleep apnea diagnosis. PMID:22254600

  16. Feature extraction using adaptive multiwavelets and synthetic detection index for rotor fault diagnosis of rotating machinery

    NASA Astrophysics Data System (ADS)

    Lu, Na; Xiao, Zhihuai; Malik, O. P.

    2015-02-01

    State identification to diagnose the condition of rotating machinery is often converted to a classification problem of values of non-dimensional symptom parameters (NSPs). To improve the sensitivity of the NSPs to the changes in machine condition, a novel feature extraction method based on adaptive multiwavelets and the synthetic detection index (SDI) is proposed in this paper. Based on the SDI maximization principle, optimal multiwavelets are searched by genetic algorithms (GAs) from an adaptive multiwavelets library and used for extracting fault features from vibration signals. By the optimal multiwavelets, more sensitive NSPs can be extracted. To examine the effectiveness of the optimal multiwavelets, conventional methods are used for comparison study. The obtained NSPs are fed into K-means classifier to diagnose rotor faults. The results show that the proposed method can effectively improve the sensitivity of the NSPs and achieve a higher discrimination rate for rotor fault diagnosis than the conventional methods.

  17. Altronic`s latest ignition system features secondary diagnostics

    SciTech Connect

    1996-07-01

    Few issues continue to cause more distress for operators of gas engines and integral compressors than spark plug life. Altronic`s new CPU-95 ignition system was developed for use on medium and high-speed gas-fueled engines driving compressors, generators and pumps. In addition to the new secondary prognostic capability, the 24 V dc-powered CPU-95 system is designed to handle all the functions of a modern digital ignition. This includes spark characteristic control, global and individual cylinder timing adjustments, and extensive user display and serial communications capabilities. But the most revolutionary feature of the CPU-95 system is its built-in secondary ignition diagnosis capabilities, which provide a prognosis of spark plug life. The system displays a number that is an indicator of how much voltage is required at each cylinder. As the spark plug gap erodes the number increases. In addition to displaying and alarming relative voltage demand, the secondary-side diagnosis can also alarm for high or low spark voltage (by cylinder), or no secondary spark. Primary-side diagnostic capabilities (also annunciated by cylinder) include open or shorted circuits. The system can also diagnose for faults in the gear tooth pickup, reset pickup, four-cycle pickup, ring gear, microprocessor, current loop, EEPROM checksum, analog timing input (4-20 mA) being out of range, and a failure to charge the capacitor.

  18. Intelligent system for automatic feature detection and selection or identification

    DOEpatents

    Sun, Chuen-Tsai; Jang, Jyh-Shing; Fu, Chi-Yung

    1997-01-01

    A neural network uses a fuzzy membership function, the parameters of which are adaptive during the training process, to parameterize the interconnection weights between an (n-1)'th layer and an n'th layer of the network. Each j'th node in each k'th layer of the network except the input layer produces its output value y.sub.k,j according to the function ##EQU1## where N.sub.k-1 is the number of nodes in layer k-1, i indexes the nodes of layer k-1 and all the w.sub.k,i,j are interconnection weights. The interconnection weights to all nodes j in the n'th layer are given by w.sub.n,i,j =w.sub.n,j (i, p.sub.n,j,1, . . . , p.sub.n,j,p.sbsb.n). The apparatus is trained by setting values for at least one of the parameters p.sub.n,j,1, . . . , p.sub.n,j,Pn. Preferably the number of parameters P.sub.n is less than the number of nodes N.sub.n-1 in layer n-1. w.sub.n,j (i,p.sub.n,j,1, . . . , p.sub.n,j,Pn) can be convex in i, and it can be bell-shaped. Sample functions for w.sub.n,j (i, p.sub.n,j,1, . . . , p.sub.n,j,Pn) include ##EQU2##

  19. Intelligent system for automatic feature detection and selection or identification

    DOEpatents

    Sun, C.T.; Shiang, P.S.; Jang, J.S.; Fu, C.Y.

    1997-09-02

    A neural network uses a fuzzy membership function, the parameters of which are adaptive during the training process, to parameterize the interconnection weights between an (n{minus}1)`th layer and an n`th layer of the network. Each j`th node in each k`th layer of the network except the input layer produces its output value y{sub k,j} according to the function shown in Equation 1 where N{sub k{minus}1} is the number of nodes in layer k{minus}1, i indexes the nodes of layer k{minus}1 and all the w{sub k,i,j} are interconnection weights. The interconnection weights to all nodes j in the n`th layer are given by w{sub n,i,j}=w{sub n,j} (i, p{sub n,j,1}, . . . , p{sub n,j},p{sub n}). The apparatus is trained by setting values for at least one of the parameters p{sub n,j,1}, . . . , p{sub n,j},Pn. Preferably the number of parameters P{sub n} is less than the number of nodes N{sub n{minus}1} in layer n{minus}1. W{sub n,j} (i,p{sub n,j,1}, . . . , p{sub n,j},Pn) can be convex in i, and it can be bell-shaped. Sample functions for w{sub n,j} (i, p{sub n,j,1}, . . . , p{sub n,j},Pn) include Equation 2, shown in the patent. 8 figs.

  20. Discriminating ultrasonic proximity detection system

    DOEpatents

    Annala, Wayne C.

    1989-01-01

    This invention uses an ultrasonic transmitter and receiver and a microprocessor to detect the presence of an object. In the reset mode the invention uses a plurality of echoes from each ultrasonic burst to create a reference table of the echo-burst-signature of the empty monitored environment. The invention then processes the reference table so that it only uses the most reliable data. In the detection mode the invention compares the echo-burst-signature of the present environment with the reference table, detecting an object if there is a consistent difference between the echo-burst-signature of the empty monitored environment recorded in the reference table and the echo-burst-signature of the present environment.

  1. Airborne change detection system for the detection of route mines

    NASA Astrophysics Data System (ADS)

    Donzelli, Thomas P.; Jackson, Larry; Yeshnik, Mark; Petty, Thomas E.

    2003-09-01

    The US Army is interested in technologies that will enable it to maintain the free flow of traffic along routes such as Main Supply Routes (MSRs). Mines emplaced in the road by enemy forces under cover of darkness represent a major threat to maintaining a rapid Operational Tempo (OPTEMPO) along such routes. One technique that shows promise for detecting enemy mining activity is Airborne Change Detection, which allows an operator to detect suspicious day-to-day changes in and around the road that may be indicative of enemy mining. This paper presents an Airborne Change Detection that is currently under development at the US Army Night Vision and Electronic Sensors Directorate (NVESD). The system has been tested using a longwave infrared (LWIR) sensor on a vertical take-off and landing unmanned aerial vehicle (VTOL UAV) and a midwave infrared (MWIR) sensor on a fixed wing aircraft. The system is described and results of the various tests conducted to date are presented.

  2. Detection of Focal Cortical Dysplasia Lesions in MRI Using Textural Features

    NASA Astrophysics Data System (ADS)

    Loyek, Christian; Woermann, Friedrich G.; Nattkemper, Tim W.

    Focal cortical dysplasia (FCD) is a frequent cause of medically refractory partial epilepsy. The visual identification of FCD lesions on magnetic resonance images (MRI) is a challenging task in standard radiological analysis. Quantitative image analysis which tries to assist in the diagnosis of FCD lesions is an active field of research. In this work we investigate the potential of different texture features, in order to explore to what extent they are suitable for detecting lesional tissue. As a result we can show first promising results based on segmentation and texture classification.

  3. Carpet Lesions Detected at CT Colonography: Clinical, Imaging, and Pathologic Features

    PubMed Central

    Lam, Vu P.; Weiss, Jennifer M.; Kennedy, Gregory D.; Kim, David H.

    2014-01-01

    Purpose To describe carpet lesions (laterally spreading tumors ≥ 3 cm) detected at computed tomographic (CT) colonography, including their clinical, imaging, and pathologic features. Materials and Methods The imaging reports for 9152 consecutive adults undergoing initial CT colonography at a tertiary center were reviewed in this HIPAA-compliant, institutional review board–approved retrospective study to identify all potential carpet lesions detected at CT colonography. Carpet lesions were defined as morphologically flat, laterally spreading tumors 3 cm or larger. For those patients with neoplastic carpet lesions, CT colonography studies were analyzed to determine maximal lesion width and height, oral contrast material coating, segmental location, and computer-aided detection (CAD) findings. Demographic data and details of clinical treatment in these patients were reviewed. Results Eighteen carpet lesions in 18 patients (0.2%; mean age, 67.1 years; eight men, 10 women) were identified and were subsequently confirmed at colonoscopy and pathologic examination among 20 potential flat masses (≥3 cm) prospectively identified at CT colonography (there were two nonneoplastic rectal false-positive findings). No additional neoplastic carpet lesions were found in the cohort undergoing colonoscopy after CT colonography and/or surgery (there were no false-negatives). Mean lesion width was 46.5 mm (range, 30–80 mm); mean lesion height was 7.9 mm (range, 4–14 mm). Surface retention of oral contrast material was noted in all 18 cases. All but two lesions were located in the distal rectosigmoid or proximal right colon. At CAD, 17 (94.4%) lesions were detected (mean, 6.2 CAD marks per lesion). Sixteen lesions (88.9%) demonstrated advanced histologic features, including a villous component (n = 11), high-grade dysplasia (n = 4), and invasive cancer (n = 5). Sixteen patients (88.9%) required surgical treatment for complete excision. Conclusion CT colonography can effectively

  4. Less is more: Avoiding the LIBS dimensionality curse through judicious feature selection for explosive detection

    PubMed Central

    Kumar Myakalwar, Ashwin; Spegazzini, Nicolas; Zhang, Chi; Kumar Anubham, Siva; Dasari, Ramachandra R.; Barman, Ishan; Kumar Gundawar, Manoj

    2015-01-01

    Despite its intrinsic advantages, translation of laser induced breakdown spectroscopy for material identification has been often impeded by the lack of robustness of developed classification models, often due to the presence of spurious correlations. While a number of classifiers exhibiting high discriminatory power have been reported, efforts in establishing the subset of relevant spectral features that enable a fundamental interpretation of the segmentation capability and avoid the ‘curse of dimensionality’ have been lacking. Using LIBS data acquired from a set of secondary explosives, we investigate judicious feature selection approaches and architect two different chemometrics classifiers –based on feature selection through prerequisite knowledge of the sample composition and genetic algorithm, respectively. While the full spectral input results in classification rate of ca.92%, selection of only carbon to hydrogen spectral window results in near identical performance. Importantly, the genetic algorithm-derived classifier shows a statistically significant improvement to ca. 94% accuracy for prospective classification, even though the number of features used is an order of magnitude smaller. Our findings demonstrate the impact of rigorous feature selection in LIBS and also hint at the feasibility of using a discrete filter based detector thereby enabling a cheaper and compact system more amenable to field operations. PMID:26286630

  5. Less is more: Avoiding the LIBS dimensionality curse through judicious feature selection for explosive detection

    NASA Astrophysics Data System (ADS)

    Kumar Myakalwar, Ashwin; Spegazzini, Nicolas; Zhang, Chi; Kumar Anubham, Siva; Dasari, Ramachandra R.; Barman, Ishan; Kumar Gundawar, Manoj

    2015-08-01

    Despite its intrinsic advantages, translation of laser induced breakdown spectroscopy for material identification has been often impeded by the lack of robustness of developed classification models, often due to the presence of spurious correlations. While a number of classifiers exhibiting high discriminatory power have been reported, efforts in establishing the subset of relevant spectral features that enable a fundamental interpretation of the segmentation capability and avoid the ‘curse of dimensionality’ have been lacking. Using LIBS data acquired from a set of secondary explosives, we investigate judicious feature selection approaches and architect two different chemometrics classifiers -based on feature selection through prerequisite knowledge of the sample composition and genetic algorithm, respectively. While the full spectral input results in classification rate of ca.92%, selection of only carbon to hydrogen spectral window results in near identical performance. Importantly, the genetic algorithm-derived classifier shows a statistically significant improvement to ca. 94% accuracy for prospective classification, even though the number of features used is an order of magnitude smaller. Our findings demonstrate the impact of rigorous feature selection in LIBS and also hint at the feasibility of using a discrete filter based detector thereby enabling a cheaper and compact system more amenable to field operations.

  6. Less is more: Avoiding the LIBS dimensionality curse through judicious feature selection for explosive detection.

    PubMed

    Kumar Myakalwar, Ashwin; Spegazzini, Nicolas; Zhang, Chi; Kumar Anubham, Siva; Dasari, Ramachandra R; Barman, Ishan; Kumar Gundawar, Manoj

    2015-01-01

    Despite its intrinsic advantages, translation of laser induced breakdown spectroscopy for material identification has been often impeded by the lack of robustness of developed classification models, often due to the presence of spurious correlations. While a number of classifiers exhibiting high discriminatory power have been reported, efforts in establishing the subset of relevant spectral features that enable a fundamental interpretation of the segmentation capability and avoid the 'curse of dimensionality' have been lacking. Using LIBS data acquired from a set of secondary explosives, we investigate judicious feature selection approaches and architect two different chemometrics classifiers -based on feature selection through prerequisite knowledge of the sample composition and genetic algorithm, respectively. While the full spectral input results in classification rate of ca.92%, selection of only carbon to hydrogen spectral window results in near identical performance. Importantly, the genetic algorithm-derived classifier shows a statistically significant improvement to ca. 94% accuracy for prospective classification, even though the number of features used is an order of magnitude smaller. Our findings demonstrate the impact of rigorous feature selection in LIBS and also hint at the feasibility of using a discrete filter based detector thereby enabling a cheaper and compact system more amenable to field operations. PMID:26286630

  7. Multi-feature-based robust face detection and coarse alignment method via multiple kernel learning

    NASA Astrophysics Data System (ADS)

    Sun, Bo; Zhang, Di; He, Jun; Yu, Lejun; Wu, Xuewen

    2015-10-01

    Face detection and alignment are two crucial tasks to face recognition which is a hot topic in the field of defense and security, whatever for the safety of social public, personal property as well as information and communication security. Common approaches toward the treatment of these tasks in recent years are often of three types: template matching-based, knowledge-based and machine learning-based, which are always separate-step, high computation cost or fragile robust. After deep analysis on a great deal of Chinese face images without hats, we propose a novel face detection and coarse alignment method, which is inspired by those three types of methods. It is multi-feature fusion with Simple Multiple Kernel Learning1 (Simple-MKL) algorithm. The proposed method is contrasted with competitive and related algorithms, and demonstrated to achieve promising results.

  8. Acoustic Longitudinal Field NIF Optic Feature Detection Map Using Time-Reversal & MUSIC

    SciTech Connect

    Lehman, S K

    2006-02-09

    We developed an ultrasonic longitudinal field time-reversal and MUltiple SIgnal Classification (MUSIC) based detection algorithm for identifying and mapping flaws in fused silica NIF optics. The algorithm requires a fully multistatic data set, that is one with multiple, independently operated, spatially diverse transducers, each transmitter of which, in succession, launches a pulse into the optic and the scattered signal measured and recorded at every receiver. We have successfully localized engineered ''defects'' larger than 1 mm in an optic. We confirmed detection and localization of 3 mm and 5 mm features in experimental data, and a 0.5 mm in simulated data with sufficiently high signal-to-noise ratio. We present the theory, experimental results, and simulated results.

  9. Thermal systems for landmine detection

    NASA Astrophysics Data System (ADS)

    D'Angelo, Marco; Del Vecchio, Luca; Esposito, Salvatore; Balsi, Marco; Jankowski, Stanislaw

    2009-06-01

    This paper presents new techniques of landmine detection and localization using thermal methods. Described methods use both dynamical and static analysis. The work is based on datasets obtained from the Humanitarian Demining Laboratory of Università La Sapienza di Roma, Italy.

  10. Feature-space assessment of electrical impedance tomography coregistered with computed tomography in detecting multiple contrast targets

    SciTech Connect

    Krishnan, Kalpagam; Liu, Jeff; Kohli, Kirpal

    2014-06-15

    Purpose: Fusion of electrical impedance tomography (EIT) with computed tomography (CT) can be useful as a clinical tool for providing additional physiological information about tissues, but requires suitable fusion algorithms and validation procedures. This work explores the feasibility of fusing EIT and CT images using an algorithm for coregistration. The imaging performance is validated through feature space assessment on phantom contrast targets. Methods: EIT data were acquired by scanning a phantom using a circuit, configured for injecting current through 16 electrodes, placed around the phantom. A conductivity image of the phantom was obtained from the data using electrical impedance and diffuse optical tomography reconstruction software (EIDORS). A CT image of the phantom was also acquired. The EIT and CT images were fused using a region of interest (ROI) coregistration fusion algorithm. Phantom imaging experiments were carried out on objects of different contrasts, sizes, and positions. The conductive medium of the phantoms was made of a tissue-mimicking bolus material that is routinely used in clinical radiation therapy settings. To validate the imaging performance in detecting different contrasts, the ROI of the phantom was filled with distilled water and normal saline. Spatially separated cylindrical objects of different sizes were used for validating the imaging performance in multiple target detection. Analyses of the CT, EIT and the EIT/CT phantom images were carried out based on the variations of contrast, correlation, energy, and homogeneity, using a gray level co-occurrence matrix (GLCM). A reference image of the phantom was simulated using EIDORS, and the performances of the CT and EIT imaging systems were evaluated and compared against the performance of the EIT/CT system using various feature metrics, detectability, and structural similarity index measures. Results: In detecting distilled and normal saline water in bolus medium, EIT as a stand

  11. Forward Obstacle Detection System by Stereo Vision

    NASA Astrophysics Data System (ADS)

    Iwata, Hiroaki; Saneyoshi, Keiji

    Forward obstacle detection is needed to prevent car accidents. We have developed forward obstacle detection system which has good detectability and the accuracy of distance only by using stereo vision. The system runs in real time by using a stereo processing system based on a Field-Programmable Gate Array (FPGA). Road surfaces are detected and the space to drive can be limited. A smoothing filter is also used. Owing to these, the accuracy of distance is improved. In the experiments, this system could detect forward obstacles 100 m away. Its error of distance up to 80 m was less than 1.5 m. It could immediately detect cutting-in objects.

  12. Toward detecting deception in intelligent systems

    NASA Astrophysics Data System (ADS)

    Santos, Eugene, Jr.; Johnson, Gregory, Jr.

    2004-08-01

    Contemporary decision makers often must choose a course of action using knowledge from several sources. Knowledge may be provided from many diverse sources including electronic sources such as knowledge-based diagnostic or decision support systems or through data mining techniques. As the decision maker becomes more dependent on these electronic information sources, detecting deceptive information from these sources becomes vital to making a correct, or at least more informed, decision. This applies to unintentional disinformation as well as intentional misinformation. Our ongoing research focuses on employing models of deception and deception detection from the fields of psychology and cognitive science to these systems as well as implementing deception detection algorithms for probabilistic intelligent systems. The deception detection algorithms are used to detect, classify and correct attempts at deception. Algorithms for detecting unexpected information rely upon a prediction algorithm from the collaborative filtering domain to predict agent responses in a multi-agent system.

  13. Automatic detection of pectoral muscle using average gradient and shape based feature.

    PubMed

    Chakraborty, Jayasree; Mukhopadhyay, Sudipta; Singla, Veenu; Khandelwal, Niranjan; Bhattacharyya, Pinakpani

    2012-06-01

    In medio-lateral oblique view of mammogram, pectoral muscle may sometimes affect the detection of breast cancer due to their similar characteristics with abnormal tissues. As a result pectoral muscle should be handled separately while detecting the breast cancer. In this paper, a novel approach for the detection of pectoral muscle using average gradient- and shape-based feature is proposed. The process first approximates the pectoral muscle boundary as a straight line using average gradient-, position-, and shape-based features of the pectoral muscle. Straight line is then tuned to a smooth curve which represents the pectoral margin more accurately. Finally, an enclosed region is generated which represents the pectoral muscle as a segmentation mask. The main advantage of the method is its' simplicity as well as accuracy. The method is applied on 200 mammographic images consisting 80 randomly selected scanned film images from Mammographic Image Analysis Society (mini-MIAS) database, 80 direct radiography (DR) images, and 40 computed radiography (CR) images from local database. The performance is evaluated based upon the false positive (FP), false negative (FN) pixel percentage, and mean distance closest point (MDCP). Taking all the images into consideration, the average FP and FN pixel percentages are 4.22%, 3.93%, 18.81%, and 6.71%, 6.28%, 5.12% for mini-MIAS, DR, and CR images, respectively. Obtained MDCP values for the same set of database are 3.34, 3.33, and 10.41 respectively. The method is also compared with two well-known pectoral muscle detection techniques and in most of the cases, it outperforms the other two approaches. PMID:22006275

  14. Automated Detection of Geomorphic Features in LiDAR Point Clouds of Various Spatial Density

    NASA Astrophysics Data System (ADS)

    Dorninger, Peter; Székely, Balázs; Zámolyi, András.; Nothegger, Clemens

    2010-05-01

    extraction and modeling of buildings (Dorninger & Pfeifer, 2008) we expected that similar generalizations for geomorphic features can be achieved. Our aim is to recognize as many features as possible from the point cloud in the same processing loop, if they can be geometrically described with appropriate accuracy (e.g., as a plane). For this, we propose to apply a segmentation process allowing determining connected, planar structures within a surface represented by a point cloud. It is based on a robust determination of local tangential planes for all points acquired (Nothegger & Dorninger, 2009). It assumes that for points, belonging to a distinct planar structure, similar tangential planes can be determined. In passing, points acquired at continuous such as vegetation can be identified and eliminated. The plane parameters are used to define a four-dimensional feature space which is used to determine seed-clusters globally for the whole are of interest. Starting from these seeds, all points defining a connected, planar region are assigned to a segment. Due to the design of the algorithm, millions of input points can be processed with acceptable processing time on standard computer systems. This allows for processing geomorphically representative areas at once. For each segment, numerous parameter are derived which can be used for further exploitation. These are, for example, location, area, aspect, slope, and roughness. To prove the applicability of our method for automated geomorphic terrain analysis, we used terrestrial and airborne laser scanning data, acquired at two locations. The data of the Doren landslide located in Vorarlberg, Austria, was acquired by a terrestrial Riegl LS-321 laser scanner in 2008, by a terrestrial Riegl LMS-Z420i laser scanner in 2009, and additionally by three airborne LiDAR measurement campaigns, organized by the Landesvermessungsamt Vorarlberg, Feldkirch, in 2003, 2006, and 2007. The measurement distance of the terrestrial measurements was

  15. Learning to Detect Vandalism in Social Content Systems: A Study on Wikipedia

    NASA Astrophysics Data System (ADS)

    Javanmardi, Sara; McDonald, David W.; Caruana, Rich; Forouzan, Sholeh; Lopes, Cristina V.

    A challenge facing user generated content systems is vandalism, i.e. edits that damage content quality. The high visibility and easy access to social networks makes them popular targets for vandals. Detecting and removing vandalism is critical for these user generated content systems. Because vandalism can take many forms, there are many different kinds of features that are potentially useful for detecting it. The complex nature of vandalism, and the large number of potential features, make vandalism detection difficult and time consuming for human editors. Machine learning techniques hold promise for developing accurate, tunable, and maintainable models that can be incorporated into vandalism detection tools. We describe a method for training classifiers for vandalism detection that yields classifiers that are more accurate on the PAN 2010 corpus than others previously developed. Because of the high turnaround in social network systems, it is important for vandalism detection tools to run in real-time. To this aim, we use feature selection to find the minimal set of features consistent with high accuracy. In addition, because some features are more costly to compute than others, we use cost-sensitive feature selection to reduce the total computational cost of executing our models. In addition to the features previously used for spam detection, we introduce new features based on user action histories. The user history features contribute significantly to classifier performance. The approach we use is general and can easily be applied to other user generated content systems.

  16. Dual Low-Rank Pursuit: Learning Salient Features for Saliency Detection.

    PubMed

    Lang, Congyan; Feng, Jiashi; Feng, Songhe; Wang, Jingdong; Yan, Shuicheng

    2016-06-01

    Saliency detection is an important procedure for machines to understand visual world as humans do. In this paper, we consider a specific saliency detection problem of predicting human eye fixations when they freely view natural images, and propose a novel dual low-rank pursuit (DLRP) method. DLRP learns saliency-aware feature transformations by utilizing available supervision information and constructs discriminative bases for effectively detecting human fixation points under the popular low-rank and sparsity-pursuit framework. Benefiting from the embedded high-level information in the supervised learning process, DLRP is able to predict fixations accurately without performing the expensive object segmentation as in the previous works. Comprehensive experiments clearly show the superiority of the proposed DLRP method over the established state-of-the-art methods. We also empirically demonstrate that DLRP provides stronger generalization performance across different data sets and inherits the advantages of both the bottom-up- and top-down-based saliency detection methods. PMID:27046853

  17. [Spectral features analysis of Pinus massoniana with pest of Dendrolimus punctatus Walker and levels detection].

    PubMed

    Xu, Zhang-Hua; Liu, Jian; Yu, Kun-Yong; Gong, Cong-Hong; Xie, Wan-Jun; Tang, Meng-Ya; Lai, Ri-Wen; Li, Zeng-Lu

    2013-02-01

    Taking 51 field measured hyperspectral data with different pest levels in Yanping, Fujian Province as objects, the spectral reflectance and first derivative features of 4 levels of healthy, mild, moderate and severe insect pest were analyzed. On the basis of 7 detecting parameters construction, the pest level detecting models were built. The results showed that (1) the spectral reflectance of Pinus massoniana with pests were significantly lower than that of healthy state, and the higher the pest level, the lower the reflectance; (2) with the increase in pest level, the spectral reflectance curves' "green peak" and "red valley" of Pinus massoniana gradually disappeared, and the red edge was leveleds (3) the pest led to spectral "green peak" red shift, red edge position blue shift, but the changes in "red valley" and near-infrared position were complicated; (4) CARI, RES, REA and REDVI were highly relevant to pest levels, and the correlations between REP, RERVI, RENDVI and pest level were weak; (5) the multiple linear regression model with the variables of the 7 detection parameters could effectively detect the pest levels of Dendrolimus punctatus Walker, with both the estimation rate and accuracy above 0.85. PMID:23697126

  18. Using Inorganic Nanomaterials to Endow Biocatalytic Systems with Unique Features.

    PubMed

    Lin, Youhui; Chen, Zhengwei; Liu, Xiang Yang

    2016-04-01

    The rapid growth in nanotechnology and biotechnology offers a wealth of opportunities for the combination of natural enzymes with different kinds of nanomaterial. Here, we highlight recent advances in constructing nanomaterial-incorporated enzymes that integrate the specific recognition and biocatalytic properties of enzymes with the attractive electronic, optical, magnetic, and catalytic properties of nanomaterials. These composite materials have some extra features that are not possessed by the enzymes themselves, such as electron transfer mediated by nanoparticles, enzyme delivery, remote activation and/or deactivation of enzymatic activity, and fabrication of catalytic entities with complementary functions. Additionally, we describe briefly the current challenges and future development of unique enzyme-nanomaterial hybrid systems. We hope that this review will help to accelerate further progress in this promising field. PMID:26822167

  19. Characteristic features of an ionization system with semiconducting cathode

    NASA Astrophysics Data System (ADS)

    Salamov, B. G.; Altındal, Ş.; Özer, M.; Çolakoğlu, K.; Bulur, E.

    1998-06-01

    The characteristic features of a dc discharge generated between parallel plate electrodes and especially the discharge stabilization by the GaAs semiconducting cathode in such a system are studied. The cathode was irradiated on the back-side with IR light in a particular wavelength range that was used to control the photoconductivity of the material. The semiconductor material was found to stabilize the discharge. The current-voltage and radiation-voltage characteristics of the gas discharge cell with a semiconducting cathode were obtained experimentally. An investigation of the effect of the voltage amplitude on the dynamics of transient processes in the plane semiconductor-discharge gap structure was made for explanation of the light intensity and current decay. Expressions are obtained for the photoelectric gain.

  20. A disaster recovery system featuring uncertainty visualization and distributed infrastructure

    NASA Astrophysics Data System (ADS)

    Grewe, L.; Krishnagiri, S.; Cristobal, J.

    2007-04-01

    This paper will present the use and implementation of uncertainty visualization in a disaster recovery tool called DiRecT. DirecT is an emergency response system that couples the visualization mechanism with a distributed computing architecture for a more reliable, failsafe infrastructure. The uncertainty visualization cues help provide the means of determining the priority of assigning resources to the entities by taking into account various factors such as their identity, location, and health. With DiRecT the incident commander would be able to quickly assess the current scenario and make critical and informed decisions. An important part of DiRect is its distributed, real-time infrastructure which supports capture, storage and delivery of data from various sources in the field. DiRect also supports personnel communication through an instant memoing feature.

  1. System of tectonic features common to Earth, Mars, and Venus

    SciTech Connect

    Watters, T.R. )

    1992-07-01

    Investigations of landforms on the terrestrial planets have revealed a system of tectonic features consisting of long, narrow, regularly spaced folds and/or thrust faults, referred to as wrinkle ridges, and conjugate sets of cross-trending strike-slip faults. These are observed in the Yakima fold belt of the Columbia Plateau, Earth, the ridged plains of the Tharsis province, Mars, and the lowland plains of Lavinia Planitia, Venus. The wrinkle ridges and strike-slip faults reflect a relatively small amount of crustal shortening in these regions of distributed deformation. The observed geometric relations between the structures are consistent with those predicted by the Coulomb-Anderson model. Although the tectonic settings of the provinces studied on the three planets are very different, the crustal materials appear to have deformed in a similar manner.

  2. Investigation of optimal feature value set in false positive reduction process for automated abdominal lymph node detection method

    NASA Astrophysics Data System (ADS)

    Nakamura, Yoshihiko; Nimura, Yukitaka; Kitasaka, Takayuki; Mizuno, Shinji; Furukawa, Kazuhiro; Goto, Hidemi; Fujiwara, Michitaka; Misawa, Kazunari; Ito, Masaaki; Nawano, Shigeru; Mori, Kensaku

    2015-03-01

    This paper presents an investigation of optimal feature value set in false positive reduction process for the automated method of enlarged abdominal lymph node detection. We have developed the automated abdominal lymph node detection method to aid for surgical planning. Because it is important to understand the location and the structure of an enlarged lymph node in order to make a suitable surgical plan. However, our previous method was not able to obtain the suitable feature value set. This method was able to detect 71.6% of the lymph nodes with 12.5 FPs per case. In this paper, we investigate the optimal feature value set in the false positive reduction process to improve the method for automated abdominal lymph node detection. By applying our improved method by using the optimal feature value set to 28 cases of abdominal 3D CT images, we detected about 74.7% of the abdominal lymph nodes with 11.8 FPs/case.

  3. An Energy efficient application specific integrated circuit for electrocardiogram feature detection and its potential for ambulatory cardiovascular disease detection

    PubMed Central

    Bhaumik, Basabi

    2016-01-01

    A novel algorithm based on forward search is developed for real-time electrocardiogram (ECG) signal processing and implemented in application specific integrated circuit (ASIC) for QRS complex related cardiovascular disease diagnosis. The authors have evaluated their algorithm using MIT-BIH database and achieve sensitivity of 99.86% and specificity of 99.93% for QRS complex peak detection. In this Letter, Physionet PTB diagnostic ECG database is used for QRS complex related disease detection. An ASIC for cardiovascular disease detection is fabricated using 130-nm CMOS high-speed process technology. The area of the ASIC is 0.5 mm2. The power dissipation is 1.73 μW at the operating frequency of 1 kHz with a supply voltage of 0.6 V. The output from the ASIC is fed to their Android application that generates diagnostic report and can be sent to a cardiologist through email. Their ASIC result shows average failed detection rate of 0.16% for six leads data of 290 patients in PTB diagnostic ECG database. They also have implemented a low-leakage version of their ASIC. The ASIC dissipates only 45 pJ with a supply voltage of 0.9 V. Their proposed ASIC is most suitable for energy efficient telemetry cardiovascular disease detection system. PMID:27284458

  4. An Energy efficient application specific integrated circuit for electrocardiogram feature detection and its potential for ambulatory cardiovascular disease detection.

    PubMed

    Jain, Sanjeev Kumar; Bhaumik, Basabi

    2016-03-01

    A novel algorithm based on forward search is developed for real-time electrocardiogram (ECG) signal processing and implemented in application specific integrated circuit (ASIC) for QRS complex related cardiovascular disease diagnosis. The authors have evaluated their algorithm using MIT-BIH database and achieve sensitivity of 99.86% and specificity of 99.93% for QRS complex peak detection. In this Letter, Physionet PTB diagnostic ECG database is used for QRS complex related disease detection. An ASIC for cardiovascular disease detection is fabricated using 130-nm CMOS high-speed process technology. The area of the ASIC is 0.5 mm(2). The power dissipation is 1.73 μW at the operating frequency of 1 kHz with a supply voltage of 0.6 V. The output from the ASIC is fed to their Android application that generates diagnostic report and can be sent to a cardiologist through email. Their ASIC result shows average failed detection rate of 0.16% for six leads data of 290 patients in PTB diagnostic ECG database. They also have implemented a low-leakage version of their ASIC. The ASIC dissipates only 45 pJ with a supply voltage of 0.9 V. Their proposed ASIC is most suitable for energy efficient telemetry cardiovascular disease detection system. PMID:27284458

  5. Detecting features in the dark energy equation of state: a wavelet approach

    SciTech Connect

    Hojjati, Alireza; Pogosian, Levon; Zhao, Gong-Bo E-mail: levon@sfu.ca

    2010-04-01

    We study the utility of wavelets for detecting the redshift evolution of the dark energy equation of state w(z) from the combination of supernovae (SNe), CMB and BAO data. We show that local features in w, such as bumps, can be detected efficiently using wavelets. To demonstrate, we first generate a mock supernovae data sample for a SNAP-like survey with a bump feature in w(z) hidden in, then successfully discover it by performing a blind wavelet analysis. We also apply our method to analyze the recently released ''Constitution'' SNe data, combined with WMAP and BAO from SDSS, and find weak hints of dark energy dynamics. Namely, we find that models with w(z) < −1 for 0.2 < z < 0.5, and w(z) > −1 for 0.5 < z < 1, are mildly favored at 95% confidence level. This is in good agreement with several recent studies using other methods, such as redshift binning with principal component analysis (PCA) (e.g. Zhao and Zhang, arXiv: 0908.1568)

  6. A research of selected textural features for detection of asbestos-cement roofing sheets using orthoimages

    NASA Astrophysics Data System (ADS)

    Książek, Judyta

    2015-10-01

    At present, there has been a great interest in the development of texture based image classification methods in many different areas. This study presents the results of research carried out to assess the usefulness of selected textural features for detection of asbestos-cement roofs in orthophotomap classification. Two different orthophotomaps of southern Poland (with ground resolution: 5 cm and 25 cm) were used. On both orthoimages representative samples for two classes: asbestos-cement roofing sheets and other roofing materials were selected. Estimation of texture analysis usefulness was conducted using machine learning methods based on decision trees (C5.0 algorithm). For this purpose, various sets of texture parameters were calculated in MaZda software. During the calculation of decision trees different numbers of texture parameters groups were considered. In order to obtain the best settings for decision trees models cross-validation was performed. Decision trees models with the lowest mean classification error were selected. The accuracy of the classification was held based on validation data sets, which were not used for the classification learning. For 5 cm ground resolution samples, the lowest mean classification error was 15.6%. The lowest mean classification error in the case of 25 cm ground resolution was 20.0%. The obtained results confirm potential usefulness of the texture parameter image processing for detection of asbestos-cement roofing sheets. In order to improve the accuracy another extended study should be considered in which additional textural features as well as spectral characteristics should be analyzed.

  7. Automatic Detection and Characterization of Subsurface Features from Mars Radar Sounder Data

    NASA Astrophysics Data System (ADS)

    Ferro, A.; Bruzzone, L.; Heggy, E.; Plaut, J. J.

    2010-12-01

    MARSIS and SHARAD are currently orbiting Mars in an attempt to explore structural and volatile elements in its subsurface. The data returned from these two experiments are complementary in their nature for providing different penetration capabilities and vertical resolutions that is crucial to constrain the ambiguities on the subsurface structural and geophysical properties. To this day, both radars have acquired a substantial large volume of data that are yet to be quantitatively analyzed with more accurate radar inversion algorithms. Manual investigation of the radargrams is a time consuming task that is often dependent on user visual ability to distinguish subsurface reflectors. Such process induces a substantial ambiguity in data analysis from user to user, limits the amount of data to be explored and reduces efficiency of fusion studies to compile MARSIS and SHARAD data in a metric process. To address this deficiency, we started the development of automated techniques for the extraction of subsurface information from the radar sounding data. Such methods will greatly improve the ability to perform scientific analysis on larger scale areas using the two data sets from MARSIS and SHARAD simultaneously [Ferro and Bruzzone, 2009]. Our automated data analysis chain has been preliminarily applied only to SHARAD data for the statistical characterization of the radargrams and the automatic detection of linear subsurface features [Ferro and Bruzzone, 2010]. Our current development has been extended for the integration of both SHARAD and MARSIS data. We identified two targets of interest to test and validate our automated tools to explore subsurface features: (1) The North Polar Layer Deposits, and (2) Elysium Planitia. On the NPLD, the technique was able to extract the position and the extension of the returns coming from basal unit from SHARAD radargrams, both in range and azimuth. Therefore, it was possible to map the depth and thickness of the icy polar cap. The

  8. Context-dependent feature selection using unsupervised contexts applied to GPR-based landmine detection

    NASA Astrophysics Data System (ADS)

    Ratto, Christopher R.; Torrione, Peter A.; Collins, Leslie M.

    2010-04-01

    Context-dependent classification techniques applied to landmine detection with ground-penetrating radar (GPR) have demonstrated substantial performance improvements over conventional classification algorithms. Context-dependent algorithms compute a decision statistic by integrating over uncertainty in the unknown, but probabilistically inferable, context of the observation. When applied to GPR, contexts may be defined by differences in electromagnetic properties of the subsurface environment, which are due to discrepancies in soil composition, moisture levels, and surface texture. Context-dependent Feature Selection (CDFS) is a technique developed for selecting a unique subset of features for classifying landmines from clutter in different environmental contexts. In past work, context definitions were assumed to be soil moisture conditions which were known during training. However, knowledge of environmental conditions could be difficult to obtain in the field. In this paper, we utilize an unsupervised learning algorithm for defining contexts which are unknown a priori. Our method performs unsupervised context identification based on similarities in physics-based and statistical features that characterize the subsurface environment of the raw GPR data. Results indicate that utilizing this contextual information improves classification performance, and provides performance improvements over non-context-dependent approaches. Implications for on-line context identification will be suggested as a possible avenue for future work.

  9. Detecting abnormality in optic nerve head images using a feature extraction analysis

    PubMed Central

    Zhu, Haogang; Poostchi, Ali; Vernon, Stephen A; Crabb, David P

    2014-01-01

    Imaging and evaluation of the optic nerve head (ONH) plays an essential part in the detection and clinical management of glaucoma. The morphological characteristics of ONHs vary greatly from person to person and this variability means it is difficult to quantify them in a standardized way. We developed and evaluated a feature extraction approach using shift-invariant wavelet packet and kernel principal component analysis to quantify the shape features in ONH images acquired by scanning laser ophthalmoscopy (Heidelberg Retina Tomograph [HRT]). The methods were developed and tested on 1996 eyes from three different clinical centers. A shape abnormality score (SAS) was developed from extracted features using a Gaussian process to identify glaucomatous abnormality. SAS can be used as a diagnostic index to quantify the overall likelihood of ONH abnormality. Maps showing areas of likely abnormality within the ONH were also derived. Diagnostic performance of the technique, as estimated by ROC analysis, was significantly better than the classification tools currently used in the HRT software – the technique offers the additional advantage of working with all images and is fully automated. PMID:25071960

  10. Automatic Road Area Extraction from Printed Maps Based on Linear Feature Detection

    NASA Astrophysics Data System (ADS)

    Callier, Sebastien; Saito, Hideo

    Raster maps are widely available in the everyday life, and can contain a huge amount of information of any kind using labels, pictograms, or color code e.g. However, it is not an easy task to extract roads from those maps due to those overlapping features. In this paper, we focus on an automated method to extract roads by using linear features detection to search for seed points having a high probability to belong to roads. Those linear features are lines of pixels of homogenous color in each direction around each pixel. After that, the seeds are then expanded before choosing to keep or to discard the extracted element. Because this method is not mainly based on color segmentation, it is also suitable for handwritten maps for example. The experimental results demonstrate that in most cases our method gives results similar to usual methods without needing any previous data or user input, but do need some knowledge on the target maps; and does work with handwritten maps if drawn following some basic rules whereas usual methods fail.

  11. Computer-aided mass detection in mammography: False positive reduction via gray-scale invariant ranklet texture features

    SciTech Connect

    Masotti, Matteo; Lanconelli, Nico; Campanini, Renato

    2009-02-15

    In this work, gray-scale invariant ranklet texture features are proposed for false positive reduction (FPR) in computer-aided detection (CAD) of breast masses. Two main considerations are at the basis of this proposal. First, false positive (FP) marks surviving our previous CAD system seem to be characterized by specific texture properties that can be used to discriminate them from masses. Second, our previous CAD system achieves invariance to linear/nonlinear monotonic gray-scale transformations by encoding regions of interest into ranklet images through the ranklet transform, an image transformation similar to the wavelet transform, yet dealing with pixels' ranks rather than with their gray-scale values. Therefore, the new FPR approach proposed herein defines a set of texture features which are calculated directly from the ranklet images corresponding to the regions of interest surviving our previous CAD system, hence, ranklet texture features; then, a support vector machine (SVM) classifier is used for discrimination. As a result of this approach, texture-based information is used to discriminate FP marks surviving our previous CAD system; at the same time, invariance to linear/nonlinear monotonic gray-scale transformations of the new CAD system is guaranteed, as ranklet texture features are calculated from ranklet images that have this property themselves by construction. To emphasize the gray-scale invariance of both the previous and new CAD systems, training and testing are carried out without any in-between parameters' adjustment on mammograms having different gray-scale dynamics; in particular, training is carried out on analog digitized mammograms taken from a publicly available digital database, whereas testing is performed on full-field digital mammograms taken from an in-house database. Free-response receiver operating characteristic (FROC) curve analysis of the two CAD systems demonstrates that the new approach achieves a higher reduction of FP marks

  12. Morphological and immunohistochemical features of the vomeronasal system in dogs.

    PubMed

    Salazar, Ignacio; Cifuentes, José M; Sánchez-Quinteiro, Pablo

    2013-01-01

    Each of the structures integrating the sense of smell in mammals has a different degree of development, even in the so-called macrosmatic animals, according to the capacity of the olfactory system to detect thousands of different chemical signals. Such morphological diversity implies analogous physiological variation. The study of the accessory olfactory system, also known as the vomeronasal system, is a useful way to analyze the heterogeneity of the sense of smell. Macrodissection and microdissection methods as well as conventional histology and immunohistochemistry protocols were used to study aspects of the vomeronasal organ and the accessory olfactory bulbs in dogs. Observations regarding the end of the anterior part of the vomeronasal duct have been emphasized. Both lectins, Ulex europaeus agglutinin I and Lycopersicum esculentum agglutinin, and one G protein, G(αi2), show a similar pattern of binding in the sensory epithelium of the vomeronasal organ and in the vomeronasal nerve and glomerular layers of the accessory olfactory bulb, whereas the expression of protein G(αo) was not observed. Taken together, our results emphasize the contribution of comparative data to our understanding of the vomeronasal system function. PMID:23161754

  13. Statistical methods for detecting differentially abundant features in clinical metagenomic samples.

    PubMed

    White, James Robert; Nagarajan, Niranjan; Pop, Mihai

    2009-04-01

    Numerous studies are currently underway to characterize the microbial communities inhabiting our world. These studies aim to dramatically expand our understanding of the microbial biosphere and, more importantly, hope to reveal the secrets of the complex symbiotic relationship between us and our commensal bacterial microflora. An important prerequisite for such discoveries are computational tools that are able to rapidly and accurately compare large datasets generated from complex bacterial communities to identify features that distinguish them.We present a statistical method for comparing clinical metagenomic samples from two treatment populations on the basis of count data (e.g. as obtained through sequencing) to detect differentially abundant features. Our method, Metastats, employs the false discovery rate to improve specificity in high-complexity environments, and separately handles sparsely-sampled features using Fisher's exact test. Under a variety of simulations, we show that Metastats performs well compared to previously used methods, and significantly outperforms other methods for features with sparse counts. We demonstrate the utility of our method on several datasets including a 16S rRNA survey of obese and lean human gut microbiomes, COG functional profiles of infant and mature gut microbiomes, and bacterial and viral metabolic subsystem data inferred from random sequencing of 85 metagenomes. The application of our method to the obesity dataset reveals differences between obese and lean subjects not reported in the original study. For the COG and subsystem datasets, we provide the first statistically rigorous assessment of the differences between these populations. The methods described in this paper are the first to address clinical metagenomic datasets comprising samples from multiple subjects. Our methods are robust across datasets of varied complexity and sampling level. While designed for metagenomic applications, our software can also be applied

  14. Near-surface Interface Detection for Coal Mining Applications using Bispectral Features and GPR

    NASA Astrophysics Data System (ADS)

    Strange, Andrew D.; Ralston, Jonathon C.; Chandran, Vinod

    2005-04-01

    The use of ground penetrating radar (GPR) for detecting the presence of near-surface interfaces is a scenario of special interest to the underground coal mining industry. The problem is difficult to solve in practice because the radar echo from the near-surface interface is often dominated by unwanted components such as antenna crosstalk and ringing, ground-bounce effects, clutter, and severe attenuation. These nuisance components are also highly sensitive to subtle variations in ground conditions, rendering the application of standard signal pre-processing techniques such as background subtraction largely ineffective in the unsupervised case. As a solution to this detection problem, we develop a novel pattern recognition-based algorithm which utilizes a neural network to classify features derived from the bispectrum of 1D early time radar data. The binary classifier is used to decide between two key cases, namely whether an interface is within, for example, 5 cm of the surface or not. This go/no-go detection capability is highly valuable for underground coal mining operations, such as longwall mining, where the need to leave a remnant coal section is essential for geological stability. The classifier was trained and tested using real GPR data with ground truth measurements. The real data was acquired from a testbed with coal–clay, coal–shale and shale–clay interfaces, which represents a test mine site. We show that, unlike traditional second order correlation based methods such as matched filtering which can fail even in known conditions, the new method reliably allows the detection of interfaces using GPR to be applied in the near-surface region. In this work, we are not addressing the problem of depth estimation, rather confining ourselves to detecting an interface within a particular depth range.

  15. TV system for detection of latent fingerprints

    NASA Astrophysics Data System (ADS)

    Li, Ping; Ban, Xianfu; Liu, Shaowu; Ding, Zhenfang

    1993-04-01

    A fingerprint is reliable evidence for recognizing criminals in detecting cases. There are many conventional chemical and physical methods in detecting fingerprints. In this paper, a newly developed portable TV system for detecting a latent fingerprint is described. This system is suited for field reconnaissance of cases as well as for laboratory testing. It can display a latent fingerprint, which is hard to identify and even cannot be displayed by conventional methods, and it can detect prints or stamps which are faded, altered, or falsified, etc.

  16. Design features of the radioactive Liquid-Fed Ceramic Melter system

    SciTech Connect

    Holton, L.K. Jr.

    1985-06-01

    During 1983, the Pacific Northwest Laboratory (PNL), at the request of the Department of Energy (DOE), undertook a program with the principal objective of testing the Liquid-Fed Ceramic Melter (LFCM) process in actual radioactive operations. This activity, termed the Radioactive LFCM (RLFCM) Operations is being conducted in existing shielded hot-cell facilities in B-Cell of the 324 Building, 300 Area, located at Hanford, Washington. This report summarizes the design features of the RLFCM system. These features include: a waste preparation and feed system which uses pulse-agitated waste preparation tanks for waste slurry agitation and an air displacement slurry pump for transferring waste slurries to the LFCM; a waste vitrification system (LFCM) - the design features, design approach, and reasoning for the design of the LFCM are described; a canister-handling turntable for positioning canisters underneath the RLFCM discharge port; a gamma source positioning and detection system for monitoring the glass fill level of the product canisters; and a primary off-gas treatment system for removing the majority of the radionuclide contamination from the RLFCM off gas. 8 refs., 48 figs., 6 tabs.

  17. Radar seeker based autonomous navigation update system using topography feature matching techniques

    NASA Astrophysics Data System (ADS)

    Lerche, H. D.; Tumbreagel, F.

    1992-11-01

    The discussed navigation update system was designed for an unmanned platform with fire and forget capability. It meets the requirement due to fully autonomous operation. The system concept will be characterized by complementary use of the radar seeker for target identification as well as for navigation function. The system works in the navigation mode during preprogrammable phases where the primary target identification function is not active or in parallel processing. The dual function radar seeker system navigates the drone during the midcourse and terminal phases of the mission. Its high resolution due to range measurement and doppler beam sharpening in context with its radar reflectivity sensing capability are the basis for topography referenced navigation computation. The detected height jumps (coming from terrain elevation and cultural objects) and radar reflectivity features will be matched together with topography referenced features. The database comprises elevation data and selected radar reflectivity features that are robust against seasonal influences. The operational benefits of the discussed system are as follows: (1) the improved navigation performance with high probability of position fixing, even over flat terrain; (2) the operation within higher altitudes; and (3) bad weather capability. The developed software modules were verified with captive flight test data running in a hardware-in-the-loop simulation.

  18. Real time recall feature for an engine data processor system

    SciTech Connect

    Bluish, J.A.; Hanson, M.B.

    1986-11-04

    This patent describes an engine data processor system having a real time recall feature, the system comprising: means for measuring operating parameters of an engine in real time; means for converting the measured operating parameters into a data table having separate words, each set of parameters defining an engine profile indicative of the value of the measured parameters; means for updating the data table at an input cyclic rate with new values of the operating parameters; means for communicating with a permanent data recordation device over an output channel; means for outputting words from the data table at an output cycle rate to the communicating means; auxiliary memory means; means for controlling the auxiliary memory means, the controlling means being capable of writing information into the auxiliary memory and reading information from the auxiliary memory; the controlling means being operable in a first mode communicating the words from the data table at the output cycle rate and storing them in the auxiliary memory; the controlling means being operable in a second mode set by a command at a particular time to discontinue storing the words from the data table in the auxiliary memory; the controlling means being operable in a third mode set by a second command at a particular time to transmit the stored words at the output cycle rate to the communicating means.

  19. Depth-based human fall detection via shape features and improved extreme learning machine.

    PubMed

    Ma, Xin; Wang, Haibo; Xue, Bingxia; Zhou, Mingang; Ji, Bing; Li, Yibin

    2014-11-01

    Falls are one of the major causes leading to injury of elderly people. Using wearable devices for fall detection has a high cost and may cause inconvenience to the daily lives of the elderly. In this paper, we present an automated fall detection approach that requires only a low-cost depth camera. Our approach combines two computer vision techniques-shape-based fall characterization and a learning-based classifier to distinguish falls from other daily actions. Given a fall video clip, we extract curvature scale space (CSS) features of human silhouettes at each frame and represent the action by a bag of CSS words (BoCSS). Then, we utilize the extreme learning machine (ELM) classifier to identify the BoCSS representation of a fall from those of other actions. In order to eliminate the sensitivity of ELM to its hyperparameters, we present a variable-length particle swarm optimization algorithm to optimize the number of hidden neurons, corresponding input weights, and biases of ELM. Using a low-cost Kinect depth camera, we build an action dataset that consists of six types of actions (falling, bending, sitting, squatting, walking, and lying) from ten subjects. Experimenting with the dataset shows that our approach can achieve up to 91.15% sensitivity, 77.14% specificity, and 86.83% accuracy. On a public dataset, our approach performs comparably to state-of-the-art fall detection methods that need multiple cameras. PMID:25375688

  20. New Cloud Activity on Uranus in 2004: First Detection of a Southern Feature at 2.2 microns

    SciTech Connect

    Hammel, H B; de Pater, I; Gibbard, S; Lockwood, G; Rages, K

    2005-02-02

    On 4 July 2004 UT, we detected one of Uranus' southern hemispheric features at K' (2.2 {micro}m); this is the first such detection in half a decade of adaptive optics imaging of Uranus at the Keck 10-m telescope. When we observed again on 8 July UT the core had faded, and by 9 July UT it was not seen at K' and barely detectable at H. The detection and subsequent disappearance of the feature indicates rapid dynamical processes in the localized vertical aerosol structure.

  1. Modeling And Detecting Anomalies In Scada Systems

    NASA Astrophysics Data System (ADS)

    Svendsen, Nils; Wolthusen, Stephen

    The detection of attacks and intrusions based on anomalies is hampered by the limits of specificity underlying the detection techniques. However, in the case of many critical infrastructure systems, domain-specific knowledge and models can impose constraints that potentially reduce error rates. At the same time, attackers can use their knowledge of system behavior to mask their manipulations, causing adverse effects to observed only after a significant period of time. This paper describes elementary statistical techniques that can be applied to detect anomalies in critical infrastructure networks. A SCADA system employed in liquefied natural gas (LNG) production is used as a case study.

  2. Inertial navigation sensor integrated obstacle detection system

    NASA Technical Reports Server (NTRS)

    Bhanu, Bir (Inventor); Roberts, Barry A. (Inventor)

    1992-01-01

    A system that incorporates inertial sensor information into optical flow computations to detect obstacles and to provide alternative navigational paths free from obstacles. The system is a maximally passive obstacle detection system that makes selective use of an active sensor. The active detection typically utilizes a laser. Passive sensor suite includes binocular stereo, motion stereo and variable fields-of-view. Optical flow computations involve extraction, derotation and matching of interest points from sequential frames of imagery, for range interpolation of the sensed scene, which in turn provides obstacle information for purposes of safe navigation.

  3. TOWARD DETECTING THE 2175 A DUST FEATURE ASSOCIATED WITH STRONG HIGH-REDSHIFT Mg II ABSORPTION LINES

    SciTech Connect

    Jiang Peng; Zhou Hongyan; Wang Junxian; Wang Tinggui; Ge Jian

    2011-05-10

    We report detections of 39 2175 A dust extinction bump candidates associated with strong Mg II absorption lines at z{approx} 1-1.8 on quasar spectra in Sloan Digital Sky Survey (SDSS) DR3. These strong Mg II absorption line systems are detected among 2951 strong Mg II absorbers with a rest equivalent width W{sub r} {lambda}2796> 1.0 A at 1.0 < z < 1.86, which is part of a full sample of 7421 strong Mg II absorbers compiled by Prochter et al. The redshift range of the absorbers is chosen to allow the 2175 A extinction features to be completely covered within the SDSS spectrograph operation wavelength range. An upper limit of the background quasar emission redshift at z = 2.1 is set to prevent the Ly{alpha} forest lines from contaminating the sensitive spectral region for the 2175 A bump measurements. The FM90 parameterization is applied to model the optical/UV extinction curve in the rest frame of Mg II absorbers of the 2175 A bump candidates. The simulation technique developed by Jiang et al. is used to derive the statistical significance of the candidate 2175 A bumps. A total of 12 absorbers are detected with 2175 A bumps at a 5{sigma} level of statistical significance, 10 are detected at a 4{sigma} level, and 17 are detected at a 3{sigma} level. Most of the candidate bumps in this work are similar to the relatively weak 2175 A bumps observed in the Large Magellanic Cloud LMC2 supershell rather than the strong ones observed in the Milky Way. This sample has greatly increased the total number of 2175 A extinction bumps measured on SDSS quasar spectra. Follow-up observations may rule out some of the possible false detections and reveal the physical and chemical natures of 2175 A quasar absorbers.

  4. Driver fatigue detection system based on DSP

    NASA Astrophysics Data System (ADS)

    Wang, Qian; Yu, Fu liang; Song, Lixin

    2012-01-01

    To detect driver fatigue states effectively and in real time, a driver fatigue detection system was built, which take ICETEK-DM6347 module as system core, near-infrared LED as light source, and CCD camera as picture gathering device. An improved PER-NORFACE detection method combined several simple and efficient image processing algorithms was proposed, which based on principle of PERCLOS method and take the human face location as the main detection target. To ensure the ability of real-time processing, the algorithms on the DM6437 DaVinci processor were optimized. Experiments show that the system could complete the driver fatigue states detection accurately and in real time.

  5. SIFT-based error compensation for ear feature matching and recognition system

    NASA Astrophysics Data System (ADS)

    Jiang, Jingying; Zhang, Qi; Ma, Congcong; Lu, Junsheng; Xu, Kexin

    2015-03-01

    The current ear feature matching and recognition system, based on Scale Invariant Feature Transform (SIFT) image matching algorithm, can realize the human ear feature matching and detect the displacement of the human ear so as to reproduce the human ear position and posture. However, due to the influence of image acquisition equipment performance and lighting conditions, too dark or too bright background could bring the locally underexposed or overexposed image. This could result in the loss of some image details so as to make it impossible to identity the image and the recognition rate would be reduced. In this talk, the application of image gray level normalization processing can reduce the sensitivity of imaging to light intensity. Accordingly, it will greatly improve the recognition rate of human ears. Furthermore, it has been found that even if the object is stationary, the image matching results still have certain fluctuation changes, which could be caused by the system error. In order to reduce the error, the Background-based Compensation Model (BCM) has been established based on the investigation of the system error brought by the working environment changes. The results show that, BCM can be used to compensate the system errors of ear recognition matching and further improve the matching accuracy of human ear.

  6. Model-based estimation of cardiovascular repolarization features: ischaemia detection and PTCA monitoring.

    PubMed

    Laguna, P; García, J; Roncal, I; Wagner, G; Lander, P; Mark, R

    1998-01-01

    The ST-T segment of the surface ECG reflects cardiac repolarization, and is quite sensitive to a number of pathological conditions, particularly ischaemia. ST-T changes generally affect the entire waveshape, and are inadequately characterized by single features such as depression of the ST segment at one particular point. Metrics which represent overall waveshape should provide more sensitive indicators of ST-T wave abnormalities, particularly when they are subtle, intermittent or periodic. This study discusses a Karhunen-Loève transform (KLT) technique for the analysis of the ST-T waveform. The KL technique was used to analyse the ST-T complexes in the ESC ST-T database. KL coefficients were plotted as a function of time, and were effective in detection of transient ischaemic episodes. Twenty per cent of the records showed bursts of periodic ischaemia suggesting local vascular instability. A comparison between kl and ST depression series has shown the KL technique as more appropriate to the study of ST-T complex variations. Using the kl series, an ischaemia detector has been developed based on a resampled, filtered, and differentiated KL series. This technique demonstrates a sensitivity of 65% and a specificity of 54%. These low values can be due to shifts of the electrical axis which are detected as ischaemic changes, real ischaemic episodes that were not annotated with the protocol used at the European ST-T database, or erroneous detections. An increase in sensitivity can be obtained at the expense of a decrease in the positive predictive value and thus becomes a useful technique for previous scanning of the ECG record and subsequent review by the expert. The technique has also been used to monitor patients during a PTCA process, demonstrating that this technique allows us to monitor PTCA-induced ischaemia. A detailed analysis has shown that in some cases a repetitive oscillatory behaviour appears, lasting for a period of around 20 s, and highly related to the

  7. Feasibility of detecting near-surface feature with Rayleigh-wave diffraction

    USGS Publications Warehouse

    Xia, J.; Nyquist, J.E.; Xu, Y.; Roth, M.J.S.; Miller, R.D.

    2007-01-01

    Detection of near-surfaces features such as voids and faults is challenging due to the complexity of near-surface materials and the limited resolution of geophysical methods. Although multichannel, high-frequency, surface-wave techniques can provide reliable shear (S)-wave velocities in different geological settings, they are not suitable for detecting voids directly based on anomalies of the S-wave velocity because of limitations on the resolution of S-wave velocity profiles inverted from surface-wave phase velocities. Therefore, we studied the feasibility of directly detecting near-surfaces features with surface-wave diffractions. Based on the properties of surface waves, we have derived a Rayleigh-wave diffraction traveltime equation. We also have solved the equation for the depth to the top of a void and an average velocity of Rayleigh waves. Using these equations, the depth to the top of a void/fault can be determined based on traveltime data from a diffraction curve. In practice, only two diffraction times are necessary to define the depth to the top of a void/fault and the average Rayleigh-wave velocity that generates the diffraction curve. We used four two-dimensional square voids to demonstrate the feasibility of detecting a void with Rayleigh-wave diffractions: a 2??m by 2??m with a depth to the top of the void of 2??m, 4??m by 4??m with a depth to the top of the void of 7??m, and 6??m by 6??m with depths to the top of the void 12??m and 17??m. We also modeled surface waves due to a vertical fault. Rayleigh-wave diffractions were recognizable for all these models after FK filtering was applied to the synthetic data. The Rayleigh-wave diffraction traveltime equation was verified by the modeled data. Modeling results suggested that FK filtering is critical to enhance diffracted surface waves. A real-world example is presented to show how to utilize the derived equation of surface-wave diffractions. ?? 2006 Elsevier B.V. All rights reserved.

  8. Key Features of the National Polar-Orbiting Operational Environmental Satellite System (NPOESS) System Architecture

    NASA Astrophysics Data System (ADS)

    Pela, F.; Tsugawa, R. K.; Andreoli, L. J.

    2004-12-01

    The National Polar-Orbiting NPOESS, a tri-agency program, supports missions of the Department of Commerce (DOC)/National Oceanic and Atmospheric Administration (NOAA), the Department of Defense (DoD), and the National Aeronautics and Space Administration (NASA). NPOESS provides a critical, timely, reliable, and high quality space-based sensing capability to acquire and process global and regional environmental imagery and specialized meteorological, climatic, terrestrial, oceanographic, solar-geophysical, and other data products. These products are delivered to national weather and environmental facilities operated by NOAA and DoD, to NASA, and to environmental remote sensing science community users to support civil and military functions. These data are also provided in real time to field terminals deployed worldwide. The NPOESS architecture is built on a foundation of affordability, and the three pillars of data quality, latency, availability. Affordability refers to an over-arching awareness of cost to provide the best value to the government for implementing a converged system; some dimensions of cost include the cost for system development and implementation, the balance between development costs and operation and maintenance costs, and the fiscal year expenditure plans that meet schedule commitments. Data quality is characterized in terms of the attributes associated with Environmental Data Records (EDRs), and the products that are delivered to the four US Operational Centrals and field users. These EDRs are generated by the system using raw data from the space-borne sensors and spacecraft, in conjunction with science algorithms and calibration factors. Data latency refers to the time period between the detection of energy by a space-borne sensor to the delivery of a corresponding EDR. The system was designed to minimize data latency, and hence provide users with timely data. Availability refers to both data availability and system operational availability

  9. Detection of a Deep 3-μm Absorption Feature in the Spectrum of Amalthea (JV)

    NASA Astrophysics Data System (ADS)

    Takato, Naruhisa; Bus, Schelte J.; Terada, Hiroshi; Pyo, Tae-Soo; Kobayashi, Naoto

    2004-12-01

    Near-infrared spectra of Jupiter's small inner satellites Amalthea and Thebe are similar to those of D-type asteroids in the 0.8- to 2.5-micrometer wavelength range. A deep absorption feature is detected at 3 micrometers in the spectra of the trailing side of Amalthea, which is similar to that of the non-ice components of Callisto and can be attributed to hydrous minerals. These surface materials cannot be explained if the satellite formed at its present orbit by accreting from a circumjovian nebula. Amalthea and Thebe may be the remnants of Jupiter's inflowing building blocks that formed in the outer part or outside of the circumjovian nebula.

  10. Detection of a deep 3-microm absorption feature in the spectrum of Amalthea (JV).

    PubMed

    Takato, Naruhisa; Bus, Schelte J; Terada, Hiroshi; Pyo, Tae-Soo; Kobayashi, Naoto

    2004-12-24

    Near-infrared spectra of Jupiter's small inner satellites Amalthea and Thebe are similar to those of D-type asteroids in the 0.8- to 2.5-micrometer wavelength range. A deep absorption feature is detected at 3 micrometers in the spectra of the trailing side of Amalthea, which is similar to that of the non-ice components of Callisto and can be attributed to hydrous minerals. These surface materials cannot be explained if the satellite formed at its present orbit by accreting from a circumjovian nebula. Amalthea and Thebe may be the remnants of Jupiter's inflowing building blocks that formed in the outer part or outside of the circumjovian nebula. PMID:15618511

  11. Gamma detectors in explosives and narcotics detection systems

    NASA Astrophysics Data System (ADS)

    Bystritsky, V. M.; Zubarev, E. V.; Krasnoperov, A. V.; Porohovoi, S. Yu.; Rapatskii, V. L.; Rogov, Yu. N.; Sadovskii, A. B.; Salamatin, A. V.; Salmin, R. A.; Slepnev, V. M.; Andreev, E. I.

    2013-11-01

    Gamma detectors based on BGO crystals were designed and developed at the Joint Institute for Nuclear Research. These detectors are used in explosives and narcotics detection systems. Key specifications and design features of the detectors are presented. A software temperature-compensation method that makes it possible to stabilize the gamma detector response and operate the detector in a temperature range from -20 to 50°C is described.

  12. Feature-based watermark localization in digital capture systems

    NASA Astrophysics Data System (ADS)

    Holub, Vojtech; Filler, Tomáš

    2014-02-01

    The "Internet of Things" is an appealing concept aiming to assign digital identity to both physical and digital everyday objects. One way of achieving this goal is to embed the identity in the object itself by using digital watermarking. In the case of printed physical objects, such as consumer packages, this identity can be later read from a digital image of the watermarked object taken by a camera. In many cases, the object might occupy only a small portion of the the image and an attempt to read the watermark payload from the whole image can lead to unnecessary processing. This paper proposes a statistical learning-based algorithm for localizing watermarked physical objects taken by a digital camera. The algorithm is specifically designed and tested on watermarked consumer packages read by an off-the-shelf barcode imaging scanner. By employing simple noise-sensitive features borrowed from blind image steganalysis and a linear classifier, we are able to estimate probabilities of watermark presence in every part of the image significantly faster than running a watermark detector. These probabilities are used to pinpoint areas that are recommended for further processing. We compare our adaptive approach with a system designed to read watermarks from a set of fixed locations and achieve significant savings in processing time while improving overall detector robustness.

  13. Technical features of a low-cost Earthquake Alert System

    SciTech Connect

    Harben, P.

    1991-08-01

    The concept and features of an Earthquake Alert System (EAS) involving a distributed network of strong motion sensors is discussed. The EAS analyzes real-time data telemetered to a central facility and issues an areawide warning of a large earthquake in advance of the spreading elastic wave energy. A low-cost solution to high-cost estimates for installation and maintenance of a dedicated EAS is presented that makes use of existing microseismic stations. Using the San Francisco Bay area as an example, we show that existing US Geological Survey microseismic monitoring stations are of sufficient density to form the elements of a prototype EAS. By installing strong motion instrumentation and a specially developed switching device, strong ground motion can be telemetered in real-time to the central microseismic station on the existing communication channels. When a large earthquake occurs, a dedicated real-time central processing unit at the central microseismic station digitizes and analyses the incoming data and issues a warning containing location and magnitude estimations. A 50-station EAS of this type in the San Francisco Bay area should cost under $70,000 to install and less than $5000 annually to maintain.

  14. Multisensor cargo bay fire detection system

    NASA Astrophysics Data System (ADS)

    Snyder, Brian L.; Anderson, Kaare J.; Renken, Christopher H.; Socha, David M.; Miller, Mark S.

    2004-08-01

    Current aircraft cargo bay fire detection systems are generally based on smoke detection. Smoke detectors in modern aircraft are predominately photoelectric particle detectors that reliably detect smoke, but also detect dust, fog, and most other small particles. False alarms caused by these contaminants can be very costly to the airlines because they can cause flights to be diverted needlessly. To minimize these expenses, a new approach to cargo bay fire detection is needed. This paper describes a novel fire detection system developed by the Goodrich Advanced Sensors Technical Center. The system uses multiple sensors of different technologies to provide a way of discriminating between real fire events and false triggers. The system uses infrared imaging along with multiple, distributed chemical sensors and smoke detectors, all feeding data to a digital signal processor. The processor merges data from the chemical sensors, smoke detectors, and processed images to determine if a fire (or potential fire) is present. Decision algorithms look at all this data in real-time and make the final decision about whether a fire is present. In the paper, we present a short background of the problem we are solving, the reasons for choosing the technologies used, the design of the system, the signal processing methods and results from extensive system testing. We will also show that multiple sensing technologies are crucial to reducing false alarms in such systems.

  15. Computer systems for automatic earthquake detection

    USGS Publications Warehouse

    Stewart, S.W.

    1974-01-01

    U.S Geological Survey seismologists in Menlo park, California, are utilizing the speed, reliability, and efficiency of minicomputers to monitor seismograph stations and to automatically detect earthquakes. An earthquake detection computer system, believed to be the only one of its kind in operation, automatically reports about 90 percent of all local earthquakes recorded by a network of over 100 central California seismograph stations. The system also monitors the stations for signs of malfunction or abnormal operation. Before the automatic system was put in operation, all of the earthquakes recorded had to be detected by manually searching the records, a time-consuming process. With the automatic detection system, the stations are efficiently monitored continuously. 

  16. RADIATION DETECTING AND TELEMETERING SYSTEM

    DOEpatents

    Richards, H.K.

    1959-12-15

    A system is presented for measuring ionizing radiation at several remote stations and transmitting the measured information by radio to a central station. At each remote station a signal proportioned to the counting rate is applied across an electrical condenser made of ferroelectric material. The voltage across the condenser will vary as a function of the incident radiation and the capacitance of the condenser will vary accordingly. This change in capacitance is used to change the frequency of a crystalcontrolled oscillator. The output of the oscillator is coupled to an antenna for transmitting a signal proportional to the incident radiation.

  17. An Interval Type-2 Neural Fuzzy System for Online System Identification and Feature Elimination.

    PubMed

    Lin, Chin-Teng; Pal, Nikhil R; Wu, Shang-Lin; Liu, Yu-Ting; Lin, Yang-Yin

    2015-07-01

    We propose an integrated mechanism for discarding derogatory features and extraction of fuzzy rules based on an interval type-2 neural fuzzy system (NFS)-in fact, it is a more general scheme that can discard bad features, irrelevant antecedent clauses, and even irrelevant rules. High-dimensional input variable and a large number of rules not only enhance the computational complexity of NFSs but also reduce their interpretability. Therefore, a mechanism for simultaneous extraction of fuzzy rules and reducing the impact of (or eliminating) the inferior features is necessary. The proposed approach, namely an interval type-2 Neural Fuzzy System for online System Identification and Feature Elimination (IT2NFS-SIFE), uses type-2 fuzzy sets to model uncertainties associated with information and data in designing the knowledge base. The consequent part of the IT2NFS-SIFE is of Takagi-Sugeno-Kang type with interval weights. The IT2NFS-SIFE possesses a self-evolving property that can automatically generate fuzzy rules. The poor features can be discarded through the concept of a membership modulator. The antecedent and modulator weights are learned using a gradient descent algorithm. The consequent part weights are tuned via the rule-ordered Kalman filter algorithm to enhance learning effectiveness. Simulation results show that IT2NFS-SIFE not only simplifies the system architecture by eliminating derogatory/irrelevant antecedent clauses, rules, and features but also maintains excellent performance. PMID:25163074

  18. Multispectral imaging system for contaminant detection

    NASA Technical Reports Server (NTRS)

    Poole, Gavin H. (Inventor)

    2003-01-01

    An automated inspection system for detecting digestive contaminants on food items as they are being processed for consumption includes a conveyor for transporting the food items, a light sealed enclosure which surrounds a portion of the conveyor, with a light source and a multispectral or hyperspectral digital imaging camera disposed within the enclosure. Operation of the conveyor, light source and camera are controlled by a central computer unit. Light reflected by the food items within the enclosure is detected in predetermined wavelength bands, and detected intensity values are analyzed to detect the presence of digestive contamination.

  19. Detecting data anomalies methods in distributed systems

    NASA Astrophysics Data System (ADS)

    Mosiej, Lukasz

    2009-06-01

    Distributed systems became most popular systems in big companies. Nowadays many telecommunications companies want to hold large volumes of data about all customers. Obviously, those data cannot be stored in single database because of many technical difficulties, such as data access efficiency, security reasons, etc. On the other hand there is no need to hold all data in one place, because companies already have dedicated systems to perform specific tasks. In the distributed systems there is a redundancy of data and each system holds only interesting data in appropriate form. Data updated in one system should be also updated in the rest of systems, which hold that data. There are technical problems to update those data in all systems in transactional way. This article is about data anomalies in distributed systems. Avail data anomalies detection methods are shown. Furthermore, a new initial concept of new data anomalies detection methods is described on the last section.

  20. Key Features of the Deployed NPP/NPOESS Ground System

    NASA Astrophysics Data System (ADS)

    Heckmann, G.; Grant, K. D.; Mulligan, J. E.

    2010-12-01

    The National Oceanic & Atmospheric Administration (NOAA), Department of Defense (DoD), and National Aeronautics & Space Administration (NASA) are jointly acquiring the next-generation weather/environmental satellite system; the National Polar-orbiting Operational Environmental Satellite System (NPOESS). NPOESS replaces the current NOAA Polar-orbiting Operational Environmental Satellites (POES) and DoD Defense Meteorological Satellite Program (DMSP). NPOESS satellites carry sensors to collect meteorological, oceanographic, climatological, and solar-geophysical data of the earth, atmosphere, and space. The ground data processing segment is the Interface Data Processing Segment (IDPS), developed by Raytheon Intelligence & Information Systems (IIS). The IDPS processes NPOESS Preparatory Project (NPP)/NPOESS satellite data to provide environmental data products/records (EDRs) to NOAA and DoD processing centers operated by the US government. The IDPS will process EDRs beginning with NPP and continuing through the lifetime of the NPOESS system. The command & telemetry segment is the Command, Control & Communications Segment (C3S), also developed by Raytheon IIS. C3S is responsible for managing the overall NPP/NPOESS missions from control & status of the space and ground assets to ensuring delivery of timely, high quality data from the Space Segment to IDPS for processing. In addition, the C3S provides the globally-distributed ground assets needed to collect and transport mission, telemetry, and command data between the satellites and processing locations. The C3S provides all functions required for day-to-day satellite commanding & state-of-health monitoring, and delivery of Stored Mission Data to each Central IDP for data products development and transfer to system subscribers. The C3S also monitors and reports system-wide health & status and data communications with external systems and between the segments. The C3S & IDPS segments were delivered & transitioned to

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

    PubMed

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

    2012-10-01

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

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

    PubMed Central

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

    2012-01-01

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

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

    NASA Astrophysics Data System (ADS)

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

    2012-10-01

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

  4. GPS Signal Feature Analysis to Detect Volcanic Plume on Mount Etna

    NASA Astrophysics Data System (ADS)

    Cannavo', Flavio; Aranzulla, Massimo; Scollo, Simona; Puglisi, Giuseppe; Imme', Giuseppina

    2014-05-01

    Volcanic ash produced during explosive eruptions can cause disruptions to aviation operations and to population living around active volcanoes. Thus, detection of volcanic plume becomes a crucial issue to reduce troubles connected to its presence. Nowadays, the volcanic plume detection is carried out by using different approaches such as satellites, radars and lidars. Recently, the capability of GPS to retrieve volcanic plumes has been also investigated and some tests applied to explosive activity of Etna have demonstrated that also the GPS may give useful information. In this work, we use the permanent and continuous GPS network of the Istituto Nazionale di Geofisica e Vulcanologia, Osservatorio Etneo (Italy) that consists of 35 stations located all around volcano flanks. Data are processed by the GAMIT package developed by Massachusetts Institute of Technology. Here we investigate the possibility to quantify the volcanic plume through the GPS signal features and to estimate its spatial distribution by means of a tomographic inversion algorithm. The method is tested on volcanic plumes produced during the lava fountain of 4-5 September 2007, already used to confirm if weak explosive activity may or may not affect the GPS signals.

  5. Toward fast feature adaptation and localization for real-time face recognition systems

    NASA Astrophysics Data System (ADS)

    Zuo, Fei; de With, Peter H.

    2003-06-01

    In a home environment, video surveillance employing face detection and recognition is attractive for new applications. Facial feature (e.g. eyes and mouth) localization in the face is an essential task for face recognition because it constitutes an indispensable step for face geometry normalization. This paper presents a new and efficient feature localization approach for real-time personal surveillance applications with low-quality images. The proposed approach consists of three major steps: (1) self-adaptive iris tracing, which is preceded by a trace-point selection process with multiple initializations to overcome the local convergence problem, (2) eye structure verification using an eye template with limited deformation freedom, and (3) eye-pair selection based on a combination of metrics. We have tested our facial feature localization method on about 100 randomly selected face images from the AR database and 30 face images downloaded from the Internet. The results show that our approach achieves a correct detection rate of 96%. Since our eye-selection technique does not involve time-consuming deformation processes, it yields relatively fast processing. The proposed algorithm has been successfully applied to a real-time home video surveillance system and proven to be an effective and computationally efficient face normalization method preceding the face recognition.

  6. Automatic detection of wheezes by evaluation of multiple acoustic feature extraction methods and C-weighted SVM

    NASA Astrophysics Data System (ADS)

    Sosa, Germán. D.; Cruz-Roa, Angel; González, Fabio A.

    2015-01-01

    This work addresses the problem of lung sound classification, in particular, the problem of distinguishing between wheeze and normal sounds. Wheezing sound detection is an important step to associate lung sounds with an abnormal state of the respiratory system, usually associated with tuberculosis or another chronic obstructive pulmonary diseases (COPD). The paper presents an approach for automatic lung sound classification, which uses different state-of-the-art sound features in combination with a C-weighted support vector machine (SVM) classifier that works better for unbalanced data. Feature extraction methods used here are commonly applied in speech recognition and related problems thanks to the fact that they capture the most informative spectral content from the original signals. The evaluated methods were: Fourier transform (FT), wavelet decomposition using Wavelet Packet Transform bank of filters (WPT) and Mel Frequency Cepstral Coefficients (MFCC). For comparison, we evaluated and contrasted the proposed approach against previous works using different combination of features and/or classifiers. The different methods were evaluated on a set of lung sounds including normal and wheezing sounds. A leave-two-out per-case cross-validation approach was used, which, in each fold, chooses as validation set a couple of cases, one including normal sounds and the other including wheezing sounds. Experimental results were reported in terms of traditional classification performance measures: sensitivity, specificity and balanced accuracy. Our best results using the suggested approach, C-weighted SVM and MFCC, achieve a 82.1% of balanced accuracy obtaining the best result for this problem until now. These results suggest that supervised classifiers based on kernel methods are able to learn better models for this challenging classification problem even using the same feature extraction methods.

  7. Automated Hydrogen Gas Leak Detection System

    NASA Technical Reports Server (NTRS)

    1995-01-01

    The Gencorp Aerojet Automated Hydrogen Gas Leak Detection System was developed through the cooperation of industry, academia, and the Government. Although the original purpose of the system was to detect leaks in the main engine of the space shuttle while on the launch pad, it also has significant commercial potential in applications for which there are no existing commercial systems. With high sensitivity, the system can detect hydrogen leaks at low concentrations in inert environments. The sensors are integrated with hardware and software to form a complete system. Several of these systems have already been purchased for use on the Ford Motor Company assembly line for natural gas vehicles. This system to detect trace hydrogen gas leaks from pressurized systems consists of a microprocessor-based control unit that operates a network of sensors. The sensors can be deployed around pipes, connectors, flanges, and tanks of pressurized systems where leaks may occur. The control unit monitors the sensors and provides the operator with a visual representation of the magnitude and locations of the leak as a function of time. The system can be customized to fit the user's needs; for example, it can monitor and display the condition of the flanges and fittings associated with the tank of a natural gas vehicle.

  8. Flat Surface Damage Detection System (FSDDS)

    NASA Technical Reports Server (NTRS)

    Williams, Martha; Lewis, Mark; Gibson, Tracy; Lane, John; Medelius, Pedro; Snyder, Sarah; Ciarlariello, Dan; Parks, Steve; Carrejo, Danny; Rojdev, Kristina

    2013-01-01

    The Flat Surface Damage Detection system (FSDDS} is a sensory system that is capable of detecting impact damages to surfaces utilizing a novel sensor system. This system will provide the ability to monitor the integrity of an inflatable habitat during in situ system health monitoring. The system consists of three main custom designed subsystems: the multi-layer sensing panel, the embedded monitoring system, and the graphical user interface (GUI). The GUI LABVIEW software uses a custom developed damage detection algorithm to determine the damage location based on the sequence of broken sensing lines. It estimates the damage size, the maximum depth, and plots the damage location on a graph. Successfully demonstrated as a stand alone technology during 2011 D-RATS. Software modification also allowed for communication with HDU avionics crew display which was demonstrated remotely (KSC to JSC} during 2012 integration testing. Integrated FSDDS system and stand alone multi-panel systems were demonstrated remotely and at JSC, Mission Operations Test using Space Network Research Federation (SNRF} network in 2012. FY13, FSDDS multi-panel integration with JSC and SNRF network Technology can allow for integration with other complementary damage detection systems.

  9. Detection probability of EBPSK-MODEM system

    NASA Astrophysics Data System (ADS)

    Yao, Yu; Wu, Lenan

    2016-07-01

    Since the impacting filter-based receiver is able to transform phase modulation into amplitude peak, a simple threshold decision can detect the Extend-Binary Phase Shift Keying (EBPSK) modulated ranging signal in noise environment. In this paper, an analysis of the EBPSK-MODEM system output gives the probability density function for EBPSK modulated signals plus noise. The equation of detection probability (pd) for fluctuating and non-fluctuating targets has been deduced. Also, a comparison of the pd for the EBPSK-MODEM system and pulse radar receiver is made, and some results are plotted. Moreover, the probability curves of such system with several modulation parameters are analysed. When modulation parameter is not smaller than 6, the detection performance of EBPSK-MODEM system is more excellent than traditional radar system. In addition to theoretical considerations, computer simulations are provided for illustrating the performance.

  10. Windshear detection and avoidance - Airborne systems survey

    NASA Technical Reports Server (NTRS)

    Bowles, Roland L.

    1990-01-01

    Functional requirements for airborne windshear detection and warning systems are discussed in terms of the threat posed to civil aircraft operations. A preliminary set of performance criteria for predictive windshear detection and warning systems is defined. Candidate airborne remote sensor technologies based on microwave Doppler radar, Doppler laser radar (lidar), and infrared radiometric techniques are discussed in the context of overall system requirements, and the performance of each sensor is assessed for representative microburst environments and ground clutter conditions. Preliminary simulation results demonstrate that all three sensors show potential for detecting windshear, and provide adequate warning time to allow flight crews to avoid the affected area or escape from the encounter. Radar simulation and analysis show that by using bin-to-bin automatic gain control, clutter filtering, limited detection range, and suitable antenna tilt management, windshear from wet microbursts can be accurately detected. Although a performance improvement can be obtained at higher radar frequency, the baseline X-band system also detected the presence of windshear hazard for a dry microburst. Simulation results of end-to-end performance for competing coherent lidar systems are presented.

  11. Multiple-feature extracting modules based leak mining system design.

    PubMed

    Cho, Ying-Chiang; Pan, Jen-Yi

    2013-01-01

    Over the years, human dependence on the Internet has increased dramatically. A large amount of information is placed on the Internet and retrieved from it daily, which makes web security in terms of online information a major concern. In recent years, the most problematic issues in web security have been e-mail address leakage and SQL injection attacks. There are many possible causes of information leakage, such as inadequate precautions during the programming process, which lead to the leakage of e-mail addresses entered online or insufficient protection of database information, a loophole that enables malicious users to steal online content. In this paper, we implement a crawler mining system that is equipped with SQL injection vulnerability detection, by means of an algorithm developed for the web crawler. In addition, we analyze portal sites of the governments of various countries or regions in order to investigate the information leaking status of each site. Subsequently, we analyze the database structure and content of each site, using the data collected. Thus, we make use of practical verification in order to focus on information security and privacy through black-box testing. PMID:24453892

  12. Multiple-Feature Extracting Modules Based Leak Mining System Design

    PubMed Central

    Cho, Ying-Chiang; Pan, Jen-Yi

    2013-01-01

    Over the years, human dependence on the Internet has increased dramatically. A large amount of information is placed on the Internet and retrieved from it daily, which makes web security in terms of online information a major concern. In recent years, the most problematic issues in web security have been e-mail address leakage and SQL injection attacks. There are many possible causes of information leakage, such as inadequate precautions during the programming process, which lead to the leakage of e-mail addresses entered online or insufficient protection of database information, a loophole that enables malicious users to steal online content. In this paper, we implement a crawler mining system that is equipped with SQL injection vulnerability detection, by means of an algorithm developed for the web crawler. In addition, we analyze portal sites of the governments of various countries or regions in order to investigate the information leaking status of each site. Subsequently, we analyze the database structure and content of each site, using the data collected. Thus, we make use of practical verification in order to focus on information security and privacy through black-box testing. PMID:24453892

  13. Genetic Particle Swarm Optimization-Based Feature Selection for Very-High-Resolution Remotely Sensed Imagery Object Change Detection.

    PubMed

    Chen, Qiang; Chen, Yunhao; Jiang, Weiguo

    2016-01-01

    In the field of multiple features Object-Based Change Detection (OBCD) for very-high-resolution remotely sensed images, image objects have abundant features and feature selection affects the precision and efficiency of OBCD. Through object-based image analysis, this paper proposes a Genetic Particle Swarm Optimization (GPSO)-based feature selection algorithm to solve the optimization problem of feature selection in multiple features OBCD. We select the Ratio of Mean to Variance (RMV) as the fitness function of GPSO, and apply the proposed algorithm to the object-based hybrid multivariate alternative detection model. Two experiment cases on Worldview-2/3 images confirm that GPSO can significantly improve the speed of convergence, and effectively avoid the problem of premature convergence, relative to other feature selection algorithms. According to the accuracy evaluation of OBCD, GPSO is superior at overall accuracy (84.17% and 83.59%) and Kappa coefficient (0.6771 and 0.6314) than other algorithms. Moreover, the sensitivity analysis results show that the proposed algorithm is not easily influenced by the initial parameters, but the number of features to be selected and the size of the particle swarm would affect the algorithm. The comparison experiment results reveal that RMV is more suitable than other functions as the fitness function of GPSO-based feature selection algorithm. PMID:27483285

  14. A system for distributed intrusion detection

    SciTech Connect

    Snapp, S.R.; Brentano, J.; Dias, G.V.; Goan, T.L.; Heberlein, L.T.; Ho, Che-Lin; Levitt, K.N.; Mukherjee, B. . Div. of Computer Science); Grance, T. ); Mansur, D.L.; Pon, K.L. ); Smaha, S.E. )

    1991-01-01

    The study of providing security in computer networks is a rapidly growing area of interest because the network is the medium over which most attacks or intrusions on computer systems are launched. One approach to solving this problem is the intrusion-detection concept, whose basic premise is that not only abandoning the existing and huge infrastructure of possibly-insecure computer and network systems is impossible, but also replacing them by totally-secure systems may not be feasible or cost effective. Previous work on intrusion-detection systems were performed on stand-alone hosts and on a broadcast local area network (LAN) environment. The focus of our present research is to extend our network intrusion-detection concept from the LAN environment to arbitarily wider areas with the network topology being arbitrary as well. The generalized distributed environment is heterogeneous, i.e., the network nodes can be hosts or servers from different vendors, or some of them could be LAN managers, like our previous work, a network security monitor (NSM), as well. The proposed architecture for this distributed intrusion-detection system consists of the following components: a host manager in each host; a LAN manager for monitoring each LAN in the system; and a central manager which is placed at a single secure location and which receives reports from various host and LAN managers to process these reports, correlate them, and detect intrusions. 11 refs., 2 figs.

  15. A feature-based eddy-current imaging system for personal computers: Final report

    SciTech Connect

    Elmo, P.M.; Shankar, R.

    1989-03-01

    Imaging eddy current nondestructive evaluation (NDE) data on low-cost, field-deployable personal computer (PC) systems is now possible. This report describes the modification of a previously developed feature-based ultrasonic (UT) testing system for automatic eddy current (EC) inspection. This EC system was applied to flat plate and circular geometries. The PC system manipulates the eddy current probe around the part to be inspected; acquires multi-frequency, dual-channel digital data and displays images of an operator-selected channel; and utilizes advanced signal processing software to generate and display impedance-plane trajectories for all frequencies. Laboratory experiments using this eddy current test system on flaw calibration standards, induced intergranular-stress-corrosion-cracking (IGSCC) in retaining ring material coupons, and induced cracks in full size retaining rings have demonstrated the system's capability to detect surface damage. Future efforts will incorporate other problem-relevant signal processing algorithms to aid in detecting and characterizing surface-damage, such as pitting and cracking. 6 refs., 20 figs.

  16. Capacitive system detects and locates fluid leaks

    NASA Technical Reports Server (NTRS)

    1966-01-01

    Electronic monitoring system automatically detects and locates minute leaks in seams of large fluid storage tanks and pipelines covered with thermal insulation. The system uses a capacitive tape-sensing element that is adhesively bonded over seams where fluid leaks are likely to occur.

  17. Detection Algorithms of the Seismic Alert System of Mexico (SASMEX)

    NASA Astrophysics Data System (ADS)

    Cuellar Martinez, A.; Espinosa Aranda, J.; Ramos Perez, S.; Ibarrola Alvarez, G.; Zavala Guerrero, M.; Sasmex

    2013-05-01

    The importance of a rapid and reliable detection of an earthquake, allows taking advantage with more opportunity time of any possible opportunity warnings to the population. Thus detection algorithms in the sensing field station (FS) of an earthquake early earning system, must have a high rate of correct detection; this condition lets perform numerical processes to obtain appropriate parameters for the alert activation. During the evolution and continuous service of the Mexican Seismic Alert System (SASMEX) in more than 23 operation years, it has used various methodologies in the detection process to get the largest opportunity time when an earthquake occurs and it is alerted. In addition to the characteristics of the acceleration signal observed in sensing field stations, it is necessary the site conditions reducing urban noise, but sometimes it is not present through of the first operation years, however, urban growth near to FS cause urban noise, which should be tolerated while carrying out the relocation process of the station, and in the algorithm design should be contemplating the robustness to reduce possible errors and false detections. This work presents some results on detection algorithms used in Mexico for early warning systems for earthquakes considering recent events and different opportunity times obtained depending of the detections on P and S phases of the earthquake detected in the station. Some methodologies are reviewed and described in detail in this work and the main features implemented in The Seismic Alert System of Mexico City (SAS), in continuous operation since 1991, and the Seismic Alert System of Oaxaca City (SASO), today both comprise the SASMEX.

  18. Advanced Atmospheric Water Vapor DIAL Detection System

    NASA Technical Reports Server (NTRS)

    Refaat, Tamer F.; Elsayed-Ali, Hani E.; DeYoung, Russell J. (Technical Monitor)

    2000-01-01

    Measurement of atmospheric water vapor is very important for understanding the Earth's climate and water cycle. The remote sensing Differential Absorption Lidar (DIAL) technique is a powerful method to perform such measurement from aircraft and space. This thesis describes a new advanced detection system, which incorporates major improvements regarding sensitivity and size. These improvements include a low noise advanced avalanche photodiode detector, a custom analog circuit, a 14-bit digitizer, a microcontroller for on board averaging and finally a fast computer interface. This thesis describes the design and validation of this new water vapor DIAL detection system which was integrated onto a small Printed Circuit Board (PCB) with minimal weight and power consumption. Comparing its measurements to an existing DIAL system for aerosol and water vapor profiling validated the detection system.

  19. A cable detection lidar system for helicopters

    NASA Technical Reports Server (NTRS)

    Grossmann, Benoist; Capbern, Alain; Defour, Martin; Fertala, Remi

    1992-01-01

    Helicopters in low-level flight are endangered by power lines or telephone wires, especially when flying at night and under poor visibility conditions. In order to prevent 'wire strike', Thomson has developed a lidar system consisting of a pulsed diode laser emitting in the near infrared region (lambda = 0.9 microns). The HOWARD (Helicopter Obstacle Warning and Detection) System utilizes a high repetition rate diode laser (PRE = 20 KHz) along with counter-rotating prisms for laser beam deflection with a total field of view of 30 degrees. This system was successfully field tested in 1991. HOWARD can detect one inch wires at ranges up to 200 meters. We are presently in the process of developing a flyable compact lidar system capable of detection ranges in the order of 400 meters.

  20. Discriminating Damage from Surface Wetting via Feature Analysis for Ultrasonic Structural Health Monitoring Systems

    NASA Astrophysics Data System (ADS)

    Lu, Yinghui; Michaels, Jennifer E.

    2008-02-01

    Benign environmental effects, including temperature and surface condition changes, can adversely affect the performance of an ultrasonic structural health monitoring system because ultrasonic waves are sensitive to such changes as well as to damage. Compared to temperature, the problem of surface condition changes is more difficult to formalize because of their variability in terms of type, location and extent. In this paper, we systematically investigate the effects of surface wetting with a simplified experiment where well controlled water drops are added onto the surface of a specimen in conjunction with damage. The approach is to find selective features which are sensitive to damage but insensitive to the applied surface wetting. Features considered are derived from the time domain ultrasonic signals and their matching pursuit decompositions. Experimental results show good performance in detecting damage in the presence of the applied surface wetting, and provide a set of differential features with potential for dealing with the problem of surface wetting for ultrasonic structural health monitoring systems.

  1. Detection of prosodics by using a speech recognition system

    NASA Astrophysics Data System (ADS)

    Hupp, N. A.

    1991-07-01

    The problem was to determine the ability of a speech recognizer to extract prosodic speech features, such as pitch and stress, and to examine these features for application to future voice recognition systems. The Speech Systems Incorporated (SSI) speech recognizer demonstrated that it could detect prosodic features and that these features do indicate the word and/or syllable that is stressed by the speaker. The research examined the effect of prosodics, such as pitch, amplitude, and duration, on word and syllable stress by using the SSI. Subjects read phases and sentences, using a given intonation and stress. The three sections of the experiment compared questions and answers, words stressed within a sentence, and noun/verb pairs, such as object and subject. The results were analyzed both on the syllable level and the word level. In all cases, there was a significant increase in pitch, amplitude, and duration when comparing stressed words and syllables to unstressed words and syllables. When comparing unstressed words only, it was also noted that the first word in a sentence has an increase in pitch, amplitude, and duration. The threshold could be set in recognition systems for each of these parameters. Current speech recognizers do not use acoustic data above the word level. This research shows that we have the capability of developing better speech systems by incorporating prosodics with new linguistic software.

  2. Features preferred in-identification system based on computer mouse movements

    NASA Astrophysics Data System (ADS)

    Jašek, Roman; Kolařík, Martin

    2016-06-01

    Biometric identification systems build features, which describe people, using various data. Usually it is not known which of features could be chosen, and a dedicated process called a feature selection is applied to resolve this uncertainty. The relevant features, that are selected with this process, then describe the people in the system as well as possible. It is likely that the relevancy of selected features also mean that these features describe the important things in the measured behavior and that they partly reveal how the behavior works. This paper uses this idea and evaluates results of many runs of feature selection runs in a system, that identifies people using their moving a computer mouse. It has been found that the most frequently selected features repeat between runs, and that these features describe the similar things of the movements.

  3. Coal-shale interface detection system

    NASA Technical Reports Server (NTRS)

    Campbell, R. A.; Hudgins, J. L.; Morris, P. W.; Reid, H., Jr.; Zimmerman, J. E. (Inventor)

    1979-01-01

    A coal-shale interface detection system for use with coal cutting equipment consists of a reciprocating hammer on which an accelerometer is mounted to measure the impact of the hammer as it penetrates the ceiling or floor surface of a mine. A pair of reflectometers simultaneously view the same surface. The outputs of the accelerometer and reflectometers are detected and jointly registered to determine when an interface between coal and shale is being cut through.

  4. Distributed fiber optic fuel leak detection system

    NASA Astrophysics Data System (ADS)

    Mendoza, Edgar; Kempen, C.; Esterkin, Yan; Sun, Sonjian

    2013-05-01

    With the increase worldwide demand for hydrocarbon fuels and the vast development of new fuel production and delivery infrastructure installations around the world, there is a growing need for reliable fuel leak detection technologies to provide safety and reduce environmental risks. Hydrocarbon leaks (gas or liquid) pose an extreme danger and need to be detected very quickly to avoid potential disasters. Gas leaks have the greatest potential for causing damage due to the explosion risk from the dispersion of gas clouds. This paper describes progress towards the development of a fast response, high sensitivity, distributed fiber optic fuel leak detection (HySenseTM) system based on the use of an optical fiber that uses a hydrocarbon sensitive fluorescent coating to detect the presence of fuel leaks present in close proximity along the length of the sensor fiber. The HySenseTM system operates in two modes, leak detection and leak localization, and will trigger an alarm within seconds of exposure contact. The fast and accurate response of the sensor provides reliable fluid leak detection for pipelines, tanks, airports, pumps, and valves to detect and minimize any potential catastrophic damage.

  5. Distributed fiber optic fuel leak detection system

    NASA Astrophysics Data System (ADS)

    Mendoza, Edgar; Kempen, C.; Esterkin, Yan; Sun, Sunjian

    2013-05-01

    With the increase worldwide demand for hydrocarbon fuels and the vast development of new fuel production and delivery infrastructure installations around the world, there is a growing need for reliable fuel leak detection technologies to provide safety and reduce environmental risks. Hydrocarbon leaks (gas or liquid) pose an extreme danger and need to be detected very quickly to avoid potential disasters. Gas leaks have the greatest potential for causing damage due to the explosion risk from the dispersion of gas clouds. This paper describes progress towards the development of a fast response, high sensitivity, distributed fiber optic fuel leak detection (HySensTM) system based on the use of an optical fiber that uses a hydrocarbon sensitive fluorescent coating to detect the presence of fuel leaks present in close proximity along the length of the sensor fiber. The HySenseTM system operates in two modes, leak detection and leak localization, and will trigger an alarm within seconds of exposure contact. The fast and accurate response of the sensor provides reliable fluid leak detection for pipelines, tanks, airports, pumps, and valves to detect and minimize any potential catastrophic damage.

  6. Automated macromolecular crystal detection system and method

    DOEpatents

    Christian, Allen T.; Segelke, Brent; Rupp, Bernard; Toppani, Dominique

    2007-06-05

    An automated macromolecular method and system for detecting crystals in two-dimensional images, such as light microscopy images obtained from an array of crystallization screens. Edges are detected from the images by identifying local maxima of a phase congruency-based function associated with each image. The detected edges are segmented into discrete line segments, which are subsequently geometrically evaluated with respect to each other to identify any crystal-like qualities such as, for example, parallel lines, facing each other, similarity in length, and relative proximity. And from the evaluation a determination is made as to whether crystals are present in each image.

  7. Edge detection techniques for iris recognition system

    NASA Astrophysics Data System (ADS)

    Tania, U. T.; Motakabber, S. M. A.; Ibrahimy, M. I.

    2013-12-01

    Nowadays security and authentication are the major parts of our daily life. Iris is one of the most reliable organ or part of human body which can be used for identification and authentication purpose. To develop an iris authentication algorithm for personal identification, this paper examines two edge detection techniques for iris recognition system. Between the Sobel and the Canny edge detection techniques, the experimental result shows that the Canny's technique has better ability to detect points in a digital image where image gray level changes even at slow rate.

  8. SOFIA Observations of SN 2010jl: Another Non-Detection of the 9.7 Micrometer Silicate Dust Feature

    NASA Technical Reports Server (NTRS)

    Williams, Brian J.; Fox, Ori D.

    2015-01-01

    We present photometric observations from the Stratospheric Observatory for Infrared Astronomy (SOFIA) at 11.1 micrometers of the Type IIn supernova (SN IIn) 2010jl. The SN is undetected by SOFIA, but the upper limits obtained, combined with new and archival detections from Spitzer at 3.6 and 4.5 micrometers, allow us to characterize the composition of the dust present. Dust in other SN IIn has been shown in previous works to reside in a circumstellar shell of material ejected by the progenitor system in the few millennia prior to explosion. Our model fits show that the dust in the system shows no evidence for the strong, ubiquitous 9.7 micrometer feature from silicate dust, suggesting the presence of carbonaceous grains. The observations are best fit with 0.01-0.05 solar mass of carbonaceous dust radiating at a temperature of approximately 550-620 degrees Kelvin. The dust composition may reveal clues concerning the nature of the progenitor system, which remains ambiguous for this subclass. Most of the single star progenitor systems proposed for SNe IIn, such as luminous blue variables, red supergiants, yellow hypergiants, and B[e] stars, all clearly show silicate dust in their pre-SN outflows. However, this post-SN result is consistent with the small sample of SNe IIn with mid-infrared observations, none of which show signs of emission from silicate dust in their infrared spectra.

  9. Statistical Fault Detection & Diagnosis Expert System

    SciTech Connect

    Wegerich, Stephan

    1996-12-18

    STATMON is an expert system that performs real-time fault detection and diagnosis of redundant sensors in any industrial process requiring high reliability. After a training period performed during normal operation, the expert system monitors the statistical properties of the incoming signals using a pattern recognition test. If the test determines that statistical properties of the signals have changed, the expert system performs a sequence of logical steps to determine which sensor or machine component has degraded.

  10. Multi-resolution Gabor wavelet feature extraction for needle detection in 3D ultrasound

    NASA Astrophysics Data System (ADS)

    Pourtaherian, Arash; Zinger, Svitlana; Mihajlovic, Nenad; de With, Peter H. N.; Huang, Jinfeng; Ng, Gary C.; Korsten, Hendrikus H. M.

    2015-12-01

    Ultrasound imaging is employed for needle guidance in various minimally invasive procedures such as biopsy guidance, regional anesthesia and brachytherapy. Unfortunately, a needle guidance using 2D ultrasound is very challenging, due to a poor needle visibility and a limited field of view. Nowadays, 3D ultrasound systems are available and more widely used. Consequently, with an appropriate 3D image-based needle detection technique, needle guidance and interventions may significantly be improved and simplified. In this paper, we present a multi-resolution Gabor transformation for an automated and reliable extraction of the needle-like structures in a 3D ultrasound volume. We study and identify the best combination of the Gabor wavelet frequencies. High precision in detecting the needle voxels leads to a robust and accurate localization of the needle for the intervention support. Evaluation in several ex-vivo cases shows that the multi-resolution analysis significantly improves the precision of the needle voxel detection from 0.23 to 0.32 at a high recall rate of 0.75 (gain 40%), where a better robustness and confidence were confirmed in the practical experiments.

  11. A fast and reliable change detection feature from bi-temporal amplitude SAR images

    NASA Astrophysics Data System (ADS)

    Garzelli, Andrea; Zoppetti, Claudia

    2015-10-01

    In this paper, we propose a change detection feature for an amplitude SAR image pair, based on both information theoretic (IT) assumptions and a CFAR criterion derived from the probabilistic model of the ratio image. In particular, the proposed method aims to introduce two main improvements with respect to the previous IT-based approaches. The first goal is to find a strategy to adaptively quantize the 2-D scatterplot instead of applying clustering. This is carried out by performing a preliminary partition of the image pixels according to a constant false alarm rate criterion that is based on the probabilistic model of the ratio image. The second goal is to test the proposed method in order to assess reliable performances in case of severe speckle noise and in case of small percentage of change within the scene. Therefore, experimental results have been carried out with simulated changes applied to synthetically-generated 1-look SAR images produced from an optical remote sensing image. True Cosmo-SkyMed SAR images have been also considered on a damage assessment scenario.

  12. FIRST SIMULTANEOUS DETECTION OF MOVING MAGNETIC FEATURES IN PHOTOSPHERIC INTENSITY AND MAGNETIC FIELD DATA

    SciTech Connect

    Lim, Eun-Kyung; Yurchyshyn, Vasyl; Goode, Philip

    2012-07-01

    The formation and the temporal evolution of a bipolar moving magnetic feature (MMF) was studied with high-spatial and temporal resolution. The photometric properties were observed with the New Solar Telescope at Big Bear Solar Observatory using a broadband TiO filter (705.7 nm), while the magnetic field was analyzed using the spectropolarimetric data obtained by Hinode. For the first time, we observed a bipolar MMF simultaneously in intensity images and magnetic field data, and studied the details of its structure. The vector magnetic field and the Doppler velocity of the MMF were also studied. A bipolar MMF with its positive polarity closer to the negative penumbra formed, accompanied by a bright, filamentary structure in the TiO data connecting the MMF and a dark penumbral filament. A fast downflow ({<=}2 km s{sup -1}) was detected at the positive polarity. The vector magnetic field obtained from the full Stokes inversion revealed that a bipolar MMF has a U-shaped magnetic field configuration. Our observations provide a clear intensity counterpart of the observed MMF in the photosphere, and strong evidence of the connection between the MMF and the penumbral filament as a serpentine field.

  13. Optoelectronic system for NO2 detection

    NASA Astrophysics Data System (ADS)

    Bielecki, Zbigniew; Pregowski, Piotr; Wojtas, Jacek

    2005-10-01

    This paper presents application of Cavity Ring-Down Spectroscopy (CRDS) and Cavity Enhanced Spectroscopic (CEAS) techniques with blue laser diodes-based system for nitrogen dioxide (NO2) detection. CES technique bases on integration of the light from a resonator. Since the integrated intensity is proportional to the decay time, the experimental signal can be related to the absorption process. The minimum detectable concentration of the absorber for a specific transition is inversely proportional to the effective sample-path length, and directly proportional to the minimum intensity fluctuation detected by a receiving system. In the presented system, the blue laser diode was mounted in a temperature-controlled housing. The light transmitted through the cavity was focused onto a PMT of H5783-03 type. The detector signal enters a lock-in amplifier and next a computer with a 16-bit data acquisition board.

  14. Possible Detection of an Emission Cyclotron Resonance Scattering Feature from the Accretion-Powered Pulsar 4U 1626-67

    NASA Technical Reports Server (NTRS)

    Iwakiri, W. B.; Terada, Y.; Tashiro, M. S.; Mihara, T.; Angelini, L.; Yamada, S.; Enoto, T.; Makishima, K.; Nakajima, M.; Yoshida, A.

    2012-01-01

    We present analysis of 4U 1626-67, a 7.7 s pulsar in a low-mass X-ray binary system, observed with the hard X-ray detector of the Japanese X-ray satellite Suzaku in 2006 March for a net exposure of 88 ks. The source was detected at an average 10-60 keY flux of approx 4 x 10-10 erg / sq cm/ s. The phase-averaged spectrum is reproduced well by combining a negative and positive power-law times exponential cutoff (NPEX) model modified at approx 37 keY by a cyclotron resonance scattering feature (CRSF). The phase-resolved analysis shows that the spectra at the bright phases are well fit by the NPEX with CRSF model. On the other hand. the spectrum in the dim phase lacks the NPEX high-energy cutoff component, and the CRSF can be reproduced by either an emission or an absorption profile. When fitting the dim phase spectrum with the NPEX plus Gaussian model. we find that the feature is better described in terms of an emission rather than an absorption profile. The statistical significance of this result, evaluated by means of an F test, is between 2.91 x 10(exp -3) and 1.53 x 10(exp -5), taking into account the systematic errors in the background evaluation of HXD-PIN. We find that the emission profile is more feasible than the absorption one for comparing the physical parameters in other phases. Therefore, we have possibly detected an emission line at the cyclotron resonance energy in the dim phase.

  15. Damage detection in initially nonlinear systems

    SciTech Connect

    Bornn, Luke; Farrar, Charles; Park, Gyuhae

    2009-01-01

    The primary goal of Structural Health Monitoring (SHM) is to detect structural anomalies before they reach a critical level. Because of the potential life-safety and economic benefits, SHM has been widely studied over the past decade. In recent years there has been an effort to provide solid mathematical and physical underpinnings for these methods; however, most focus on systems that behave linearly in their undamaged state - a condition that often does not hold in complex 'real world' systems and systems for which monitoring begins mid-lifecycle. In this work, we highlight the inadequacy of linear-based methodology in handling initially nonlinear systems. We then show how the recently developed autoregressive support vector machine (AR-SVM) approach to time series modeling can be used for detecting damage in a system that exhibits initially nonlinear response. This process is applied to data acquired from a structure with induced nonlinearity tested in a laboratory environment.

  16. Fault detection and diagnosis of HVAC systems

    SciTech Connect

    Han, C.Y.; Xiao, Y.; Ruther, C.J.

    1999-07-01

    This paper presents a model-based fault detection and diagnosis (FDD) system for building heating, ventilating, and air conditioning (HVAC). Model-based fault detection is based on the strategy of determining the difference or the residuals between the normal and the existing patterns. Their approach was to attack the problem on many levels of abstraction: from the signal level, controller programming level, and system component, all the way up to the information and knowledge processing level. The various issues of real implementation of the system and the processing of real-time on-line data in actual systems of campus buildings using the proven technology and off-the-shelf commercial tools are discussed. The research was based on input and output points and software control programs found in typical direct digital control systems used for variable-air-volume air handlers and VAV cooling and hot water reheat terminal units.

  17. System for detection of hazardous events

    DOEpatents

    Kulesz, James J.; Worley, Brian A.

    2006-05-23

    A system for detecting the occurrence of anomalies, includes a plurality of spaced apart nodes, with each node having adjacent nodes, each of the nodes having one or more sensors associated with the node and capable of detecting anomalies, and each of the nodes having a controller connected to the sensors associated with the node. The system also includes communication links between adjacent nodes, whereby the nodes form a network. Each controller is programmed to query its adjacent nodes to assess the status of the adjacent nodes and the communication links.

  18. System For Detection Of Hazardous Events

    DOEpatents

    Kulesz, James J [Oak Ridge, TN; Worley, Brian A [Knoxville, TN

    2005-08-16

    A system for detecting the occurrence of anomalies, includes a plurality of spaced apart nodes, with each node having adjacent nodes, each of the nodes having one or more sensors associated with the node and capable of detecting anomalies, and each of the nodes having a controller connected to the sensors associated with the node. The system also includes communication links between adjacent nodes, whereby the nodes form a network. Each controller is programmed to query its adjacent nodes to assess the status of the adjacent nodes and the communication links.

  19. Position Sensitive Detection System for Charged Particles

    SciTech Connect

    Coello, E. A.; Favela, F.; Curiel, Q.; Chavez, E; Huerta, A.; Varela, A.; Shapira, Dan

    2012-01-01

    The position sensitive detection system presented in this work employs the Anger logic algorithm to determine the position of the light spark produced by the passage of charged particles on a 170 x 170 x 10 mm3 scintillator material (PILOT-U). The detection system consists of a matrix of nine photomultipliers, covering a fraction of the back area of the scintillators. Tests made with a non-collimated alpha particle source together with a Monte Carlo simulation that reproduces the data, suggest an intrinsic position resolution of up to 6 mm is achieved.

  20. System for particle concentration and detection

    DOEpatents

    Morales, Alfredo M.; Whaley, Josh A.; Zimmerman, Mark D.; Renzi, Ronald F.; Tran, Huu M.; Maurer, Scott M.; Munslow, William D.

    2013-03-19

    A new microfluidic system comprising an automated prototype insulator-based dielectrophoresis (iDEP) triggering microfluidic device for pathogen monitoring that can eventually be run outside the laboratory in a real world environment has been used to demonstrate the feasibility of automated trapping and detection of particles. The system broadly comprised an aerosol collector for collecting air-borne particles, an iDEP chip within which to temporarily trap the collected particles and a laser and fluorescence detector with which to induce a fluorescence signal and detect a change in that signal as particles are trapped within the iDEP chip.