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. Wavelet features for failure detection and identification in vibration systems

    NASA Astrophysics Data System (ADS)

    Deckert, James C.; Rhenals, Alonso E.; Tenney, Robert R.; Willsky, Alan S.

    1992-12-01

    The result of this effort is an extremely flexible and powerful methodology for failure detection and identification (FDI) in vibrating systems. The essential elements of this methodology are: (1) an off-line set of techniques to identify high-energy, statistically significant features in the continuous wavelet transform (CWT); (2) a CWT-based preprocessor to extract the most useful features from the sensor signal; and (3) simple artificial neural networks (incorporating a mechanism to defer any decision if the current feature sample is determined to be ambiguous) for the subsequent classification task. For the helicopter intermediate gearbox test-stand data and centrifugal and fire pump shipboard (mild operating condition) data used, the algorithms designed using this method achieved perfect detection performance (1.000 probability of detection, and 0.000 false alarm probability), with a probability less than 0.04 that a decision would be deferred-based on only 500 milliseconds of data from each sample case. While this effort shows the exceptional promise of our wavelet-based method for FDI in vibrating systems, more demanding applications, which also have other sources of high-energy vibration, raise additional technical issues that could provide the focus for a Phase 2 effort.

  4. 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.

  5. Feature discrimination and detection probability in synthetic aperture radar imaging system

    NASA Technical Reports Server (NTRS)

    Lipes, R. G.; Butman, S. A.

    1977-01-01

    Images obtained using synthetic aperture radar (SAR) systems can only represent the intensities of resolution cells in the scene of interest probabilistically since radar receiver noise and Rayleigh scattering of the transmitted radiation are always present. Consequently, when features to be identified differ only by their contribution to the mean power of the radar return, discrimination can be treated by detection theory. In this paper, we develop a 'sufficient statistic' for discriminating between competing features and compare it with some suboptimal methods frequently used. Discrimination is measured by probability of detection error and depends on number of samples or 'looks', signal-to-noise ratio (SNR), and ratio of mean power returns from the competing features. Our results show discrimination and image quality rapidly saturate with SNR (very small improvement for SNR not less than 10 dB) but continue to improve with increasing number of looks.

  6. 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.

  7. 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.

  8. 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

  9. Consistent performance measurement of a system to detect masses in mammograms based on blind feature extraction

    PubMed Central

    2013-01-01

    Background Breast cancer continues to be a leading cause of cancer deaths among women, especially in Western countries. In the last two decades, many methods have been proposed to achieve a robust mammography‐based computer aided detection (CAD) system. A CAD system should provide high performance over time and in different clinical situations. I.e., the system should be adaptable to different clinical situations and should provide consistent performance. Methods We tested our system seeking a measure of the guarantee of its consistent performance. The method is based on blind feature extraction by independent component analysis (ICA) and classification by neural networks (NN) or SVM classifiers. The test mammograms were from the Digital Database for Screening Mammography (DDSM). This database was constructed collaboratively by four institutions over more than 10 years. We took advantage of this to train our system using the mammograms from each institution separately, and then testing it on the remaining mammograms. We performed another experiment to compare the results and thus obtain the measure sought. This experiment consists in to form the learning sets with all available prototypes regardless of the institution in which them were generated, obtaining in that way the overall results. Results The smallest variation from comparing the results of the testing set in each experiment (performed by training the system using the mammograms from one institution and testing with the remaining) with those of the overall result, considering the success rate for an intermediate decision maker threshold, was roughly 5%, and the largest variation was roughly 17%. But, if we considere the area under ROC curve, the smallest variation was close to 4%, and the largest variation was about a 6%. Conclusions Considering the heterogeneity in the datasets used to train and test our system in each case, we think that the variation of performance obtained when the results are

  10. Feature detection for spatial templates

    SciTech Connect

    Robinson, K.

    1996-02-01

    The Color Medical Image System (CMIS), a program that uses segmented mapping techniques to obtain high resolution digital images, is currently trying to develop techniques to transfer microscopic glass slides to electronic image libraries. One technique that has been attempted is to use correlation techniques to scan the image. However, when segments of high magnification are used, it is difficult and time consuming to perform correlation techniques. This project investigates feature detection in microscopic images. Various techniques are implemented to detect the section of the image containing the most feature information, thereby making the correlation process more efficient. Three tests are implemented that eliminate the background in the image and calculate the mean (1st order technique), variance (2nd order technique), and ratio test (1st order technique) of the remaining pixel values. Background elimination involves deleting all pixel values above a certain experimental value from any calculations made. The source code for each of the three tests was implemented and tested on a number of images using the green color band. Each program outputs the box containing the most features and writes that section to a file to be displayed to the screen. A visual rank was also recorded so as to compare it the output of the tests. Each of the three tests proved to be successful. After comparing the visual rank to the output of the tests, it was determined that both first and second order techniques are effective in detecting features in microscopic images. Although all of the purposes and goals were met, this investigation should be expanded to include texturized images and the use of all three color bands.

  11. Finding features for real-time premature ventricular contraction detection using a fuzzy neural network system.

    PubMed

    Lim, Joon S

    2009-03-01

    Fuzzy neural networks (FNNs) have been successfully applied to generate predictive rules for medical or diagnostic data. This brief presents an approach to detect premature ventricular contractions (PVCs) using the neural network with weighted fuzzy membership functions (NEWFMs). The NEWFM classifies normal and PVC beats by the trained bounded sum of weighted fuzzy membership functions (BSWFMs) using wavelet transformed coefficients from the MIT-BIH PVC database. The eight generalized coefficients, locally related to the time signal, are extracted by the nonoverlap area distribution measurement method. The eight generalized coefficients are used for the three PVC data sets with reliable accuracy rates of 99.80%, 99.21%, and 98.78%, respectively, which means that the selected features are less dependent on the data sets. It is shown that the locations of the eight features are not only around the QRS complex that represents ventricular depolarization in the electrocardiogram (ECG) containing a Q wave, an R wave, and an S wave, but also the QR segment from the Q wave to the R wave has more discriminate information than the RS segment from the R wave to the S wave. The BSWFMs of the eight features trained by NEWFM are shown visually, which makes the features explicitly interpretable. Since each BSWFM combines multiple weighted fuzzy membership functions into one using the bounded sum, the eight small-sized BSWFMs can realize real-time PVC detection in a mobile environment.

  12. 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

  13. Development of an automated detection system for microcalcifications on mammograms by using the higher-order autocorrelation features

    NASA Astrophysics Data System (ADS)

    Ohe, Yoshitaka; Shinohara, Norimitsu; Hara, Takeshi; Fujita, Hiroshi; Endo, Tokiko; Iwase, Takuji

    2004-05-01

    The purpose of this work is to develop a new pattern recognition method using the higher-order autocorrelation features (HOAFs), and to apply this to our microcalcification detection system on mammographic images. Microcalcification is a typical sign of breast cancer and tends to show up as very subtle shadows. We developed a triple-ring filter for detecting microcalcifications, and the prototype detection system is nearly complete. However, our prototype system does not allow for the detection of three types of microcalcifications, two of which are amorphous and linear microcalcifications and the third is obscured microcalcifications which is often confused with the background or circumference that have almost the same density. We targeted the amorphous type of microcalcification, which has a low contrast and easily goes undetected. The various features of microcalcifications and false-positive (FP) shadows were extracted and trained using the multi-regression analysis, and unknown images were recognized as a result of this training. As a result, amorphous microcalcifications were successfully detected with no increase in the number of FPs compared with our existing detection method.

  14. 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

  15. 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.

  16. 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.

  17. Detection of the 2175 Å Dust Feature in Mg II Absorption Systems

    NASA Astrophysics Data System (ADS)

    Malhotra, Sangeeta

    1997-10-01

    The broad absorption bump at 2175 Å due to dust, which is ubiquitous in the Galaxy and is seen in the Magellanic clouds, is also seen in a composite spectrum of Mg II absorbers. The composite absorber spectrum is obtained by taking the geometric mean of 92 quasar spectra after aligning them in the rest frame of 96 absorbers. By aligning the spectra according to absorber redshifts, we reinforce the spectral features of the absorbers and smooth over possible bumps and wiggles in the emission spectra as well as small features in the flat-fielding of the spectra. The width of the observed absorption feature is 200-300 Å (FWHM), or 0.4-0.6 μm-1, and the central wavelength is 2240 Å. These are somewhat different from the central wavelength of 2176 Å and FWHM = 0.8-1.25 μm-1 found in the Galaxy. Simulations show that this discrepancy between the properties of the 2175 Å feature in Mg II absorbers and the Galactic interstellar medium can be mostly explained by the different methods used to measure them.

  18. 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.

  19. Feature detection and letter identification.

    PubMed

    Pelli, Denis G; Burns, Catherine W; Farell, Bart; Moore-Page, Deborah C

    2006-12-01

    Seeking to understand how people recognize objects, we have examined how they identify letters. We expected this 26-way classification of familiar forms to challenge the popular notion of independent feature detection ("probability summation"), but find instead that this theory parsimoniously accounts for our results. We measured the contrast required for identification of a letter briefly presented in visual noise. We tested a wide range of alphabets and scripts (English, Arabic, Armenian, Chinese, Devanagari, Hebrew, and several artificial ones), three- and five-letter words, and various type styles, sizes, contrasts, durations, and eccentricities, with observers ranging widely in age (3 to 68) and experience (none to fluent). Foreign alphabets are learned quickly. In just three thousand trials, new observers attain the same proficiency in letter identification as fluent readers. Surprisingly, despite this training, the observers-like clinical letter-by-letter readers-have the same meager memory span for random strings of these characters as observers seeing them for the first time. We compare performance across tasks and stimuli that vary in difficulty by pitting the human against the ideal observer, and expressing the results as efficiency. We find that efficiency for letter identification is independent of duration, overall contrast, and eccentricity, and only weakly dependent on size, suggesting that letters are identified by a similar computation across this wide range of viewing conditions. Efficiency is also independent of age and years of reading. However, efficiency does vary across alphabets and type styles, with more complex forms yielding lower efficiencies, as one might expect from Gestalt theories of perception. In fact, we find that efficiency is inversely proportional to perimetric complexity (perimeter squared over "ink" area) and nearly independent of everything else. This, and the surprisingly fixed ratio of detection and identification

  20. 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.

  1. 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

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

    PubMed

    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.

  3. 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.

  4. Feature detection techniques for preprocessing proteomic data.

    PubMed

    Sellers, Kimberly F; Miecznikowski, Jeffrey C

    2010-01-01

    Numerous gel-based and nongel-based technologies are used to detect protein changes potentially associated with disease. The raw data, however, are abundant with technical and structural complexities, making statistical analysis a difficult task. Low-level analysis issues (including normalization, background correction, gel and/or spectral alignment, feature detection, and image registration) are substantial problems that need to be addressed, because any large-level data analyses are contingent on appropriate and statistically sound low-level procedures. Feature detection approaches are particularly interesting due to the increased computational speed associated with subsequent calculations. Such summary data corresponding to image features provide a significant reduction in overall data size and structure while retaining key information. In this paper, we focus on recent advances in feature detection as a tool for preprocessing proteomic data. This work highlights existing and newly developed feature detection algorithms for proteomic datasets, particularly relating to time-of-flight mass spectrometry, and two-dimensional gel electrophoresis. Note, however, that the associated data structures (i.e., spectral data, and images containing spots) used as input for these methods are obtained via all gel-based and nongel-based methods discussed in this manuscript, and thus the discussed methods are likewise applicable.

  5. Detection and tracking of facial features

    NASA Astrophysics Data System (ADS)

    De Silva, Liyanage C.; Aizawa, Kiyoharu; Hatori, Mitsutoshi

    1995-04-01

    Detection and tracking of facial features without using any head mounted devices may become required in various future visual communication applications, such as teleconferencing, virtual reality etc. In this paper we propose an automatic method of face feature detection using a method called edge pixel counting. Instead of utilizing color or gray scale information of the facial image, the proposed edge pixel counting method utilized the edge information to estimate the face feature positions such as eyes, nose and mouth in the first frame of a moving facial image sequence, using a variable size face feature template. For the remaining frames, feature tracking is carried out alternatively using a method called deformable template matching and edge pixel counting. One main advantage of using edge pixel counting in feature tracking is that it does not require the condition of a high inter frame correlation around the feature areas as is required in template matching. Some experimental results are shown to demonstrate the effectiveness of the proposed method.

  6. 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.

  7. Detecting Nematode Features from Digital Images

    PubMed Central

    de la Blanca, N. Pérez; Fdez-Valdivia, J.; Castillo, P.; Gómez-Barcina, A.

    1992-01-01

    Procedures for estimating and calibrating nematode features from digitial images are described and evaluated by illustration and mathematical formulae. Technical problems, such as capturing and cleaning raw images, standardizing the grey level range of images, and the detection of characteristics of the body habitus, presence or absence of stylet knobs, and tail and lip region shape are discussed. This study is the first of a series aimed at developing a set of automated methods to permit more rapid, objective characterizations of nematode features than is achievable by cumbersome conventional methods. PMID:19282998

  8. 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

  9. 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.

  10. Feature matching method in shaped light mode VFD defect detection

    NASA Astrophysics Data System (ADS)

    Jin, Xuanhong; Dai, Shuguang; Mu, Pingan

    2010-08-01

    In recent years, Vacuum Fluorescent Display (VFD) module in the car audio panel has been widely used. However, due to process reasons, VFD display production process will produce defects, not only affect the appearance, but also affect the display correctly. So building a car VFD display panel defect detection system is of great significance. Machine vision technology is introduced into the automotive VFD display defect detection in order to achieve fast and accurate detection of defects. Shaped light mode is a typical flaw detection mode which is based on characteristics of vehicle VFD panel. According to the image features, learning of the gray matching and feature matching method, we integrated use of feature matching method and the gray level matching method to achieve defect detection.

  11. 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

  12. Breast Cancer Detection with Reduced Feature Set

    PubMed Central

    Kılıç, Niyazi; Bilgili, Erdem

    2015-01-01

    This paper explores feature reduction properties of independent component analysis (ICA) on breast cancer decision support system. Wisconsin diagnostic breast cancer (WDBC) dataset is reduced to one-dimensional feature vector computing an independent component (IC). The original data with 30 features and reduced one feature (IC) are used to evaluate diagnostic accuracy of the classifiers such as k-nearest neighbor (k-NN), artificial neural network (ANN), radial basis function neural network (RBFNN), and support vector machine (SVM). The comparison of the proposed classification using the IC with original feature set is also tested on different validation (5/10-fold cross-validations) and partitioning (20%–40%) methods. These classifiers are evaluated how to effectively categorize tumors as benign and malignant in terms of specificity, sensitivity, accuracy, F-score, Youden's index, discriminant power, and the receiver operating characteristic (ROC) curve with its criterion values including area under curve (AUC) and 95% confidential interval (CI). This represents an improvement in diagnostic decision support system, while reducing computational complexity. PMID:26078774

  13. 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

  14. Melanoma detection algorithm based on feature fusion.

    PubMed

    Barata, Catarina; Emre Celebi, M; Marques, Jorge S

    2015-08-01

    A Computer Aided-Diagnosis (CAD) System for melanoma diagnosis usually makes use of different types of features to characterize the lesions. The features are often combined into a single vector that can belong to a high dimensional space (early fusion). However, it is not clear if this is the optimal strategy and works on other fields have shown that early fusion has some limitations. In this work, we address this issue and investigate which is the best approach to combine different features comparing early and late fusion. Experiments carried on the datasets PH2 (single source) and EDRA (multi source) show that late fusion performs better, leading to classification scores of Sensitivity = 98% and Specificity = 90% (PH(2)) and Sensitivity = 83% and Specificity = 76% (EDRA). PMID:26736837

  15. 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.

  16. 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.

  17. Feature detection algorithms in computed images

    NASA Astrophysics Data System (ADS)

    Gurbuz, Ali Cafer

    2008-10-01

    The problem of sensing a medium by several sensors and retrieving interesting features is a very general one. The basic framework is generally the same for applications from MRI, tomography, Radar SAR imaging to subsurface imaging, even though the data acquisition processes, sensing geometries and sensed properties are different. In this thesis we introduced a new perspective to the problem of remote sensing and information retrieval by studying the problem of subsurface imaging using GPR and seismic sensors. We have shown that if the sensed medium is sparse in some domain then it can be imaged using many fewer measurements than required by the standard methods. This leads to much lower data acquisition times and better images. We have used the ideas from Compressive Sensing, which show that a small number of random measurements about a signal are sufficient to completely characterize it, if the signal is sparse or compressible in some domain. Although we have applied our ideas to the subsurface imaging problem, our results are general and can be extended to other remote sensing applications. A second objective in remote sensing is information retrieval which involves searching for important features in the computed image. In this thesis we focus on detecting buried structures like pipes, and tunnels in computed GPR or seismic images. The problem of finding these structures in high clutter and noise conditions, and finding them faster than the standard shape detecting methods is analyzed. One of the most important contributions of this thesis is where the sensing and the information retrieval stages are unified in a single framework using compressive sensing. Instead of taking lots of standard measurements to compute the image of the medium and search the necessary information in the computed image, only a small number of measurements as random projections are used to infer the information without generating the image of the medium.

  18. 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.

  19. Auditory feature detection for stimuli presented from different directions

    NASA Astrophysics Data System (ADS)

    Bronkhorst, Adelbert W.; Townsend, James T.

    2002-05-01

    The processing of stimulus features by the visual system is commonly studied by measuring response times (RTs) for detection of targets presented with a varying number of distracters. In audition, however, interpretation of the interaction between target and distracters is complicated by effects like masking, binaural gain and fusion. In the present paradigm, which uses stimuli presented from different directions in the horizontal plane, these confounding effects were either minimized or kept constant. Targets were synthetic vowels and RTs were measured for detection of three features: F0, spectrum (type of vowel) and direction. Both single features and conjunctions were studied. Independent variables were the type of distracters (synthetic vowels or spectrally shaped noise) and their number. Results show that the effect of the number of distracters on the RT is smallest for the spectrum feature; RTs for the detection of F0 are largest and vary considerably across subjects. For conjunctions of features, the RTs lie, as expected, between the RTs for the single features. The effect of the type of distracter on the RT is small-this indicates that the processing involved in segregating vowels from noise is not more efficient than that used for segregating vowels from other vowels.

  20. Epidemic features affecting the performance of outbreak detection algorithms

    PubMed Central

    2012-01-01

    Background Outbreak detection algorithms play an important role in effective automated surveillance. Although many algorithms have been designed to improve the performance of outbreak detection, few published studies have examined how epidemic features of infectious disease impact on the detection performance of algorithms. This study compared the performance of three outbreak detection algorithms stratified by epidemic features of infectious disease and examined the relationship between epidemic features and performance of outbreak detection algorithms. Methods Exponentially weighted moving average (EWMA), cumulative sum (CUSUM) and moving percentile method (MPM) algorithms were applied. We inserted simulated outbreaks into notifiable infectious disease data in China Infectious Disease Automated-alert and Response System (CIDARS), and compared the performance of the three algorithms with optimized parameters at a fixed false alarm rate of 5% classified by epidemic features of infectious disease. Multiple linear regression was adopted to analyse the relationship of the algorithms’ sensitivity and timeliness with the epidemic features of infectious diseases. Results The MPM had better detection performance than EWMA and CUSUM through all simulated outbreaks, with or without stratification by epidemic features (incubation period, baseline counts and outbreak magnitude). The epidemic features were associated with both sensitivity and timeliness. Compared with long incubation, short incubation had lower probability (β* = −0.13, P < 0.001) but needed shorter time to detect outbreaks (β* = −0.57, P < 0.001). Lower baseline counts were associated with higher probability (β* = −0.20, P < 0.001) and longer time (β* = 0.14, P < 0.001). The larger outbreak magnitude was correlated with higher probability (β* = 0.55, P < 0.001) and shorter time (β* = −0.23, P < 0.001). Conclusions The results of this study suggest

  1. A feature-based model of symmetry detection.

    PubMed Central

    Scognamillo, Renata; Rhodes, Gillian; Morrone, Concetta; Burr, David

    2003-01-01

    Symmetry detection is important for many biological visual systems, including those of mammals, insects and birds. We constructed a symmetry-detection algorithm with two stages: location of the visually salient features of the image, then evaluating the symmetry of these features over a long range, by means of a simple Gaussian filter. The algorithm detects the axis of maximum symmetry for human faces (or any arbitrary image) and calculates the magnitude of the asymmetry. We have evaluated the algorithm on the dataset of Rhodes et al. (1998 Psychonom. Bull. Rev. 5, 659-669) and found that the algorithm is able to discriminate small variations of symmetry created by computer-manipulating the symmetry levels in individual faces, and that the values measured by the algorithm correlate well with human psycho-physical symmetry ratings. PMID:12965001

  2. 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.

  3. Automated detection of glaucoma using structural and non structural features.

    PubMed

    Salam, Anum A; Khalil, Tehmina; Akram, M Usman; Jameel, Amina; Basit, Imran

    2016-01-01

    Glaucoma is a chronic disease often called "silent thief of sight" as it has no symptoms and if not detected at an early stage it may cause permanent blindness. Glaucoma progression precedes some structural changes in the retina which aid ophthalmologists to detect glaucoma at an early stage and stop its progression. Fundoscopy is among one of the biomedical imaging techniques to analyze the internal structure of retina. Our proposed technique provides a novel algorithm to detect glaucoma from digital fundus image using a hybrid feature set. This paper proposes a novel combination of structural (cup to disc ratio) and non-structural (texture and intensity) features to improve the accuracy of automated diagnosis of glaucoma. The proposed method introduces a suspect class in automated diagnosis in case of any conflict in decision from structural and non-structural features. The evaluation of proposed algorithm is performed using a local database containing fundus images from 100 patients. This system is designed to refer glaucoma cases from rural areas to specialists and the motivation behind introducing suspect class is to ensure high sensitivity of proposed system. The average sensitivity and specificity of proposed system are 100 and 87 % respectively. PMID:27652092

  4. Structure damage detection based on random forest recursive feature elimination

    NASA Astrophysics Data System (ADS)

    Zhou, Qifeng; Zhou, Hao; Zhou, Qingqing; Yang, Fan; Luo, Linkai

    2014-05-01

    Feature extraction is a key former step in structural damage detection. In this paper, a structural damage detection method based on wavelet packet decomposition (WPD) and random forest recursive feature elimination (RF-RFE) is proposed. In order to gain the most effective feature subset and to improve the identification accuracy a two-stage feature selection method is adopted after WPD. First, the damage features are sorted according to original random forest variable importance analysis. Second, using RF-RFE to eliminate the least important feature and reorder the feature list each time, then get the new feature importance sequence. Finally, k-nearest neighbor (KNN) algorithm, as a benchmark classifier, is used to evaluate the extracted feature subset. A four-storey steel shear building model is chosen as an example in method verification. The experimental results show that using the fewer features got from proposed method can achieve higher identification accuracy and reduce the detection time cost.

  5. 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...

  6. 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

  7. 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

  8. 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

  9. 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

  10. 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.

  11. 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.

  12. Defects' geometric feature recognition based on infrared image edge detection

    NASA Astrophysics Data System (ADS)

    Junyan, Liu; Qingju, Tang; Yang, Wang; Yumei, Lu; Zhiping, Zhang

    2014-11-01

    Edge detection is an important technology in image segmentation, feature extraction and other digital image processing areas. Boundary contains a wealth of information in the image, so to extract defects' edges in infrared images effectively enables the identification of defects' geometric features. This paper analyzed the detection effect of classic edge detection operators, and proposed fuzzy C-means (FCM) clustering-Canny operator algorithm to achieve defects' edges in the infrared images. Results show that the proposed algorithm has better effect than the classic edge detection operators, which can identify the defects' geometric feature much more completely and clearly. The defects' diameters have been calculated based on the image edge detection results.

  13. 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

  14. 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

  15. 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.

  16. 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.

  17. Automated Feature Detection and Solar Flare Prediction Using SDO Data

    NASA Astrophysics Data System (ADS)

    Qahwaji, Rami; Ahmed, Omar; Colak, Tufan

    The importance of real-time processing of solar data especially for space weather applica-tions is increasing continuously, especially with the launch of SDO which will provide sev-eral times more data compared to previous solar satellites. In this paper, we will show the initial results of applying our Automated Solar Activity Prediction (ASAP) system for the short-term prediction of significant solar flares to SDO data. This automated system is cur-rently working in real-time mode with SOHO/MDI images and its results are available online (http://spaceweather.inf.brad.ac.uk/) whenever a new solar image available. This system inte-grates image processing and machine learning to deliver these predictions. A machine learning-based system is designed to analyse years of sunspots and flares data to extract knowledge and to create associations that can be represented using computer-based learning rules. An imaging-based real time system that provides automated detection, grouping and then clas-sification of recent sunspots based on the McIntosh classification and integrated within this system. The results of current feature detections and flare predictions of ASAP using SOHO data will be compared to those results of ASAP using SDO data and will also be presented in this paper.

  18. 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.

  19. 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.

  20. 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.

  1. 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.

  2. 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.

  3. 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%.

  4. Colitis detection on abdominal CT scans by rich feature hierarchies

    NASA Astrophysics Data System (ADS)

    Liu, Jiamin; Lay, Nathan; Wei, Zhuoshi; Lu, Le; Kim, Lauren; Turkbey, Evrim; Summers, Ronald M.

    2016-03-01

    Colitis is inflammation of the colon due to neutropenia, inflammatory bowel disease (such as Crohn disease), infection and immune compromise. Colitis is often associated with thickening of the colon wall. The wall of a colon afflicted with colitis is much thicker than normal. For example, the mean wall thickness in Crohn disease is 11-13 mm compared to the wall of the normal colon that should measure less than 3 mm. Colitis can be debilitating or life threatening, and early detection is essential to initiate proper treatment. In this work, we apply high-capacity convolutional neural networks (CNNs) to bottom-up region proposals to detect potential colitis on CT scans. Our method first generates around 3000 category-independent region proposals for each slice of the input CT scan using selective search. Then, a fixed-length feature vector is extracted from each region proposal using a CNN. Finally, each region proposal is classified and assigned a confidence score with linear SVMs. We applied the detection method to 260 images from 26 CT scans of patients with colitis for evaluation. The detection system can achieve 0.85 sensitivity at 1 false positive per image.

  5. 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

  6. 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

  7. 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…

  8. Efficient vessel feature detection for endoscopic image analysis.

    PubMed

    Lin, Bingxiong; Sun, Yu; Sanchez, Jaime E; Qian, Xiaoning

    2015-04-01

    Distinctive feature detection is an essential task in computer-assisted minimally invasive surgery (MIS). For special conditions in an MIS imaging environment, such as specular reflections and texture homogeneous areas, the feature points extracted by general feature point detectors are less distinctive and repeatable in MIS images. We observe that abundant blood vessels are available on tissue surfaces and can be extracted as a new set of image features. In this paper, two types of blood vessel features are proposed for endoscopic images: branching points and branching segments. Two novel methods, ridgeness-based circle test and ridgeness-based branching segment detection are presented to extract branching points and branching segments, respectively. Extensive in vivo experiments were conducted to evaluate the performance of the proposed methods and compare them with the state-of-the-art methods. The numerical results verify that, in MIS images, the blood vessel features can produce a large number of points.More importantly, those points are more robust and repeatable than the other types of feature points. In addition, due to the difference in feature types, vessel features can be combined with other general features, which makes them new tools for MIS image analysis. These proposed methods are efficient and the code and datasets are made available to the public.

  9. 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…

  10. Picture Detection in Rapid Serial Visual Presentation: Features or Identity?

    ERIC Educational Resources Information Center

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

    2010-01-01

    A pictured object can be readily detected in a rapid serial visual presentation 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…

  11. 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.

  12. 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.

  13. Extended fractal feature for first-stage SAR target detection

    NASA Astrophysics Data System (ADS)

    Kaplan, Lance M.; Murenzi, Romain; Namuduri, Kameswara R.

    1999-08-01

    The Extended Fractal (EF) feature has been shown to lower the false alarm rate for the focus of attention (FOA) stage of a synthetic aperture radar (SAR) automatic target recognition (ATR) system. The feature is both contrast and size sensitive, and thus, can discriminate between targets and many types of cultural clutter at the earliest stages of the ATR. In this paper we modify the EF feature so that one can 'tune' the size sensitivity to the specific targets of interest. We show how to optimize the EF feature using target chip data from the public MSTAR database. We demonstrate improvements in performance for FOA algorithms that include the new feature by comparing the receiver operating characteristic (ROC) curves for all possible combinations of FOA algorithms incorporating EF, two-parameter CFAR, and variance features. Finally, we perform timing experiments on the fused detector to demonstrate the feasibility for implementation of the detector in a real system.

  14. Saliency Detection Using Sparse and Nonlinear Feature Representation

    PubMed Central

    Zhao, Qingjie; Manzoor, Muhammad Farhan; Ishaq Khan, Saqib

    2014-01-01

    An important aspect of visual saliency detection is how features that form an input image are represented. A popular theory supports sparse feature representation, an image being represented with a basis dictionary having sparse weighting coefficient. Another method uses a nonlinear combination of image features for representation. In our work, we combine the two methods and propose a scheme that takes advantage of both sparse and nonlinear feature representation. To this end, we use independent component analysis (ICA) and covariant matrices, respectively. To compute saliency, we use a biologically plausible center surround difference (CSD) mechanism. Our sparse features are adaptive in nature; the ICA basis function are learnt at every image representation, rather than being fixed. We show that Adaptive Sparse Features when used with a CSD mechanism yield better results compared to fixed sparse representations. We also show that covariant matrices consisting of nonlinear integration of color information alone are sufficient to efficiently estimate saliency from an image. The proposed dual representation scheme is then evaluated against human eye fixation prediction, response to psychological patterns, and salient object detection on well-known datasets. We conclude that having two forms of representation compliments one another and results in better saliency detection. PMID:24895644

  15. 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.

  16. 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.

  17. A behavioral role for feature detection by sensory bursts.

    PubMed

    Marsat, Gary; Pollack, Gerald S

    2006-10-11

    Brief episodes of high-frequency firing of sensory neurons, or bursts, occur in many systems, including mammalian auditory and visual systems, and are believed to signal the occurrence of particularly important stimulus features, i.e., to function as feature detectors. However, the behavioral relevance of sensory bursts has not been established in any system. Here, we show that bursts in an identified auditory interneuron of crickets reliably signal salient stimulus features and reliably predict behavioral responses. Our results thus demonstrate the close link between sensory bursts and behavior.

  18. 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.

  19. Feature analysis for detecting people from remotely sensed images

    NASA Astrophysics Data System (ADS)

    Sirmacek, Beril; Reinartz, Peter

    2013-01-01

    We propose a novel approach using airborne image sequences for detecting dense crowds and individuals. Although airborne images of this resolution range are not enough to see each person in detail, we can still notice a change of color and intensity components of the acquired image in the location where a person exists. Therefore, we propose a local feature detection-based probabilistic framework to detect people automatically. Extracted local features behave as observations of the probability density function (PDF) of the people locations to be estimated. Using an adaptive kernel density estimation method, we estimate the corresponding PDF. First, we use estimated PDF to detect boundaries of dense crowds. After that, using background information of dense crowds and previously extracted local features, we detect other people in noncrowd regions automatically for each image in the sequence. To test our crowd and people detection algorithm, we use airborne images taken over Munich during the Oktoberfest event, two different open-air concerts, and an outdoor festival. In addition, we apply tests on GeoEye-1 satellite images. Our experimental results indicate possible use of the algorithm in real-life mass events.

  20. Computed Tomography Features of Incidentally Detected Diffuse Thyroid Disease

    PubMed Central

    Rho, Myung Ho

    2014-01-01

    Objective. This study aimed to evaluate the CT features of incidentally detected DTD in the patients who underwent thyroidectomy and to assess the diagnostic accuracy of CT diagnosis. Methods. We enrolled 209 consecutive patients who received preoperative neck CT and subsequent thyroid surgery. Neck CT in each case was retrospectively investigated by a single radiologist. We evaluated the diagnostic accuracy of individual CT features and the cut-off CT criteria for detecting DTD by comparing the CT features with histopathological results. Results. Histopathological examination of the 209 cases revealed normal thyroid (n = 157), Hashimoto thyroiditis (n = 17), non-Hashimoto lymphocytic thyroiditis (n = 34), and diffuse hyperplasia (n = 1). The CT features suggestive of DTD included low attenuation, inhomogeneous attenuation, increased glandular size, lobulated margin, and inhomogeneous enhancement. ROC curve analysis revealed that CT diagnosis of DTD based on the CT classification of “3 or more” abnormal CT features was superior. When the “3 or more” CT classification was selected, the sensitivity, specificity, positive and negative predictive values, and accuracy of CT diagnosis for DTD were 55.8%, 95.5%, 80.6%, 86.7%, and 85.6%, respectively. Conclusion. Neck CT may be helpful for the detection of incidental DTD. PMID:25548565

  1. PCA feature extraction for change detection in multidimensional unlabeled data.

    PubMed

    Kuncheva, Ludmila I; Faithfull, William J

    2014-01-01

    When classifiers are deployed in real-world applications, it is assumed that the distribution of the incoming data matches the distribution of the data used to train the classifier. This assumption is often incorrect, which necessitates some form of change detection or adaptive classification. While there has been a lot of work on change detection based on the classification error monitored over the course of the operation of the classifier, finding changes in multidimensional unlabeled data is still a challenge. Here, we propose to apply principal component analysis (PCA) for feature extraction prior to the change detection. Supported by a theoretical example, we argue that the components with the lowest variance should be retained as the extracted features because they are more likely to be affected by a change. We chose a recently proposed semiparametric log-likelihood change detection criterion that is sensitive to changes in both mean and variance of the multidimensional distribution. An experiment with 35 datasets and an illustration with a simple video segmentation demonstrate the advantage of using extracted features compared to raw data. Further analysis shows that feature extraction through PCA is beneficial, specifically for data with multiple balanced classes.

  2. 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...

  3. Combining power spectrum and bispectrum measurements to detect oscillatory features

    NASA Astrophysics Data System (ADS)

    Fergusson, J. R.; Gruetjen, H. F.; Shellard, E. P. S.; Liguori, M.

    2015-01-01

    The simplest inflationary models present us with few observable parameters to discriminate between them. A detection of features in the spectra of primordial density perturbations could provide valuable insights and lead to stringent tests of models of the early Universe. So far, searches for oscillatory features have not produced statistically significant results. In this work we consider a combined search for features in the power spectrum and bispectrum. We show that possible dependencies between the estimates of feature model amplitudes based on the two- and three-point correlators are largely statistically independent under the assumption of the null hypothesis of a nearly Gaussian featureless cosmic microwave background. Building on this conclusion we propose an optimal amplitude estimator for a combined search and study the look-elsewhere effect in feature model surveys. In particular we construct analytic models for the distribution of amplitude estimates that allow for a reliable assessment of the significance of potential findings. We also propose a well-behaved integrated statistic that is designed to detect evidence for models exhibiting features at multiple frequencies.

  4. 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

  5. Detecting landmines using weighted density distribution function features

    NASA Astrophysics Data System (ADS)

    Stanley, Ronald J.; Theera-Umpon, Nipon; Gader, Paul D.; Somanchi, Satish; Ho, Dominic K.

    2001-08-01

    Land mine detection using metal detector (MD) and ground penetrating radar (GPR) sensors in hand-held units is a difficult problem. Detection difficulties arise due to: 1) the varying composition and type of metal in land mines, 2) the time-varying nature of background and 3) the variation in height and velocity of the hand-held unit in data measurement. This research introduces new spatially distributed MD features for differentiating land mine signatures from background. The spatially distributed features involve correlating sequences of MD energy values with six weighted density distribution functions. These features are evaluated using a standard back propagation neural network on real data sets containing more than 2,300 mine encounters of different size, shape, content and metal composition that are measured under different soil conditions.

  6. Feature based passive acoustic detection of underwater threats

    NASA Astrophysics Data System (ADS)

    Stolkin, Rustam; Sutin, Alexander; Radhakrishnan, Sreeram; Bruno, Michael; Fullerton, Brian; Ekimov, Alexander; Raftery, Michael

    2006-05-01

    Stevens Institute of Technology is performing research aimed at determining the acoustical parameters that are necessary for detecting and classifying underwater threats. This paper specifically addresses the problems of passive acoustic detection of small targets in noisy urban river and harbor environments. We describe experiments to determine the acoustic signatures of these threats and the background acoustic noise. Based on these measurements, we present an algorithm for robustly discriminating threat presence from severe acoustic background noise. Measurements of the target's acoustic radiation signal were conducted in the Hudson River. The acoustic noise in the Hudson River was also recorded for various environmental conditions. A useful discriminating feature can be extracted from the acoustic signal of the threat, calculated by detecting packets of multi-spectral high frequency sound which occur repetitively at low frequency intervals. We use experimental data to show how the feature varies with range between the sensor and the detected underwater threat. We also estimate the effective detection range by evaluating this feature for hydrophone signals, recorded in the river both with and without threat presence.

  7. Acoustic leak detection system

    SciTech Connect

    Peacock, M.J.

    1993-08-03

    An acoustic leak detection system is described for determining the location of leaks in storage tanks, comprising: (a) sensor means for detecting a leak signal; (b) data acquisition means for digitizing and storing leak signals meeting preset criterion; and (c) analysis means for analyzing the digitized signals and computing the location of the source of the leak signals.

  8. Visual Saliency Detection Based on Multiscale Deep CNN Features

    NASA Astrophysics Data System (ADS)

    Li, Guanbin; Yu, Yizhou

    2016-11-01

    Visual saliency is a fundamental problem in both cognitive and computational sciences, including computer vision. In this paper, we discover that a high-quality visual saliency model can be learned from multiscale features extracted using deep convolutional neural networks (CNNs), which have had many successes in visual recognition tasks. For learning such saliency models, we introduce a neural network architecture, which has fully connected layers on top of CNNs responsible for feature extraction at three different scales. The penultimate layer of our neural network has been confirmed to be a discriminative high-level feature vector for saliency detection, which we call deep contrast feature. To generate a more robust feature, we integrate handcrafted low-level features with our deep contrast feature. To promote further research and evaluation of visual saliency models, we also construct a new large database of 4447 challenging images and their pixelwise saliency annotations. Experimental results demonstrate that our proposed method is capable of achieving state-of-the-art performance on all public benchmarks, improving the F- measure by 6.12% and 10.0% respectively on the DUT-OMRON dataset and our new dataset (HKU-IS), and lowering the mean absolute error by 9% and 35.3% respectively on these two datasets.

  9. Feature selection on movement imagery discrimination and attention detection

    PubMed Central

    Dias, N. S.; Kamrunnahar, M.; Mendes, P. M.; Schiff, S. J.; Correia, J. H.

    2010-01-01

    Noninvasive brain–computer interfaces (BCI) translate subject's electroencephalogram (EEG) features into device commands. Large feature sets should be down-selected for efficient feature translation. This work proposes two different feature down-selection algorithms for BCI: (a) a sequential forward selection; and (b) an across-group variance. Power rar ratios (PRs) were extracted from the EEG data for movement imagery discrimination. Event-related potentials (ERPs) were employed in the discrimination of cue-evoked responses. While center-out arrows, commonly used in calibration sessions, cued the subjects in the first experiment (for both PR and ERP analyses), less stimulating arrows that were centered in the visual field were employed in the second experiment (for ERP analysis). The proposed algorithms outperformed other three popular feature selection algorithms in movement imagery discrimination. In the first experiment, both algorithms achieved classification errors as low as 12.5% reducing the feature set dimensionality by more than 90%. The classification accuracy of ERPs dropped in the second experiment since centered cues reduced the amplitude of cue-evoked ERPs. The two proposed algorithms effectively reduced feature dimensionality while increasing movement imagery discrimination and detected cue-evoked ERPs that reflect subject attention. PMID:20112135

  10. Idaho Explosive Detection System

    ScienceCinema

    Klinger, Jeff

    2016-07-12

    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

  11. 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

  12. 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.

  13. 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.

  14. Association rule mining in intrusion detection systems

    NASA Astrophysics Data System (ADS)

    Zhao, Dong; Lu, Yan-sheng

    2004-04-01

    In a modern computer system, intrusion detection has become an essential and critical component. Data mining generally refers to the process of extracting models from large stores of data. The intrusion detection system first apply data mining programs to audit data to compute frequent patterns, extract features, and then use classification algorithms to compute detection models. The most important step of this process is to determine relations between fields in the database records to construct features. The standard association rules have not enough expressiveness. Intrusion detection system can extract the association rule with negations and with varying support thresholds to get better performance rather than extract the standard association rule.

  15. 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.

  16. Functional Feature Selection by Weighted Projections in Pathological Voice Detection

    NASA Astrophysics Data System (ADS)

    Sánchez Giraldo, Luis; Martínez Tabares, Fernando; Castellanos Domínguez, Germán

    In this paper, we introduce an adaptation of a multivariate feature selection method to deal with functional features. In our case, observations are described by a set of functions defined over a common domain (e.g. a time interval). The feature selection method consists on combining variable weighting with a feature extraction projection. Although the employed method was primarily intended for observations described by vectors in ℝ n , we propose a simple extension that allows us to select a set of functional features, which is well suited for classification. This study is complemented by the incorporation of Functional Principal Component Analysis (FPCA) that project functions into a finite dimensional space were we can perform classification easily. Another remarkable property of FPCA is that it can provide insight about the nature of the functional features. The proposed algorithms are tested on a pathological voice detection task. Two databases are considered: Massachusetts Eye and Ear Infirmary Voice Laboratory voice disorders database and Universidad Politécnica de Madrid voice database. As a result, we obtain a canonical function whose time average is enough to reach similar performances to the ones reported in the literature.

  17. 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

  18. DAB: Multiplex and system support features

    NASA Astrophysics Data System (ADS)

    Riley, J. L.

    This Report describes the multiplex and system support features of the Eureka 147/DAB digital audio system. It sets out the requirements of all users along the broadcast chain from service providers and broadcaster through to the listener. The contents of the transmission frame are examined drawing the distinction between the main service multiplex and the provision of control information in a separate fast data channel. The concept of the DAB service structure is introduced and the inherent system flexibility for altering the service arrangement is explained. A wide range of service information features builds on those provided in earlier systems, such as RDS (Radio Data System) and is intended to make it easier for a listener to find any required service and to add a further dimension to audio broadcasting. The choices available to users in all of these areas are examined.

  19. 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.

  20. 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

  1. An auditory feature detection circuit for sound pattern recognition.

    PubMed

    Schöneich, Stefan; Kostarakos, Konstantinos; Hedwig, Berthold

    2015-09-01

    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.

  2. Portable pathogen detection system

    SciTech Connect

    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.

  3. Solar system fault detection

    NASA Astrophysics Data System (ADS)

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

    1984-05-01

    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 combing 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.

  4. 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.

  5. 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.

  6. 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.

  7. Idaho Explosives Detection System

    SciTech Connect

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

    2004-10-01

    The Idaho Explosives Detection System (IEDS) was developed at the Idaho National Laboratory (INL) to respond to threats imposed by delivery trucks 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-minute measurement time. System performance was successfully demonstrated with explosives at the INL in June 2004 and at Andrews Air Force Base in July 2004.

  8. 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…

  9. Crowding is unlike ordinary masking: distinguishing feature integration from detection.

    PubMed

    Pelli, Denis G; Palomares, Melanie; Majaj, Najib J

    2004-12-30

    A letter in the peripheral visual field is much harder to identify in the presence of nearby letters. This is "crowding." Both crowding and ordinary masking are special cases of "masking," which, in general, refers to any effect of a "mask" pattern on the discriminability of a signal. Here we characterize crowding, and propose a diagnostic test to distinguish it from ordinary masking. In ordinary masking, the signal disappears. In crowding, it remains visible, but is ambiguous, jumbled with its neighbors. Masks are usually effective only if they overlap the signal, but the crowding effect extends over a large region. The width of that region is proportional to signal eccentricity from the fovea and independent of signal size, mask size, mask contrast, signal and mask font, and number of masks. At 4 deg eccentricity, the threshold contrast for identification of a 0.32 deg signal letter is elevated (up to six-fold) by mask letters anywhere in a 2.3 deg region, 7 times wider than the signal. In ordinary masking, threshold contrast rises as a power function of mask contrast, with a shallow log-log slope of 0.5 to 1, whereas, in crowding, threshold is a sigmoidal function of mask contrast, with a steep log-log slope of 2 at close spacing. Most remarkably, although the threshold elevation decreases exponentially with spacing, the threshold and saturation contrasts of crowding are independent of spacing. Finally, ordinary masking is similar for detection and identification, but crowding occurs only for identification, not detection. More precisely, crowding occurs only in tasks that cannot be done based on a single detection by coarsely coded feature detectors. These results (and observers' introspections) suggest that ordinary masking blocks feature detection, so the signal disappears, while crowding (like "illusory conjunction") is excessive feature integration - detected features are integrated over an inappropriately large area because there are no smaller integration

  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. 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.

  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. 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.

  14. Shape and texture based novel features for automated juxtapleural nodule detection in lung CTs.

    PubMed

    Taşcı, Erdal; Uğur, Aybars

    2015-05-01

    Lung cancer is one of the types of cancer with highest mortality rate in the world. In case of early detection and diagnosis, the survival rate of patients significantly increases. In this study, a novel method and system that provides automatic detection of juxtapleural nodule pattern have been developed from cross-sectional images of lung CT (Computerized Tomography). Shape-based and both shape and texture based 7 features are contributed to the literature for lung nodules. System that we developed consists of six main stages called preprocessing, lung segmentation, detection of nodule candidate regions, feature extraction, feature selection (with five feature ranking criteria) and classification. LIDC dataset containing cross-sectional images of lung CT has been utilized, 1410 nodule candidate regions and 40 features have been extracted from 138 cross-sectional images for 24 patients. Experimental results for 10 classifiers are obtained and presented. Adding our derived features to known 33 features has increased nodule recognition performance from 0.9639 to 0.9679 AUC value on generalized linear model regression (GLMR) for 22 selected features and being reached one of the most successful results in the literature. PMID:25732079

  15. 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.

  16. Hdr Imaging for Feature Detection on Detailed Architectural Scenes

    NASA Astrophysics Data System (ADS)

    Kontogianni, G.; Stathopoulou, E. K.; Georgopoulos, A.; Doulamis, A.

    2015-02-01

    3D reconstruction relies on accurate detection, extraction, description and matching of image features. This is even truer for complex architectural scenes that pose needs for 3D models of high quality, without any loss of detail in geometry or color. Illumination conditions influence the radiometric quality of images, as standard sensors cannot depict properly a wide range of intensities in the same scene. Indeed, overexposed or underexposed pixels cause irreplaceable information loss and degrade digital representation. Images taken under extreme lighting environments may be thus prohibitive for feature detection/extraction and consequently for matching and 3D reconstruction. High Dynamic Range (HDR) images could be helpful for these operators because they broaden the limits of illumination range that Standard or Low Dynamic Range (SDR/LDR) images can capture and increase in this way the amount of details contained in the image. Experimental results of this study prove this assumption as they examine state of the art feature detectors applied both on standard dynamic range and HDR images.

  17. DETECTION OR WARNING SYSTEM

    DOEpatents

    Tillman, J E

    1953-10-20

    This patent application describes a sensitive detection or protective system capable of giving an alarm or warning upon the entrance or intrusion of any body into a defined area or zone protected by a radiation field of suitable direction or extent.

  18. Automated multidimensional single molecule fluorescence microscopy feature detection and tracking.

    PubMed

    Rolfe, Daniel J; McLachlan, Charles I; Hirsch, Michael; Needham, Sarah R; Tynan, Christopher J; Webb, Stephen E D; Martin-Fernandez, Marisa L; Hobson, Michael P

    2011-10-01

    Characterisation of multi-protein interactions in cellular networks can be achieved by optical microscopy using multidimensional single molecule fluorescence imaging. Proteins of different species, individually labelled with a single fluorophore, can be imaged as isolated spots (features) of different colour light in different channels, and their diffusive behaviour in cells directly measured through time. Challenges in data analysis have, however, thus far hindered its application in biology. A set of methods for the automated analysis of multidimensional single molecule microscopy data from cells is presented, incorporating Bayesian segmentation-based feature detection, image registration and particle tracking. Single molecules of different colours can be simultaneously detected in noisy, high background data with an arbitrary number of channels, acquired simultaneously or time-multiplexed, and then tracked through time. The resulting traces can be further analysed, for example to detect intensity steps, count discrete intensity levels, measure fluorescence resonance energy transfer (FRET) or changes in polarisation. Examples are shown illustrating the use of the algorithms in investigations of the epidermal growth factor receptor (EGFR) signalling network, a key target for cancer therapeutics, and with simulated data.

  19. 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

  20. 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

  1. Bro Intrusion Detection System

    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

  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. 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.

  4. 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.

  5. 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

  6. 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.

  7. 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.

  8. 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.

  9. 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.

  10. 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.

  11. 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.

  12. Unique features of animal mitochondrial translation systems

    PubMed Central

    Watanabe, Kimitsuna

    2010-01-01

    In animal mitochondria, several codons are non-universal and their meanings differ depending on the species. In addition, the tRNA structures that decipher codons are sometimes unusually truncated. These features seem to be related to the shortening of mitochondrial (mt) genomes, which occurred during the evolution of mitochondria. These organelles probably originated from the endosymbiosis of an aerobic eubacterium into an ancestral eukaryote. It is plausible that these events brought about the various characteristic features of animal mt translation systems, such as genetic code variations, unusually truncated tRNA and rRNA structures, unilateral tRNA recognition mechanisms by aminoacyl-tRNA synthetases, elongation factors and ribosomes, and compensation for RNA deficits by enlarged proteins. In this article, we discuss molecular mechanisms for these phenomena. Finally, we describe human mt diseases that are caused by modification defects in mt tRNAs. PMID:20075606

  13. Detection of hypertensive retinopathy using vessel measurements and textural features.

    PubMed

    Agurto, Carla; Joshi, Vinayak; Nemeth, Sheila; Soliz, Peter; Barriga, Simon

    2014-01-01

    Features that indicate hypertensive retinopathy have been well described in the medical literature. This paper presents a new system to automatically classify subjects with hypertensive retinopathy (HR) using digital color fundus images. Our method consists of the following steps: 1) normalization and enhancement of the image; 2) determination of regions of interest based on automatic location of the optic disc; 3) segmentation of the retinal vasculature and measurement of vessel width and tortuosity; 4) extraction of color features; 5) classification of vessel segments as arteries or veins; 6) calculation of artery-vein ratios using the six widest (major) vessels for each category; 7) calculation of mean red intensity and saturation values for all arteries; 8) calculation of amplitude-modulation frequency-modulation (AM-FM) features for entire image; and 9) classification of features into HR and non-HR using linear regression. This approach was tested on 74 digital color fundus photographs taken with TOPCON and CANON retinal cameras using leave-one out cross validation. An area under the ROC curve (AUC) of 0.84 was achieved with sensitivity and specificity of 90% and 67%, respectively. PMID:25571216

  14. 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.

  15. 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.

  16. Feature detection and SLAM on embedded processors for micro-robot navigation

    NASA Astrophysics Data System (ADS)

    Robinette, Paul; Collins, Thomas R.

    2013-05-01

    We have developed software that allows a micro-robot to localize itself at a 1Hz rate using only onboard hardware. The Surveyor SRV-1 robot and its Blackfin processors were used to perform FAST feature detection on images. Good features selected from these images were then described using the SURF descriptor algorithm. An onboard Gumstix then correlated the features reported by the two processors and used GTSAM to develop an estimate of robot localization and landmark positions. Localization errors in this system were on the same order of magnitude as the size of the robot itself, giving the robot the potential to autonomously operate in a real-world environment.

  17. 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

  18. 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

  19. 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

  20. First and second-order features for detection of masses in digital breast tomosynthesis

    NASA Astrophysics Data System (ADS)

    Samala, Ravi K.; Wei, Jun; Chan, Heang-Ping; Hadjiiski, Lubomir; Cha, Kenny; Helvie, Mark A.

    2016-03-01

    We are developing novel methods for prescreening of mass candidates in computer-aided detection (CAD) system for digital breast tomosynthesis (DBT). With IRB approval and written informed consent, 186 views from 94 breasts were imaged using a GE GEN2 prototype DBT system. The data set was randomly separated into training and test sets by cases. Gradient field convergence features based on first-order features were used to select the initial set of mass candidates. Eigenvalues based on second-order features from the Hessian matrix were extracted for the mass candidate locations in the DBT volume. The features from the first- and second-order analysis form the feature vector that was input to a linear discriminant analysis (LDA) classifier to generate a candidate-likelihood score. The likelihood scores were ranked and the top N candidates were passed onto the subsequent detection steps. The improvement between using only first-order features and the combination of first and second-order features was analyzed using a rank-sensitivity plot. 3D objects were obtained with two-stage 3D clustering followed by active contour segmentation. Morphological, gradient field, and texture features were extracted and feature selection was performed using stepwise feature selection. A combination of LDA and rule-based classifiers was used for FP reduction. The LDA classifier output a masslikelihood score for each object that was used as a decision variable for FROC analysis. At breast-based sensitivities of 70% and 80%, prescreening using first-order and second-order features resulted in 0.7 and 1.0 FPs/DBT.

  1. 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.

  2. 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.

  3. 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.

  4. 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).

  5. 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.

  6. 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.

  7. Polyp Detection via Imbalanced Learning and Discriminative Feature Learning.

    PubMed

    Bae, Seung-Hwan; Yoon, Kuk-Jin

    2015-11-01

    Recent achievement of the learning-based classification leads to the noticeable performance improvement in automatic polyp detection. Here, building large good datasets is very crucial for learning a reliable detector. However, it is practically challenging due to the diversity of polyp types, expensive inspection, and labor-intensive labeling tasks. For this reason, the polyp datasets usually tend to be imbalanced, i.e., the number of non-polyp samples is much larger than that of polyp samples, and learning with those imbalanced datasets results in a detector biased toward a non-polyp class. In this paper, we propose a data sampling-based boosting framework to learn an unbiased polyp detector from the imbalanced datasets. In our learning scheme, we learn multiple weak classifiers with the datasets rebalanced by up/down sampling, and generate a polyp detector by combining them. In addition, for enhancing discriminability between polyps and non-polyps that have similar appearances, we propose an effective feature learning method using partial least square analysis, and use it for learning compact and discriminative features. Experimental results using challenging datasets show obvious performance improvement over other detectors. We further prove effectiveness and usefulness of the proposed methods with extensive evaluation.

  8. 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.)

  9. Pixel color feature enhancement for road signs detection

    NASA Astrophysics Data System (ADS)

    Zhang, Qieshi; Kamata, Sei-ichiro

    2010-02-01

    Road signs play an important role in our daily life which used to guide drivers to notice variety of road conditions and cautions. They provide important visual information that can help drivers operating their vehicles in a manner for enhancing traffic safety. The occurrence of some accidents can be reduced by using automatic road signs recognition system which can alert the drivers. This research attempts to develop a warning system to alert the drivers to notice the important road signs early enough to refrain road accidents from happening. For solving this, a non-linear weighted color enhancement method by pixels is presented. Due to the advantage of proposed method, different road signs can be detected from videos effectively. With suitably coefficients and operations, the experimental results have proved that the proposed method is robust, accurate and powerful in road signs detection.

  10. 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.

  11. Safety features of subcritical fluid fueled systems

    NASA Astrophysics Data System (ADS)

    Bell, Charles R.

    1995-09-01

    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 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.

  12. Fuzzy system for detecting microcalcifications in mammograms

    NASA Astrophysics Data System (ADS)

    Strickland, Robin N.; Theodosiou, Theodosis

    1998-10-01

    We present a fuzzy classifier for detecting microcalcification sin digitized mammograms. The classifier post-processes the output form a wavelets-based multiscale correlation filter. Each local peak in the correlation filter output is represented by a set of five features describing the shape, size and definition of the peak. These features are used in linguistic rules by a fuzzy system that is trained to distinguish between microcalcification sand normal mammogram texture. In borderline cases where microcalcifications are buried in dense tissue or appear only faintly, simply drawing a straight threshold across the feature vector values will likely not produce the correct classification. the fuzzy system allows the effective 'threshold' to be drawn across ranges of features values depending upon how they interact with one another. Compared to wavelet processing alone, the fuzzy detection system produces a significant increase in true positive fraction when tested on a public domain mammogram database.

  13. 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

  14. Glaucoma detection using novel optic disc localization, hybrid feature set and classification techniques.

    PubMed

    Akram, M Usman; Tariq, Anam; Khalid, Shehzad; Javed, M Younus; Abbas, Sarmad; Yasin, Ubaid Ullah

    2015-12-01

    Glaucoma is a chronic and irreversible neuro-degenerative disease in which the neuro-retinal nerve that connects the eye to the brain (optic nerve) is progressively damaged and patients suffer from vision loss and blindness. The timely detection and treatment of glaucoma is very crucial to save patient's vision. Computer aided diagnostic systems are used for automated detection of glaucoma that calculate cup to disc ratio from colored retinal images. In this article, we present a novel method for early and accurate detection of glaucoma. The proposed system consists of preprocessing, optic disc segmentation, extraction of features from optic disc region of interest and classification for detection of glaucoma. The main novelty of the proposed method lies in the formation of a feature vector which consists of spatial and spectral features along with cup to disc ratio, rim to disc ratio and modeling of a novel mediods based classier for accurate detection of glaucoma. The performance of the proposed system is tested using publicly available fundus image databases along with one locally gathered database. Experimental results using a variety of publicly available and local databases demonstrate the superiority of the proposed approach as compared to the competitors.

  15. 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.

  16. 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.

  17. Mass detection with digitized screening mammograms by using Gabor features

    NASA Astrophysics Data System (ADS)

    Zheng, Yufeng; Agyepong, Kwabena

    2007-03-01

    Breast cancer is the leading cancer among American women. The current lifetime risk of developing breast cancer is 13.4% (one in seven). Mammography is the most effective technology presently available for breast cancer screening. With digital mammograms computer-aided detection (CAD) has proven to be a useful tool for radiologists. In this paper, we focus on mass detection that is a common category of breast cancers relative to calcification and architecture distortion. We propose a new mass detection algorithm utilizing Gabor filters, termed as "Gabor Mass Detection" (GMD). There are three steps in the GMD algorithm, (1) preprocessing, (2) generating alarms and (3) classification (reducing false alarms). Down-sampling, quantization, denoising and enhancement are done in the preprocessing step. Then a total of 30 Gabor filtered images (along 6 bands by 5 orientations) are produced. Alarm segments are generated by thresholding four Gabor images of full orientations (Stage-I classification) with image-dependent thresholds computed via histogram analysis. Next a set of edge histogram descriptors (EHD) are extracted from 24 Gabor images (6 by 4) that will be used for Stage-II classification. After clustering EHD features with fuzzy C-means clustering method, a k-nearest neighbor classifier is used to reduce the number of false alarms. We initially analyzed 431 digitized mammograms (159 normal images vs. 272 cancerous images, from the DDSM project, University of South Florida) with the proposed GMD algorithm. And a ten-fold cross validation was used for testing the GMD algorithm upon the available data. The GMD performance is as follows: sensitivity (true positive rate) = 0.88 at false positives per image (FPI) = 1.25, and the area under the ROC curve = 0.83. The overall performance of the GMD algorithm is satisfactory and the accuracy of locating masses (highlighting the boundaries of suspicious areas) is relatively high. Furthermore, the GMD algorithm can

  18. 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.

  19. 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

  20. 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.

  1. Vehicle detection by means of stereo vision-based obstacles features extraction and monocular pattern analysis.

    PubMed

    Toulminet, Gwenaëlle; Bertozzi, Massimo; Mousset, Stéphane; Bensrhair, Abdelaziz; Broggi, Alberto

    2006-08-01

    This paper presents a stereo vision system for the detection and distance computation of a preceding vehicle. It is divided in two major steps. Initially, a stereo vision-based algorithm is used to extract relevant three-dimensional (3-D) features in the scene, these features are investigated further in order to select the ones that belong to vertical objects only and not to the road or background. These 3-D vertical features are then used as a starting point for preceding vehicle detection; by using a symmetry operator, a match against a simplified model of a rear vehicle's shape is performed using a monocular vision-based approach that allows the identification of a preceding vehicle. In addition, using the 3-D information previously extracted, an accurate distance computation is performed.

  2. 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.

  3. 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

  4. 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

  5. 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

  6. 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

  7. Intelligent Leak Detection System

    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

  8. Bilateral Symmetry Detection on the Basis of Scale Invariant Feature Transform

    PubMed Central

    Akbar, Habib; Hayat, Khizar; Haq, Nuhman ul; Bajwa, Usama Ijaz

    2014-01-01

    The automatic detection of bilateral symmetry is a challenging task in computer vision and pattern recognition. This paper presents an approach for the detection of bilateral symmetry in digital single object images. Our method relies on the extraction of Scale Invariant Feature Transform (SIFT) based feature points, which serves as the basis for the ascertainment of the centroid of the object; the latter being the origin under the Cartesian coordinate system to be converted to the polar coordinate system in order to facilitate the selection symmetric coordinate pairs. This is followed by comparing the gradient magnitude and orientation of the corresponding points to evaluate the amount of symmetry exhibited by each pair of points. The experimental results show that our approach draw the symmetry line accurately, provided that the observed centroid point is true. PMID:25144655

  9. 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.

  10. 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.

  11. 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.

  12. Features of the Deployed NPOESS Ground System

    NASA Astrophysics Data System (ADS)

    Smith, D.; Grant, K. D.; Route, G.; Heckmann, G.

    2009-12-01

    NOAA, DoD, and NASA are jointly acquiring the National Polar-orbiting Operational Environmental Satellite System (NPOESS) replacing the current NOAA Polar-orbiting Operational Environmental Satellites (POES) and the DoD's Defense Meteorological Satellite Program (DMSP). The NPOESS satellites will carry a suite of sensors to collect meteorological, oceanographic, climatological, and solar-geophysical observations 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 satellite data to provide environmental data products (aka, Environmental Data Records or EDRs) to US NOAA and DoD processing centers. The IDPS will process EDRs beginning with the NPOESS Preparatory Project (NPP) and through the lifetime of the NPOESS system. The command and telemetry segment is the Command, Control and Communications Segment (C3S), also developed by Raytheon IIS. C3S is responsible for managing the overall NPOESS mission from control and status of the space and ground assets to ensuring delivery of timely, high quality data from the Space Segment (SS) to IDPS for processing. In addition, the C3S provides the globally distributed ground assets necessary to collect and transport mission, telemetry, and command data between the satellites and the processing locations. The C3S provides all functions required for day-to-day commanding and state-of-health monitoring of the NPP and NPOESS satellites, and delivery of SMD 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 NPOESS segments. The NPOESS C3S and IDPS ground segments have been delivered and transitioned to operations for NPP. C3S was transitioned to operations at the NOAA Satellite Operations Facility in Suitland MD in August

  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. 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.

  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. 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.

  17. 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.

  18. 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

  19. Predictive deconvolution and hybrid feature selection for computer-aided detection of prostate cancer.

    PubMed

    Maggio, Simona; Palladini, Alessandro; Marchi, Luca De; Alessandrini, Martino; Speciale, Nicolò; Masetti, Guido

    2010-02-01

    Computer-aided detection (CAD) schemes are decision making support tools, useful to overcome limitations of problematic clinical procedures. Trans-rectal ultrasound image based CAD would be extremely important to support prostate cancer diagnosis. An effective approach to realize a CAD scheme for this purpose is described in this work, employing a multi-feature kernel classification model based on generalized discriminant analysis. The mutual information of feature value and tissue pathological state is used to select features essential for tissue characterization. System-dependent effects are reduced through predictive deconvolution of the acquired radio-frequency signals. A clinical study, performed on ground truth images from biopsy findings, provides a comparison of the classification model applied before and after deconvolution, showing in the latter case a significant gain in accuracy and area under the receiver operating characteristic curve.

  20. 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.

  1. 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.

  2. 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…

  3. 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...

  4. 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.

  5. Towards real-time detection and tracking of spatio-temporal features: Blob-filaments in fusion plasma

    DOE PAGESBeta

    Wu, Lingfei; Wu, Kesheng; Sim, Alex; Churchill, Michael; Choi, Jong Youl; Stathopoulos, Andreas; Chang, Choong -Seock; Klasky, Scott A.

    2016-06-01

    A novel algorithm and implementation of real-time identification and tracking of blob-filaments in fusion reactor data is presented. Similar spatio-temporal features are important in many other applications, for example, ignition kernels in combustion and tumor cells in a medical image. This work presents an approach for extracting these features by dividing the overall task into three steps: local identification of feature cells, grouping feature cells into extended feature, and tracking movement of feature through overlapping in space. Through our extensive work in parallelization, we demonstrate that this approach can effectively make use of a large number of compute nodes tomore » detect and track blob-filaments in real time in fusion plasma. Here, on a set of 30GB fusion simulation data, we observed linear speedup on 1024 processes and completed blob detection in less than three milliseconds using Edison, a Cray XC30 system at NERSC.« less

  6. 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.

  7. Rapid deployment intrusion detection system

    SciTech Connect

    Graham, R.H.

    1997-08-01

    A rapidly deployable security system is one that provides intrusion detection, assessment, communications, and annunciation capabilities; is easy to install and configure; can be rapidly deployed, and is reusable. A rapidly deployable intrusion detection system (RADIDS) has many potential applications within the DOE Complex: back-up protection for failed zones in a perimeter intrusion detection and assessment system, intrusion detection and assessment capabilities in temporary locations, protection of assets during Complex reconfiguration, and protection in hazardous locations, protection of assets during Complex reconfiguration, and protection in hazardous locations. Many DOE user-need documents have indicated an interest in a rapidly deployable intrusion detection system. The purpose of the RADIDS project is to design, develop, and implement such a system. 2 figs.

  8. Expert system structures for fault detection in spaceborne power systems

    NASA Technical Reports Server (NTRS)

    Watson, Karan; Russell, B. Don; Hackler, Irene

    1988-01-01

    This paper presents an architecture for an expert system structure suitable for use with power system fault detection algorithms. The system described is not for the purpose of reacting to faults which have occurred, but rather for the purpose of performing on-line diagnostics and parameter evaluation to determine potential or incipient fault conditions. The system is also designed to detect high impedance or arcing faults which cannot be detected by conventional protection devices. This system is part of an overall monitoring computer hierarchy which would provide a full evaluation of the status of the power system and react to both incipient and catastrophic faults. An approximate hardware structure is suggested and software requirements are discussed. Modifications to CLIPS software, to capitalize on features offered by expert systems, are presented. It is suggested that such a system would have significant advantages over existing protection philosophy.

  9. Detection of abnormal events via optical flow feature analysis.

    PubMed

    Wang, Tian; Snoussi, Hichem

    2015-03-24

    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.

  10. 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

  11. 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

  12. 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

  13. 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.

  14. 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

  15. 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.

  16. Feature detection and orientation tuning in the Drosophila central complex.

    PubMed

    Seelig, Johannes D; Jayaraman, Vivek

    2013-11-14

    Many animals, including insects, are known to use visual landmarks to orient in their environment. In Drosophila melanogaster, behavioural genetics studies have identified a higher brain structure called the central complex as being required for the fly's innate responses to vertical visual features and its short- and long-term memory for visual patterns. But whether and how neurons of the fly central complex represent visual features are unknown. Here we use two-photon calcium imaging in head-fixed walking and flying flies to probe visuomotor responses of ring neurons--a class of central complex neurons that have been implicated in landmark-driven spatial memory in walking flies and memory for visual patterns in tethered flying flies. We show that dendrites of ring neurons are visually responsive and arranged retinotopically. Ring neuron receptive fields comprise both excitatory and inhibitory subfields, resembling those of simple cells in the mammalian primary visual cortex. Ring neurons show strong and, in some cases, direction-selective orientation tuning, with a notable preference for vertically oriented features similar to those that evoke innate responses in flies. Visual responses were diminished during flight, but, in contrast with the hypothesized role of the central complex in the control of locomotion, not modulated during walking. Taken together, these results indicate that ring neurons represent behaviourally relevant visual features in the fly's environment, enabling downstream central complex circuits to produce appropriate motor commands. More broadly, this study opens the door to mechanistic investigations of circuit computations underlying visually guided action selection in the Drosophila central complex.

  17. 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.

  18. 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.

  19. 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.

  20. 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

  1. Randomness fault detection system

    NASA Technical Reports Server (NTRS)

    Russell, B. Don (Inventor); Aucoin, B. Michael (Inventor); Benner, Carl L. (Inventor)

    1996-01-01

    A method and apparatus are provided for detecting a fault on a power line carrying a line parameter such as a load current. The apparatus monitors and analyzes the load current to obtain an energy value. The energy value is compared to a threshold value stored in a buffer. If the energy value is greater than the threshold value a counter is incremented. If the energy value is greater than a high value threshold or less than a low value threshold then a second counter is incremented. If the difference between two subsequent energy values is greater than a constant then a third counter is incremented. A fault signal is issued if the counter is greater than a counter limit value and either the second counter is greater than a second limit value or the third counter is greater than a third limit value.

  2. Some features of secretory systems in plants.

    PubMed

    Juniper, B E; Gilchrist, A J; Robins, R J

    1977-09-01

    Recent work on secretion in plants is reviewed, with emphasis on the anatomy and physiology of root cap cells in higher plants, the stalked glands of Drosera capensis, and the secretory mechanism of Dionaea muscipula. Cells of the root cap of higher plants switch from a geo-perceptive role to one of mucilage secretion at maturation. Features of this process, the role of the Golgi and the pathway for mucilage distribution are reviewed. In contrast, the stalked glands of the leaves of Drosera capensis are much longer lived and have a complex anatomy. The mechanisms for mucilage secretion, protein absorption and the role of the cell membranes in the internal secretion of the protein are described, using data from X-ray microscopv. The secretion of fluid and protein by Dionaea is stimulated by various nitrogen-containing compounds. Uric acid, often excreted by captured insects, is particularly effective in this respect.

  3. Portable weighing system with alignment features

    SciTech Connect

    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.

  4. 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.

  5. 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.

  6. 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.

  7. 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.

  8. 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.

  9. 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.

  10. 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.

  11. 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

  12. 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.

  13. 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.

  14. Essential Features of Australian Training Systems.

    ERIC Educational Resources Information Center

    Australian Commonwealth/State Training Advisory Committee, Canberra.

    This document provides a variety of material on the Australian training systems. Section 1 summarizes apprenticeship and traineeship training and administration in Australia and provides a broad overview of the responsibilities and roles of industry, government, and trade unions. It also outlines the financial support provided by the state and…

  15. 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

    2015-01-01

    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).

  16. 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

  17. 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

  18. 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.

  19. Rotor acoustic monitoring system (RAMS): a fatigue crack detection system

    NASA Astrophysics Data System (ADS)

    Schoess, Jeffrey N.

    1996-05-01

    The Rotor Acoustic Monitoring System (RAMS) is an embedded structural health monitoring system to demonstrate the ability to detect rotor head fatigue cracks and provide early warning of propagating fatigue cracks in rotor components of Navy helicopters. The concept definition effort was performed to assess the feasibility of detecting rotor head fatigue cracks using bulk- wave wide-bandwidth acoustic emission technology. A wireless piezo-based transducer system is being designed to capture rotor fatigue data in real time and perform acoustic emission (AE) event detection, feature extraction, and classification. A flight test effort will be performed to characterize rotor acoustic background noise and flight environment characteristics. The long- term payoff of the RAMS technology includes structural integrity verification and leak detection for large industrial tanks, and nuclear plant cooling towers could be performed using the RAMS AE technology. A summary of the RAMS concept, bench-level AE fatigue testing, and results are presented.

  20. 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.

  1. Rotation-Invariant Features for Multi-Oriented Text Detection in Natural Images

    PubMed Central

    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

  2. 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.

  3. 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.

  4. 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.

  5. 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

  6. 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.

  7. 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.

  8. 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.

  9. 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.

  10. Automatic layout feature extraction for lithography hotspot detection based on deep neural network

    NASA Astrophysics Data System (ADS)

    Matsunawa, Tetsuaki; Nojima, Shigeki; Kotani, Toshiya

    2016-03-01

    Lithography hotspot detection in the physical verification phase is one of the most important techniques in today's optical lithography based manufacturing process. Although lithography simulation based hotspot detection is widely used, it is also known to be time-consuming. To detect hotspots in a short runtime, several machine learning based methods have been proposed. However, it is difficult to realize highly accurate detection without an increase in false alarms because an appropriate layout feature is undefined. This paper proposes a new method to automatically extract a proper layout feature from a given layout for improvement in detection performance of machine learning based methods. Experimental results show that using a deep neural network can achieve better performance than other frameworks using manually selected layout features and detection algorithms, such as conventional logistic regression or artificial neural network.

  11. 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.

  12. Impact of weighted density distribution function features on land mine detection using hand-held units

    NASA Astrophysics Data System (ADS)

    Stanley, Ronald J.; Somanchi, Satish; Gader, Paul D.

    2002-08-01

    Landmine detection using metal detector (MD) and ground penetrating radar (GPR) sensors in hand-held units is a difficult problem. Detection difficulties arise due to: 1) the varying composition and type of metal in landmines, 2) the time-varying nature of background and 3) the variation in height and velocity of the hand-held unit in data measurement. In prior research, spatially distributed MD features were explored for differentiating landmine signatures from background and non-landmine objects. These features were computed based on correlating sequences of MD energy values with six weighted density distribution functions. In this research the effectiveness of these features to detect landmines of varying metal composition and type is investigated. Experimental results are presented from statistical analysis for feature assessment. Preliminary experimental results are also presented for evaluating the impact on MD feature calculations from varying height and sweep rate of the hand-held unit for data acquisition.

  13. 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.

  14. 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.

  15. 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.

  16. Sequential feature selection for detecting buried objects using forward looking ground penetrating radar

    NASA Astrophysics Data System (ADS)

    Shaw, Darren; Stone, Kevin; Ho, K. C.; Keller, James M.; Luke, Robert H.; Burns, Brian P.

    2016-05-01

    Forward looking ground penetrating radar (FLGPR) has the benefit of detecting objects at a significant standoff distance. The FLGPR signal is radiated over a large surface area and the radar signal return is often weak. Improving detection, especially for buried in road targets, while maintaining an acceptable false alarm rate remains to be a challenging task. Various kinds of features have been developed over the years to increase the FLGPR detection performance. This paper focuses on investigating the use of as many features as possible for detecting buried targets and uses the sequential feature selection technique to automatically choose the features that contribute most for improving performance. Experimental results using data collected at a government test site are presented.

  17. 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

  18. 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.

  19. 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

  20. 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.

  1. 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

  2. Joint spatial-spectral feature space clustering for speech activity detection from ECoG signals.

    PubMed

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

    2014-04-01

    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 as a classifier. We also defined the cortical areas that held the most information about the discrimination of speech and nonspeech time intervals. Additionally, the results shed light on the distinct cortical areas associated with the two syllables repetition tasks and may contribute to the development of portable ECoG-based communication.

  3. 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.

  4. 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.

  5. 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.

  6. 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.

  7. 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.

  8. 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…

  9. Adaptive feature extraction techniques for subpixel target detections in hyperspectral remote sensing

    NASA Astrophysics Data System (ADS)

    Yuen, Peter W. T.; Bishop, Gary J.

    2004-12-01

    Most target detection algorithms employed in hyperspectral remote sensing rely on a measurable difference between the spectral signature of the target and background. Matched filter techniques which utilise a set of library spectra as filter for target detection are often found to be unsatisfactory because of material variability and atmospheric effects in the field data. The aim of this paper is to report an algorithm which extracts features directly from the scene to act as matched filters for target detection. Methods based upon spectral unmixing using geometric simplex volume maximisation (SVM) and independent component analysis (ICA) were employed to generate features of the scene. Target and background like features are then differentiated, and automatically selected, from the endmember set of the unmixed result according to their statistics. Anomalies are then detected from the selected endmember set and their corresponding spectral characteristics are subsequently extracted from the scene, serving as a bank of matched filters for detection. This method, given the acronym SAFED, has a number of advantages for target detection, compared to previous techniques which use orthogonal subspace of the background feature. This paper reports the detection capability of this new technique by using an example simulated hyperspectral scene. Similar results using hyperspectral military data show high detection accuracy with negligible false alarms. Further potential applications of this technique for false alarm rate (FAR) reduction via multiple approach fusion (MAF), and, as a means for thresholding the anomaly detection technique, are outlined.

  10. 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.

  11. 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.

  12. Semi autonomous mine detection system

    NASA Astrophysics Data System (ADS)

    Few, Doug; Versteeg, Roelof; 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, 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.

  13. 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.

  14. 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.

  15. 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.

  16. 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

  17. 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

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

    PubMed

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

    2016-07-19

    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

  19. Early breast cancer detection with digital mammograms using Haar-like features and AdaBoost algorithm

    NASA Astrophysics Data System (ADS)

    Zheng, Yufeng; Yang, Clifford; Merkulov, Alex; Bandari, Malavika

    2016-05-01

    The current computer-aided detection (CAD) methods are not sufficiently accurate in detecting masses, especially in dense breasts and/or small masses (typically at their early stages). A small mass may not be perceived when it is small and/or homogeneous with surrounding tissues. Possible reasons for the limited performance of existing CAD methods are lack of multiscale analysis and unification of variant masses. The speed of CAD analysis is important for field applications. We propose a new CAD model for mass detection, which extracts simple Haar-like features for fast detection, uses AdaBoost approach for feature selection and classifier training, applies cascading classifiers for reduction of false positives, and utilizes multiscale detection for variant sizes of masses. In addition to Haar features, local binary pattern (LBP) and histograms of oriented gradient (HOG) are extracted and applied to mass detection. The performance of a CAD system can be measured with true positive rate (TPR) and false positives per image (FPI). We are collecting our own digital mammograms for the proposed research. The proposed CAD model will be initially demonstrated with mass detection including architecture distortion.

  20. Investigation of automated feature extraction techniques for applications in cancer detection from multispectral histopathology images

    NASA Astrophysics Data System (ADS)

    Harvey, Neal R.; Levenson, Richard M.; Rimm, David L.

    2003-05-01

    Recent developments in imaging technology mean that it is now possible to obtain high-resolution histological image data at multiple wavelengths. This allows pathologists to image specimens over a full spectrum, thereby revealing (often subtle) distinctions between different types of tissue. With this type of data, the spectral content of the specimens, combined with quantitative spatial feature characterization may make it possible not only to identify the presence of an abnormality, but also to classify it accurately. However, such are the quantities and complexities of these data, that without new automated techniques to assist in the data analysis, the information contained in the data will remain inaccessible to those who need it. We investigate the application of a recently developed system for the automated analysis of multi-/hyper-spectral satellite image data to the problem of cancer detection from multispectral histopathology image data. The system provides a means for a human expert to provide training data simply by highlighting regions in an image using a computer mouse. Application of these feature extraction techniques to examples of both training and out-of-training-sample data demonstrate that these, as yet unoptimized, techniques already show promise in the discrimination between benign and malignant cells from a variety of samples.

  1. 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,…

  2. 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.

  3. Automatic Ship Detection in Single-Pol SAR Images Using Texture Features in Artificial Neural Networks

    NASA Astrophysics Data System (ADS)

    Khesali, E.; Enayati, H.; Modiri, M.; Mohseni Aref, M.

    2015-12-01

    This paper presents a novel method for detecting ships from high-resolution synthetic aperture radar (SAR) images. This method categorizes ship targets from single-pol SAR images using texture features in artificial neural networks. As such, the method tries to overcome the lack of an operational solution that is able to reliably detect ships with one SAR channel. The method has the following three main stages: 1) feature extraction; 2) feature selection; and 3) ship detection. The first part extracts different texture features from SAR image. These textures include occurrence and co occurrence measures with different window sizes. Then, best features are selected. Finally, the artificial neural network is used to extract ship pixels from sea ones. In post processing stage some morphological filters are used to improve the result. The effectiveness of the proposed method is verified using Sentinel-1 data in VV polarization. Experimental results indicate that the proposed algorithm can be implemented with time-saving, high precision ship extraction, feature analysis, and detection. The results also show that using texture features the algorithm properly discriminates speckle noise from ships.

  4. Novel radon transform-based method for linear feature detection in open water SAR images

    NASA Astrophysics Data System (ADS)

    Chen, Jie; Sun, Jiyin; Xu, Suqin; Chen, Biao

    2007-11-01

    Open water SAR images frequently exhibit long dark or bright linear features, some of which are ship wakes, internal wave or internal wave wakes of under water moving objects. The detection of these line features is very impotent in both civil and military fields. Considering to the drawbacks of conventional Radon transform, this paper proposed a novel liner feature detection method. It use the gliding window and firstly apply a Radon transform to the aim image, use a "mean matrix" to normalize the aim image in the Radon domain, and then search for the peaks or troughs in an ellipse region instead of the whole region. This algorithm is tested on a set of simulated SAR images of ship wakes. The results demonstrate that this algorithm's robustness in the presence of noise, as well as its ability to detect and localize linear features that are somewhat not so straight.

  5. A Harmonic Linear Dynamical System for Prominent ECG Feature Extraction

    PubMed Central

    Nguyen Thi, Ngoc Anh; Yang, Hyung-Jeong; Kim, SunHee; Do, Luu Ngoc

    2014-01-01

    Unsupervised mining of electrocardiography (ECG) time series is a crucial task in biomedical applications. To have efficiency of the clustering results, the prominent features extracted from preprocessing analysis on multiple ECG time series need to be investigated. In this paper, a Harmonic Linear Dynamical System is applied to discover vital prominent features via mining the evolving hidden dynamics and correlations in ECG time series. The discovery of the comprehensible and interpretable features of the proposed feature extraction methodology effectively represents the accuracy and the reliability of clustering results. Particularly, the empirical evaluation results of the proposed method demonstrate the improved performance of clustering compared to the previous main stream feature extraction approaches for ECG time series clustering tasks. Furthermore, the experimental results on real-world datasets show scalability with linear computation time to the duration of the time series. PMID:24719648

  6. A harmonic linear dynamical system for prominent ECG feature extraction.

    PubMed

    Thi, Ngoc Anh Nguyen; Yang, Hyung-Jeong; Kim, SunHee; Do, Luu Ngoc

    2014-01-01

    Unsupervised mining of electrocardiography (ECG) time series is a crucial task in biomedical applications. To have efficiency of the clustering results, the prominent features extracted from preprocessing analysis on multiple ECG time series need to be investigated. In this paper, a Harmonic Linear Dynamical System is applied to discover vital prominent features via mining the evolving hidden dynamics and correlations in ECG time series. The discovery of the comprehensible and interpretable features of the proposed feature extraction methodology effectively represents the accuracy and the reliability of clustering results. Particularly, the empirical evaluation results of the proposed method demonstrate the improved performance of clustering compared to the previous main stream feature extraction approaches for ECG time series clustering tasks. Furthermore, the experimental results on real-world datasets show scalability with linear computation time to the duration of the time series.

  7. 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

  8. 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

  9. 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.

  10. 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.

  11. 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.

  12. Microbial mine detection system (MMDS)

    NASA Astrophysics Data System (ADS)

    Fliermans, Carl B.; Lopez-de-Victoria, Geralyne

    1998-09-01

    The Savannah River Technology Center (SRTC) is developing the Microbial Mine Detection System (MMDS), a cost-effective, safe and reliable method to detect land mines using microorganisms as the primary biosensor detector. SRTC research has shown that various naturally occurring microbial species are stimulated by nitrogen, trinitrotoluene (TNT), dinitrotoluene (DNT), nitrates, nitrites, nitrous oxide, and the chemical components found in explosive materials. Several of the 10,000 indigenous bacteria already existing in the SRTC Subsurface Microbiology Culture Collection (SMCC) possess characteristics that would support discrete detection of land mines during metabolic activity or growth. SRTC scientists are screening and identifying bacteria residing in the SMCC, and other collections associated with specific land mines, for their attraction to explosive off-gasses. After contacting explosives or off-gasses, the micro-organisms will activate via bioluminescence and identify the location of the land mines. Once identified, down selected and mesocosmly defined, the micro-organisms can then be prepared for field deployment. This deployment process requires minimal user training and is envisioned to be administered in hand-held, vehicular mounted and airborne platforms. Microbial detection systems are a renewable resource, easy to preserve, inexpensive to maintain under field conditions, and provide a high-probability response recognition technology.

  13. 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

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

    PubMed Central

    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

  15. 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.

  16. 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.

  17. 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.

  18. 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.

  19. 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).

  20. 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).

  1. DETECTION OF SHARP SYMMETRIC FEATURES IN THE CIRCUMBINARY DISK AROUND AK Sco

    SciTech Connect

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

    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.

  2. 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.

  3. Nucleic acid detection system and method for detecting influenza

    DOEpatents

    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.

  4. 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.

  5. 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.

  6. 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.

  7. 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.

  8. 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

  9. 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.

  10. 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.

  11. 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

  12. 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.

  13. 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.

  14. 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.

  15. 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.

  16. Automated Detection of Low-Contrast Solar Features Using the Phase-Congruency Algorithm

    NASA Astrophysics Data System (ADS)

    Feng, Song; Xu, Zhi; Wang, Feng; Deng, Hui; Yang, Yunfei; Ji, Kaifan

    2014-10-01

    We propose a new feature-detection technique based on phase-congruency (PC) measurements to automatically recognize or enhance faint features in solar observations, such as off-limb coronal loops and umbral dots. Compared with other feature-detection methods that are based on gradient illuminance and imaging filtering, PC-based measurements are particular efficient for recognizing faint features, which generally have a low-intensity contrast to their surroundings. In the present article, we carry out a PC-based measurement of the local weighted mean phase angle (LWMPA) at each point in an image to indicate or highlight low-contrast features. We first used artificial images to check the detection accuracy and sensitivity to the noise of this approach. Subsequently, we applied this approach to an EUV observation obtained by the Solar Dynamics Observatory/Atmospheric Imaging Assembly to highlight off-limb coronal loops, and a photospheric observation obtained by the Hinode/Solar Optical Telescope to recognize faint dots within the cores of sunspots and pores. The results illustrate that this PC-based measurement of the LWMPA is a robust detection method for faint structures in solar observations.

  17. 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

  18. Intensity and Range Image Based Features for Object Detection in Mobile Mapping Data

    NASA Astrophysics Data System (ADS)

    Palmer, R.; Borck, M.; West, G.; Tan, T.

    2012-07-01

    Mobile mapping is used for asset management, change detection, surveying and dimensional analysis. There is a great desire to automate these processes given the very large amounts of data, especially when 3-D point cloud data is combined with co-registered imagery - termed "3-D images". One approach requires low-level feature extraction from the images and point cloud data followed by pattern recognition and machine learning techniques to recognise the various high level features (or objects) in the images. This paper covers low-level feature analysis and investigates a number of different feature extraction methods for their usefulness. The features of interest include those based on the "bag of words" concept in which many low-level features are used e.g. histograms of gradients, as well as those describing the saliency (how unusual a region of the image is). These mainly image based features have been adapted to deal with 3-D images. The performance of the various features are discussed for typical mobile mapping scenarios and recommendations made as to the best features to use.

  19. Hot spot detection based on feature space representation of visual search.

    PubMed

    Hu, Xiao-Peng; Dempere-Marco, Laura; Yang, Guang-Zhong

    2003-09-01

    This paper presents a new framework for capturing intrinsic visual search behavior of different observers in image understanding by analysing saccadic eye movements in feature space. The method is based on the information theory for identifying salient image features based on which visual search is performed. We demonstrate how to obtain feature space fixation density functions that are normalized to the image content along the scan paths. This allows a reliable identification of salient image features that can be mapped back to spatial space for highlighting regions of interest and attention selection. A two-color conjunction search experiment has been implemented to illustrate the theoretical framework of the proposed method including feature selection, hot spot detection, and back-projection. The practical value of the method is demonstrated with computed tomography image of centrilobular emphysema, and we discuss how the proposed framework can be used as a basis for decision support in medical image understanding.

  20. Autonomous pathogen detection system 2001

    SciTech Connect

    Langlois, R G; Wang, A; Colston, B; Masquelier, D; Jones, L; Venkateswaran, K S; Nasarabadi, S; Brown, S; Ramponi, A; Milanovich, F P

    2001-01-09

    The objective of this project is to design, fabricate and field-demonstrate a fully Autonomous Pathogen Detector (identifier) System (APDS). This will be accomplished by integrating a proven flow cytometer and real-time polymerase chain reaction (PCR) detector with sample collection, sample preparation and fluidics to provide a compact, autonomously operating instrument capable of simultaneously detecting multiple pathogens and/or toxins. The APDS will be designed to operate in fixed locations, where it continuously monitors air samples and automatically reports the presence of specific biological agents. The APDS will utilize both multiplex immuno and nucleic acid assays to provide ''quasi-orthogonal'', multiple agent detection approaches to minimize false positives and increase the reliability of identification. Technical advancements across several fronts must first be made in order to realize the full extent of the APDS. Commercialization will be accomplished through three progressive generations of instruments. The APDS is targeted for domestic applications in which (1) the public is at high risk of exposure to covert releases of bioagent such as in major subway systems and other transportation terminals, large office complexes, and convention centers; and (2) as part of a monitoring network of sensors integrated with command and control systems for wide area monitoring of urban areas and major gatherings (e.g., inaugurations, Olympics, etc.). In this latter application there is potential that a fully developed APDS could add value to Defense Department monitoring architectures.

  1. 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.

  2. 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

  3. 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.

  4. Optical fibre gas detections systems

    NASA Astrophysics Data System (ADS)

    Culshaw, Brian

    2016-05-01

    This tutorial review covers the principles of and prospects for fibre optic sensor technology in gas detection. Many of the potential benefits common to fibre sensor technology also apply in the context of gas sensing - notably long distance - many km - access to multiple remote measurement points; invariably intrinsic safety; access to numerous important gas species and often uniquely high levels of selectivity and/or sensitivity. Furthermore, the range of fibre sensor network architectures - single point, multiple point and distributed - enable unprecedented flexibility in system implementation. Additionally, competitive technologies and regulatory issues contribute to final application potential.

  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. 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.

  7. Hematopoietic tumors of the female genital system: imaging features with pathologic correlation.

    PubMed

    Salem, Usama; Menias, Christine O; Shaaban, Akram; Bhosale, Priya R; Youssef, Ayda; Elsayes, Khaled M

    2014-08-01

    Various hematopoietic neoplasms can involve the female genital system. The most common hematological malignancy that involves the female genital system is lymphoma and secondary involvement is more common than primary genital lymphoma. Rarely, leukemic infiltration and extramedullary plasmacytomas of the female genital tract may also occur. Being infrequent, these lesions are commonly misdiagnosed radiologically. Therefore, understanding these malignancies of the female genital system and recognizing their imaging features are of utmost clinical importance. Although definitive diagnosis can be made only by histological analysis, imaging of these tumors plays an important role in detecting lesion extensions, guiding biopsies, staging disease, planning therapy, and detecting recurrence.

  8. Diagnostic Performance of 3D Standing CT Imaging For Detection of Knee Osteoarthritis Features

    PubMed Central

    Segal, Neil A; Nevitt, Michael C.; Lynch, John A; Niu, Jingbo; Torner, James C; Guermazi, Ali

    2016-01-01

    Objective To determine the diagnostic performance of standing computerized tomography (SCT) of the knee for osteophytes and subchondral cysts compared to fixed-flexion radiography, using magnetic resonance imaging (MRI) as the reference standard. Methods Twenty participants were recruited from the Multicenter Osteoarthritis Study (MOST). Participants' knees were imaged with SCT while standing in a knee-positioning frame, and with PA fixed-flexion radiography and 1T MRI. Medial and lateral marginal osteophytes and subchondral cysts were scored on bilateral radiographs and coronal SCT images using the OARSI grading system and on coronal MRI using Whole Organ MRI Scoring (WORMS). Imaging modalities were read separately with images in random order. Sensitivity, specificity, and accuracy for the detection of lesions were calculated and differences between modalities were tested using McNemar's test. Results Participants' mean age was 66.8 years, BMI was 29.6kg/m2 and 50% were women. Of the 160 surfaces (medial and lateral femur and tibia for 40 knees), MRI revealed 84 osteophytes and 10 subchondral cysts. In comparison with osteophytes and subchondral cysts detected by MRI, SCT was significantly more sensitive (93% and 100%; p<0.004) and accurate (95% and 99%; p<0.001 for osteophytes) than plain radiographs (sensitivity: 60% and 10% and accuracy 79% and 94% respectively). For osteophytes, differences in sensitivity and accuracy were greatest at the medial femur (p=0.002). Conclusions In comparison with MRI, SCT imaging was more sensitive and accurate for detection of osteophytes and subchondral cysts than conventional fixed-flexion radiography. Additional study is warranted to assess diagnostic performance of SCT measures of joint space width, progression of OA features and the patellofemoral joint. PMID:26313455

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

    NASA Astrophysics Data System (ADS)

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

    2007-02-01

    In a series of papers, Nicastro et al. have reported 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 this result 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 ks of usable exposure time and more than 2.6×104 counts per 50 mÅ at 21.6 Å. We concentrate on the spectrally clean region (21.3 <λ<22.5 ), where sharp features due to the astrophysically abundant O VII may reveal an intervening, warm-hot intergalactic medium (WHIM). 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(Ni)~14.6 (3 σ) 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 cannot 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 our analysis resolves the issues encountered by Williams et al. and recovers the full resolution and statistical quality of the RGS data. We highlight the differences between our analysis and those published by Williams et al. as this may explain our disparate conclusions.

  10. 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.

  11. 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.

  12. Biosensor method and system based on feature vector extraction

    SciTech Connect

    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.

  13. 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.

  14. 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.

  15. 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

  16. 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.

  17. 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.

  18. 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.

  19. 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.

  20. An FPGA-Based Rapid Wheezing Detection System

    PubMed Central

    Lin, Bor-Shing; Yen, Tian-Shiue

    2014-01-01

    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

  1. 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

  2. Stereo vision-based vehicle detection using a road feature and disparity histogram

    NASA Astrophysics Data System (ADS)

    Lee, Chung-Hee; Lim, Young-Chul; Kwon, Soon; Lee, Jong-Hun

    2011-02-01

    This paper presents stereo vision-based vehicle detection approach on the road using a road feature and disparity histogram. It is not easy to detect only vehicles robustly on the road in various traffic situations, for example, a nonflat road or a multiple-obstacle situation. This paper focuses on the improvement of vehicle detection performance in various real traffic situations. The approach consists of three steps, namely obstacle localization, obstacle segmentation, and vehicle verification. First, we extract a road feature from v-disparity maps binarized using the most frequent values in each row and column, and adopt the extracted road feature as an obstacle criterion in column detection. However, many obstacles still coexist in each localized obstacle area. Thus, we divide the localized obstacle area into multiple obstacles using a disparity histogram and remerge the divided obstacles using four criteria parameters, namely the obstacle size, distance, and angle between the divided obstacles, and the difference of disparity values. Finally, we verify the vehicles using a depth map and gray image to improve the performance. We verify the performance of our proposed method by conducting experiments in various real traffic situations. The average recall rate of vehicle detection is 95.5%.

  3. 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.

  4. [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

  5. 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.

  6. 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.

  7. 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.

  8. 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.

  9. 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

  10. 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.

  11. 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.

  12. 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.

  13. 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.

  14. 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

  15. 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

  16. 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

  17. Ischemia episode detection in ECG using kernel density estimation, support vector machine and feature selection

    PubMed Central

    2012-01-01

    Background Myocardial ischemia can be developed into more serious diseases. Early Detection of the ischemic syndrome in electrocardiogram (ECG) more accurately and automatically can prevent it from developing into a catastrophic disease. To this end, we propose a new method, which employs wavelets and simple feature selection. Methods For training and testing, the European ST-T database is used, which is comprised of 367 ischemic ST episodes in 90 records. We first remove baseline wandering, and detect time positions of QRS complexes by a method based on the discrete wavelet transform. Next, for each heart beat, we extract three features which can be used for differentiating ST episodes from normal: 1) the area between QRS offset and T-peak points, 2) the normalized and signed sum from QRS offset to effective zero voltage point, and 3) the slope from QRS onset to offset point. We average the feature values for successive five beats to reduce effects of outliers. Finally we apply classifiers to those features. Results We evaluated the algorithm by kernel density estimation (KDE) and support vector machine (SVM) methods. Sensitivity and specificity for KDE were 0.939 and 0.912, respectively. The KDE classifier detects 349 ischemic ST episodes out of total 367 ST episodes. Sensitivity and specificity of SVM were 0.941 and 0.923, respectively. The SVM classifier detects 355 ischemic ST episodes. Conclusions We proposed a new method for detecting ischemia in ECG. It contains signal processing techniques of removing baseline wandering and detecting time positions of QRS complexes by discrete wavelet transform, and feature extraction from morphology of ECG waveforms explicitly. It was shown that the number of selected features were sufficient to discriminate ischemic ST episodes from the normal ones. We also showed how the proposed KDE classifier can automatically select kernel bandwidths, meaning that the algorithm does not require any numerical values of the parameters

  18. An automatic LCD panel quality detection system

    NASA Astrophysics Data System (ADS)

    Guo, Bianfang; Hou, Wenguang; Ding, Mingyue

    2009-10-01

    Automatic detection using computer vision expands rapidly along with the development of image processing technology. In this paper, we developed a rapid LCD quality detection system for automobile instrument panel production, which has wide range of usage and good stability.Our automatic detection system consists of four parts: panel fixture, signal generator module, image acquisition module and image processing software. Experiments demonstrated that our system is feasible, efficient and fast compared to manual detection.

  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.

  20. Effects of child restraint system features on installation errors.

    PubMed

    Klinich, Kathleen D; Manary, Miriam A; Flannagan, Carol A C; Ebert, Sheila M; Malik, Laura A; Green, Paul A; Reed, Matthew P

    2014-03-01

    This study examined how child restraint system (CRS) features contribute to CRS installation errors. Sixteen convertible CRS, selected to include a wide range of features, were used in volunteer testing with 32 subjects. Subjects were recruited based on their education level (high or low) and experience with installing CRS (none or experienced). Each subject was asked to perform four child restraint installations in the right-rear passenger seat of a 2006 Pontiac G6 sedan using a crash dummy as a child surrogate. Each subject installed two CRS forward-facing (FF), one with LATCH and one with the vehicle seatbelt, and two CRS rear-facing (RF), one with LATCH and one with the seatbelt. After each installation, the experimenter evaluated 42 factors for each installation, such as choice of belt routing path, tightness of installation, and harness snugness. Analyses used linear mixed models to identify CRS installation outcomes associated with CRS features. LATCH connector type, LATCH strap adjustor type, and the presence of belt lockoffs were associated with the tightness of the CRS installation. The type of harness shoulder height adjuster was associated with the rate of achieving a snug harness. Correct tether use was associated with the tether storage method. In general, subject assessments of the ease-of-use of CRS features were not highly correlated with the quality of their installation, suggesting a need for feedback with incorrect installations. The data from this study provide quantitative assessments of some CRS features that were associated with reductions in CRS installation errors. These results provide child restraint designers with design guidelines for developing easier-to-use products. Research on providing effective feedback during the child restraint installation process is recommended. PMID:23731627

  1. Effects of child restraint system features on installation errors.

    PubMed

    Klinich, Kathleen D; Manary, Miriam A; Flannagan, Carol A C; Ebert, Sheila M; Malik, Laura A; Green, Paul A; Reed, Matthew P

    2014-03-01

    This study examined how child restraint system (CRS) features contribute to CRS installation errors. Sixteen convertible CRS, selected to include a wide range of features, were used in volunteer testing with 32 subjects. Subjects were recruited based on their education level (high or low) and experience with installing CRS (none or experienced). Each subject was asked to perform four child restraint installations in the right-rear passenger seat of a 2006 Pontiac G6 sedan using a crash dummy as a child surrogate. Each subject installed two CRS forward-facing (FF), one with LATCH and one with the vehicle seatbelt, and two CRS rear-facing (RF), one with LATCH and one with the seatbelt. After each installation, the experimenter evaluated 42 factors for each installation, such as choice of belt routing path, tightness of installation, and harness snugness. Analyses used linear mixed models to identify CRS installation outcomes associated with CRS features. LATCH connector type, LATCH strap adjustor type, and the presence of belt lockoffs were associated with the tightness of the CRS installation. The type of harness shoulder height adjuster was associated with the rate of achieving a snug harness. Correct tether use was associated with the tether storage method. In general, subject assessments of the ease-of-use of CRS features were not highly correlated with the quality of their installation, suggesting a need for feedback with incorrect installations. The data from this study provide quantitative assessments of some CRS features that were associated with reductions in CRS installation errors. These results provide child restraint designers with design guidelines for developing easier-to-use products. Research on providing effective feedback during the child restraint installation process is recommended.

  2. Predicting error in detecting mammographic masses among radiology trainees using statistical models based on BI-RADS features

    SciTech Connect

    Grimm, Lars J. Ghate, Sujata V.; Yoon, Sora C.; Kim, Connie; Kuzmiak, Cherie M.; Mazurowski, Maciej A.

    2014-03-15

    Purpose: The purpose of this study is to explore Breast Imaging-Reporting and Data System (BI-RADS) features as predictors of individual errors made by trainees when detecting masses in mammograms. Methods: Ten radiology trainees and three expert breast imagers reviewed 100 mammograms comprised of bilateral medial lateral oblique and craniocaudal views on a research workstation. The cases consisted of normal and biopsy proven benign and malignant masses. For cases with actionable abnormalities, the experts recorded breast (density and axillary lymph nodes) and mass (shape, margin, and density) features according to the BI-RADS lexicon, as well as the abnormality location (depth and clock face). For each trainee, a user-specific multivariate model was constructed to predict the trainee's likelihood of error based on BI-RADS features. The performance of the models was assessed using area under the receive operating characteristic curves (AUC). Results: Despite the variability in errors between different trainees, the individual models were able to predict the likelihood of error for the trainees with a mean AUC of 0.611 (range: 0.502–0.739, 95% Confidence Interval: 0.543–0.680,p < 0.002). Conclusions: Patterns in detection errors for mammographic masses made by radiology trainees can be modeled using BI-RADS features. These findings may have potential implications for the development of future educational materials that are personalized to individual trainees.

  3. 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

  4. Aircraft detection from VHR images based on circle-frequency filter and multilevel features.

    PubMed

    Gao, Feng; Xu, Qizhi; 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.

  5. 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.

  6. 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

  7. 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

  8. 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.

  9. 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.

  10. 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

  11. 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.

  12. 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

  13. 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.

  14. Combining multiple feature representations and AdaBoost ensemble learning for reducing false-positive detections in computer-aided detection of masses on mammograms.

    PubMed

    Choi, Jae Young; Kim, Dae Hoe; Plataniotis, Konstantinos N; Ro, Yong Man

    2012-01-01

    One of the drawbacks of current Computer-aided Detection (CADe) systems is a high number of false-positive (FP) detections, especially for detecting mass abnormalities. In a typical CADe system, classifier design is one of the key steps for determining FP detection rates. This paper presents the effective classifier ensemble system for tackling FP reduction problem in CADe. To construct ensemble consisting of correct classifiers while disagreeing with each other as much as possible, we develop a new ensemble construction solution that combines data resampling underpinning AdaBoost learning with the use of different feature representations. In addition, to cope with the limitation of weak classifiers in conventional AdaBoost, our method has an effective mechanism for tuning the level of weakness of base classifiers. Further, for combining multiple decision outputs of ensemble members, a weighted sum fusion strategy is used to maximize a complementary effect for correct classification. Comparative experiments have been conducted on benchmark mammogram dataset. Results show that the proposed classifier ensemble outperforms the best single classifier in terms of reducing the FP detections of masses.

  15. 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.

  16. Multi-channels statistical and morphological features based mitosis detection in breast cancer histopathology.

    PubMed

    Irshad, Humayun; Roux, Ludovic; Racoceanu, Daniel

    2013-01-01

    Accurate counting of mitosis in breast cancer histopathology plays a critical role in the grading process. Manual counting of mitosis is tedious and subject to considerable inter- and intra-reader variations. This work aims at improving the accuracy of mitosis detection by selecting the color channels that better capture the statistical and morphological features having mitosis discrimination from other objects. The proposed framework includes comprehensive analysis of first and second order statistical features together with morphological features in selected color channels and a study on balancing the skewed dataset using SMOTE method for increasing the predictive accuracy of mitosis classification. The proposed framework has been evaluated on MITOS data set during an ICPR 2012 contest and ranked second from 17 finalists. The proposed framework achieved 74% detection rate, 70% precision and 72% F-Measure. In future work, we plan to apply our mitosis detection tool to images produced by different types of slide scanners, including multi-spectral and multi-focal microscopy.

  17. 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

  18. 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

  19. Damage-detection system for LNG carriers

    NASA Technical Reports Server (NTRS)

    Mastandrea, J. R.; Scherb, M. V.

    1978-01-01

    System utilizes array of acoustical transducers to detect cracks and leaks in liquefied natural gas (LNG) containers onboard ships. In addition to detecting leaks, device indicates location and leak rate.

  20. 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.

  1. 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

  2. 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

  3. 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.

  4. 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.

  5. 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

  6. Edge detection of street trees in high-resolution remote sensing images using spectrum features

    NASA Astrophysics Data System (ADS)

    Zhao, Haohao; Xiao, Pengfeng; Feng, Xuezhi

    2013-10-01

    In this paper a method of Fourier spectrum features based edge detection of urban street trees is described. The QuickBird image was first transformed by 2-D discrete Fourier transform. Then the energy of the component in spatial frequency was calculated. The energy distribution of the angle in max energy was used for further study. Different frequency segments was analyzed, the frequency that can best describe the street tree edge was chosen as the cut-off frequency of the street trees edge. Odd Gabor filter in frequency domain with the cut-off frequency and the max-energy angle was applied for the edge detection. The road center line is extracted by a Gabor filter in frequency domain. Then the edge of the street trees is restricted by the road center line. The edge detection result is analyzed by Canny criteria, and the ΣV=1.00, and C=0.89.

  7. Wave field features of shallow vertical discontinuity and their application in non-destructive detection

    USGS Publications Warehouse

    Liu, J.; Xia, J.; Luo, Y.; Chen, C.; Li, X.; Huang, Y.

    2007-01-01

    The geotechnical integrity of critical infrastructure can be seriously compromised by the presence of fractures or crevices. Non-destructive techniques to accurately detect fractures in critical infrastructure such as dams and highways could be of significant benefit to the geotechnical industry. This paper investigates the application of shallow seismic and georadar methods to the detection of a vertical discontinuity using numerical simulations. The objective is to address the kinematical analysis of a vertical discontinuity, determine the resulting wave field characteristics, and provide the basis for determining the existence of vertical discontinuities based on the recorded signals. Simulation results demonstrate that: (1) A reflection from a vertical discontinuity produces a hyperbolic feature on a seismic or georadar profile; (2) In order for a reflection from a vertical discontinuity to be produced, a reflecting horizon below the discontinuity must exist, the offset between source and receiver (x0) must be non-zero, on the same side of the vertical discontinuity; (3) The range of distances from the vertical discontinuity where a reflection event is observed is proportional to its length and to x0; (4) Should the vertical crevice (or fracture) pass through a reflecting horizon, dual hyperbolic features can be observed on the records, and this can be used as a determining factor that the vertical crevice passes through the interface; and (5) diffractions from the edges of the discontinuity can be recorded with relatively smaller amplitude than reflections and their ranges are not constrained by the length of discontinuity. If the length of discontinuity is short enough, diffractions are the dominant feature. Real-world examples show that the shallow seismic reflection method and the georadar method are capable of recording the hyperbolic feature, which can be interpreted as vertical discontinuity. Thus, these methods show some promise as effective non

  8. Feature Space Mapping as a universal adaptive system

    NASA Astrophysics Data System (ADS)

    Duch, Włodzisław; Diercksen, Geerd H. F.

    1995-06-01

    The most popular realizations of adaptive systems are based on the neural network type of algorithms, in particular feedforward multilayered perceptrons trained by backpropagation of error procedures. In this paper an alternative approach based on multidimensional separable localized functions centered at the data clusters is proposed. In comparison with the neural networks that use delocalized transfer functions this approach allows for full control of the basins of attractors of all stationary points. Slow learning procedures are replaced by the explicit construction of the landscape function followed by the optimization of adjustable parameters using gradient techniques or genetic algorithms. Retrieving information does not require searches in multidimensional subspaces but it is factorized into a series of one-dimensional searches. Feature Space Mapping is applicable to learning not only from facts but also from general laws and may be treated as a fuzzy expert system (neurofuzzy system). The number of nodes (fuzzy rules) is growing as the network creates new nodes for novel data but the search time is sublinear in the number of rules or data clusters stored. Such a system may work as a universal classificator, approximator and reasoning system. Examples of applications for the identification of spectra (classification), intelligent databases (association) and for the analysis of simple electrical circuits (expert system type) are given.

  9. Task-Specific Codes for Face Recognition: How they Shape the Neural Representation of Features for Detection and Individuation

    PubMed Central

    2008-01-01

    Background The variety of ways in which faces are categorized makes face recognition challenging for both synthetic and biological vision systems. Here we focus on two face processing tasks, detection and individuation, and explore whether differences in task demands lead to differences both in the features most effective for automatic recognition and in the featural codes recruited by neural processing. Methodology/Principal Findings Our study appeals to a computational framework characterizing the features representing object categories as sets of overlapping image fragments. Within this framework, we assess the extent to which task-relevant information differs across image fragments. Based on objective differences we find among task-specific representations, we test the sensitivity of the human visual system to these different face descriptions independently of one another. Both behavior and functional magnetic resonance imaging reveal effects elicited by objective task-specific levels of information. Behaviorally, recognition performance with image fragments improves with increasing task-specific information carried by different face fragments. Neurally, this sensitivity to the two tasks manifests as differential localization of neural responses across the ventral visual pathway. Fragments diagnostic for detection evoke larger neural responses than non-diagnostic ones in the right posterior fusiform gyrus and bilaterally in the inferior occipital gyrus. In contrast, fragments diagnostic for individuation evoke larger responses than non-diagnostic ones in the anterior inferior temporal gyrus. Finally, for individuation only, pattern analysis reveals sensitivity to task-specific information within the right “fusiform face area”. Conclusions/Significance Our results demonstrate: 1) information diagnostic for face detection and individuation is roughly separable; 2) the human visual system is independently sensitive to both types of information; 3) neural

  10. 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.

  11. 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.

  12. 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.

  13. 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.

  14. 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

  15. 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.

  16. 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.

  17. 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.

  18. Spectral feature characterization methods for blood stain detection in crime scene backgrounds

    NASA Astrophysics Data System (ADS)

    Yang, Jie; Mathew, Jobin J.; Dube, Roger R.; Messinger, David W.

    2016-05-01

    Blood stains are one of the most important types of evidence for forensic investigation. They contain valuable DNA information, and the pattern of the stains can suggest specifics about the nature of the violence that transpired at the scene. Blood spectral signatures containing unique reflectance or absorption features are important both for forensic on-site investigation and laboratory testing. They can be used for target detection and identification applied to crime scene hyperspectral imagery, and also be utilized to analyze the spectral variation of blood on various backgrounds. Non-blood stains often mislead the detection and can generate false alarms at a real crime scene, especially for dark and red backgrounds. This paper measured the reflectance of liquid blood and 9 kinds of non-blood samples in the range of 350 nm - 2500 nm in various crime scene backgrounds, such as pure samples contained in petri dish with various thicknesses, mixed samples with different colors and materials of fabrics, and mixed samples with wood, all of which are examined to provide sub-visual evidence for detecting and recognizing blood from non-blood samples in a realistic crime scene. The spectral difference between blood and non-blood samples are examined and spectral features such as "peaks" and "depths" of reflectance are selected. Two blood stain detection methods are proposed in this paper. The first method uses index to denote the ratio of "depth" minus "peak" over"depth" add"peak" within a wavelength range of the reflectance spectrum. The second method uses relative band depth of the selected wavelength ranges of the reflectance spectrum. Results show that the index method is able to discriminate blood from non-blood samples in most tested crime scene backgrounds, but is not able to detect it from black felt. Whereas the relative band depth method is able to discriminate blood from non-blood samples on all of the tested background material types and colors.

  19. 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.

  20. 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.

  1. 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

  2. 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.

  3. 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.

  4. 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##

  5. 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.

  6. 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.

  7. 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.

  8. Expandable coating cocoon leak detection system

    NASA Technical Reports Server (NTRS)

    Hauser, R. L.; Kochansky, M. C.

    1972-01-01

    Development of system and materials for detecting leaks in cocoon protective coatings are discussed. Method of applying materials for leak determination is presented. Pressurization of system following application of materials will cause formation of bubble if leak exists.

  9. 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.

  10. 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.

  11. Flowthrough Bacteria-Detection System

    NASA Technical Reports Server (NTRS)

    Grana, D. C.; Wilkins, J. R.

    1983-01-01

    Online system allows repetitive cycling of sample intake, bacteria counting and sterilization. System measures bacteria count by using sample/incubate/ measure cycle. Steps in cycle are on/off operations to cycle automated easily.

  12. 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.

  13. 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.

  14. 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

  15. 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

  16. Canadian pipeline installs leak-detection system

    SciTech Connect

    Yoon, M.S.; Mensik, M.; Luk, W.Y.

    1988-05-30

    Site-acceptance tests for a recently installed leak-detection system on a pipeline in southern Alberta indicated that the system will reduce spillage because of leaks. The tests on the Porcupine Hills Pipeline also indicated that pipeline isolation and spill containment are enhanced by the use of the system. Covered here are the selection, design, and implementation of the real time leak-detection system and its extension to offshore and arctic applications.

  17. 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.

  18. 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.

  19. 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.

  20. 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

  1. 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

  2. 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.

  3. Cortical feature analysis and machine learning improves detection of “MRI-negative” focal cortical dysplasia

    PubMed Central

    Ahmed, Bilal; Brodley, Carla E.; Blackmon, Karen E.; Kuzniecky, Ruben; Barash, Gilad; Carlson, Chad; Quinn, Brian T.; Doyle, Werner; French, Jacqueline; Devinsky, Orrin; Thesen, Thomas

    2015-01-01

    Focal cortical dysplasia (FCD) is the most common cause of pediatric epilepsy and the third most common lesion in adults with treatment-resistant epilepsy. Advances in MRI have revolutionized the diagnosis of FCD, resulting in higher success rates for resective epilepsy surgery. However, many histologically confirmed FCD patients have normal pre-surgical MRI studies (‘MRI-negative’), making pre-surgical diagnosis difficult. The purpose of this study is to test whether a novel MRI post-processing method successfully detects histopathologically-verified FCD in a sample of patients without visually appreciable lesions. We applied an automated quantitative morphometry approach which computed five surface-based MRI features and combined them in a machine learning model to classify lesional and non-lesional vertices. Accuracy was defined by classifying contiguous vertices as “lesional” when they fell within the surgical resection region. Our multivariate method correctly detected the lesion in 6 of 7 MRI-positive patients, which is comparable with the detection rates that have been reported in univariate vertex-based morphometry studies. More significantly, in patients that were MRI-negative, machine learning correctly identified 14 out of 24 FCD lesions (58%). This was achieved after separating abnormal thickness and thinness into distinct classifiers, as well as separating sulcal and gyral regions. Results demonstrate that MRI-negative images contain sufficient information to aid in the in-vivo detection of visually elusive FCD lesions. PMID:26037845

  4. The Emotion Recognition System Based on Autoregressive Model and Sequential Forward Feature Selection of Electroencephalogram Signals

    PubMed Central

    Hatamikia, Sepideh; Maghooli, Keivan; Nasrabadi, Ali Motie

    2014-01-01

    Electroencephalogram (EEG) is one of the useful biological signals to distinguish different brain diseases and mental states. In recent years, detecting different emotional states from biological signals has been merged more attention by researchers and several feature extraction methods and classifiers are suggested to recognize emotions from EEG signals. In this research, we introduce an emotion recognition system using autoregressive (AR) model, sequential forward feature selection (SFS) and K-nearest neighbor (KNN) classifier using EEG signals during emotional audio-visual inductions. The main purpose of this paper is to investigate the performance of AR features in the classification of emotional states. To achieve this goal, a distinguished AR method (Burg's method) based on Levinson-Durbin's recursive algorithm is used and AR coefficients are extracted as feature vectors. In the next step, two different feature selection methods based on SFS algorithm and Davies–Bouldin index are used in order to decrease the complexity of computing and redundancy of features; then, three different classifiers include KNN, quadratic discriminant analysis and linear discriminant analysis are used to discriminate two and three different classes of valence and arousal levels. The proposed method is evaluated with EEG signals of available database for emotion analysis using physiological signals, which are recorded from 32 participants during 40 1 min audio visual inductions. According to the results, AR features are efficient to recognize emotional states from EEG signals, and KNN performs better than two other classifiers in discriminating of both two and three valence/arousal classes. The results also show that SFS method improves accuracies by almost 10-15% as compared to Davies–Bouldin based feature selection. The best accuracies are %72.33 and %74.20 for two classes of valence and arousal and %61.10 and %65.16 for three classes, respectively. PMID:25298928

  5. 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

  6. 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)

  7. 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.

  8. 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

  9. 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.

  10. 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

  11. 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.

  12. 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.

  13. Multisensor system for mine detection

    NASA Astrophysics Data System (ADS)

    Duvoisin, Herbert A., III; Steinway, William J.; Tomassi, Mark S.; Thomas, James E.; Morris, Carl A.; Kahn, Barry A.; Stern, Peter H.; Krywick, Scott; Johnson, Kevin; Dennis, Kevin; Betts, George; Blood, Ben; Simoneaux, Wanda; Miller, John L.

    1998-10-01

    A multi-sensor approach to buried object discrimination has been developed by Coleman Research Corporation (CRC) as a practical successor to currently prevalent metal detectors. The CRC multi-sensor unit integrates with and complements standard metal detectors to enable the detection of low- metallic and non-metallic anti-tank and anti-personnel mines as well as the older metallic-jacketed mines. The added sensors include Ground Penetration Radar (GPR) and Infrared (IR). The GPR consists of a lightweight (less than 1 LB) snap on antenna unit, a belt attached electronics unit (less than 5 LB) and batteries. The IR consists of a lightweight (less than 3 LB) head mounted camera, a heads-up virtual display, and a belt attached processing unit (Figure 1.1). The output from Automatic Target Recognition algorithms provide the detection of metallic and non-metallic mines in real-time on the IR display and as an audio alert from the GPR and MD.

  14. Origin of additional spectral features in modulated reflectance spectra of 2-dimensional semiconductor systems

    SciTech Connect

    Mukherjee, Amlan; Ghosh, Sandip

    2014-03-28

    High resolution photoreflectance (PR) spectroscopy study on a single GaAs/AlGaAs quantum well representing a two-dimensional (2D) system, shows additional distinct spectral features on the high energy side of the first confined heavy-hole and light-hole exciton transitions. The PR experiments involved a special dual detection technique which significantly improved the measurement sensitivity. Photoluminescence excitation spectroscopy data on the sample showed broadened step-like features around these energies. A detailed lineshape analysis, including first principles simulations, was performed to understand the origins of these additional PR spectral features. They are shown to arise primarily from inhomogeneously broadened first excited state transition of the excitons, rather than from a change in the joint density of states at the exciton continuum edge. The analysis suggests that such features are more likely in the case of 2D excitons as compared to 3D excitons in bulk material. Apart from its significance for post-growth characterization, identification of these additional PR features enables direct estimation of the exciton binding energy.

  15. [A P-wave detection method based on multi-feature].

    PubMed

    Song, Lixin; Guan, Lili; Wang, Qian; Wang, Yuhong

    2014-04-01

    Generally, P-wave is the wave of low-frequency and low-amplitude, and it could be affected by baseline drift, electromyography (EMG) interference and other noises easily. Not every heart beat contains the P-wave, and it is also a major problem to determine the P-wave exist or not in a heart beat. In order to solve the limitation of suiting the diverse morphological P-wave using wavelet-amplitude-transform algorithm and the limitation of selecting the pseudo-P-wave sample using the wavelet transform and neural network, we presented new P-wave detecting method based on wave-amplitude threshold and using the multi-feature as the input of neural networks. Firstly, we removed the noise of ECG through the wavelet transform, then determined the position of the candidate P-wave by calculating modulus maxima of the wavelet transform, and then determine the P-wave exist or not by wave-amplitude threshold method initially. Finally we determined whether the P-wave existed or not by the neural networks. The method is validated based on the QT database which is supplied with manual labels made by physicians. We compared the detection effect of ECG P-waves, which was obtained with the method developed in the study, with the algorithm of wavelet threshold value and the method based on "wavelet-amplitude-slope", and verified the feasibility of the proposed algorithm. The detected ECG signal, which is recorded in the hospital ECG division, was consistent with the doctor's labels. Furthermore, after detecting the 13 sets of ECG which were 15 min long, the detection rate for the correct P-wave is 99.911%.

  16. Fuzzy MUAP recognition in HSR-EMG detection basing on morphological features.

    PubMed

    Mebarkia, Kamel; Bekka, Raїs El'hadi; Reffad, Aicha; Disselhorst-Klug, Catherine

    2014-08-01

    The idea of 'besides the MU properties and depending on the recording techniques, MUAPs can have unique pattern' was adopted. The aim of this work was to recognise whether a Laplacian-detected MUAP is isolated or overlapped basing on novel morphological features using fuzzy classifier. Training data set was constructed to elaborate and test the 'if-then' fuzzy rules using signals provided by three muscles: the abductor pollicis brevis (APB), the first dorsal interosseous (FDI) and the biceps brachii (BB) muscles of 11 healthy subjects. The proposed fuzzy classier recognized automatically the isolated MUAPs with a performance of 95.03% which was improved to 97.8% by adjusting the certainty grades of rules using genetic algorithms (GA). Synthetic signals were used as reference to further evaluate the performance of the elaborated classifier. The recognition of the isolated MUAPs depends largely on noise level and is acceptable down to the signal to noise ratio of 20 dB with a detection probability of 0.96. The recognition of overlapped MUAPs depends slightly on the noise level with a detection probability of about 0.8. The corresponding misrecognition is caused principally by the synchronisation and the small overlapping degree.

  17. Evaluation of automatic feature detection algorithms in EEG: application to interburst intervals.

    PubMed

    Chauvet, Pierre E; Tich, Sylvie Nguyen The; Schang, Daniel; Clément, Alain

    2014-11-01

    In this paper, we present a new method to compare and improve algorithms for feature detection in neonatal EEG. The method is based on the algorithm׳s ability to compute accurate statistics to predict the results of EEG visual analysis. This method is implemented inside a Java software called EEGDiag, as part of an e-health Web portal dedicated to neonatal EEG. EEGDiag encapsulates a component-based implementation of the detection algorithms called analyzers. Each analyzer is defined by a list of modules executed sequentially. As the libraries of modules are intended to be enriched by its users, we developed a process to evaluate the performance of new modules and analyzers using a database of expertized and categorized EEGs. The evaluation is based on the Davies-Bouldin index (DBI) which measures the quality of cluster separation, so that it will ease the building of classifiers on risk categories. For the first application we tested this method on the detection of interburst intervals (IBI) using a database of 394 EEG acquired on premature newborns. We have defined a class of IBI detectors based on a threshold of the standard deviation on contiguous short time windows, inspired by previous work. Then we determine which detector and what threshold values are the best regarding DBI, as well as the robustness of this choice. This method allows us to make counter-intuitive choices, such as removing the 50 Hz filter (power supply) to save time. PMID:25212119

  18. 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.

  19. Detecting small geothermal features at Northern Pacific volcanoes with ASTER thermal infrared data

    NASA Astrophysics Data System (ADS)

    Wessels, R.; Senyukov, S.; Tranbenkova, A.; Ramsey, M. S.; Schneider, D. J.

    2004-12-01

    The Alaska Volcano Observatory (AVO) and the Kamchatkan Volcanic Eruption Response Team (KVERT) monitor the eruptive state of volcanoes throughout the Aleutian, Kamchatkan, and Kurile arcs. This is accomplished in part by analyzing thermal infrared (TIR) data from the Advanced Very High Resolution Radiometer (AVHRR) and Moderate-resolution Imaging Spectroradiometer (MODIS) sensors at least twice per day for major thermal anomalies. The AVHRR and MODIS 1-km spatial resolution data have been very useful for detecting large and/or high-temperature thermal signatures such as Strombolian activity as well as lava and pyroclastic flows. Such anomalies commonly indicate a major eruptive event is in progress. However, in order to observe and quantify small and/or lower temperature thermal features such as fumaroles and lava domes, higher spatial resolution data with better radiometric and spectral resolution are required. We have reviewed 2600 available night and day time TIR scenes acquired by the Advanced Spaceborne Thermal Emission and Reflection Radiometer (ASTER) over the volcanoes of the northern Pacific. The current archive spans from March, 2000 to present. ASTER is the only instrument that routinely acquires high spatial resolution (30 - 90 m) night time data over volcanic targets. These data sets typically contain 5 TIR (8-12 microns) with 90 meter spatial resolution and 6 shortwave infrared (SWIR) bands (1-3 microns) with 30 meter spatial resolution. After the general survey of the volcanic arcs, we have focused our efforts on several targets. Mt. Hague, in the Emmons Lake complex on the Alaska Peninsula, has had mostly cloud-free ASTER observations for twenty night time TIR and six daytime TIR since August 2000. A small lake in the lower crater of Mt. Hague has had a history of appearing and disappearing over the last few years. The ASTER data combined with several recent field observations allow us to track the changes in lake area and associated temperatures

  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. 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.

  2. 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.

  3. 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.

  4. Computer aided detection system for clustered microcalcifications

    PubMed Central

    Ge, Jun; Hadjiiski, Lubomir M.; Sahiner, Berkman; Wei, Jun; Helvie, Mark A.; Zhou, Chuan; Chan, Heang-Ping

    2009-01-01

    We have developed a computer-aided detection (CAD) system to detect clustered microcalcification automatically on full-field digital mammograms (FFDMs) and a CAD system for screen-film mammograms (SFMs). The two systems used the same computer vision algorithms but their false positive (FP) classifiers were trained separately with sample images of each modality. In this study, we compared the performance of the CAD systems for detection of clustered microcalcifications on pairs of FFDM and SFM obtained from the same patient. For case-based performance evaluation, the FFDM CAD system achieved detection sensitivities of 70%, 80%, and 90% at an average FP cluster rate of 0.07, 0.16, and 0.63 per image, compared with an average FP cluster rate of 0.15, 0.38, and 2.02 per image for the SFM CAD system. The difference was statistically significant with the alternative free-response receiver operating characteristic (AFROC) analysis. When evaluated on data sets negative for microcalcification clusters, the average FP cluster rates of the FFDM CAD system were 0.04, 0.11, and 0.33 per image at detection sensitivity level of 70%, 80%, and 90%, compared with an average FP cluster rate of 0.08, 0.14, and 0.50 per image for the SFM CAD system. When evaluated for malignant cases only, the difference of the performance of the two CAD systems was not statistically significant with AFROC analysis. PMID:17264365

  5. 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.

  6. 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. 

  7. 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.

  8. 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.

  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. Pulmonary Nodule Detection Model Based on SVM and CT Image Feature-Level Fusion with Rough Sets

    PubMed Central

    Lu, Huiling; Zhang, Junjie; Shi, Hongbin

    2016-01-01

    In order to improve the detection accuracy of pulmonary nodules in CT image, considering two problems of pulmonary nodules detection model, including unreasonable feature structure and nontightness of feature representation, a pulmonary nodules detection algorithm is proposed based on SVM and CT image feature-level fusion with rough sets. Firstly, CT images of pulmonary nodule are analyzed, and 42-dimensional feature components are extracted, including six new 3-dimensional features proposed by this paper and others 2-dimensional and 3-dimensional features. Secondly, these features are reduced for five times with rough set based on feature-level fusion. Thirdly, a grid optimization model is used to optimize the kernel function of support vector machine (SVM), which is used as a classifier to identify pulmonary nodules. Finally, lung CT images of 70 patients with pulmonary nodules are collected as the original samples, which are used to verify the effectiveness and stability of the proposed model by four groups' comparative experiments. The experimental results show that the effectiveness and stability of the proposed model based on rough set feature-level fusion are improved in some degrees. PMID:27722173

  12. Detection Rate, Distribution, Clinical and Pathological Features of Colorectal Serrated Polyps

    PubMed Central

    Cao, Hai-Long; Chen, Xue; Du, Shao-Chun; Song, Wen-Jing; Wang, Wei-Qiang; Xu, Meng-Que; Wang, Si-Nan; Piao, Mei-Yu; Cao, Xiao-Cang; Wang, Bang-Mao

    2016-01-01

    Background: Colorectal serrated polyp is considered as histologically heterogeneous lesions with malignant potential in western countries. However, few Asian studies have investigated the comprehensive clinical features of serrated polyps in symptomatic populations. The aim of the study was to evaluate the features of colorectal serrated polyps in a Chinese symptomatic population. Methods: Data from all consecutive symptomatic patients were documented from a large colonoscopy database and were analyzed. Chi-square test or Fisher's exact test and logistic regression analysis were used for the data processing. Results: A total of 9191 (31.7%) patients were detected with at least one colorectal polyp. The prevalence of serrated polyps was 0.53% (153/28,981). The proportions of hyperplastic polyp (HP), sessile serrated adenoma/polyp (SSA/P), and traditional serrated adenoma (TSA) of all serrated polyps were 41.2%, 7.2%, and 51.6%, respectively, which showed a lower proportion of HP and SSA/P and a higher proportion of TSA. Serrated polyps appeared more in males and elder patients while there was no significant difference in the subtype distribution in gender and age. The proportions of large and proximal serrated polyps were 13.7% (21/153) and 46.4% (71/153), respectively. In total, 98.9% (89/90) serrated adenomas were found with dysplasia. Moreover, 14 patients with serrated polyps were found with synchronous advanced colorectal neoplasia, and large serrated polyps (LSPs) (odds ratio: 3.446, 95% confidence interval: 1.010–11.750, P < 0.05), especially large HPs, might have an association with synchronous advanced neoplasia (AN). Conclusions: The overall detection rate of colorectal serrated polyps in Chinese symptomatic patient population was low, and distribution pattern of three subtypes is different from previous reports. Moreover, LSPs, especially large HPs, might be associated with an increased risk of synchronous AN. PMID:27748334

  13. 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.

  14. 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

  15. 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.

  16. 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.

  17. 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.

  18. A primitive study of voxel feature generation by multiple stacked denoising autoencoders for detecting cerebral aneurysms on MRA

    NASA Astrophysics Data System (ADS)

    Nemoto, Mitsutaka; Hayashi, Naoto; Hanaoka, Shouhei; Nomura, Yukihiro; Miki, Soichiro; Yoshikawa, Takeharu; Ohtomo, Kuni

    2016-03-01

    The purpose of this study is to evaluate the feasibility of a novel feature generation, which is based on multiple deep neural networks (DNNs) with boosting, for computer-assisted detection (CADe). It is hard and time-consuming to optimize the hyperparameters for DNNs such as stacked denoising autoencoder (SdA). The proposed method allows using SdA based features without the burden of the hyperparameter setting. The proposed method was evaluated by an application for detecting cerebral aneurysms on magnetic resonance angiogram (MRA). A baseline CADe process included four components; scaling, candidate area limitation, candidate detection, and candidate classification. Proposed feature generation method was applied to extract the optimal features for candidate classification. Proposed method only required setting range of the hyperparameters for SdA. The optimal feature set was selected from a large quantity of SdA based features by multiple SdAs, each of which was trained using different hyperparameter set. The feature selection was operated through ada-boost ensemble learning method. Training of the baseline CADe process and proposed feature generation were operated with 200 MRA cases, and the evaluation was performed with 100 MRA cases. Proposed method successfully provided SdA based features just setting the range of some hyperparameters for SdA. The CADe process by using both previous voxel features and SdA based features had the best performance with 0.838 of an area under ROC curve and 0.312 of ANODE score. The results showed that proposed method was effective in the application for detecting cerebral aneurysms on MRA.

  19. Contaminant detection on poultry carcasses using hyperspectral data: Part II. Algorithms for selection of sets of ratio features

    NASA Astrophysics Data System (ADS)

    Nakariyakul, Songyot; Casasent, David P.

    2007-09-01

    We consider new methods to select useful sets of ratio features in hyperspectral data to detect contaminant regions on chicken carcasses using data provided by ARS (Athens, GA). A ratio feature is the ratio of the response at each pixel for two different wavebands. Ratio features perform a type of normalization and can thus help reduce false alarms, if a good normalization algorithm is not available. Thus, they are of interest. We present a new algorithm for the general problem of such feature selection in high-dimensional data. The four contaminant types of interest are three types of feces from different gastrointestinal regions (duodenum, ceca, and colon) and ingesta (undigested food) from the gizzard. To select the best two sets of ratio features from this 492-band HS data requires an exhaustive search of more than seven billion combinations of two sets of ratio features, which is very excessive. Thus, we propose our new fast ratio feature selection algorithm that requires evaluation of a much fewer number of sets of ratio features and is capable of giving quasi-optimal or optimal sets of ratio features. This new feature selection method has not been previously presented. It is shown to offer promise for an excellent detection rate and a low false alarm rate for this application. Our tests use data with different feed types and different contaminant types.

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

    PubMed

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

    2012-10-01

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

  1. 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.

  2. 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

  3. 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

  4. 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.

  5. Three-dimensional morphological and signal intensity features for detection of intervertebral disc degeneration from magnetic resonance images

    PubMed Central

    Neubert, A; Fripp, J; Engstrom, C; Walker, D; Weber, M-A; Schwarz, R; Crozier, S

    2013-01-01

    Background and objectives Advances in MRI hardware and sequences are continually increasing the amount and complexity of data such as those generated in high-resolution three-dimensional (3D) scanning of the spine. Efficient informatics tools offer considerable opportunities for research and clinically based analyses of magnetic resonance studies. In this work, we present and validate a suite of informatics tools for automated detection of degenerative changes in lumbar intervertebral discs (IVD) from both 3D isotropic and routine two-dimensional (2D) clinical T2-weighted MRI. Materials and methods An automated segmentation approach was used to extract morphological (traditional 2D radiological measures and novel 3D shape descriptors) and signal appearance (extracted from signal intensity histograms) features. The features were validated against manual reference, compared between 2D and 3D MRI scans and used for quantification and classification of IVD degeneration across magnetic resonance datasets containing IVD with early and advanced stages of degeneration. Results and conclusions Combination of the novel 3D-based shape and signal intensity features on 3D (area under receiver operating curve (AUC) 0.984) and 2D (AUC 0.988) magnetic resonance data deliver a significant improvement in automated classification of IVD degeneration, compared to the combination of previously used 2D radiological measurement and signal intensity features (AUC 0.976 and 0.983, respectively). Further work is required regarding the usefulness of 2D and 3D shape data in relation to clinical scores of lower back pain. The results reveal the potential of the proposed informatics system for computer-aided IVD diagnosis from MRI in large-scale research studies and as a possible adjunct for clinical diagnosis. PMID:23813538

  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. Detection of E. coli using a microfluidics-based antibody biochip detection system.

    PubMed

    Stokes, D L; Griffin, G D; Vo-Dinh, T

    2001-02-01

    This work demonstrates the detection of E. coli using a 2-dimensional photosensor array biochip which is efficiently equipped with a microfluidics sample/reagent delivery system for on-chip monitoring of bioassays. The biochip features a 4 x 4 array of independently operating photodiodes that are integrated along with amplifiers, discriminators and logic circuitry on a single platform. The microfluidics system includes a single 0.4 mL reaction chamber which houses a sampling platform that selectively captures detection probes from a sample through the use of immobilized bioreceptors. The independently operating photodiodes allow simultaneous monitoring of multiple samples. In this study the sampling platform is a cellulosic membrane that is exposed to E. coli organisms and subsequently analyzed using a sandwich immunoassay involving a Cy5-labeled antibody probe. The combined effectiveness of the integrated circuit (IC) biochip and the immunoassay is evaluated for assays performed both by conventional laboratory means followed by detection with the IC biochip, and through the use of the microfluidics system for on-chip detection. Highlights of the studies show that the biochip has a linear dynamic range of three orders of magnitude observed for conventional assays, and can detect 20 E. coli organisms. Selective detection of E. coli in a complex medium, milk diluent, is also reported for both off-chip and on-chip assays.

  8. 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

  9. 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

  10. 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.

  11. 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.

  12. 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.

  13. Visual system based on artificial retina for motion detection.

    PubMed

    Barranco, Francisco; Díaz, Javier; Ros, Eduardo; del Pino, Begoña

    2009-06-01

    We present a bioinspired model for detecting spatiotemporal features based on artificial retina response models. Event-driven processing is implemented using four kinds of cells encoding image contrast and temporal information. We have evaluated how the accuracy of motion processing depends on local contrast by using a multiscale and rank-order coding scheme to select the most important cues from retinal inputs. We have also developed some alternatives by integrating temporal feature results and obtained a new improved bioinspired matching algorithm with high stability, low error and low cost. Finally, we define a dynamic and versatile multimodal attention operator with which the system is driven to focus on different target features such as motion, colors, and textures.

  14. Expert System Detects Power-Distribution Faults

    NASA Technical Reports Server (NTRS)

    Walters, Jerry L.; Quinn, Todd M.

    1994-01-01

    Autonomous Power Expert (APEX) computer program is prototype expert-system program detecting faults in electrical-power-distribution system. Assists human operators in diagnosing faults and deciding what adjustments or repairs needed for immediate recovery from faults or for maintenance to correct initially nonthreatening conditions that could develop into faults. Written in Lisp.

  15. Low-level feature extraction for edge detection using genetic programming.

    PubMed

    Fu, Wenlong; Johnston, Mark; Zhang, Mengjie

    2014-08-01

    Edge detection is a subjective task. Traditionally, a moving window approach is used, but the window size in edge detection is a tradeoff between localization accuracy and noise rejection. An automatic technique for searching a discriminated pixel's neighbors to construct new edge detectors is appealing to satisfy different tasks. In this paper, we propose a genetic programming (GP) system to automatically search pixels (a discriminated pixel and its neighbors) to construct new low-level subjective edge detectors for detecting edges in natural images, and analyze the pixels selected by the GP edge detectors. Automatically searching pixels avoids the problem of blurring edges from a large window and noise influence from a small window. Linear and second-order filters are constructed from the pixels with high occurrences in these GP edge detectors. The experiment results show that the proposed GP system has good performance. A comparison between the filters with the pixels selected by GP and all pixels in a fixed window indicates that the set of pixels selected by GP is compact but sufficiently rich to construct good edge detectors.

  16. 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.

  17. 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.

  18. Genetic Particle Swarm Optimization-Based Feature Selection for Very-High-Resolution Remotely Sensed Imagery Object Change Detection.

    PubMed

    Chen, Qiang; Chen, Yunhao; Jiang, Weiguo

    2016-07-30

    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.

  19. 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

  20. Genetic Particle Swarm Optimization–Based Feature Selection for Very-High-Resolution Remotely Sensed Imagery Object Change Detection

    PubMed Central

    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

  1. The detection of a broad interstellar extinction feature near 1700 A

    NASA Technical Reports Server (NTRS)

    Carnochan, David J.

    1989-01-01

    A statistical examination of 126 extinction curves has revealed the presence of a second broad absorption feature similar in nature to the 2200 A feature. The feature is centered on wavelength 1706 A, has a full half-width of 350 A, and a mean central height of 0.21 magnitudes. The strength of the feature increases with E(B-V) supporting an interstellar origin, and on average it is 18 times weaker than the 2200 A feature.

  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. 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.

  4. Boomerang mobile counter shooter detection system

    NASA Astrophysics Data System (ADS)

    Mazurek, Jeffrey A.; Barger, James E.; Brinn, Marshall; Mullen, Richard J.; Price, David; Ritter, Scott E.; Schmitt, Dave

    2005-05-01

    Boomerang is an acoustic system installed on military vehicles that is designed to detect relative shooter azimuth/range/elevation from incoming small arms fire. It performs passive acoustic detection and computer-based signal processing. Aural and visual alerts are used to 1) inform vehicle occupants that a bullet has passed within close proximity to the vehicle and 2) indicate the position of the shooter relative to the vehicle"s direction of travel. Boomerang operates when the vehicle is stationary or moving (no motion compensation) using a single, compact, mast-mounted array of microphones. The system is calibrated to detect infantry small arms. This calibration, however, does not preclude the system from detecting larger and smaller supersonic rounds. In this paper, we discuss the design, development, testing and production of 50 Boomerang I systems over a 65-day period in late 2003/early 2004. These systems were deployed during Operation Iraqi Freedom II with Marine and Army units. Feedback from operational units identified specific deficiencies and desired improvements that were incorporated into a system re-design effort, Boomerang II. The Boomerang II system has been extensively tested for performance and environmental fitness regarding RF compatibility with tactical radios (SINCGARS), heat, cold, shock and vibration. The US Army Aberdeen Proving Grounds conducted successful RF compatibility tests, road tests and 'live fire' testing. Summary results of open field 'live fire' static tests are presented as well as performance results on a remote-controlled HMMWV operating at 45 MPH.

  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. 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.

  7. 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.

  8. 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.

  9. CT-detected solitary thyroid calcification: an important imaging feature for papillary carcinoma

    PubMed Central

    Yang, Tian-tian; Huang, Yong; Jing, Xu-quan; Gai, Xiu-juan; Li, Wen-wu

    2016-01-01

    Purpose To evaluate computed tomography (CT) detection of solitary thyroid calcification for identifying thyroid papillary carcinoma and to determine whether the predictive ability changes when the size increases after enhancement. Materials and methods CT scans on all 96 patients with thyroid nodules who underwent both enhanced CT examination of neck and thyroidectomy from 2014 to 2016 in the Shandong Cancer Hospital affiliated to Shandong University were reviewed. The cases without calcification and the cases with peripheral calcification, multiple coarse calcifications, or punctate calcification were excluded. Imaging features, including location and size of the lesions, were reviewed on plain and contrast-enhanced CT. The patients were grouped by histological results. The comparisons were evaluated by using Fisher’s exact test and binary logistic regression. Results The study population consisted of 96 patients (74 females, 22 males; mean age 49.8±11.3 years). Papillary thyroid carcinoma was observed in both solitary calcified thyroid nodules (85.4%) and solely coarse calcifications surrounded by low-density focus (58.2%). The difference was significant (P=0.006). Of 64 patients with an amplification of lesions after contrast enhancement, 58 (90.6%) were diagnosed with a malignant lesion. At the same time, of the 32 patients with no increase in size, 10 (31.2%) were diagnosed with carcinoma and 22 (68.8%) with nodular goiter. This difference was significant (P<0.001), and after binary logistic regression, increasing size was an independent risk factor for cancer. Conclusion Solitary calcified thyroid nodules detected on CT represent a high risk for papillary thyroid carcinoma, especially when the size of the lesions increases after contrast- enhanced CT. PMID:27785075

  10. 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.

  11. Maximum Temperature Detection System for Integrated Circuits

    NASA Astrophysics Data System (ADS)

    Frankiewicz, Maciej; Kos, Andrzej

    2015-03-01

    The paper describes structure and measurement results of the system detecting present maximum temperature on the surface of an integrated circuit. The system consists of the set of proportional to absolute temperature sensors, temperature processing path and a digital part designed in VHDL. Analogue parts of the circuit where designed with full-custom technique. The system is a part of temperature-controlled oscillator circuit - a power management system based on dynamic frequency scaling method. The oscillator cooperates with microprocessor dedicated for thermal experiments. The whole system is implemented in UMC CMOS 0.18 μm (1.8 V) technology.

  12. Novel systems for corrosion detection in piping

    SciTech Connect

    Raad, J.A. de; Fingerhut, M.P.

    1995-12-31

    Predictive maintenance requires accurate quantitative information. Nondestructive testing (NDT) tools have been able provide the necessary information, economically. Examination of the full surface of components is often required, which is contrary to the more typical spot location measurements. In addition, predictive maintenance inspection often requires the examination of hot and or insulated components. These challenges have been satisfied by recent developments in NDT and are applicable to a broad range of facility types such as tank terminals and pulp and paper plants. For non-insulated and above ground piping systems magnetic flux leakage (MFL) tools have recently been introduced into the marketplace. These tools allow very quick and reliable detection of local and extensive general corrosion, in carbon steel pipes or vessel walls, with nominal wall thicknesses of up to 15 mm. A relatively new method for detection of corrosion under insulated components is the RTD-Incotest, pulse eddy current (PEC) system. This system can also provide the components remaining wall thickness at general corrosion locations. Demand for corrosion detection under insulation on piping has also been satisfied by new dynamic Real-Time-Radiography systems. These systems are relatively fast, but the concept itself and its weight require close human access to the pipe, hence, some method of accessing above ground piping is required. Nevertheless, the systems satisfy a market demand. Time-of-flight-Diffraction (TOFD) for detection and sizing of weld root corrosion as well as coherent color enhanced thickness mapping will also be introduced.

  13. 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.

  14. Features of the urban atmosphere as detected by RASS and 3D sonics

    NASA Astrophysics Data System (ADS)

    Piringer, Martin; Lotteraner, Christoph

    2010-05-01

    In contrast to the classical homogeneous atmospheric boundary layer, the urban boundary layer is more complex due to several specific features and processes caused by the buildings which introduce a large amount of vertical surfaces, high roughness elements, and artificial materials. The most well-known result is the urban heat island, but urban areas also influence the wind field, precipitation, atmospheric stability, and the mixing height. The last two are essential to determine the spread of pollutants in urban areas. Atmospheric stability can properly be derived with 3D sonics via the sensible heat flux; The mixing height can be deduced from vertical temperature profiles provided by Sodar-RASS systems. The contribution will highlight the methodologies, give examples, and critically discuss advantages and shortcomings of the instruments and the methods applied.

  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. 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.

  17. 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.

  18. 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.

  19. 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.

  20. 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.

  1. 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.

  2. Human visual system-based smoking event detection

    NASA Astrophysics Data System (ADS)

    Odetallah, Amjad D.; Agaian, Sos S.

    2012-06-01

    Human action (e.g. smoking, eating, and phoning) analysis is an important task in various application domains like video surveillance, video retrieval, human-computer interaction systems, and so on. Smoke detection is a crucial task in many video surveillance applications and could have a great impact to raise the level of safety of urban areas, public parks, airplanes, hospitals, schools and others. The detection task is challenging since there is no prior knowledge about the object's shape, texture and color. In addition, its visual features will change under different lighting and weather conditions. This paper presents a new scheme of a system for detecting human smoking events, or small smoke, in a sequence of images. In developed system, motion detection and background subtraction are combined with motion-region-saving, skin-based image segmentation, and smoke-based image segmentation to capture potential smoke regions which are further analyzed to decide on the occurrence of smoking events. Experimental results show the effectiveness of the proposed approach. As well, the developed method is capable of detecting the small smoking events of uncertain actions with various cigarette sizes, colors, and shapes.

  3. System for Automatic Detection of Clustered Microcalcifications in Digital Mammograms

    NASA Astrophysics Data System (ADS)

    Bazzani, A.; Bollini, D.; Brancaccio, R.; Campanini, R.; Lanconelli, N.; Romani, D.; Bevilacqua, A.

    In this paper, we investigate the performance of a Computer Aided Diagnosis (CAD) system for the detection of clustered microcalcifications in mammograms. Our detection algorithm consists of the combination of two different methods. The first, based on difference-image techniques and gaussianity statistical tests, finds out the most obvious signals. The second, is able to discover more subtle microcalcifications by exploiting a multiresolution analysis by means of the wavelet transform. We can separately tune the two methods, so that each one of them is able to detect signals with similar features. By combining signals coming out from the two parts through a logical OR operation, we can discover microcalcifications with different characteristics. Our algorithm yields a sensitivity of 91.4% with 0.4 false positive cluster per image on the 40 images of the Nijmegen database.

  4. Spark discharge trace element detection system

    DOEpatents

    Adler-Golden, S.; Bernstein, L.S.; Bien, F.

    1988-08-23

    A spark discharge trace element detection system is provided which includes a spark chamber including a pair of electrodes for receiving a sample of gas to be analyzed at no greater than atmospheric pressure. A voltage is provided across the electrodes for generating a spark in the sample. The intensity of the emitted radiation in at least one primary selected narrow band of the radiation is detected. Each primary band corresponds to an element to be detected in the gas. The intensity of the emission in each detected primary band is integrated during the afterglow time interval of the spark emission and a signal representative of the integrated intensity of the emission in each selected primary bond is utilized to determine the concentration of the corresponding element in the gas. 12 figs.

  5. Spark discharge trace element detection system

    DOEpatents

    Adler-Golden, Steven; Bernstein, Lawrence S.; Bien, Fritz

    1988-01-01

    A spark discharge trace element detection system is provided which includes a spark chamber including a pair of electrodes for receiving a sample of gas to be analyzed at no greater than atmospheric pressure. A voltage is provided across the electrodes for generating a spark in the sample. The intensity of the emitted radiation in at least one primary selected narrow band of the radiation is detected. Each primary band corresponds to an element to be detected in the gas. The intensity of the emission in each detected primary band is integrated during the afterglow time interval of the spark emission and a signal representative of the integrated intensity of the emission in each selected primary bond is utilized to determine the concentration of the corresponding element in the gas.

  6. Short-range biological standoff detection system (SR-BSDS)

    NASA Astrophysics Data System (ADS)

    Suliga, William; Burnham, Ralph L.; Deely, Timothy; Gavert, William; Pronko, Mark S.; Verdun, Gustavo; Verdun, Horacio R.; Cannaliato, V. James; Ginley, William J.; Hyttinen, Larry; Strawbridge, John B.

    1999-11-01

    interrogation. System status, data and discrimination interrogation results will be transmitted over a wireless modem to a Command Post. All the above will continue to operate without additional operator intervention. The autonomous operation feature was recently demonstrated at Dugway Proving Ground in July of 1999. Field testing to date, both at Aberdeen Proving Ground, MD and Dugway Proving Ground, UT in 1998 and 1999 successfully demonstrated the system's detection, discrimination, scanning functions and autonomous operation. With the initial field testing and system demonstration testing successfully complete, emphasis is on several areas of enhancements in preparation for additional DPG testing and system delivery for field implementation in 2000.

  7. Detection of a novel pigment network feature in reticulated black solar lentigo by high-resolution epiluminescence microscopy.

    PubMed

    Haas, Norbert; Hermes, Barbara; Henz, Beate M

    2002-06-01

    Epiluminescence light microscopy (ELM) of pigmented skin lesions has led to a catalog of pigment network (PN) features. The objective of this study was to determine whether high-resolution ELM detects additional pigment structures not seen with conventional ELM. Epiluminescence light microscopy was performed by placing the lens of a standard light microscope directly on the skin surface, with resulting enhanced optical resolution compared with ELM systems currently in general use. Eight reticulated black solar lentigines were studied. All lesions were viewed and analyzed using dermatoscopic criteria for the PN. In addition, they were all photographed, excised, and examined histologically. Two subtypes of black solar lentigines could be distinguished using generally accepted dermatoscopic criteria for the PN. Furthermore, a new pigment structure was detected, namely, pigment spots of equal color, shape, and size, which were regularly superimposed on and juxtaposed to the PN. Clinicopathologic correlation showed these spots to represent individual hyperpigmented corneocytes. Because such cells result from physiologic excessive pigment translocation via the epidermal melanocytic unit, we called them pigmented corneocytes. Pigmented corneocytes were seen in seven of eight black solar lentigines. The ELM technique presented here allows for more detailed analysis and classification of the PN components. Pigmented corneocytes are proposed as an additional dermatoscopic criterion.

  8. Immunity-Based Aircraft Fault Detection System

    NASA Technical Reports Server (NTRS)

    Dasgupta, D.; KrishnaKumar, K.; Wong, D.; Berry, M.

    2004-01-01

    In the study reported in this paper, we have developed and applied an Artificial Immune System (AIS) algorithm for aircraft fault detection, as an extension to a previous work on intelligent flight control (IFC). Though the prior studies had established the benefits of IFC, one area of weakness that needed to be strengthened was the control dead band induced by commanding a failed surface. Since the IFC approach uses fault accommodation with no detection, the dead band, although it reduces over time due to learning, is present and causes degradation in handling qualities. If the failure can be identified, this dead band can be further A ed to ensure rapid fault accommodation and better handling qualities. The paper describes the application of an immunity-based approach that can detect a broad spectrum of known and unforeseen failures. The approach incorporates the knowledge of the normal operational behavior of the aircraft from sensory data, and probabilistically generates a set of pattern detectors that can detect any abnormalities (including faults) in the behavior pattern indicating unsafe in-flight operation. We developed a tool called MILD (Multi-level Immune Learning Detection) based on a real-valued negative selection algorithm that can generate a small number of specialized detectors (as signatures of known failure conditions) and a larger set of generalized detectors for unknown (or possible) fault conditions. Once the fault is detected and identified, an adaptive control system would use this detection information to stabilize the aircraft by utilizing available resources (control surfaces). We experimented with data sets collected under normal and various simulated failure conditions using a piloted motion-base simulation facility. The reported results are from a collection of test cases that reflect the performance of the proposed immunity-based fault detection algorithm.

  9. DCE Bio Detection System Final Report

    SciTech Connect

    Lind, Michael A.; Batishko, Charles R.; Morgen, Gerald P.; Owsley, Stanley L.; Dunham, Glen C.; Warner, Marvin G.; Willett, Jesse A.

    2007-12-01

    The DCE (DNA Capture Element) Bio-Detection System (Biohound) was conceived, designed, built and tested by PNNL under a MIPR for the US Air Force under the technical direction of Dr. Johnathan Kiel and his team at Brooks City Base in San Antonio Texas. The project was directed toward building a measurement device to take advantage of a unique aptamer based assay developed by the Air Force for detecting biological agents. The assay uses narrow band quantum dots fluorophores, high efficiency fluorescence quenchers, magnetic micro-beads beads and selected aptamers to perform high specificity, high sensitivity detection of targeted biological materials in minutes. This final report summarizes and documents the final configuration of the system delivered to the Air Force in December 2008

  10. Methods, systems and devices for detecting threatening objects and for classifying magnetic data

    DOEpatents

    Kotter, Dale K.; Roybal, Lyle G.; Rohrbaugh, David T.; Spencer, David F.

    2012-01-24

    A method for detecting threatening objects in a security screening system. The method includes a step of classifying unique features of magnetic data as representing a threatening object. Another step includes acquiring magnetic data. Another step includes determining if the acquired magnetic data comprises a unique feature.

  11. Digital radiographic systems detect boiler tube cracks

    SciTech Connect

    Walker, S.

    2008-06-15

    Boiler water wall leaks have been a major cause of steam plant forced outages. But conventional nondestructive evaluation techniques have a poor track record of detecting corrosion fatigue cracking on the inside surface of the cold side of waterwall tubing. EPRI is performing field trials of a prototype direct-digital radiographic system that promises to be a game changer. 8 figs.

  12. Detection of abrupt changes in dynamic systems

    NASA Technical Reports Server (NTRS)

    Willsky, A. S.

    1984-01-01

    Some of the basic ideas associated with the detection of abrupt changes in dynamic systems are presented. Multiple filter-based techniques and residual-based method and the multiple model and generalized likelihood ratio methods are considered. Issues such as the effect of unknown onset time on algorithm complexity and structure and robustness to model uncertainty are discussed.

  13. Grading Systems, Features of Assessment and Students' Approaches to Learning

    ERIC Educational Resources Information Center

    Dahlgren, Lars Owe; Fejes, Andreas; Abrandt-Dahlgren, Madeleine; Trowald, Nils

    2009-01-01

    The Bologna process aims at harmonising the higher education systems in the Europe. One of the most important tools proposed for such a purpose is the European Credit Transfer System. A significant element of this system is a common seven-step grading scale. It has previously been shown that assessment characteristics impact on students'…

  14. The Unicorn Collection Management System: Its Structure and Features.

    ERIC Educational Resources Information Center

    Young, Jacky; Veatch, James R., Jr.

    1988-01-01

    Discusses the design principles behind the Unicorn Collection Management System, an integrated library system which includes modules for bibliographic and inventory control, circulation, academic reserves, serials control, authority control, acquisition, electronic mail, bulletin board, and enhanced public access. The flexibility of the system is…

  15. Optical Detection System for Ultrasonic Surface Displacements.

    NASA Astrophysics Data System (ADS)

    Godfrey, Martin William

    Available from UMI in association with The British Library. The work was carried out with the aim of developing an optical interferometric detection system. This was to be applied to the quantitative measurement of low amplitude, high frequency surface displacements (<1nm at several MHz). Two forms of interferometric detector are investigated. The performance and limitations in particular measurement situations are assessed for both types of interferometer. The first type of detector investigated is a miniature stabilised interferometer. The design of a stabilisation system is given, along with ways in which it can be optimised for a particular environment. The second type of detector studied is a quadrature interferometer. Various methods of processing the two channels of information from this device are discussed. The design of a new method of processing the signals is given, and its performance determined. The interferometric sensor is combined with a waveform digitiser and microcomputer to form an integrated detection system. Analysis of the waveforms obtained is performed by a system of Pascal programs developed for this purpose. The detection system is applied to tasks such as the calibration of other forms of transducer and the characterisation of artificial sources of acoustic emission. The results of experimental studies are given and the applications of such a system discussed.

  16. Region based stellate features combined with variable selection using AdaBoost learning in mammographic computer-aided detection.

    PubMed

    Kim, Dae Hoe; Choi, Jae Young; Ro, Yong Man

    2015-08-01

    In this paper, a new method is developed for extracting so-called region-based stellate features to correctly differentiate spiculated malignant masses from normal tissues on mammograms. In the proposed method, a given region of interest (ROI) for feature extraction is divided into three individual subregions, namely core, inner, and outer parts. The proposed region-based stellate features are then extracted to encode the different and complementary stellate pattern information by computing the statistical characteristics for each of the three different subregions. To further maximize classification performance, a novel variable selection algorithm based on AdaBoost learning is incorporated for choosing an optimal subset of variables of region-based stellate features. In particular, we develop a new variable selection metric (criteria) that effectively determines variable importance (ranking) within the conventional AdaBoost framework. Extensive and comparative experiments have been performed on the popular benchmark mammogram database (DB). Results show that our region-based stellate features (extracted from automatically segmented ROIs) considerably outperform other state-of-the-art features developed for mammographic spiculated mass detection or classification. Our results also indicate that combining region-based stellate features with the proposed variable selection strategy has an impressive effect on improving spiculated mass classification and detection.

  17. Real-Time EEG-Based Happiness Detection System

    PubMed Central

    2013-01-01

    We propose to use real-time EEG signal to classify happy and unhappy emotions elicited by pictures and classical music. We use PSD as a feature and SVM as a classifier. The average accuracies of subject-dependent model and subject-independent model are approximately 75.62% and 65.12%, respectively. Considering each pair of channels, temporal pair of channels (T7 and T8) gives a better result than the other area. Considering different frequency bands, high-frequency bands (Beta and Gamma) give a better result than low-frequency bands. Considering different time durations for emotion elicitation, that result from 30 seconds does not have significant difference compared with the result from 60 seconds. From all of these results, we implement real-time EEG-based happiness detection system using only one pair of channels. Furthermore, we develop games based on the happiness detection system to help user recognize and control the happiness. PMID:24023532

  18. Incidentally Detected Kaposi Sarcoma of Adrenal Gland with Anaplastic Features in an HIV Negative Patient

    PubMed Central

    Celik, Murat; Sen, Erdem; Cebeci, Hakan; Ata, Ozlem; Yavas, Cagdas

    2016-01-01

    Kaposi sarcoma (KS), a vascular tumor caused by infection with human herpesvirus 8 (HHV8), is a systemic disease that can present with cutaneous lesions with or without visceral involvement. Very few cases of KS, most of which were associated with AIDS, have been reported in the adrenal gland. Anaplastic transformation of KS is a rare clinical presentation known as an aggressive disease with local recurrence and metastatic potential. We report here a 47-year-old HIV negative male presented with extra-adrenal symptoms and had an incidentally detected anaplastic adrenal KS exhibited aggressive clinical course. To the best of our knowledge, this is the first case of anaplastic primary adrenal KS without mucocutaneous involvement but subsequently developed other side adrenal metastases in an HIV negative patient. PMID:27747121

  19. Statistical Features of Complex Systems ---Toward Establishing Sociological Physics---

    NASA Astrophysics Data System (ADS)

    Kobayashi, Naoki; Kuninaka, Hiroto; Wakita, Jun-ichi; Matsushita, Mitsugu

    2011-07-01

    Complex systems have recently attracted much attention, both in natural sciences and in sociological sciences. Members constituting a complex system evolve through nonlinear interactions among each other. This means that in a complex system the multiplicative experience or, so to speak, the history of each member produces its present characteristics. If attention is paid to any statistical property in any complex system, the lognormal distribution is the most natural and appropriate among the standard or ``normal'' statistics to overview the whole system. In fact, the lognormality emerges rather conspicuously when we examine, as familiar and typical examples of statistical aspects in complex systems, the nursing-care period for the aged, populations of prefectures and municipalities, and our body height and weight. Many other examples are found in nature and society. On the basis of these observations, we discuss the possibility of sociological physics.

  20. Remote Sensing of Martian Terrain Hazards via Visually Salient Feature Detection

    NASA Astrophysics Data System (ADS)

    Al-Milli, S.; Shaukat, A.; Spiteri, C.; Gao, Y.

    2014-04-01

    The main objective of the FASTER remote sensing system is the detection of rocks on planetary surfaces by employing models that can efficiently characterise rocks in terms of semantic descriptions. The proposed technique abates some of the algorithmic limitations of existing methods with no training requirements, lower computational complexity and greater robustness towards visual tracking applications over long-distance planetary terrains. Visual saliency models inspired from biological systems help to identify important regions (such as rocks) in the visual scene. Surface rocks are therefore completely described in terms of their local or global conspicuity pop-out characteristics. These local and global pop-out cues are (but not limited to); colour, depth, orientation, curvature, size, luminance intensity, shape, topology etc. The currently applied methods follow a purely bottom-up strategy of visual attention for selection of conspicuous regions in the visual scene without any topdown control. Furthermore the choice of models used (tested and evaluated) are relatively fast among the state-of-the-art and have very low computational load. Quantitative evaluation of these state-ofthe- art models was carried out using benchmark datasets including the Surrey Space Centre Lab Testbed, Pangu generated images, RAL Space SEEKER and CNES Mars Yard datasets. The analysis indicates that models based on visually salient information in the frequency domain (SRA, SDSR, PQFT) are the best performing ones for detecting rocks in an extra-terrestrial setting. In particular the SRA model seems to be the most optimum of the lot especially that it requires the least computational time while keeping errors competitively low. The salient objects extracted using these models can then be merged with the Digital Elevation Models (DEMs) generated from the same navigation cameras in order to be fused to the navigation map thus giving a clear indication of the rock locations.