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Sample records for change detection based

  1. Scene change detection based on multimodal integration

    NASA Astrophysics Data System (ADS)

    Zhu, Yingying; Zhou, Dongru

    2003-09-01

    Scene change detection is an essential step to automatic and content-based video indexing, retrieval and browsing. In this paper, a robust scene change detection and classification approach is presented, which analyzes audio, visual and textual sources and accounts for their inter-relations and coincidence to semantically identify and classify video scenes. Audio analysis focuses on the segmentation of audio stream into four types of semantic data such as silence, speech, music and environmental sound. Further processing on speech segments aims at locating speaker changes. Video analysis partitions visual stream into shots. Text analysis can provide a supplemental source of clues for scene classification and indexing information. We integrate the video and audio analysis results to identify video scenes and use the text information detected by the video OCR technology or derived from transcripts available to refine scene classification. Results from single source segmentation are in some cases suboptimal. By combining visual, aural features adn the accessorial text information, the scence extraction accuracy is enhanced, and more semantic segmentations are developed. Experimental results are proven to rather promising.

  2. Point pattern match-based change detection in a constellation of previously detected objects

    SciTech Connect

    Paglieroni, David W.

    2016-06-07

    A method and system is provided that applies attribute- and topology-based change detection to objects that were detected on previous scans of a medium. The attributes capture properties or characteristics of the previously detected objects, such as location, time of detection, detection strength, size, elongation, orientation, etc. The locations define a three-dimensional network topology forming a constellation of previously detected objects. The change detection system stores attributes of the previously detected objects in a constellation database. The change detection system detects changes by comparing the attributes and topological consistency of newly detected objects encountered during a new scan of the medium to previously detected objects in the constellation database. The change detection system may receive the attributes of the newly detected objects as the objects are detected by an object detection system in real time.

  3. Attribute and topology based change detection in a constellation of previously detected objects

    SciTech Connect

    Paglieroni, David W.; Beer, Reginald N.

    2016-01-19

    A system that applies attribute and topology based change detection to networks of objects that were detected on previous scans of a structure, roadway, or area of interest. The attributes capture properties or characteristics of the previously detected objects, such as location, time of detection, size, elongation, orientation, etc. The topology of the network of previously detected objects is maintained in a constellation database that stores attributes of previously detected objects and implicitly captures the geometrical structure of the network. A change detection system detects change by comparing the attributes and topology of new objects detected on the latest scan to the constellation database of previously detected objects.

  4. A SAR ATR algorithm based on coherent change detection

    SciTech Connect

    Harmony, D.W.

    2000-12-01

    This report discusses an automatic target recognition (ATR) algorithm for synthetic aperture radar (SAR) imagery that is based on coherent change detection techniques. The algorithm relies on templates created from training data to identify targets. Objects are identified or rejected as targets by comparing their SAR signatures with templates using the same complex correlation scheme developed for coherent change detection. Preliminary results are presented in addition to future recommendations.

  5. Diffusion Geometry Based Nonlinear Methods for Hyperspectral Change Detection

    DTIC Science & Technology

    2010-05-12

    Schaum and A. Stocker, “Hyperspectral change detection and supervised matched filtering based on covariance equalization,” Proceedings of the SPIE, vol...5425, pp. 77- 90 (2004). 10. A. Schaum and A. Stocker, “Linear chromodynamics models for hyperspectral target detection,” Proceedings of the IEEE...Aerospace Conference (February 2003). 11. A. Schaum and A. Stocker, “Linear chromodynamics models for hyperspectral target detection

  6. Region Based Forest Change Detection from CARTOSAT-1 Stereo Imagery

    NASA Astrophysics Data System (ADS)

    Tian, J.; Leitloff, J.; Krauß, T.; Reinartz, P.

    2011-09-01

    Tree height is a fundamental parameter for describing the forest situation and changes. The latest development of automatic Digital Surface Model (DSM) generation techniques allows new approaches of forest change detection from satellite stereo imagery. This paper shows how DSMs can support the change detection in forest area. A novel region based forest change detection method is proposed using single-channel CARTOSAT-1 stereo imagery. In the first step, DSMs from two dates are generated based on automatic matching technology. After co-registration and normalising by using LiDAR data, the mean-shift segmentation is applied to the original pan images, and the images of both dates are classified to forest and non-forest areas by analysing their histograms and height differences. In the second step, a rough forest change detection map is generated based on the comparison of the two forest map. Then the GLCM texture from the nDSM and the Cartosat-1 images of the resulting regions are analyzed and compared, the real changes are extracted by SVM based classification.

  7. Convolutional neural network features based change detection in satellite images

    NASA Astrophysics Data System (ADS)

    Mohammed El Amin, Arabi; Liu, Qingjie; Wang, Yunhong

    2016-07-01

    With the popular use of high resolution remote sensing (HRRS) satellite images, a huge research efforts have been placed on change detection (CD) problem. An effective feature selection method can significantly boost the final result. While hand-designed features have proven difficulties to design features that effectively capture high and mid-level representations, the recent developments in machine learning (Deep Learning) omit this problem by learning hierarchical representation in an unsupervised manner directly from data without human intervention. In this letter, we propose approaching the change detection problem from a feature learning perspective. A novel deep Convolutional Neural Networks (CNN) features based HR satellite images change detection method is proposed. The main guideline is to produce a change detection map directly from two images using a pretrained CNN. This method can omit the limited performance of hand-crafted features. Firstly, CNN features are extracted through different convolutional layers. Then, a concatenation step is evaluated after an normalization step, resulting in a unique higher dimensional feature map. Finally, a change map was computed using pixel-wise Euclidean distance. Our method has been validated on real bitemporal HRRS satellite images according to qualitative and quantitative analyses. The results obtained confirm the interest of the proposed method.

  8. Change detection of built-up land: A framework of combining pixel-based detection and object-based recognition

    NASA Astrophysics Data System (ADS)

    Xiao, Pengfeng; Zhang, Xueliang; Wang, Dongguang; Yuan, Min; Feng, Xuezhi; Kelly, Maggi

    2016-09-01

    This study proposed a new framework that combines pixel-level change detection and object-level recognition to detect changes of built-up land from high-spatial resolution remote sensing images. First, an adaptive differencing method was designed to detect changes at the pixel level based on both spectral and textural features. Next, the changed pixels were subjected to a set of morphological operations to improve the completeness and to generate changed objects, achieving the transition of change detection from the pixel level to the object level. The changed objects were further recognised through the difference of morphological building index in two phases to indicate changed objects on built-up land. The transformation from changed pixels to changed objects makes the proposed framework distinct with both the pixel-based and the object-based change detection methods. Compared with the pixel-based methods, the proposed framework can improve the change detection capability through the transformation and successive recognition of objects. Compared with the object-based method, the proposed framework avoids the issue of multitemporal segmentation and can generate changed objects directly from changed pixels. The experimental results show the effectiveness of the transformation from changed pixels to changed objects and the successive object-based recognition on improving the detection accuracy, which justify the application potential of the proposed change detection framework.

  9. Refractive index change detection based on porous silicon microarray

    NASA Astrophysics Data System (ADS)

    Chen, Weirong; Jia, Zhenhong; Li, Peng; Lv, Guodong; Lv, Xiaoyi

    2016-05-01

    By combining photolithography with the electrochemical anodization method, a microarray device of porous silicon (PS) photonic crystal was fabricated on the crystalline silicon substrate. The optical properties of the microarray were analyzed with the transfer matrix method. The relationship between refractive index and reflectivity of each array element of the microarray at 633 nm was also studied, and the array surface reflectivity changes were observed through digital imaging. By means of the reflectivity measurement method, reflectivity changes below 10-3 can be observed based on PS microarray. The results of this study can be applied to the detection of biosensor arrays.

  10. Object-based rapid change detection for disaster management

    NASA Astrophysics Data System (ADS)

    Thunig, Holger; Michel, Ulrich; Ehlers, Manfred; Reinartz, Peter

    2011-11-01

    Rapid change detection is used in cases of natural hazards and disasters. This analysis lead to quick information about areas of damage. In certain cases the lack of information after catastrophe events is obstructing supporting measures within disaster management. Earthquakes, tsunamis, civil war, volcanic eruption, droughts and floods have much in common: people are directly affected, landscapes and buildings are destroyed. In every case geospatial data is necessary to gain knowledge as basement for decision support. Where to go first? Which infrastructure is usable? How much area is affected? These are essential questions which need to be answered before appropriate, eligible help can be established. This study presents an innovative strategy to retrieve post event information by use of an object-based change detection approach. Within a transferable framework, the developed algorithms can be implemented for a set of remote sensing data among different investigation areas. Several case studies are the base for the retrieved results. Within a coarse dividing into statistical parts and the segmentation in meaningful objects, the framework is able to deal with different types of change. By means of an elaborated normalized temporal change index (NTCI) panchromatic datasets are used to extract areas which are destroyed, areas which were not affected and in addition areas which are developing new for cases where rebuilding has already started. The results of the study are also feasible for monitoring urban growth.

  11. Vibration-based monitoring to detect mass changes in satellites

    NASA Astrophysics Data System (ADS)

    Maji, Arup; Vernon, Breck

    2012-04-01

    Vibration-based structural health monitoring could be a useful form of determining the health and safety of space structures. A particular concern is the possibility of a foreign object that attaches itself to a satellite in orbit for adverse reasons. A frequency response analysis was used to determine the changes in mass and moment of inertia of the space structure based on a change in the natural frequencies of the structure or components of the structure. Feasibility studies were first conducted on a 7 in x 19 in aluminum plate with various boundary conditions. Effect of environmental conditions on the frequency response was determined. The baseline frequency response for the plate was then used as the basis for detection of the addition, and possibly the location, of added masses on the plate. The test results were compared to both analytical solutions and finite element models created in SAP2000. The testing was subsequently expanded to aluminum alloy satellite panels and a mock satellite with dummy payloads. Statistical analysis was conducted on variations of frequency due to added mass and thermal changes to determine the threshold of added mass that can be detected.

  12. GPU based detection of topological changes in Voronoi diagrams

    NASA Astrophysics Data System (ADS)

    Bernaschi, M.; Lulli, M.; Sbragaglia, M.

    2017-04-01

    The Voronoi diagrams are an important tool having theoretical and practical applications in a large number of fields. We present a new procedure, implemented as a set of CUDA kernels, which detects, in a general and efficient way, topological changes in case of dynamic Voronoi diagrams whose generating points move in time. The solution that we provide has been originally developed to identify plastic events during simulations of soft-glassy materials based on a lattice Boltzmann model with frustrated-short range attractive and mid/long-range repulsive-interactions. Along with the description of our approach, we present also some preliminary physics results.

  13. Detecting abrupt dynamic change based on changes in the fractal properties of spatial images

    NASA Astrophysics Data System (ADS)

    Liu, Qunqun; He, Wenping; Gu, Bin; Jiang, Yundi

    2016-08-01

    Many abrupt climate change events often cannot be detected timely by conventional abrupt detection methods until a few years after these events have occurred. The reason for this lag in detection is that abundant and long-term observational data are required for accurate abrupt change detection by these methods, especially for the detection of a regime shift. So, these methods cannot help us understand and forecast the evolution of the climate system in a timely manner. Obviously, spatial images, generated by a coupled spatiotemporal dynamical model, contain more information about a dynamic system than a single time series, and we find that spatial images show the fractal properties. The fractal properties of spatial images can be quantitatively characterized by the Hurst exponent, which can be estimated by two-dimensional detrended fluctuation analysis (TD-DFA). Based on this, TD-DFA is used to detect an abrupt dynamic change of a coupled spatiotemporal model. The results show that the TD-DFA method can effectively detect abrupt parameter changes in the coupled model by monitoring the changing in the fractal properties of spatial images. The present method provides a new way for abrupt dynamic change detection, which can achieve timely and efficient abrupt change detection results.

  14. Updating National Topographic Data Base Using Change Detection Methods

    NASA Astrophysics Data System (ADS)

    Keinan, E.; Felus, Y. A.; Tal, Y.; Zilberstien, O.; Elihai, Y.

    2016-06-01

    The traditional method for updating a topographic database on a national scale is a complex process that requires human resources, time and the development of specialized procedures. In many National Mapping and Cadaster Agencies (NMCA), the updating cycle takes a few years. Today, the reality is dynamic and the changes occur every day, therefore, the users expect that the existing database will portray the current reality. Global mapping projects which are based on community volunteers, such as OSM, update their database every day based on crowdsourcing. In order to fulfil user's requirements for rapid updating, a new methodology that maps major interest areas while preserving associated decoding information, should be developed. Until recently, automated processes did not yield satisfactory results, and a typically process included comparing images from different periods. The success rates in identifying the objects were low, and most were accompanied by a high percentage of false alarms. As a result, the automatic process required significant editorial work that made it uneconomical. In the recent years, the development of technologies in mapping, advancement in image processing algorithms and computer vision, together with the development of digital aerial cameras with NIR band and Very High Resolution satellites, allow the implementation of a cost effective automated process. The automatic process is based on high-resolution Digital Surface Model analysis, Multi Spectral (MS) classification, MS segmentation, object analysis and shape forming algorithms. This article reviews the results of a novel change detection methodology as a first step for updating NTDB in the Survey of Israel.

  15. Change Detection Based on Persistent Scatterer Interferometry - a New Method of Monitoring Building Changes

    NASA Astrophysics Data System (ADS)

    Yang, C. H.; Kenduiywo, B. K.; Soergel, U.

    2016-06-01

    Persistent Scatterer Interferometry (PSI) is a technique to detect a network of extracted persistent scatterer (PS) points which feature temporal phase stability and strong radar signal throughout time-series of SAR images. The small surface deformations on such PS points are estimated. PSI particularly works well in monitoring human settlements because regular substructures of man-made objects give rise to large number of PS points. If such structures and/or substructures substantially alter or even vanish due to big change like construction, their PS points are discarded without additional explorations during standard PSI procedure. Such rejected points are called big change (BC) points. On the other hand, incoherent change detection (ICD) relies on local comparison of multi-temporal images (e.g. image difference, image ratio) to highlight scene modifications of larger size rather than detail level. However, image noise inevitably degrades ICD accuracy. We propose a change detection approach based on PSI to synergize benefits of PSI and ICD. PS points are extracted by PSI procedure. A local change index is introduced to quantify probability of a big change for each point. We propose an automatic thresholding method adopting change index to extract BC points along with a clue of the period they emerge. In the end, PS ad BC points are integrated into a change detection image. Our method is tested at a site located around north of Berlin main station where steady, demolished, and erected building substructures are successfully detected. The results are consistent with ground truth derived from time-series of aerial images provided by Google Earth. In addition, we apply our technique for traffic infrastructure, business district, and sports playground monitoring.

  16. Correlation based efficient face recognition and color change detection

    NASA Astrophysics Data System (ADS)

    Elbouz, M.; Alfalou, A.; Brosseau, C.; Alam, M. S.; Qasmi, S.

    2013-01-01

    Identifying the human face via correlation is a topic attracting widespread interest. At the heart of this technique lies the comparison of an unknown target image to a known reference database of images. However, the color information in the target image remains notoriously difficult to interpret. In this paper, we report a new technique which: (i) is robust against illumination change, (ii) offers discrimination ability to detect color change between faces having similar shape, and (iii) is specifically designed to detect red colored stains (i.e. facial bleeding). We adopt the Vanderlugt correlator (VLC) architecture with a segmented phase filter and we decompose the color target image using normalized red, green, and blue (RGB), and hue, saturation, and value (HSV) scales. We propose a new strategy to effectively utilize color information in signatures for further increasing the discrimination ability. The proposed algorithm has been found to be very efficient for discriminating face subjects with different skin colors, and those having color stains in different areas of the facial image.

  17. Object-Based Classification and Change Detection of Hokkaido, Japan

    NASA Astrophysics Data System (ADS)

    Park, J. G.; Harada, I.; Kwak, Y.

    2016-06-01

    Topography and geology are factors to characterize the distribution of natural vegetation. Topographic contour is particularly influential on the living conditions of plants such as soil moisture, sunlight, and windiness. Vegetation associations having similar characteristics are present in locations having similar topographic conditions unless natural disturbances such as landslides and forest fires or artificial disturbances such as deforestation and man-made plantation bring about changes in such conditions. We developed a vegetation map of Japan using an object-based segmentation approach with topographic information (elevation, slope, slope direction) that is closely related to the distribution of vegetation. The results found that the object-based classification is more effective to produce a vegetation map than the pixel-based classification.

  18. Object-based change detection for landslide monitoring based on SPOT imagery

    NASA Astrophysics Data System (ADS)

    Hölbling, Daniel; Friedl, Barbara; Eisank, Clemens

    2014-05-01

    The steadily increasing availability of Earth observation (EO) data from a wide range of sensors facilitates the long-time monitoring of mass movements and retrospective analysis. Pixel-based approaches are most commonly used for detecting changes based on optical remote sensing data. However, single pixels are not suitable for depicting natural phenomena such as landslides in their full complexity and their transformation over time. By applying semi-automated object-based change detection limitations inherent to pixel-based methods can be overcome to a certain extent. For instance, the problem of variant spectral reflectance for the same pixel location in images from different points in time can be minimized. Therefore, atmospheric and radiometric correction of input data sets - although highly recommended - seems to be not that important for developing a straightforward change detection approach based on object-based image analysis (OBIA). The object-based change detection approach was developed for a subset of the Baichi catchment, which is located in the Shihmen Reservoir watershed in northern Taiwan. The study area is characterized by mountainous terrain with steep slopes and is regularly affected by severe landslides and debris flows. Several optical satellite images, i.e. SPOT images from different years and seasons with a spatial resolution ranging from 2.5 to 6.25 m, have been used for monitoring the past evolution of landslides and landslide affected areas. A digital elevation model (DEM) with 5 m spatial resolution was integrated in the analysis for supporting the differentiation of landslides and debris flows. The landslide changes were identified by comparing feature values of segmentation-derived image objects between two subsequent images in eCognition (Trimble) software. To increase the robustness and transferability of the approach we identified changes by using the relative difference in values of band-specific relational features, spectral

  19. Efficient fold-change detection based on protein-protein interactions.

    PubMed

    Buijsman, W; Sheinman, M

    2014-02-01

    Various biological sensory systems exhibit a response to a relative change of the stimulus, often referred to as fold-change detection. In the past few years, fold-change detecting mechanisms, based on transcriptional networks, have been proposed. Here we present a fold-change detecting mechanism, based on protein-protein interactions, consisting of two interacting proteins. This mechanism does not consume chemical energy and is not subject to transcriptional and translational noise, in contrast to previously proposed mechanisms. We show by analytical and numerical calculations that the mechanism is robust and can have a fast, precise, and efficient response for parameters that are relevant to eukaryotic cells.

  20. Efficient fold-change detection based on protein-protein interactions

    NASA Astrophysics Data System (ADS)

    Buijsman, W.; Sheinman, M.

    2014-02-01

    Various biological sensory systems exhibit a response to a relative change of the stimulus, often referred to as fold-change detection. In the past few years, fold-change detecting mechanisms, based on transcriptional networks, have been proposed. Here we present a fold-change detecting mechanism, based on protein-protein interactions, consisting of two interacting proteins. This mechanism does not consume chemical energy and is not subject to transcriptional and translational noise, in contrast to previously proposed mechanisms. We show by analytical and numerical calculations that the mechanism is robust and can have a fast, precise, and efficient response for parameters that are relevant to eukaryotic cells.

  1. Environmental monitoring based on automatic change detection from remotely sensed data: kernel-based approach

    NASA Astrophysics Data System (ADS)

    Shah-Hosseini, Reza; Homayouni, Saeid; Safari, Abdolreza

    2015-01-01

    In the event of a natural disaster, such as a flood or earthquake, using fast and efficient methods for estimating the extent of the damage is critical. Automatic change mapping and estimating are important in order to monitor environmental changes, e.g., deforestation. Traditional change detection (CD) approaches are time consuming, user dependent, and strongly influenced by noise and/or complex spectral classes in a region. Change maps obtained by these methods usually suffer from isolated changed pixels and have low accuracy. To deal with this, an automatic CD framework-which is based on the integration of change vector analysis (CVA) technique, kernel-based C-means clustering (KCMC), and kernel-based minimum distance (KBMD) classifier-is proposed. In parallel with the proposed algorithm, a support vector machine (SVM) CD method is presented and analyzed. In the first step, a differential image is generated via two approaches in high dimensional Hilbert space. Next, by using CVA and automatically determining a threshold, the pseudo-training samples of the change and no-change classes are extracted. These training samples are used for determining the initial value of KCMC parameters and training the SVM-based CD method. Then optimizing a cost function with the nature of geometrical and spectral similarity in the kernel space is employed in order to estimate the KCMC parameters and to select the precise training samples. These training samples are used to train the KBMD classifier. Last, the class label of each unknown pixel is determined using the KBMD classifier and SVM-based CD method. In order to evaluate the efficiency of the proposed algorithm for various remote sensing images and applications, two different datasets acquired by Quickbird and Landsat TM/ETM+ are used. The results show a good flexibility and effectiveness of this automatic CD method for environmental change monitoring. In addition, the comparative analysis of results from the proposed method

  2. Molecular Recognition: Detection of Colorless Compounds Based on Color Change

    ERIC Educational Resources Information Center

    Khalafi, Lida; Kashani, Samira; Karimi, Javad

    2016-01-01

    A laboratory experiment is described in which students measure the amount of cetirizine in allergy-treatment tablets based on molecular recognition. The basis of recognition is competition of cetirizine with phenolphthalein to form an inclusion complex with ß-cyclodextrin. Phenolphthalein is pinkish under basic condition, whereas it's complex form…

  3. A segmentation-based approach to SAR change detection and mapping

    NASA Astrophysics Data System (ADS)

    Garzelli, Andrea; Zoppetti, Claudia

    2016-10-01

    The potentials of SAR sensors in change detection applications have been recently strengthened by the high spatial resolution and the short revisit time provided by the new generation SAR-based missions, such as COSMO- SkyMed, TerraSAR-X, and RadarSat 3. Classical pixel-based change detection methods exploit first-order statistics variations in multitemporal acquisitions. Higher-order statistics may improve the reliability of the results, while plain object-based change detection are rarely applied to SAR images due to the low signal-to-noise ratio which characterizes 1-look VHR SAR image products. The paper presents a hybrid approach considering both a pixel-based selection of likely-changed pixels and a segmentation-driven step based on the assumption that structural changes correspond to some clusters in a multiscale amplitude/texture representation. Experiments on simulated and true SAR image pairs demonstrate the advantages of the proposed approach.

  4. The study of target damage assessment system based on image change detection

    NASA Astrophysics Data System (ADS)

    Zhao, Ping; Yang, Fan; Feng, Xinxi

    2009-10-01

    Target Damage Assessment (TDA) system is an important component of the intelligent command and control system. The method of building TDA based on Image Change Detection can greatly improve the system efficiency and accuracy, thus get a fast and precise assessment results. This paper firstly analyzes the structure of TDA system. Then studies the key technology in this system. Finally, gives an evaluation criteria based on image change detection of the target damage assessment system.

  5. An automatic multi-lead electrocardiogram segmentation algorithm based on abrupt change detection.

    PubMed

    Illanes-Manriquez, Alfredo

    2010-01-01

    Automatic detection of electrocardiogram (ECG) waves provides important information for cardiac disease diagnosis. In this paper a new algorithm is proposed for automatic ECG segmentation based on multi-lead ECG processing. Two auxiliary signals are computed from the first and second derivatives of several ECG leads signals. One auxiliary signal is used for R peak detection and the other for ECG waves delimitation. A statistical hypothesis testing is finally applied to one of the auxiliary signals in order to detect abrupt mean changes. Preliminary experimental results show that the detected mean changes instants coincide with the boundaries of the ECG waves.

  6. Towards real-time change detection in videos based on existing 3D models

    NASA Astrophysics Data System (ADS)

    Ruf, Boitumelo; Schuchert, Tobias

    2016-10-01

    Image based change detection is of great importance for security applications, such as surveillance and reconnaissance, in order to find new, modified or removed objects. Such change detection can generally be performed by co-registration and comparison of two or more images. However, existing 3d objects, such as buildings, may lead to parallax artifacts in case of inaccurate or missing 3d information, which may distort the results in the image comparison process, especially when the images are acquired from aerial platforms like small unmanned aerial vehicles (UAVs). Furthermore, considering only intensity information may lead to failures in detection of changes in the 3d structure of objects. To overcome this problem, we present an approach that uses Structure-from-Motion (SfM) to compute depth information, with which a 3d change detection can be performed against an existing 3d model. Our approach is capable of the change detection in real-time. We use the input frames with the corresponding camera poses to compute dense depth maps by an image-based depth estimation algorithm. Additionally we synthesize a second set of depth maps, by rendering the existing 3d model from the same camera poses as those of the image-based depth map. The actual change detection is performed by comparing the two sets of depth maps with each other. Our method is evaluated on synthetic test data with corresponding ground truth as well as on real image test data.

  7. Change detection in high resolution SAR images based on multiscale texture features

    NASA Astrophysics Data System (ADS)

    Wen, Caihuan; Gao, Ziqiang

    2011-12-01

    This paper studied on change detection algorithm of high resolution (HR) Synthetic Aperture Radar (SAR) images based on multi-scale texture features. Firstly, preprocessed multi-temporal Terra-SAR images were decomposed by 2-D dual tree complex wavelet transform (DT-CWT), and multi-scale texture features were extracted from those images. Then, log-ratio operation was utilized to get difference images, and the Bayes minimum error theory was used to extract change information from difference images. Lastly, precision assessment was done. Meanwhile, we compared with the result of method based on texture features extracted from gray-level cooccurrence matrix (GLCM). We had a conclusion that, change detection algorithm based on multi-scale texture features has a great more improvement, which proves an effective method to change detect of high spatial resolution SAR images.

  8. Application of singular spectrum-based change-point analysis to EMG-onset detection.

    PubMed

    Vaisman, Lev; Zariffa, José; Popovic, Milos R

    2010-08-01

    While many approaches have been proposed to identify the signal onset in EMG recordings, there is no standardized method for performing this task. Here, we propose to use a change-point detection procedure based on singular spectrum analysis to determine the onset of EMG signals. This method is suitable for automated real-time implementation, can be applied directly to the raw signal, and does not require any prior knowledge of the EMG signal's properties. The algorithm proposed by Moskvina and Zhigljavsky (2003) was applied to EMG segments recorded from wrist and trunk muscles. Wrist EMG data was collected from 9 Parkinson's disease patients with and without tremor, while trunk EMG data was collected from 13 healthy able-bodied individuals. Along with the change-point detection analysis, two threshold-based onset detection methods were applied, as well as visual estimates of the EMG onset by trained practitioners. In the case of wrist EMG data without tremor, the change-point analysis showed comparable or superior frequency and quality of detection results, as compared to other automatic detection methods. In the case of wrist EMG data with tremor and trunk EMG data, performance suffered because other changes occurring in these signals caused larger changes in the detection statistic than the changes caused by the initial muscle activation, suggesting that additional criteria are needed to identify the onset from the detection statistic other than its magnitude alone. Once this issue is resolved, change-point detection should provide an effective EMG-onset detection method suitable for automated real-time implementation.

  9. Multipolarimetric SAR image change detection based on multiscale feature-level fusion

    NASA Astrophysics Data System (ADS)

    Sun, X.; Zhang, J.; Zhai, L.

    2015-06-01

    Many methodologies of change detection have been discussed in the literature, but most of them are tested on only optical images or traditional synthetic-aperture radar (SAR) images. Few studies have investigated multipolarimetric SAR image change detection. In this study, we presented a type of multipolarimetric SAR image change detection approach based on nonsubsampled contourlet transform and multiscale feature-level fusion techniques. In this approach, Instead of denoising an image in advance, the nonsubsampled contourlet transform multiscale decomposition was used to reduce the effect of speckle noise by processing only the low-frequency sub-band coefficients of the decomposed image, and the multiscale feature-level fusion technique was employed to integrate the rich information obtained from various polarization images. Because SAR image information is dependent on scale, a multiscale multipolarimetric feature-level fusion strategy is introduced into the change detection to improve change detection precision; this feature-level fusion can not only achieve complementation of information with different polarizations and on different scales, but also has better robustness against noise. Compared with PCA methods, the proposed method constructs better differential images, resulting in higher change detection precision.

  10. Change Detection in Synthetic Aperture Radar Images Based on Deep Neural Networks.

    PubMed

    Gong, Maoguo; Zhao, Jiaojiao; Liu, Jia; Miao, Qiguang; Jiao, Licheng

    2016-01-01

    This paper presents a novel change detection approach for synthetic aperture radar images based on deep learning. The approach accomplishes the detection of the changed and unchanged areas by designing a deep neural network. The main guideline is to produce a change detection map directly from two images with the trained deep neural network. The method can omit the process of generating a difference image (DI) that shows difference degrees between multitemporal synthetic aperture radar images. Thus, it can avoid the effect of the DI on the change detection results. The learning algorithm for deep architectures includes unsupervised feature learning and supervised fine-tuning to complete classification. The unsupervised feature learning aims at learning the representation of the relationships between the two images. In addition, the supervised fine-tuning aims at learning the concepts of the changed and unchanged pixels. Experiments on real data sets and theoretical analysis indicate the advantages, feasibility, and potential of the proposed method. Moreover, based on the results achieved by various traditional algorithms, respectively, deep learning can further improve the detection performance.

  11. Detection of a sudden change of the field time series based on the Lorenz system.

    PubMed

    Da, ChaoJiu; Li, Fang; Shen, BingLu; Yan, PengCheng; Song, Jian; Ma, DeShan

    2017-01-01

    We conducted an exploratory study of the detection of a sudden change of the field time series based on the numerical solution of the Lorenz system. First, the time when the Lorenz path jumped between the regions on the left and right of the equilibrium point of the Lorenz system was quantitatively marked and the sudden change time of the Lorenz system was obtained. Second, the numerical solution of the Lorenz system was regarded as a vector; thus, this solution could be considered as a vector time series. We transformed the vector time series into a time series using the vector inner product, considering the geometric and topological features of the Lorenz system path. Third, the sudden change of the resulting time series was detected using the sliding t-test method. Comparing the test results with the quantitatively marked time indicated that the method could detect every sudden change of the Lorenz path, thus the method is effective. Finally, we used the method to detect the sudden change of the pressure field time series and temperature field time series, and obtained good results for both series, which indicates that the method can apply to high-dimension vector time series. Mathematically, there is no essential difference between the field time series and vector time series; thus, we provide a new method for the detection of the sudden change of the field time series.

  12. Detection of a sudden change of the field time series based on the Lorenz system

    PubMed Central

    Li, Fang; Shen, BingLu; Yan, PengCheng; Song, Jian; Ma, DeShan

    2017-01-01

    We conducted an exploratory study of the detection of a sudden change of the field time series based on the numerical solution of the Lorenz system. First, the time when the Lorenz path jumped between the regions on the left and right of the equilibrium point of the Lorenz system was quantitatively marked and the sudden change time of the Lorenz system was obtained. Second, the numerical solution of the Lorenz system was regarded as a vector; thus, this solution could be considered as a vector time series. We transformed the vector time series into a time series using the vector inner product, considering the geometric and topological features of the Lorenz system path. Third, the sudden change of the resulting time series was detected using the sliding t-test method. Comparing the test results with the quantitatively marked time indicated that the method could detect every sudden change of the Lorenz path, thus the method is effective. Finally, we used the method to detect the sudden change of the pressure field time series and temperature field time series, and obtained good results for both series, which indicates that the method can apply to high-dimension vector time series. Mathematically, there is no essential difference between the field time series and vector time series; thus, we provide a new method for the detection of the sudden change of the field time series. PMID:28141832

  13. 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.

  14. Region-based automatic building and forest change detection on Cartosat-1 stereo imagery

    NASA Astrophysics Data System (ADS)

    Tian, J.; Reinartz, P.; d'Angelo, P.; Ehlers, M.

    2013-05-01

    In this paper a novel region-based method is proposed for change detection using space borne panchromatic Cartosat-1 stereo imagery. In the first step, Digital Surface Models (DSMs) from two dates are generated by semi-global matching. The geometric lateral resolution of the DSMs is 5 m × 5 m and the height accuracy is in the range of approximately 3 m (RMSE). In the second step, mean-shift segmentation is applied on the orthorectified images of two dates to obtain initial regions. A region intersection following a merging strategy is proposed to get minimum change regions and multi-level change vectors are extracted for these regions. Finally change detection is achieved by combining these features with weighted change vector analysis. The result evaluations demonstrate that the applied DSM generation method is well suited for Cartosat-1 imagery, and the extracted height values can largely improve the change detection accuracy, moreover it is shown that the proposed change detection method can be used robustly for both forest and industrial areas.

  15. Change detection from synthetic aperture radar images based on neighborhood-based ratio and extreme learning machine

    NASA Astrophysics Data System (ADS)

    Gao, Feng; Dong, Junyu; Li, Bo; Xu, Qizhi; Xie, Cui

    2016-10-01

    Change detection is of high practical value to hazard assessment, crop growth monitoring, and urban sprawl detection. A synthetic aperture radar (SAR) image is the ideal information source for performing change detection since it is independent of atmospheric and sunlight conditions. Existing SAR image change detection methods usually generate a difference image (DI) first and use clustering methods to classify the pixels of DI into changed class and unchanged class. Some useful information may get lost in the DI generation process. This paper proposed an SAR image change detection method based on neighborhood-based ratio (NR) and extreme learning machine (ELM). NR operator is utilized for obtaining some interested pixels that have high probability of being changed or unchanged. Then, image patches centered at these pixels are generated, and ELM is employed to train a model by using these patches. Finally, pixels in both original SAR images are classified by the pretrained ELM model. The preclassification result and the ELM classification result are combined to form the final change map. The experimental results obtained on three real SAR image datasets and one simulated dataset show that the proposed method is robust to speckle noise and is effective to detect change information among multitemporal SAR images.

  16. Distance metric-based forest cover change detection using MODIS time series

    NASA Astrophysics Data System (ADS)

    Huang, Xiaoman; Friedl, Mark A.

    2014-06-01

    More than 12 years of global observations are now available from NASA's Moderate Resolution Imaging Spectroradiometer (MODIS). As this time series grows, the MODIS archive provides new opportunities for identification and characterization of land cover at regional to global spatial scales and interannual to decadal temporal scales. In particular, the high temporal frequency of MODIS provides a rich basis for monitoring land cover dynamics. At the same time, the relatively coarse spatial resolution of MODIS (250-500 m) presents significant challenges for land cover change studies. In this paper, we present a distance metric-based change detection method for identifying changed pixels at annual time steps using 500 m MODIS time series data. The approach we describe uses distance metrics to measure (1) the similarity between a pixel's annual time series to annual time series for pixels of the same land cover class and (2) the similarity between annual time series from different years at the same pixel. Pre-processing, including gap-filling, smoothing and temporal subsetting of MODIS 500 m Nadir BRDF-adjusted Reflectance (NBAR) time series is essential to the success of our method. We evaluated our approach using three case studies. We first explored the ability of our method to detect change in temperate and boreal forest training sites in North America and Eurasia. We applied our method to map regional forest change in the Pacific Northwest region of the United States, and in tropical forests of the Xingu River Basin in Mato Grosso, Brazil. Results from these case studies show that the method successfully identified pixels affected by logging and fire disturbance in temperate and boreal forest sites. Change detection results in the Pacific Northwest compared well with a Landsat-based disturbance map, yielding a producer's accuracy of 85%. Assessment of change detection results for the Xingu River Basin demonstrated that detection accuracy improves as the fraction of

  17. Object-based change detection: dimension of damage in residential areas of Abu Suruj, Sudan

    NASA Astrophysics Data System (ADS)

    Demharter, Timo; Michel, Ulrich; Ehlers, Manfred; Reinartz, Peter

    2011-11-01

    Given the importance of Change Detection, especially in the field of crisis management, this paper discusses the advantage of object-based Change Detection. This project and the used methods give an opportunity to coordinate relief actions strategically. The principal objective of this project was to develop an algorithm which allows to detect rapidly damaged and destroyed buildings in the area of Abu Suruj. This Sudanese village is located in West-Darfur and has become the victim of civil war. The software eCognition Developer was used to per-form an object-based Change Detection on two panchromatic Quickbird 2 images from two different time slots. The first image shows the area before, the second image shows the area after the massacres in this region. Seeking a classification for the huts of the Sudanese town Abu Suruj was reached by first segmenting the huts and then classifying them on the basis of geo-metrical and brightness-related values. The huts were classified as "new", "destroyed" and "preserved" with the help of a automated algorithm. Finally the results were presented in the form of a map which displays the different conditions of the huts. The accuracy of the project is validated by an accuracy assessment resulting in an Overall Classification Accuracy of 90.50 percent. These change detection results allow aid organizations to provide quick and efficient help where it is needed the most.

  18. Point Cloud Based Change Detection - an Automated Approach for Cloud-based Services

    NASA Astrophysics Data System (ADS)

    Collins, Patrick; Bahr, Thomas

    2016-04-01

    The fusion of stereo photogrammetric point clouds with LiDAR data or terrain information derived from SAR interferometry has a significant potential for 3D topographic change detection. In the present case study latest point cloud generation and analysis capabilities are used to examine a landslide that occurred in the village of Malin in Maharashtra, India, on 30 July 2014, and affected an area of ca. 44.000 m2. It focuses on Pléiades high resolution satellite imagery and the Airbus DS WorldDEMTM as a product of the TanDEM-X mission. This case study was performed using the COTS software package ENVI 5.3. Integration of custom processes and automation is supported by IDL (Interactive Data Language). Thus, ENVI analytics is running via the object-oriented and IDL-based ENVITask API. The pre-event topography is represented by the WorldDEMTM product, delivered with a raster of 12 m x 12 m and based on the EGM2008 geoid (called pre-DEM). For the post-event situation a Pléiades 1B stereo image pair of the AOI affected was obtained. The ENVITask "GeneratePointCloudsByDenseImageMatching" was implemented to extract passive point clouds in LAS format from the panchromatic stereo datasets: • A dense image-matching algorithm is used to identify corresponding points in the two images. • A block adjustment is applied to refine the 3D coordinates that describe the scene geometry. • Additionally, the WorldDEMTM was input to constrain the range of heights in the matching area, and subsequently the length of the epipolar line. The "PointCloudFeatureExtraction" task was executed to generate the post-event digital surface model from the photogrammetric point clouds (called post-DEM). Post-processing consisted of the following steps: • Adding the geoid component (EGM 2008) to the post-DEM. • Pre-DEM reprojection to the UTM Zone 43N (WGS-84) coordinate system and resizing. • Subtraction of the pre-DEM from the post-DEM. • Filtering and threshold based classification of

  19. A novel method for detecting abrupt dynamic change based on the changing Hurst exponent of spatial images

    NASA Astrophysics Data System (ADS)

    He, Wen-Ping; Liu, Qun-Qun; Gu, Bin; Zhao, Shan-Shan

    2016-10-01

    The climate system is a classical spatiotemporal evolutionary dynamic system with spatiotemporal correlation characteristics. Based on this, two-dimensional detrended fluctuation analysis (TD-DFA) is used to estimate the Hurst exponent of two-dimensional images. Then, we monitored the change of the Hurst exponent of the images to identify an abrupt dynamic change. We tested the performance of this method with a coupled spatiotemporal dynamic model and found that it works well. The changes in the Hurst exponents of the spatial images are stable when there is no dynamic change in the system, but there will be a clear non-stationary change of the Hurst exponents; for example, the abrupt mean values change if the dynamics of the system change. Thus, the TD-DFA method is suitable for detecting an abrupt dynamic change from natural and artificial images. The spatial images of the NCEP reanalysis of the daily average temperature exhibited fractality. Based on this, we found three non-stationary changes in the Hurst exponents for the NCEP reanalysis of the daily average temperature or for the annual average temperature in the region (60°S-60°N). It can be concluded that the climate system may have incurred three dynamic changes since 1961 on decadal timescales, i.e., in approximately the mid-1970s, the mid-1980s, and between the late 1990s and the early 2000s.

  20. Object-oriented change detection based on weighted polarimetric scattering differences on POLSAR images

    NASA Astrophysics Data System (ADS)

    Shi, X.; Lu, L.; Yang, S.; Huang, G.; Zhao, Z.

    2015-06-01

    For wide application of change detection with SAR imagery, current processing technologies and methods are mostly based on pixels. It is difficult for pixel-based technologies to utilize spatial characteristics of images and topological relations of objects. Object-oriented technology takes objects as processing unit, which takes advantage of the shape and texture information of image. It can greatly improve the efficiency and reliability of change detection. Recently, with the development of polarimetric synthetic aperture radar (PolSAR), more backscattering features on different polarization state can be available for usage of object-oriented change detection study. In this paper, the object-oriented strategy will be employed. Considering the fact that the different target or target's state behaves different backscattering characteristics dependent on polarization state, an object-oriented change detection method that based on weighted polarimetric scattering difference of PolSAR images is proposed. The method operates on the objects generated by generalized statistical region merging (GSRM) segmentation processing. The merit of GSRM method is that image segmentation is executed on polarimetric coherence matrix, which takes full advantages of polarimetric backscattering features. And then, the measurement of polarimetric scattering difference is constructed by combining the correlation of covariance matrix and the difference of scattering power. Through analysing the effects of the covariance matrix correlation and the scattering echo power difference on the polarimetric scattering difference, the weighted method is used to balance the influences caused by the two parts, so that more reasonable weights can be chosen to decrease the false alarm rate. The effectiveness of the algorithm that proposed in this letter is tested by detection of the growth of crops with two different temporal radarsat-2 fully PolSAR data. First, objects are produced by GSRM algorithm

  1. A Fast Framework for Abrupt Change Detection Based on Binary Search Trees and Kolmogorov Statistic.

    PubMed

    Qi, Jin-Peng; Qi, Jie; Zhang, Qing

    2016-01-01

    Change-Point (CP) detection has attracted considerable attention in the fields of data mining and statistics; it is very meaningful to discuss how to quickly and efficiently detect abrupt change from large-scale bioelectric signals. Currently, most of the existing methods, like Kolmogorov-Smirnov (KS) statistic and so forth, are time-consuming, especially for large-scale datasets. In this paper, we propose a fast framework for abrupt change detection based on binary search trees (BSTs) and a modified KS statistic, named BSTKS (binary search trees and Kolmogorov statistic). In this method, first, two binary search trees, termed as BSTcA and BSTcD, are constructed by multilevel Haar Wavelet Transform (HWT); second, three search criteria are introduced in terms of the statistic and variance fluctuations in the diagnosed time series; last, an optimal search path is detected from the root to leaf nodes of two BSTs. The studies on both the synthetic time series samples and the real electroencephalograph (EEG) recordings indicate that the proposed BSTKS can detect abrupt change more quickly and efficiently than KS, t-statistic (t), and Singular-Spectrum Analyses (SSA) methods, with the shortest computation time, the highest hit rate, the smallest error, and the highest accuracy out of four methods. This study suggests that the proposed BSTKS is very helpful for useful information inspection on all kinds of bioelectric time series signals.

  2. A Fast Framework for Abrupt Change Detection Based on Binary Search Trees and Kolmogorov Statistic

    PubMed Central

    Qi, Jin-Peng; Qi, Jie; Zhang, Qing

    2016-01-01

    Change-Point (CP) detection has attracted considerable attention in the fields of data mining and statistics; it is very meaningful to discuss how to quickly and efficiently detect abrupt change from large-scale bioelectric signals. Currently, most of the existing methods, like Kolmogorov-Smirnov (KS) statistic and so forth, are time-consuming, especially for large-scale datasets. In this paper, we propose a fast framework for abrupt change detection based on binary search trees (BSTs) and a modified KS statistic, named BSTKS (binary search trees and Kolmogorov statistic). In this method, first, two binary search trees, termed as BSTcA and BSTcD, are constructed by multilevel Haar Wavelet Transform (HWT); second, three search criteria are introduced in terms of the statistic and variance fluctuations in the diagnosed time series; last, an optimal search path is detected from the root to leaf nodes of two BSTs. The studies on both the synthetic time series samples and the real electroencephalograph (EEG) recordings indicate that the proposed BSTKS can detect abrupt change more quickly and efficiently than KS, t-statistic (t), and Singular-Spectrum Analyses (SSA) methods, with the shortest computation time, the highest hit rate, the smallest error, and the highest accuracy out of four methods. This study suggests that the proposed BSTKS is very helpful for useful information inspection on all kinds of bioelectric time series signals. PMID:27413364

  3. Detection of perturbed quantization class stego images based on possible change modes

    NASA Astrophysics Data System (ADS)

    Zhang, Yi; Liu, Fenlin; Yang, Chunfang; Luo, Xiangyang; Song, Xiaofeng

    2015-11-01

    To improve the detection performance for perturbed quantization (PQ) class [PQ, energy-adaptive PQ (PQe), and texture-adaptive PQ (PQt)] stego images, a detection method based on possible change modes is proposed. First, by using the relationship between the changeable coefficients used for carrying secret messages and the second quantization steps, the modes having even second quantization steps are identified as possible change modes. Second, by referencing the existing features, the modified features that can accurately capture the embedding changes based on possible change modes are extracted. Next, feature sensitivity analyses based on the modifications performed before and after the embedding are carried out. These analyses show that the modified features are more sensitive to the original features. Experimental results indicate that detection performance of the modified features is better than that of the corresponding original features for three typical feature models [Cartesian calibrated PEVny (ccPEV), Cartesian calibrated co-occurrence matrix features (CF), and JPEG rich model (JRM)], and the integrated feature consisting of enhanced histogram features (EHF) and the modified JRM outperforms two current state-of-the-art feature models, namely, phase aware projection model (PHARM) and Gabor rich model (GRM).

  4. Trajectory-Based Analysis of Urban Land-Cover Change Detection

    NASA Astrophysics Data System (ADS)

    Zhang, Y. H.; Liu, H. P.

    2016-06-01

    China have occurred unprecedented urban growth over the last two decades. It is reported that the level of China's urbanization increased from 18 % in 1978 to 41 % in 2003, and this figure may exceed 65 % by 2050. The change detection of long time serious remote sensing images is the effective way to acquire the data of urban land-cover change to understand the pattern of urbanization. In this paper, we proposed the similarity index (SI) and apply it in long time series urban land-cover change detection. First of all, we built possible change trajectories in four times based on the normalized difference vegetation index (NDVI) and modified normalized difference water index (MNDWI) that extracted from time series Landsat images. Secondly, we applied SI in similarity comparison between the observed change trajectory and the possible trajectories. Lastly, verifying the accuracy of the results. The overall accuracy in four periods is 85.7 % and the overall accuracy of each two years is about 90 % and kappa statistic is about 0.85. The results show that this method is effective for time series land-cover change detection.

  5. Object-Based Change Detection Using High-Resolution Remotely Sensed Data and GIS

    NASA Astrophysics Data System (ADS)

    Sofina, N.; Ehlers, M.

    2012-08-01

    High resolution remotely sensed images provide current, detailed, and accurate information for large areas of the earth surface which can be used for change detection analyses. Conventional methods of image processing permit detection of changes by comparing remotely sensed multitemporal images. However, for performing a successful analysis it is desirable to take images from the same sensor which should be acquired at the same time of season, at the same time of a day, and - for electro-optical sensors - in cloudless conditions. Thus, a change detection analysis could be problematic especially for sudden catastrophic events. A promising alternative is the use of vector-based maps containing information about the original urban layout which can be related to a single image obtained after the catastrophe. The paper describes a methodology for an object-based search of destroyed buildings as a consequence of a natural or man-made catastrophe (e.g., earthquakes, flooding, civil war). The analysis is based on remotely sensed and vector GIS data. It includes three main steps: (i) generation of features describing the state of buildings; (ii) classification of building conditions; and (iii) data import into a GIS. One of the proposed features is a newly developed 'Detected Part of Contour' (DPC). Additionally, several features based on the analysis of textural information corresponding to the investigated vector objects are calculated. The method is applied to remotely sensed images of areas that have been subjected to an earthquake. The results show the high reliability of the DPC feature as an indicator for change.

  6. Change detection in synthetic aperture radar images based on image fusion and fuzzy clustering.

    PubMed

    Gong, Maoguo; Zhou, Zhiqiang; Ma, Jingjing

    2012-04-01

    This paper presents an unsupervised distribution-free change detection approach for synthetic aperture radar (SAR) images based on an image fusion strategy and a novel fuzzy clustering algorithm. The image fusion technique is introduced to generate a difference image by using complementary information from a mean-ratio image and a log-ratio image. In order to restrain the background information and enhance the information of changed regions in the fused difference image, wavelet fusion rules based on an average operator and minimum local area energy are chosen to fuse the wavelet coefficients for a low-frequency band and a high-frequency band, respectively. A reformulated fuzzy local-information C-means clustering algorithm is proposed for classifying changed and unchanged regions in the fused difference image. It incorporates the information about spatial context in a novel fuzzy way for the purpose of enhancing the changed information and of reducing the effect of speckle noise. Experiments on real SAR images show that the image fusion strategy integrates the advantages of the log-ratio operator and the mean-ratio operator and gains a better performance. The change detection results obtained by the improved fuzzy clustering algorithm exhibited lower error than its preexistences.

  7. Empirical likelihood based detection procedure for change point in mean residual life functions under random censorship.

    PubMed

    Chen, Ying-Ju; Ning, Wei; Gupta, Arjun K

    2016-05-01

    The mean residual life (MRL) function is one of the basic parameters of interest in survival analysis that describes the expected remaining time of an individual after a certain age. The study of changes in the MRL function is practical and interesting because it may help us to identify some factors such as age and gender that may influence the remaining lifetimes of patients after receiving a certain surgery. In this paper, we propose a detection procedure based on the empirical likelihood for the changes in MRL functions with right censored data. Two real examples are also given: Veterans' administration lung cancer study and Stanford heart transplant to illustrate the detecting procedure. Copyright © 2016 John Wiley & Sons, Ltd.

  8. Urban Change Detection of Pingtan City based on Bi-temporal Remote Sensing Images

    NASA Astrophysics Data System (ADS)

    Degang, JIANG; Jinyan, XU; Yikang, GAO

    2017-02-01

    In this paper, a pair of SPOT 5-6 images with the resolution of 0.5m is selected. An object-oriented classification method is used to the two images and five classes of ground features were identified as man-made objects, farmland, forest, waterbody and unutilized land. An auxiliary ASTER GDEM was used to improve the classification accuracy. And the change detection based on the classification results was performed. Accuracy assessment was carried out finally. Consequently, satisfactory results were obtained. The results show that great changes of the Pingtan city have been detected as the expansion of the city area and the intensity increase of man-made buildings, roads and other infrastructures with the establishment of Pingtan comprehensive experimental zone. Wide range of open sea area along the island coast zones has been reclaimed for port and CBDs construction.

  9. Distance metric-based forest cover change detection using MODIS time series

    NASA Astrophysics Data System (ADS)

    Huang, X.; Friedl, M. A.; Sulla-menashe, D. J.

    2012-12-01

    More than a decade of global observations are now available from NASA's Moderate Resolution Imaging Spectroradiometer (MODIS). The MODIS archive provides new opportunities for identification and characterization of land cover change processes at regional to global scales. In particular, the high temporal frequency of MODIS data provides new opportunities for remotely sensed land cover change detection. At the same time, the relatively coarse spatial resolution of MODIS (250-500m) presents significant challenges for land cover change studies. In this paper we present a simple yet effective distance metric-based change detection method for identifying changed pixels at annual time steps from MODIS time series data. The approach we have developed uses statistical metrics that measure the within-year distance of a pixel to pixels of the same land cover class, and the between-year distance between measurements acquired at two points in time. Both metrics make explicit use of annual MODIS time series at each pixel. The former metric measures the similarity of a pixel's temporal profile to the average multi-year baseline time series, and the latter measures the similarity of the pixel's temporal profile before and after the year of interest. We evaluate our approach using two case studies for data from 2003-2010. In the first case study we apply our method to detect sites that have experienced land cover change in the training site database used to create the MODIS land cover product, focusing on forested land cover sites. In the second case study we examine forest conversion in the Xingu River Basin in Mato Grosso, Brazil. We also describe the pre-processing steps necessary to apply our method, including gap-filling, smoothing and temporal sub-setting of MODIS Nadir BRDF-adjusted Reflectance (NBAR) time series. These steps are crucial for baseline characterization of forest classes in both case studies, but require refinements specific to each dataset. Results show that

  10. Change detection of bitemporal multispectral images based on FCM and D-S theory

    NASA Astrophysics Data System (ADS)

    Shi, Aiye; Gao, Guirong; Shen, Shaohong

    2016-12-01

    In this paper, we propose a change detection method of bitemporal multispectral images based on the D-S theory and fuzzy c-means (FCM) algorithm. Firstly, the uncertainty and certainty regions are determined by thresholding method applied to the magnitudes of difference image (MDI) and spectral angle information (SAI) of bitemporal images. Secondly, the FCM algorithm is applied to the MDI and SAI in the uncertainty region, respectively. Then, the basic probability assignment (BPA) functions of changed and unchanged classes are obtained by the fuzzy membership values from the FCM algorithm. In addition, the optimal value of fuzzy exponent of FCM is adaptively determined by conflict degree between the MDI and SAI in uncertainty region. Finally, the D-S theory is applied to obtain the new fuzzy partition matrix for uncertainty region and further the change map is obtained. Experiments on bitemporal Landsat TM images and bitemporal SPOT images validate that the proposed method is effective.

  11. Change detection based on features invariant to monotonic transforms and spatially constrained matching

    NASA Astrophysics Data System (ADS)

    Rodrigues, Marco Túlio A. N.; Balbino de Mesquita, Daniel; Nascimento, Erickson R.; Schwartz, William Robson

    2016-01-01

    In several image processing applications, discovering regions that have changed in a set of images acquired from a scene at different times and possibly from different viewpoints plays a very important role. Remote sensing, visual surveillance, medical diagnosis, civil infrastructure, and underwater sensing are examples of such applications that operate in dynamic environments. We propose an approach to detect such changes automatically by using image analysis techniques and segmentation based on superpixels in two stages: (1) the tuning stage, which is focused on adjusting the parameters; and (2) the unsupervised stage that is executed in real scenarios without an appropriate ground truth. Unlike most common approaches, which are pixel-based, our approach combines superpixel extraction, hierarchical clustering, and segment matching. Experimental results demonstrate the effectiveness of the proposed approach compared to a remote sensing technique and a background subtraction technique, demonstrating the robustness of our algorithm against illumination variations.

  12. Detecting Unidentified Changes

    PubMed Central

    Howe, Piers D. L.; Webb, Margaret E.

    2014-01-01

    Does becoming aware of a change to a purely visual stimulus necessarily cause the observer to be able to identify or localise the change or can change detection occur in the absence of identification or localisation? Several theories of visual awareness stress that we are aware of more than just the few objects to which we attend. In particular, it is clear that to some extent we are also aware of the global properties of the scene, such as the mean luminance or the distribution of spatial frequencies. It follows that we may be able to detect a change to a visual scene by detecting a change to one or more of these global properties. However, detecting a change to global property may not supply us with enough information to accurately identify or localise which object in the scene has been changed. Thus, it may be possible to reliably detect the occurrence of changes without being able to identify or localise what has changed. Previous attempts to show that this can occur with natural images have produced mixed results. Here we use a novel analysis technique to provide additional evidence that changes can be detected in natural images without also being identified or localised. It is likely that this occurs by the observers monitoring the global properties of the scene. PMID:24454727

  13. Object-Oriented Change Detection Based on Multi-Scale Approach

    NASA Astrophysics Data System (ADS)

    Jia, Yonghong; Zhou, Mingting; Jinshan, Ye

    2016-06-01

    The change detection of remote sensing images means analysing the change information quantitatively and recognizing the change types of the surface coverage data in different time phases. With the appearance of high resolution remote sensing image, object-oriented change detection method arises at this historic moment. In this paper, we research multi-scale approach for high resolution images, which includes multi-scale segmentation, multi-scale feature selection and multi-scale classification. Experimental results show that this method has a stronger advantage than the traditional single-scale method of high resolution remote sensing image change detection.

  14. Change detection in satellite images

    NASA Astrophysics Data System (ADS)

    Thonnessen, U.; Hofele, G.; Middelmann, W.

    2005-05-01

    Change detection plays an important role in different military areas as strategic reconnaissance, verification of armament and disarmament control and damage assessment. It is the process of identifying differences in the state of an object or phenomenon by observing it at different times. The availability of spaceborne reconnaissance systems with high spatial resolution, multi spectral capabilities, and short revisit times offer new perspectives for change detection. Before performing any kind of change detection it is necessary to separate changes of interest from changes caused by differences in data acquisition parameters. In these cases it is necessary to perform a pre-processing to correct the data or to normalize it. Image registration and, corresponding to this task, the ortho-rectification of the image data is a further prerequisite for change detection. If feasible, a 1-to-1 geometric correspondence should be aspired for. Change detection on an iconic level with a succeeding interpretation of the changes by the observer is often proposed; nevertheless an automatic knowledge-based analysis delivering the interpretation of the changes on a semantic level should be the aim of the future. We present first results of change detection on a structural level concerning urban areas. After pre-processing, the images are segmented in areas of interest and structural analysis is applied to these regions to extract descriptions of urban infrastructure like buildings, roads and tanks of refineries. These descriptions are matched to detect changes and similarities.

  15. Land use change detection based on multi-date imagery from different satellite sensor systems

    NASA Technical Reports Server (NTRS)

    Stow, Douglas A.; Collins, Doretta; Mckinsey, David

    1990-01-01

    An empirical study is conducted to assess the accuracy of land use change detection using satellite image data acquired ten years apart by sensors with differing spatial resolutions. The primary goals of the investigation were to (1) compare standard change detection methods applied to image data of varying spatial resolution, (2) assess whether to transform the raster grid of the higher resolution image data to that of the lower resolution raster grid or vice versa in the registration process, (3) determine if Landsat/Thermatic Mapper or SPOT/High Resolution Visible multispectral data provide more accurate detection of land use changes when registered to historical Landsat/MSS data. It is concluded that image ratioing of multisensor, multidate satellite data produced higher change detection accuracies than did principal components analysis, and that it is useful as a land use change enhancement method.

  16. Interactive Change Detection Using High Resolution Remote Sensing Images Based on Active Learning with Gaussian Processes

    NASA Astrophysics Data System (ADS)

    Ru, Hui; Yu, Huai; Huang, Pingping; Yang, Wen

    2016-06-01

    Although there have been many studies for change detection, the effective and efficient use of high resolution remote sensing images is still a problem. Conventional supervised methods need lots of annotations to classify the land cover categories and detect their changes. Besides, the training set in supervised methods often has lots of redundant samples without any essential information. In this study, we present a method for interactive change detection using high resolution remote sensing images with active learning to overcome the shortages of existing remote sensing image change detection techniques. In our method, there is no annotation of actual land cover category at the beginning. First, we find a certain number of the most representative objects in unsupervised way. Then, we can detect the change areas from multi-temporal high resolution remote sensing images by active learning with Gaussian processes in an interactive way gradually until the detection results do not change notably. The artificial labelling can be reduced substantially, and a desirable detection result can be obtained in a few iterations. The experiments on Geo-Eye1 and WorldView2 remote sensing images demonstrate the effectiveness and efficiency of our proposed method.

  17. Wavelet-based detection of abrupt changes in natural frequencies of time-variant systems

    NASA Astrophysics Data System (ADS)

    Dziedziech, K.; Staszewski, W. J.; Basu, B.; Uhl, T.

    2015-12-01

    Detection of abrupt changes in natural frequencies from vibration responses of time-variant systems is a challenging task due to the complex nature of physics involved. It is clear that the problem needs to be analysed in the combined time-frequency domain. The paper proposes an application of the input-output wavelet-based Frequency Response Function for this analysis. The major focus and challenge relate to ridge extraction of the above time-frequency characteristics. It is well known that classical ridge extraction procedures lead to ridges that are smooth. However, this property is not desired when abrupt changes in the dynamics are considered. The methods presented in the paper are illustrated using simulated and experimental multi-degree-of-freedom systems. The results are compared with the classical Frequency Response Function and with the output only analysis based on the wavelet auto-power response spectrum. The results show that the proposed method captures correctly the dynamics of the analysed time-variant systems.

  18. Object based change detection of Central Asian Tugai vegetation with very high spatial resolution satellite imagery

    NASA Astrophysics Data System (ADS)

    Gärtner, Philipp; Förster, Michael; Kurban, Alishir; Kleinschmit, Birgit

    2014-09-01

    Ecological restoration of degraded riparian Tugai forests in north-western China is a key driver to combat desertification in this region. Recent restoration efforts attempt to recover the forest along with its most dominant tree species, Populus euphratica. The present research observed the response of natural vegetation using an object based change detection method on QuickBird (2005) and WorldView2 (2011) data. We applied the region growing approach to derived Normalized Difference Vegetation Index (NDVI) values in order to identify single P. euphratica trees, delineate tree crown areas and quantify crown diameter changes. Results were compared to 59 reference trees. The findings confirmed a positive tree crown growth and suggest a crown diameter increase of 1.14 m, on average. On a single tree basis, tree crown diameters of larger crowns were generally underestimated. Small crowns were slightly underestimated in QuickBird and overestimated in Worldview2 images. The results of the automated tree crown delineation show a moderate relation to field reference data with R20052: 0.36 and R20112: 0.48. The object based image analysis (OBIA) method proved to be applicable in sparse riparian Tugai forests and showed great suitability to evaluate ecological restoration efforts in an endangered ecosystem.

  19. SAR-based change detection using hypothesis testing and Markov random field modelling

    NASA Astrophysics Data System (ADS)

    Cao, W.; Martinis, S.

    2015-04-01

    The objective of this study is to automatically detect changed areas caused by natural disasters from bi-temporal co-registered and calibrated TerraSAR-X data. The technique in this paper consists of two steps: Firstly, an automatic coarse detection step is applied based on a statistical hypothesis test for initializing the classification. The original analytical formula as proposed in the constant false alarm rate (CFAR) edge detector is reviewed and rewritten in a compact form of the incomplete beta function, which is a builtin routine in commercial scientific software such as MATLAB and IDL. Secondly, a post-classification step is introduced to optimize the noisy classification result in the previous step. Generally, an optimization problem can be formulated as a Markov random field (MRF) on which the quality of a classification is measured by an energy function. The optimal classification based on the MRF is related to the lowest energy value. Previous studies provide methods for the optimization problem using MRFs, such as the iterated conditional modes (ICM) algorithm. Recently, a novel algorithm was presented based on graph-cut theory. This method transforms a MRF to an equivalent graph and solves the optimization problem by a max-flow/min-cut algorithm on the graph. In this study this graph-cut algorithm is applied iteratively to improve the coarse classification. At each iteration the parameters of the energy function for the current classification are set by the logarithmic probability density function (PDF). The relevant parameters are estimated by the method of logarithmic cumulants (MoLC). Experiments are performed using two flood events in Germany and Australia in 2011 and a forest fire on La Palma in 2009 using pre- and post-event TerraSAR-X data. The results show convincing coarse classifications and considerable improvement by the graph-cut post-classification step.

  20. [Building Change Detection Based on Multi-Level Rules Classification with Airborne LiDAR Data and Aerial Images].

    PubMed

    Gong, Yi-long; Yan, Li

    2015-05-01

    The present paper proposes a new building change detection method combining Lidar point cloud with aerial image, using multi-level rules classification algorithm, to solve building change detection problem between these two kinds of heterogeneous data. Then, a morphological post-processing method combined with area threshold is proposed. Thus, a complete building change detection processing flow that can be applied to actual production is proposed. Finally, the effectiveness of the building change detection method is evaluated, processing the 2010 airborne LiDAR point cloud data and 2009 high resolution aerial image of Changchun City, Jilin province, China; in addition, compared with the object-oriented building change detection method based on support vector machine (SVM) classification, more analysis and evaluation of the suggested method is given. Experiment results show that the performance of the proposed building change detection method is ideal. Its Kappa index is 0. 90, and correctness is 0. 87, which is higher than the object-oriented building change detection method based on SVM classification.

  1. Closed-form density-based framework for automatic detection of cellular morphology changes

    PubMed Central

    Duong, Tarn; Goud, Bruno; Schauer, Kristine

    2012-01-01

    A primary method for studying cellular function is to examine cell morphology after a given manipulation. Fluorescent markers attached to proteins/intracellular structures of interest in conjunction with 3D fluorescent microscopy are frequently exploited for functional analysis. Despite the central role of morphology comparisons in cell biological approaches, few statistical tools are available that allow biological scientists without a high level of statistical training to quantify the similarity or difference of fluorescent images containing multifactorial information. We transform intracellular structures into kernels and develop a multivariate two-sample test that is nonparametric and asymptotically normal to directly and quantitatively compare cellular morphologies. The asymptotic normality bypasses the computationally intensive calculations used by the usual resampling techniques to compute the P-value. Because all parameters required for the statistical test are estimated directly from the data, it does not require any subjective decisions. Thus, we provide a black-box method for unbiased, automated comparison of cell morphology. We validate the performance of our test statistic for finite synthetic samples and experimental data. Employing our test for the comparison of the morphology of intracellular multivesicular bodies, we detect changes in their distribution after disruption of the cellular microtubule cytoskeleton with high statistical significance in fixed samples and live cell analysis. These results demonstrate that density-based comparison of multivariate image information is a powerful tool for automated detection of cell morphology changes. Moreover, the underlying mathematics of our test statistic is a general technique, which can be applied in situations where two data samples are compared. PMID:22586080

  2. An Unsupervised Change Detection Based on Test Statistic and KI from Multi-Temporal and Full Polarimetric SAR Images

    NASA Astrophysics Data System (ADS)

    Zhao, J. Q.; Yang, J.; Li, P. X.; Liu, M. Y.; Shi, Y. M.

    2016-06-01

    Accurate and timely change detection of Earth's surface features is extremely important for understanding relationships and interactions between people and natural phenomena. Many traditional methods of change detection only use a part of polarization information and the supervised threshold selection. Those methods are insufficiency and time-costing. In this paper, we present a novel unsupervised change-detection method based on quad-polarimetric SAR data and automatic threshold selection to solve the problem of change detection. First, speckle noise is removed for the two registered SAR images. Second, the similarity measure is calculated by the test statistic, and automatic threshold selection of KI is introduced to obtain the change map. The efficiency of the proposed method is demonstrated by the quad-pol SAR images acquired by Radarsat-2 over Wuhan of China.

  3. Effect of topographic correction on forest change detection using spectral trend analysis of Landsat pixel-based composites

    NASA Astrophysics Data System (ADS)

    Chance, Curtis M.; Hermosilla, Txomin; Coops, Nicholas C.; Wulder, Michael A.; White, Joanne C.

    2016-02-01

    Pixel-based image compositing enables production of large-area surface reflectance images that are largely devoid of clouds, cloud shadows, or haze. Change detection with spectral trend analysis uses a dense time series of images, such as pixel-based composites, to quantify the year, amount, and magnitude of landscape changes. Topographically-related shadows found in mountainous terrain may confound trend-based forest change detection approaches. In this study, we evaluate the impact of topographic correction on trend-based forest change detection outcomes by comparing the amount and location of changes identified on an image composite with and without a topographic correction. Moreover, we evaluated two different approaches to topographic correction that are relevant to pixel-based image composites: the first corrects each pixel according to the day of year (DOY) the pixel was acquired, whilst the second corrects all pixels to a single reference date (August 1st), which was also the target date for generating the pixel-based image composite. Our results indicate that a greater area of change is detected when no topographic correction is applied to the image composite, however, the difference in change area detected between no correction and either the DOY or the August 1st correction is minor and less than 1% (0.54-0.85%). The spatial correspondence of these different approaches is 96.2% for the DOY correction and 97.7% for the August 1st correction. The largest differences between the correction processes occur in valleys (0.71-1.14%), upper slopes (0.71-1.09%), and ridges (0.73-1.09%). While additional tests under different conditions and in other environments are encouraged, our results indicate that topographic correction may not be justified in change detection routines computing spectral trends from pixel-based composites.

  4. Unsupervised change detection in VHR remote sensing imagery - an object-based clustering approach in a dynamic urban environment

    NASA Astrophysics Data System (ADS)

    Leichtle, Tobias; Geiß, Christian; Wurm, Michael; Lakes, Tobia; Taubenböck, Hannes

    2017-02-01

    Monitoring of changes is one of the most important inherent capabilities of remote sensing. The steadily increasing amount of available very-high resolution (VHR) remote sensing imagery requires highly automatic methods and thus, largely unsupervised concepts for change detection. In addition, new procedures that address this challenge should be capable of handling remote sensing data acquired by different sensors. Thereby, especially in rapidly changing complex urban environments, the high level of detail present in VHR data indicates the deployment of object-based concepts for change detection. This paper presents a novel object-based approach for unsupervised change detection with focus on individual buildings. First, a principal component analysis together with a unique procedure for determination of the number of relevant principal components is performed as a predecessor for change detection. Second, k-means clustering is applied for discrimination of changed and unchanged buildings. In this manner, several groups of object-based difference features that can be derived from multi-temporal VHR data are evaluated regarding their discriminative properties for change detection. In addition, the influence of deviating viewing geometries when using VHR data acquired by different sensors is quantified. Overall, the proposed workflow returned viable results in the order of κ statistics of 0.8-0.9 and beyond for different groups of features, which demonstrates its suitability for unsupervised change detection in dynamic urban environments. With respect to imagery from different sensors, deviating viewing geometries were found to deteriorate the change detection result only slightly in the order of up to 0.04 according to κ statistics, which underlines the robustness of the proposed approach.

  5. 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.

  6. The regularized iteratively reweighted object-based MAD method for change detection in bi-temporal, multispectral data

    NASA Astrophysics Data System (ADS)

    Xu, Qiangqiang; Liu, Zhengjun; Li, Fangfang; Yang, Mingze; Ren, Haicheng

    2016-10-01

    As the resolution for multispectral images continuously improving, the phenomena that "synonyms spectrum", "foreign body with spectrum" become more apparently, therefore, in the results for pixel-based iteratively reweighted multivariate alteration detection (IR -MAD) may appear many issues such as broken patch, much pseudo-change, more noise, the low overall detection rate et al. In order to improving the above problems. In this paper, the pixel-based IR-MAD algorithm can be transferred to the object domain. the object-based IR-MAD(OB_IRMAD) method apply different and meaningful combinations of features rather than the original pixels. these features are classified into meaningful combinations, so that the results for object-based change detection can get higher reliability and accuracy. To stabilize solutions to the IR-MAD problem, some of regularization may be needed. A case with ZY-3 multispectral image at one point in time in province of Xinjiang border port demonstrate the effectiveness and feasibility of the OB_IRMAD. Compared as, using the same date and region we do the pixel-level IR-MAD change detection and artificial visual change detection. Finally, we calculate the various evaluation indexes for accuracy utilizing confuse matrix and compare the accuracy of the two detection results. The results show: in the ways of overall accuracy, correct rate, error rate, the OB_IRMAD is better than pixel-level IRMAD, change polygon more rules and indicates a less noisy.

  7. Automatic co-registration of space-based sensors for precision change detection and analysis

    NASA Technical Reports Server (NTRS)

    Bryant, N.; Zobrist, A.; Logan, T.

    2003-01-01

    A variety of techniques were developed at JPL to assure sub-pixel co-registration of scenes and ortho-rectification of satellite imagery to other georeferenced information to permit precise change detection and analysis of low and moderate resolution space sensors.

  8. SAR change detection MTI

    NASA Astrophysics Data System (ADS)

    Scarborough, Steven; Lemanski, Christopher; Nichols, Howard; Owirka, Gregory; Minardi, Michael; Hale, Todd

    2006-05-01

    This paper examines the theory, application, and results of using single-channel synthetic aperture radar (SAR) data with Moving Reference Processing (MRP) to focus and geolocate moving targets. Moving targets within a standard SAR imaging scene are defocused, displaced, or completely missing in the final image. Building on previous research at AFRL, the SAR-MRP method focuses and geolocates moving targets by reprocessing the SAR data to focus the movers rather than the stationary clutter. SAR change detection is used so that target detection and focusing is performed more robustly. In the cases where moving target returns possess the same range versus slow-time histories, a geolocation ambiguity results. This ambiguity can be resolved in a number of ways. This paper concludes by applying the SAR-MRP method to high-frequency radar measurements from persistent continuous-dwell SAR observations of a moving target.

  9. Conditionally fluorescent molecular probes for detecting single base changes in double-stranded DNA.

    PubMed

    Chen, Sherry Xi; Zhang, David Yu; Seelig, Georg

    2013-09-01

    Small variations in nucleic acid sequences can have far-reaching phenotypic consequences. Reliably distinguishing closely related sequences is therefore important for research and clinical applications. Here, we demonstrate that conditionally fluorescent DNA probes are capable of distinguishing variations of a single base in a stretch of target DNA. These probes use a novel programmable mechanism in which each single nucleotide polymorphism generates two thermodynamically destabilizing mismatch bubbles rather than the single mismatch formed during typical hybridization-based assays. Up to a 12,000-fold excess of a target that contains a single nucleotide polymorphism is required to generate the same fluorescence as one equivalent of the intended target, and detection works reliably over a wide range of conditions. Using these probes we detected point mutations in a 198 base-pair subsequence of the Escherichia coli rpoB gene. That our probes are constructed from multiple oligonucleotides circumvents synthesis limitations and enables long continuous DNA sequences to be probed.

  10. Conditionally fluorescent molecular probes for detecting single base changes in double-stranded DNA

    NASA Astrophysics Data System (ADS)

    Chen, Sherry Xi; Zhang, David Yu; Seelig, Georg

    2013-09-01

    Small variations in nucleic acid sequences can have far-reaching phenotypic consequences. Reliably distinguishing closely related sequences is therefore important for research and clinical applications. Here, we demonstrate that conditionally fluorescent DNA probes are capable of distinguishing variations of a single base in a stretch of target DNA. These probes use a novel programmable mechanism in which each single nucleotide polymorphism generates two thermodynamically destabilizing mismatch bubbles rather than the single mismatch formed during typical hybridization-based assays. Up to a 12,000-fold excess of a target that contains a single nucleotide polymorphism is required to generate the same fluorescence as one equivalent of the intended target, and detection works reliably over a wide range of conditions. Using these probes we detected point mutations in a 198 base-pair subsequence of the Escherichia coli rpoB gene. That our probes are constructed from multiple oligonucleotides circumvents synthesis limitations and enables long continuous DNA sequences to be probed.

  11. SAR Object Change Detection Study.

    DTIC Science & Technology

    1980-03-01

    based techniques when applied to Synthetic Aperature Radar (SAR imagery. DOUGLA 3. PRASKA, 2LT, USAF Project Engineer viii Section 1 INTRODUCTION AND...to assess the applicability of three region-based change-detection methods to synthetic aperture radar imagery. I/ Ac .0ion For K:CTAB [ ft i . i...Section 2, the algorithms developed were applied to synthetic -aperture radar image data furnished by RADC. Some preprocessing of all images was required

  12. Change Detection: Training and Transfer

    PubMed Central

    Gaspar, John G.; Neider, Mark B.; Simons, Daniel J.; McCarley, Jason S.; Kramer, Arthur F.

    2013-01-01

    Observers often fail to notice even dramatic changes to their environment, a phenomenon known as change blindness. If training could enhance change detection performance in general, then it might help to remedy some real-world consequences of change blindness (e.g. failing to detect hazards while driving). We examined whether adaptive training on a simple change detection task could improve the ability to detect changes in untrained tasks for young and older adults. Consistent with an effective training procedure, both young and older adults were better able to detect changes to trained objects following training. However, neither group showed differential improvement on untrained change detection tasks when compared to active control groups. Change detection training led to improvements on the trained task but did not generalize to other change detection tasks. PMID:23840775

  13. MIRANDA 35 GHz SAR Based Non-Coherent Change Detection

    NASA Astrophysics Data System (ADS)

    Noetel, Denis; Kloeppel, Frank; Sieger, Stefan; Janssen, Daniel; Pohl, Nils

    2017-03-01

    Up to now, the high effort involved with airborne Synthetic Aperture Radar (SAR) systems has led to the effect that SAR image processing is mostly restricted to single images of a scene. Recent developments like the MIRANDA 35 and its matching flight platform allow for frequent revisits of the same scene at very high repeatability. MIRANDA 35 has been designed as a lightweight and compact SAR sensor for altitudes of up to 1 km while providing a resolution of 15 cm. In combination with a highly flexible ultralight aircraft we have recently obtained 28 measurements of a scene within 2 h of flight time at very high repeatability. Two sets of flight courses, each with 7 flyovers of the reference scene and 7 of a modified change scene, are investigated here using statistical change detection methods like Informational Difference and Universal Image Quality Index.

  14. MODIS 250m burned area mapping based on an algorithm using change point detection and Markov random fields.

    NASA Astrophysics Data System (ADS)

    Mota, Bernardo; Pereira, Jose; Campagnolo, Manuel; Killick, Rebeca

    2013-04-01

    Area burned in tropical savannas of Brazil was mapped using MODIS-AQUA daily 250m resolution imagery by adapting one of the European Space Agency fire_CCI project burned area algorithms, based on change point detection and Markov random fields. The study area covers 1,44 Mkm2 and was performed with data from 2005. The daily 1000 m image quality layer was used for cloud and cloud shadow screening. The algorithm addresses each pixel as a time series and detects changes in the statistical properties of NIR reflectance values, to identify potential burning dates. The first step of the algorithm is robust filtering, to exclude outlier observations, followed by application of the Pruned Exact Linear Time (PELT) change point detection technique. Near-infrared (NIR) spectral reflectance changes between time segments, and post change NIR reflectance values are combined into a fire likelihood score. Change points corresponding to an increase in reflectance are dismissed as potential burn events, as are those occurring outside of a pre-defined fire season. In the last step of the algorithm, monthly burned area probability maps and detection date maps are converted to dichotomous (burned-unburned maps) using Markov random fields, which take into account both spatial and temporal relations in the potential burned area maps. A preliminary assessment of our results is performed by comparison with data from the MODIS 1km active fires and the 500m burned area products, taking into account differences in spatial resolution between the two sensors.

  15. Change detection based on deep feature representation and mapping transformation for multi-spatial-resolution remote sensing images

    NASA Astrophysics Data System (ADS)

    Zhang, Puzhao; Gong, Maoguo; Su, Linzhi; Liu, Jia; Li, Zhizhou

    2016-06-01

    Multi-spatial-resolution change detection is a newly proposed issue and it is of great significance in remote sensing, environmental and land use monitoring, etc. Though multi-spatial-resolution image-pair are two kinds of representations of the same reality, they are often incommensurable superficially due to their different modalities and properties. In this paper, we present a novel multi-spatial-resolution change detection framework, which incorporates deep-architecture-based unsupervised feature learning and mapping-based feature change analysis. Firstly, we transform multi-resolution image-pair into the same pixel-resolution through co-registration, followed by details recovery, which is designed to remedy the spatial details lost in the registration. Secondly, the denoising autoencoder is stacked to learn local and high-level representation/feature from the local neighborhood of the given pixel, in an unsupervised fashion. Thirdly, motivated by the fact that multi-resolution image-pair share the same reality in the unchanged regions, we try to explore the inner relationships between them by building a mapping neural network. And it can be used to learn a mapping function based on the most-unlikely-changed feature-pairs, which are selected from all the feature-pairs via a coarse initial change map generated in advance. The learned mapping function can bridge the different representations and highlight changes. Finally, we can build a robust and contractive change map through feature similarity analysis, and the change detection result is obtained through the segmentation of the final change map. Experiments are carried out on four real datasets, and the results confirmed the effectiveness and superiority of the proposed method.

  16. Space-based detection of wetlands' surface water level changes from L-band SAR interferometry

    USGS Publications Warehouse

    Wdowinski, S.; Kim, S.-W.; Amelung, F.; Dixon, T.H.; Miralles-Wilhelm, F.; Sonenshein, R.

    2008-01-01

    Interferometric processing of JERS-1 L-band Synthetic Aperture Radar (SAR) data acquired over south Florida during 1993-1996 reveals detectable surface changes in the Everglades wetlands. Although our study is limited to south Florida it has implication for other large-scale wetlands, because south Florida wetlands have diverse vegetation types and both managed and natural flow environments. Our analysis reveals that interferometric coherence level is sensitive to wetland vegetation type and to the interferogram time span. Interferograms with time spans less than six months maintain phase observations for all wetland types, allowing characterization of water level changes in different wetland environments. The most noticeable changes occur between the managed and the natural flow wetlands. In the managed wetlands, fringes are organized, follow patterns related to some of the managed water control structures and have high fringe-rate. In the natural flow areas, fringes are irregular and have a low fringe-rate. The high fringe rate in managed areas reflects dynamic water topography caused by high flow rate due to gate operation. Although this organized fringe pattern is not characteristic of most large-scale wetlands, the high level of water level change enables accurate estimation of the wetland InSAR technique, which lies in the range of 5-10??cm. The irregular and low rate fringe pattern in the natural flow area reflects uninterrupted flow that diffuses water efficiently and evenly. Most of the interferograms in the natural flow area show an elongated fringe located along the transitional zone between salt- and fresh-water wetlands, reflecting water level changes due to ocean tides. ?? 2007 Elsevier Inc. All rights reserved.

  17. Researches on the Land-Use Change Detection of Mine Area Based on Tm/etm Images

    NASA Astrophysics Data System (ADS)

    Qin, Y.; Liling, H.; Min, Y.; Yi, L.

    2013-07-01

    With the economic development and population growth, land use status is changing rapidly in Chinese urban. Since the remote sensing technology can analyze and detect the land use information quickly and accurately, it has been widely applied to obtain the land use/land cover change (LUCC) information today. The land surface destroy occurred in mineral resources exploration will result in a lot of environmental problems in coal mine areas. But there are few research in small and medium-sized cities and coal mine areas. So Peixian is used as the study area in the paper. The Landsat TM/ETM images spanning 3 years and thematic map are adopted to detect the land-use change of the area. In order to improve the classification results, we built an optimized classification model adapting classic SVM method, which was defined "a feature weighted SVM classifier using mixed kernel function". Based on geostatistic and multi-scale statistical knowledge, we calculated the transformation matrix and dynamic index of land-use types, from which we conducted quantitative analysis and the driving force on the mine land-use change of Peixian. Then, we can achieve resource dynamic change detection of four years in Peixian area, analyze the effect of the surface land-use change due to mineral exploration and obtain the causes of land-use change.

  18. Electrophysiological correlates of change detection.

    PubMed

    Eimer, Martin; Mazza, Veronica

    2005-05-01

    To identify electrophysiological correlates of change detection, event-related brain potentials (ERPs) were recorded while participants monitored displays containing four faces in order to detect a face identity change across successive displays. Successful change detection was mirrored by an N2pc component at posterior electrodes contralateral to the side of a change, suggesting close links between conscious change detection and attention. ERPs on undetected-change trials differed from detected-change and no-change trials. We suggest that short-latency ERP differences between these trial types reflect trial-by-trial fluctuations in advance task preparation, whereas differences in the P3 time range are due to variations in the duration of perceptual and decision-related processing. Overall, these findings demonstrate that ERPs are a useful tool for dissociating processes underlying change blindness and change detection.

  19. 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.

  20. 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

  1. Land cover change detection in Chinese Zhejiang Province based on object-oriented approach

    NASA Astrophysics Data System (ADS)

    Liu, Dong; Zhua, Qiankun; Li, Yan

    2016-10-01

    Many factors such as geomorphic features, economic development, expansion of cities, the implementation of new policy, etc. are changing land cover. Therefore, it is necessary to monitor Land-Use and Land-Cover Change (LUCC) by using new technologies and methods at high tempo-spatial resolution. Based on one supervised classification approach combining object-oriented method and binary decision tree, this study mapped land cover of Zhejiang Province, China, at the scale of 1:250000 in 2000, 2005, and 2010. After image segmentation, object features such as normalized difference vegetation index (NDVI), modified normalized difference water index (MNDWI), area, the ratio of length to width, density, etc. were applied to assign each object to specific class. Being verified through confusion matrix, the mapping results were satisfactory. Taking land cover map in 2010 as an example, the lowest user accuracy was 84.14%, with the average of 92.15%; The lowest production accuracy was 62.00%, with the average was 86.69%; The overall accuracy was 0.8928; The Kappa coefficient was 0.8752. Under the influence of geomorphic features and economic development, changes of land cover in Zhejiang were mainly distributed in the areas with lower elevation and higher GDP from 2000 to 2010. Under the influence of natural factors and human activities, many croplands and wetlands were lost from 2000 to 2010. For croplands, there were 2106.608 km2 croplands changed into other types from 2000 to 2005, and 1897.809 km2 from 2005 to 2010. Most of croplands were changed into artificial lands, with 1520.601km2 from 2000 to 2005 and 1446.826 km2 from 2005 to 2010. For wetland, there were 209.085 km2 wetlands changed into other types from 2000 to 2005, and 292.975 km2 from 2005 to 2010. Most of wetlands were changed into croplands, with 134.652 km2 from 2000 to 2005 and 122.979 km2 from 2005 to 2010.

  2. An ontology-based annotation of cardiac implantable electronic devices to detect therapy changes in a national registry.

    PubMed

    Rosier, Arnaud; Mabo, Philippe; Chauvin, Michel; Burgun, Anita

    2015-05-01

    The patient population benefitting from cardiac implantable electronic devices (CIEDs) is increasing. This study introduces a device annotation method that supports the consistent description of the functional attributes of cardiac devices and evaluates how this method can detect device changes from a CIED registry. We designed the Cardiac Device Ontology, an ontology of CIEDs and device functions. We annotated 146 cardiac devices with this ontology and used it to detect therapy changes with respect to atrioventricular pacing, cardiac resynchronization therapy, and defibrillation capability in a French national registry of patients with implants (STIDEFIX). We then analyzed a set of 6905 device replacements from the STIDEFIX registry. Ontology-based identification of therapy changes (upgraded, downgraded, or similar) was accurate (6905 cases) and performed better than straightforward analysis of the registry codes (F-measure 1.00 versus 0.75 to 0.97). This study demonstrates the feasibility and effectiveness of ontology-based functional annotation of devices in the cardiac domain. Such annotation allowed a better description and in-depth analysis of STIDEFIX. This method was useful for the automatic detection of therapy changes and may be reused for analyzing data from other device registries.

  3. 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

  4. A Method Based on Radiative Cooling for Detecting Structural Changes in Undercooled Metallic Liquids

    NASA Technical Reports Server (NTRS)

    Rulison, Aaron J.; Rhim, Won-Kyu

    1995-01-01

    We introduce a structure-sensitive parameter for undercooled melts which can be measured in containerless processing experiments. We have established that the ratio, R(T), of hemispherical total emissivity epsilon(sub T)(T) to constant-pressure specific heat c(sub p)(T) can serve as an indicator which is sensitive to any changes in short range atomic order in undercooled metallic melts. R(T) (triple bonds) epsilon(sub T)(T)/c(sub p)(T) values for nickel, zirconium, and silicon have been obtained using the high temperature electrostatic levitator while the levitated melts were undergoing purely radiative cooling into the deeply undercooled region. R(T) plots for undercooled liquid nickel and zirconium indicate no significant change in short-range structure from their melting temperatures to 15% undercooling. In contrast, liquid silicon shows marked short-range structural changes beginning above its melting temperature and extending throughout the undercooled region. The short-range structure of liquid silicon is related to the highly-directional covalent bonding which characterizes its solid form. The nickel and zirconium data show that epsilon(sub T) varies linearly with T, in support of metal emissivity theories.

  5. Optimum electrode configuration selection for electrical resistance change based damage detection in composites using an effective independence measure

    NASA Astrophysics Data System (ADS)

    Escalona, Luis; Díaz-Montiel, Paulina; Venkataraman, Satchi

    2016-04-01

    Laminated carbon fiber reinforced polymer (CFRP) composite materials are increasingly used in aerospace structures due to their superior mechanical properties and reduced weight. Assessing the health and integrity of these structures requires non-destructive evaluation (NDE) techniques to detect and measure interlaminar delamination and intralaminar matrix cracking damage. The electrical resistance change (ERC) based NDE technique uses the inherent changes in conductive properties of the composite to characterize internal damage. Several works that have explored the ERC technique have been limited to thin cross-ply laminates with simple linear or circular electrode arrangements. This paper investigates a method of optimum selection of electrode configurations for delamination detection in thick cross-ply laminates using ERC. Inverse identification of damage requires numerical optimization of the measured response with a model predicted response. Here, the electrical voltage field in the CFRP composite laminate is calculated using finite element analysis (FEA) models for different specified delamination size and locations, and location of ground and current electrodes. Reducing the number of sensor locations and measurements is needed to reduce hardware requirements, and computational effort needed for inverse identification. This paper explores the use of effective independence (EI) measure originally proposed for sensor location optimization in experimental vibration modal analysis. The EI measure is used for selecting the minimum set of resistance measurements among all possible combinations of selecting a pair of electrodes among the n electrodes. To enable use of EI to ERC required, it is proposed in this research a singular value decomposition SVD to obtain a spectral representation of the resistance measurements in the laminate. The effectiveness of EI measure in eliminating redundant electrode pairs is demonstrated by performing inverse identification of

  6. A Critique of Patch-Based Landscape Indicators for Detection of Temporal Change in Fragmentation

    EPA Science Inventory

    Since O’Neill et al. (1988), analysis of landscape indicators based on measurements from land-cover maps has been a core area of research in landscape ecology. Landscape indicator research has focused on development of new measurements, statistical properties, and indictor behav...

  7. Manmade target extraction based on multistage decision and its application for change detection in polarimetric synthetic aperture radar image

    NASA Astrophysics Data System (ADS)

    Cong, Runmin; Han, Ping; Li, Chongyi; He, Jiaji; Zhang, Zaiji

    2016-09-01

    Targets of interest are different in various applications in which manmade targets, such as aircraft, ships, and buildings, are given more attention. Manmade target extraction methods using synthetic aperture radar (SAR) images are designed in response to various demands, which include civil uses, business purposes, and military industries. This plays an increasingly vital role in monitoring, military reconnaissance, and precision strikes. Achieving accurate and complete results through traditional methods is becoming more challenging because of the scattered complexity of polarization in polarimetric synthetic aperture radar (PolSAR) image. A multistage decision-based method is proposed composed of power decision, dominant scattering mechanism decision, and reflection symmetry decision. In addition, the theories of polarimetric contrast enhancement, generalized Y decomposition, and maximum eigenvalue ratio are applied to assist the decision. Fully PolSAR data are adopted to evaluate and verify the approach. Experimental results show that the method can achieve an effective result with a lower false alarm rate and clear contours. Finally, on this basis, a universal framework of change detection for manmade targets is presented as an application of our method. Two sets of measured data are also used to evaluate and verify the effectiveness of the change-detection algorithm.

  8. Detecting Driver Mental Fatigue Based on EEG Alpha Power Changes during Simulated Driving

    PubMed Central

    GHARAGOZLOU, Faramarz; NASL SARAJI, Gebraeil; MAZLOUMI, Adel; NAHVI, Ali; MOTIE NASRABADI, Ali; RAHIMI FOROUSHANI, Abbas; ARAB KHERADMAND, Ali; ASHOURI, Mohammadreza; SAMAVATI, Mehdi

    2015-01-01

    Background: Driver fatigue is one of the major implications in transportation safety and accounted for up to 40% of road accidents. This study aimed to analyze the EEG alpha power changes in partially sleep-deprived drivers while performing a simulated driving task. Methods: Twelve healthy male car drivers participated in an overnight study. Continuous EEG and EOG records were taken during driving on a virtual reality simulator on a monotonous road. Simultaneously, video recordings from the driver face and behavior were performed in lateral and front views and rated by two trained observers. Moreover, the subjective self-assessment of fatigue was implemented in every 10-min interval during the driving using Fatigue Visual Analog Scale (F-VAS). Power spectrum density and fast Fourier transform (FFT) were used to determine the absolute and relative alpha powers in the initial and final 10 minutes of driving. Results: The findings showed a significant increase in the absolute alpha power (P = 0.006) as well as F-VAS scores during the final section of driving (P = 0.001). Meanwhile, video ratings were consistent with subjective self-assessment of fatigue. Conclusion: The increase in alpha power in the final section of driving indicates the decrease in the level of alertness and attention and the onset of fatigue, which was consistent with F-VAS and video ratings. The study suggested that variations in alpha power could be a good indicator for driver mental fatigue, but for using as a countermeasure device needed further investigations. PMID:26811821

  9. Image Change Detection via Ensemble Learning

    SciTech Connect

    Martin, Benjamin W; Vatsavai, Raju

    2013-01-01

    The concept of geographic change detection is relevant in many areas. Changes in geography can reveal much information about a particular location. For example, analysis of changes in geography can identify regions of population growth, change in land use, and potential environmental disturbance. A common way to perform change detection is to use a simple method such as differencing to detect regions of change. Though these techniques are simple, often the application of these techniques is very limited. Recently, use of machine learning methods such as neural networks for change detection has been explored with great success. In this work, we explore the use of ensemble learning methodologies for detecting changes in bitemporal synthetic aperture radar (SAR) images. Ensemble learning uses a collection of weak machine learning classifiers to create a stronger classifier which has higher accuracy than the individual classifiers in the ensemble. The strength of the ensemble lies in the fact that the individual classifiers in the ensemble create a mixture of experts in which the final classification made by the ensemble classifier is calculated from the outputs of the individual classifiers. Our methodology leverages this aspect of ensemble learning by training collections of weak decision tree based classifiers to identify regions of change in SAR images collected of a region in the Staten Island, New York area during Hurricane Sandy. Preliminary studies show that the ensemble method has approximately 11.5% higher change detection accuracy than an individual classifier.

  10. Luminescence detection of cysteine based on Ag⁺-mediated conformational change of terbium ion-promoted G-quadruplex.

    PubMed

    Tan, Hongliang; Tang, Gonge; Ma, Chanjiao; Li, Qian

    2016-02-18

    In this work, we developed a simple and sensitive method for the detection of cysteine (Cys) by employing terbium ion (Tb(3+))-promoted G-qudraplex (G4/Tb) as a luminescent probe, which is based on Ag(+)-mediated conformational change of G4/Tb. Due to Ag(+) is able to compete with Tb(3+) to bind guanine at G4, the presence of Ag(+) can lead to the formation of G4/Tb-Ag(+) complex and disrupt the structure of G4/Tb. Meanwhile, the binding of Ag(+) with G4/Tb will also cause the alteration of the excited state of G4 and more efficient energy transfer from G4 to Tb(3+), enhancing the luminescence of G4/Tb. However, upon the addition of Cys, Ag(+) will be released from G4/Tb-Ag(+) complex because of the high affinity of Cys to Ag(+). This results in the re-formation of the conformation of G4/Tb and the decrease of the luminescence of G4/Tb. So, Ag(+)-enhanced luminescence of G4/Tb is associated with its conformational transformation. As a luminescent probe for Cys, G4/Tb not only shows excellent selectivity and sensitivity with a detection limit of 20 nM, but also possesses the features of simple preparation, easy reproducibility, and eliminating the interferences from background fluorescence. We envision that the presented strategy might provide new insight into the biosensing applications of lanthanide complex.

  11. Comparison of pixel -based and artificial neural networks classification methods for detecting forest cover changes in Malaysia

    NASA Astrophysics Data System (ADS)

    Deilmai, B. R.; Kanniah, K. D.; Rasib, A. W.; Ariffin, A.

    2014-02-01

    According to the FAO (Food and Agriculture Organization), Malaysia lost 8.6% of its forest cover between 1990 and 2005. In forest cover change detection, remote sensing plays an important role. A lot of change detection methods have been developed, and most of them are semi-automated. These methods are time consuming and difficult to apply. One of the new and robust methods for change detection is artificial neural network (ANN). In this study, (ANN) classification scheme is used to detect the forest cover changes in the Johor state in Malaysia. Landsat Thematic Mapper images covering a period of 9 years (2000 and 2009) are used. Results obtained with ANN technique was compared with Maximum likelihood classification (MLC) to investigate whether ANN can perform better in the tropical environment. Overall accuracy of the ANN and MLC techniques are 75%, 68% (2000) and 80%, 75% (2009) respectively. Using the ANN method, it was found that forest area in Johor decreased as much as 1298 km2 between 2000 and 2009. The results also showed the potential and advantages of neural network in classification and change detection analysis.

  12. Evaluation of experimental UAV video change detection

    NASA Astrophysics Data System (ADS)

    Bartelsen, J.; Saur, G.; Teutsch, C.

    2016-10-01

    During the last ten years, the availability of images acquired from unmanned aerial vehicles (UAVs) has been continuously increasing due to the improvements and economic success of flight and sensor systems. From our point of view, reliable and automatic image-based change detection may contribute to overcoming several challenging problems in military reconnaissance, civil security, and disaster management. Changes within a scene can be caused by functional activities, i.e., footprints or skid marks, excavations, or humidity penetration; these might be recognizable in aerial images, but are almost overlooked when change detection is executed manually. With respect to the circumstances, these kinds of changes may be an indication of sabotage, terroristic activity, or threatening natural disasters. Although image-based change detection is possible from both ground and aerial perspectives, in this paper we primarily address the latter. We have applied an extended approach to change detection as described by Saur and Kr uger,1 and Saur et al.2 and have built upon the ideas of Saur and Bartelsen.3 The commercial simulation environment Virtual Battle Space 3 (VBS3) is used to simulate aerial "before" and "after" image acquisition concerning flight path, weather conditions and objects within the scene and to obtain synthetic videos. Video frames, which depict the same part of the scene, including "before" and "after" changes and not necessarily from the same perspective, are registered pixel-wise against each other by a photogrammetric concept, which is based on a homography. The pixel-wise registration is used to apply an automatic difference analysis, which, to a limited extent, is able to suppress typical errors caused by imprecise frame registration, sensor noise, vegetation and especially parallax effects. The primary concern of this paper is to seriously evaluate the possibilities and limitations of our current approach for image-based change detection with respect

  13. Detecting changes in ultrasound backscattered statistics by using Nakagami parameters: Comparisons of moment-based and maximum likelihood estimators.

    PubMed

    Lin, Jen-Jen; Cheng, Jung-Yu; Huang, Li-Fei; Lin, Ying-Hsiu; Wan, Yung-Liang; Tsui, Po-Hsiang

    2017-05-01

    The Nakagami distribution is an approximation useful to the statistics of ultrasound backscattered signals for tissue characterization. Various estimators may affect the Nakagami parameter in the detection of changes in backscattered statistics. In particular, the moment-based estimator (MBE) and maximum likelihood estimator (MLE) are two primary methods used to estimate the Nakagami parameters of ultrasound signals. This study explored the effects of the MBE and different MLE approximations on Nakagami parameter estimations. Ultrasound backscattered signals of different scatterer number densities were generated using a simulation model, and phantom experiments and measurements of human liver tissues were also conducted to acquire real backscattered echoes. Envelope signals were employed to estimate the Nakagami parameters by using the MBE, first- and second-order approximations of MLE (MLE1 and MLE2, respectively), and Greenwood approximation (MLEgw) for comparisons. The simulation results demonstrated that, compared with the MBE and MLE1, the MLE2 and MLEgw enabled more stable parameter estimations with small sample sizes. Notably, the required data length of the envelope signal was 3.6 times the pulse length. The phantom and tissue measurement results also showed that the Nakagami parameters estimated using the MLE2 and MLEgw could simultaneously differentiate various scatterer concentrations with lower standard deviations and reliably reflect physical meanings associated with the backscattered statistics. Therefore, the MLE2 and MLEgw are suggested as estimators for the development of Nakagami-based methodologies for ultrasound tissue characterization.

  14. Object-based change detection in rapid urbanization regions with remotely sensed observations: a case study of Shenzhen, China

    NASA Astrophysics Data System (ADS)

    He, Lihuang; Dong, Guihua; Wang, Wei-Min; Yang, Lijun; Liang, Hong

    2013-10-01

    China, the most populous country on Earth, has experienced rapid urbanization which is one of the main causes of many environmental and ecological problems. Therefore, the monitoring of rapid urbanization regions and the environment is of critical importance for their sustainable development. In this study, the object-based classification is employed to detect the change of land cover in Shenzhen, which is located in South China and has been urbanized rapidly in recent three decades. First, four Landsat TM images, which were acquired on 1990, 2000 and 2010, respectively, are selected from the image database. Atmospheric corrections are conducted on these images with improved dark-object subtraction technique and surface meteorological observations. Geometric correction is processed with ground control points derived from topographic maps. Second, a region growing multi-resolution segmentation and a soft nearest neighbour classifier are used to finish object-based classification. After analyzing the fraction of difference classes over time series, we conclude that the comparison of derived land cover classes with socio-economic statistics demonstrates the strong positive correlation between built-up classes and urban population as well as gross GDP and GDPs in second and tertiary industries. Two different mechanisms of urbanization, namely new land development and redevelopment, are revealed. Consequently, we found that, the districts of Shenzhen were urbanized through different mechanisms.

  15. 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.

  16. Land-cover change detection

    USGS Publications Warehouse

    Chen, Xuexia; Giri, Chandra; Vogelmann, James

    2012-01-01

    Land cover is the biophysical material on the surface of the earth. Land-cover types include grass, shrubs, trees, barren, water, and man-made features. Land cover changes continuously.  The rate of change can be either dramatic and abrupt, such as the changes caused by logging, hurricanes and fire, or subtle and gradual, such as regeneration of forests and damage caused by insects (Verbesselt et al., 2001).  Previous studies have shown that land cover has changed dramatically during the past sevearal centuries and that these changes have severely affected our ecosystems (Foody, 2010; Lambin et al., 2001). Lambin and Strahlers (1994b) summarized five types of cause for land-cover changes: (1) long-term natural changes in climate conditions, (2) geomorphological and ecological processes, (3) human-induced alterations of vegetation cover and landscapes, (4) interannual climate variability, and (5) human-induced greenhouse effect.  Tools and techniques are needed to detect, describe, and predict these changes to facilitate sustainable management of natural resources.

  17. Imaging, object detection, and change detection with a polarized multistatic GPR array

    SciTech Connect

    Beer, N. Reginald; Paglieroni, David W.

    2015-07-21

    A polarized detection system performs imaging, object detection, and change detection factoring in the orientation of an object relative to the orientation of transceivers. The polarized detection system may operate on one of several modes of operation based on whether the imaging, object detection, or change detection is performed separately for each transceiver orientation. In combined change mode, the polarized detection system performs imaging, object detection, and change detection separately for each transceiver orientation, and then combines changes across polarizations. In combined object mode, the polarized detection system performs imaging and object detection separately for each transceiver orientation, and then combines objects across polarizations and performs change detection on the result. In combined image mode, the polarized detection system performs imaging separately for each transceiver orientation, and then combines images across polarizations and performs object detection followed by change detection on the result.

  18. Anomalous change detection in imagery

    DOEpatents

    Theiler, James P.; Perkins, Simon J.

    2011-05-31

    A distribution-based anomaly detection platform is described that identifies a non-flat background that is specified in terms of the distribution of the data. A resampling approach is also disclosed employing scrambled resampling of the original data with one class specified by the data and the other by the explicit distribution, and solving using binary classification.

  19. A wavelet-based approach to detect climate change on the coherent and turbulent component of the atmospheric circulation

    NASA Astrophysics Data System (ADS)

    Faranda, Davide; Defrance, Dimitri

    2016-06-01

    The modifications of atmospheric circulation induced by anthropogenic effects are difficult to capture because wind fields feature a complex spectrum where the signal of large-scale coherent structures (planetary, baroclinic waves and other long-term oscillations) is mixed up with turbulence. Our purpose is to study the effects of climate changes on these two components separately by applying a wavelet analysis to the 700 hPa wind fields obtained in climate simulations for different forcing scenarios. We study the coherent component of the signal via a correlation analysis to detect the persistence of large-scale or long-lasting structures, whereas we use the theory of autoregressive moving-average stochastic processes to measure the spectral complexity of the turbulent component. Under strong anthropogenic forcing, we detect a significant climate change signal. The analysis suggests that coherent structures will play a dominant role in future climate, whereas turbulent spectra will approach a classical Kolmogorov behaviour.

  20. Investigation of ionospheric TEC changes related to the 2008 Wenchuan earthquake based on statistic analysis and signal detection

    NASA Astrophysics Data System (ADS)

    Li, Jianyong; Meng, Guojie; Wang, Min; Liao, Hua; Shen, Xuhui

    2009-10-01

    Ionospheric TEC (total electron content) time series are derived from GPS measurements at 13 stations around the epicenter of the 2008 Wenchuan earthquake. Defining anomaly bounds for a sliding window by quartile and 2-standard deviation of TEC values, this paper analyzed the characteristics of ionospheric changes before and after the destructive event. The Neyman-Pearson signal detection method is employed to compute the probabilities of TEC abnormalities. Result shows that one week before the Wenchuan earthquake, ionospheric TEC over the epicenter and its vicinities displays obvious abnormal disturbances, most of which are positive anomalies. The largest TEC abnormal changes appeared on May 9, three days prior to the seismic event. Signal detection shows that the largest possibility of TEC abnormity on May 9 is 50.74%, indicating that ionospheric abnormities three days before the main shock are likely related to the preparation process of the M S8.0 Wenchuan earthquake.

  1. Parametric probability distributions for anomalous change detection

    SciTech Connect

    Theiler, James P; Foy, Bernard R; Wohlberg, Brendt E; Scovel, James C

    2010-01-01

    The problem of anomalous change detection arises when two (or possibly more) images are taken of the same scene, but at different times. The aim is to discount the 'pervasive differences' that occur thoughout the imagery, due to the inevitably different conditions under which the images were taken (caused, for instance, by differences in illumination, atmospheric conditions, sensor calibration, or misregistration), and to focus instead on the 'anomalous changes' that actually take place in the scene. In general, anomalous change detection algorithms attempt to model these normal or pervasive differences, based on data taken directly from the imagery, and then identify as anomalous those pixels for which the model does not hold. For many algorithms, these models are expressed in terms of probability distributions, and there is a class of such algorithms that assume the distributions are Gaussian. By considering a broader class of distributions, however, a new class of anomalous change detection algorithms can be developed. We consider several parametric families of such distributions, derive the associated change detection algorithms, and compare the performance with standard algorithms that are based on Gaussian distributions. We find that it is often possible to significantly outperform these standard algorithms, even using relatively simple non-Gaussian models.

  2. Detecting Change in Longitudinal Social Networks

    DTIC Science & Technology

    2011-01-01

    marketing campaigns and media on social behavior. Initial Construct populations, social and knowledge networks, can be hypothetical or real (Carley...patent data bases, phone-networks, email- based-networks, social- media networks and more. Page 6 of 37 Current methods of change detection in...CUSUM C Sta measured fo o be successf Average Bet ct either incre or each socia g increases in the data for fective for ch ork. tistic Over Tim

  3. On Radar Resolution in Coherent Change Detection.

    SciTech Connect

    Bickel, Douglas L.

    2015-11-01

    It is commonly observed that resolution plays a role in coherent change detection. Although this is the case, the relationship of the resolution in coherent change detection is not yet defined . In this document, we present an analytical method of evaluating this relationship using detection theory. Specifically we examine the effect of resolution on receiver operating characteristic curves for coherent change detection.

  4. Detection of land-cover and land-use change in the Hohe Tauern National Park, Austria) using an object-based classification approach: first results

    NASA Astrophysics Data System (ADS)

    Kern, Katharina; Sulzer, Wolfgang; Lieb, Gerhard K.

    2009-09-01

    Land-cover and land-use (LCLU) change reflects environmental as well as social changes. High-mountain environments such as the Hohe Tauern Range (Austria; its National Park are very sensitive to these changes. Remotely sensed data are ideally suited to detect and analyze land-cover and land-use changes on different spatial and temporal scales. In this study a Landsat Thematic Mapper (TM) time-series from 1988 to 2003, aerial photographs and auxiliary GIS data were used detect land-cover and land-use changes in the National Park and its surroundings. The objective of the study was to develop and test a transferable method for change detection in comparable high-mountain environments. After applying a series of pre-processing steps, object-based image analysis, on the basis of Definiens Developer, was used to extracted land-cover and land-use information from the Landsat images. To generate meaningful image objects, a multiresolutions segmentation algorithm was combined with image object merging. The resultant image objects were then classified by using a hierarchically organized classification method. In a further step the resultant outputs of the land-cover and landuse classifications build the basis for the post-classification change detection. Moreover the advantages and disadvantages of the developed method, the first results as well as future adaptations of the method and applications are discussed in this paper.

  5. Spatial-Temporal Detection of Changes on the Southern Coast of the Baltic Sea Based on Multitemporal Aerial Photographs

    NASA Astrophysics Data System (ADS)

    Michalowska, K.; Glowienka, E.; Pekala, A.

    2016-06-01

    Digital photogrammetry and remote sensing solutions applied under the project and combined with the geographical information system made it possible to utilize data originating from various sources and dating back to different periods. Research works made use of archival and up-to-date aerial images, satellite images, orthophotomaps. Multitemporal data served for mapping and monitoring intermediate conditions of the Baltic Sea shore zone without a need for a direct interference in the environment. The main objective of research was to determine the dynamics and volume of sea shore changes along the selected part of coast in the period of 1951-2004, and to assess the tendencies of shore development in that area. For each of the six annual data sets, the following were determined: front dune base line, water line and the beach width. The location of the dune base line, which reflects the course of the shoreline in a given year was reconstructed based on stereoscopic study of images from each annual set. Unidirectional changes in the period of 1951-2004 occurred only within 10% of the examined shore section length. The examined shore is marked by a high and considerable dynamics of changes. Almost half of the shore, in particular the middle coast shows big changes, in excess of 2 m/year. The limits of shoreline changes ranged from 120 to -90 m, and their velocity from 0 to 11 m/year, save that the middle and west parts of the examined coast section were subjected to definitely more intense shore transformations. Research based on the analysis of multitemporal aerial images made it possible to reconstruct the intermediate conditions of the Baltic Sea shoreline and determine the volume and rate of changes in the location of dune base line in the examined period of 53 years, and to find out tendencies of shore development and dynamics.

  6. Efficient ensemble system based on the copper binding motif for highly sensitive and selective detection of cyanide ions in 100% aqueous solutions by fluorescent and colorimetric changes.

    PubMed

    Jung, Kwan Ho; Lee, Keun-Hyeung

    2015-09-15

    A peptide-based ensemble for the detection of cyanide ions in 100% aqueous solutions was designed on the basis of the copper binding motif. 7-Nitro-2,1,3-benzoxadiazole-labeled tripeptide (NBD-SSH, NBD-SerSerHis) formed the ensemble with Cu(2+), leading to a change in the color of the solution from yellow to orange and a complete decrease of fluorescence emission. The ensemble (NBD-SSH-Cu(2+)) sensitively and selectively detected a low concentration of cyanide ions in 100% aqueous solutions by a colorimetric change as well as a fluorescent change. The addition of cyanide ions instantly removed Cu(2+) from the ensemble (NBD-SSH-Cu(2+)) in 100% aqueous solutions, resulting in a color change of the solution from orange to yellow and a "turn-on" fluorescent response. The detection limits for cyanide ions were lower than the maximum allowable level of cyanide ions in drinking water set by the World Health Organization. The peptide-based ensemble system is expected to be a potential and practical way for the detection of submicromolar concentrations of cyanide ions in 100% aqueous solutions.

  7. Nationwide Hybrid Change Detection of Buildings

    NASA Astrophysics Data System (ADS)

    Hron, V.; Halounova, L.

    2016-06-01

    The Fundamental Base of Geographic Data of the Czech Republic (hereinafter FBGD) is a national 2D geodatabase at a 1:10,000 scale with more than 100 geographic objects. This paper describes the design of the permanent updating mechanism of buildings in FBGD. The proposed procedure belongs to the category of hybrid change detection (HCD) techniques which combine pixel-based and object-based evaluation. The main sources of information for HCD are cadastral information and bi-temporal vertical digital aerial photographs. These photographs have great information potential because they contain multispectral, position and also elevation information. Elevation information represents a digital surface model (DSM) which can be obtained using the image matching technique. Pixel-based evaluation of bi-temporal DSMs enables fast localization of places with potential building changes. These coarse results are subsequently classified through the object-based image analysis (OBIA) using spectral, textural and contextual features and GIS tools. The advantage of the two-stage evaluation is the pre-selection of locations where image segmentation (a computationally demanding part of OBIA) is performed. It is not necessary to apply image segmentation to the entire scene, but only to the surroundings of detected changes, which contributes to significantly faster processing and lower hardware requirements. The created technology is based on open-source software solutions that allow easy portability on multiple computers and parallelization of processing. This leads to significant savings of financial resources which can be expended on the further development of FBGD.

  8. Automated Change Detection for Synthetic Aperture Sonar

    DTIC Science & Technology

    2014-01-01

    analysis ( CCA ). The method was tested using data collected with a high-frequency SAS in a sandy shallow-water environment. By using precise co...coherent-based change detection results using canonical correlation analysis ( CCA ) described by Azimi-Sadjadi and Srinivasan,18 G-Michael and Tucker15 and...Sternlicht and G-Michael,19 where the preliminary studies were performed on simulated SAR and SAS imagery. The motivation behind CCA comes from recent

  9. Total least squares for anomalous change detection

    SciTech Connect

    Theiler, James P; Matsekh, Anna M

    2010-01-01

    A family of difference-based anomalous change detection algorithms is derived from a total least squares (TLSQ) framework. This provides an alternative to the well-known chronochrome algorithm, which is derived from ordinary least squares. In both cases, the most anomalous changes are identified with the pixels that exhibit the largest residuals with respect to the regression of the two images against each other. The family of TLSQ-based anomalous change detectors is shown to be equivalent to the subspace RX formulation for straight anomaly detection, but applied to the stacked space. However, this family is not invariant to linear coordinate transforms. On the other hand, whitened TLSQ is coordinate invariant, and furthermore it is shown to be equivalent to the optimized covariance equalization algorithm. What whitened TLSQ offers, in addition to connecting with a common language the derivations of two of the most popular anomalous change detection algorithms - chronochrome and covariance equalization - is a generalization of these algorithms with the potential for better performance.

  10. Development of a HOG-based real-time PCR method to detect stress response changes in mycotoxigenic moulds.

    PubMed

    Rodríguez, Alicia; Medina, Ángel; Córdoba, Juan J; Magan, Naresh

    2016-08-01

    There is a need to understand the mechanism of adaptation of toxigenic fungal species which are able to colonise highly specialised foods such as cured meats where there is a high osmotic stress due to the presence up to 20-22% NaCl during the ripening process. A new tool able to detect changes in stress related genes would be useful to understand the ecological reasons for the ability of these species to grow in specialised niches. In this work a real-time PCR (qPCR) using SYBR Green was developed. Primers were designed from the Hog1 gene involved in osmo-adaptation in fungi. For this, conserved regions resulting from the alignment of 26 published partial sequences of such gene were used. Specificity of primers HogF2/R2 was demonstrated when amplified, producing a unique 131-bp PCR product with a Tm value of 84 °C. The qPCR method showed an efficiency of 98%, R(2) value > 0.99 and a detection limit of 0.7 log Hog1 gene copies. The qPCR method to measure changes in the Hog1 gene expression in relation to growth in ionic and non-ionic stressed environments (using 10-40% NaCl and sorbitol concentrations) was found to be suitable for two mycotoxigenic species (Penicillium nordicum, P. expansum). This assay will be a valuable tool for generating relevant Hog1 expression data from different mould species in relation to different stresses in food habitats. It will also be a good tool for a better understanding of the ability of xerophilic and xerotolerant species to colonise extreme environments.

  11. Description and validation of a new set of object-based temporal geostatistical features for land-use/land-cover change detection

    NASA Astrophysics Data System (ADS)

    Gil-Yepes, Jose L.; Ruiz, Luis A.; Recio, Jorge A.; Balaguer-Beser, Ángel; Hermosilla, Txomin

    2016-11-01

    A new set of temporal features derived from codispersion and cross-semivariogram geostatistical functions is proposed, described, extracted, and evaluated for object-based land-use/land-cover change detection using high resolution images. Five features were extracted from the codispersion function and another six from the cross-semivariogram. The set of features describes the temporal behaviour of the internal structure of the image objects defined in a cadastral database. The set of extracted features was combined with spectral information and a feature selection study was performed using forward stepwise discriminant analysis, principal component analysis, as well as correlation and feature interpretation analysis. The temporal feature set was validated using high resolution aerial images from an agricultural area located in south-east Spain, in order to solve a tree crop change detection problem. Direct classification using decision tree classifier was used as change detection method. Different classifications were performed comparing various feature group combinations in order to obtain the most suitable features for this study. Results showed that the new sets of cross-semivariogram and codispersion features provided high global accuracy classification results (83.55% and 85.71% respectively), showing high potential for detecting changes related to the internal structure of agricultural tree crop parcels. A significant increase in accuracy value was obtained when combining features from both groups with spectral information (94.59%).

  12. 3D change detection - Approaches and applications

    NASA Astrophysics Data System (ADS)

    Qin, Rongjun; Tian, Jiaojiao; Reinartz, Peter

    2016-12-01

    Due to the unprecedented technology development of sensors, platforms and algorithms for 3D data acquisition and generation, 3D spaceborne, airborne and close-range data, in the form of image based, Light Detection and Ranging (LiDAR) based point clouds, Digital Elevation Models (DEM) and 3D city models, become more accessible than ever before. Change detection (CD) or time-series data analysis in 3D has gained great attention due to its capability of providing volumetric dynamics to facilitate more applications and provide more accurate results. The state-of-the-art CD reviews aim to provide a comprehensive synthesis and to simplify the taxonomy of the traditional remote sensing CD techniques, which mainly sit within the boundary of 2D image/spectrum analysis, largely ignoring the particularities of 3D aspects of the data. The inclusion of 3D data for change detection (termed 3D CD), not only provides a source with different modality for analysis, but also transcends the border of traditional top-view 2D pixel/object-based analysis to highly detailed, oblique view or voxel-based geometric analysis. This paper reviews the recent developments and applications of 3D CD using remote sensing and close-range data, in support of both academia and industry researchers who seek for solutions in detecting and analyzing 3D dynamics of various objects of interest. We first describe the general considerations of 3D CD problems in different processing stages and identify CD types based on the information used, being the geometric comparison and geometric-spectral analysis. We then summarize relevant works and practices in urban, environment, ecology and civil applications, etc. Given the broad spectrum of applications and different types of 3D data, we discuss important issues in 3D CD methods. Finally, we present concluding remarks in algorithmic aspects of 3D CD.

  13. Phenylboronic acid-based (19)F MRI probe for the detection and imaging of hydrogen peroxide utilizing its large chemical-shift change.

    PubMed

    Nonaka, Hiroshi; An, Qi; Sugihara, Fuminori; Doura, Tomohiro; Tsuchiya, Akira; Yoshioka, Yoshichika; Sando, Shinsuke

    2015-01-01

    Herein, we report on a new (19)F MRI probe for the detection and imaging of H2O2. Our designed 2-fluorophenylboronic acid-based (19)F probe promptly reacted with H2O2 to produce 2-fluorophenol via boronic acid oxidation. The accompanying (19)F chemical-shift change reached 31 ppm under our experimental conditions. Such a large chemical-shift change allowed for the imaging of H2O2 by (19)F chemical-shift-selective MRI.

  14. Land Use Land Cover Changes in Detection of Water Quality: A Study Based on Remote Sensing and Multivariate Statistics.

    PubMed

    Hua, Ang Kean

    2017-01-01

    Malacca River water quality is affected due to rapid urbanization development. The present study applied LULC changes towards water quality detection in Malacca River. The method uses LULC, PCA, CCA, HCA, NHCA, and ANOVA. PCA confirmed DS, EC, salinity, turbidity, TSS, DO, BOD, COD, As, Hg, Zn, Fe, E. coli, and total coliform. CCA confirmed 14 variables into two variates; first variate involves residential and industrial activities; and second variate involves agriculture, sewage treatment plant, and animal husbandry. HCA and NHCA emphasize that cluster 1 occurs in urban area with Hg, Fe, total coliform, and DO pollution; cluster 3 occurs in suburban area with salinity, EC, and DS; and cluster 2 occurs in rural area with salinity and EC. ANOVA between LULC and water quality data indicates that built-up area significantly polluted the water quality through E. coli, total coliform, EC, BOD, COD, TSS, Hg, Zn, and Fe, while agriculture activities cause EC, TSS, salinity, E. coli, total coliform, arsenic, and iron pollution; and open space causes contamination of turbidity, salinity, EC, and TSS. Research finding provided useful information in identifying pollution sources and understanding LULC with river water quality as references to policy maker for proper management of Land Use area.

  15. Land Use Land Cover Changes in Detection of Water Quality: A Study Based on Remote Sensing and Multivariate Statistics

    PubMed Central

    2017-01-01

    Malacca River water quality is affected due to rapid urbanization development. The present study applied LULC changes towards water quality detection in Malacca River. The method uses LULC, PCA, CCA, HCA, NHCA, and ANOVA. PCA confirmed DS, EC, salinity, turbidity, TSS, DO, BOD, COD, As, Hg, Zn, Fe, E. coli, and total coliform. CCA confirmed 14 variables into two variates; first variate involves residential and industrial activities; and second variate involves agriculture, sewage treatment plant, and animal husbandry. HCA and NHCA emphasize that cluster 1 occurs in urban area with Hg, Fe, total coliform, and DO pollution; cluster 3 occurs in suburban area with salinity, EC, and DS; and cluster 2 occurs in rural area with salinity and EC. ANOVA between LULC and water quality data indicates that built-up area significantly polluted the water quality through E. coli, total coliform, EC, BOD, COD, TSS, Hg, Zn, and Fe, while agriculture activities cause EC, TSS, salinity, E. coli, total coliform, arsenic, and iron pollution; and open space causes contamination of turbidity, salinity, EC, and TSS. Research finding provided useful information in identifying pollution sources and understanding LULC with river water quality as references to policy maker for proper management of Land Use area. PMID:28377790

  16. Evaluation of change detection techniques for monitoring coastal zone environments

    NASA Technical Reports Server (NTRS)

    Weismiller, R. A.; Kristof, S. J.; Scholz, D. K.; Anuta, P. E.; Momin, S. M.

    1977-01-01

    Development of satisfactory techniques for detecting change in coastal zone environments is required before operational monitoring procedures can be established. In an effort to meet this need a study was directed toward developing and evaluating different types of change detection techniques, based upon computer aided analysis of LANDSAT multispectral scanner (MSS) data, to monitor these environments. The Matagorda Bay estuarine system along the Texas coast was selected as the study area. Four change detection techniques were designed and implemented for evaluation: (1) post classification comparison change detection, (2) delta data change detection, (3) spectral/temporal change classification, and (4) layered spectral/temporal change classification. Each of the four techniques was used to analyze a LANDSAT MSS temporal data set to detect areas of change of the Matagorda Bay region.

  17. Detection of climate change impacts on boreal soil carbon cycling: A model-based analysis of carbon stock and flux changes over the coming decades

    NASA Astrophysics Data System (ADS)

    Fan, Z.; Neff, J.

    2009-12-01

    Future changes in organic carbon (OC) cycling of northern soils due to climate change may have significant impacts on global C cycling. However such changes are still complex and poorly understood in part because boreal soils have unique factors that preserve OC (e.g. permafrost) and loss pathways that include CO2, CH4 and dissolved organic carbon (DOC) fluxes. Additionally, boreal soils contain large stocks of OC that challenge attempts to measure OC loss through repeat measurements of OC pools. With multiple pathways of OC loss and challenges to OC monitoring, it becomes critical to determine which component or property of boreal soil OC (e.g. thickness of OC layer, 14C in solid, liquid, or gas phase) is likely to most sensitive to potential climate changes and when changes in these components would become detectable using laboratory or field measurement. The objective of this study is to provide theoretical answers to the above questions using one single complex biogeochemical model along with various sensitivity analyses. Several existing models have been incorporated into the biogeochemical model, including 1) a multi-isotope OC dynamic model simulating the dynamics of OC layers through time, 2) soil thermal dynamics model simulating the soil heat transported by conduction and by convection via movement of liquid water and water vapor, 3) DOC dynamics model simulating the production, fate, and transport of DOC, and 4) CO2 dynamics model simulating the production and transport of CO2. Six synthesis sites with a factorial combination of drainage class (i.e., well-drained, intermediate well-drained, and poorly drained) and permafrost status (i.e., with or without permafrost underlain) were studied in this research. The results highlight the importance of DOC fluxes from the OC layers to the mineral soils; however the importance of DOC fluxes varied among sites and was strongly dependent on the soil physical properties including soil texture and moisture content

  18. Anxiety, conscious awareness and change detection.

    PubMed

    Gregory, Sally M; Lambert, Anthony

    2012-03-01

    Attentional scanning was studied in anxious and non-anxious participants, using a modified change detection paradigm. Participants detected changes in pairs of emotional scenes separated by two task irrelevant slides, which contained an emotionally valenced scene (the 'distractor scene') and a visual mask. In agreement with attentional control theory, change detection latencies were slower overall for anxious participants. Change detection in anxious, but not non-anxious, participants was influenced by the emotional valence and exposure duration of distractor scenes. When negative distractor scenes were presented at subliminal exposure durations, anxious participants detected changes more rapidly than when supraliminal negative scenes or subliminal positive scenes were presented. We propose that for anxious participants, subliminal presentation of emotionally negative distractor scenes stimulated attention into a dynamic state in the absence of attentional engagement. Presentation of the same scenes at longer exposure times was accompanied by conscious awareness, attentional engagement, and slower change detection.

  19. Detecting hydrological changes through conceptual model

    NASA Astrophysics Data System (ADS)

    Viola, Francesco; Caracciolo, Domenico; Pumo, Dario; Francipane, Antonio; Valerio Noto, Leonardo

    2015-04-01

    Circulation Models (GCMs) for the future scenarios 2046-2065 and 2081-2100. Land use changes (i.e., changes in the fraction of impervious area due to increasing urbanization) are explicitly simulated, while the reference hydrological responses are assessed by the spatially distributed, process-based hydrological model tRIBS, the TIN-based Real-time Integrated Basin Simulator. Several scenarios have been created, describing hypothetical centuries with steady conditions, climate change conditions, land use change conditions and finally complex conditions involving both transient climatic modifications and gradual land use changes. A conceptual lumped model, the EHSM (EcoHydrological Streamflow Model) is calibrated for the above mentioned scenarios with regard to different time-windows. The calibrated parameters show high sensitivity to anthropic variations in land use and/or climatic variability. Land use changes are clearly visible from parameters evolution especially when steady climatic conditions are considered. When the increase in urbanization is coupled with rainfall reduction the ability to detect human interventions through the analysis of conceptual model parameters is weakened.

  20. A High Speed Detection Platform Based on Surface-Enhanced Raman Scattering for Monitoring Antibiotic-Induced Chemical Changes in Bacteria Cell Wall

    PubMed Central

    Liu, Ting-Ting; Lin, You-Hsuan; Hung, Chia-Sui; Liu, Tian-Jiun; Chen, Yu; Huang, Yung-Ching; Tsai, Tsung-Heng; Wang, Huai-Hsien; Wang, Da-Wei; Wang, Juen-Kai; Wang, Yuh-Lin; Lin, Chi-Hung

    2009-01-01

    Rapid and accurate diagnosis for pathogens and their antibiotic susceptibility is critical for controlling bacterial infections. Conventional methods for determining bacterium's sensitivity to antibiotic depend mostly on measuring the change of microbial proliferation in response to the drug. Such “biological assay” inevitably takes time, ranging from days for fast-growing bacteria to weeks for slow-growers. Here, a novel tool has been developed to detect the “chemical features” of bacterial cell wall that enables rapid identification of drug resistant bacteria within hours. The surface-enhanced Raman scattering (SERS) technique based on our newly developed SERS-active substrate was applied to assess the fine structures of the bacterial cell wall. The SERS profiles recorded by such a platform are sensitive and stable, that could readily reflect different bacterial cell walls found in Gram-positive, Gram-negative, or mycobacteria groups. Moreover, characteristic changes in SERS profile were noticed in the drug-sensitive bacteria at the early period (i.e., ∼1 hr) of antibiotic exposure, which could be used to differentiate them from the drug-resistant ones. The SERS-based diagnosis could be applied to a single bacterium. The high-speed SERS detection represents a novel approach for microbial diagnostics. The single-bacterium detection capability of SERS makes possible analyses directly on clinical specimen instead of pure cultured bacteria. PMID:19421405

  1. One new method for road data shape change detection

    NASA Astrophysics Data System (ADS)

    Tang, Luliang; Li, Qingquan; Xu, Feng; Chang, Xiaomeng

    2009-10-01

    Similarity is a psychological cognition; this paper defines the Difference Distance and puts forward the Similarity Measuring Model for linear spatial data (SMM-L) based on the integration of the Distance View and the Feature Set View which are the views for similarity cognition. Based on the study of the relationship between the spatial data change and the similarity, a change detection algorithm for linear spatial data is developed, and a test on road data change detection is realized.

  2. Lake Chapala change detection using time series

    NASA Astrophysics Data System (ADS)

    López-Caloca, Alejandra; Tapia-Silva, Felipe-Omar; Escalante-Ramírez, Boris

    2008-10-01

    The Lake Chapala is the largest natural lake in Mexico. It presents a hydrological imbalance problem caused by diminishing intakes from the Lerma River, pollution from said volumes, native vegetation and solid waste. This article presents a study that allows us to determine with high precision the extent of the affectation in both extension and volume reduction of the Lake Chapala in the period going from 1990 to 2007. Through satellite images this above-mentioned period was monitored. Image segmentation was achieved through a Markov Random Field model, extending the application towards edge detection. This allows adequately defining the lake's limits as well as determining new zones within the lake, both changes pertaining the Lake Chapala. Detected changes are related to a hydrological balance study based on measuring variables such as storage volumes, evapotranspiration and water balance. Results show that the changes in the Lake Chapala establish frail conditions which pose a future risk situation. Rehabilitation of the lake requires a hydrologic balance in its banks and aquifers.

  3. Detecting Concentration Changes with Cooperative Receptors

    NASA Astrophysics Data System (ADS)

    Bo, Stefano; Celani, Antonio

    2016-03-01

    Cells constantly need to monitor the state of the environment to detect changes and timely respond. The detection of concentration changes of a ligand by a set of receptors can be cast as a problem of hypothesis testing, and the cell viewed as a Neyman-Pearson detector. Within this framework, we investigate the role of receptor cooperativity in improving the cell's ability to detect changes. We find that cooperativity decreases the probability of missing an occurred change. This becomes especially beneficial when difficult detections have to be made. Concerning the influence of cooperativity on how fast a desired detection power is achieved, we find in general that there is an optimal value at finite levels of cooperation, even though easy discrimination tasks can be performed more rapidly by noncooperative receptors.

  4. Indigenous people's detection of rapid ecological change.

    PubMed

    Aswani, Shankar; Lauer, Matthew

    2014-06-01

    When sudden catastrophic events occur, it becomes critical for coastal communities to detect and respond to environmental transformations because failure to do so may undermine overall ecosystem resilience and threaten people's livelihoods. We therefore asked how capable of detecting rapid ecological change following massive environmental disruptions local, indigenous people are. We assessed the direction and periodicity of experimental learning of people in the Western Solomon Islands after a tsunami in 2007. We compared the results of marine science surveys with local ecological knowledge of the benthos across 3 affected villages and 3 periods before and after the tsunami. We sought to determine how people recognize biophysical changes in the environment before and after catastrophic events such as earthquakes and tsunamis and whether people have the ability to detect ecological changes over short time scales or need longer time scales to recognize changes. Indigenous people were able to detect changes in the benthos over time. Detection levels differed between marine science surveys and local ecological knowledge sources over time, but overall patterns of statistically significant detection of change were evident for various habitats. Our findings have implications for marine conservation, coastal management policies, and disaster-relief efforts because when people are able to detect ecological changes, this, in turn, affects how they exploit and manage their marine resources.

  5. [Spatiotemporal dynamics of vegetation cover based on trajectory change detection: a case study in Dapeng Peninsula of Shenzhen].

    PubMed

    Liang, Yao-qin; Xie, Fang-yi; Li, Jing; Li, Gui-cai; Zeng, Hui

    2010-05-01

    By using the second-time developed ArcEngine component at pixel level, this paper studied the spatiotemporal dynamics of vegetation cover in the Dapeng Peninsula of Shenzhen, China in 1986-2007, and analyzed the characters and causes of the dynamics. To quantify this dynamics, the NDVI changes in 1986-2007 were extracted from 10 time-series TM/ETM+ remote sensing images, and the results showed that from 1986 to 2007, there were four trajectories of vegetation cover change in the Peninsula, including stable (a), stable-rising-stable (aba), stable-descending-stable (aca), and stable-descending-stable-rising-stable (acaba). The area with these four types occupied 71.54% of the total. Among the four types, type "a" was most common, occupying 1/3 of the study area, mainly in the mountains; and type "acaba" was the typical one, which was closely related to the deforestation and reforestation after the human disturbances of original vegetation. The areas at higher elevation or steeper slopes exhibited smaller vegetation change, mainly because of the constrained human disturbances. Timing of the vegetation cover change showed a relative stability in the mid-90s of 20th century, but a dramatic change after 2003, coinciding with the growth of Shenzhen City.

  6. Detectability of onsets versus offsets in the change detection paradigm.

    PubMed

    Cole, Geoff G; Kentridge, Robert W; Gellatly, Angus R H; Heywood, Charles A

    2003-01-01

    The human visual system is particularly sensitive to abrupt onset of new objects that appear in the visual field. Onsets have been shown to capture attention even when other transients simultaneously occur. This has led some authors to argue for the special role that object onset plays in attentional capture. However, evidence from the change detection paradigm appears contradictory to such findings. Studies of change blindness demonstrate that the onset of new objects can often go unnoticed. Assessing the relative detectability of onsets compared with other visual transients in a change detection procedure may help resolve this contradiction. We report the results of four experiments investigating the efficacy with which onsets capture attention compared with offsets. In Experiment 1, we employed a standard flicker procedure and assessed whether participants were more likely to detect the change following a frame containing an onset or following a frame containing an offset. In Experiment 2, we employed the one-shot method and investigated whether participants detected more onsets or offsets. Experiment 3 used the same method but assessed whether onsets would be detected more rapidly than offsets. In Experiment 4, we investigated whether the effect obtained in Experiments 1-3 using simple shapes would replicate when images of real-world objects were used. Results showed that onsets were less susceptible to change blindness than were offsets. We argue that the preservation of information is greater in onsets than in offsets.

  7. Change Detection Experiments Using Low Cost UAVs

    NASA Technical Reports Server (NTRS)

    Logan, Michael J.; Vranas, Thomas L.; Motter, Mark; Hines, Glenn D.; Rahman, Zia-ur

    2005-01-01

    This paper presents the progress in the development of a low-cost change-detection system. This system is being developed to provide users with the ability to use a low-cost unmanned aerial vehicle (UAV) and image processing system that can detect changes in specific fixed ground locations using video provided by an autonomous UAV. The results of field experiments conducted with the US Army at Ft. A.P.Hill are presented.

  8. Change Point Detection in Correlation Networks

    PubMed Central

    Barnett, Ian; Onnela, Jukka-Pekka

    2016-01-01

    Many systems of interacting elements can be conceptualized as networks, where network nodes represent the elements and network ties represent interactions between the elements. In systems where the underlying network evolves, it is useful to determine the points in time where the network structure changes significantly as these may correspond to functional change points. We propose a method for detecting change points in correlation networks that, unlike previous change point detection methods designed for time series data, requires minimal distributional assumptions. We investigate the difficulty of change point detection near the boundaries of the time series in correlation networks and study the power of our method and competing methods through simulation. We also show the generalizable nature of the method by applying it to stock price data as well as fMRI data. PMID:26739105

  9. Change Point Detection in Correlation Networks

    NASA Astrophysics Data System (ADS)

    Barnett, Ian; Onnela, Jukka-Pekka

    2016-01-01

    Many systems of interacting elements can be conceptualized as networks, where network nodes represent the elements and network ties represent interactions between the elements. In systems where the underlying network evolves, it is useful to determine the points in time where the network structure changes significantly as these may correspond to functional change points. We propose a method for detecting change points in correlation networks that, unlike previous change point detection methods designed for time series data, requires minimal distributional assumptions. We investigate the difficulty of change point detection near the boundaries of the time series in correlation networks and study the power of our method and competing methods through simulation. We also show the generalizable nature of the method by applying it to stock price data as well as fMRI data.

  10. Cellular localization of lead using an antibody-based detection system and enzyme activity changes in the gills and digestive gland of the blue mussel Mytilus edulis.

    PubMed

    Einsporn, Sonja; Bressling, Jana; Koehler, Angela

    2009-02-01

    Marine organisms are continuously exposed to heavy metals in their environment. Bivalve mollusks such as the blue mussel Mytilus edulis accumulate high levels of heavy metals effecting cellular homeostasis and functions. Lead (Pb) exposure (2.5 mg/L of lead (II) nitrate for 10 d) and depuration (10 d in clean seawater) experiments were conducted to study its intracellular fate in the gills and digestive gland of M. edulis. For this purpose, an antibody-based detection method for ultrastructural localization and a subcellular fractionation approach for chemical analysis of Pb were used. In addition, effects of Pb on enzyme activities involved in oxyradical scavenging, such as the conjugative enzyme glutathione-S-transferase and the antioxidative enzyme catalase, were determined. The ultrastructural studies showed that Pb was mainly detected in lysosomes of gill epithelial cells and digestive cells. Lead was also detected in cell nuclei and granular hemocytes. Higher metal concentrations were measured by chemical analysis in subcellular fractions of the gills compared to those of the digestive gland. Increased activities of glutathione-S-transferase were found in gills after exposure and remained elevated during the depuration period, whereas glutathione-S-transferase activity remained unaffected in the digestive gland. Catalase activities showed no changes after Pb exposure, neither in the gills nor in the digestive gland. We conclude that gill cells are major sites of uptake and accumulation for dissolved Pb and are involved in sequestration and detoxification of this metal in M. edulis.

  11. Sensor for detecting changes in magnetic fields

    DOEpatents

    Praeg, W.F.

    1980-02-26

    A sensor is described for detecting changes in the magnetic field of the equilibrium-field coil of a Tokamak plasma device that comprises a pair of bifilar wires disposed circumferentially, one inside and one outside the equilibrium-field coil. Each is shorted at one end. The difference between the voltages detected at the other ends of the bifilar wires provides a measure of changing flux in the equilibrium-field coil. This difference can be used to detect faults in the coil in time to take action to protect the coil.

  12. Sensor for detecting changes in magnetic fields

    DOEpatents

    Praeg, Walter F.

    1981-01-01

    A sensor for detecting changes in the magnetic field of the equilibrium-field coil of a Tokamak plasma device comprises a pair of bifilar wires disposed circumferentially, one inside and one outside the equilibrium-field coil. Each is shorted at one end. The difference between the voltages detected at the other ends of the bifilar wires provides a measure of changing flux in the equilibrium-field coil. This difference can be used to detect faults in the coil in time to take action to protect the coil.

  13. Parallax mitigation for hyperspectral change detection

    NASA Astrophysics Data System (ADS)

    Vongsy, Karmon; Eismann, Michael T.; Mendenhall, Michael J.; Velten, Vincent J.

    2014-06-01

    A pixel-level Generalized Likelihood Ratio Test (GLRT) statistic for hyperspectral change detection is developed to mitigate false change caused by image parallax. Change detection, in general, represents the difficult problem of discriminating significant changes opposed to insignificant changes caused by radiometric calibration, image registration issues, and varying view geometries. We assume that the images have been registered, and each pixel pair provides a measurement from the same spatial region in the scene. Although advanced image registration methods exist that can reduce mis-registration to subpixel levels; residual spatial mis-registration can still be incorrectly detected as significant changes. Similarly, changes in sensor viewing geometry can lead to parallax error in an urban cluttered scene where height structures, such as buildings, appear to move. Our algorithm looks to the inherent relationship between the image views and the theory of stereo vision to perform parallax mitigation leading to a search result in the assumed parallax direction. Mitigation of the parallax-induced false alarms is demonstrated using hyperspectral data in the experimental analysis. The algorithm is examined and compared to the existing chronochrome anomalous change detection algorithm to assess performance.

  14. Comparing Several Algorithms for Change Detection of Wetland

    NASA Astrophysics Data System (ADS)

    Yan, F.; Zhang, S.; Chang, L.

    2015-12-01

    As "the kidneys of the landscape" and "ecological supermarkets", wetland plays an important role in ecological equilibrium and environmental protection.Therefore, it is of great significance to understand the dynamic changes of the wetland. Nowadays, many index and many methods have been used in dynamic Monitoring of Wetland. However, there are no single method and no single index are adapted to detect dynamic change of wetland all over the world. In this paper, three digital change detection algorithms are applied to 2005 and 2010 Landsat Thematic Mapper (TM) images of a portion of the Northeast China to detect wetland dynamic between the two dates. The change vector analysis method (CVA) uses 6 bands of TM images to detect wetland dynamic. The tassled cap transformation is used to create three change images (change in brightness, greenness, and wetness). A new method--- Comprehensive Change Detection Method (CCDM) is introduced to detect forest dynamic change. The CCDM integrates spectral-based change detection algorithms including a Multi-Index Integrated Change Analysis (MIICA) model and a novel change model called Zone, which extracts change information from two Landsat image pairs. The MIICA model is the core module of the change detection strategy and uses four spectral indices (differenced Normalized Burn Ratio (dNBR), differenced Normalized Difference Vegetation Index (dNDVI), the Change Vector (CV) and a new index called the Relative Change Vector Maximum (RCVMAX)) to obtain the changes that occurred between two image dates. The CCDM also includes a knowledge-based system, which uses critical information on historical and current land cover conditions and trends and the likelihood of land cover change, to combine the changes from MIICA and Zone. Related test proved that CCDM method is simple, easy to operate, widely applicable, and capable of capturing a variety of natural and anthropogenic disturbances potentially associated with land cover changes on

  15. The impact of misregistration on change detection

    NASA Technical Reports Server (NTRS)

    Townshend, John R. G.; Justice, Christopher O.; Gurney, Charlotte; Mcmanus, James

    1992-01-01

    The impact of images misregistration on the detection of changes in land cover was studied using spatially degraded Landsat MSS images. Emphasis is placed on simulated images of the Normalized Difference Vegetation Index (NDVI) at spatial resolutions of 250 and 500 m. It is pointed out that there is the need to achieve high values of registration accuracy. The evidence from simulations suggests that misregistrations can have a marked effect on the ability of remotely sensed data to detect changes in land cover. Even subpixel misregistrations can have a major impact, and the most marked proportional changes will tend to occur at the finest misregistrations.

  16. Automated change detection for synthetic aperture sonar

    NASA Astrophysics Data System (ADS)

    G-Michael, Tesfaye; Marchand, Bradley; Tucker, J. D.; Sternlicht, Daniel D.; Marston, Timothy M.; Azimi-Sadjadi, Mahmood R.

    2014-05-01

    In this paper, an automated change detection technique is presented that compares new and historical seafloor images created with sidescan synthetic aperture sonar (SAS) for changes occurring over time. The method consists of a four stage process: a coarse navigational alignment; fine-scale co-registration using the scale invariant feature transform (SIFT) algorithm to match features between overlapping images; sub-pixel co-registration to improves phase coherence; and finally, change detection utilizing canonical correlation analysis (CCA). The method was tested using data collected with a high-frequency SAS in a sandy shallow-water environment. By using precise co-registration tools and change detection algorithms, it is shown that the coherent nature of the SAS data can be exploited and utilized in this environment over time scales ranging from hours through several days.

  17. Holistic processing improves change detection but impairs change identification.

    PubMed

    Mathis, Katherine M; Kahan, Todd A

    2014-10-01

    It has been just over a century since Gestalt psychologists described the factors that contribute to the holistic processing of visually presented stimuli. Recent research indicates that holistic processing may come at a cost; specifically, the perception of holistic forms may reduce the visibility of constituent parts. In the present experiment, we examined change detection and change identification accuracy with Kanizsa rectangle patterns that were arranged to either form a Gestalt whole or not. Results from an experiment with 62 participants support this trade-off in processing holistic forms. Holistic processing improved the detection of change but obstructed its identification. Results are discussed in terms of both their theoretical significance and their application in areas ranging from baggage screening and the detection of changes in radiological images to the systems that are used to generate composite images of perpetrators on the basis of eyewitness reports.

  18. Highly selective cysteine detection and bioimaging in zebrafish through emission color change of water-soluble conjugated polymer-based assay complex.

    PubMed

    Kwon, Na Young; Kim, Daigeun; Jang, Geunseok; Lee, Jung Hyo; So, Ju-Hoon; Kim, Cheol-Hee; Kim, Tae Hyeon; Lee, Taek Seung

    2012-03-01

    A new concept for rapid, label-free cysteine sensing method is proposed via possible naked eye-detection of red-to-blue emission color change. Intermolecular exciton migration in conjugated polyelectrolyte-based assay complex is adopted to enhance selectivity and sensitivity for cysteine sensing by formation and dissociation of polymer-Hg(2+)-thymine assay complex in the absence and presence of cysteine, respectively. The assay complex shows red emission due to cooperative aggregation of conjugated polyelectrolyte, thymine, and Hg(2+). Upon exposure to cysteine, the assay complex dissociates into individual molecules showing transparent, blue-emitting solution, because cysteine extracts Hg(2+) from the assay complex via more favorable binding between cysteine and Hg(2+).

  19. Land Cover Change Detection Using Saliency Andwavelet Transformation

    NASA Astrophysics Data System (ADS)

    Zhang, Haopeng; Jiang, Zhiguo; Cheng, Yan

    2016-06-01

    How to obtain accurate difference map remains an open challenge in change detection. To tackle this problem, we propose a change detection method based on saliency detection and wavelet transformation. We do frequency-tuned saliency detection in initial difference image (IDI) obtained by logarithm ratio to get a salient difference image (SDI). Then, we calculate local entropy of SDI to obtain an entropic salient difference image (ESDI). The final difference image (FDI) is the wavelet fusion of IDI and ESDI, and Otsu thresholding is used to extract difference map from FDI. Experimental results validate the effectiveness and feasibility.

  20. NOVELTY DETECTION UNDER CHANGING ENVIRONMENTAL CONDITIONS

    SciTech Connect

    H. SOHN; K. WORDER; C. R. FARRAR

    2001-04-01

    The primary objective of novelty detection is to examine a system's dynamic response to determine if the system significantly deviates from an initial baseline condition. In reality, the system is often subject to changing environmental and operation conditions that affect its dynamic characteristics. Such variations include changes in loading, boundary conditions, temperature, and moisture. Most damage diagnosis techniques, however, generally neglect the effects of these changing ambient conditions. Here, a novelty detection technique is developed explicitly taking into account these natural variations of the system in order to minimize false positive indications of true system changes. Auto-associative neural networks are employed to discriminate system changes of interest such as structural deterioration and damage from the natural variations of the system.

  1. A Generalizable Hierarchical Bayesian Model for Persistent SAR Change Detection

    DTIC Science & Technology

    2012-04-01

    6] K. Ranney and M. Soumekh, “Signal subspace change detection in averaged multilook sar imagery,” Geoscience and Remote Sensing, IEEE Transactions on...A Generalizable Hierarchical Bayesian Model for Persistent SAR Change Detection Gregory E. Newstadta, Edmund G. Zelniob, and Alfred O. Hero IIIa...Base, OH, 45433, USA ABSTRACT This paper proposes a hierarchical Bayesian model for multiple-pass, multiple antenna synthetic aperture radar ( SAR

  2. Change detection and characterization of volcanic activity using ground based low-light and near infrared cameras to monitor incandescence and thermal signatures

    NASA Astrophysics Data System (ADS)

    Harrild, M.; Webley, P.; Dehn, J.

    2014-12-01

    Knowledge and understanding of precursory events and thermal signatures are vital for monitoring volcanogenic processes, as activity can often range from low level lava effusion to large explosive eruptions, easily capable of ejecting ash up to aircraft cruise altitudes. Using ground based remote sensing techniques to monitor and detect this activity is essential, but often the required equipment and maintenance is expensive. Our investigation explores the use of low-light cameras to image volcanic activity in the visible to near infrared (NIR) portion of the electromagnetic spectrum. These cameras are ideal for monitoring as they are cheap, consume little power, are easily replaced and can provide near real-time data. We focus here on the early detection of volcanic activity, using automated scripts, that capture streaming online webcam imagery and evaluate image pixel brightness values to determine relative changes and flag increases in activity. The script is written in Python, an open source programming language, to reduce the overall cost to potential consumers and increase the application of these tools across the volcanological community. In addition, by performing laboratory tests to determine the spectral response of these cameras, a direct comparison of collocated low-light and thermal infrared cameras has allowed approximate eruption temperatures and effusion rates to be determined from pixel brightness. The results of a field campaign in June, 2013 to Stromboli volcano, Italy, are also presented here. Future field campaigns to Latin America will include collaborations with INSIVUMEH in Guatemala, to apply our techniques to Fuego and Santiaguito volcanoes.

  3. Change detection and characterization of volcanic activity using ground based low-light and near infrared cameras to monitor incandescence and thermal signatures

    NASA Astrophysics Data System (ADS)

    Harrild, Martin; Webley, Peter; Dehn, Jonathan

    2015-04-01

    Knowledge and understanding of precursory events and thermal signatures are vital for monitoring volcanogenic processes, as activity can often range from low level lava effusion to large explosive eruptions, easily capable of ejecting ash up to aircraft cruise altitudes. Using ground based remote sensing techniques to monitor and detect this activity is essential, but often the required equipment and maintenance is expensive. Our investigation explores the use of low-light cameras to image volcanic activity in the visible to near infrared (NIR) portion of the electromagnetic spectrum. These cameras are ideal for monitoring as they are cheap, consume little power, are easily replaced and can provide near real-time data. We focus here on the early detection of volcanic activity, using automated scripts, that capture streaming online webcam imagery and evaluate image pixel brightness values to determine relative changes and flag increases in activity. The script is written in Python, an open source programming language, to reduce the overall cost to potential consumers and increase the application of these tools across the volcanological community. In addition, by performing laboratory tests to determine the spectral response of these cameras, a direct comparison of collocated low-light and thermal infrared cameras has allowed approximate eruption temperatures and effusion rates to be determined from pixel brightness. The results of a field campaign in June, 2013 to Stromboli volcano, Italy, are also presented here. Future field campaigns to Latin America will include collaborations with INSIVUMEH in Guatemala, to apply our techniques to Fuego and Santiaguito volcanoes.

  4. Hardware accelerator design for change detection in smart camera

    NASA Astrophysics Data System (ADS)

    Singh, Sanjay; Dunga, Srinivasa Murali; Saini, Ravi; Mandal, A. S.; Shekhar, Chandra; Chaudhury, Santanu; Vohra, Anil

    2011-10-01

    Smart Cameras are important components in Human Computer Interaction. In any remote surveillance scenario, smart cameras have to take intelligent decisions to select frames of significant changes to minimize communication and processing overhead. Among many of the algorithms for change detection, one based on clustering based scheme was proposed for smart camera systems. However, such an algorithm could achieve low frame rate far from real-time requirements on a general purpose processors (like PowerPC) available on FPGAs. This paper proposes the hardware accelerator capable of detecting real time changes in a scene, which uses clustering based change detection scheme. The system is designed and simulated using VHDL and implemented on Xilinx XUP Virtex-IIPro FPGA board. Resulted frame rate is 30 frames per second for QVGA resolution in gray scale.

  5. Automatic change detection in spaceborne SAR imagery

    NASA Astrophysics Data System (ADS)

    Corr, Douglas G.; Whitehouse, Simon W.; Mott, David H.; Baldwin, Jim F.

    1996-06-01

    This paper describes a new technique of the automatic detection of change within synthetic aperture radar (SAR) images produced from satellite data. The interpretation of this type of imagery is difficult due to the combined effect of speckle, low resolution and the complexity of the radar signatures. The change detection technique that has been developed overcomes these problems by automatically measuring the degree of change between two images. The principle behind the technique used is that when satellite repeat orbits are at almost the same position in space then unless the scene has changed, the speckle pattern in the image will be unchanged. Comparison of images therefore reveals real change, not change due to fluctuating speckle patterns. The degree of change between two SAR images was measured by using the coherence function. Coherence has been studied for a variety of scene types: agricultural, forestry, domestic housing, small and large scale industrial complexes. Fuzzy set techniques, as well as direct threshold methods, have bee applied to the coherence data to determine places where change has occurred. The method has been validated using local information on building changes due to construction or demolition.

  6. Acoustic change detection algorithm using an FM radio

    NASA Astrophysics Data System (ADS)

    Goldman, Geoffrey H.; Wolfe, Owen

    2012-06-01

    The U.S. Army is interested in developing low-cost, low-power, non-line-of-sight sensors for monitoring human activity. One modality that is often overlooked is active acoustics using sources of opportunity such as speech or music. Active acoustics can be used to detect human activity by generating acoustic images of an area at different times, then testing for changes among the imagery. A change detection algorithm was developed to detect physical changes in a building, such as a door changing positions or a large box being moved using acoustics sources of opportunity. The algorithm is based on cross correlating the acoustic signal measured from two microphones. The performance of the algorithm was shown using data generated with a hand-held FM radio as a sound source and two microphones. The algorithm could detect a door being opened in a hallway.

  7. Automatic change detection using mobile laser scanning

    NASA Astrophysics Data System (ADS)

    Hebel, M.; Hammer, M.; Gordon, M.; Arens, M.

    2014-10-01

    Automatic change detection in 3D environments requires the comparison of multi-temporal data. By comparing current data with past data of the same area, changes can be automatically detected and identified. Volumetric changes in the scene hint at suspicious activities like the movement of military vehicles, the application of camouflage nets, or the placement of IEDs, etc. In contrast to broad research activities in remote sensing with optical cameras, this paper addresses the topic using 3D data acquired by mobile laser scanning (MLS). We present a framework for immediate comparison of current MLS data to given 3D reference data. Our method extends the concept of occupancy grids known from robot mapping, which incorporates the sensor positions in the processing of the 3D point clouds. This allows extracting the information that is included in the data acquisition geometry. For each single range measurement, it becomes apparent that an object reflects laser pulses in the measured range distance, i.e., space is occupied at that 3D position. In addition, it is obvious that space is empty along the line of sight between sensor and the reflecting object. Everywhere else, the occupancy of space remains unknown. This approach handles occlusions and changes implicitly, such that the latter are identifiable by conflicts of empty space and occupied space. The presented concept of change detection has been successfully validated in experiments with recorded MLS data streams. Results are shown for test sites at which MLS data were acquired at different time intervals.

  8. Bivariate gamma distributions for image registration and change detection.

    PubMed

    Chatelain, Florent; Tourneret, Jean-Yves; Inglada, Jordi; Ferrari, André

    2007-07-01

    This paper evaluates the potential interest of using bivariate gamma distributions for image registration and change detection. The first part of this paper studies estimators for the parameters of bivariate gamma distributions based on the maximum likelihood principle and the method of moments. The performance of both methods are compared in terms of estimated mean square errors and theoretical asymptotic variances. The mutual information is a classical similarity measure which can be used for image registration or change detection. The second part of the paper studies some properties of the mutual information for bivariate Gamma distributions. Image registration and change detection techniques based on bivariate gamma distributions are finally investigated. Simulation results conducted on synthetic and real data are very encouraging. Bivariate gamma distributions are good candidates allowing us to develop new image registration algorithms and new change detectors.

  9. Attending to faces: change detection, familiarization, and inversion effects.

    PubMed

    Barton, Jason J S; Deepak, Shaunak; Malik, Numaan

    2003-01-01

    We tested detection of changes to eye position, eye color (brightness), mouth position, and mouth color in frontal views of faces. Two faces were presented sequentially for 555 ms each, with a blank screen of 120 ms separating the two. Faces were presented either both upright or both inverted. Measures of detection (d') were calculated for several different degrees of change for each of the four dimensions of change. We first compared results to an earlier experiment that used an oddity design, in which subjects indicated which of three simultaneously viewed and otherwise identical faces had been altered on one of these four dimensions. Subjects in both of these experiments were partially cued, in that they knew the four possible types of changes that could occur on a given trial. The change-detection results correlated well with the oddity data. They confirmed that face inversion had little effect upon detection of changes in eye color, a moderate effect upon detection of eye-position or mouth-color changes, and caused a drastic reduction in the detection of mouth-position changes. An experiment in which uncued and fully cued subjects were compared showed that cueing significantly improved detection of feature color changes, but there was little difference between upright and inverted faces. Full cueing eliminated all effects of inversion. Compared to partial cueing, changes in mouth color were poorly detected by uncued subjects. Last, a change in the frequency of the base (unaltered) face in an experiment from 75% to 40% showed that increased short-term familiarity decreased the detection of eye changes and increased the detection of mouth changes, regardless of face orientation and the type of change made (color or position). We conclude that uncued subjects encode the spatial relations of features more than the colors of features, that mouth color in particular is not considered a relevant dimension for encoding, and that familiarization redistributes attention

  10. Automated baseline change detection phase I. Final report

    SciTech Connect

    1995-12-01

    The Automated Baseline Change Detection (ABCD) project is supported by the DOE Morgantown Energy Technology Center (METC) as part of its ER&WM cross-cutting technology program in robotics. Phase 1 of the Automated Baseline Change Detection project is summarized in this topical report. The primary objective of this project is to apply robotic and optical sensor technology to the operational inspection of mixed toxic and radioactive waste stored in barrels, using Automated Baseline Change Detection (ABCD), based on image subtraction. Absolute change detection is based on detecting any visible physical changes, regardless of cause, between a current inspection image of a barrel and an archived baseline image of the same barrel. Thus, in addition to rust, the ABCD system can also detect corrosion, leaks, dents, and bulges. The ABCD approach and method rely on precise camera positioning and repositioning relative to the barrel and on feature recognition in images. In support of this primary objective, there are secondary objectives to determine DOE operational inspection requirements and DOE system fielding requirements.

  11. Change detection in urban areas by object-based analysis and on-the-fly comparison of multi-view ALS data

    NASA Astrophysics Data System (ADS)

    Hebel, Marcus; Arens, Michael; Stilla, Uwe

    2013-12-01

    The use of helicopters as a sensor platform offers flexible fields of application due to adaptable flying speed at low flight levels. Modern helicopters are equipped with radar altimeters, inertial navigation systems (INS), forward-looking cameras and even laser scanners for automatic obstacle avoidance. If the 3D geometry of the terrain is already available, the analysis of airborne laser scanner (ALS) measurements may also be used for terrain-referenced navigation and change detection. In this paper, we present a framework for on-the-fly comparison of current ALS data to given reference data of an urban area. In contrast to classical difference methods, our approach extends the concept of occupancy grids known from robot mapping. However, it does not blur the measured information onto the grid cells. The proposed change detection method applies the Dempster-Shafer theory to identify conflicting evidence along the laser pulse propagation path. Additional attributes are considered to decide whether detected changes are of man-made origin or occurring due to seasonal effects. The concept of online change detection has been successfully validated in offline experiments with recorded ALS data streams. Results are shown for an urban test site at which multi-view ALS data were acquired at an interval of 1 year.

  12. Detecting changes during pregnancy with Raman spectroscopy

    NASA Astrophysics Data System (ADS)

    Vargis, Elizabeth; Robertson, Kesha; Al-Hendy, Ayman; Reese, Jeff; Mahadevan-Jansen, Anita

    2010-02-01

    Preterm labor is the second leading cause of neonatal mortality and leads to a myriad of complications like delayed development and cerebral palsy. Currently, there is no way to accurately predict preterm labor, making its prevention and treatment virtually impossible. While there are some at-risk patients, over half of all preterm births do not fall into any high-risk category. This study seeks to predict and prevent preterm labor by using Raman spectroscopy to detect changes in the cervix during pregnancy. Since Raman spectroscopy has been used to detect cancers in vivo in organs like the cervix and skin, it follows that spectra will change over the course of pregnancy. Previous studies have shown that fluorescence decreased during pregnancy and increased during post-partum exams to pre-pregnancy levels. We believe significant changes will occur in the Raman spectra obtained during the course of pregnancy. In this study, Raman spectra from the cervix of pregnant mice and women will be acquired. Specific changes that occur due to cervical softening or changes in hormonal levels will be observed to understand the likelihood that a female mouse or a woman will enter labor.

  13. Evaluation of object level change detection techniques

    NASA Astrophysics Data System (ADS)

    Irvine, John M.; Bergeron, Stuart; Hugo, Doug; O'Brien, Michael A.

    2007-04-01

    A variety of change detection (CD) methods have been developed and employed to support imagery analysis for applications including environmental monitoring, mapping, and support to military operations. Evaluation of these methods is necessary to assess technology maturity, identify areas for improvement, and support transition to operations. This paper presents a methodology for conducting this type of evaluation, discusses the challenges, and illustrates the techniques. The evaluation of object-level change detection methods is more complicated than for automated techniques for processing a single image. We explore algorithm performance assessments, emphasizing the definition of the operating conditions (sensor, target, and environmental factors) and the development of measures of performance. Specific challenges include image registration; occlusion due to foliage, cultural clutter and terrain masking; diurnal differences; and differences in viewing geometry. Careful planning, sound experimental design, and access to suitable imagery with image truth and metadata are critical.

  14. A new maximum-likelihood change estimator for two-pass SAR coherent change detection

    SciTech Connect

    Wahl, Daniel E.; Yocky, David A.; Jakowatz, Jr., Charles V.; Simonson, Katherine Mary

    2016-01-11

    In past research, two-pass repeat-geometry synthetic aperture radar (SAR) coherent change detection (CCD) predominantly utilized the sample degree of coherence as a measure of the temporal change occurring between two complex-valued image collects. Previous coherence-based CCD approaches tend to show temporal change when there is none in areas of the image that have a low clutter-to-noise power ratio. Instead of employing the sample coherence magnitude as a change metric, in this paper, we derive a new maximum-likelihood (ML) temporal change estimate—the complex reflectance change detection (CRCD) metric to be used for SAR coherent temporal change detection. The new CRCD estimator is a surprisingly simple expression, easy to implement, and optimal in the ML sense. As a result, this new estimate produces improved results in the coherent pair collects that we have tested.

  15. A new maximum-likelihood change estimator for two-pass SAR coherent change detection

    DOE PAGES

    Wahl, Daniel E.; Yocky, David A.; Jakowatz, Jr., Charles V.; ...

    2016-01-11

    In past research, two-pass repeat-geometry synthetic aperture radar (SAR) coherent change detection (CCD) predominantly utilized the sample degree of coherence as a measure of the temporal change occurring between two complex-valued image collects. Previous coherence-based CCD approaches tend to show temporal change when there is none in areas of the image that have a low clutter-to-noise power ratio. Instead of employing the sample coherence magnitude as a change metric, in this paper, we derive a new maximum-likelihood (ML) temporal change estimate—the complex reflectance change detection (CRCD) metric to be used for SAR coherent temporal change detection. The new CRCD estimatormore » is a surprisingly simple expression, easy to implement, and optimal in the ML sense. As a result, this new estimate produces improved results in the coherent pair collects that we have tested.« less

  16. Unsupervised Change Detection in SAR Images Using Gaussian Mixture Models

    NASA Astrophysics Data System (ADS)

    Kiana, E.; Homayouni, S.; Sharifi, M. A.; Farid-Rohani, M.

    2015-12-01

    In this paper, we propose a method for unsupervised change detection in Remote Sensing Synthetic Aperture Radar (SAR) images. This method is based on the mixture modelling of the histogram of difference image. In this process, the difference image is classified into three classes; negative change class, positive change class and no change class. However the SAR images suffer from speckle noise, the proposed method is able to map the changes without speckle filtering. To evaluate the performance of this method, two dates of SAR data acquired by Uninhabited Aerial Vehicle Synthetic from an agriculture area are used. Change detection results show better efficiency when compared to the state-of-the-art methods.

  17. A Hopfield neural network for image change detection.

    PubMed

    Pajares, Gonzalo

    2006-09-01

    This paper outlines an optimization relaxation approach based on the analog Hopfield neural network (HNN) for solving the image change detection problem between two images. A difference image is obtained by subtracting pixel by pixel both images. The network topology is built so that each pixel in the difference image is a node in the network. Each node is characterized by its state, which determines if a pixel has changed. An energy function is derived, so that the network converges to stable states. The analog Hopfield's model allows each node to take on analog state values. Unlike most widely used approaches, where binary labels (changed/unchanged) are assigned to each pixel, the analog property provides the strength of the change. The main contribution of this paper is reflected in the customization of the analog Hopfield neural network to derive an automatic image change detection approach. When a pixel is being processed, some existing image change detection procedures consider only interpixel relations on its neighborhood. The main drawback of such approaches is the labeling of this pixel as changed or unchanged according to the information supplied by its neighbors, where its own information is ignored. The Hopfield model overcomes this drawback and for each pixel allows a tradeoff between the influence of its neighborhood and its own criterion. This is mapped under the energy function to be minimized. The performance of the proposed method is illustrated by comparative analysis against some existing image change detection methods.

  18. Evaluation of change detection techniques for monitoring coastal zone environments

    NASA Technical Reports Server (NTRS)

    Weismiller, R. A. (Principal Investigator); Kristof, S. J.; Scholz, D. K.; Anuta, P. E.; Momin, S. M.

    1977-01-01

    The author has identified the following significant results. Four change detection techniques were designed and implemented for evaluation: (1) post classification comparison change detection, (2) delta data change detection, (3) spectral/temporal change classification, and (4) layered spectral/temporal change classification. The post classification comparison technique reliably identified areas of change and was used as the standard for qualitatively evaluating the other three techniques. The layered spectral/temporal change classification and the delta data change detection results generally agreed with the post classification comparison technique results; however, many small areas of change were not identified. Major discrepancies existed between the post classification comparison and spectral/temporal change detection results.

  19. Statistically normalized coherent change detection for synthetic aperture sonar imagery

    NASA Astrophysics Data System (ADS)

    G-Michael, Tesfaye; Tucker, J. D.; Roberts, Rodney G.

    2016-05-01

    Coherent Change Detection (CCD) is a process of highlighting an area of activity in scenes (seafloor) under survey and generated from pairs of synthetic aperture sonar (SAS) images of approximately the same location observed at two different time instances. The problem of CCD and subsequent anomaly feature extraction/detection is complicated due to several factors such as the presence of random speckle pattern in the images, changing environmental conditions, and platform instabilities. These complications make the detection of weak target activities even more difficult. Typically, the degree of similarity between two images measured at each pixel locations is the coherence between the complex pixel values in the two images. Higher coherence indicates little change in the scene represented by the pixel and lower coherence indicates change activity in the scene. Such coherence estimation scheme based on the pixel intensity correlation is an ad-hoc procedure where the effectiveness of the change detection is determined by the choice of threshold which can lead to high false alarm rates. In this paper, we propose a novel approach for anomalous change pattern detection using the statistical normalized coherence and multi-pass coherent processing. This method may be used to mitigate shadows by reducing the false alarms resulting in the coherent map due to speckles and shadows. Test results of the proposed methods on a data set of SAS images will be presented, illustrating the effectiveness of the normalized coherence in terms statistics from multi-pass survey of the same scene.

  20. Automated Change Detection Using Synthetic Aperture Sonar Imagery

    DTIC Science & Technology

    2010-06-01

    using shadow outlining, scene matching using control-point matching, and visualization capabilities. This system was developed for sidescan sonar ...surveys using instrumentation such as the high-frequency Marine Sonic Technology sidescan sonar . In this paper, the authors describe modifications to...the sidescan -based system required to perform change detection using Synthetic Aperture Sonar (SAS) bottom imagery. Index Terms—Acoustic signal

  1. Improved forest change detection with terrain illumination corrected landsat images

    Technology Transfer Automated Retrieval System (TEKTRAN)

    An illumination correction algorithm has been developed to improve the accuracy of forest change detection from Landsat reflectance data. This algorithm is based on an empirical rotation model and was tested on the Landsat imagery pair over Cherokee National Forest, Tennessee, Uinta-Wasatch-Cache N...

  2. Detecting ecological change on coral reefs

    NASA Astrophysics Data System (ADS)

    Dustan, P.

    2011-12-01

    Remote sensing offers the potential to observe the response of coral reef ecosystems to environmental perturbations on a geographical scale not previously accessible. However, coral reef environments are optically, spatially, and temporally complex habitats which all present significant challenges for extracting meaningful information. Virtually every member of the reef community possesses some degree of photosynthetic capability. The community thus generates a matrix of fine scale features with bio-optical signatures that blend as the scale of observation increases. Furthermore, to have any validity, the remotely sensed signal must be "calibrated" to the bio-optics of the reef, a difficult and resource intensive process due to a convergence of photosynthetic light harvesting by green, red, and brown algal pigment systems. To make matters more complex, reefs are overlain by a seawater skin with its own set of hydrological optical challenges. Rather than concentrating on classification, my research has attempted to track change by following the variation in geo-referenced pixel brightness over time with a technique termed temporal texture. Environmental periodicities impart a phenology to the variation in brightness and departures from the norm are easily detected as statistical outliers. This opens the door to using current orbiting technology to efficiently examine large areas of sea for change. If hot spots are detected, higher resolution sensors and field studies can be focused as resources permit. While this technique does not identify the type of change, it is sensitive, simple to compute, easy to automate and grounded in ecological niche theory

  3. Immunohistochemical Detection of Changes in Tumor Hypoxia

    SciTech Connect

    Russell, James Carlin, Sean; Burke, Sean A.; Wen Bixiu; Yang, Kwang Mo; Ling, C. Clifton

    2009-03-15

    Purpose: Although hypoxia is a known prognostic factor, its effect will be modified by the rate of reoxygenation and the extent to which the cells are acutely hypoxic. We tested the ability of exogenous and endogenous markers to detect reoxygenation in a xenograft model. Our technique might be applicable to stored patient samples. Methods and Materials: The human colorectal carcinoma line, HT29, was grown in nude mice. Changes in tumor hypoxia were examined by injection of pimonidazole, followed 24 hours later by EF5. Cryosections were stained for these markers and for carbonic anhydrase IX (CAIX) and hypoxia-inducible factor 1{alpha} (HIF1{alpha}). Tumor hypoxia was artificially manipulated by carbogen exposure. Results: In unstressed tumors, all four markers showed very similar spatial distributions. After carbogen treatment, pimonidazole and EF5 could detect decreased hypoxia. HIF1{alpha} staining was also decreased relative to CAIX, although the effect was less pronounced than for EF5. Control tumors displayed small regions that had undergone spontaneous changes in tumor hypoxia, as judged by pimonidazole relative to EF5; most of these changes were reflected by CAIX and HIF1{alpha}. Conclusion: HIF1{alpha} can be compared with either CAIX or a previously administered nitroimidazole to provide an estimate of reoxygenation.

  4. SAR image change detection using watershed and spectral clustering

    NASA Astrophysics Data System (ADS)

    Niu, Ruican; Jiao, L. C.; Wang, Guiting; Feng, Jie

    2011-12-01

    A new method of change detection in SAR images based on spectral clustering is presented in this paper. Spectral clustering is employed to extract change information from a pair images acquired on the same geographical area at different time. Watershed transform is applied to initially segment the big image into non-overlapped local regions, leading to reduce the complexity. Experiments results and system analysis confirm the effectiveness of the proposed algorithm.

  5. Land-use/land-cover change detection using change-vector analysis in posterior probability space

    NASA Astrophysics Data System (ADS)

    Chen, Xuehong; Chen, Jin; Shen, Miaogen; Yang, Wei

    2008-10-01

    Land use/land cover change is an important field in global environmental change research. Remote sensing is a valuable data source from which land use/land cover change information can be extracted efficiently. A number of techniques for accomplishing change detection using satellite imagery have been formulated, applied, and evaluated, which can be generally grouped into two types. (1) Those based on spectral classification of the input data such as post-classification comparison and direct two-date classification; and (2) those based on radiometric change between different acquisition dates. The shortage of type 1 is cumulative error in image classification of an individual date. However, radiometric change approaches has a strict requirement for reliable image radiometry. In light of the above mentioned drawbacks of those two types of change detection methods, this paper presents a new method named change vector analysis in posterior probability space (CVAPS). Change-vector analysis (CVA) is one of the most successful radiometric change-based approaches. CVAPS approach incorporates post-classification comparison method and CVA approach, which is expected to inherit the advantages of two traditional methods and avoid their defects at the same time. CVAPS includes the following four steps. (1) Images in different periods are classified by certain classifier which can provide posterior probability output. Then, the posterior probability can be treated as a vector, the dimension of which is equal to the number of classes. (2) A procedure similar with CVA is employed. Compared with traditional CVA, new method analyzes the change vector in posterior probability space instead of spectral feature space. (3) A semiautomatic method, named Double-Window Flexible Pace Search (DFPS), is employed to determine the threshold of change magnitude. (4) Change category is discriminated by cosines of the change vectors. CVAPS approach was applied and validated by a case study of

  6. Detecting changes in dynamic and complex acoustic environments

    PubMed Central

    Boubenec, Yves; Lawlor, Jennifer; Górska, Urszula; Shamma, Shihab; Englitz, Bernhard

    2017-01-01

    Natural sounds such as wind or rain, are characterized by the statistical occurrence of their constituents. Despite their complexity, listeners readily detect changes in these contexts. We here address the neural basis of statistical decision-making using a combination of psychophysics, EEG and modelling. In a texture-based, change-detection paradigm, human performance and reaction times improved with longer pre-change exposure, consistent with improved estimation of baseline statistics. Change-locked and decision-related EEG responses were found in a centro-parietal scalp location, whose slope depended on change size, consistent with sensory evidence accumulation. The potential's amplitude scaled with the duration of pre-change exposure, suggesting a time-dependent decision threshold. Auditory cortex-related potentials showed no response to the change. A dual timescale, statistical estimation model accounted for subjects' performance. Furthermore, a decision-augmented auditory cortex model accounted for performance and reaction times, suggesting that the primary cortical representation requires little post-processing to enable change-detection in complex acoustic environments. DOI: http://dx.doi.org/10.7554/eLife.24910.001 PMID:28262095

  7. Towards a Framework for Change Detection in Data Sets

    NASA Astrophysics Data System (ADS)

    Böttcher, Mirko; Nauck, Detlef; Ruta, Dymitr; Spott, Martin

    Since the world with its markets, innovations and customers is changing faster than ever before, the key to survival for businesses is the ability to detect, assess and respond to changing conditions rapidly and intelligently. Discovering changes and reacting to or acting upon them before others do has therefore become a strategical issue for many companies. However, existing data analysis techniques are insufflent for this task since they typically assume that the domain under consideration is stable over time. This paper presents a framework that detects changes within a data set at virtually any level of granularity. The underlying idea is to derive a rule-based description of the data set at different points in time and to subsequently analyse how these rules change. Nevertheless, further techniques are required to assist the data analyst in interpreting and assessing their changes. Therefore the framework also contains methods to discard rules that are non-drivers for change and to assess the interestingness of detected changes.

  8. Detecting changes in dynamic and complex acoustic environments.

    PubMed

    Boubenec, Yves; Lawlor, Jennifer; Górska, Urszula; Shamma, Shihab; Englitz, Bernhard

    2017-03-06

    Natural sounds such as wind or rain, are characterized by the statistical occurrence of their constituents. Despite their complexity, listeners readily detect changes in these contexts. We here address the neural basis of statistical decision-making using a combination of psychophysics, EEG and modelling. In a texture-based, change-detection paradigm, human performance and reaction times improved with longer pre-change exposure, consistent with improved estimation of baseline statistics. Change-locked and decision-related EEG responses were found in a centro-parietal scalp location, whose slope depended on change size, consistent with sensory evidence accumulation. The potential's amplitude scaled with the duration of pre-change exposure, suggesting a time-dependent decision threshold. Auditory cortex-related potentials showed no response to the change. A dual timescale, statistical estimation model accounted for subjects' performance. Furthermore, a decision-augmented auditory cortex model accounted for performance and reaction times, suggesting that the primary cortical representation requires little post-processing to enable change-detection in complex acoustic environments.

  9. Detecting past changes of effective population size

    PubMed Central

    Nikolic, Natacha; Chevalet, Claude

    2014-01-01

    Understanding and predicting population abundance is a major challenge confronting scientists. Several genetic models have been developed using microsatellite markers to estimate the present and ancestral effective population sizes. However, to get an overview on the evolution of population requires that past fluctuation of population size be traceable. To address the question, we developed a new model estimating the past changes of effective population size from microsatellite by resolving coalescence theory and using approximate likelihoods in a Monte Carlo Markov Chain approach. The efficiency of the model and its sensitivity to gene flow and to assumptions on the mutational process were checked using simulated data and analysis. The model was found especially useful to provide evidence of transient changes of population size in the past. The times at which some past demographic events cannot be detected because they are too ancient and the risk that gene flow may suggest the false detection of a bottleneck are discussed considering the distribution of coalescence times. The method was applied on real data sets from several Atlantic salmon populations. The method called VarEff (Variation of Effective size) was implemented in the R package VarEff and is made available at https://qgsp.jouy.inra.fr and at http://cran.r-project.org/web/packages/VarEff. PMID:25067949

  10. Environmental Change Detection Using Multi-Temporal SAR Imagery

    NASA Astrophysics Data System (ADS)

    Fazel, Mohammad A.; Homayouni, Saeid; Aghakarimi, Armin

    2013-04-01

    Monitoring of environmental phenomena in short-, mid- and long-term periods is the first step of any study or plan for natural resource management. As a result, detection and identification of the environmental changes became a main area of research for different applications. Remotely sensed data and especially Synthetic Aperture Radar (SAR) imagery thanks to its independence to weather conditions and sun illumination, and its spatial and temporal resolution ability is a valuable source of information for change detection analysis and provides reliable data for information extraction for various applications. In general, change detection methods are grouped into supervised and unsupervised methods. Supervised methods work based on multi-temporal land-cover mapping of satellite images. While, unsupervised techniques include the very simple idea of image differencing to more sophisticated statistical modeling of changes in images. Unsupervised methods because of their advantages are more important in many applications. In recent years, the use of kernel based methods in change detection applications became an interesting topic in remote sensing community. Kernel-based methods and machine learning algorithms are the unsupervised paradigms which introduced powerful tools to deal with nonlinear classification. In this paper, we have presented a fully unsupervised framework for detecting the Urmia Lake changes during 2007 to 2010. This method uses the kernel-based clustering technique. The kernel k-means algorithm separates the changes from no-change classes of speckle free images. This method is a non-linear algorithm which considers the contextual information. For this purpose, at first, difference maps are calculated from multi-temporal data. Then these maps are projected into a higher dimensional space by using kernel function. Finally an unsupervised k-means clustering algorithm is used to obtain change and no-change classes. The proposed methodology is applied to

  11. Urban development change detection based on Multi-Temporal Satellite Images as a fast tracking approach--a case study of Ahwaz County, southwestern Iran.

    PubMed

    Malmir, Maryam; Zarkesh, Mir Masoud Kheirkhah; Monavari, Seyed Masoud; Jozi, Seyed Ali; Sharifi, Esmail

    2015-03-01

    Rapid land-use/land-cover changes in suburbs of metropolitan cities of Iran have recently caused serious environmental damages. Detection of these changes can be a very important step in urban planning and optimal use of natural resources. Accordingly, the present study was carried out to track land-use/land-cover (LULC) changes of Ahwaz County in southwestern Iran using remote sensing techniques over a period of 26 years, from 1987 to 2013. For this, ISODATA algorithm and Maximum Likelihood were initially used for unsupervised and supervised classifications of the satellite images. The accuracy of the LULC maps was checked by the Kappa Coefficient and the Overall Accuracy methods. As the final step, the LULC changes were detected using the cross-tabulation technique. The obtained results indicated that urban and agricultural areas have been increased about 57.5 and 84.5 %, respectively, from 1987 to 2013. Further, the area of poorly vegetated regions, in the same period, has been decreased to approximately 36 %. The largest land conversion area belongs to the poorly vegetated regions, which have been declined to about 10,371 and 1,334 ha during 1987-2007 and 2007-2013, respectively. Approximately 1,670 and 382 ha of the agricultural lands have also been changed to built-up areas by about 1,670 and 382 ha during the same periods. As a result, it was found that the northwest, southwest, and south of the county were highly subjected to urban development. This would be of great importance for urban planning decision-making faced by the planners of the city in the present and future.

  12. Development of a spatial analysis method using ground-based repeat photography to detect changes in the alpine treeline ecotone, Glacier National Park, Montana, U.S.A.

    USGS Publications Warehouse

    Roush, W.; Munroe, J.S.; Fagre, D.B.

    2007-01-01

    Repeat photography is a powerful tool for detection of landscape change over decadal timescales. Here a novel method is presented that applies spatial analysis software to digital photo-pairs, allowing vegetation change to be categorized and quantified. This method is applied to 12 sites within the alpine treeline ecotone of Glacier National Park, Montana, and is used to examine vegetation changes over timescales ranging from 71 to 93 years. Tree cover at the treeline ecotone increased in 10 out of the 12 photo-pairs (mean increase of 60%). Establishment occurred at all sites, infilling occurred at 11 sites. To demonstrate the utility of this method, patterns of tree establishment at treeline are described and the possible causes of changes within the treeline ecotone are discussed. Local factors undoubtedly affect the magnitude and type of the observed changes, however the ubiquity of the increase in tree cover implies a common forcing mechanism. Mean minimum summer temperatures have increased by 1.5??C over the past century and, coupled with variations in the amount of early spring snow water equivalent, likely account for much of the increase in tree cover at the treeline ecotone. Lastly, shortcomings of this method are presented along with possible solutions and areas for future research. ?? 2007 Regents of the University of Colorado.

  13. 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.

  14. Extended image differencing for change detection in UAV video mosaics

    NASA Astrophysics Data System (ADS)

    Saur, Günter; Krüger, Wolfgang; Schumann, Arne

    2014-03-01

    Change detection is one of the most important tasks when using unmanned aerial vehicles (UAV) for video reconnaissance and surveillance. We address changes of short time scale, i.e. the observations are taken in time distances from several minutes up to a few hours. Each observation is a short video sequence acquired by the UAV in near-nadir view and the relevant changes are, e.g., recently parked or moved vehicles. In this paper we extend our previous approach of image differencing for single video frames to video mosaics. A precise image-to-image registration combined with a robust matching approach is needed to stitch the video frames to a mosaic. Additionally, this matching algorithm is applied to mosaic pairs in order to align them to a common geometry. The resulting registered video mosaic pairs are the input of the change detection procedure based on extended image differencing. A change mask is generated by an adaptive threshold applied to a linear combination of difference images of intensity and gradient magnitude. The change detection algorithm has to distinguish between relevant and non-relevant changes. Examples for non-relevant changes are stereo disparity at 3D structures of the scene, changed size of shadows, and compression or transmission artifacts. The special effects of video mosaicking such as geometric distortions and artifacts at moving objects have to be considered, too. In our experiments we analyze the influence of these effects on the change detection results by considering several scenes. The results show that for video mosaics this task is more difficult than for single video frames. Therefore, we extended the image registration by estimating an elastic transformation using a thin plate spline approach. The results for mosaics are comparable to that of single video frames and are useful for interactive image exploitation due to a larger scene coverage.

  15. Analytical and numerical studies of approximate phase velocity matching based nonlinear S0 mode Lamb waves for the detection of evenly distributed microstructural changes

    NASA Astrophysics Data System (ADS)

    Wan, X.; Tse, P. W.; Xu, G. H.; Tao, T. F.; Zhang, Q.

    2016-04-01

    the primary and secondary horizontal displacements generated from nonlinear S0 mode Lamb waves are closest to the real value, which indicates that using horizontal displacements is more suitable for detecting evenly distributed microstructural changes in large thin plate-like structure. Successful application to evaluating material at different levels of evenly distributed fatigue damage is also numerically conducted.

  16. Segmentation of Arteries in Minimally Invasive Surgery Using Change Detection

    NASA Astrophysics Data System (ADS)

    Akbari, Hamed; Kosugi, Yukio; Kojima, Kazuyuki

    In laparoscopic surgery, the lack of tactile sensation and 3D visual feedback make it difficult to identify the position of a blood vessel intraoperatively. An unintentional partial tear or complete rupture of a blood vessel may result in a serious complication; moreover, if the surgeon cannot manage this situation, open surgery will be necessary. Differentiation of arteries from veins and other structures and the ability to independently detect them has a variety of applications in surgical procedures involving the head, neck, lung, heart, abdomen, and extremities. We have used the artery's pulsatile movement to detect and differentiate arteries from veins. The algorithm for change detection in this study uses edge detection for unsupervised image registration. Changed regions are identified by subtracting the systolic and diastolic images. As a post-processing step, region properties, including color average, area, major and minor axis lengths, perimeter, and solidity, are used as inputs of the LVQ (Learning Vector Quantization) network. The output results in two object classes: arteries and non-artery regions. After post-processing, arteries can be detected in the laparoscopic field. The registration method used here is evaluated in comparison with other linear and nonlinear elastic methods. The performance of this method is evaluated for the detection of arteries in several laparoscopic surgeries on an animal model and on eleven human patients. The performance evaluation criteria are based on false negative and false positive rates. This algorithm is able to detect artery regions, even in cases where the arteries are obscured by other tissues.

  17. Census cities experiment in urban change detection

    NASA Technical Reports Server (NTRS)

    Wray, J. R. (Principal Investigator)

    1973-01-01

    The author has identified the following significant results. Work continues on mapping of 1970 urban land use from 1970 census contemporaneous aircraft photography. In addition, change detection analysis from 1972 aircraft photography is underway for several urban test sites. Land use maps, mosaics, and census overlays for the two largest urban test sites are nearing publication readiness. Preliminary examinations of ERTS-1 imagery of San Francisco Bay have been conducted which show that tracts of land of more than 10 acres in size which are undergoing development in an urban setting can be identified. In addition, each spectral band is being evaluated as to its utility for urban analyses. It has been found that MSS infrared band 7 helps to differentiate intra-urban land use details not found in other MSS bands or in the RBV coverage of the same scene. Good quality false CIR composites have been generated from 9 x 9 inch positive MSS bands using the Diazo process.

  18. Knowledge-based pitch detection

    NASA Astrophysics Data System (ADS)

    Dove, W. P.

    1986-06-01

    Many problems in signal processing involve a mixture of numerical and symbolic knowledge. Examples of problems of this sort include the recognition of speech and the analysis of images. This thesis focuses on the problem of employing a mixture of symbolic and numerical knowledge within a single system, through the development of a system directed at a modified pitch detection problem. For this thesis, the conventional pitch detection problem was modified by providing a phonetic transcript and sex/age information as input to the system, in addition to the acoustic waveform. The Pitch Detector's Assistant (PDA) system that was developed is an interactive facility for evaluating ways of approaching this problem. The PDA system allows the user to interrupt processing at any point, change either input data, derived data, or problem knowledge and continue execution.

  19. A targeted change-detection procedure by combining change vector analysis and post-classification approach

    NASA Astrophysics Data System (ADS)

    Ye, Su; Chen, Dongmei; Yu, Jie

    2016-04-01

    In remote sensing, conventional supervised change-detection methods usually require effective training data for multiple change types. This paper introduces a more flexible and efficient procedure that seeks to identify only the changes that users are interested in, here after referred to as ;targeted change detection;. Based on a one-class classifier ;Support Vector Domain Description (SVDD);, a novel algorithm named ;Three-layer SVDD Fusion (TLSF); is developed specially for targeted change detection. The proposed algorithm combines one-class classification generated from change vector maps, as well as before- and after-change images in order to get a more reliable detecting result. In addition, this paper introduces a detailed workflow for implementing this algorithm. This workflow has been applied to two case studies with different practical monitoring objectives: urban expansion and forest fire assessment. The experiment results of these two case studies show that the overall accuracy of our proposed algorithm is superior (Kappa statistics are 86.3% and 87.8% for Case 1 and 2, respectively), compared to applying SVDD to change vector analysis and post-classification comparison.

  20. On event-based optical flow detection

    PubMed Central

    Brosch, Tobias; Tschechne, Stephan; Neumann, Heiko

    2015-01-01

    Event-based sensing, i.e., the asynchronous detection of luminance changes, promises low-energy, high dynamic range, and sparse sensing. This stands in contrast to whole image frame-wise acquisition by standard cameras. Here, we systematically investigate the implications of event-based sensing in the context of visual motion, or flow, estimation. Starting from a common theoretical foundation, we discuss different principal approaches for optical flow detection ranging from gradient-based methods over plane-fitting to filter based methods and identify strengths and weaknesses of each class. Gradient-based methods for local motion integration are shown to suffer from the sparse encoding in address-event representations (AER). Approaches exploiting the local plane like structure of the event cloud, on the other hand, are shown to be well suited. Within this class, filter based approaches are shown to define a proper detection scheme which can also deal with the problem of representing multiple motions at a single location (motion transparency). A novel biologically inspired efficient motion detector is proposed, analyzed and experimentally validated. Furthermore, a stage of surround normalization is incorporated. Together with the filtering this defines a canonical circuit for motion feature detection. The theoretical analysis shows that such an integrated circuit reduces motion ambiguity in addition to decorrelating the representation of motion related activations. PMID:25941470

  1. NO2 Detection Using Microcantilever Based Potentiometry

    PubMed Central

    Qazi, Muhammad; Koley, Goutam

    2008-01-01

    A highly sensitive and novel sensor platform for gases and volatile chemicals using microcantilever based potentiometry is reported. A resonant cantilever is used to detect the changes in surface work functions of functionalized substrates caused by adsorption of target gas molecules. Surface work function (SWF) changes were measured for different functionalization layers made of transition metal oxide thin films with the flow of NO2. The rate of change in SWF for In2O3 and SnO2 were found to be ∼80 and ∼100 μV/sec, respectively, for 70 ppm NO2. A sensitivity of 64 μV/sec for SWF change was also found for 70 ppm NO2 concentration for isolated clusters of ZnO nanowires, indicating that this technique is applicable even for nano-clusters of sensing materials where amperometric detection is impossible due to material discontinuity. NO2 detection as low as 400 ppb was possible using highly insulating In2O3 and SnO2 thin films (resistivity > 1 TΩ/□. Two different forms of nano scale graphite were compared with the transition oxide based functionalization layer for sensing sub-ppm NO2 sensing. It was observed that nanostructured graphite (NG) shows much higher sensitivity and lower response time than transition metal oxides. PMID:27873919

  2. NO₂ Detection Using Microcantilever Based Potentiometry.

    PubMed

    Qazi, Muhammad; Koley, Goutam

    2008-11-12

    A highly sensitive and novel sensor platform for gases and volatile chemicals using microcantilever based potentiometry is reported. A resonant cantilever is used to detect the changes in surface work functions of functionalized substrates caused by adsorption of target gas molecules. Surface work function (SWF) changes were measured for different functionalization layers made of transition metal oxide thin films with the flow of NO₂. The rate of change in SWF for In₂O₃ and SnO₂ were found to be ~80 and ~100 μV/sec, respectively, for 70 ppm NO₂. A sensitivity of 64 μV/sec for SWF change was also found for 70 ppm NO₂ concentration for isolated clusters of ZnO nanowires, indicating that this technique is applicable even for nano-clusters of sensing materials where amperometric detection is impossible due to material discontinuity. NO₂ detection as low as 400 ppb was possible using highly insulating In₂O₃ and SnO₂ thin films (resistivity > 1 TΩ/⎕). Two different forms of nano scale graphite were compared with the transition oxide based functionalization layer for sensing sub-ppm NO₂ sensing. It was observed that nanostructured graphite (NG) shows much higher sensitivity and lower response time than transition metal oxides.

  3. Discriminative genre-independent audio-visual scene change detection

    NASA Astrophysics Data System (ADS)

    Wilson, Kevin W.; Divakaran, Ajay

    2009-01-01

    We present a technique for genre-independent scene-change detection using audio and video features in a discriminative support vector machine (SVM) framework. This work builds on our previous work by adding a video feature based on the MPEG-7 "scalable color" descriptor. Adding this feature improves our detection rate over all genres by 5% to 15% for a fixed false positive rate of 10%. We also find that the genres that benefit the most are those with which the previous audio-only was least effective.

  4. Impact of LANDSAT MSS sensor differences on change detection analysis

    NASA Technical Reports Server (NTRS)

    Likens, W. C.; Wrigley, R. C.

    1983-01-01

    Some 512 by 512 pixel subwindows for simultaneously acquired scene pairs obtained by LANDSAT 2,3 and 4 multispectral band scanners were coregistered using LANDSAT 4 scenes as the base to which the other images were registered. Scattergrams between the coregistered scenes (a form of contingency analysis) were used to radiometrically compare data from the various sensors. Mode values were derived and used to visually fit a linear regression. Root mean square errors of the registration varied between .1 and 1.5 pixels. There appear to be no major problem preventing the use of LANDSAT 4 MSS with previous MSS sensors for change detection, provided the noise interference can be removed or minimized. Data normalizations for change detection should be based on the data rather than solely on calibration information. This allows simultaneous normalization of the atmosphere as well as the radiometry.

  5. Change detection experiments using Gotcha public release SAR data

    NASA Astrophysics Data System (ADS)

    Stojanovic, Ivana; Novak, Les

    2013-05-01

    In this paper we compare coherent change detection performance obtained using the maximum likelihood estimate (MLE) of the SAR image-pair coherence versus using the complex correlation coefficient coherence estimate (CCD). We also compare the non-coherent change detection performance (PD vs. PFA) versus the performance of the coherent change detection algorithms.

  6. Street environment change detection from mobile laser scanning point clouds

    NASA Astrophysics Data System (ADS)

    Xiao, Wen; Vallet, Bruno; Brédif, Mathieu; Paparoditis, Nicolas

    2015-09-01

    Mobile laser scanning (MLS) has become a popular technique for road inventory, building modelling, infrastructure management, mobility assessment, etc. Meanwhile, due to the high mobility of MLS systems, it is easy to revisit interested areas. However, change detection using MLS data of street environment has seldom been studied. In this paper, an approach that combines occupancy grids and a distance-based method for change detection from MLS point clouds is proposed. Unlike conventional occupancy grids, our occupancy-based method models space based on scanning rays and local point distributions in 3D without voxelization. A local cylindrical reference frame is presented for the interpolation of occupancy between rays according to the scanning geometry. The Dempster-Shafer theory (DST) is utilized for both intra-data evidence fusion and inter-data consistency assessment. Occupancy of reference point cloud is fused at the location of target points and then the consistency is evaluated directly on the points. A point-to-triangle (PTT) distance-based method is combined to improve the occupancy-based method. Because it is robust to penetrable objects, e.g. vegetation, which cause self-conflicts when modelling occupancy. The combined method tackles irregular point density and occlusion problems, also eliminates false detections on penetrable objects.

  7. Multiscale object-oriented change detection over urban areas

    NASA Astrophysics Data System (ADS)

    Wang, Jianmei; Li, Deren

    2006-10-01

    Urban growth induces urban spatial expansion in many cities in China. There is a great need for up-to-date information for effective urban decision-making and sustainable development. Many researches have demonstrated that satellite images, especial high resolution images, are very suitable for urban growth studies. However, change detection technique is the key to keep current with the rapid urban growth rate, taking advantage of tremendous amounts of satellite data. In this paper, a multi-scale object-oriented change detection approach integrating GIS and remote sensing is introduced. Firstly, a subset of image is cropped based on existing parcel boundaries stored in GIS database, then a multi-scale watershed transform is carried out to obtain the image objects. The image objects are classified into different land cover types by supervised classification based on their spectral, geometry and texture attributes. Finally a rule-based system is set up to judge every parcel one by one whether or not change happened comparing to existing GIS land use types. In order to verify the application validity of the presented methodology, the rural-urban fringe of Shanghai in China with the support of QuickBird date and GIS is tested, the result shown that it is effective to detect illegal land use parcel.

  8. Groundwater storage change detection using micro-gravimetric technology

    NASA Astrophysics Data System (ADS)

    El-Diasty, Mohammed

    2016-06-01

    In this paper, new perspectives and developments in applying a ground-based micro-gravimetric method to detect groundwater storage change in Waterloo Moraine are investigated. Four epochs of gravity survey were conducted using absolute gravimeter (FG5), two relative gravity meters (CG5) and two geodetic global positioning systems (GPS) in the Waterloo Moraine in May and August of 2010 and 2011, respectively. Data were processed using the parametric least-squares method and integrated with geological and hydrological studies. The gravity differences between May and August for 2010 and 2011 epochs were inverted to provide the estimated total water storage changes. Changes in soil water content obtained from land surface models of Ecological Assimilation of Land and Climate Observations (EALCO) and the Global Land Data Assimilation System (GLDAS) program were employed to estimate the groundwater storage change. The ratios between the estimated groundwater storage changes and measured water table changes (specific yields) were determined at a local monitoring well located in the survey area. The results showed that the estimates of specific yields between May and August of 2010 and 2011 were consistent at a significant confidence level and are also within the range of the specific yield from geological and hydrological studies. Therefore, the micro-gravimetric (absolute and relative gravity meters) technology has demonstrated the great potential in detecting groundwater storage change and specific yield for local scale aquifers such as Waterloo Moraine.

  9. Eye Movements and Display Change Detection during Reading

    ERIC Educational Resources Information Center

    Slattery, Timothy J.; Angele, Bernhard; Rayner, Keith

    2011-01-01

    In the boundary change paradigm (Rayner, 1975), when a reader's eyes cross an invisible boundary location, a preview word is replaced by a target word. Readers are generally unaware of such changes due to saccadic suppression. However, some readers detect changes on a few trials and a small percentage of them detect many changes. Two experiments…

  10. Bacteriophage-Based Pathogen Detection

    NASA Astrophysics Data System (ADS)

    Ripp, Steven

    Considered the most abundant organism on Earth, at a population approaching 1031, bacteriophage, or phage for short, mediate interactions with myriad bacterial hosts that has for decades been exploited in phage typing schemes for signature identification of clinical, food-borne, and water-borne pathogens. With over 5,000 phage being morphologically characterized and grouped as to susceptible host, there exists an enormous cache of bacterial-specific sensors that has more recently been incorporated into novel bio-recognition assays with heightened sensitivity, specificity, and speed. These assays take many forms, ranging from straightforward visualization of labeled phage as they attach to their specific bacterial hosts to reporter phage that genetically deposit trackable signals within their bacterial hosts to the detection of progeny phage or other uniquely identifiable elements released from infected host cells. A comprehensive review of these and other phage-based detection assays, as directed towards the detection and monitoring of bacterial pathogens, will be provided in this chapter.

  11. A method for detecting changes in long time series

    SciTech Connect

    Downing, D.J.; Lawkins, W.F.; Morris, M.D.; Ostrouchov, G.

    1995-09-01

    Modern scientific activities, both physical and computational, can result in time series of many thousands or even millions of data values. Here the authors describe a statistically motivated algorithm for quick screening of very long time series data for the presence of potentially interesting but arbitrary changes. The basic data model is a stationary Gaussian stochastic process, and the approach to detecting a change is the comparison of two predictions of the series at a time point or contiguous collection of time points. One prediction is a ``forecast``, i.e. based on data from earlier times, while the other a ``backcast``, i.e. based on data from later times. The statistic is the absolute value of the log-likelihood ratio for these two predictions, evaluated at the observed data. A conservative procedure is suggested for specifying critical values for the statistic under the null hypothesis of ``no change``.

  12. Fault Diagnostics Using Statistical Change Detection in the Bispectral Domain

    NASA Astrophysics Data System (ADS)

    Eugene Parker, B.; Ware, H. A.; Wipf, D. P.; Tompkins, W. R.; Clark, B. R.; Larson, E. C.; Vincent Poor, H.

    2000-07-01

    It is widely accepted that structural defects in rotating machinery components (e.g. bearings and gears) can be detected through monitoring of vibration and/or sound emissions. Traditional diagnostic vibration analysis attempts to match spectral lines with a priori -known defect frequencies that are characteristic of the affected machinery components. Emphasis herein is on use of bispectral-based statistical change detection algorithms for machinery health monitoring. The bispectrum, a third-order statistic, helps identify pairs of phase-related spectral components, which is useful for fault detection and isolation. In particular, the bispectrum helps sort through the clutter of usual (second-order) vibration spectra to extract useful information associated with the health of particular components. Seeded and non-seeded helicopter gearbox fault results (CH-46E and CH-47D, respectively) show that bispectral algorithms can detect faults at the level of an individual component (i.e. bearings or gears). Fault isolation is implicit with detection based on characteristic a priori -known defect frequencies. Important attributes of the bispectral SCD approach include: (1) it does not require a priori training data as is needed for traditional pattern-classifier-based approaches (and thereby avoids the significant time and cost investments necessary to obtain such data); (2) being based on higher-order moment-based energy detection, it makes no assumptions about the statistical model of the bispectral sequences that are generated; (3) it is operating-regime independent (i.e. works across different operating conditions, flight regimes, torque levels, etc., without knowledge of same); (4) it can be used to isolate faults to the level of specific machinery components (e.g. bearings and gears); and (5) it can be implemented using relatively inexpensive computer hardware, since only low-frequency vibrations need to be processed. The bispectral SCD algorithm thus represents a

  13. Nanomaterials based biosensors for cancer biomarker detection

    NASA Astrophysics Data System (ADS)

    Malhotra, Bansi D.; Kumar, Saurabh; Mouli Pandey, Chandra

    2016-04-01

    Biosensors have enormous potential to contribute to the evolution of new molecular diagnostic techniques for patients suffering with cancerous diseases. A major obstacle preventing faster development of biosensors pertains to the fact that cancer is a highly complex set of diseases. The oncologists currently rely on a few biomarkers and histological characterization of tumors. Some of the signatures include epigenetic and genetic markers, protein profiles, changes in gene expression, and post-translational modifications of proteins. These molecular signatures offer new opportunities for development of biosensors for cancer detection. In this context, conducting paper has recently been found to play an important role towards the fabrication of a biosensor for cancer biomarker detection. In this paper we will focus on results of some of the recent studies obtained in our laboratories relating to fabrication and application of nanomaterial modified paper based biosensors for cancer biomarker detection.

  14. Automated baseline change detection -- Phases 1 and 2. Final report

    SciTech Connect

    Byler, E.

    1997-10-31

    The primary objective of this project is to apply robotic and optical sensor technology to the operational inspection of mixed toxic and radioactive waste stored in barrels, using Automated Baseline Change Detection (ABCD), based on image subtraction. Absolute change detection is based on detecting any visible physical changes, regardless of cause, between a current inspection image of a barrel and an archived baseline image of the same barrel. Thus, in addition to rust, the ABCD system can also detect corrosion, leaks, dents, and bulges. The ABCD approach and method rely on precise camera positioning and repositioning relative to the barrel and on feature recognition in images. The ABCD image processing software was installed on a robotic vehicle developed under a related DOE/FETC contract DE-AC21-92MC29112 Intelligent Mobile Sensor System (IMSS) and integrated with the electronics and software. This vehicle was designed especially to navigate in DOE Waste Storage Facilities. Initial system testing was performed at Fernald in June 1996. After some further development and more extensive integration the prototype integrated system was installed and tested at the Radioactive Waste Management Facility (RWMC) at INEEL beginning in April 1997 through the present (November 1997). The integrated system, composed of ABCD imaging software and IMSS mobility base, is called MISS EVE (Mobile Intelligent Sensor System--Environmental Validation Expert). Evaluation of the integrated system in RWMC Building 628, containing approximately 10,000 drums, demonstrated an easy to use system with the ability to properly navigate through the facility, image all the defined drums, and process the results into a report delivered to the operator on a GUI interface and on hard copy. Further work is needed to make the brassboard system more operationally robust.

  15. Visible light communication based motion detection.

    PubMed

    Sewaiwar, Atul; Tiwari, Samrat Vikramaditya; Chung, Yeon-Ho

    2015-07-13

    In this paper, a unique and novel visible light communication based motion detection is presented. The proposed motion detection is performed based on white light LEDs and an array of photodetectors from existing visible light communication (VLC) links, thus providing VLC with three functionalities of illumination, communication and motion detection. The motion is detected by observing the pattern created by intentional obstruction of the VLC link. Experimental and simulation results demonstrate the validity of the proposed VLC based motion detection technique. The VLC based motion detection can benefit smart devices control in VLC based smart home environments.

  16. Detection of concealed ground targets in CARABAS SAR images using change detection

    NASA Astrophysics Data System (ADS)

    Ulander, Lars M.; Froelind, Per-Olov; Gustavsson, Anders; Hellsten, Hans; Larsson, Bjoern

    1999-08-01

    The paper describes a new method to detect man-made objects hidden under foliage or camouflage. The method is based on change detection and thus multiple revisits of the same area. It uses SAR image data provided by the low-frequency and ultra-wideband CARABAS SAR system which operate in the 20 - 90 MHz frequency range. Experimental results show a drastic reduction in false-alarm rate compared to methods based on single-pass SAR images. Small- to medium-sized trucks are consistently detected with a false-alarm rate of the order of 0.1 - 1 per km2. This level of false-alarm rate is quite sufficient for most military or civilian applications of interest.

  17. Detecting holocene changes in thermohaline circulation.

    PubMed

    Keigwin, L D; Boyle, E A

    2000-02-15

    Throughout the last glacial cycle, reorganizations of deep ocean water masses were coincident with rapid millennial-scale changes in climate. Climate changes have been less severe during the present interglacial, but evidence for concurrent deep ocean circulation change is ambiguous.

  18. What factors determine Belgian general practitioners’ approaches to detecting and managing substance abuse? A qualitative study based on the I-Change Model

    PubMed Central

    2014-01-01

    Background General practitioners (GPs) are considered to play a major role in detecting and managing substance abuse. However, little is known about how or why they decide to manage it. This study investigated the factors that influence GP behaviours with regard to the abuse of alcohol, illegal drugs, hypnotics, and tranquilisers among working Belgians. Methods Twenty Belgian GPs were interviewed. De Vries’ Integrated Change Model was used to guide the interviews and qualitative data analyses. Results GPs perceived higher levels of substance abuse in urban locations and among lower socioeconomic groups. Guidelines, if they existed, were primarily used in Flanders. Specific training was unevenly applied but considered useful. GPs who accepted abuse management cited strong interpersonal skills and available multidisciplinary networks as facilitators. GPs relied on their clinical common sense to detect abuse or initiate management. Specific patients’ situations and their social, psychological, or professional dysfunctions were cited as cues to action. GPs were strongly influenced by their personal representations of abuse, which included the balance between their professional responsibilities toward their patients and the patients’ responsibilities in managing their own health as well the GPs’ abilities to cope with unsatisfying patient outcomes without reaching professional exhaustion. GPs perceived substance abuse along a continuum ranging from a chronic disease (whose management was part of their responsibility) to a moral failing of untrustworthy people. Alcohol and cannabis were more socially acceptable than other drugs. Personal experiences of emotional burdens (including those regarding substance abuse) increased feelings of empathy or rejection toward patients. Multidisciplinary practices and professional experiences were cited as important factors with regard to engaging GPs in substance abuse management. Time constraints and personal investments were

  19. Change Detection in Naturalistic Pictures among Children with Autism

    ERIC Educational Resources Information Center

    Burack, Jacob A.; Joseph, Shari; Russo, Natalie; Shore, David I.; Porporino, Mafalda; Enns, James T.

    2009-01-01

    Persons with autism often show strong reactions to changes in the environment, suggesting that they may detect changes more efficiently than typically developing (TD) persons. However, Fletcher-Watson et al. (Br J Psychol 97:537-554, 2006) reported no differences between adults with autism and TD adults with a change-detection task. In this study,…

  20. [Early detection of cervical cancer in Chile: time for change].

    PubMed

    Léniz Martelli, Javiera; Van De Wyngard, Vanessa; Lagos, Marcela; Barriga, María Isabel; Puschel Illanes, Klaus; Ferreccio Readi, Catterina

    2014-08-01

    Mortality rates for cervical cancer (CC) in Chile are higher than those of developed countries and it has an unequal socioeconomic distribution. The recognition of human papilloma virus (HPV) as the causal agent of cervical cancer in the early 80's changed the prevention paradigms. Current goals are to prevent HPV infection by vaccination before the onset of sexual activity and to detect HPV infection in women older than 30 years. This article reviews CC prevention and early detection methods, discusses relevant evidence to support a change in Chile and presents an innovation proposal. A strategy of primary screening based on HPV detection followed by triage of HPV-positive women by colposcopy in primary care or by cytological or molecular reflex testing is proposed. Due to the existence in Chile of a well-organized nationwide CC prevention program, the replacement of a low-sensitivity screening test such as the Papanicolau test with a highly sensitive one such as HPV detection, could quickly improve the effectiveness of the program. The program also has a network of personnel qualified to conduct naked-eye inspections of the cervix, who could easily be trained to perform triage colposcopy. The incorporation of new prevention strategies could reduce the deaths of Chilean women and correct inequities.

  1. Epigenetic changes detected in micropropagated hop plants.

    PubMed

    Peredo, Elena L; Arroyo-García, Rosa; Revilla, M Angeles

    2009-07-01

    Micropropagation is a widely used technique in hops (Humulus lupulus L.). However, to the best of our knowledge, the genetic and epigenetic stability of the microplants has never been tested before. In the present study, two hop accessions were established in vitro and micropropagated for 2 years. The genetic and epigenetic stability of the in vitro plants was analyzed with several molecular techniques: random amplified DNA polymorphism (RAPD), retrotransposon microsatellite amplified polymorphism (REMAP), and methylation-sensitive amplification polymorphism (MSAP). No genetic variation among control and treated plants was found, even after 12 cycles of micropropagation. Epigenetic variation was detected, first, when field and in vitro samples were compared. Nearly a 30% of the detected fragments presented the same pattern of alterations in all the vitroplants. Second, lower levels of epigenetic variation were detected among plants from the different subcultures. Part of this detected variation seemed to be accumulated along the 12 sequential subcultures tested.

  2. Urban change detection procedures using Landsat digital data

    NASA Technical Reports Server (NTRS)

    Jensen, J. R.; Toll, D. L.

    1982-01-01

    Landsat multispectral scanner data was applied to an urban change detection problem in Denver, CO. A dichotomous key yielding ten stages of residential development at the urban fringe was developed. This heuristic model allowed one to identify certain stages of development which are difficult to detect when performing digital change detection using Landsat data. The stages of development were evaluated in terms of their spectral and derived textural characteristics. Landsat band 5 (0.6-0.7 micron) and texture data produced change detection maps which were approximately 81 percent accurate. Results indicated that the stage of development and the spectral/textural features affect the change in the spectral values used for change detection. These preliminary findings will hopefully prove valuable for improved change detection at the urban fringe.

  3. Unsupervised detection and analysis of changes in everyday physical activity data.

    PubMed

    Sprint, Gina; Cook, Diane J; Schmitter-Edgecombe, Maureen

    2016-10-01

    Sensor-based time series data can be utilized to monitor changes in human behavior as a person makes a significant lifestyle change, such as progress toward a fitness goal. Recently, wearable sensors have increased in popularity as people aspire to be more conscientious of their physical health. Automatically detecting and tracking behavior changes from wearable sensor-collected physical activity data can provide a valuable monitoring and motivating tool. In this paper, we formalize the problem of unsupervised physical activity change detection and address the problem with our Physical Activity Change Detection (PACD) approach. PACD is a framework that detects changes between time periods, determines significance of the detected changes, and analyzes the nature of the changes. We compare the abilities of three change detection algorithms from the literature and one proposed algorithm to capture different types of changes as part of PACD. We illustrate and evaluate PACD on synthetic data and using Fitbit data collected from older adults who participated in a health intervention study. Results indicate PACD detects several changes in both datasets. The proposed change algorithms and analysis methods are useful data mining techniques for unsupervised, window-based change detection with potential to track users' physical activity and motivate progress toward their health goals.

  4. Water Detection Based on Color Variation

    NASA Technical Reports Server (NTRS)

    Rankin, Arturo L.

    2012-01-01

    This software has been designed to detect water bodies that are out in the open on cross-country terrain at close range (out to 30 meters), using imagery acquired from a stereo pair of color cameras mounted on a terrestrial, unmanned ground vehicle (UGV). This detector exploits the fact that the color variation across water bodies is generally larger and more uniform than that of other naturally occurring types of terrain, such as soil and vegetation. Non-traversable water bodies, such as large puddles, ponds, and lakes, are detected based on color variation, image intensity variance, image intensity gradient, size, and shape. At ranges beyond 20 meters, water bodies out in the open can be indirectly detected by detecting reflections of the sky below the horizon in color imagery. But at closer range, the color coming out of a water body dominates sky reflections, and the water cue from sky reflections is of marginal use. Since there may be times during UGV autonomous navigation when a water body does not come into a perception system s field of view until it is at close range, the ability to detect water bodies at close range is critical. Factors that influence the perceived color of a water body at close range are the amount and type of sediment in the water, the water s depth, and the angle of incidence to the water body. Developing a single model of the mixture ratio of light reflected off the water surface (to the camera) to light coming out of the water body (to the camera) for all water bodies would be fairly difficult. Instead, this software detects close water bodies based on local terrain features and the natural, uniform change in color that occurs across the surface from the leading edge to the trailing edge.

  5. Occupancy change detection system and method

    SciTech Connect

    Bruemmer, David J; Few, Douglas A

    2009-09-01

    A robot platform includes perceptors, locomotors, and a system controller. The system controller executes instructions for producing an occupancy grid map of an environment around the robot, scanning the environment to generate a current obstacle map relative to a current robot position, and converting the current obstacle map to a current occupancy grid map. The instructions also include processing each grid cell in the occupancy grid map. Within the processing of each grid cell, the instructions include comparing each grid cell in the occupancy grid map to a corresponding grid cell in the current occupancy grid map. For grid cells with a difference, the instructions include defining a change vector for each changed grid cell, wherein the change vector includes a direction from the robot to the changed grid cell and a range from the robot to the changed grid cell.

  6. Symmetrized local co-registration optimization for anomalous change detection

    SciTech Connect

    Wohlberg, Brendt E; Theiler, James P

    2009-01-01

    The goal of anomalous change detection (ACD) is to identify what unusual changes have occurred in a scene, based on two images of the scene taken at different times and under different conditions. The actual anomalous changes need to be distinguished from the incidental differences that occur throughout the imagery, and one of the most common and confounding of these incidental differences is due to the misregistration of the images, due to limitations of the registration pre-processing applied to the image pair. We propose a general method to compensate for residual misregistration in any ACD algorithm which constructs an estimate of the degree of 'anomalousness' for every pixel in the image pair. The method computes a modified misregistration-insensitive anomalousness by making local re-registration adjustments to minimize the local anomalousness. In this paper we describe a symmetrized version of our initial algorithm, and find significant performance improvements in the anomalous change detection ROC curves for a number of real and synthetic data sets.

  7. Multi-lane detection based on multiple vanishing points detection

    NASA Astrophysics Data System (ADS)

    Li, Chuanxiang; Nie, Yiming; Dai, Bin; Wu, Tao

    2015-03-01

    Lane detection plays a significant role in Advanced Driver Assistance Systems (ADAS) for intelligent vehicles. In this paper we present a multi-lane detection method based on multiple vanishing points detection. A new multi-lane model assumes that a single lane, which has two approximately parallel boundaries, may not parallel to others on road plane. Non-parallel lanes associate with different vanishing points. A biological plausibility model is used to detect multiple vanishing points and fit lane model. Experimental results show that the proposed method can detect both parallel lanes and non-parallel lanes.

  8. Time series analysis of ground-based microwave measurements at K- and V-bands to detect temporal changes in water vapor and temperature profiles

    NASA Astrophysics Data System (ADS)

    Panda, Sibananda; Sahoo, Swaroop; Pandithurai, Govindan

    2017-01-01

    Ground-based microwave measurements performed at water vapor and oxygen absorption line frequencies are widely used for remote sensing of tropospheric water vapor density and temperature profiles, respectively. Recent work has shown that Bayesian optimal estimation can be used for improving accuracy of radiometer retrieved water vapor and temperature profiles. This paper focuses on using Bayesian optimal estimation along with time series of independent frequency measurements at K- and V-bands. The measurements are used along with statistically significant but short background data sets to retrieve and sense temporal variations and gradients in water vapor and temperature profiles. To study this capability, the Indian Institute of Tropical Meteorology (IITM) deployed a microwave radiometer at Mahabubnagar, Telangana, during August 2011 as part of the Integrated Ground Campaign during the Cloud Aerosol Interaction and Precipitation Enhancement Experiment (CAIPEEX-IGOC). In this study, temperature profiles for the first time have been estimated using short but statistically significant background information so as to improve the accuracy of the retrieved profiles as well as to be able to detect gradients. Estimated water vapor and temperature profiles are compared with those taken from the reanalysis data updated by the Earth System Research Laboratory, National Oceanic and Atmospheric Administration (NOAA), to determine the range of possible errors. Similarly, root mean square errors are evaluated for a month for water vapor and temperature profiles to estimate the accuracy of the retrievals. It is found that water vapor and temperature profiles can be estimated with an acceptable accuracy by using a background information data set compiled over a period of 1 month.

  9. Two stages of parafoveal processing during reading: Evidence from a display change detection task.

    PubMed

    Angele, Bernhard; Slattery, Timothy J; Rayner, Keith

    2016-08-01

    We used a display change detection paradigm (Slattery, Angele, & Rayner Human Perception and Performance, 37, 1924-1938 2011) to investigate whether display change detection uses orthographic regularity and whether detection is affected by the processing difficulty of the word preceding the boundary that triggers the display change. Subjects were significantly more sensitive to display changes when the change was from a nonwordlike preview than when the change was from a wordlike preview, but the preview benefit effect on the target word was not affected by whether the preview was wordlike or nonwordlike. Additionally, we did not find any influence of preboundary word frequency on display change detection performance. Our results suggest that display change detection and lexical processing do not use the same cognitive mechanisms. We propose that parafoveal processing takes place in two stages: an early, orthography-based, preattentional stage, and a late, attention-dependent lexical access stage.

  10. A Dual-Process Account of Auditory Change Detection

    ERIC Educational Resources Information Center

    McAnally, Ken I.; Martin, Russell L.; Eramudugolla, Ranmalee; Stuart, Geoffrey W.; Irvine, Dexter R. F.; Mattingley, Jason B.

    2010-01-01

    Listeners can be "deaf" to a substantial change in a scene comprising multiple auditory objects unless their attention has been directed to the changed object. It is unclear whether auditory change detection relies on identification of the objects in pre- and post-change scenes. We compared the rates at which listeners correctly identify changed…

  11. Competitive SWIFT cluster templates enhance detection of aging changes

    PubMed Central

    Rebhahn, Jonathan A.; Roumanes, David R.; Qi, Yilin; Khan, Atif; Thakar, Juilee; Rosenberg, Alex; Lee, F. Eun‐Hyung; Quataert, Sally A.; Sharma, Gaurav

    2015-01-01

    Abstract Clustering‐based algorithms for automated analysis of flow cytometry datasets have achieved more efficient and objective analysis than manual processing. Clustering organizes flow cytometry data into subpopulations with substantially homogenous characteristics but does not directly address the important problem of identifying the salient differences in subpopulations between subjects and groups. Here, we address this problem by augmenting SWIFT—a mixture model based clustering algorithm reported previously. First, we show that SWIFT clustering using a “template” mixture model, in which all subpopulations are represented, identifies small differences in cell numbers per subpopulation between samples. Second, we demonstrate that resolution of inter‐sample differences is increased by “competition” wherein a joint model is formed by combining the mixture model templates obtained from different groups. In the joint model, clusters from individual groups compete for the assignment of cells, sharpening differences between samples, particularly differences representing subpopulation shifts that are masked under clustering with a single template model. The benefit of competition was demonstrated first with a semisynthetic dataset obtained by deliberately shifting a known subpopulation within an actual flow cytometry sample. Single templates correctly identified changes in the number of cells in the subpopulation, but only the competition method detected small changes in median fluorescence. In further validation studies, competition identified a larger number of significantly altered subpopulations between young and elderly subjects. This enrichment was specific, because competition between templates from consensus male and female samples did not improve the detection of age‐related differences. Several changes between the young and elderly identified by SWIFT template competition were consistent with known alterations in the elderly, and additional

  12. Competitive SWIFT cluster templates enhance detection of aging changes.

    PubMed

    Rebhahn, Jonathan A; Roumanes, David R; Qi, Yilin; Khan, Atif; Thakar, Juilee; Rosenberg, Alex; Lee, F Eun-Hyung; Quataert, Sally A; Sharma, Gaurav; Mosmann, Tim R

    2016-01-01

    Clustering-based algorithms for automated analysis of flow cytometry datasets have achieved more efficient and objective analysis than manual processing. Clustering organizes flow cytometry data into subpopulations with substantially homogenous characteristics but does not directly address the important problem of identifying the salient differences in subpopulations between subjects and groups. Here, we address this problem by augmenting SWIFT--a mixture model based clustering algorithm reported previously. First, we show that SWIFT clustering using a "template" mixture model, in which all subpopulations are represented, identifies small differences in cell numbers per subpopulation between samples. Second, we demonstrate that resolution of inter-sample differences is increased by "competition" wherein a joint model is formed by combining the mixture model templates obtained from different groups. In the joint model, clusters from individual groups compete for the assignment of cells, sharpening differences between samples, particularly differences representing subpopulation shifts that are masked under clustering with a single template model. The benefit of competition was demonstrated first with a semisynthetic dataset obtained by deliberately shifting a known subpopulation within an actual flow cytometry sample. Single templates correctly identified changes in the number of cells in the subpopulation, but only the competition method detected small changes in median fluorescence. In further validation studies, competition identified a larger number of significantly altered subpopulations between young and elderly subjects. This enrichment was specific, because competition between templates from consensus male and female samples did not improve the detection of age-related differences. Several changes between the young and elderly identified by SWIFT template competition were consistent with known alterations in the elderly, and additional altered subpopulations

  13. Automated image based prominent nucleoli detection

    PubMed Central

    Yap, Choon K.; Kalaw, Emarene M.; Singh, Malay; Chong, Kian T.; Giron, Danilo M.; Huang, Chao-Hui; Cheng, Li; Law, Yan N.; Lee, Hwee Kuan

    2015-01-01

    Introduction: Nucleolar changes in cancer cells are one of the cytologic features important to the tumor pathologist in cancer assessments of tissue biopsies. However, inter-observer variability and the manual approach to this work hamper the accuracy of the assessment by pathologists. In this paper, we propose a computational method for prominent nucleoli pattern detection. Materials and Methods: Thirty-five hematoxylin and eosin stained images were acquired from prostate cancer, breast cancer, renal clear cell cancer and renal papillary cell cancer tissues. Prostate cancer images were used for the development of a computer-based automated prominent nucleoli pattern detector built on a cascade farm. An ensemble of approximately 1000 cascades was constructed by permuting different combinations of classifiers such as support vector machines, eXclusive component analysis, boosting, and logistic regression. The output of cascades was then combined using the RankBoost algorithm. The output of our prominent nucleoli pattern detector is a ranked set of detected image patches of patterns of prominent nucleoli. Results: The mean number of detected prominent nucleoli patterns in the top 100 ranked detected objects was 58 in the prostate cancer dataset, 68 in the breast cancer dataset, 86 in the renal clear cell cancer dataset, and 76 in the renal papillary cell cancer dataset. The proposed cascade farm performs twice as good as the use of a single cascade proposed in the seminal paper by Viola and Jones. For comparison, a naive algorithm that randomly chooses a pixel as a nucleoli pattern would detect five correct patterns in the first 100 ranked objects. Conclusions: Detection of sparse nucleoli patterns in a large background of highly variable tissue patterns is a difficult challenge our method has overcome. This study developed an accurate prominent nucleoli pattern detector with the potential to be used in the clinical settings. PMID:26167383

  14. Load and Rate of Change of Load Detection System.

    DTIC Science & Technology

    The present invention relates to a system for detecting and recording the level and rate of change of landing loads in the struts of aircraft landing...to a minimum pressure to record the level and rate of change of pressure detected by the sensor.

  15. Multiscale Region-Level VHR Image Change Detection via Sparse Change Descriptor and Robust Discriminative Dictionary Learning

    PubMed Central

    Xu, Yuan; Ding, Kun; Huo, Chunlei; Zhong, Zisha; Li, Haichang; Pan, Chunhong

    2015-01-01

    Very high resolution (VHR) image change detection is challenging due to the low discriminative ability of change feature and the difficulty of change decision in utilizing the multilevel contextual information. Most change feature extraction techniques put emphasis on the change degree description (i.e., in what degree the changes have happened), while they ignore the change pattern description (i.e., how the changes changed), which is of equal importance in characterizing the change signatures. Moreover, the simultaneous consideration of the classification robust to the registration noise and the multiscale region-consistent fusion is often neglected in change decision. To overcome such drawbacks, in this paper, a novel VHR image change detection method is proposed based on sparse change descriptor and robust discriminative dictionary learning. Sparse change descriptor combines the change degree component and the change pattern component, which are encoded by the sparse representation error and the morphological profile feature, respectively. Robust change decision is conducted by multiscale region-consistent fusion, which is implemented by the superpixel-level cosparse representation with robust discriminative dictionary and the conditional random field model. Experimental results confirm the effectiveness of the proposed change detection technique. PMID:25918748

  16. Change Detection in Rough Time Series

    DTIC Science & Technology

    2014-09-01

    support models. While at DSTO he has worked on applications for modelling strategic decisions, intelligence analysis, and decision support systems ...changing nature of expected droughts into the future thus indicates increasing stress on the MDB river and lake system such that pre-existing irrigation ...or inaccurate sensor data, subjective ratings of vague variables, imperfect intelligence reports, algorithmic derived measures indicating degrees

  17. Uncertainty in Estimation of Bioenergy Induced Lulc Change: Development of a New Change Detection Technique.

    NASA Astrophysics Data System (ADS)

    Singh, N.; Vatsavai, R. R.; Patlolla, D.; Bhaduri, B. L.; Lim, S. J.

    2015-12-01

    Recent estimates of bioenergy induced land use land cover change (LULCC) have large uncertainty due to misclassification errors in the LULC datasets used for analysis. These uncertainties are further compounded when data is modified by merging classes, aggregating pixels and change in classification methods over time. Hence the LULCC computed using these derived datasets is more a reflection of change in classification methods, change in input data and data manipulation rather than reflecting actual changes ion ground. Furthermore results are constrained by geographic extent, update frequency and resolution of the dataset. To overcome this limitation we have developed a change detection system to identify yearly as well as seasonal changes in LULC patterns. Our method uses hierarchical clustering which works by grouping objects into a hierarchy based on phenological similarity of different vegetation types. The algorithm explicitly models vegetation phenology to reduce spurious changes. We apply our technique on globally available Moderate Resolution Imaging Spectroradiometer (MODIS) NDVI data at 250-meter resolution. We analyze 10 years of bi-weekly data to predict changes in the mid-western US as a case study. The results of our analysis are presented and its advantages over existing techniques are discussed.

  18. Automatic background updating for video-based vehicle detection

    NASA Astrophysics Data System (ADS)

    Hu, Chunhai; Li, Dongmei; Liu, Jichuan

    2008-03-01

    Video-based vehicle detection is one of the most valuable techniques for the Intelligent Transportation System (ITS). The widely used video-based vehicle detection technique is the background subtraction method. The key problem of this method is how to subtract and update the background effectively. In this paper an efficient background updating scheme based on Zone-Distribution for vehicle detection is proposed to resolve the problems caused by sudden camera perturbation, sudden or gradual illumination change and the sleeping person problem. The proposed scheme is robust and fast enough to satisfy the real-time constraints of vehicle detection.

  19. Synthetic circuit for exact adaptation and fold-change detection.

    PubMed

    Kim, Jongmin; Khetarpal, Ishan; Sen, Shaunak; Murray, Richard M

    2014-05-01

    Biological organisms use their sensory systems to detect changes in their environment. The ability of sensory systems to adapt to static inputs allows wide dynamic range as well as sensitivity to input changes including fold-change detection, a response that depends only on fold changes in input, and not on absolute changes. This input scale invariance underlies an important strategy for search that depends solely on the spatial profile of the input. Synthetic efforts to reproduce the architecture and response of cellular circuits provide an important step to foster understanding at the molecular level. We report the bottom-up assembly of biochemical systems that show exact adaptation and fold-change detection. Using a malachite green aptamer as the output, a synthetic transcriptional circuit with the connectivity of an incoherent feed-forward loop motif exhibits pulse generation and exact adaptation. A simple mathematical model was used to assess the amplitude and duration of pulse response as well as the parameter regimes required for fold-change detection. Upon parameter tuning, this synthetic circuit exhibits fold-change detection for four successive rounds of two-fold input changes. The experimental realization of fold-change detection circuit highlights the programmability of transcriptional switches and the ability to obtain predictive dynamical systems in a cell-free environment for technological applications.

  20. Synthetic circuit for exact adaptation and fold-change detection

    PubMed Central

    Kim, Jongmin; Khetarpal, Ishan; Murray, Richard M.

    2014-01-01

    Biological organisms use their sensory systems to detect changes in their environment. The ability of sensory systems to adapt to static inputs allows wide dynamic range as well as sensitivity to input changes including fold-change detection, a response that depends only on fold changes in input, and not on absolute changes. This input scale invariance underlies an important strategy for search that depends solely on the spatial profile of the input. Synthetic efforts to reproduce the architecture and response of cellular circuits provide an important step to foster understanding at the molecular level. We report the bottom-up assembly of biochemical systems that show exact adaptation and fold-change detection. Using a malachite green aptamer as the output, a synthetic transcriptional circuit with the connectivity of an incoherent feed-forward loop motif exhibits pulse generation and exact adaptation. A simple mathematical model was used to assess the amplitude and duration of pulse response as well as the parameter regimes required for fold-change detection. Upon parameter tuning, this synthetic circuit exhibits fold-change detection for four successive rounds of two-fold input changes. The experimental realization of fold-change detection circuit highlights the programmability of transcriptional switches and the ability to obtain predictive dynamical systems in a cell-free environment for technological applications. PMID:24728988

  1. Detecting land-use/land-cover change in rural-urban fringe areas using extended change-vector analysis

    NASA Astrophysics Data System (ADS)

    He, Chunyang; Wei, Anni; Shi, Peijun; Zhang, Qiaofeng; Zhao, Yuanyuan

    2011-08-01

    Detecting land-use/land-cover (LULC) changes in rural-urban fringe areas (RUFAs) timely and accurately using satellite imagery is essential for land-use planning and management in China. Although traditional spectral-based change-vector analysis (CVA) can effectively detect LULC change in many cases, it encounters difficulties in RUFAs because of deficiencies in the spectral information of satellite images. To detect LULC changes in RUFAs effectively, this paper proposes an extended CVA approach that incorporates textural change information into the traditional spectral-based CVA. The extended CVA was applied to three different pilot RUFAs in China with different remotely sensed data, including Landsat Thematic Mapper (TM), China-Brazil Earth Resources Satellite (CBERS) and Advanced Land Observing Satellite (ALOS) images. The results demonstrated the improvement of the extended CVA compared to the traditional spectral-based CVA with the overall accuracy increased between 4.66% and 8.00% and the kappa coefficient increased between 0.10 and 0.15, respectively. The advantage of the extended CVA lies in its integration of both spectral and textural change information to detect LULC changes, allowing for effective discrimination of LULC changes that are spectrally similar but texturally different in RUFAs. The extended CVA has great potential to be widely used for LULC-change detection in RUFAs, which are often heterogeneous and fragmental in nature, with rich textural information.

  2. Mutual Comparative Filtering for Change Detection in Videos with Unstable Illumination Conditions

    NASA Astrophysics Data System (ADS)

    Sidyakin, Sergey V.; Vishnyakov, Boris V.; Vizilter, Yuri V.; Roslov, Nikolay I.

    2016-06-01

    In this paper we propose a new approach for change detection and moving objects detection in videos with unstable, abrupt illumination changes. This approach is based on mutual comparative filters and background normalization. We give the definitions of mutual comparative filters and outline their strong advantage for change detection purposes. Presented approach allows us to deal with changing illumination conditions in a simple and efficient way and does not have drawbacks, which exist in models that assume different color transformation laws. The proposed procedure can be used to improve a number of background modelling methods, which are not specifically designed to work under illumination changes.

  3. A new statistical approach to climate change detection and attribution

    NASA Astrophysics Data System (ADS)

    Ribes, Aurélien; Zwiers, Francis W.; Azaïs, Jean-Marc; Naveau, Philippe

    2017-01-01

    We propose here a new statistical approach to climate change detection and attribution that is based on additive decomposition and simple hypothesis testing. Most current statistical methods for detection and attribution rely on linear regression models where the observations are regressed onto expected response patterns to different external forcings. These methods do not use physical information provided by climate models regarding the expected response magnitudes to constrain the estimated responses to the forcings. Climate modelling uncertainty is difficult to take into account with regression based methods and is almost never treated explicitly. As an alternative to this approach, our statistical model is only based on the additivity assumption; the proposed method does not regress observations onto expected response patterns. We introduce estimation and testing procedures based on likelihood maximization, and show that climate modelling uncertainty can easily be accounted for. Some discussion is provided on how to practically estimate the climate modelling uncertainty based on an ensemble of opportunity. Our approach is based on the " models are statistically indistinguishable from the truth" paradigm, where the difference between any given model and the truth has the same distribution as the difference between any pair of models, but other choices might also be considered. The properties of this approach are illustrated and discussed based on synthetic data. Lastly, the method is applied to the linear trend in global mean temperature over the period 1951-2010. Consistent with the last IPCC assessment report, we find that most of the observed warming over this period (+0.65 K) is attributable to anthropogenic forcings (+0.67 ± 0.12 K, 90 % confidence range), with a very limited contribution from natural forcings (-0.01± 0.02 K).

  4. Transistor-based particle detection systems and methods

    SciTech Connect

    Jain, Ankit; Nair, Pradeep R.; Alam, Muhammad Ashraful

    2015-06-09

    Transistor-based particle detection systems and methods may be configured to detect charged and non-charged particles. Such systems may include a supporting structure contacting a gate of a transistor and separating the gate from a dielectric of the transistor, and the transistor may have a near pull-in bias and a sub-threshold region bias to facilitate particle detection. The transistor may be configured to change current flow through the transistor in response to a change in stiffness of the gate caused by securing of a particle to the gate, and the transistor-based particle detection system may configured to detect the non-charged particle at least from the change in current flow.

  5. High-Sensitivity Surface-Enhanced Raman Scattering (SERS) Substrate Based on a Gold Colloid Solution with a pH Change for Detection of Trace-Level Polycyclic Aromatic Hydrocarbons in Aqueous Solution.

    PubMed

    Shi, Xiaofeng; Liu, Shu; Han, Xiaohong; Ma, Jun; Jiang, Yongchao; Yu, Guifeng

    2015-05-01

    In this study, a gold colloid solution whose parameters were optimized, and without any surfactants, was developed as a surface-enhanced Raman scattering (SERS) substrate for the detection of trace-level polycyclic aromatic hydrocarbons (PAHs). A gold colloid solution with 57 nm gold particles and pH 13 was prepared to be the SERS substrate. It had impressive enhancement that was two orders of magnitude higher than that of a gold colloid solution with 57 nm gold particles and without pH change (pH 6). Even with a compact field-based Raman spectrometer, naphthalene, phenanthrene, anthracene, fluoranthene, and pyrene were detected, with limits of detection at 6.8 nM, 3.4 nM, 1.8 nM, 0.68 nM (680 pM), and 0.44 nM (440 pM), respectively. The significant enhancement was ascribed to an electromagnetic mechanism and a charge-transfer mechanism. Quantitative analyses for these five PAHs in water were also performed. The SERS intensities of PAHs were found to have good linear dependence relations with the concentrations in low concentration. This high-sensitivity, easily prepared substrate offers a promising technology for the quantitative detection of trace-level PAHs.

  6. Changing Diagnostic Methods and Increased Detection of Verotoxigenic Escherichia coli, Ireland

    PubMed Central

    Rice, Thomas; Quinn, Noreen; Lucey, Brigid

    2016-01-01

    The recent paradigm shift in infectious disease diagnosis from culture-based to molecular-based approaches is exemplified in the findings of a national study assessing the detection of verotoxigenic Escherichia coli infections in Ireland. The methodologic changes have been accompanied by a dramatic increase in detections of non-O157 verotoxigenic E. coli serotypes. PMID:27322897

  7. Improved change detection with local co-registration adjustments

    SciTech Connect

    Wohlberg, Brendt E; Theiler, James P

    2009-01-01

    We introduce a simple approach for compensating for residual misregistration error on the performance of anomalous change detection algorithms. Using real data with a simulation framework for anomalous change and with a real anomalous change, we illustrate the approach and investigate its effectiveness.

  8. Relative Saliency in Change Signals Affects Perceptual Comparison and Decision Processes in Change Detection

    ERIC Educational Resources Information Center

    Yang, Cheng-Ta

    2011-01-01

    Change detection requires perceptual comparison and decision processes on different features of multiattribute objects. How relative salience between two feature-changes influences the processes has not been addressed. This study used the systems factorial technology to investigate the processes when detecting changes in a Gabor patch with visual…

  9. Detecting data and schema changes in scientific documents

    SciTech Connect

    Adiwijaya, I; Critchlow, T; Musick, R

    1999-06-08

    Data stored in a data warehouse must be kept consistent and up-to-date with the underlying information sources. By providing the capability to identify, categorize and detect changes in these sources, only the modified data needs to be transferred and entered into the warehouse. Another alternative, periodically reloading from scratch, is obviously inefficient. When the schema of an information source changes, all components that interact with, or make use of, data originating from that source must be updated to conform to the new schema. In this paper, the authors present an approach to detecting data and schema changes in scientific documents. Scientific data is of particular interest because it is normally stored as semi-structured documents, and it incurs frequent schema updates. They address the change detection problem by detecting data and schema changes between two versions of the same semi-structured document. This paper presents a graph representation of semi-structured documents and their schema before describing their approach to detecting changes while parsing the document. It also discusses how analysis of a collection of schema changes obtained from comparing several individual can be used to detect complex schema changes.

  10. Detection of light transformations and concomitant changes in surface albedo.

    PubMed

    Gerhard, Holly E; Maloney, Laurence T

    2010-07-16

    We report two experiments demonstrating that (1) observers are sensitive to information about changes in the light field not captured by local scene statistics and that (2) they can use this information to enhance detection of changes in surface albedo. Observers viewed scenes consisting of matte surfaces at many orientations illuminated by a collimated light source. All surfaces were achromatic, all lights neutral. In the first experiment, observers attempted to discriminate small changes in direction of the collimated light source (light transformations) from matched changes in the albedos of all surfaces (non-light transformations). Light changes and non-light changes shared the same local scene statistics and edge ratios, but the latter were not consistent with any change in direction to the collimated source. We found that observers could discriminate light changes as small as 5 degrees with sensitivity d' > 1 and accurately judge the direction of change. In a second experiment, we measured observers' ability to detect a change in the surface albedo of an isolated surface patch during either a light change or a surface change. Observers were more accurate in detecting isolated albedo changes during light changes. Measures of sensitivity d' were more than twice as great.

  11. Detecting and isolating abrupt changes in linear switching systems

    NASA Astrophysics Data System (ADS)

    Nazari, Sohail; Zhao, Qing; Huang, Biao

    2015-04-01

    In this paper, a novel fault detection and isolation (FDI) method for switching linear systems is developed. All input and output signals are assumed to be corrupted with measurement noises. In the proposed method, a 'lifted' linear model named as stochastic hybrid decoupling polynomial (SHDP) is introduced. The SHDP model governs the dynamics of the switching linear system with all different modes, and is independent of the switching sequence. The error-in-variable (EIV) representation of SHDP is derived, and is used for the fault residual generation and isolation following the well-adopted local approach. The proposed FDI method can detect and isolate the fault-induced abrupt changes in switching models' parameters without estimating the switching modes. Furthermore, in this paper, the analytical expressions of the gradient vector and Hessian matrix are obtained based on the EIV SHDP formulation, so that they can be used to implement the online fault detection scheme. The performance of the proposed method is then illustrated by simulation examples.

  12. Detection and Attribution of Regional Climate Change

    SciTech Connect

    Bala, G; Mirin, A

    2007-01-19

    We developed a high resolution global coupled modeling capability to perform breakthrough studies of the regional climate change. The atmospheric component in our simulation uses a 1{sup o} latitude x 1.25{sup o} longitude grid which is the finest resolution ever used for the NCAR coupled climate model CCSM3. Substantial testing and slight retuning was required to get an acceptable control simulation. The major accomplishment is the validation of this new high resolution configuration of CCSM3. There are major improvements in our simulation of the surface wind stress and sea ice thickness distribution in the Arctic. Surface wind stress and ocean circulation in the Antarctic Circumpolar Current are also improved. Our results demonstrate that the FV version of the CCSM coupled model is a state of the art climate model whose simulation capabilities are in the class of those used for IPCC assessments. We have also provided 1000 years of model data to Scripps Institution of Oceanography to estimate the natural variability of stream flow in California. In the future, our global model simulations will provide boundary data to high-resolution mesoscale model that will be used at LLNL. The mesoscale model would dynamically downscale the GCM climate to regional scale on climate time scales.

  13. Long-Term Land Cover and Land Use Change Detection in the Kilombero Floodplain (Tanzania) Using Multitemporal Metrics-Based Compositing

    NASA Astrophysics Data System (ADS)

    Thonfeld, Frank; Amler, Esther; Kirimi, Fridah; Steinbach, Stefanie; Menz, Gunter

    2016-08-01

    Land cover and land use changes have great impact on the hydrological characteristics of the Kilombero floodplain wetland (Tanzania) and its surroundings. Conservation interests meet the wetland's ecosystem function as a site for growing food production. The system, processes and ecosystem responses are, however, not yet fully understood. We make use of Landsat data to assess long-term land cover and land use changes in the catchment. In this paper we describe an approach to circumvent the challenge of frequent cloud cover and to consider the dynamics of the wetland during the classification. The approach results in temporal composites for four different decades and hierarchically organized maps including two levels of detail and providing information about dynamic classes.

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

    NASA Astrophysics Data System (ADS)

    Tanaka, Hirokazu; Katura, Takusige

    2011-08-01

    Using a machine-learning classification algorithm applied to near-infrared spectroscopy (NIRS) signals, we classify a success (change detection) or a failure (change blindness) in detecting visual changes for a change-detection task. Five subjects perform a change-detection task, and their brain activities are continuously monitored. A support-vector-machine algorithm is applied to classify the change-detection and change-blindness trials, and correct classification probability of 70-90% is obtained for four subjects. Two types of temporal shapes in classification probabilities are found: one exhibiting a maximum value after the task is completed (postdictive type), and another exhibiting a maximum value during the task (predictive type). As for the postdictive type, the classification probability begins to increase immediately after the task completion and reaches its maximum in about the time scale of neuronal hemodynamic response, reflecting a subjective report of change detection. As for the predictive type, the classification probability shows an increase at the task initiation and is maximal while subjects are performing the task, predicting the task performance in detecting a change. We conclude that decoding change detection and change blindness from NIRS signal is possible and argue some future applications toward brain-machine interfaces.

  15. Spatial Temporal Land Use Change Detection Using Google Earth Data

    NASA Astrophysics Data System (ADS)

    Wibowo, Adi; Osman Salleh, Khairulmaini; Sitanala Frans, F. Th. R.; Mulyo Semedi, Jarot

    2016-11-01

    Land use as representation of human activities had different type. Human activity needs land for home, food, school, work, and leisure. Land use changed depends on human activity in the world within spatial and temporal term. This study aims to identify land use change using Google Earth data spatially and temporally. To answer the aim of this research, Google Earth data within five-year used for the analysis. This technique use for detection and mapping the land use change. The result saw the spatial-temporal land use change each year. This result addressed very importance of Google Earth Data as spatial temporal land use detection for land use mapping.

  16. Fast Change Point Detection for Electricity Market Analysis

    SciTech Connect

    Berkeley, UC; Gu, William; Choi, Jaesik; Gu, Ming; Simon, Horst; Wu, Kesheng

    2013-08-25

    Electricity is a vital part of our daily life; therefore it is important to avoid irregularities such as the California Electricity Crisis of 2000 and 2001. In this work, we seek to predict anomalies using advanced machine learning algorithms. These algorithms are effective, but computationally expensive, especially if we plan to apply them on hourly electricity market data covering a number of years. To address this challenge, we significantly accelerate the computation of the Gaussian Process (GP) for time series data. In the context of a Change Point Detection (CPD) algorithm, we reduce its computational complexity from O($n^{5}$) to O($n^{2}$). Our efficient algorithm makes it possible to compute the Change Points using the hourly price data from the California Electricity Crisis. By comparing the detected Change Points with known events, we show that the Change Point Detection algorithm is indeed effective in detecting signals preceding major events.

  17. Detection of Greenhouse-Gas-Induced Climatic Change

    SciTech Connect

    Jones, P.D.; Wigley, T.M.L.

    1998-05-26

    The objective of this report is to assemble and analyze instrumental climate data and to develop and apply climate models as a basis for (1) detecting greenhouse-gas-induced climatic change, and (2) validation of General Circulation Models.

  18. A stopping theoretic approach to minimal time detection of system parameter change

    NASA Technical Reports Server (NTRS)

    Mazumdar, Ravi R.

    1986-01-01

    The problem of minimal time detection of abrupt parameter changes in linear stochastic systems considered. The problem is posed as an optimal stopping problem for the detection in change of the induced probability measure. Under the assumption of a prior distribution for the time of change (or disruption) a stopping rule is given which minimizes the average detection delay when there is knowledge of the new measure after the change. When the new induced measure is unknown, a stopping rule is given, based only on the noisy observations and is shown to be better than the a priori knowlege of the disruption time.

  19. Electrophysiological evidence for different types of change detection and change blindness.

    PubMed

    Busch, Niko A; Fründ, Ingo; Herrmann, Christoph S

    2010-08-01

    Numerous studies have demonstrated that observers often fail to notice large changes in visual scenes, a phenomenon known as change blindness. Some experiments have suggested that phenomenological experience in change blindness experiments is more diverse than the common distinction between change detection and change blindness allows to resolve. Recently, it has been debated whether changes in visual scenes can be detected ("sensed") without a corresponding perception of the changing object ("seeing") and whether these phenomena build on fundamentally different perceptual processes. The present study investigated whether phenomenologically different perceptual processes such as sensing and seeing rely on different or similar neural processes. We studied ERP effects of visual change processing (as compared to change blindness) when observers merely detected the presence of a change ("sensing") and when they identified the changing object in addition to detection ("seeing"). Although the visual awareness negativity (VAN)/selection negativity was similar for detection with and without identification, a change-related positivity and the N2pc contralateral to changes were found exclusively when the change was fully identified. This finding indicates that change identification requires perceptual and neural processes that are not involved in mere detection. In a second experiment, we demonstrated that the VAN and N2pc effects are similar to effects of selective attention in a visual search task. By contrast, the change-related positivity was specific for conscious processing of visual changes. The results suggest that changes can be detected ("sensed") without perception of the changing object. Furthermore, sensing and seeing seem to rely on different neural processes and seem to constitute different types of visual perception. These findings bear implications for how different categories of visual awareness are related to different stages in visual processing.

  20. Automated detection of changes in sequential color ocular fundus images

    NASA Astrophysics Data System (ADS)

    Sakuma, Satoshi; Nakanishi, Tadashi; Takahashi, Yasuko; Fujino, Yuichi; Tsubouchi, Tetsuro; Nakanishi, Norimasa

    1998-06-01

    A recent trend is the automatic screening of color ocular fundus images. The examination of such images is used in the early detection of several adult diseases such as hypertension and diabetes. Since this type of examination is easier than CT, costs less, and has no harmful side effects, it will become a routine medical examination. Normal ocular fundus images are found in more than 90% of all people. To deal with the increasing number of such images, this paper proposes a new approach to process them automatically and accurately. Our approach, based on individual comparison, identifies changes in sequential images: a previously diagnosed normal reference image is compared to a non- diagnosed image.

  1. Robust real-time change detection in high jitter.

    SciTech Connect

    Simonson, Katherine Mary; Ma, Tian J.

    2009-08-01

    A new method is introduced for real-time detection of transient change in scenes observed by staring sensors that are subject to platform jitter, pixel defects, variable focus, and other real-world challenges. The approach uses flexible statistical models for the scene background and its variability, which are continually updated to track gradual drift in the sensor's performance and the scene under observation. Two separate models represent temporal and spatial variations in pixel intensity. For the temporal model, each new frame is projected into a low-dimensional subspace designed to capture the behavior of the frame data over a recent observation window. Per-pixel temporal standard deviation estimates are based on projection residuals. The second approach employs a simple representation of jitter to generate pixelwise moment estimates from a single frame. These estimates rely on spatial characteristics of the scene, and are used gauge each pixel's susceptibility to jitter. The temporal model handles pixels that are naturally variable due to sensor noise or moving scene elements, along with jitter displacements comparable to those observed in the recent past. The spatial model captures jitter-induced changes that may not have been seen previously. Change is declared in pixels whose current values are inconsistent with both models.

  2. Vibration-Based Damage Detection in Rotating Machinery

    SciTech Connect

    Farrar, C.R.; Duffey, T.A.

    1999-06-28

    Damage detection as determined from changes in the vibration characteristics of a system has been a popular research topic for the last thirty years. Numerous damage identification algorithms have been proposed for detecting and locating damage in structural and mechanical systems. To date, these damage-detection methods have shown mixed results. A particular application of vibration-based damage detection that has perhaps enjoyed the greatest success is that of damage detection in rotating machinery. This paper summarizes the state of technology in vibration-based damage detection applied to rotating machinery. The review interprets the damage detection process in terms of a statistical pattern recognition paradigm that encompasses all vibration-based damage detection methods and applications. The motivation for the study reported herein is to identify the reasons that vibration-based damage detection has been successfully applied to rotating machinery, but has yet to show robust applications to civil engineering infrastructure. The paper concludes by comparing and contrasting the vibration-based damage detection applied to rotating machinery with large civil engineering infrastructure applications.

  3. Detection of changes in DNA methylation induced by different doses of ground-base ion radiation in rice(oryza sativa L.)

    NASA Astrophysics Data System (ADS)

    Zhao, Qian; Sun, Yeqing; Wang, Wei; Wen, Bin

    Spaceflight represents a very complex environmental condition with highly ionizing radiations (HZE). To further investigate the incentives of ion effects in space environment, we performed on-ground simulated HZE particle radiations to rice seeds with different cumulative doses (0Gy, 0.01Gy, 0.02Gy, 0.1Gy, 0.2Gy, 1Gy , 2Gy, 5Gy, 20Gy ). Using Methylation-Sensitive Amplification Polymorphism (MSAP) analysis technology, differential polymorphism sites of DNA methylation of seedlings were analysed and acquired. The results showed that changes of methylation and demethylation on CCGG sites had taken place after irradiated treatments in all doses. It was noted that there was a stimulating effect in low-dose radiation(≤1 Gy). The minimum proportion of DNA methylation polymorphism level was 3.15% in 0.1Gy, whereas the maximum proportion was 9.87% in 2Gy, interestingly the proportion reduced with radiation doses increased, suggesting the dosage effects of radiation. We further found that the CG site tended to have a higher proportion of cytosine methylation alterations than CNG site in six of the eight dose groups. The results also indicated that different dose treatment groups showed various frequencies of methylation variation patterns: The type of CG hypermethylation was higher than CG hypormethylation in four low-dose groups (<≤2 Gy) ,whereas the result presented the opposite trends in all high-dose groups(>≥1 Gy). In addition, the type of CNG hypormethylation was obviously higher than the CNG hypermethylation in seven dose groups. This result indicated that the methylation variation patterns caused by radiation had site preferences. To investigate the mechanisms of sequences underlying alterations in DNA methylation after ion irradiation, we isolated, cloned and sequenced a subset of bands which showed obvious mutational bias. BLAST analysis indicated that many sequences showed significant homology to known function genes, most of which were related to

  4. Optical flow based finger stroke detection

    NASA Astrophysics Data System (ADS)

    Zhu, Zhongdi; Li, Bin; Wang, Kongqiao

    2010-07-01

    Finger stroke detection is an important topic in hand based Human Computer Interaction (HCI) system. Few research studies have carried out effective solutions to this problem. In this paper, we present a novel approach for stroke detection based on mono vision. Via analyzing the optical flow field within the finger area, our method is able to detect finger stroke under various camera position and visual angles. We present a thorough evaluation for each component of the algorithm, and show its efficiency and effectiveness on solving difficult stroke detection problems.

  5. Signal detection using change point analysis in postmarket surveillance†

    PubMed Central

    Xu, Zhiheng; Kass-Hout, Taha; Anderson-Smits, Colin; Gray, Gerry

    2015-01-01

    Purpose Signal detection methods have been used extensively in postmarket surveillance to identify elevated risks of adverse events associated with medical products (drugs, vaccines, and devices). However, current popular disproportionality methods ignore useful information such as trends when the data are aggregated over time for signal detection. Methods In this paper, we applied change point analysis (CPA) to trend analysis of medical products in a spontaneous adverse event reporting system. CPA was used to detect the time point at which statistical properties of a sequence of observations change over time. Two CPA approaches, change in mean and change in variance, were demonstrated by an example using neurostimulator adverse event dataset. Results Two significant change points associated with upward trends were detected in June 2008 (n = 20, p < 0.001) and May 2011 (n = 51, p = 0.003). Further investigation confirmed battery issues and expansion of the indication for use could be possible causes for the occurrence of these change points. Two time points showed extremely low number of loss of therapy events, two cases in October 2009 and three in November 2009, which could be the result of reporting issues such as underreporting. Conclusion As a complimentary tool to current signal detection efforts at FDA, CPA can be used to detect changes in the association between medical products and adverse events over time. Detecting these changes could be critical for public health regulation, adverse events surveillance, product recalls, and regulators’ understanding of the connection between adverse events and other events regarding regulated products. © 2015 The Authors. Pharmacoepidemiology and Drug Safety published by John Wiley & Sons, Ltd. PMID:25903221

  6. Vegetation cover change detection in Chamela-Cuixamala, Mexico

    NASA Astrophysics Data System (ADS)

    De la Barreda Bautista, Betsabé; López-Caloca, Alejandra A.

    2009-09-01

    In Mexico, and everywhere else, the ecosystems are constantly changing either by natural factors or anthropogenic activity. Remote sensing has been a key tool to monitoring these changes throughout history and also to understanding the ecological dynamics. Hence, sustainable development plans have been created in order to improve the decisionmaking process; thus, this paper analyses deforestation impact in a very important natural resourcing area in Mexico, considering land cover changes. The study area is located in the coast of Jalisco, Mexico, where deforestation and fragmentation as well as high speed touristic development have been the causes of enormous biodiversity losses; the Chamela-Cuixamala Biosphere Reserve is located within this area. It has great species richness and vast endemism. The exploitation of this biome is widespread all over the country and it has already had an impact in the reserve. The change detection multi-temporal study uses Landsat satellite imagery during the 1970-2003 time period. Thus, the objective of change detection analysis is to detect and localize environmental changes through time. The change detection method consists in producing an image of change likelihood (by post-classification, multivariate alteration detection) and thresholding it in order to produce the change map. Experimental results confirmed that the patterns of land use and land cover changes have increased significantly over the last decade. This study also analyzes the deforestation impact on biodiversity. The analysis validation was carried out using field and statistic data. Spatial-temporal changing range enables the analysis of the structural and dynamic effects on the ecosystem and it enhances better decision-making and public environmental policies to decrease or eliminate deforestation, the creation of natural protected areas as a biodiversity conservation method, and counteracting the global warming phenomena.

  7. Detection and Attribution of Anthropogenic Climate Change Impacts

    NASA Technical Reports Server (NTRS)

    Rosenzweig, Cynthia; Neofotis, Peter

    2013-01-01

    Human-influenced climate change is an observed phenomenon affecting physical and biological systems across the globe. The majority of observed impacts are related to temperature changes and are located in the northern high- and midlatitudes. However, new evidence is emerging that demonstrates that impacts are related to precipitation changes as well as temperature, and that climate change is impacting systems and sectors beyond the Northern Hemisphere. In this paper, we highlight some of this new evidence-focusing on regions and sectors that the Intergovernmental Panel on Climate Change Fourth Assessment Report (IPCC AR4) noted as under-represented-in the context of observed climate change impacts, direct and indirect drivers of change (including carbon dioxide itself), and methods of detection. We also present methods and studies attributing observed impacts to anthropogenic forcing. We argue that the expansion of methods of detection (in terms of a broader array of climate variables and data sources, inclusion of the major modes of climate variability, and incorporation of other drivers of change) is key to discerning the climate sensitivities of sectors and systems in regions where the impacts of climate change currently remain elusive. Attributing such changes to human forcing of the climate system, where possible, is important for development of effective mitigation and adaptation. Current challenges in documenting adaptation and the role of indigenous knowledge in detection and attribution are described.

  8. Statistical method for detecting structural change in the growth process.

    PubMed

    Ninomiya, Yoshiyuki; Yoshimoto, Atsushi

    2008-03-01

    Due to competition among individual trees and other exogenous factors that change the growth environment, each tree grows following its own growth trend with some structural changes in growth over time. In the present article, a new method is proposed to detect a structural change in the growth process. We formulate the method as a simple statistical test for signal detection without constructing any specific model for the structural change. To evaluate the p-value of the test, the tube method is developed because the regular distribution theory is insufficient. Using two sets of tree diameter growth data sampled from planted forest stands of Cryptomeria japonica in Japan, we conduct an analysis of identifying the effect of thinning on the growth process as a structural change. Our results demonstrate that the proposed method is useful to identify the structural change caused by thinning. We also provide the properties of the method in terms of the size and power of the test.

  9. Change point detection of the Persian Gulf sea surface temperature

    NASA Astrophysics Data System (ADS)

    Shirvani, A.

    2017-01-01

    In this study, the Student's t parametric and Mann-Whitney nonparametric change point models (CPMs) were applied to detect change point in the annual Persian Gulf sea surface temperature anomalies (PGSSTA) time series for the period 1951-2013. The PGSSTA time series, which were serially correlated, were transformed to produce an uncorrelated pre-whitened time series. The pre-whitened PGSSTA time series were utilized as the input file of change point models. Both the applied parametric and nonparametric CPMs estimated the change point in the PGSSTA in 1992. The PGSSTA follow the normal distribution up to 1992 and thereafter, but with a different mean value after year 1992. The estimated slope of linear trend in PGSSTA time series for the period 1951-1992 was negative; however, that was positive after the detected change point. Unlike the PGSSTA, the applied CPMs suggested no change point in the Niño3.4SSTA time series.

  10. Change detection inflates confidence on a subsequent recognition task.

    PubMed

    Fitzgerald, Ryan J; Oriet, Chris; Price, Heather L

    2011-11-01

    A face viewed under good encoding conditions is more likely to be remembered than a face viewed under poor encoding conditions. In four experiments we investigated how encoding conditions affected confidence in recognising faces from line-ups. Participants performed a change detection task followed by a recognition task and then rated how confident they were in their recognition accuracy. In the first two experiments the same faces were repeated across trials. In the final two experiments novel faces were used on each trial. Target-present and target-absent line-ups were utilised. In each experiment participants had greater recognition confidence after change detection than after change blindness. The finding that change detection inflates confidence, even for inaccurate recognitions, indicates recognition certainty can be a product of perceived encoding conditions rather than authentic memory strength.

  11. Coherent Change Detection: Theoretical Description and Experimental Results

    DTIC Science & Technology

    2006-08-01

    multilook polarimetric and interferometric SAR imagery,” IEEE Transactions on Geoscience and Remote Sensing, vol. 32, no. 5, pp. 1017–1027, 1994. 50. J. W...scene changes using repeat pass Synthetic Aperture Radar ( SAR ) imagery. As SAR is a coherent imaging system two forms of change detection may be...changes to the sub-resolution cell scattering structure that may be undetectable using inco- herent techniques. The repeat pass SAR imagery however, must

  12. Daytime Water Detection Based on Sky Reflections

    NASA Technical Reports Server (NTRS)

    Rankin, Arturo; Matthies, Larry; Bellutta, Paolo

    2011-01-01

    A water body s surface can be modeled as a horizontal mirror. Water detection based on sky reflections and color variation are complementary. A reflection coefficient model suggests sky reflections dominate the color of water at ranges > 12 meters. Water detection based on sky reflections: (1) geometrically locates the pixel in the sky that is reflecting on a candidate water pixel on the ground (2) predicts if the ground pixel is water based on color similarity and local terrain features. Water detection has been integrated on XUVs.

  13. Contour detection based on wavelet differentiation

    NASA Astrophysics Data System (ADS)

    Bezuglov, D.; Kuzin, A.; Voronin, V.

    2016-05-01

    This work proposes a novel algorithm for contour detection based on high-performance algorithm of wavelet analysis for multimedia applications. To solve the noise effect on the result of peaking in this paper we consider the direct and inverse wavelet differentiation. Extensive experimental evaluation on noisy images demonstrates that our contour detection method significantly outperform competing algorithms. The proposed algorithm provides a means of coupling our system to recognition application such as detection and identification of vehicle number plate.

  14. Geometric change detection in urban environments using images.

    PubMed

    Taneja, Aparna; Ballan, Luca; Pollefeys, Marc

    2015-11-01

    We propose a method to detect changes in the geometry of a city using panoramic images captured by a car driving around the city. The proposed method can be used to significantly optimize the process of updating the 3D model of an urban environment that is changing over time, by restricting this process to only those areas where changes are detected. With this application in mind, we designed our algorithm to specifically detect only structural changes in the environment, ignoring any changes in its appearance, and ignoring also all the changes which are not relevant for update purposes such as cars, people etc. The approach also accounts for the challenges involved in a large scale application of change detection, such as inaccuracies in the input geometry, errors in the geo-location data of the images as well as the limited amount of information due to sparse imagery. We evaluated our approach on a small scale setup using high resolution, densely captured images and a large scale setup covering an entire city using instead the more realistic scenario of low resolution, sparsely captured images. A quantitative evaluation was also conducted for the large scale setup consisting of 14,000 images.

  15. Intelligent-based Structural Damage Detection Model

    SciTech Connect

    Lee, Eric Wai Ming; Yu, K.F.

    2010-05-21

    This paper presents the application of a novel Artificial Neural Network (ANN) model for the diagnosis of structural damage. The ANN model, denoted as the GRNNFA, is a hybrid model combining the General Regression Neural Network Model (GRNN) and the Fuzzy ART (FA) model. It not only retains the important features of the GRNN and FA models (i.e. fast and stable network training and incremental growth of network structure) but also facilitates the removal of the noise embedded in the training samples. Structural damage alters the stiffness distribution of the structure and so as to change the natural frequencies and mode shapes of the system. The measured modal parameter changes due to a particular damage are treated as patterns for that damage. The proposed GRNNFA model was trained to learn those patterns in order to detect the possible damage location of the structure. Simulated data is employed to verify and illustrate the procedures of the proposed ANN-based damage diagnosis methodology. The results of this study have demonstrated the feasibility of applying the GRNNFA model to structural damage diagnosis even when the training samples were noise contaminated.

  16. Detecting Chemically Modified DNA Bases Using Surface Enhanced Raman Spectroscopy.

    PubMed

    Barhoumi, Aoune; Halas, Naomi J

    2011-12-15

    Post-translational modifications of DNA- changes in the chemical structure of individual bases that occur without changes in the DNA sequence- are known to alter gene expression. They are believed to result in frequently deleterious phenotypic changes, such as cancer. Methylation of adenine, methylation and hydroxymethylation of cytosine, and guanine oxidation are the primary DNA base modifications identified to date. Here we show it is possible to use surface enhanced Raman spectroscopy (SERS) to detect these primary DNA base modifications. SERS detection of modified DNA bases is label-free and requires minimal additional sample preparation, reducing the possibility of additional chemical modifications induced prior to measurement. This approach shows the feasibility of DNA base modification assessment as a potentially routine analysis that may be further developed for clinical diagnostics.

  17. Sociolect-Based Community Detection

    SciTech Connect

    Reynolds, William N.; Salter, William J.; Farber, Robert M.; Corley, Courtney D.; Dowling, Chase P.; Beeman, William O.; Smith-Lovin, Lynn; Choi, Joon Nak

    2013-06-06

    "Sociolects" are specialized vocabularies used by social subgroups defined by common interests or origins. We applied methods to retrieve large quantities of Twitter data based on expert-identified sociolects and then applied and developed network-analysis methods to relate sociolect use to network (sub-) structure. We show that novel methods including consideration of node populations, as well as edge counts, provide substantially enhanced performance compared to standard assortativity. We explain these methods, show their utility in analyzing large corpora of social media data, and discuss their further extensions and potential applications.

  18. Collaborative Regression-based Anatomical Landmark Detection

    PubMed Central

    Gao, Yaozong; Shen, Dinggang

    2015-01-01

    Anatomical landmark detection plays an important role in medical image analysis, e.g., for registration, segmentation and quantitative analysis. Among various existing methods for landmark detection, regression-based methods recently have drawn much attention due to robustness and efficiency. In such methods, landmarks are localized through voting from all image voxels, which is completely different from classification-based methods that use voxel-wise classification to detect landmarks. Despite robustness, the accuracy of regression-based landmark detection methods is often limited due to 1) inclusion of uninformative image voxels in the voting procedure, and 2) lack of effective ways to incorporate inter-landmark spatial dependency into the detection step. In this paper, we propose a collaborative landmark detection framework to address these limitations. The concept of collaboration is reflected in two aspects. 1) Multi-resolution collaboration. A multi-resolution strategy is proposed to hierarchically localize landmarks by gradually excluding uninformative votes from faraway voxels. Moreover, for the informative voxels near the landmark, a spherical sampling strategy is also designed in the training stage to improve their prediction accuracy. 2) Inter-landmark collaboration. A confidence-based landmark detection strategy is proposed to improve the detection accuracy of “difficult-to-detect” landmarks by using spatial guidance from “easy-to-detect” landmarks. To evaluate our method, we conducted experiments extensively on three datasets for detecting prostate landmarks and head & neck landmarks in computed tomography (CT) images, and also dental landmarks in cone beam computed tomography (CBCT) images. The results show the effectiveness of our collaborative landmark detection framework in improving landmark detection accuracy, compared to other state-of-the-art methods. PMID:26579736

  19. Collaborative regression-based anatomical landmark detection

    NASA Astrophysics Data System (ADS)

    Gao, Yaozong; Shen, Dinggang

    2015-12-01

    Anatomical landmark detection plays an important role in medical image analysis, e.g. for registration, segmentation and quantitative analysis. Among the various existing methods for landmark detection, regression-based methods have recently attracted much attention due to their robustness and efficiency. In these methods, landmarks are localised through voting from all image voxels, which is completely different from the classification-based methods that use voxel-wise classification to detect landmarks. Despite their robustness, the accuracy of regression-based landmark detection methods is often limited due to (1) the inclusion of uninformative image voxels in the voting procedure, and (2) the lack of effective ways to incorporate inter-landmark spatial dependency into the detection step. In this paper, we propose a collaborative landmark detection framework to address these limitations. The concept of collaboration is reflected in two aspects. (1) Multi-resolution collaboration. A multi-resolution strategy is proposed to hierarchically localise landmarks by gradually excluding uninformative votes from faraway voxels. Moreover, for informative voxels near the landmark, a spherical sampling strategy is also designed at the training stage to improve their prediction accuracy. (2) Inter-landmark collaboration. A confidence-based landmark detection strategy is proposed to improve the detection accuracy of ‘difficult-to-detect’ landmarks by using spatial guidance from ‘easy-to-detect’ landmarks. To evaluate our method, we conducted experiments extensively on three datasets for detecting prostate landmarks and head & neck landmarks in computed tomography images, and also dental landmarks in cone beam computed tomography images. The results show the effectiveness of our collaborative landmark detection framework in improving landmark detection accuracy, compared to other state-of-the-art methods.

  20. Context sensitivity in the detection of changes in facial emotion

    PubMed Central

    Yamashita, Yuichi; Fujimura, Tomomi; Katahira, Kentaro; Honda, Manabu; Okada, Masato; Okanoya, Kazuo

    2016-01-01

    In social contexts, reading subtle changes in others’ facial expressions is a crucial communication skill. To measure this ability, we developed an expression-change detection task, wherein a series of pictures of changes in an individual’s facial expressions within contextual scenes were presented. The results demonstrated that the detection of subtle changes was highly sensitive to contextual stimuli. That is, participants identified the direction of facial-expression changes more accurately and more quickly when they were ‘appropriate’—consistent with the valence of the contextual stimulus change—than when they were ‘inappropriate’. Moreover, individual differences in sensitivity to contextual stimuli were correlated with scores on the Toronto Alexithymia Scale, a commonly used measure of alexithymia tendencies. These results suggest that the current behavioural task might facilitate investigations of the role of context in human social cognition. PMID:27291099

  1. Structural Change Can Be Detected in Advanced-Glaucoma Eyes

    PubMed Central

    Belghith, Akram; Medeiros, Felipe A.; Bowd, Christopher; Liebmann, Jeffrey M.; Girkin, Christopher A.; Weinreb, Robert N.; Zangwill, Linda M.

    2016-01-01

    Purpose To compare spectral-domain optical coherence tomography (SD-OCT) standard structural measures and a new three-dimensional (3D) volume optic nerve head (ONH) change detection method for detecting change over time in severely advanced-glaucoma (open-angle glaucoma [OAG]) patients. Methods Thirty-five eyes of 35 patients with very advanced glaucoma (defined as a visual field mean deviation < −21 dB) and 46 eyes of 30 healthy subjects to estimate aging changes were included. Circumpapillary retinal fiber layer thickness (cpRNFL), minimum rim width (MRW), and macular retinal ganglion cell–inner plexiform layer (GCIPL) thicknesses were measured using the San Diego Automated Layer Segmentation Algorithm (SALSA). Progression was defined as structural loss faster than 95th percentile of healthy eyes. Three-dimensional volume ONH change was estimated using the Bayesian-kernel detection scheme (BKDS), which does not require extensive retinal layer segmentation. Results The number of progressing glaucoma eyes identified was highest for 3D volume BKDS (13, 37%), followed by GCPIL (11, 31%), cpRNFL (4, 11%), and MRW (2, 6%). In advanced-OAG eyes, only the mean rate of GCIPL change reached statistical significance, −0.18 μm/y (P = 0.02); the mean rates of cpRNFL and MRW change were not statistically different from zero. In healthy eyes, the mean rates of cpRNFL, MRW, and GCIPL change were significantly different from zero. (all P < 0.001). Conclusions Ganglion cell–inner plexiform layer and 3D volume BKDS show promise for identifying change in severely advanced glaucoma. These results suggest that structural change can be detected in very advanced disease. Longer follow-up is needed to determine whether changes identified are false positives or true progression. PMID:27454660

  2. Folded Compact Range Development and Coherent Change Detection Measurement Project

    SciTech Connect

    Sorensen, K.W.

    1995-03-01

    A novel, folded compact range configuration has been developed at the Sandia National Laboratories compact range antenna and radar cross section measurement facility, operated by the Radar/Antenna Department 2343, as a means of performing indoor, environmentally-controlled, far-field simulations of synthetic aperture radar (SAR) coherent change detection (CCD) measurements. This report describes the development of the folded compact range configuration, as well as the initial set of coherent change detection measurements made with the system. These measurements have been highly successful, and have demonstrated the viability of the folded compact range concept in simulating SAR CCD measurements. It is felt that follow-on measurements have the potential of contributing significantly to the body of knowledge available to the scientific community involved in CCD image generation and processing, and that this tool will be a significant aid in the research and development of change detection methodologies.

  3. Reconstruction of interrupted SAR imagery for persistent surveillance change detection

    NASA Astrophysics Data System (ADS)

    Stojanovic, Ivana; Karl, W. C.; Novak, Les

    2012-05-01

    In this paper we apply a sparse signal recovery technique for synthetic aperture radar (SAR) image formation from interrupted phase history data. Timeline constraints imposed on multi-function modern radars result in interrupted SAR data collection, which in turn leads to corrupted imagery that degrades reliable change detection. In this paper we extrapolate the missing data by applying the basis pursuit denoising algorithm (BPDN) in the image formation step, effectively, modeling the SAR scene as sparse. We investigate the effects of regular and random interruptions on the SAR point spread function (PSF), as well as on the quality of both coherent (CCD) and non-coherent (NCCD) change detection. We contrast the sparse reconstruction to the matched filter (MF) method, implemented via Fourier processing with missing data set to zero. To illustrate the capabilities of the gap-filling sparse reconstruction algorithm, we evaluate change detection performance using a pair of images from the GOTCHA data set.

  4. Change detection in coastal zone environments. [by Landsat MSS data analysis

    NASA Technical Reports Server (NTRS)

    Weismiller, R. A.; Kristof, S. J.; Scholz, D. K.; Anuta, P. E.; Momin, S. A.

    1977-01-01

    A study was conducted with the objective to develop and evaluate various change detection techniques based upon computer-aided analysis of Landsat multispectral scanner (MSS) data to monitor coastal zone environments. The study site selected includes a portion of the Matagorda Bay estuarine system located along the Texas Coast. The principal data sources for the study were MSS data collected on November, 27, 1972 and February 25, 1975. The MSS data were processed and a multidata eight-channel data set at a scale of 1:24,000 was obtained. A description is presented of four change detection techniques which were designed and implented for evaluation, taking into account postclassification comparison change detection, delta data change detection, spectral/temporal change classification, and layered spectral/temporal approach. The results of the investigation are discussed.

  5. Evaluation of change detection techniques for monitoring coastal zone environments

    NASA Technical Reports Server (NTRS)

    Weismiller, R. A.; Kristof, S. J.; Scholz, D. K.; Anuta, P. E.; Momin, S. M.

    1977-01-01

    Procedures for detecting changes in Landsat multispectral scanning imagery of coastal zone environments are discussed. Four detection procedures are examined: a comparison of independently produced spectral classifications; a classification of a multispectral difference data set; a single analysis of a multidate data set; and a maximum likelihood classification using multistage decision logic. The relatively complex maximum likelihood classification technique was found to yield results closest to those obtained with the comparison of independently produced spectral classifications, the chosen standard.

  6. Support Vector Machines for Multitemporal and Multisensor Change Detection in a Mining Area

    NASA Astrophysics Data System (ADS)

    Hecheltjen, Antje; Waske, Bjorn; Thonfeld, Frank; Braun, Matthias; Menz, Gunter

    2010-12-01

    Long-term change detection often implies the challenge of incorporating multitemporal data from different sensors. Most of the conventional change detection algorithms are designed for bi-temporal datasets from the same sensors detecting only the existence of changes. The labeling of change areas remains a difficult task. To overcome such drawbacks, much attention has been given lately to algorithms arising from machine learning, such as Support Vector Machines (SVMs). While SVMs have been applied successfully for land cover classifications, the exploitation of this approach for change detection is still in its infancy. Few studies have already proven the applicability of SVMs for bi- and multitemporal change detection using data from one sensor only. In this paper we demonstrate the application of SVM for multitemporal and -sensor change detection. Our study site covers lignite open pit mining areas in the German state North Rhine-Westphalia. The dataset consists of bi-temporal Landsat data and multi-temporal ERS SAR data covering two time slots (2001 and 2009). The SVM is conducted using the IDL program imageSVM. Change is deduced from one time slot to the next resulting in two change maps. In contrast to change detection, which is based on post-classification comparison, change detection is seen here as a specific classification problem. Thus, changes are directly classified from a layer-stack of the two years. To reduce the number of change classes, we created a change mask using the magnitude of Change Vector Analysis (CVA). Training data were selected for different change classes (e.g. forest to mining or mining to agriculture) as well as for the no-change classes (e.g. agriculture). Subsequently, they were divided in two independent sets for training the SVMs and accuracy assessment, respectively. Our study shows the applicability of SVMs to classify changes via SVMs. The proposed method yielded a change map of reclaimed and active mines. The use of ERS SAR

  7. Investigation on automatic change detection using pixel-changes and DSM-changes with ALOS-PRISM triplet images

    NASA Astrophysics Data System (ADS)

    Sasagawa, A.; Baltsavias, E.; Kocaman Aksakal, S.; Wegner, J. D.

    2013-10-01

    A new algorithm for automatic change detection is presented. It detects a pixel-change and DSM-change from two orthoimages and two DSMs, then it extracts the polygons in elevation-changed areas. Pixel-change is detected by using least squares fitting technique. This method can extract the visible changed areas between two orthoimages, while DSM-change is detected by difference DSM. From these two changes, polygons in elevation-changed areas are extracted using the longest matched line selection techniques. This method can automatically detect not only visible changed areas such as vegetated areas, new road construction areas and so on, but also elevation-changed areas such as new building construction, land improvement areas and so on with footprint polygon extraction. We have tested our method using the two sets of ALOS-PRISM triplet images observed over a testfield in Tsukuba, Japan. We confirmed that this method has an effect finding changed areas. Also we compared the number of extracted polygons between manual operation and our automatic method.

  8. Detecting regional patterns of changing CO2 flux in Alaska

    NASA Astrophysics Data System (ADS)

    Parazoo, Nicholas C.; Commane, Roisin; Wofsy, Steven C.; Koven, Charles D.; Sweeney, Colm; Lawrence, David M.; Lindaas, Jakob; Chang, Rachel Y.-W.; Miller, Charles E.

    2016-07-01

    With rapid changes in climate and the seasonal amplitude of carbon dioxide (CO2) in the Arctic, it is critical that we detect and quantify the underlying processes controlling the changing amplitude of CO2 to better predict carbon cycle feedbacks in the Arctic climate system. We use satellite and airborne observations of atmospheric CO2 with climatically forced CO2 flux simulations to assess the detectability of Alaskan carbon cycle signals as future warming evolves. We find that current satellite remote sensing technologies can detect changing uptake accurately during the growing season but lack sufficient cold season coverage and near-surface sensitivity to constrain annual carbon balance changes at regional scale. Airborne strategies that target regular vertical profile measurements within continental interiors are more sensitive to regional flux deeper into the cold season but currently lack sufficient spatial coverage throughout the entire cold season. Thus, the current CO2 observing network is unlikely to detect potentially large CO2 sources associated with deep permafrost thaw and cold season respiration expected over the next 50 y. Although continuity of current observations is vital, strategies and technologies focused on cold season measurements (active remote sensing, aircraft, and tall towers) and systematic sampling of vertical profiles across continental interiors over the full annual cycle are required to detect the onset of carbon release from thawing permafrost.

  9. Detecting regional patterns of changing CO2 flux in Alaska

    PubMed Central

    Parazoo, Nicholas C.; Wofsy, Steven C.; Koven, Charles D.; Sweeney, Colm; Lawrence, David M.; Lindaas, Jakob; Chang, Rachel Y.-W.; Miller, Charles E.

    2016-01-01

    With rapid changes in climate and the seasonal amplitude of carbon dioxide (CO2) in the Arctic, it is critical that we detect and quantify the underlying processes controlling the changing amplitude of CO2 to better predict carbon cycle feedbacks in the Arctic climate system. We use satellite and airborne observations of atmospheric CO2 with climatically forced CO2 flux simulations to assess the detectability of Alaskan carbon cycle signals as future warming evolves. We find that current satellite remote sensing technologies can detect changing uptake accurately during the growing season but lack sufficient cold season coverage and near-surface sensitivity to constrain annual carbon balance changes at regional scale. Airborne strategies that target regular vertical profile measurements within continental interiors are more sensitive to regional flux deeper into the cold season but currently lack sufficient spatial coverage throughout the entire cold season. Thus, the current CO2 observing network is unlikely to detect potentially large CO2 sources associated with deep permafrost thaw and cold season respiration expected over the next 50 y. Although continuity of current observations is vital, strategies and technologies focused on cold season measurements (active remote sensing, aircraft, and tall towers) and systematic sampling of vertical profiles across continental interiors over the full annual cycle are required to detect the onset of carbon release from thawing permafrost. PMID:27354511

  10. Optical and SAR data integration for automatic change pattern detection

    NASA Astrophysics Data System (ADS)

    Mishra, B.; Susaki, J.

    2014-09-01

    Automatic change pattern mapping in urban and sub-urban area is important but challenging due to the diversity of urban land use pattern. With multi-sensor imagery, it is possible to generate multidimensional unique information of Earth surface features that allow developing a relationship between a response of each feature to synthetic aperture radar (SAR) and optical sensors to track the change automatically. Thus, a SAR and optical data integration framework for change detection and a relationship for automatic change pattern detection were developed. It was carried out in three steps: (i) Computation of indicators from SAR and optical images, namely: normalized difference ratio (NDR) from multi-temporal SAR images and the normalized difference vegetation index difference (NDVI) from multi-temporal optical images, (ii) computing the change magnitude image from NDR and ΔNDVI and delineating the change area and (iii) the development of an empirical relationship, for automatic change pattern detection. The experiment was carried out in an outskirts part of Ho Chi Minh City, one of the fastest growing cities in the world. The empirical relationship between the response of surface feature to optical and SAR imagery has successfully delineated six changed classes in a very complex urban sprawl area that was otherwise impossible with multi-spectral imagery. The improvement of the change detection results by making use of the unique information on both sensors, optical and SAR, is also noticeable with a visual inspection and the kappa index was increased by 0.13 (0.75 to 0.88) in comparison to only optical images.

  11. Using adversary text to detect adversary phase changes.

    SciTech Connect

    Speed, Ann Elizabeth; Doser, Adele Beatrice; Warrender, Christina E.

    2009-05-01

    The purpose of this work was to help develop a research roadmap and small proof ofconcept for addressing key problems and gaps from the perspective of using text analysis methods as a primary tool for detecting when a group is undergoing a phase change. Self- rganizing map (SOM) techniques were used to analyze text data obtained from the tworld-wide web. Statistical studies indicate that it may be possible to predict phase changes, as well as detect whether or not an example of writing can be attributed to a group of interest.

  12. Landsat change detection can aid in water quality monitoring

    NASA Technical Reports Server (NTRS)

    Macdonald, H. C.; Steele, K. F.; Waite, W. P.; Shinn, M. R.

    1977-01-01

    Comparison between Landsat-1 and -2 imagery of Arkansas provided evidence of significant land use changes during the 1972-75 time period. Analysis of Arkansas historical water quality information has shown conclusively that whereas point source pollution generally can be detected by use of water quality data collected by state and federal agencies, sampling methodologies for nonpoint source contamination attributable to surface runoff are totally inadequate. The expensive undertaking of monitoring all nonpoint sources for numerous watersheds can be lessened by implementing Landsat change detection analyses.

  13. Dissolve Detection Using Intensity Change Information of Edge Pixels

    NASA Astrophysics Data System (ADS)

    Kwon, Chul-Hyun; Han, Doo-Jin; Kim, Hyun-Sool; Lee, Myung-Ho; Park, Sang-Hui

    Shot transition detection is a core technology in video browsing, indexing systems and information retrieval. In this paper we propose a dissolve detection algorithm using the characteristics of edge in MPEG compressed video. Using the intensity change information of edge pixels obtained by Sobel edge detector, we detect the location of a dissolve and its precise duration. We also present a new reliable method to eliminate the false dissolves. The proposed algorithm is tested in various types of videos, and the experimental results show that the proposed algorithm is effective and robust.

  14. Climate Change Detection and Attribution of Infrared Spectrum Measurements

    NASA Technical Reports Server (NTRS)

    Phojanamongkolkij, Nipa; Parker, Peter A.; Mlynczak, Martin G.

    2012-01-01

    Climate change occurs when the Earth's energy budget changes due to natural or possibly anthropogenic forcings. These forcings cause the climate system to adjust resulting in a new climate state that is warmer or cooler than the original. The key question is how to detect and attribute climate change. The inference of infrared spectral signatures of climate change has been discussed in the literature for nearly 30 years. Pioneering work in the 1980s noted that distinct spectral signatures would be evident in changes in the infrared radiance emitted by the Earth and its atmosphere, and that these could be observed from orbiting satellites. Since then, a number of other studies have advanced the concepts of spectral signatures of climate change. Today the concept of using spectral signatures to identify and attribute atmospheric composition change is firmly accepted and is the foundation of the Climate Absolute Radiance and Refractivity Observatory (CLARREO) satellite mission being developed at NASA. In this work, we will present an overview of the current climate change detection concept using climate model calculations as surrogates for climate change. Any future research work improving the methodology to achieve this concept will be valuable to our society.

  15. Detection of cardiac activity changes from human speech

    NASA Astrophysics Data System (ADS)

    Tovarek, Jaromir; Partila, Pavol; Voznak, Miroslav; Mikulec, Martin; Mehic, Miralem

    2015-05-01

    Impact of changes in blood pressure and pulse from human speech is disclosed in this article. The symptoms of increased physical activity are pulse, systolic and diastolic pressure. There are many methods of measuring and indicating these parameters. The measurements must be carried out using devices which are not used in everyday life. In most cases, the measurement of blood pressure and pulse following health problems or other adverse feelings. Nowadays, research teams are trying to design and implement modern methods in ordinary human activities. The main objective of the proposal is to reduce the delay between detecting the adverse pressure and to the mentioned warning signs and feelings. Common and frequent activity of man is speaking, while it is known that the function of the vocal tract can be affected by the change in heart activity. Therefore, it can be a useful parameter for detecting physiological changes. A method for detecting human physiological changes by speech processing and artificial neural network classification is described in this article. The pulse and blood pressure changes was induced by physical exercises in this experiment. The set of measured subjects was formed by ten healthy volunteers of both sexes. None of the subjects was a professional athlete. The process of the experiment was divided into phases before, during and after physical training. Pulse, systolic, diastolic pressure was measured and voice activity was recorded after each of them. The results of this experiment describe a method for detecting increased cardiac activity from human speech using artificial neural network.

  16. Quantitative naturalistic methods for detecting change points in psychotherapy research: an illustration with alliance ruptures.

    PubMed

    Eubanks-Carter, Catherine; Gorman, Bernard S; Muran, J Christopher

    2012-01-01

    Analysis of change points in psychotherapy process could increase our understanding of mechanisms of change. In particular, naturalistic change point detection methods that identify turning points or breakpoints in time series data could enhance our ability to identify and study alliance ruptures and resolutions. This paper presents four categories of statistical methods for detecting change points in psychotherapy process: criterion-based methods, control chart methods, partitioning methods, and regression methods. Each method's utility for identifying shifts in the alliance is illustrated using a case example from the Beth Israel Psychotherapy Research program. Advantages and disadvantages of the various methods are discussed.

  17. Sugar sensing based on induced pH changes.

    PubMed

    Kim, Youngmi; Hilderbrand, Scott A; Weissleder, Ralph; Tung, Ching-Hsuan

    2007-06-14

    A sensory assembly consisting of a pH sensitive NIR dye and an arylboronic acid shows ratiometric absorption changes with increased fluorescence intensity upon addition of sugar in aqueous media; this demonstrates a new signal transduction mechanism for the detection of sugar based on pH changes induced in the microenvironment of the sensory assembly.

  18. Detecting human influence in observed changes in precipitation

    NASA Astrophysics Data System (ADS)

    Polson, Debbie; Hegerl, Gabriele; Bollasina, Massimo; Wilcox, Laura; Zhang, Xuebin; Osborn, Timothy; Balan Sarojini, Beena

    2015-04-01

    Human induced changes to the precipitation could cause some of the most serious impacts of climate change, with potential consequences for water resources, health, agriculture and ecosystems. However, quantifying and understanding the drivers of changes to precipitation is challenging due to its large spatial and temporal variability, the lack of long-term observational records over much of the globe and the counteracting affects of greenhouse gases and aerosols. Nevertheless, detection and attribution studies have shown that human influence has changed both global and regional precipitation over the latter half of the 20th century. Using climates models to derive fingerprints of external forcing, we are able to show that greenhouse gas warming has driven large scale changes in precipitation. Greenhouse gas forcing is detectable in observed changes to zonal mean precipitation over land (Polson et al., 2012a). It has also been shown to have caused the intensification of the water cycle, enhancing existing patterns of the precipitation in the tropics and subtropics, over both land and ocean (Polson et al., 2012b). While at global scales, the influence of greenhouse gases is detectable in observations, separating the response of precipitation to anthropogenic aerosol forcing is more difficult. However, in some regions the influence of aerosols dominate, making it possible to detect aerosol forcing. Observed precipitation in the monsoon regions underwent substantial changes during the second half of the twentieth century, with drying from the 1950s to mid-1980s and increasing precipitation in recent decades. Climate model simulations are used to derive fingerprints of individual climate forcings (i.e., greenhouse gas, anthropogenic aerosol, and natural) and detection and attribution methods applied to determine which, if any, have driven these changes to monsoon precipitation. Even when accounting for internal variability of the climate, a clear signal of anthropogenic

  19. Daytime Water Detection Based on Sky Reflections

    NASA Technical Reports Server (NTRS)

    Rankin, Arturo L.; Matthies, Larry H.; Bellutta, Paolo

    2011-01-01

    Robust water detection is a critical perception requirement for unmanned ground vehicle (UGV) autonomous navigation. This is particularly true in wide-open areas where water can collect in naturally occurring terrain depressions during periods of heavy precipitation and form large water bodies. One of the properties of water useful for detecting it is that its surface acts as a horizontal mirror at large incidence angles. Water bodies can be indirectly detected by detecting reflections of the sky below the horizon in color imagery. The Jet Propulsion Laboratory (JPL) has implemented a water detector based on sky reflections that geometrically locates the pixel in the sky that is reflecting on a candidate water pixel on the ground and predicts if the ground pixel is water based on color similarity and local terrain features. This software detects water bodies in wide-open areas on cross-country terrain at mid- to far-range using imagery acquired from a forward-looking stereo pair of color cameras mounted on a terrestrial UGV. In three test sequences approaching a pond under a clear, overcast, and cloudy sky, the true positive detection rate was 100% when the UGV was beyond 7 meters of the water's leading edge and the largest false positive detection rate was 0.58%. The sky reflection based water detector has been integrated on an experimental unmanned vehicle and field tested at Ft. Indiantown Gap, PA, USA.

  20. Region duplication forgery detection technique based on SURF and HAC.

    PubMed

    Mishra, Parul; Mishra, Nishchol; Sharma, Sanjeev; Patel, Ravindra

    2013-01-01

    Region duplication forgery detection is a special type of forgery detection approach and widely used research topic under digital image forensics. In copy move forgery, a specific area is copied and then pasted into any other region of the image. Due to the availability of sophisticated image processing tools, it becomes very hard to detect forgery with naked eyes. From the forged region of an image no visual clues are often detected. For making the tampering more robust, various transformations like scaling, rotation, illumination changes, JPEG compression, noise addition, gamma correction, and blurring are applied. So there is a need for a method which performs efficiently in the presence of all such attacks. This paper presents a detection method based on speeded up robust features (SURF) and hierarchical agglomerative clustering (HAC). SURF detects the keypoints and their corresponding features. From these sets of keypoints, grouping is performed on the matched keypoints by HAC that shows copied and pasted regions.

  1. Region Duplication Forgery Detection Technique Based on SURF and HAC

    PubMed Central

    Mishra, Parul; Sharma, Sanjeev; Patel, Ravindra

    2013-01-01

    Region duplication forgery detection is a special type of forgery detection approach and widely used research topic under digital image forensics. In copy move forgery, a specific area is copied and then pasted into any other region of the image. Due to the availability of sophisticated image processing tools, it becomes very hard to detect forgery with naked eyes. From the forged region of an image no visual clues are often detected. For making the tampering more robust, various transformations like scaling, rotation, illumination changes, JPEG compression, noise addition, gamma correction, and blurring are applied. So there is a need for a method which performs efficiently in the presence of all such attacks. This paper presents a detection method based on speeded up robust features (SURF) and hierarchical agglomerative clustering (HAC). SURF detects the keypoints and their corresponding features. From these sets of keypoints, grouping is performed on the matched keypoints by HAC that shows copied and pasted regions. PMID:24311972

  2. Detecting Changes in Terrain Using Unmanned Aerial Vehicles

    NASA Technical Reports Server (NTRS)

    Rahman, Zia-ur; Hines, Glenn D.; Logan, Michael J.

    2005-01-01

    In recent years, small unmanned aerial vehicles (UAVs) have been used for more than the thrill they bring to model airplane enthusiasts. Their flexibility and low cost have made them a viable option for low-altitude reconnaissance. In a recent effort, we acquired video data from a small UAV during several passes over the same flight path. The objective of the exercise was to determine if objects had been added to the terrain along the flight path between flight passes. Several issues accrue to this simple-sounding problem: (1) lighting variations may cause false detection of objects because of changes in shadow orientation and strength between passes; (2) variations in the flight path due to wind-speed, and heading change may cause misalignment of gross features making the task of detecting changes between the frames very difficult; and (3) changes in the aircraft orientation and altitude lead to a change in size of the features from frame-to-frame making a comparison difficult. In this paper, we discuss our efforts to perform this change detection, and the lessons that we learned from this exercise.

  3. Pedestrian detection based on redundant wavelet transform

    NASA Astrophysics Data System (ADS)

    Huang, Lin; Ji, Liping; Hu, Ping; Yang, Tiejun

    2016-10-01

    Intelligent video surveillance is to analysis video or image sequences captured by a fixed or mobile surveillance camera, including moving object detection, segmentation and recognition. By using it, we can be notified immediately in an abnormal situation. Pedestrian detection plays an important role in an intelligent video surveillance system, and it is also a key technology in the field of intelligent vehicle. So pedestrian detection has very vital significance in traffic management optimization, security early warn and abnormal behavior detection. Generally, pedestrian detection can be summarized as: first to estimate moving areas; then to extract features of region of interest; finally to classify using a classifier. Redundant wavelet transform (RWT) overcomes the deficiency of shift variant of discrete wavelet transform, and it has better performance in motion estimation when compared to discrete wavelet transform. Addressing the problem of the detection of multi-pedestrian with different speed, we present an algorithm of pedestrian detection based on motion estimation using RWT, combining histogram of oriented gradients (HOG) and support vector machine (SVM). Firstly, three intensities of movement (IoM) are estimated using RWT and the corresponding areas are segmented. According to the different IoM, a region proposal (RP) is generated. Then, the features of a RP is extracted using HOG. Finally, the features are fed into a SVM trained by pedestrian databases and the final detection results are gained. Experiments show that the proposed algorithm can detect pedestrians accurately and efficiently.

  4. Detection of epigenetic changes using ANOVA with spatially varying coefficients.

    PubMed

    Guanghua, Xiao; Xinlei, Wang; Quincey, LaPlant; Nestler, Eric J; Xie, Yang

    2013-03-13

    Identification of genome-wide epigenetic changes, the stable changes in gene function without a change in DNA sequence, under various conditions plays an important role in biomedical research. High-throughput epigenetic experiments are useful tools to measure genome-wide epigenetic changes, but the measured intensity levels from these high-resolution genome-wide epigenetic profiling data are often spatially correlated with high noise levels. In addition, it is challenging to detect genome-wide epigenetic changes across multiple conditions, so efficient statistical methodology development is needed for this purpose. In this study, we consider ANOVA models with spatially varying coefficients, combined with a hierarchical Bayesian approach, to explicitly model spatial correlation caused by location-dependent biological effects (i.e., epigenetic changes) and borrow strength among neighboring probes to compare epigenetic changes across multiple conditions. Through simulation studies and applications in drug addiction and depression datasets, we find that our approach compares favorably with competing methods; it is more efficient in estimation and more effective in detecting epigenetic changes. In addition, it can provide biologically meaningful results.

  5. A change detection approach to moving object detection in low frame-rate video

    SciTech Connect

    Porter, Reid B; Harvey, Neal R; Theiler, James P

    2009-01-01

    Moving object detection is of significant interest in temporal image analysis since it is a first step in many object identification and tracking applications. A key component in almost all moving object detection algorithms is a pixel-level classifier, where each pixel is predicted to be either part of a moving object or part of the background. In this paper we investigate a change detection approach to the pixel-level classification problem and evaluate its impact on moving object detection. The change detection approach that we investigate was previously applied to multi-and hyper-spectral datasets, where images were typically taken several days, or months apart. In this paper, we apply the approach to low-frame rate (1-2 frames per second) video datasets.

  6. Recent advances in biosensor based endotoxin detection.

    PubMed

    Das, A P; Kumar, P S; Swain, S

    2014-01-15

    Endotoxins also referred to as pyrogens are chemically lipopolysaccharides habitually found in food, environment and clinical products of bacterial origin and are unavoidable ubiquitous microbiological contaminants. Pernicious issues of its contamination result in high mortality and severe morbidities. Standard traditional techniques are slow and cumbersome, highlighting the pressing need for evoking agile endotoxin detection system. The early and prompt detection of endotoxin assumes prime importance in health care, pharmacological and biomedical sectors. The unparalleled recognition abilities of LAL biosensors perched with remarkable sensitivity, high stability and reproducibility have bestowed it with persistent reliability and their possible fabrication for commercial applicability. This review paper entails an overview of various trends in current techniques available and other possible alternatives in biosensor based endotoxin detection together with its classification, epidemiological aspects, thrust areas demanding endotoxin control, commercially available detection sensors and a revolutionary unprecedented approach narrating the influence of omics for endotoxin detection.

  7. Detection of infrastructure manipulation with knowledge-based video surveillance

    NASA Astrophysics Data System (ADS)

    Muench, David; Hilsenbeck, Barbara; Kieritz, Hilke; Becker, Stefan; Grosselfinger, Ann-Kristin; Huebner, Wolfgang; Arens, Michael

    2016-10-01

    We are living in a world dependent on sophisticated technical infrastructure. Malicious manipulation of such critical infrastructure poses an enormous threat for all its users. Thus, running a critical infrastructure needs special attention to log the planned maintenance or to detect suspicious events. Towards this end, we present a knowledge-based surveillance approach capable of logging visual observable events in such an environment. The video surveillance modules are based on appearance-based person detection, which further is used to modulate the outcome of generic processing steps such as change detection or skin detection. A relation between the expected scene behavior and the underlying basic video surveillance modules is established. It will be shown that the combination already provides sufficient expressiveness to describe various everyday situations in indoor video surveillance. The whole approach is qualitatively and quantitatively evaluated on a prototypical scenario in a server room.

  8. Developing nucleic acid-based electrical detection systems

    PubMed Central

    Gabig-Ciminska, Magdalena

    2006-01-01

    Development of nucleic acid-based detection systems is the main focus of many research groups and high technology companies. The enormous work done in this field is particularly due to the broad versatility and variety of these sensing devices. From optical to electrical systems, from label-dependent to label-free approaches, from single to multi-analyte and array formats, this wide range of possibilities makes the research field very diversified and competitive. New challenges and requirements for an ideal detector suitable for nucleic acid analysis include high sensitivity and high specificity protocol that can be completed in a relatively short time offering at the same time low detection limit. Moreover, systems that can be miniaturized and automated present a significant advantage over conventional technology, especially if detection is needed in the field. Electrical system technology for nucleic acid-based detection is an enabling mode for making miniaturized to micro- and nanometer scale bio-monitoring devices via the fusion of modern micro- and nanofabrication technology and molecular biotechnology. The electrical biosensors that rely on the conversion of the Watson-Crick base-pair recognition event into a useful electrical signal are advancing rapidly, and recently are receiving much attention as a valuable tool for microbial pathogen detection. Pathogens may pose a serious threat to humans, animal and plants, thus their detection and analysis is a significant element of public health. Although different conventional methods for detection of pathogenic microorganisms and their toxins exist and are currently being applied, improvements of molecular-based detection methodologies have changed these traditional detection techniques and introduced a new era of rapid, miniaturized and automated electrical chip detection technologies into pathogen identification sector. In this review some developments and current directions in nucleic acid-based electrical

  9. Robust Detection of Examinees with Aberrant Answer Changes

    ERIC Educational Resources Information Center

    Belov, Dmitry I.

    2015-01-01

    The statistical analysis of answer changes (ACs) has uncovered multiple testing irregularities on large-scale assessments and is now routinely performed at testing organizations. However, AC data has an uncertainty caused by technological or human factors. Therefore, existing statistics (e.g., number of wrong-to-right ACs) used to detect examinees…

  10. Experiments in Coherent Change Detection for Synthetic Aperture Sonar

    DTIC Science & Technology

    2010-06-01

    over time. ACD techniques, long used in airborne radar applications, are just beginning to be applied to sidescan sonar . In Coherent Change Detection...accurate geo- registration), the complexity of the propagation environment, and the radiometric inconsistencies of conventional sidescan sonars ...will follow suit. As conventional sidescan sonars exhibit resolution that degrades with range and are typically limited to creation of backscatter

  11. Climate change and the detection of trends in annual runoff

    USGS Publications Warehouse

    McCabe, G.J.; Wolock, D.M.

    1997-01-01

    This study examines the statistical likelihood of detecting a trend in annual runoff given an assumed change in mean annual runoff, the underlying year-to-year variability in runoff, and serial correlation of annual runoff. Means, standard deviations, and lag-1 serial correlations of annual runoff were computed for 585 stream gages in the conterminous United States, and these statistics were used to compute the probability of detecting a prescribed trend in annual runoff. Assuming a linear 20% change in mean annual runoff over a 100 yr period and a significance level of 95%, the average probability of detecting a significant trend was 28% among the 585 stream gages. The largest probability of detecting a trend was in the northwestern U.S., the Great Lakes region, the northeastern U.S., the Appalachian Mountains, and parts of the northern Rocky Mountains. The smallest probability of trend detection was in the central and southwestern U.S., and in Florida. Low probabilities of trend detection were associated with low ratios of mean annual runoff to the standard deviation of annual runoff and with high lag-1 serial correlation in the data.

  12. 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.

  13. Multiple support vector machines for land cover change detection: An application for mapping urban extensions

    NASA Astrophysics Data System (ADS)

    Nemmour, Hassiba; Chibani, Youcef

    The reliability of support vector machines for classifying hyper-spectral images of remote sensing has been proven in various studies. In this paper, we investigate their applicability for land cover change detection. First, SVM-based change detection is presented and performed for mapping urban growth in the Algerian capital. Different performance indicators, as well as a comparison with artificial neural networks, are used to support our experimental analysis. In a second step, a combination framework is proposed to improve change detection accuracy. Two combination rules, namely, Fuzzy Integral and Attractor Dynamics, are implemented and evaluated with respect to individual SVMs. Recognition rates achieved by individual SVMs, compared to neural networks, confirm their efficiency for land cover change detection. Furthermore, the relevance of SVM combination is highlighted.

  14. Genetic base changes for December 2014

    Technology Transfer Automated Retrieval System (TEKTRAN)

    Genetic bases were updated previously in the United States in 1965, 1974, 1984, 1989, 1995, 2000, 2005, and 2010, and the next base change is scheduled for December 2014. Base changes for yield, health, fertility, and type traits in December 2014 are reported. Stepwise genetic bases allow predicted ...

  15. Laser-based detection of chemical contraband

    NASA Astrophysics Data System (ADS)

    Clemmer, Robert G.; Kelly, James F.; Martin, Steven W.; Mong, Gary M.; Sharpe, Steven W.

    1997-02-01

    The goal of our work is tow fold; 1) develop a portable and rapid laser based air sampler for detection of specific chemical contraband and 2) compile a spectral data base in both the near- and mid-IR of sufficiently high quality to be useful for gas phase spectroscopic identification of chemical contraband. During the synthesis or 'cooking' of many illicit chemical substances, relatively high concentrations of volatile solvents, chemical precursors and byproducts are unavoidably released to the atmosphere. In some instances, the final product may have sufficient vapor pressure to be detectable in the surrounding air. The detection of a single high-value effluent or the simultaneous detection of two or more low-value effluents can be used as reliable indicators of a nearby clandestine cooking operation. The designation of high- versus low-value effluent reflects both the commercial availability and legitimate usage of a specific chemical. This paper will describe PNNL's progress and efforts towards the development of a portable laser based air sampling system for the detection of clandestine manufacturing of methamphetamine. Although our current efforts ar focused on methamphetamine, we see no fundamental limitations on detection of other forms of chemical contraband manufacturing. This also includes the synthesis of certain classes of chemical weapons that have recently been deployed by terrorist groups.

  16. Environmental monitoring in peat bog areas by change detection methods

    NASA Astrophysics Data System (ADS)

    Michel, Ulrich; Mildes, Wiebke

    2016-10-01

    Remote sensing image analysis systems and geographic information systems (GIS) show great promise for the integration of a wide variety of spatial information supporting tasks such as urban and regional planning, natural resource management, agricultural studies and topographic or thematic mapping. Current and future remote sensing programs are based on a variety of sensors that will provide timely and repetitive multisensor earth observation on a global scale. GIS offer efficient tools for handling, manipulating, analyzing and presenting spatial data that are required for sensible decision making in various areas. The Environmental Monitoring project may serve as a convincing example of the operational use of integrated GIS/remote sensing technologies. The overall goal of the project is to assess the capabilities of satellite remote sensing for the analysis of land use changes, especially in moor areas. These areas are recognized as areas crucial to the mission of the Department of Environment and, therefore, to be placed under an extended level of protection. It is of critical importance, however, to have accurate and current information about the ecological and economic state of these sensitive areas. In selected pasture and moor areas, methods for multisensor data fusion have being developed and tested. The results of this testing show which techniques are useful for pasture and moor monitoring at an operational level. A hierarchical method is used for extracting bog land classes with respect to the environmental protection goals. A highly accurate classification of the following classes was accomplished: deciduous- and mixed forest, coniferous forest, water, very wet areas, meadowland/farmland with vegetation, meadowland/farmland with partly vegetation, meadowland/ farmland without vegetation, peat quarrying with maximum of 50% vegetation, de- and regeneration stages. In addition, a change detection analysis is performed in comparison with the existing

  17. Satellite Observations for Detecting and Tracking Changes in Atmospheric Composition

    NASA Technical Reports Server (NTRS)

    Neil, Doreen O.; Kondragunbta, Shobha; Osterman, Gregory; Pickering, Kenneth; Pinder, Robert W.; Prados, Ana I.; Szykman, James

    2009-01-01

    The satellite observations provide constraints on detailed atmospheric modeling, including emissions inventories, indications of transport, harmonized data over vast areas suitable for trends analysis, and a link between spatial scales ranging from local to global, and temporal scales from diurnal to interannual. 1 The National Oceanic and Atmospheric Administration's (NOAA) long-term commitments help provide these observations in cooperation with international meteorological organizations. NASA s long-term commitments will advance scientifically important observations as part of its Earth Science Program, and will assist the transition of the science measurements to applied analyses through the Applied Science Program. Both NASA and NOAA have begun to provide near realtime data and tools to visualize and analyze satellite data,2 while maintaining data quality, validation, and standards. Consequently, decision-makers can expect satellite data services to support air quality decision making now and in the future. The international scientific community's Integrated Global Atmosphere Chemistry Observation System Report3 outlined a plan for ground-based, airborne and satellite measurements and models to integrate the observations into a four-dimensional representation of the atmosphere (space and time) to support assessment and policy information needs. This plan is being carried out under the Global Earth Observation System of Systems (GEOSS). Demonstrations of such an integrated capability4 provide new understanding of the changing atmosphere and link policy decisions to benefits for society. In this article, we highlight the use of satellite data to constrain biomass burning emissions, to assess oxides of nitrogen (NO(x)) emission reductions, and to contribute to state implementation plans, as examples of the use of satellite observations for detecting and tracking changes in atmospheric composition.

  18. Fiber-Optic Based Compact Gas Leak Detection System

    NASA Technical Reports Server (NTRS)

    deGroot, Wim A.

    1995-01-01

    A propellant leak detection system based on Raman scattering principles is introduced. The proposed system is flexible and versatile as the result of the use of optical fibers. It is shown that multiple species can be monitored simultaneously. In this paper oxygen, nitrogen, carbon monoxide, and hydrogen are detected and monitored. The current detection sensitivity for both hydrogen and carbon monoxide is 1% partial pressure at ambient conditions. The sensitivity for oxygen and nitrogen is 0.5% partial pressure. The response time to changes in species concentration is three minutes. This system can be used to monitor multiple species at several locations.

  19. Detecting Abrupt Changes in a Piecewise Locally Stationary Time Series

    PubMed Central

    Last, Michael; Shumway, Robert

    2007-01-01

    Non-stationary time series arise in many settings, such as seismology, speech-processing, and finance. In many of these settings we are interested in points where a model of local stationarity is violated. We consider the problem of how to detect these change-points, which we identify by finding sharp changes in the time-varying power spectrum. Several different methods are considered, and we find that the symmetrized Kullback-Leibler information discrimination performs best in simulation studies. We derive asymptotic normality of our test statistic, and consistency of estimated change-point locations. We then demonstrate the technique on the problem of detecting arrival phases in earthquakes. PMID:19190715

  20. Detection and localization of change points in temporal networks with the aid of stochastic block models

    NASA Astrophysics Data System (ADS)

    De Ridder, Simon; Vandermarliere, Benjamin; Ryckebusch, Jan

    2016-11-01

    A framework based on generalized hierarchical random graphs (GHRGs) for the detection of change points in the structure of temporal networks has recently been developed by Peel and Clauset (2015 Proc. 29th AAAI Conf. on Artificial Intelligence). We build on this methodology and extend it to also include the versatile stochastic block models (SBMs) as a parametric family for reconstructing the empirical networks. We use five different techniques for change point detection on prototypical temporal networks, including empirical and synthetic ones. We find that none of the considered methods can consistently outperform the others when it comes to detecting and locating the expected change points in empirical temporal networks. With respect to the precision and the recall of the results of the change points, we find that the method based on a degree-corrected SBM has better recall properties than other dedicated methods, especially for sparse networks and smaller sliding time window widths.

  1. [Application of optical flow dynamic texture in land use/cover change detection].

    PubMed

    Yan, Li; Gong, Yi-Long; Zhang, Yi; Duan, Wei

    2014-11-01

    In the present study, a novel change detection approach for high resolution remote sensing images is proposed based on the optical flow dynamic texture (OFDT), which could achieve the land use & land cover change information automatically with a dynamic description of ground-object changes. This paper describes the ground-object gradual change process from the principle using optical flow theory, which breaks the ground-object sudden change hypothesis in remote sensing change detection methods in the past. As the steps of this method are simple, it could be integrated in the systems and software such as Land Resource Management and Urban Planning software that needs to find ground-object changes. This method takes into account the temporal dimension feature between remote sensing images, which provides a richer set of information for remote sensing change detection, thereby improving the status that most of the change detection methods are mainly dependent on the spatial dimension information. In this article, optical flow dynamic texture is the basic reflection of changes, and it is used in high resolution remote sensing image support vector machine post-classification change detection, combined with spectral information. The texture in the temporal dimension which is considered in this article has a smaller amount of data than most of the textures in the spatial dimensions. The highly automated texture computing has only one parameter to set, which could relax the onerous manual evaluation present status. The effectiveness of the proposed approach is evaluated with the 2011 and 2012 QuickBird datasets covering Duerbert Mongolian Autonomous County of Daqing City, China. Then, the effects of different optical flow smooth coefficient and the impact on the description of the ground-object changes in the method are deeply analyzed: The experiment result is satisfactory, with an 87.29% overall accuracy and an 0.850 7 Kappa index, and the method achieves better

  2. Water Detection Based on Sky Reflections

    NASA Technical Reports Server (NTRS)

    Rankin, Arturo L.; Matthies, Larry H.

    2010-01-01

    This software has been designed to detect water bodies that are out in the open on cross-country terrain at mid- to far-range (approximately 20 100 meters), using imagery acquired from a stereo pair of color cameras mounted on a terrestrial, unmanned ground vehicle (UGV). Non-traversable water bodies, such as large puddles, ponds, and lakes, are indirectly detected by detecting reflections of the sky below the horizon in color imagery. The appearance of water bodies in color imagery largely depends on the ratio of light reflected off the water surface to the light coming out of the water body. When a water body is far away, the angle of incidence is large, and the light reflected off the water surface dominates. We have exploited this behavior to detect water bodies out in the open at mid- to far-range. When a water body is detected at far range, a UGV s path planner can begin to look for alternate routes to the goal position sooner, rather than later. As a result, detecting water hazards at far range generally reduces the time required to reach a goal position during autonomous navigation. This software implements a new water detector based on sky reflections that geometrically locates the exact pixel in the sky that is reflecting on a candidate water pixel on the ground, and predicts if the ground pixel is water based on color similarity and local terrain features

  3. Mine detection training based on expert skill

    NASA Astrophysics Data System (ADS)

    Staszewski, James J.; Davison, Alan

    2000-08-01

    Studies show that soldiers' mine detection capabilities with the PSS-12 hand-held detector are substandard and that their probabilities of detecting (PD) low-metal mines are dangerously low. Highly experienced PSS-12 operators, however, achieve PDS over 0.90 on high- and low-metal anti- tank (AT) and anti-personnel (AP) mines. Significantly, experts' detection techniques differ from conventional military PSS-12 operating procedures. We report three studies investigating whether instruction based on expert skill could bridge the observed performance gap. Basic research on human expertise has shown that instruction based on detailed scientific analyses of experts' behaviors and thought processes boosts skill acquisition dramatically. These studies tested the effects of an experimental detection training program based on knowledge and techniques learned from analysis of PSS-12 expertise. In Study I soldiers who had completed standard mine detection training participated as operators/trainees. This experiment used a pretest-posttest design. Mine simulants served as targets in testing gand training. Targets simulate d5 different mines and represented high- and low-metal AT and AP mine types. Pretest performance failed to distinguish the treatment and control groups. Both achieved very low PDs on low metal mines. Treatment-group soldiers then received approximately 15 hours of experimental, hands-on training. Posttest results showed that the treatment groups PD on minimal metal targets was more than 6 times that of the control group. Study 2 tested a subset of the treated soldiers in the same setting, now wearing body armor. Results replicated those of Study 1. Study 3 tested treatment group soldiers on real mine targets. Several mines from each mine type were used. The surface of the test lanes was expected to increase detection difficulty. Soldiers nonetheless achieved an aggregate PD of 0.97 and showed significant improvement in detecting low-metal mines.

  4. Detection of Epigenetic Changes Using ANOVA with Spatially Varying Coefficients

    PubMed Central

    Xiao, Guanghua; Wang, Xinlei; LaPlant, Quincey; Nestler, Eric; Xie, Yang

    2016-01-01

    Identification of genome-wide epigenetic changes, the stable changes in gene function without a change in DNA sequence, under various conditions plays an important role in biomedical research. High-throughput epigenetic experiments are useful tools to measure genome-wide epigenetic changes, but the measured intensity levels from these high-resolution genome-wide epigenetic profiling data are often spatially correlated with high noise levels. In addition, no formal statistical method was developed to compare genome-wide epigenetic changes across multiple conditions. In this study, we consider ANOVA models with spatially varying coefficients, combined with a hierarchical Bayes approach, to explicitly model spatial correlation caused by location-dependent biological effects (i.e., epigenetic changes) and borrow strength among neighboring probes to compare epigenetic changes across multiple conditions. Through simulation studies and applications in drug addiction and depression models, we find that our approach compares favorably with competing methods; it is more efficient in estimation and more effective in detecting epigenetic changes. In addition, it can provide biologically meaningful results. PMID:23502341

  5. Detecting Land Cover Change by Trend and Seasonality of Remote Sensing Time Series

    NASA Astrophysics Data System (ADS)

    Oliveira, J. C.; Epiphanio, J. N.; Mello, M. P.

    2013-05-01

    Natural resource managers demand knowledge of information on the spatiotemporal dynamics of land use and land cover change, and detection and characteristics change over time is an initial step for the understanding of the mechanism of change. The propose of this research is the use the approach BFAST (Breaks For Additive Seasonal and Trend) for detects trend and seasonal changes within Normalized Difference Vegetation Index (NDVI) time series. BFAST integrates the decomposition of time series into trend, seasonal, and noise components with methods for detecting change within time series without the need to select a reference period, set a threshold, or define a change trajectory. BFAST iteratively estimates the time and number of changes, and characterizes change by its magnitude and direction. The general model is of the form Yt = Tt + St + et (t= 1,2,3,…, n) where Yt is the observed data at time t, Tt is the trend component, St is the seasonal component, and et is the remainder component. In this study was used MODIS NDVI time series datasets (MOD13Q1) over 11 years (2000 - 2010) on an intensive agricultural area in Mato Grosso - Brazil. At first it was applied a filter for noise reduction (4253H twice) over spectral curve of each MODIS pixel, and subsequently each time series was decomposed into seasonal, trend, and remainder components by BFAST. Were detected one abrupt change from a single pixel of forest and two abrupt changes on trend component to a pixel of the agricultural area. Figure 1 shows the number of phonological change with base in seasonal component for study area. This paper demonstrated the ability of the BFAST to detect long-term phenological change by analyzing time series while accounting for abrupt and gradual changes. The algorithm iteratively estimates the dates and number of changes occurring within seasonal and trend components, and characterizes changes by extracting the magnitude and direction of change. Changes occurring in the

  6. Genetic Base Changes for January 2010

    Technology Transfer Automated Retrieval System (TEKTRAN)

    Genetic bases were updated previously in the United States in 1965, 1974, 1984, 1989, 1995, 2000, and 2005, and the next base change is scheduled for January 2010. Changing the base every 5 years subtracts accumulated genetic gain so that all animals are compared with a more recent cow population, w...

  7. Light Scattering based detection of food pathogens

    Technology Transfer Automated Retrieval System (TEKTRAN)

    The current methods for detecting foodborne pathogens are mostly destructive (i.e., samples need to be pretreated), and require time, personnel, and laboratories for analyses. Optical methods including light scattering based techniques have gained a lot of attention recently due to its their rapid a...

  8. A structural framework for anomalous change detection and characterization

    SciTech Connect

    Prasad, Lakshman; Theiler, James P

    2009-01-01

    We present a spatially adaptive scheme for automatically searching a pair of images of a scene for unusual and interesting changes. Our motivation is to bring into play structural aspects of image features alongside the spectral attributes used for anomalous change detection (ACD). We leverage a small but informative subset of pixels, namely edge pixels of the images, as anchor points of a Delaunay triangulation to jointly decompose the images into a set of triangular regions, called trixels, which are spectrally uniform. Such decomposition helps in image regularization by simple-function approximation on a feature-adaptive grid. Applying ACD to this trixel grid instead of pixels offers several advantages. It allows: (1) edge-preserving smoothing of images, (2) speed-up of spatial computations by significantly reducing the representation of the images, and (3) the easy recovery of structure of the detected anomalous changes by associating anomalous trixels with polygonal image features. The latter facility further enables the application of shape-theoretic criteria and algorithms to characterize the changes and recognize them as interesting or not. This incorporation of spatial information has the potential to filter out some spurious changes, such as due to parallax, shadows, and misregistration, by identifying and filtering out those that are structurally similar and spatially pervasive. Our framework supports the joint spatial and spectral analysis of images, potentially enabling the design of more robust ACD algorithms.

  9. Theory of optimal weighting of data to detect climatic change

    NASA Technical Reports Server (NTRS)

    Bell, T. L.

    1986-01-01

    A search for climatic change predicted by climate models can easily yield unconvincing results because of 'climatic noise,' the inherent, unpredictable variability of time-average atmospheric data. A weighted average of data that maximizes the probability of detecting predicted climatic change is presented. To obtain the optimal weights, an estimate of the covariance matrix of the data from a prior data set is needed. This introduces additional sampling error into the method. This is presently taken into account. A form of the weighted average is found whose probability distribution is independent of the true (but unknown) covariance statistics of the data and of the climate model prediction.

  10. Changes of protein stiffness during folding detect protein folding intermediates.

    PubMed

    Małek, Katarzyna E; Szoszkiewicz, Robert

    2014-01-01

    Single-molecule force-quench atomic force microscopy (FQ-AFM) is used to detect folding intermediates of a simple protein by detecting changes of molecular stiffness of the protein during its folding process. Those stiffness changes are obtained from shape and peaks of an autocorrelation of fluctuations in end-to-end length of the folding molecule. The results are supported by predictions of the equipartition theorem and agree with existing Langevin dynamics simulations of a simplified model of a protein folding. In the light of the Langevin simulations the experimental data probe an ensemble of random-coiled collapsed states of the protein, which are present both in the force-quench and thermal-quench folding pathways.

  11. Water Detection Based on Object Reflections

    NASA Technical Reports Server (NTRS)

    Rankin, Arturo L.; Matthies, Larry H.

    2012-01-01

    Water bodies are challenging terrain hazards for terrestrial unmanned ground vehicles (UGVs) for several reasons. Traversing through deep water bodies could cause costly damage to the electronics of UGVs. Additionally, a UGV that is either broken down due to water damage or becomes stuck in a water body during an autonomous operation will require rescue, potentially drawing critical resources away from the primary operation and increasing the operation cost. Thus, robust water detection is a critical perception requirement for UGV autonomous navigation. One of the properties useful for detecting still water bodies is that their surface acts as a horizontal mirror at high incidence angles. Still water bodies in wide-open areas can be detected by geometrically locating the exact pixels in the sky that are reflecting on candidate water pixels on the ground, predicting if ground pixels are water based on color similarity to the sky and local terrain features. But in cluttered areas where reflections of objects in the background dominate the appearance of the surface of still water bodies, detection based on sky reflections is of marginal value. Specifically, this software attempts to solve the problem of detecting still water bodies on cross-country terrain in cluttered areas at low cost.

  12. Land cover changed object detection in remote sensing data with medium spatial resolution

    NASA Astrophysics Data System (ADS)

    Yang, Xiao tong; Liu, Huiping; Gao, Xiaofeng

    2015-06-01

    Land cover change information is crucial to analyse the process and the change patterns of environments and ecological systems. Recent studies have incorporated object-based image analysis for its ability to generate meaningful geographical objects into studies of change detection. In this research, we developed a systematic methodology to realise multi-type land cover changed object detection with medium spatial resolution remote sensing images in Beijing, China. Optimum index factor (OIF) was applied to determine the best change indicators and the chi-square transformation was carried out to determine the change threshold of the 4 classes of changed object. The clustering change vectors in the feature space were proposed to discriminate the change types. According to the accuracy assessment, the overall accuracy of changed/unchanged object detection was approximately 93.9% with an overall kappa of 0.824, and the change type discrimination also achieved an overall accuracy of 81.67%, indicating the effectiveness of the proposed method.

  13. Urban land-cover change detection through sub-pixel imperviousness mapping using remotely sensed data

    USGS Publications Warehouse

    Yang, Limin; Xian, George Z.; Klaver, Jacqueline M.; Deal, Brian

    2003-01-01

    We developed a Sub-pixel Imperviousness Change Detection (SICD) approach to detect urban land-cover changes using Landsat and high-resolution imagery. The sub-pixel percent imperviousness was mapped for two dates (09 March 1993 and 11 March 2001) over western Georgia using a regression tree algorithm. The accuracy of the predicted imperviousness was reasonable based on a comparison using independent reference data. The average absolute error between predicted and reference data was 16.4 percent for 1993 and 15.3 percent for 2001. The correlation coefficient (r) was 0.73 for 1993 and 0.78 for 2001, respectively. Areas with a significant increase (greater than 20 percent) in impervious surface from 1993 to 2001 were mostly related to known land-cover/land-use changes that occurred in this area, suggesting that the spatial change of an impervious surface is a useful indicator for identifying spatial extent, intensity, and, potentially, type of urban land-cover/land-use changes. Compared to other pixel-based change-detection methods (band differencing, rationing, change vector, post-classification), information on changes in sub-pixel percent imperviousness allow users to quantify and interpret urban land-cover/land-use changes based on their own definition. Such information is considered complementary to products generated using other change-detection methods. In addition, the procedure for mapping imperviousness is objective and repeatable, hence, can be used for monitoring urban land-cover/land-use change over a large geographic area. Potential applications and limitations of the products developed through this study in urban environmental studies are also discussed.

  14. Vision-based threat detection in dynamic environments.

    SciTech Connect

    Carlson, Jeffrey J.

    2007-08-01

    This report addresses the development of automated video-screening technology to assist security forces in protecting our homeland against terrorist threats. A prevailing threat is the covert placement of bombs inside crowded public facilities. Although video-surveillance systems are increasingly common, current systems cannot detect the placement of bombs. It is also unlikely that security personnel could detect a bomb or its placement by observing video from surveillance cameras. The problems lie in the large number of cameras required to monitor large areas, the limited number of security personnel employed to protect these areas, and the intense diligence required to effectively screen live video from even a single camera. Different from existing video-detection systems designed to operate in nearly static environments, we are developing technology to detect changes in the background of dynamic environments: environments where motion and human activities are persistent over long periods. Our goal is to quickly detect background changes, even if the background is visible to the camera less than 5 percent of the time and possibly never free from foreground activity. Our approach employs statistical scene models based on mixture densities. We hypothesized that the background component of the mixture has a small variance compared to foreground components. Experiments demonstrate this hypothesis is true under a wide variety of operating conditions. A major focus involved the development of robust background estimation techniques that exploit this property. We desire estimation algorithms that can rapidly produce accurate background estimates and detection algorithms that can reliably detect background changes with minimal nuisance alarms. Another goal is to recognize unusual activities or foreground conditions that could signal an attack (e.g., large numbers of running people, people falling to the floor, etc.). Detection of background changes and/or unusual

  15. Mutation detection in plasmid-based biopharmaceuticals.

    PubMed

    Oliveira, Pedro H; Prather, Kristala L J; Prazeres, Duarte M F; Monteiro, Gabriel A

    2011-04-01

    As the number of applications involving therapeutic plasmid DNA (pDNA) increases worldwide, there is a growing concern over maintaining rigorous quality control through a panel of high-quality assays. For this reason, efficient, cost-effective and sensitive technologies enabling the identification of genetic variants and unwanted side products are needed to successfully establish the identity and stability of a plasmid-based biopharmaceutical. This review highlights several bioinformatic tools for ab initio detection of potentially unstable DNA regions, as well as techniques used for mutation detection in nucleic acids, with particular emphasis on pDNA.

  16. Resonant energy transfer based biosensor for detection of multivalent proteins.

    SciTech Connect

    Song, X.; Swanson, Basil I.

    2001-01-01

    We have developed a new fluorescence-based biosensor for sensitive detection of species involved in a multivslent interaction. The biosensor system utilizes specific interactions between proteins and cell surface receptors, which trigger a receptor aggregation process. Distance-dependent fluorescence self-quenching and resonant energy transfer mechanisms were coupled with a multivalent interaction to probe the receptor aggregation process, providing a sensitive and specific signal transduction method for such a binding event. The fluorescence change induced by the aggregation process can be monitored by different instrument platforms, e.g. fluorimetry and flow cytometry. In this article, a sensitive detection of pentavalent cholera toxin which recognizes ganglioside GM1 has been demonstrated through the resonant energy transfer scheme, which can achieve a double color change simultaneously. A detection sensitivity as high as 10 pM has been achieved within a few minutes (c.a. 5 minutes). The simultaneous double color change (an increase of acceptor fluorescence and a decrease of donor fluorescence intensity) of two similar fluorescent probes provides particularly high detection reliability owing to the fact that they act as each other's internal reference. Any external perturbation such as environmental temperature change causes no significant change in signal generation. Besides the application for biological sensing, the method also provides a useful tool for investigation of kinetics and thermodynamics of a multivalent interaction. Keywords: Biosensor, Fluorescence resonant energy transfer, Multivalent interaction, Cholera Toxin, Ganglioside GM1, Signal Transduction

  17. Topographic Change Detection Using Full-Waveform Imaging Lidar

    NASA Technical Reports Server (NTRS)

    Blair, Bryan; Hofton, Michele A.; Smith, David E. (Technical Monitor)

    2001-01-01

    The capability of wide-footprint (i.e. 10m or greater), full-waveform laser altimeters to penetrate beneath dense vegetation to directly measure the sub-canopy topography provides us with a unique capability for sensing topographic change in the presence of vegetation. We evaluate the feasibility of using a geolocated laser altimeter return waveform instead of individual elevation measurements to measure vertical elevation change within a laser footprint. The method, dubbed the return pulse correlation method, maximizes the shape similarity of nea-coincident, vertically- geolocated laser return waveforms from two observation epochs as they are vertically-shifted relative to each other. First, we evaluate the inherent accuracy of the pulse correlation method using models and simulations under "bare-Earth" conditions. We then analyze the effects of vegetation and vegetation growth on the change detection capability. The use of this method, combined with order of magnitude improvements to laser altimeter swath widths (from 1 km to 10 km) and the potential for a future spaceborne imaging lidar, may provide subcentimeter level relative change detection beneath vegetation to complement IFSAR's ability to make similar measurements in low or vegetation-free conditions.

  18. Vegetation change detection in the Savannah River swamp

    SciTech Connect

    Jensen, J.R.; Christensen, E.J.; Mackey, H.E. Jr.

    1986-01-01

    Portions of Pen Branch, Four Mile Creek, Steel Creek, and Beaver Dam Creek deltas in the Savannah River swamp were evaluated for wetlands vegetation change using aircraft multispectral scanner (MSS) data acquired at 2440 meters altitude. Areas of 190 hectares on the Pen Branch, Four Mile Creek, and Beaver Dam Creek deltas, and a 240-hectare portion of Steel Creek delta were registered, classified, and wetlands vegetation change detection categories determined. Pen Branch and Four Mile Creek deltas each lost about 12 hectares of swamp forest from 1981 to 1984. Secondary successional forest regrew on portions of the Four Mile Creek delta (2.4 hectares) and the Beaver Dam Creek delta (15.4 hectares). About 5 hectares of swamp forest regrew on the Steel Creek delta. This may be the first study to detect wetlands vegetation change over several years using aircraft MSS data. One reason could be due to difficulties similar to those encountered in this study. Data distortion from aircraft movement in some areas of the swamp made image-to-image registration difficult. Best results were obtained on Beaver Dam Creek and Steel Creek deltas which had average registration accuracies within one data element, or pixel, of 5.6 x 5.6 meters. Phenological differences and shadows caused difficulties in vegetation-type discrimination and classification. As a result, the number of vegetation change classes were sometimes limited.

  19. Detecting a trend change in cross-border epidemic transmission

    NASA Astrophysics Data System (ADS)

    Maeno, Yoshiharu

    2016-09-01

    A method for a system of Langevin equations is developed for detecting a trend change in cross-border epidemic transmission. The equations represent a standard epidemiological SIR compartment model and a meta-population network model. The method analyzes a time series of the number of new cases reported in multiple geographical regions. The method is applicable to investigating the efficacy of the implemented public health intervention in managing infectious travelers across borders. It is found that the change point of the probability of travel movements was one week after the WHO worldwide alert on the SARS outbreak in 2003. The alert was effective in managing infectious travelers. On the other hand, it is found that the probability of travel movements did not change at all for the flu pandemic in 2009. The pandemic did not affect potential travelers despite the WHO alert.

  20. Ionizing particle detection based on phononic crystals

    SciTech Connect

    Aly, Arafa H. E-mail: arafa.hussien@science.bsu.edu.eg; Mehaney, Ahmed; Eissa, Mostafa F.

    2015-08-14

    Most conventional radiation detectors are based on electronic or photon collections. In this work, we introduce a new and novel type of ionizing particle detector based on phonon collection. Helium ion radiation treats tumors with better precision. There are nine known isotopes of helium, but only helium-3 and helium-4 are stable. Helium-4 is formed in fusion reactor technology and in enormous quantities during Big Bang nucleo-synthesis. In this study, we introduce a technique for helium-4 ion detection (sensing) based on the innovative properties of the new composite materials known as phononic crystals (PnCs). PnCs can provide an easy and cheap technique for ion detection compared with conventional methods. PnC structures commonly consist of a periodic array of two or more materials with different elastic properties. The two materials are polymethyl-methacrylate and polyethylene polymers. The calculations showed that the energies lost to target phonons are maximized at 1 keV helium-4 ion energy. There is a correlation between the total phonon energies and the transmittance of PnC structures. The maximum transmission for phonons due to the passage of helium-4 ions was found in the case of making polyethylene as a first layer in the PnC structure. Therefore, the concept of ion detection based on PnC structure is achievable.

  1. Skeleton-Based Abnormal Gait Detection.

    PubMed

    Nguyen, Trong-Nguyen; Huynh, Huu-Hung; Meunier, Jean

    2016-10-26

    Human gait analysis plays an important role in musculoskeletal disorder diagnosis. Detecting anomalies in human walking, such as shuffling gait, stiff leg or unsteady gait, can be difficult if the prior knowledge of such a gait pattern is not available. We propose an approach for detecting abnormal human gait based on a normal gait model. Instead of employing the color image, silhouette, or spatio-temporal volume, our model is created based on human joint positions (skeleton) in time series. We decompose each sequence of normal gait images into gait cycles. Each human instant posture is represented by a feature vector which describes relationships between pairs of bone joints located in the lower body. Such vectors are then converted into codewords using a clustering technique. The normal human gait model is created based on multiple sequences of codewords corresponding to different gait cycles. In the detection stage, a gait cycle with normality likelihood below a threshold, which is determined automatically in the training step, is assumed as an anomaly. The experimental results on both marker-based mocap data and Kinect skeleton show that our method is very promising in distinguishing normal and abnormal gaits with an overall accuracy of 90.12%.

  2. Skeleton-Based Abnormal Gait Detection

    PubMed Central

    Nguyen, Trong-Nguyen; Huynh, Huu-Hung; Meunier, Jean

    2016-01-01

    Human gait analysis plays an important role in musculoskeletal disorder diagnosis. Detecting anomalies in human walking, such as shuffling gait, stiff leg or unsteady gait, can be difficult if the prior knowledge of such a gait pattern is not available. We propose an approach for detecting abnormal human gait based on a normal gait model. Instead of employing the color image, silhouette, or spatio-temporal volume, our model is created based on human joint positions (skeleton) in time series. We decompose each sequence of normal gait images into gait cycles. Each human instant posture is represented by a feature vector which describes relationships between pairs of bone joints located in the lower body. Such vectors are then converted into codewords using a clustering technique. The normal human gait model is created based on multiple sequences of codewords corresponding to different gait cycles. In the detection stage, a gait cycle with normality likelihood below a threshold, which is determined automatically in the training step, is assumed as an anomaly. The experimental results on both marker-based mocap data and Kinect skeleton show that our method is very promising in distinguishing normal and abnormal gaits with an overall accuracy of 90.12%. PMID:27792181

  3. Ionizing particle detection based on phononic crystals

    NASA Astrophysics Data System (ADS)

    Aly, Arafa H.; Mehaney, Ahmed; Eissa, Mostafa F.

    2015-08-01

    Most conventional radiation detectors are based on electronic or photon collections. In this work, we introduce a new and novel type of ionizing particle detector based on phonon collection. Helium ion radiation treats tumors with better precision. There are nine known isotopes of helium, but only helium-3 and helium-4 are stable. Helium-4 is formed in fusion reactor technology and in enormous quantities during Big Bang nucleo-synthesis. In this study, we introduce a technique for helium-4 ion detection (sensing) based on the innovative properties of the new composite materials known as phononic crystals (PnCs). PnCs can provide an easy and cheap technique for ion detection compared with conventional methods. PnC structures commonly consist of a periodic array of two or more materials with different elastic properties. The two materials are polymethyl-methacrylate and polyethylene polymers. The calculations showed that the energies lost to target phonons are maximized at 1 keV helium-4 ion energy. There is a correlation between the total phonon energies and the transmittance of PnC structures. The maximum transmission for phonons due to the passage of helium-4 ions was found in the case of making polyethylene as a first layer in the PnC structure. Therefore, the concept of ion detection based on PnC structure is achievable.

  4. Detection of Rooftop Cooling Unit Faults Based on Electrical Measurements

    SciTech Connect

    Armstrong, Peter R.; Laughman, C R.; Leeb, S B.; Norford, L K.

    2006-01-31

    Non-intrusive load monitoring (NILM) is accomplished by sampling voltage and current at high rates and reducing the resulting start transients or harmonic contents to concise ''signatures''. Changes in these signatures can be used to detect, and in many cases directly diagnose, equipment and component faults associated with roof-top cooling units. Use of the NILM for fault detection and diagnosis (FDD) is important because (1) it complements other FDD schemes that are based on thermo-fluid sensors and analyses and (2) it is minimally intrusive (one measuring point in the relatively protected confines of the control panel) and therefore inherently reliable. This paper describes changes in the power signatures of fans and compressors that were found, experimentally and theoretically, to be useful for fault detection.

  5. Multi-driver attribution of detected hydrological change

    NASA Astrophysics Data System (ADS)

    Harrigan, Shaun; Murphy, Conor; Hall, Julia; Wilby, Robert L.; Sweeney, John

    2014-05-01

    There is growing evidence that significant links between large-scale climate indices and streamflow over decadal time-scales can be established. However identifying the dominant driving mechanism(s) of detected changes in streamflow (i.e. attribution) at the catchment scale is a challenging task due to the confounding influence of human disturbances such as land-use changes, water abstractions, and river engineering. This study addresses this challenge by examining the utility of the multiple working hypotheses framework in moving towards more rigorous attribution of changes using the Boyne catchment in the east of Ireland as a case study. Previous research on this catchment found that a large upward change point in streamflow during the mid-1970s corresponded with a shift in the North Atlantic Oscillation (NAO) index towards a more positive phase, bringing increased precipitation, and hence increased risk of flooding. Here, the single-driver analysis is extended to include multiple factors causing change within the catchment (both climatic and internal) in order to establish relative contributions of hypothesised drivers. Rainfall-runoff models were employed to reconstruct streamflow to isolate the effect of climate taking account of both model structure and parameter uncertainty. The Mann-Kendall test for monotonic trend and Pettitt change point test were applied to explore signatures of change. Results show that the detected increase in annual mean and high flows was not predominantly driven by changes in precipitation as a result of a shift in the NAO index. Rather we assert that the dominant driver of change was arterial drainage and the contemporaneous onset of agricultural field drainage in the 1970s and early 1980s. It is also demonstrated that attribution can be more complex at different time-scales with multiple drivers acting simultaneously. This study emphasises the quantity and range of data types needed for rigorous attribution, especially when

  6. Indigenous knowledge and long-term ecological change: detection, interpretation, and responses to changing ecological conditions in Pacific Island communities.

    PubMed

    Lauer, Matthew; Aswani, Shankar

    2010-05-01

    When local resource users detect, understand, and respond to environmental change they can more effectively manage environmental resources. This article assesses these abilities among artisanal fishers in Roviana Lagoon, Solomon Islands. In a comparison of two villages, it documents local resource users' abilities to monitor long-term ecological change occurring to seagrass meadows near their communities, their understandings of the drivers of change, and their conceptualizations of seagrass ecology. Local observations of ecological change are compared with historical aerial photography and IKONOS satellite images that show 56 years of actual changes in seagrass meadows from 1947 to 2003. Results suggest that villagers detect long-term changes in the spatial cover of rapidly expanding seagrass meadows. However, for seagrass meadows that showed no long-term expansion or contraction in spatial cover over one-third of respondents incorrectly assumed changes had occurred. Examples from a community-based management initiative designed around indigenous ecological knowledge and customary sea tenure governance show how local observations of ecological change shape marine resource use and practices which, in turn, can increase the management adaptability of indigenous or hybrid governance systems.

  7. Indigenous Knowledge and Long-term Ecological Change: Detection, Interpretation, and Responses to Changing Ecological Conditions in Pacific Island Communities

    PubMed Central

    Aswani, Shankar

    2010-01-01

    When local resource users detect, understand, and respond to environmental change they can more effectively manage environmental resources. This article assesses these abilities among artisanal fishers in Roviana Lagoon, Solomon Islands. In a comparison of two villages, it documents local resource users’ abilities to monitor long-term ecological change occurring to seagrass meadows near their communities, their understandings of the drivers of change, and their conceptualizations of seagrass ecology. Local observations of ecological change are compared with historical aerial photography and IKONOS satellite images that show 56 years of actual changes in seagrass meadows from 1947 to 2003. Results suggest that villagers detect long-term changes in the spatial cover of rapidly expanding seagrass meadows. However, for seagrass meadows that showed no long-term expansion or contraction in spatial cover over one-third of respondents incorrectly assumed changes had occurred. Examples from a community-based management initiative designed around indigenous ecological knowledge and customary sea tenure governance show how local observations of ecological change shape marine resource use and practices which, in turn, can increase the management adaptability of indigenous or hybrid governance systems. PMID:20336296

  8. SERS-based detection of biomolecules

    NASA Astrophysics Data System (ADS)

    Cialla, Dana; Pollok, Sibyll; Steinbrücker, Carolin; Weber, Karina; Popp, Jürgen

    2014-12-01

    In order to detect biomolecules, different approaches using for instance biological, spectroscopic or imaging techniques are established. Due to the broad variety of these methods, this review is focused on surface enhanced Raman spectroscopy (SERS) as an analytical tool in biomolecule detection. Here, the molecular specificity of Raman spectroscopy is combined with metallic nanoparticles as sensor platform, which enhances the signal intensity by several orders of magnitude. Within this article, the characterization of diverse biomolecules by means of SERS is explained and moreover current application fields are presented. The SERS intensity and as a consequence thereof the reliable detection of the biomolecule of interest is effected by distance, orientation and affinity of the molecule towards the metal surface. Furthermore, the great capability of the SERS technique for cutting-edge applications like pathogen detection and cancer diagnosis is highlighted. We wish to motivate by this comprehensive and critical summary researchers from various scientific background to create their own ideas and schemes for a SERS-based detection and analysis of biomolecules.

  9. Effects of spatial configurations on visual change detection: an account of bias changes.

    PubMed

    Boduroglu, Aysecan; Shah, Priti

    2009-12-01

    In order to determine whether people encode spatial configuration information when encoding visual displays, in four experiments, we investigated whether changes in task-irrelevant spatial configuration information would influence color change detection accuracy. In a change detection task, when objects in the test display were presented in new random locations, rather than identical or different locations preserving the overall configuration, participants were more likely to report that the colors had changed. This consistent bias across four experiments suggested that people encode task-irrelevant spatial configuration along with object information. Experiment 4 also demonstrated that only a low-false-alarm group of participants effectively bound spatial configuration information to object information, suggesting that these types of binding processes are open to strategic influences.

  10. An EEG-Based Fatigue Detection and Mitigation System.

    PubMed

    Huang, Kuan-Chih; Huang, Teng-Yi; Chuang, Chun-Hsiang; King, Jung-Tai; Wang, Yu-Kai; Lin, Chin-Teng; Jung, Tzyy-Ping

    2016-06-01

    Research has indicated that fatigue is a critical factor in cognitive lapses because it negatively affects an individual's internal state, which is then manifested physiologically. This study explores neurophysiological changes, measured by electroencephalogram (EEG), due to fatigue. This study further demonstrates the feasibility of an online closed-loop EEG-based fatigue detection and mitigation system that detects physiological change and can thereby prevent fatigue-related cognitive lapses. More importantly, this work compares the efficacy of fatigue detection and mitigation between the EEG-based and a nonEEG-based random method. Twelve healthy subjects participated in a sustained-attention driving experiment. Each participant's EEG signal was monitored continuously and a warning was delivered in real-time to participants once the EEG signature of fatigue was detected. Study results indicate suppression of the alpha- and theta-power of an occipital component and improved behavioral performance following a warning signal; these findings are in line with those in previous studies. However, study results also showed reduced warning efficacy (i.e. increased response times (RTs) to lane deviations) accompanied by increased alpha-power due to the fluctuation of warnings over time. Furthermore, a comparison of EEG-based and nonEEG-based random approaches clearly demonstrated the necessity of adaptive fatigue-mitigation systems, based on a subject's cognitive level, to deliver warnings. Analytical results clearly demonstrate and validate the efficacy of this online closed-loop EEG-based fatigue detection and mitigation mechanism to identify cognitive lapses that may lead to catastrophic incidents in countless operational environments.

  11. Anomalies in the detection of change: When changes in sample size are mistaken for changes in proportions.

    PubMed

    Fiedler, Klaus; Kareev, Yaakov; Avrahami, Judith; Beier, Susanne; Kutzner, Florian; Hütter, Mandy

    2016-01-01

    Detecting changes, in performance, sales, markets, risks, social relations, or public opinions, constitutes an important adaptive function. In a sequential paradigm devised to investigate detection of change, every trial provides a sample of binary outcomes (e.g., correct vs. incorrect student responses). Participants have to decide whether the proportion of a focal feature (e.g., correct responses) in the population from which the sample is drawn has decreased, remained constant, or increased. Strong and persistent anomalies in change detection arise when changes in proportional quantities vary orthogonally to changes in absolute sample size. Proportional increases are readily detected and nonchanges are erroneously perceived as increases when absolute sample size increases. Conversely, decreasing sample size facilitates the correct detection of proportional decreases and the erroneous perception of nonchanges as decreases. These anomalies are however confined to experienced samples of elementary raw events from which proportions have to be inferred inductively. They disappear when sample proportions are described as percentages in a normalized probability format. To explain these challenging findings, it is essential to understand the inductive-learning constraints imposed on decisions from experience.

  12. Vehicle Localization by LIDAR Point Correlation Improved by Change Detection

    NASA Astrophysics Data System (ADS)

    Schlichting, A.; Brenner, C.

    2016-06-01

    LiDAR sensors are proven sensors for accurate vehicle localization. Instead of detecting and matching features in the LiDAR data, we want to use the entire information provided by the scanners. As dynamic objects, like cars, pedestrians or even construction sites could lead to wrong localization results, we use a change detection algorithm to detect these objects in the reference data. If an object occurs in a certain number of measurements at the same position, we mark it and every containing point as static. In the next step, we merge the data of the single measurement epochs to one reference dataset, whereby we only use static points. Further, we also use a classification algorithm to detect trees. For the online localization of the vehicle, we use simulated data of a vertical aligned automotive LiDAR sensor. As we only want to use static objects in this case as well, we use a random forest classifier to detect dynamic scan points online. Since the automotive data is derived from the LiDAR Mobile Mapping System, we are able to use the labelled objects from the reference data generation step to create the training data and further to detect dynamic objects online. The localization then can be done by a point to image correlation method using only static objects. We achieved a localization standard deviation of about 5 cm (position) and 0.06° (heading), and were able to successfully localize the vehicle in about 93 % of the cases along a trajectory of 13 km in Hannover, Germany.

  13. Seasonal Change Detection and Attribution of Surface Temperature changes over Interior Peninsular Region of India

    NASA Astrophysics Data System (ADS)

    Pattanayak, Sonali; Nagesh Kumar, Dasika

    2015-04-01

    A good number of studies have investigated recent trends in the observed and simulated hydrometeorological variables across the world. It has been challenging for the research community to address whether the significant change in climate over the course of 2nd half of 20th century is caused either due to natural or manmade effects. Although evidences for an anthropogenic contribution to climatic trends have been accumulated rapidly worldwide, for India these are scarce. Hence the formal efforts have been undertaken to distinguish whether the recent changes in seasonal temperature over India occurred due to natural internal variation of climate system or human influence using rigorous detection and attribution (D&A) procedure. The surface temperature is the most widely cited indicator of climate fluctuation. Hence maximum and minimum temperatures (Tmax & Tmin) which are among the six most commonly used variables for impact assessment studies are analyzed here. Seasonal divisions are based on conventional meteorological seasons: January-February (winter); March-May (pre monsoon); June-September (monsoon); October-December (post monsoon). Time span considered for this study is 1950-2005. Climate Research Unit (Version 3.21) gridded monthly temperature datasets are considered as observed data. Initially TFPW-MK (Trend Free Pre Whitening Mann Kendall) test is used to search the significant trends in the four seasons over all India. Temporal change detection analysis in evapotranspiration (which is one of the key processes in hydrological cycle) is essential for progress in water resources planning and management. Hence along with Tmax and Tmin, potential evapotranspiration (PET) has also been analyzed for the similar conditions. Significant upward trends in Tmax, Tmin and PET are observed over most of the grid points in Interior Peninsula (IP) region over India. Significant correlation was obtained between PET and Tmax compared to PET and Tmin. Trends in Tmin clearly

  14. A robust method for detecting single-nucleotide changes as polymorphic markers by PCR.

    PubMed

    Michaels, S D; Amasino, R M

    1998-05-01

    Numerous techniques in plant molecular genetic analysis, such as mapping and positional cloning techniques, rely on the availability of molecular markers that can differentiate between alleles at a particular locus. PCR-based cleaved amplified polymorphic sequences (CAPS) markers have been widely used as a means of rapidly and reliably detecting a single-base change that creates a unique restriction site in one of a pair of alleles. However, the majority of single-nucleotide changes do not create such sites and thus cannot be used to create CAPS markers. In this paper, a modification of the CAPS technique that allows detection of most single-nucleotide changes by utilizing mismatched PCR primers is described. The mismatches in the PCR primers, in combination with the single-nucleotide change, create a unique restriction site in one of the alleles.

  15. Raman LIDAR Detection of Cloud Base

    NASA Technical Reports Server (NTRS)

    Demoz, Belay; Starr, David; Whiteman, David; Evans, Keith; Hlavka, Dennis; Peravali, Ravindra

    1999-01-01

    Advantages introduced by Raman lidar systems for cloud base determination during precipitating periods are explored using two case studies of light rain and virga conditions. A combination of the Raman lidar derived profiles of water vapor mixing ratio and aerosol scattering ratio, together with the Raman scattered signals from liquid drops, can minimize or even eliminate some of the problems associated with cloud boundary detection using elastic backscatter lidars.

  16. Biotoxin Detection Using Cell-Based Sensors

    PubMed Central

    Banerjee, Pratik; Kintzios, Spyridon; Prabhakarpandian, Balabhaskar

    2013-01-01

    Cell-based biosensors (CBBs) utilize the principles of cell-based assays (CBAs) by employing living cells for detection of different analytes from environment, food, clinical, or other sources. For toxin detection, CBBs are emerging as unique alternatives to other analytical methods. The main advantage of using CBBs for probing biotoxins and toxic agents is that CBBs respond to the toxic exposures in the manner related to actual physiologic responses of the vulnerable subjects. The results obtained from CBBs are based on the toxin-cell interactions, and therefore, reveal functional information (such as mode of action, toxic potency, bioavailability, target tissue or organ, etc.) about the toxin. CBBs incorporate both prokaryotic (bacteria) and eukaryotic (yeast, invertebrate and vertebrate) cells. To create CBB devices, living cells are directly integrated onto the biosensor platform. The sensors report the cellular responses upon exposures to toxins and the resulting cellular signals are transduced by secondary transducers generating optical or electrical signals outputs followed by appropriate read-outs. Examples of the layout and operation of cellular biosensors for detection of selected biotoxins are summarized. PMID:24335754

  17. ROLE OF SPATIAL RESOLUTION AND SPECTRAL CONTENT IN CHANGE DETECTION.

    USGS Publications Warehouse

    Milazzo, Valerie A.

    1984-01-01

    Summary form only given, as follows. Advancements in remote sensing technology have brought improvements and sophistication to modern remote sensor systems, especially those aboard earth resources satellites. These improvements have considerbly expanded the capabilities of the newer sensor systems, particularly the capability to achieve greatly increased spatial and spectral resolution levels. The debate still lingers, however, over whether future systems should maximize spatial resolution or spectral information, or both. As yet, the high costs and large volumes of data associated with even modest incremental improvements in spatial and spectral content have precluded the design of a single system that attempts to fully optimize both. Thus, the user is faced with having to choose between those systems providing high spatial resolutions but limited spectral information and those which offer a broad range of spectral data but hold spatial resolution to a less than optimum level. In this study, the contribution of both spatial resolution and spectral content to land cover change detection is examined. Ten-meter SPOT simulation imagery is compared with multispectral images acquired by the Thematic Mapper sensor system for use in the visual interpretation and mapping of changes. Several image processing and enhancement techniques are utilized to maximize the spatial and spectral data content offered by each system. Results indicate that when using visual image interpretation techniques to detect change, higher spatial resolutions are generally preferred over increased spectral content.

  18. Automatic detection of unattended changes in room acoustics.

    PubMed

    Frey, Johannes Daniel; Wendt, Mike; Jacobsen, Thomas

    2015-01-01

    Previous research has shown that the human auditory system continuously monitors its acoustic environment, detecting a variety of irregularities (e.g., deviance from prior stimulation regularity in pitch, loudness, duration, and (perceived) sound source location). Detection of irregularities can be inferred from a component of the event-related brain potential (ERP), referred to as the mismatch negativity (MMN), even in conditions in which participants are instructed to ignore the auditory stimulation. The current study extends previous findings by demonstrating that auditory irregularities brought about by a change in room acoustics elicit a MMN in a passive oddball protocol (acoustic stimuli with differing room acoustics, that were otherwise identical, were employed as standard and deviant stimuli), in which participants watched a fiction movie (silent with subtitles). While the majority of participants reported no awareness for any changes in the auditory stimulation, only one out of 14 participants reported to have become aware of changing room acoustics or sound source location. Together, these findings suggest automatic monitoring of room acoustics.

  19. Change and Anomaly Detection in Real-Time GPS Data

    NASA Astrophysics Data System (ADS)

    Granat, R.; Pierce, M.; Gao, X.; Bock, Y.

    2008-12-01

    The California Real-Time Network (CRTN) is currently generating real-time GPS position data at a rate of 1-2Hz at over 80 locations. The CRTN data presents the possibility of studying dynamical solid earth processes in a way that complements existing seismic networks. To realize this possibility we have developed a prototype system for detecting changes and anomalies in the real-time data. Through this system, we can can correlate changes in multiple stations in order to detect signals with geographical extent. Our approach involves developing a statistical model for each GPS station in the network, and then using those models to segment the time series into a number of discrete states described by the model. We use a hidden Markov model (HMM) to describe the behavior of each station; fitting the model to the data requires neither labeled training examples nor a priori information about the system. As such, HMMs are well suited to this problem domain, in which the data remains largely uncharacterized. There are two main components to our approach. The first is the model fitting algorithm, regularized deterministic annealing expectation- maximization (RDAEM), which provides robust, high-quality results. The second is a web service infrastructure that connects the data to the statistical modeling analysis and allows us to easily present the results of that analysis through a web portal interface. This web service approach facilitates the automatic updating of station models to keep pace with dynamical changes in the data. Our web portal interface is critical to the process of interpreting the data. A Google Maps interface allows users to visually interpret state changes not only on individual stations but across the entire network. Users can drill down from the map interface to inspect detailed results for individual stations, download the time series data, and inspect fitted models. Alternatively, users can use the web portal look at the evolution of changes on the

  20. Visual change detection recruits auditory cortices in early deafness.

    PubMed

    Bottari, Davide; Heimler, Benedetta; Caclin, Anne; Dalmolin, Anna; Giard, Marie-Hélène; Pavani, Francesco

    2014-07-01

    Although cross-modal recruitment of early sensory areas in deafness and blindness is well established, the constraints and limits of these plastic changes remain to be understood. In the case of human deafness, for instance, it is known that visual, tactile or visuo-tactile stimuli can elicit a response within the auditory cortices. Nonetheless, both the timing of these evoked responses and the functional contribution of cross-modally recruited areas remain to be ascertained. In the present study, we examined to what extent auditory cortices of deaf humans participate in high-order visual processes, such as visual change detection. By measuring visual ERPs, in particular the visual MisMatch Negativity (vMMN), and performing source localization, we show that individuals with early deafness (N=12) recruit the auditory cortices when a change in motion direction during shape deformation occurs in a continuous visual motion stream. Remarkably this "auditory" response for visual events emerged with the same timing as the visual MMN in hearing controls (N=12), between 150 and 300 ms after the visual change. Furthermore, the recruitment of auditory cortices for visual change detection in early deaf was paired with a reduction of response within the visual system, indicating a shift from visual to auditory cortices of part of the computational process. The present study suggests that the deafened auditory cortices participate at extracting and storing the visual information and at comparing on-line the upcoming visual events, thus indicating that cross-modally recruited auditory cortices can reach this level of computation.

  1. Fast dual graph-based hotspot detection

    NASA Astrophysics Data System (ADS)

    Kahng, Andrew B.; Park, Chul-Hong; Xu, Xu

    2006-10-01

    As advanced technologies in wafer manufacturing push patterning processes toward lower-k I subwavelength printing, lithography for mass production potentially suffers from decreased patterning fidelity. This results in generation of many hotspots, which are actual device patterns with relatively large CD and image errors with respect to on-wafer targets. Hotspots can be formed under a variety of conditions such as the original design being unfriendly to the RET that is applied, unanticipated pattern combinations in rule-based OPC, or inaccuracies in model-based OPC. When these hotspots fall on locations that are critical to the electrical performance of a device, device performance and parametric yield can be significantly degraded. Previous rule-based hotspot detection methods suffer from long runtimes for complicated patterns. Also, the model generation process that captures process variation within simulation-based approaches brings significant overheads in terms of validation, measurement and parameter calibration. In this paper, we first describe a novel detection algorithm for hotspots induced by lithographic uncertainty. Our goal is to rapidly detect all lithographic hotspots without significant accuracy degradation. In other words, we propose a filtering method: as long as there are no "false negatives", i.e., we successfully have a superset of actual hotspots, then our method can dramatically reduce the layout area for golden hotspot analysis. The first step of our hotspot detection algorithm is to build a layout graph which reflects pattern-related CD variation. Given a layout L, the layout graph G = (V, E c union E p) consists of nodes V, corner edges E c and proximity edges E p. A face in the layout graph includes several close features and the edges between them. Edge weight can be calculated from a traditional 2-D model or a lookup table. We then apply a three-level hotspot detection: (1) edge-level detection finds the hotspot caused by two close

  2. SU-E-J-191: Automated Detection of Anatomic Changes in H'N Patients

    SciTech Connect

    Usynin, A; Ramsey, C

    2014-06-01

    Purpose: To develop a novel statistics-based method for automated detection of anatomical changes using cone-beam CT data. A method was developed that can provide a reliable and automated early warning system that enables a “just-in-time” adaptation of the treatment plan. Methods: Anatomical changes were evaluated by comparing the original treatment planning CT with daily CBCT images taken prior treatment delivery. The external body contour was computed on a given CT slice and compared against the corresponding contour on the daily CBCT. In contrast to threshold-based techniques, a statistical approach was employed to evaluate the difference between the contours using a given confidence level. The detection tool used the two-sample Kolmogorov-Smirnov test, which is a non-parametric technique that compares two samples drawn from arbitrary probability distributions. 11 H'N patients were retrospectively selected from a clinical imaging database with a total of 186 CBCT images. Six patients in the database were confirmed to have anatomic changes during the course of radiotherapy. Five of the H'N patients did not have significant changes. The KS test was applied to the contour data using a sliding window analysis. The confidence level of 0.99 was used to moderate false detection. Results: The algorithm was able to correctly detect anatomical changes in 6 out of 6 patients with an excellent spatial accuracy as early as at the 14th elapsed day. The algorithm provided a consistent and accurate delineation of the detected changes. The output of the anatomical change tool is easy interpretable, and can be shown overlaid on a 3D rendering of the patient's anatomy. Conclusion: The detection method provides the basis for one of the key components of Adaptive Radiation Therapy. The method uses tools that are readily available in the clinic, including daily CBCT imaging, and image co-registration facilities.

  3. Trend Analysis and Detection of Changes in the Stratospheric Circulation

    NASA Technical Reports Server (NTRS)

    Oman, Luke; Douglass, A. R.; Rodriquez, J. M.; Stolarski, R. S.; Waugh, D. W.

    2010-01-01

    Increases in the circulation of the stratosphere appear to be a robust result of climate change in chemistry-climate models over decadal time scales. To date observations have yet to show a significant change in this circulation. It is important for the design of future observational missions to identify suitable atmospheric constituents and to determine the accuracy and length of record needed to identify a significant trend that can be attributed to circulation change. First, we determine what atmospheric variables can be used as proxies for stratospheric circulation changes. A few examples are changes in tropical lower stratospheric ozone, phase lag of the water vapor tape recorder, CO2, and SF6. Then, using both the Goddard Earth Observing System Chemistry-Climate Model (GEOS CCM) and observations from satellites and balloon soundings, we calculate the number of years needed to detect a significant trend, taking into account observational uncertainty. Model simulations will be evaluated to see how well they represent observed variability. In addition, the impacts of autocorrelation among the output or data and gaps in the observational record will be discussed.

  4. A Weld Defects Detection System Based on a Spectrometer

    PubMed Central

    Bebiano, Daniel; Alfaro, Sadek C. A.

    2009-01-01

    Improved product quality and production methods, and decreased production costs are important objectives of industries. Welding processes are part of this goal. There are many studies about monitoring and controlling welding process. This work presents a non-intrusive on-line monitoriment system and some algorithms capable of detecting GTAW weld defects. Some experiments were made to simulate weld defects by disturbing the electric arc. The data comes from a spectrometer which captures perturbations on the electric arc by the radiation emission of chosen lines. Algorithms based on change detection methods are used to indicate the presence and localization of those defects. PMID:22574049

  5. A weld defects detection system based on a spectrometer.

    PubMed

    Bebiano, Daniel; Alfaro, Sadek C A

    2009-01-01

    Improved product quality and production methods, and decreased production costs are important objectives of industries. Welding processes are part of this goal. There are many studies about monitoring and controlling welding process. This work presents a non-intrusive on-line monitoriment system and some algorithms capable of detecting GTAW weld defects. Some experiments were made to simulate weld defects by disturbing the electric arc. The data comes from a spectrometer which captures perturbations on the electric arc by the radiation emission of chosen lines. Algorithms based on change detection methods are used to indicate the presence and localization of those defects.

  6. Spatio-temporal change detection from multidimensional arrays: Detecting deforestation from MODIS time series

    NASA Astrophysics Data System (ADS)

    Lu, Meng; Pebesma, Edzer; Sanchez, Alber; Verbesselt, Jan

    2016-07-01

    Growing availability of long-term satellite imagery enables change modeling with advanced spatio-temporal statistical methods. Multidimensional arrays naturally match the structure of spatio-temporal satellite data and can provide a clean modeling process for complex spatio-temporal analysis over large datasets. Our study case illustrates the detection of breakpoints in MODIS imagery time series for land cover change in the Brazilian Amazon using the BFAST (Breaks For Additive Season and Trend) change detection framework. BFAST includes an Empirical Fluctuation Process (EFP) to alarm the change and a change point time locating process. We extend the EFP to account for the spatial autocorrelation between spatial neighbors and assess the effects of spatial correlation when applying BFAST on satellite image time series. In addition, we evaluate how sensitive EFP is to the assumption that its time series residuals are temporally uncorrelated, by modeling it as an autoregressive process. We use arrays as a unified data structure for the modeling process, R to execute the analysis, and an array database management system to scale computation. Our results point to BFAST as a robust approach against mild temporal and spatial correlation, to the use of arrays to ease the modeling process of spatio-temporal change, and towards communicable and scalable analysis.

  7. The detection of climate change due to the enhanced greenhouse effect

    NASA Technical Reports Server (NTRS)

    Schiffer, Robert A.; Unninayar, Sushel

    1991-01-01

    The greenhouse effect is accepted as an undisputed fact from both theoretical and observational considerations. In Earth's atmosphere, the primary greenhouse gas is water vapor. The specific concern today is that increasing concentrations of anthropogenically introduced greenhouse gases will, sooner or later, irreversibly alter the climate of Earth. Detecting climate change has been complicated by uncertainties in historical observations and measurements. Thus, the primary concern for the GEDEX project is how can climate change and enhanced greenhouse effects be unambiguously detected and quantified. Specifically examined are the areas of: Earth surface temperature; the free atmosphere (850 millibars and above); space-based measurements; measurement uncertainties; and modeling the observed temperature record.

  8. Multi-Scale Change Detection Research of Remotely Sensed Big Data in CyberGIS

    NASA Astrophysics Data System (ADS)

    Xing, J.; Sieber, R.

    2015-12-01

    Big remotely sensed data, the heterogeneity of satellite platforms and file formats along with increasing volumes and velocities, offers new types of analyses. This makes big remotely sensed data a good candidate for CyberGIS, the aim of which is to enable knowledge discovery of big data in the cloud. We apply CyberGIS to feature-based multi-scale land use/cover change (LUCC) detection. There have been attempts to do multi-scale LUCC. However, studies were done with small data and could not consider the mismatch between multi-scale analysis and computational scale. They have yet to consider the possibilities for scalar research across numerous temporal and spatial scales afforded by big data, especially if we want to advance beyond pixel-based analysis and also reduce preprocessing requirements. We create a geospatial cyberinfrastructure (GCI) to handle multi-spatio-temporal scale change detection. We first clarify different meanings of scale in CyberGIS and LUCC to derive a feature scope layer in the GCI based on Stommel modelling. Our analysis layer contains a multi-scale segmentation-based method based on normalized cut image segmentation and wavelet-based image scaling algorithms. Our computer resource utilization layer uses Wang and Armstrong's (2009) method for mainly for memory, I/O and CPU time. Our case is urban-rural change detection in the Greater Montreal Area (5 time periods, 2006-2012, 100 virtual machines), 36,000km2 and varying from 0.6m to 38m resolution. We present a ground truthed accuracy assessment of a change matrix that is composed of 6 feature classes at 12 different spatio-temporal scales, and the performance of the change detection GCI for multi-scale LUCC study. The GCI allows us to extract and coordinate different types of changes by varying spatio-temporal scales from the big imagery datasets.

  9. Detecting and visualizing structural changes in groundwater head time series

    NASA Astrophysics Data System (ADS)

    van Geer, Frans

    2013-04-01

    Since the fifties of the past century the dynamic behavior of the groundwater head has been monitored at many locations throughout the Netherlands and elsewhere. The data base of the Geological Survey of the Netherlands contains over 30,000 groundwater time series. For many water management purposes characteristics of the dynamic behavior are required, such as average, median, percentile etc.. These characteristics are estimated from the time series. In principle, the longer the time series, the more reliable the estimate. However, due to natural as well as man induced changes, the characteristics of a long time series are often changing in time as well. For water management it is important to be able to distinguish extreme values as part of the 'normal' pattern from structural changes in the groundwater regime. Whether or not structural changes are present in the time series can't be decided completely objective. Choices have to be made concerning the length of the period and the statistical parameters. Here a method is proposed to visualize the probability of structural changes in the time series using well known basic statistical tests. The visualization method is based on the mean values and standard deviation in a moving window. Apart from several characteristics that are calculated for each period separately, all pairs of two periods are compared and the difference is statistically tested. The results of these well known tests are combined in a visualization to supply to the user comprehensive information to examine structural changes in time series.

  10. Image change detection systems, methods, and articles of manufacture

    DOEpatents

    Jones, James L.; Lassahn, Gordon D.; Lancaster, Gregory D.

    2010-01-05

    Aspects of the invention relate to image change detection systems, methods, and articles of manufacture. According to one aspect, a method of identifying differences between a plurality of images is described. The method includes loading a source image and a target image into memory of a computer, constructing source and target edge images from the source and target images to enable processing of multiband images, displaying the source and target images on a display device of the computer, aligning the source and target edge images, switching displaying of the source image and the target image on the display device, to enable identification of differences between the source image and the target image.

  11. A New Maximum-Likelihood Change Estimator for Two-Pass SAR Coherent Change Detection.

    SciTech Connect

    Wahl, Daniel E.; Yocky, David A.; Jakowatz, Charles V,

    2014-09-01

    In this paper, we derive a new optimal change metric to be used in synthetic aperture RADAR (SAR) coherent change detection (CCD). Previous CCD methods tend to produce false alarm states (showing change when there is none) in areas of the image that have a low clutter-to-noise power ratio (CNR). The new estimator does not suffer from this shortcoming. It is a surprisingly simple expression, easy to implement, and is optimal in the maximum-likelihood (ML) sense. The estimator produces very impressive results on the CCD collects that we have tested.

  12. Advances in neutron based bulk explosive detection

    NASA Astrophysics Data System (ADS)

    Gozani, Tsahi; Strellis, Dan

    2007-08-01

    Neutron based explosive inspection systems can detect a wide variety of national security threats. The inspection is founded on the detection of characteristic gamma rays emitted as the result of neutron interactions with materials. Generally these are gamma rays resulting from thermal neutron capture and inelastic scattering reactions in most materials and fast and thermal neutron fission in fissile (e.g.235U and 239Pu) and fertile (e.g.238U) materials. Cars or trucks laden with explosives, drugs, chemical agents and hazardous materials can be detected. Cargo material classification via its main elements and nuclear materials detection can also be accomplished with such neutron based platforms, when appropriate neutron sources, gamma ray spectroscopy, neutron detectors and suitable decision algorithms are employed. Neutron based techniques can be used in a variety of scenarios and operational modes. They can be used as stand alones for complete scan of objects such as vehicles, or for spot-checks to clear (or validate) alarms indicated by another inspection system such as X-ray radiography. The technologies developed over the last two decades are now being implemented with good results. Further advances have been made over the last few years that increase the sensitivity, applicability and robustness of these systems. The advances range from the synchronous inspection of two sides of vehicles, increasing throughput and sensitivity and reducing imparted dose to the inspected object and its occupants (if any), to taking advantage of the neutron kinetic behavior of cargo to remove systematic errors, reducing background effects and improving fast neutron signals.

  13. Detection of base-pair mismatches in DNA using graphene-based nanopore device

    NASA Astrophysics Data System (ADS)

    Kundu, Sourav; Karmakar, S. N.

    2016-04-01

    We present a unique way to detect base-pair mismatches in DNA, leading to a different epigenetic disorder by the method of nanopore sequencing. Based on a tight-binding formulation of a graphene-based nanopore device, using the Green’s function approach we study the changes in the electronic transport properties of the device as we translocate a double-stranded DNA through the nanopore embedded in a zigzag graphene nanoribbon. In the present work we are not only successful in detecting the usual AT and GC pairs but also a set of possible mismatches in the complementary base pairing.

  14. Detection of base-pair mismatches in DNA using graphene-based nanopore device.

    PubMed

    Kundu, Sourav; Karmakar, S N

    2016-04-01

    We present a unique way to detect base-pair mismatches in DNA, leading to a different epigenetic disorder by the method of nanopore sequencing. Based on a tight-binding formulation of a graphene-based nanopore device, using the Green's function approach we study the changes in the electronic transport properties of the device as we translocate a double-stranded DNA through the nanopore embedded in a zigzag graphene nanoribbon. In the present work we are not only successful in detecting the usual AT and GC pairs but also a set of possible mismatches in the complementary base pairing.

  15. Robust sensing methodology for detecting change with bistable circuitry dynamics tailoring

    NASA Astrophysics Data System (ADS)

    Harne, R. L.; Wang, K. W.

    2013-05-01

    In contrast to monitoring natural frequency shift, bifurcation-based sensing techniques utilize dramatic switches in response amplitude to detect structural change. We demonstrate a highly sensitive bifurcation-based sensing method requiring only the monitored structure, a transduction mechanism, and bistable electric circuitry. The system configuration is broadly applicable from, e.g., microscale mass sensing to structural health monitoring. In contrast to single bifurcation events of past techniques, the present methodology introduces new bifurcations that may be utilized sequentially for monitoring numerous thresholds of structural parameter change. We show that bifurcation-based sensing potential and versatility is greatly advanced.

  16. A robust multi-kernel change detection framework for detecting leaf beetle defoliation using Landsat 7 ETM+ data

    NASA Astrophysics Data System (ADS)

    Anees, Asim; Aryal, Jagannath; O'Reilly, Małgorzata M.; Gale, Timothy J.; Wardlaw, Tim

    2016-12-01

    A robust non-parametric framework, based on multiple Radial Basic Function (RBF) kernels, is proposed in this study, for detecting land/forest cover changes using Landsat 7 ETM+ images. One of the widely used frameworks is to find change vectors (difference image) and use a supervised classifier to differentiate between change and no-change. The Bayesian Classifiers e.g. Maximum Likelihood Classifier (MLC), Naive Bayes (NB), are widely used probabilistic classifiers which assume parametric models, e.g. Gaussian function, for the class conditional distributions. However, their performance can be limited if the data set deviates from the assumed model. The proposed framework exploits the useful properties of Least Squares Probabilistic Classifier (LSPC) formulation i.e. non-parametric and probabilistic nature, to model class posterior probabilities of the difference image using a linear combination of a large number of Gaussian kernels. To this end, a simple technique, based on 10-fold cross-validation is also proposed for tuning model parameters automatically instead of selecting a (possibly) suboptimal combination from pre-specified lists of values. The proposed framework has been tested and compared with Support Vector Machine (SVM) and NB for detection of defoliation, caused by leaf beetles (Paropsisterna spp.) in Eucalyptus nitens and Eucalyptus globulus plantations of two test areas, in Tasmania, Australia, using raw bands and band combination indices of Landsat 7 ETM+. It was observed that due to multi-kernel non-parametric formulation and probabilistic nature, the LSPC outperforms parametric NB with Gaussian assumption in change detection framework, with Overall Accuracy (OA) ranging from 93.6% (κ = 0.87) to 97.4% (κ = 0.94) against 85.3% (κ = 0.69) to 93.4% (κ = 0.85), and is more robust to changing data distributions. Its performance was comparable to SVM, with added advantages of being probabilistic and capable of handling multi-class problems

  17. Assessment of Data Fusion Algorithms for Earth Observation Change Detection Processes

    PubMed Central

    Molina, Iñigo; Martinez, Estibaliz; Morillo, Carmen; Velasco, Jesus; Jara, Alvaro

    2016-01-01

    In this work a parametric multi-sensor Bayesian data fusion approach and a Support Vector Machine (SVM) are used for a Change Detection problem. For this purpose two sets of SPOT5-PAN images have been used, which are in turn used for Change Detection Indices (CDIs) calculation. For minimizing radiometric differences, a methodology based on zonal “invariant features” is suggested. The choice of one or the other CDI for a change detection process is a subjective task as each CDI is probably more or less sensitive to certain types of changes. Likewise, this idea might be employed to create and improve a “change map”, which can be accomplished by means of the CDI’s informational content. For this purpose, information metrics such as the Shannon Entropy and “Specific Information” have been used to weight the changes and no-changes categories contained in a certain CDI and thus introduced in the Bayesian information fusion algorithm. Furthermore, the parameters of the probability density functions (pdf’s) that best fit the involved categories have also been estimated. Conversely, these considerations are not necessary for mapping procedures based on the discriminant functions of a SVM. This work has confirmed the capabilities of probabilistic information fusion procedure under these circumstances. PMID:27706048

  18. Assessment of Data Fusion Algorithms for Earth Observation Change Detection Processes.

    PubMed

    Molina, Iñigo; Martinez, Estibaliz; Morillo, Carmen; Velasco, Jesus; Jara, Alvaro

    2016-09-30

    In this work a parametric multi-sensor Bayesian data fusion approach and a Support Vector Machine (SVM) are used for a Change Detection problem. For this purpose two sets of SPOT5-PAN images have been used, which are in turn used for Change Detection Indices (CDIs) calculation. For minimizing radiometric differences, a methodology based on zonal "invariant features" is suggested. The choice of one or the other CDI for a change detection process is a subjective task as each CDI is probably more or less sensitive to certain types of changes. Likewise, this idea might be employed to create and improve a "change map", which can be accomplished by means of the CDI's informational content. For this purpose, information metrics such as the Shannon Entropy and "Specific Information" have been used to weight the changes and no-changes categories contained in a certain CDI and thus introduced in the Bayesian information fusion algorithm. Furthermore, the parameters of the probability density functions (pdf's) that best fit the involved categories have also been estimated. Conversely, these considerations are not necessary for mapping procedures based on the discriminant functions of a SVM. This work has confirmed the capabilities of probabilistic information fusion procedure under these circumstances.

  19. Adversary phase change detection using SOMs and text data.

    SciTech Connect

    Speed, Ann Elizabeth; Doser, Adele Beatrice; Warrender, Christina E.

    2010-05-01

    In this work, we developed a self-organizing map (SOM) technique for using web-based text analysis to forecast when a group is undergoing a phase change. By 'phase change', we mean that an organization has fundamentally shifted attitudes or behaviors. For instance, when ice melts into water, the characteristics of the substance change. A formerly peaceful group may suddenly adopt violence, or a violent organization may unexpectedly agree to a ceasefire. SOM techniques were used to analyze text obtained from organization postings on the world-wide web. Results suggest it may be possible to forecast phase changes, and determine if an example of writing can be attributed to a group of interest.

  20. The influence of various graphical and numeric trend display formats on the detection of simulated changes.

    PubMed

    Kennedy, R R; Merry, A F; Warman, G R; Webster, C S

    2009-11-01

    Integration of a large amount of information is important in anaesthesia but there is little research to guide the development of data displays. Anaesthetists from two hospitals participated in five related screen based simulation studies comparing various formats for display of historical or 'trend' data. Participants were asked to indicate when they first noticed a change in each displayed variable. Accuracy and latency (i.e. delay) in detection of changes were recorded. Latency was shorter with a graphic display of historical data than with a numeric display. Increasing number of variables or reduction of y-axis height increased the latency of detection. If the same number of data points were included, there was no difference between graphical and numerical displays of historical data. There was no difference in accuracy between graphical or numerical displays. These results suggest that the way trend data is presented can influence the speed of detection of changes.

  1. Block diagonal representations for covariance based anomalous change detectors

    SciTech Connect

    Matsekh, Anna; Theiler, James

    2009-01-01

    Change detection methods are of crucial importance in many remote sensing applications such as monitoring and surveillance, where the goal is to identify and separate changes of interest from pervasive changes inevitably present in images taken at different times and in different environmental and illumination conditions. Anomalous change detection (ACD) methods aim to identify rare, unusual, or anomalous changes among the changes of interest. Covariance-based ACD methods provide a powerful tool for detection of unusual changes in hyper-spectral images. In this paper we study the properties of the eigenvalue spectra of a family of ACD matrices in order to better understand the algebraic and numerical behavior of the covariance-based quadratic ACD methods. We propose to use singular vectors of covariance matrices of two hyper-spectral images in whitened coordinates for obtaining block-diagonal representations of the matrices of quadratic ACD methods. SVD transformation gives an equivalent representation of ACD matrices in compact block-diagonal form. In the paper we show that the eigenvalue spectrum of a block-diagonal ACD matrix can be identified analytically as a function of the singular value spectrum of the corresponding covariance matrix in whitened coordinates.

  2. Daytime Water Detection Based on Color Variation

    NASA Technical Reports Server (NTRS)

    Rankin, Arturo L.; Matthies, Larry H.

    2010-01-01

    Robust water detection is a critical perception requirement for unmanned ground vehicle (UGV) autonomous navigation. This is particularly true in wide open areas where water can collect in naturally occurring terrain depressions during periods of heavy precipitation and form large water bodies (such as ponds). At far range, reflections of the sky provide a strong cue for water. But at close range, the color coming out of a water body dominates sky reflections and the water cue from sky reflections is of marginal use. We model this behavior by using water body intensity data from multiple frames of RGB imagery to estimate the total reflection coefficient contribution from surface reflections and the combination of all other factors. Then we describe an algorithm that uses one of the color cameras in a forward- looking, UGV-mounted stereo-vision perception system to detect water bodies in wide open areas. This detector exploits the knowledge that the change in saturation-to-brightness ratio across a water body from the leading to trailing edge is uniform and distinct from other terrain types. In test sequences approaching a pond under clear, overcast, and cloudy sky conditions, the true positive and false negative water detection rates were (95.76%, 96.71%, 98.77%) and (0.45%, 0.60%, 0.62%), respectively. This software has been integrated on an experimental unmanned vehicle and field tested at Ft. Indiantown Gap, PA.

  3. Motion detection based on recurrent network dynamics

    PubMed Central

    Joukes, Jeroen; Hartmann, Till S.; Krekelberg, Bart

    2014-01-01

    The detection of visual motion requires temporal delays to compare current with earlier visual input. Models of motion detection assume that these delays reside in separate classes of slow and fast thalamic cells, or slow and fast synaptic transmission. We used a data-driven modeling approach to generate a model that instead uses recurrent network dynamics with a single, fixed temporal integration window to implement the velocity computation. This model successfully reproduced the temporal response dynamics of a population of motion sensitive neurons in macaque middle temporal area (MT) and its constituent parts matched many of the properties found in the motion processing pathway (e.g., Gabor-like receptive fields (RFs), simple and complex cells, spatially asymmetric excitation and inhibition). Reverse correlation analysis revealed that a simplified network based on first and second order space-time correlations of the recurrent model behaved much like a feedforward motion energy (ME) model. The feedforward model, however, failed to capture the full speed tuning and direction selectivity properties based on higher than second order space-time correlations typically found in MT. These findings support the idea that recurrent network connectivity can create temporal delays to compute velocity. Moreover, the model explains why the motion detection system often behaves like a feedforward ME network, even though the anatomical evidence strongly suggests that this network should be dominated by recurrent feedback. PMID:25565992

  4. Musicians Are Better than Non-musicians in Frequency Change Detection: Behavioral and Electrophysiological Evidence.

    PubMed

    Liang, Chun; Earl, Brian; Thompson, Ivy; Whitaker, Kayla; Cahn, Steven; Xiang, Jing; Fu, Qian-Jie; Zhang, Fawen

    2016-01-01

    Objective: The objectives of this study were: (1) to determine if musicians have a better ability to detect frequency changes under quiet and noisy conditions; (2) to use the acoustic change complex (ACC), a type of electroencephalographic (EEG) response, to understand the neural substrates of musician vs. non-musician difference in frequency change detection abilities. Methods: Twenty-four young normal hearing listeners (12 musicians and 12 non-musicians) participated. All participants underwent psychoacoustic frequency detection tests with three types of stimuli: tones (base frequency at 160 Hz) containing frequency changes (Stim 1), tones containing frequency changes masked by low-level noise (Stim 2), and tones containing frequency changes masked by high-level noise (Stim 3). The EEG data were recorded using tones (base frequency at 160 and 1200 Hz, respectively) containing different magnitudes of frequency changes (0, 5, and 50% changes, respectively). The late-latency evoked potential evoked by the onset of the tones (onset LAEP or N1-P2 complex) and that evoked by the frequency change contained in the tone (the acoustic change complex or ACC or N1'-P2' complex) were analyzed. Results: Musicians significantly outperformed non-musicians in all stimulus conditions. The ACC and onset LAEP showed similarities and differences. Increasing the magnitude of frequency change resulted in increased ACC amplitudes. ACC measures were found to be significantly different between musicians (larger P2' amplitude) and non-musicians for the base frequency of 160 Hz but not 1200 Hz. Although the peak amplitude in the onset LAEP appeared to be larger and latency shorter in musicians than in non-musicians, the difference did not reach statistical significance. The amplitude of the onset LAEP is significantly correlated with that of the ACC for the base frequency of 160 Hz. Conclusion: The present study demonstrated that musicians do perform better than non-musicians in detecting

  5. Musicians Are Better than Non-musicians in Frequency Change Detection: Behavioral and Electrophysiological Evidence

    PubMed Central

    Liang, Chun; Earl, Brian; Thompson, Ivy; Whitaker, Kayla; Cahn, Steven; Xiang, Jing; Fu, Qian-Jie; Zhang, Fawen

    2016-01-01

    Objective: The objectives of this study were: (1) to determine if musicians have a better ability to detect frequency changes under quiet and noisy conditions; (2) to use the acoustic change complex (ACC), a type of electroencephalographic (EEG) response, to understand the neural substrates of musician vs. non-musician difference in frequency change detection abilities. Methods: Twenty-four young normal hearing listeners (12 musicians and 12 non-musicians) participated. All participants underwent psychoacoustic frequency detection tests with three types of stimuli: tones (base frequency at 160 Hz) containing frequency changes (Stim 1), tones containing frequency changes masked by low-level noise (Stim 2), and tones containing frequency changes masked by high-level noise (Stim 3). The EEG data were recorded using tones (base frequency at 160 and 1200 Hz, respectively) containing different magnitudes of frequency changes (0, 5, and 50% changes, respectively). The late-latency evoked potential evoked by the onset of the tones (onset LAEP or N1-P2 complex) and that evoked by the frequency change contained in the tone (the acoustic change complex or ACC or N1′-P2′ complex) were analyzed. Results: Musicians significantly outperformed non-musicians in all stimulus conditions. The ACC and onset LAEP showed similarities and differences. Increasing the magnitude of frequency change resulted in increased ACC amplitudes. ACC measures were found to be significantly different between musicians (larger P2′ amplitude) and non-musicians for the base frequency of 160 Hz but not 1200 Hz. Although the peak amplitude in the onset LAEP appeared to be larger and latency shorter in musicians than in non-musicians, the difference did not reach statistical significance. The amplitude of the onset LAEP is significantly correlated with that of the ACC for the base frequency of 160 Hz. Conclusion: The present study demonstrated that musicians do perform better than non-musicians in

  6. An Approach to Alleviate the False Alarm in Building Change Detection from Urban Vhr Image

    NASA Astrophysics Data System (ADS)

    Chen, J.; Hou, J. L.; Deng, M.

    2016-06-01

    Building change detection from very-high-resolution (VHR) urban remote sensing image frequently encounter the challenge of serious false alarm caused by different illumination or viewing angles in bi-temporal images. An approach to alleviate the false alarm in urban building change detection is proposed in this paper. Firstly, as shadows casted by urban buildings are of distinct spectral and shape feature, it adopts a supervised object-based classification technique to extract them in this paper. Secondly, on the opposite direction of sunlight illumination, a straight line is drawn along the principal orientation of building in every extracted shadow region. Starting from the straight line and moving toward the sunlight direction, a rectangular area is constructed to cover partial shadow and rooftop of each building. Thirdly, an algebra and geometry invariant based method is used to abstract the spatial topological relationship of the potential unchanged buildings from all central points of the rectangular area. Finally, based on an oriented texture curvature descriptor, an index is established to determine the actual false alarm in building change detection result. The experiment results validate that the proposed method can be used as an effective framework to alleviate the false alarm in building change detection from urban VHR image.

  7. Onboard Data Processor for Change-Detection Radar Imaging

    NASA Technical Reports Server (NTRS)

    Lou, Yunling; Muellerschoen, Ronald J.; Chien, Steve A.; Saatchi, Sasan S.; Clark, Duane

    2008-01-01

    A computer system denoted a change-detection onboard processor (CDOP) is being developed as a means of processing the digitized output of a synthetic-aperture radar (SAR) apparatus aboard an aircraft or spacecraft to generate images showing changes that have occurred in the terrain below between repeat passes of the aircraft or spacecraft over the terrain. When fully developed, the CDOP is intended to be capable of generating SAR images and/or SAR differential interferograms in nearly real time. The CDOP is expected to be especially useful for understanding some large-scale natural phenomena and/or mitigating natural hazards: For example, it could be used for near-real-time observation of surface changes caused by floods, landslides, forest fires, volcanic eruptions, earthquakes, glaciers, and sea ice movements. It could also be used to observe such longer-term surface changes as those associated with growth of vegetation (relevant to estimation of wildfire fuel loads). The CDOP is, essentially, an interferometric SAR processor designed to operate aboard a radar platform.

  8. Street-side vehicle detection, classification and change detection using mobile laser scanning data

    NASA Astrophysics Data System (ADS)

    Xiao, Wen; Vallet, Bruno; Schindler, Konrad; Paparoditis, Nicolas

    2016-04-01

    Statistics on street-side car parks, e.g. occupancy rates, parked vehicle types, parking durations, are of great importance for urban planning and policy making. Related studies, e.g. vehicle detection and classification, mostly focus on static images or video. Whereas mobile laser scanning (MLS) systems are increasingly utilized for urban street environment perception due to their direct 3D information acquisition, high accuracy and movability. In this paper, we design a complete system for car park monitoring, including vehicle recognition, localization, classification and change detection, from laser scanning point clouds. The experimental data are acquired by an MLS system using high frequency laser scanner which scans the streets vertically along the system's moving trajectory. The point clouds are firstly classified as ground, building façade, and street objects which are then segmented using state-of-the-art methods. Each segment is treated as an object hypothesis, and its geometric features are extracted. Moreover, a deformable vehicle model is fitted to each object. By fitting an explicit model to the vehicle points, detailed information, such as precise position and orientation, can be obtained. The model parameters are also treated as vehicle features. Together with the geometric features, they are applied to a supervised learning procedure for vehicle or non-vehicle recognition. The classes of detected vehicles are also investigated. Whether vehicles have changed across two datasets acquired at different times is detected to estimate the durations. Here, vehicles are trained pair-wisely. Two same or different vehicles are paired up as training samples. As a result, the vehicle recognition, classification and change detection accuracies are 95.9%, 86.0% and 98.7%, respectively. Vehicle modelling improves not only the recognition rate, but also the localization precision compared to bounding boxes.

  9. Exponentially Weighted Moving Average Change Detection Around the Country (and the World)

    NASA Astrophysics Data System (ADS)

    Brooks, E.; Wynne, R. H.; Thomas, V. A.; Blinn, C. E.; Coulston, J.

    2014-12-01

    With continuous, freely available moderate-resolution imagery of the Earth's surface available, and with the promise of more imagery to come, change detection based on continuous process models continues to be a major area of research. One such method, exponentially weighted moving average change detection (EWMACD), is based on a mixture of harmonic regression (HR) and statistical quality control, a branch of statistics commonly used to detect aberrations in industrial and medical processes. By using HR to approximate per-pixel seasonal curves, the resulting residuals characterize information about the pixels which stands outside of the periodic structure imposed by HR. Under stable pixels, these residuals behave as might be expected, but in the presence of changes (growth, stress, removal), the residuals clearly show these changes when they are used as inputs into an EWMA chart. In prior work in Alabama, USA, EWMACD yielded an overall accuracy of 85% on a random sample of known thinned stands, in some cases detecting thinnings as sparse as 25% removal. It was also shown to correctly identify the timing of the thinning activity, typically within a single image date of the change. The net result of the algorithm was to produce date-by-date maps of afforestation and deforestation on a variable scale of severity. In other research, EWMACD has also been applied to detect land use and land cover changes in central Java, Indonesia, despite the heavy incidence of clouds and a monsoonal climate. Preliminary results show that EWMACD accurately identifies land use conversion (agricultural to residential, for example) and also identifies neighborhoods where the building density has increased, removing neighborhood vegetation. In both cases, initial results indicate the potential utility of EWMACD to detect both gross and subtle ecosystem disturbance, but further testing across a range of ecosystems and disturbances is clearly warranted.

  10. Reset Tree-Based Optical Fault Detection

    PubMed Central

    Lee, Dong-Geon; Choi, Dooho; Seo, Jungtaek; Kim, Howon

    2013-01-01

    In this paper, we present a new reset tree-based scheme to protect cryptographic hardware against optical fault injection attacks. As one of the most powerful invasive attacks on cryptographic hardware, optical fault attacks cause semiconductors to misbehave by injecting high-energy light into a decapped integrated circuit. The contaminated result from the affected chip is then used to reveal secret information, such as a key, from the cryptographic hardware. Since the advent of such attacks, various countermeasures have been proposed. Although most of these countermeasures are strong, there is still the possibility of attack. In this paper, we present a novel optical fault detection scheme that utilizes the buffers on a circuit's reset signal tree as a fault detection sensor. To evaluate our proposal, we model radiation-induced currents into circuit components and perform a SPICE simulation. The proposed scheme is expected to be used as a supplemental security tool. PMID:23698267

  11. Contour based object detection using part bundles

    PubMed Central

    Lu, ChengEn; Adluru, Nagesh; Ling, Haibin; Zhu, Guangxi; Latecki, Longin Jan

    2016-01-01

    In this paper we propose a novel framework for contour based object detection from cluttered environments. Given a contour model for a class of objects, it is first decomposed into fragments hierarchically. Then, we group these fragments into part bundles, where a part bundle can contain overlapping fragments. Given a new image with set of edge fragments we develop an efficient voting method using local shape similarity between part bundles and edge fragments that generates high quality candidate part configurations. We then use global shape similarity between the part configurations and the model contour to find optimal configuration. Furthermore, we show that appearance information can be used for improving detection for objects with distinctive texture when model contour does not sufficiently capture deformation of the objects.

  12. An SPR based sensor for allergens detection.

    PubMed

    Ashley, J; Piekarska, M; Segers, C; Trinh, L; Rodgers, T; Willey, R; Tothill, I E

    2017-02-15

    A simple, sensitive and label-free optical sensor method was developed for allergens analysis using α-casein as the biomarker for cow's milk detection, to be used directly in final rinse samples of cleaning in place systems (CIP) of food manufacturers. A Surface Plasmon Resonance (SPR) sensor chip consisting of four sensing arrays enabling the measurement of samples and control binding events simultaneously on the sensor surface was employed in this work. SPR offers several advantages in terms of label free detection, real time measurements and superior sensitivity when compared to ELISA based techniques. The gold sensor chip was used to immobilise α-casein-polyclonal antibody using EDC/NHS coupling procedure. The performance of the assay and the sensor was first optimised and characterised in pure buffer conditions giving a detection limit of 58ngmL(-1) as a direct binding assay. The assay sensitivity can be further improved by using sandwich assay format and amplified with nanoparticles. However, at this stage this is not required as the detection limit achieved exceeded the required allergens detection levels of 2µgmL(-1) for α-S1-casein. The sensor demonstrated good selectivity towards the α-casein as the target analyte and adequate recoveries from CIP final rinse wash samples. The sensor would be useful tool for monitoring allergen levels after cleaning procedures, providing additional data that may better inform upon wider food allergen risk management decision(s) that are made by food manufacturer. In particular, this sensor could potentially help validate or optimise cleaning practices for a given food manufacturing process.

  13. Illumination robust change detection with CMOS imaging sensors

    NASA Astrophysics Data System (ADS)

    Rengarajan, Vijay; Gupta, Sheetal B.; Rajagopalan, A. N.; Seetharaman, Guna

    2015-05-01

    Change detection between two images in the presence of degradations is an important problem in the computer vision community, more so for the aerial scenario which is particularly challenging. Cameras mounted on moving platforms such as aircrafts or drones are subject to general six-dimensional motion as the motion is not restricted to a single plane. With CMOS cameras increasingly in vogue due to their low power consumption, the inevitability of rolling-shutter (RS) effect adds to the challenge. This is caused by sequential exposure of rows in CMOS cameras unlike conventional global shutter cameras where all pixels are exposed simultaneously. The RS effect is particularly pronounced in aerial imaging since each row of the imaging sensor is likely to experience a different motion. For fast-moving platforms, the problem is further compounded since the rows are also affected by motion blur. Moreover, since the two images are shot at different times, illumination differences are common. In this paper, we propose a unified computational framework that elegantly exploits the scarcity constraint to deal with the problem of change detection in images degraded by RS effect, motion blur as well as non-global illumination differences. We formulate an optimization problem where each row of the distorted image is approximated as a weighted sum of the corresponding rows in warped versions of the reference image due to camera motion within the exposure period to account for geometric as well as photometric differences. The method has been validated on both synthetic and real data.

  14. Fusion techniques using distributed Kalman filtering for detecting changes in systems

    NASA Technical Reports Server (NTRS)

    Belcastro, Celeste M.; Fischl, Robert; Kam, Moshe

    1991-01-01

    A comparison is made of the performances of two detection strategies that are based on different data fusion techniques. The strategies detect changes in a linear system. One detection strategy involves combining the estimates and error covariance matrices of distributed Kalman filters, generating a residual from the used estimates, comparing this residual to a threshold, and making a decision. The other detection strategy involves a distributed decision process in which estimates from distributed Kalman filters are used to generate distributed residuals which are compared locally to a threshold. Local decisions are made and these decisions are then fused into a global decision. The performances of each of these detection schemes are compared, and it is concluded that better performance is achieved when local decisions are made and then fused into a global decision.

  15. Mining remote sensing image data: an integration of fuzzy set theory and image understanding techniques for environmental change detection

    NASA Astrophysics Data System (ADS)

    Eklund, Peter W.; You, Jane; Deer, Peter

    2000-04-01

    This paper presents an image understanding approach to mine remotely sensed image data from different source dates for environmental change detection. It is focused on the immediate needs for knowledge discovery from large sets of image data for environmental monitoring. In contrast to the traditional approaches for change detection, we introduce a wavelet-based hierarchical scheme which integrates fuzzy set theory and image understanding techniques for knowledge discovery of the remote image data. The proposed approach includes algorithms for hierarchical change detection, region representations and classification. The effectiveness of the proposed algorithms is demonstrated throughout the completion of three tasks, namely hierarchial detection of change by fuzzy post classification comparisons, localization of change by B-spline based region representation, and categorization of change by hierarchial texture classification.

  16. Volumetric Forest Change Detection Through Vhr Satellite Imagery

    NASA Astrophysics Data System (ADS)

    Akca, Devrim; Stylianidis, Efstratios; Smagas, Konstantinos; Hofer, Martin; Poli, Daniela; Gruen, Armin; Sanchez Martin, Victor; Altan, Orhan; Walli, Andreas; Jimeno, Elisa; Garcia, Alejandro

    2016-06-01

    Quick and economical ways of detecting of planimetric and volumetric changes of forest areas are in high demand. A research platform, called FORSAT (A satellite processing platform for high resolution forest assessment), was developed for the extraction of 3D geometric information from VHR (very-high resolution) imagery from satellite optical sensors and automatic change detection. This 3D forest information solution was developed during a Eurostars project. FORSAT includes two main units. The first one is dedicated to the geometric and radiometric processing of satellite optical imagery and 2D/3D information extraction. This includes: image radiometric pre-processing, image and ground point measurement, improvement of geometric sensor orientation, quasiepipolar image generation for stereo measurements, digital surface model (DSM) extraction by using a precise and robust image matching approach specially designed for VHR satellite imagery, generation of orthoimages, and 3D measurements in single images using mono-plotting and in stereo images as well as triplets. FORSAT supports most of the VHR optically imagery commonly used for civil applications: IKONOS, OrbView - 3, SPOT - 5 HRS, SPOT - 5 HRG, QuickBird, GeoEye-1, WorldView-1/2, Pléiades 1A/1B, SPOT 6/7, and sensors of similar type to be expected in the future. The second unit of FORSAT is dedicated to 3D surface comparison for change detection. It allows users to import digital elevation models (DEMs), align them using an advanced 3D surface matching approach and calculate the 3D differences and volume changes between epochs. To this end our 3D surface matching method LS3D is being used. FORSAT is a single source and flexible forest information solution with a very competitive price/quality ratio, allowing expert and non-expert remote sensing users to monitor forests in three and four dimensions from VHR optical imagery for many forest information needs. The capacity and benefits of FORSAT have been tested in

  17. A new approach for surface water change detection: Integration of pixel level image fusion and image classification techniques

    NASA Astrophysics Data System (ADS)

    Rokni, Komeil; Ahmad, Anuar; Solaimani, Karim; Hazini, Sharifeh

    2015-02-01

    Normally, to detect surface water changes, water features are extracted individually using multi-temporal satellite data, and then analyzed and compared to detect their changes. This study introduced a new approach for surface water change detection, which is based on integration of pixel level image fusion and image classification techniques. The proposed approach has the advantages of producing a pansharpened multispectral image, simultaneously highlighting the changed areas, as well as providing a high accuracy result. In doing so, various fusion techniques including Modified IHS, High Pass Filter, Gram Schmidt, and Wavelet-PC were investigated to merge the multi-temporal Landsat ETM+ 2000 and TM 2010 images to highlight the changes. The suitability of the resulting fused images for change detection was evaluated using edge detection, visual interpretation, and quantitative analysis methods. Subsequently, artificial neural network (ANN), support vector machine (SVM), and maximum likelihood (ML) classification techniques were applied to extract and map the highlighted changes. Furthermore, the applicability of the proposed approach for surface water change detection was evaluated in comparison with some common change detection methods including image differencing, principal components analysis, and post classification comparison. The results indicate that Lake Urmia lost about one third of its surface area in the period 2000-2010. The results illustrate the effectiveness of the proposed approach, especially Gram Schmidt-ANN and Gram Schmidt-SVM for surface water change detection.

  18. Early auditory change detection implicitly facilitated by ignored concurrent visual change during a Braille reading task.

    PubMed

    Aoyama, Atsushi; Haruyama, Tomohiro; Kuriki, Shinya

    2013-09-01

    Unconscious monitoring of multimodal stimulus changes enables humans to effectively sense the external environment. Such automatic change detection is thought to be reflected in auditory and visual mismatch negativity (MMN) and mismatch negativity fields (MMFs). These are event-related potentials and magnetic fields, respectively, evoked by deviant stimuli within a sequence of standard stimuli, and both are typically studied during irrelevant visual tasks that cause the stimuli to be ignored. Due to the sensitivity of MMN/MMF to potential effects of explicit attention to vision, however, it is unclear whether multisensory co-occurring changes can purely facilitate early sensory change detection reciprocally across modalities. We adopted a tactile task involving the reading of Braille patterns as a neutral ignore condition, while measuring magnetoencephalographic responses to concurrent audiovisual stimuli that were infrequently deviated either in auditory, visual, or audiovisual dimensions; 1000-Hz standard tones were switched to 1050-Hz deviant tones and/or two-by-two standard check patterns displayed on both sides of visual fields were switched to deviant reversed patterns. The check patterns were set to be faint enough so that the reversals could be easily ignored even during Braille reading. While visual MMFs were virtually undetectable even for visual and audiovisual deviants, significant auditory MMFs were observed for auditory and audiovisual deviants, originating from bilateral supratemporal auditory areas. Notably, auditory MMFs were significantly enhanced for audiovisual deviants from about 100 ms post-stimulus, as compared with the summation responses for auditory and visual deviants or for each of the unisensory deviants recorded in separate sessions. Evidenced by high tactile task performance with unawareness of visual changes, we conclude that Braille reading can successfully suppress explicit attention and that simultaneous multisensory changes can

  19. A Saliency Guided Semi-Supervised Building Change Detection Method for High Resolution Remote Sensing Images

    PubMed Central

    Hou, Bin; Wang, Yunhong; Liu, Qingjie

    2016-01-01

    Characterizations of up to date information of the Earth’s surface are an important application providing insights to urban planning, resources monitoring and environmental studies. A large number of change detection (CD) methods have been developed to solve them by utilizing remote sensing (RS) images. The advent of high resolution (HR) remote sensing images further provides challenges to traditional CD methods and opportunities to object-based CD methods. While several kinds of geospatial objects are recognized, this manuscript mainly focuses on buildings. Specifically, we propose a novel automatic approach combining pixel-based strategies with object-based ones for detecting building changes with HR remote sensing images. A multiresolution contextual morphological transformation called extended morphological attribute profiles (EMAPs) allows the extraction of geometrical features related to the structures within the scene at different scales. Pixel-based post-classification is executed on EMAPs using hierarchical fuzzy clustering. Subsequently, the hierarchical fuzzy frequency vector histograms are formed based on the image-objects acquired by simple linear iterative clustering (SLIC) segmentation. Then, saliency and morphological building index (MBI) extracted on difference images are used to generate a pseudo training set. Ultimately, object-based semi-supervised classification is implemented on this training set by applying random forest (RF). Most of the important changes are detected by the proposed method in our experiments. This study was checked for effectiveness using visual evaluation and numerical evaluation. PMID:27618903

  20. A Saliency Guided Semi-Supervised Building Change Detection Method for High Resolution Remote Sensing Images.

    PubMed

    Hou, Bin; Wang, Yunhong; Liu, Qingjie

    2016-08-27

    Characterizations of up to date information of the Earth's surface are an important application providing insights to urban planning, resources monitoring and environmental studies. A large number of change detection (CD) methods have been developed to solve them by utilizing remote sensing (RS) images. The advent of high resolution (HR) remote sensing images further provides challenges to traditional CD methods and opportunities to object-based CD methods. While several kinds of geospatial objects are recognized, this manuscript mainly focuses on buildings. Specifically, we propose a novel automatic approach combining pixel-based strategies with object-based ones for detecting building changes with HR remote sensing images. A multiresolution contextual morphological transformation called extended morphological attribute profiles (EMAPs) allows the extraction of geometrical features related to the structures within the scene at different scales. Pixel-based post-classification is executed on EMAPs using hierarchical fuzzy clustering. Subsequently, the hierarchical fuzzy frequency vector histograms are formed based on the image-objects acquired by simple linear iterative clustering (SLIC) segmentation. Then, saliency and morphological building index (MBI) extracted on difference images are used to generate a pseudo training set. Ultimately, object-based semi-supervised classification is implemented on this training set by applying random forest (RF). Most of the important changes are detected by the proposed method in our experiments. This study was checked for effectiveness using visual evaluation and numerical evaluation.

  1. A comprehensive change detection method for updating the National Land Cover Database to circa 2011

    USGS Publications Warehouse

    Jin, Suming; Yang, Limin; Danielson, Patrick; Homer, Collin G.; Fry, Joyce; Xian, George

    2013-01-01

    The importance of characterizing, quantifying, and monitoring land cover, land use, and their changes has been widely recognized by global and environmental change studies. Since the early 1990s, three U.S. National Land Cover Database (NLCD) products (circa 1992, 2001, and 2006) have been released as free downloads for users. The NLCD 2006 also provides land cover change products between 2001 and 2006. To continue providing updated national land cover and change datasets, a new initiative in developing NLCD 2011 is currently underway. We present a new Comprehensive Change Detection Method (CCDM) designed as a key component for the development of NLCD 2011 and the research results from two exemplar studies. The CCDM integrates spectral-based change detection algorithms including a Multi-Index Integrated Change Analysis (MIICA) model and a novel change model called Zone, which extracts change information from two Landsat image pairs. The MIICA model is the core module of the change detection strategy and uses four spectral indices (CV, RCVMAX, dNBR, and dNDVI) to obtain the changes that occurred between two image dates. The CCDM also includes a knowledge-based system, which uses critical information on historical and current land cover conditions and trends and the likelihood of land cover change, to combine the changes from MIICA and Zone. For NLCD 2011, the improved and enhanced change products obtained from the CCDM provide critical information on location, magnitude, and direction of potential change areas and serve as a basis for further characterizing land cover changes for the nation. An accuracy assessment from the two study areas show 100% agreement between CCDM mapped no-change class with reference dataset, and 18% and 82% disagreement for the change class for WRS path/row p22r39 and p33r33, respectively. The strength of the CCDM is that the method is simple, easy to operate, widely applicable, and capable of capturing a variety of natural and

  2. Structural Damage Detection Using Changes in Natural Frequencies: Theory and Applications

    NASA Astrophysics Data System (ADS)

    He, K.; Zhu, W. D.

    2011-07-01

    A vibration-based method that uses changes in natural frequencies of a structure to detect damage has advantages over conventional nondestructive tests in detecting various types of damage, including loosening of bolted joints, using minimum measurement data. Two major challenges associated with applications of the vibration-based damage detection method to engineering structures are addressed: accurate modeling of structures and the development of a robust inverse algorithm to detect damage, which are defined as the forward and inverse problems, respectively. To resolve the forward problem, new physics-based finite element modeling techniques are developed for fillets in thin-walled beams and for bolted joints, so that complex structures can be accurately modeled with a reasonable model size. To resolve the inverse problem, a logistical function transformation is introduced to convert the constrained optimization problem to an unconstrained one, and a robust iterative algorithm using a trust-region method, called the Levenberg-Marquardt method, is developed to accurately detect the locations and extent of damage. The new methodology can ensure global convergence of the iterative algorithm in solving under-determined system equations and deal with damage detection problems with relatively large modeling error and measurement noise. The vibration-based damage detection method is applied to various structures including lightning masts, a space frame structure and one of its components, and a pipeline. The exact locations and extent of damage can be detected in the numerical simulation where there is no modeling error and measurement noise. The locations and extent of damage can be successfully detected in experimental damage detection.

  3. Covariance based outlier detection with feature selection.

    PubMed

    Zwilling, Chris E; Wang, Michelle Y

    2016-08-01

    The present covariance based outlier detection algorithm selects from a candidate set of feature vectors that are best at identifying outliers. Features extracted from biomedical and health informatics data can be more informative in disease assessment and there are no restrictions on the nature and number of features that can be tested. But an important challenge for an algorithm operating on a set of features is for it to winnow the effective features from the ineffective ones. The powerful algorithm described in this paper leverages covariance information from the time series data to identify features with the highest sensitivity for outlier identification. Empirical results demonstrate the efficacy of the method.

  4. A liquid-crystal-based DNA biosensor for pathogen detection

    NASA Astrophysics Data System (ADS)

    Khan, Mashooq; Khan, Abdur Rahim; Shin, Jae-Ho; Park, Soo-Young

    2016-03-01

    A liquid-crystal (LC)-filled transmission electron microscopy (TEM) grid cell coated with the cationic surfactant dodecyltrimethylammonium bromide (DTAB), to which a single-stranded deoxyribonucleic acid probe (ssDNAprobe) was adsorbed at the LC/aqueous interface (TEMDTAB/DNA), was applied for the highly specific detection of target DNA molecules. The DTAB-coated E7 (used LC mixture) in the TEM grid (TEMDTAB) exhibited a homeotropic orientation, and changed to a planar orientation upon adsorption of the ssDNAprobe. The TEMDTAB/DNA was then exposed to complementary (target) ssDNA, which resulted in a planar-to-homeotropic configurational change of E7 that could be observed through a polarized optical microscope under crossed polarizers. The optimum adsorption density (2 μM) of ssDNAprobe enabled the detection of ≥0.05 nM complementary ssDNA. This TEMDTAB/DNA biosensor could differentiate complementary ssDNA from mismatched ssDNA as well as double-stranded DNA. It also successfully detected the genomic DNAs of the bacterium Erwinia carotovora and the fungi Rhazictonia solani. Owe to the high specificity, sensitivity, and label-free detection, this biosensor may broaden the applications of LC-based biosensors to pathogen detection.

  5. A liquid-crystal-based DNA biosensor for pathogen detection

    PubMed Central

    Khan, Mashooq; Khan, Abdur Rahim; Shin, Jae-Ho; Park, Soo-Young

    2016-01-01

    A liquid-crystal (LC)-filled transmission electron microscopy (TEM) grid cell coated with the cationic surfactant dodecyltrimethylammonium bromide (DTAB), to which a single-stranded deoxyribonucleic acid probe (ssDNAprobe) was adsorbed at the LC/aqueous interface (TEMDTAB/DNA), was applied for the highly specific detection of target DNA molecules. The DTAB-coated E7 (used LC mixture) in the TEM grid (TEMDTAB) exhibited a homeotropic orientation, and changed to a planar orientation upon adsorption of the ssDNAprobe. The TEMDTAB/DNA was then exposed to complementary (target) ssDNA, which resulted in a planar-to-homeotropic configurational change of E7 that could be observed through a polarized optical microscope under crossed polarizers. The optimum adsorption density (2 μM) of ssDNAprobe enabled the detection of ≥0.05 nM complementary ssDNA. This TEMDTAB/DNA biosensor could differentiate complementary ssDNA from mismatched ssDNA as well as double-stranded DNA. It also successfully detected the genomic DNAs of the bacterium Erwinia carotovora and the fungi Rhazictonia solani. Owe to the high specificity, sensitivity, and label-free detection, this biosensor may broaden the applications of LC-based biosensors to pathogen detection. PMID:26940532

  6. A comparative study of four change detection methods for aerial photography applications

    NASA Astrophysics Data System (ADS)

    Abramovich, Gil; Brooksby, Glen; Bush, Stephen F.; Manickam, Swaminathan; Ozcanli, Ozge; Garrett, Benjamin D.

    2010-04-01

    We present four new change detection methods that create an automated change map from a probability map. In this case, the probability map was derived from a 3D model. The primary application of interest is aerial photographic applications, where the appearance, disappearance or change in position of small objects of a selectable class (e.g., cars) must be detected at a high success rate in spite of variations in magnification, lighting and background across the image. The methods rely on an earlier derivation of a probability map. We describe the theory of the four methods, namely Bernoulli variables, Markov Random Fields, connected change, and relaxation-based segmentation, evaluate and compare their performance experimentally on a set probability maps derived from aerial photographs.

  7. Change detection for hyperspectral sensing in a transformed low-dimensional space

    SciTech Connect

    Foy, Bernard R; Theiler, James

    2010-01-01

    We present an approach to the problem of change in hyperspectral imagery that operates in a two-dimensional space. The coordinates in the space are related to Mahalanobis distances for the combined ('stacked') data and the individual hyperspectral scenes. Although it is only two-dimensional, this space is rich enough to include several well-known change detection algorithms, including the hyperbolic anomalous change detector, based on Gaussian scene clutter, and the EC-uncorrelated detector based on heavy-tailed (elliptically contoured) clutter. Because this space is only two-dimensional, adaptive machine learning methods can produce new change detectors without being stymied by the curse of dimensionality. We investigate, in particular, the utility of the support vector machine for learning boundaries in this 2-D space, and compare the performance of the resulting nonlinearly adaptjve detector to change detectors that have themselves shown good performance.

  8. Detection of Deforestation and Land Conversion in Rondonia, Brazil Using Change Detection Techniques

    NASA Technical Reports Server (NTRS)

    Guild, Liane S.; Cohen, Warren B,; Kauffman, J. Boone; Peterson, David L. (Technical Monitor)

    2001-01-01

    Fires associated with tropical deforestation, land conversion, and land use greatly contribute to emissions as well as the depletion of carbon and nutrient pools. The objective of this research was to compare change detection techniques for identifying deforestation and cattle pasture formation during a period of early colonization and agricultural expansion in the vicinity of Jamari, Rond6nia. Multi-date Landsat Thematic Mapper (TM) data between 1984 and 1992 was examined in a 94 370-ha area of active deforestation to map land cover change. The Tasseled Cap (TC) transformation was used to enhance the contrast between forest, cleared areas, and regrowth. TC images were stacked into a composite multi-date TC and used in a principal components (PC) transformation to identify change components. In addition, consecutive TC image pairs were differenced and stacked into a composite multi-date differenced image. A maximum likelihood classification of each image composite was compared for identification of land cover change. The multi-date TC composite classification had the best accuracy of 78.1% (kappa). By 1984, only 5% of the study area had been cleared, but by 1992, 11% of the area had been deforested, primarily for pasture and 7% lost due to hydroelectric dam flooding. Finally, discrimination of pasture versus cultivation was improved due to the ability to detect land under sustained clearing opened to land exhibiting regrowth with infrequent clearing.

  9. Performance of a community detection algorithm based on semidefinite programming

    NASA Astrophysics Data System (ADS)

    Ricci-Tersenghi, Federico; Javanmard, Adel; Montanari, Andrea

    2016-03-01

    The problem of detecting communities in a graph is maybe one the most studied inference problems, given its simplicity and widespread diffusion among several disciplines. A very common benchmark for this problem is the stochastic block model or planted partition problem, where a phase transition takes place in the detection of the planted partition by changing the signal-to-noise ratio. Optimal algorithms for the detection exist which are based on spectral methods, but we show these are extremely sensible to slight modification in the generative model. Recently Javanmard, Montanari and Ricci-Tersenghi [1] have used statistical physics arguments, and numerical simulations to show that finding communities in the stochastic block model via semidefinite programming is quasi optimal. Further, the resulting semidefinite relaxation can be solved efficiently, and is very robust with respect to changes in the generative model. In this paper we study in detail several practical aspects of this new algorithm based on semidefinite programming for the detection of the planted partition. The algorithm turns out to be very fast, allowing the solution of problems with O(105) variables in few second on a laptop computer.

  10. The relationship between change detection and recognition of centrally attended objects in motion pictures.

    PubMed

    Angelone, Bonnie L; Levin, Daniel T; Simons, Daniel J

    2003-01-01

    Observers typically detect changes to central objects more readily than changes to marginal objects, but they sometimes miss changes to central, attended objects as well. However, even if observers do not report such changes, they may be able to recognize the changed object. In three experiments we explored change detection and recognition memory for several types of changes to central objects in motion pictures. Observers who failed to detect a change still performed at above chance levels on a recognition task in almost all conditions. In addition, observers who detected the change were no more accurate in their recognition than those who did not detect the change. Despite large differences in the detectability of changes across conditions, those observers who missed the change did not vary in their ability to recognize the changing object.

  11. Change Detection Module for New Orleans City of USA Using

    NASA Astrophysics Data System (ADS)

    Singh, Dharmendra

    accuracy. The New Orleans city of USA is taken as study area because this is reported that this city is shrinking. RADARSAT SLC (Single look complex) images acquired from January 2002 to March 2007 were obtained for the study area. Image pairs with perpendicular baselines less than 100 km are chosen. Selection of suitable image pairs is crucial since baseline distance between them affects the altitude ambiguity in resultant change detection map. Coherence is computed for the image pairs. If the coherence is greater than 0.25, such image pairs are considered for further analysis. Three pass differential InSAR is used for the analysis of change detection. Images 1 and 2 of the study area with lesser temporal span (minimum of 24 day interval) is chosen to make a digital elevation model and then images 1 and 3 of the same area with one year of temporal span is chosen to make an interferogram. The topographic phase estimated with images 1 and 2 is then subtracted to make a differential interferogram showing change from image 2 to 3. Image pairs with approximately one month temporal span, are considered for generating interferogram. Changes occurred in every one year is measured by subtracting topographic phase of the year corresponding to master image, from interferogram. From the change detection map obtained from both methods show that areas of larger changes are identified near Lake Borgne, and in the boundaries of Mississippi river. Lake Borgne is reported to be identified as an area of major land subsidence as found by other studies also. On comparing our result with this interferometric study, it is found that both are showing some common regions with high changes near water bodies. Surface deformation can be monitored quantitatively in the scale of mm with the help of temporal analysis of D-InSAR.

  12. Waveguide-Based Biosensors for Pathogen Detection

    PubMed Central

    Mukundan, Harshini; Anderson, Aaron S.; Grace, W. Kevin; Grace, Karen M.; Hartman, Nile; Martinez, Jennifer S.; Swanson, Basil I.

    2009-01-01

    Optical phenomena such as fluorescence, phosphorescence, polarization, interference and non-linearity have been extensively used for biosensing applications. Optical waveguides (both planar and fiber-optic) are comprised of a material with high permittivity/high refractive index surrounded on all sides by materials with lower refractive indices, such as a substrate and the media to be sensed. This arrangement allows coupled light to propagate through the high refractive index waveguide by total internal reflection and generates an electromagnetic wave—the evanescent field—whose amplitude decreases exponentially as the distance from the surface increases. Excitation of fluorophores within the evanescent wave allows for sensitive detection while minimizing background fluorescence from complex, “dirty” biological samples. In this review, we will describe the basic principles, advantages and disadvantages of planar optical waveguide-based biodetection technologies. This discussion will include already commercialized technologies (e.g., Corning’s EPIC® Ô, SRU Biosystems’ BIND™, Zeptosense®, etc.) and new technologies that are under research and development. We will also review differing assay approaches for the detection of various biomolecules, as well as the thin-film coatings that are often required for waveguide functionalization and effective detection. Finally, we will discuss reverse-symmetry waveguides, resonant waveguide grating sensors and metal-clad leaky waveguides as alternative signal transducers in optical biosensing. PMID:22346727

  13. Laser-Based Pulsed Photoacoustic Ammonia Detection

    NASA Astrophysics Data System (ADS)

    Vallespi, Arturo; Slezak, Verónica; Peuriot, Alejandro; Santiago, Guillermo

    2013-09-01

    Detecting ammonia traces is relevant in health, manufacturing, and security areas, among others. As ammonia presents a strong absorption band (the mode) around 10 m, some of the physical properties which may influence its detection by means of pulsed photoacoustic (PA) spectroscopy with a TEA laser have been studied. The characteristics of the ammonia molecule and the laser intensity may result in a nonlinear dependence of the PA signal amplitude on the laser fluence. Ammonia absorption can be described as a simple two-level system with power broadening. As is a polar molecule, it strongly undergoes adsorption phenomena in contact with different surfaces. Therefore, physical adsorption-desorption at the cell’s wall is studied. A theoretical model, based on Langmuir’s assumptions, fits well to the experimental results with stainless steel. Related to these studies, measurements led to the conclusion that, at the used fluenced values, dissociation by multiphotonic absorption at the 10P(32) laser line may be discarded. A calibration of the system was performed, and a detection limit around 190 ppb (at 224 ) was achieved.

  14. Optimal Regulatory Circuit Topologies for Fold-Change Detection.

    PubMed

    Adler, Miri; Szekely, Pablo; Mayo, Avi; Alon, Uri

    2017-02-22

    Evolution repeatedly converges on only a few regulatory circuit designs that achieve a given function. This simplicity helps us understand biological networks. However, why so few circuits are rediscovered by evolution is unclear. We address this question for the case of fold-change detection (FCD): a response to relative changes of input rather than absolute changes. Two types of FCD circuits recur in biological systems-the incoherent feedforward and non-linear integral-feedback loops. We performed an analytical screen of all three-node circuits in a class comprising ∼500,000 topologies. We find that FCD is rare, but still there are hundreds of FCD topologies. The two experimentally observed circuits are among the very few minimal circuits that optimally trade off speed, noise resistance, and response amplitude. This suggests a way to understand why evolution converges on only few topologies for a given function and provides FCD designs for synthetic construction and future discovery.

  15. Effect of projective viewpoint in detecting temporal density changes

    NASA Astrophysics Data System (ADS)

    Raundahl, Jakob; Nielsen, Mads; Olsen, Ole F.; Bagger, Yu Z.

    2004-05-01

    An important question in mammographic image analysis is the importance of the projected view of the breast. Can temporal changes in density be detected equally well using either one of the commonly available views Medio-Lateral (ML) and Cranio-Caudal (CC) or a combination of the two? Two sets of mammograms of 50 patients in a double-blind, placebo controlled hormone replacement therapy (HRT) experiment were used. One set of ML and CC view from 1999 and one from 2001. HRT increases density which means that the degree of separation of the populations (one group receiving HRT and the other placebo) can be used as a measure of how much density change information is carried in a particular view or combination of views. Earlier results have shown a high correlation between CC and ML views leading to the conclusion that only one of them is needed for density assessment purposes. A similar high correlation coefficient was observed in this study (0.85), while the correlation between changes was a bit lower (0.71). Using both views to separate the patients receiving hormones from the ones receiving placebo increased the area under corresponding ROC curves from 0.76 +/- 0.04 to 0.79 +/- 0.04.

  16. Detection and attribution of streamflow timing changes to climate change in the Western United States

    USGS Publications Warehouse

    Hidalgo, H.G.; Das, T.; Dettinger, M.D.; Cayan, D.R.; Pierce, D.W.; Barnett, T.P.; Bala, G.; Mirin, A.; Wood, A.W.; Bonfils, Celine; Santer, B.D.; Nozawa, T.

    2009-01-01

    This article applies formal detection and attribution techniques to investigate the nature of observed shifts in the timing of streamflow in the western United States. Previous studies have shown that the snow hydrology of the western United States has changed in the second half of the twentieth century. Such changes manifest themselves in the form of more rain and less snow, in reductions in the snow water contents, and in earlier snowmelt and associated advances in streamflow "center" timing (the day in the "water-year" on average when half the water-year flow at a point has passed). However, with one exception over a more limited domain, no other study has attempted to formally attribute these changes to anthropogenic increases of greenhouse gases in the atmosphere. Using the observations together with a set of global climate model simulations and a hydrologic model (applied to three major hydrological regions of the western United States_the California region, the upper Colorado River basin, and the Columbia River basin), it is found that the observed trends toward earlier "center" timing of snowmelt-driven streamflows in the western United States since 1950 are detectably different from natural variability (significant at the p < 0.05 level). Furthermore, the nonnatural parts of these changes can be attributed confidently to climate changes induced by anthropogenic greenhouse gases, aerosols, ozone, and land use. The signal from the Columbia dominates the analysis, and it is the only basin that showed a detectable signal when the analysis was performed on individual basins. It should be noted that although climate change is an important signal, other climatic processes have also contributed to the hydrologic variability of large basins in the western United States. ?? 2009 American Meteorological Society.

  17. Capacitance-based damage detection sensing for aerospace structural composites

    NASA Astrophysics Data System (ADS)

    Bahrami, P.; Yamamoto, N.; Chen, Y.; Manohara, H.

    2014-04-01

    Damage detection technology needs improvement for aerospace engineering application because detection within complex composite structures is difficult yet critical to avoid catastrophic failure. Damage detection is challenging in aerospace structures because not all the damage detection technology can cover the various defect types (delamination, fiber fracture, matrix crack etc.), or conditions (visibility, crack length size, etc.). These defect states are expected to become even more complex with future introduction of novel composites including nano-/microparticle reinforcement. Currently, non-destructive evaluation (NDE) methods with X-ray, ultrasound, or eddy current have good resolutions (< 0.1 mm), but their detection capabilities is limited by defect locations and orientations and require massive inspection devices. System health monitoring (SHM) methods are often paired with NDE technologies to signal out sensed damage, but their data collection and analysis currently requires excessive wiring and complex signal analysis. Here, we present a capacitance sensor-based, structural defect detection technology with improved sensing capability. Thin dielectric polymer layer is integrated as part of the structure; the defect in the structure directly alters the sensing layer's capacitance, allowing full-coverage sensing capability independent of defect size, orientation or location. In this work, capacitance-based sensing capability was experimentally demonstrated with a 2D sensing layer consisting of a dielectric layer sandwiched by electrodes. These sensing layers were applied on substrate surfaces. Surface indentation damage (~1mm diameter) and its location were detected through measured capacitance changes: 1 to 250 % depending on the substrates. The damage detection sensors are light weight, and they can be conformably coated and can be part of the composite structure. Therefore it is suitable for aerospace structures such as cryogenic tanks and rocket

  18. Change Detection & Characterization: a New Tool for Imaging Informatics and Cancer Research

    PubMed Central

    Patriarche, Julia W; Erickson, Bradley J

    2007-01-01

    Modern imaging systems are able to produce a rich and diverse array of information, regarding various facets of anatomy and function. The quantity of information produced by these systems is so bountiful, however, as to have the potential to become a hindrance to clinical assessment. In the context of serial image evaluation, computer-based change detection and characterization is one important mechanism to process the information produced by imaging systems, so as to reduce the quantity of data, direct the attention of the physician to regions of the data which are the most informative for their purposes, and present the data in the form in which it will be the most useful. Change detection and characterization algorithms may serve as a basis for the creation of an objective definition of progression, which will reduce inter and intra-observer variability, and facilitate earlier detection of disease and recurrence, which in turn may lead to improved outcomes. Decreased observer variability combined with increased acuity should make it easier to discover promising therapies. Quantitative measures of the response to these therapies should provide a means to compare the effectiveness of treatments under investigation. Change detection may be applicable to a broad range of cancers, in essentially all anatomical regions. The source of information upon which change detection comparisons may be based is likewise broad. Validation of algorithms for the longitudinal assessment of cancer patients is expected to be challenging, though not insurmountable, as the many facets of the problem mean that validation will likely need to be approached from a variety of vantage points. Change detection and characterization is quickly becoming a very active field of investigation, and it is expected that this burgeoning field will help to facilitate cancer care both in the clinic and research. PMID:19390659

  19. Change Detection in a Short Time Sequence of Polarimetric C-Band SAR Data

    NASA Astrophysics Data System (ADS)

    Nielsen, Allan Aasbjerg; Conradsen, Knut; Skriver, Henning

    2016-08-01

    Based on an omnibus likelihood ratio test statistic for the equality of several variance-covariance matrices following the complex Wishart distribution and a factorization of this test statistic with associated p-values, change analysis in a time series of multilook, polarimetric SAR data in the covariance matrix representation is carried out. The omnibus test statistic and its factorization detect if and when change(s) occur. The technique is demonstrated on airborne EMISAR C-band data but may be applied to ALOS, COSMO-SkyMed, RadarSat-2 Sentinel-1, TerraSAR-X, and Yaogan data also.

  20. Advances in nucleic acid-based detection methods.

    PubMed Central

    Wolcott, M J

    1992-01-01

    Laboratory techniques based on nucleic acid methods have increased in popularity over the last decade with clinical microbiologists and other laboratory scientists who are concerned with the diagnosis of infectious agents. This increase in popularity is a result primarily of advances made in nucleic acid amplification and detection techniques. Polymerase chain reaction, the original nucleic acid amplification technique, changed the way many people viewed and used nucleic acid techniques in clinical settings. After the potential of polymerase chain reaction became apparent, other methods of nucleic acid amplification and detection were developed. These alternative nucleic acid amplification methods may become serious contenders for application to routine laboratory analyses. This review presents some background information on nucleic acid analyses that might be used in clinical and anatomical laboratories and describes some recent advances in the amplification and detection of nucleic acids. PMID:1423216

  1. Shoreline Delineation and Land Reclamation Change Detection Using Landsat Image

    NASA Astrophysics Data System (ADS)

    Rosli, M. I.; Ahmad, M. A.; Kaamin, M.; Izhar, M. F. N.

    2016-07-01

    This study is conducted on the usage of remote sensing images from several different years in order to analyze the changes of shoreline and land cover of the area. Remote sensing images used in this study are the data captured by the Landsat satellite. The images are projecting the land surface in 30 by 30 meter resolution and it is processed by the ENVI software. ENVI is able to change each digital number of the pixels on the images into specific value according to the applied model for classification in which could be used as an approach in calculating the area different classes based from the images itself. Therefore, using this method, the changes on the coastal area are possible to be determined. Analysis of the shoreline and land reclamation around the coastal area is integrated with the land use changes to determine its impact. The study shows that Batu Pahat area might have undergone land reclamation whereas in Pasir Gudang is experiencing substantial amount of erosion. Besides, the changes of land use in both areas were considered to be rapid and due to the results obtained from this study, the issues may be brought about for the local authority awareness action.

  2. PDM-16QAM vector signal generation and detection based on intensity modulation and direct detection

    NASA Astrophysics Data System (ADS)

    Chen, Long; Yu, Jianjun; Li, Xinying

    2016-07-01

    We experimentally demonstrate a novel and simple method to generate and detect high speed polarization-division-multiplexing 16-ary quadrature-amplitude-modulation (PDM-16QAM) vector signal enabled by Mach-Zehnder modulator-based (MZM-based) optical-carrier-suppression (OCS) intensity modulation and direct detection. Due to the adoption of OCS intensity modulation, carrier beating can be avoided at the receiver, and thus polarization de-multiplexing can be implemented by digital-signal-processing-based (DSP-based) cascaded multi-modulus algorithm (CMMA) equalization instead of a polarization tracking system. The change of both amplitude and phase information due to the adoption of OCS modulation can be equalized by DSP-based amplitude and phase precoding at the transmitter. Up to 64-Gb/s PDM-16QAM vector signal is generated and detected after 2-km single-mode fiber-28 (SMF-28) or 20-km large-effective-area fiber (LEAF) transmission with a bit-error-ratio (BER) less than the hard-decision forward-error-correction (HD-FEC) threshold of 3.8×10-3.

  3. 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.

  4. Stimulus change detection in phasic auditory units in the frog midbrain: frequency and ear specific adaptation

    PubMed Central

    Ponnath, Abhilash; Hoke, Kim L.

    2013-01-01

    Neural adaptation, a reduction in the response to a maintained stimulus, is an important mechanism for detecting stimulus change. Contributing to change detection is the fact that adaptation is often stimulus specific: adaptation to a particular stimulus reduces excitability to a specific subset of stimuli, while the ability to respond to other stimuli is unaffected. Phasic cells (e.g., cells responding to stimulus onset) are good candidates for detecting the most rapid changes in natural auditory scenes, as they exhibit fast and complete adaptation to an initial stimulus presentation. We made recordings of single phasic auditory units in the frog midbrain to determine if adaptation was specific to stimulus frequency and ear of input. In response to an instantaneous frequency step in a tone, 28 % of phasic cells exhibited frequency specific adaptation based on a relative frequency change (delta-f = ±16 %). Frequency specific adaptation was not limited to frequency steps, however, as adaptation was also overcome during continuous frequency modulated stimuli and in response to spectral transients interrupting tones. The results suggest that adaptation is separated for peripheral (e.g., frequency) channels. This was tested directly using dichotic stimuli. In 45 % of binaural phasic units, adaptation was ear specific: adaptation to stimulation of one ear did not affect responses to stimulation of the other ear. Thus, adaptation exhibited specificity for stimulus frequency and lateralization at the level of the midbrain. This mechanism could be employed to detect rapid stimulus change within and between sound sources in complex acoustic environments. PMID:23344947

  5. Image animation for theme enhancement and change detection. [LANDSAT 1

    NASA Technical Reports Server (NTRS)

    Evans, W. E.

    1976-01-01

    Animated displays are useful in enhancing subtle temporally related changes in scenes viewed by satellites capable of providing repetitive coverage. The detectability of fixed features is also improved through the help of the powerful visual integration process. To expedite the process of assembling and displaying well-registered, time-lapse sequences and to provide means for making quantitative measurements of radiances, displacements, and areas, an electronic satellite image analysis console was constructed. During the LANDSAT-1 program, this equipment was applied to the needs of a number of earth resource investigators with interests principally related to dynamic hydrology. The measurement of the areal extent of snow cover within defined drainage basins is discussed as a representative applications example.

  6. Change Detection of Mobile LIDAR Data Using Cloud Computing

    NASA Astrophysics Data System (ADS)

    Liu, Kun; Boehm, Jan; Alis, Christian

    2016-06-01

    Change detection has long been a challenging problem although a lot of research has been conducted in different fields such as remote sensing and photogrammetry, computer vision, and robotics. In this paper, we blend voxel grid and Apache Spark together to propose an efficient method to address the problem in the context of big data. Voxel grid is a regular geometry representation consisting of the voxels with the same size, which fairly suites parallel computation. Apache Spark is a popular distributed parallel computing platform which allows fault tolerance and memory cache. These features can significantly enhance the performance of Apache Spark and results in an efficient and robust implementation. In our experiments, both synthetic and real point cloud data are employed to demonstrate the quality of our method.

  7. [Vegetation change in Shenzhen City based on NDVI change classification].

    PubMed

    Li, Yi-Jing; Zeng, Hui; Wel, Jian-Bing

    2008-05-01

    Based on the TM images of 1988 and 2003 as well as the land-use change survey data in 2004, the vegetation change in Shenzhen City was assessed by a NDVI (normalized difference vegetation index) change classification method, and the impacts from natural and social constraining factors were analyzed. The results showed that as a whole, the rapid urbanization in 1988-2003 had less impact on the vegetation cover in the City, but in its plain areas with low altitude, the vegetation cover degraded more obviously. The main causes of the localized ecological degradation were the invasion of built-ups to woods and orchards, land transformation from woods to orchards at the altitude of above 100 m, and low percentage of green land in some built-ups areas. In the future, the protection and construction of vegetation in Shenzhen should focus on strengthening the protection and restoration of remnant woods, trying to avoid the built-ups' expansion to woods and orchards where are better vegetation-covered, rectifying the unreasonable orchard constructions at the altitude of above 100 m, and consolidating the greenbelt construction inside the built-ups. It was considered that the NDVI change classification method could work well in efficiently uncovering the trend of macroscale vegetation change, and avoiding the effect of random noise in data.

  8. Personality and attention: Levels of neuroticism and extraversion can predict attentional performance during a change detection task.

    PubMed

    Hahn, Sowon; Buttaccio, Daniel R; Hahn, Jungwon; Lee, Taehun

    2015-01-01

    The present study demonstrates that levels of extraversion and neuroticism can predict attentional performance during a change detection task. After completing a change detection task built on the flicker paradigm, participants were assessed for personality traits using the Revised Eysenck Personality Questionnaire (EPQ-R). Multiple regression analyses revealed that higher levels of extraversion predict increased change detection accuracies, while higher levels of neuroticism predict decreased change detection accuracies. In addition, neurotic individuals exhibited decreased sensitivity A' and increased fixation dwell times. Hierarchical regression analyses further revealed that eye movement measures mediate the relationship between neuroticism and change detection accuracies. Based on the current results, we propose that neuroticism is associated with decreased attentional control over the visual field, presumably due to decreased attentional disengagement. Extraversion can predict increased attentional performance, but the effect is smaller than the relationship between neuroticism and attention.

  9. Land-cover change detection for the tropics using remote sensing and geographic information systems

    NASA Astrophysics Data System (ADS)

    Read, Jane M.

    1999-12-01

    Changing land-cover in the tropics is a central issue in global change research. This dissertation used Landsat-TM data to examine processes of land-use and land-cover changes for a lowland tropical site in Sarapiqui, Costa Rica. Performances of selected image-processing methods to detect and identify land-cover changes were evaluated. A land-cover time-series from 1960 to 1996 for the site was generated using maps derived from aerial photographs and Landsat-TM classifications. Changes in land-cover from 1986 to 1996 were evaluated using standard landscape indices, and interpreted in terms of their historical context. Dominant changes in the site during this decade included the breakup of extensive cattle ranches for large-scale plantation enterprises and small-scale farming. Colonization processes, improvements in access, and changes in export markets were identified as the major driving forces of change. Evaluation of change-detection methods revealed that postclassification comparison performed significantly better than image differencing algorithms. Image differencing using mid- infrared bonds performed the best of the differencing algorithms tested. Selection of a suitable change-detection method can be aided through examination of the individual bond statistics for the specific area and problem in question. The univariate bond differencing technique has potential for identification of 'hot spots' of change using Landsat-TM data. Spatial pattern-recognition techniques to characterize complexity of Landsat-TM data were evaluated. Fractal dimension calculated using the triangular prism surface area method, and Moran's I index of spatial autocorrelation, clearly distinguished different land-cover types. Shannon's diversity index and the contagion metric were not found to be useful in characterizing the images. The use of fractal dimension, in conjunction with standard non-spatial descriptive band statistics, are seen as having great potential in characterizing

  10. Change detection on UGV patrols with respect to a reference tour using VIS imagery

    NASA Astrophysics Data System (ADS)

    Müller, Thomas

    2015-05-01

    Autonomous driving robots (UGVs, Unmanned Ground Vehicles) equipped with visual-optical (VIS) cameras offer a high potential to automatically detect suspicious occurrences and dangerous or threatening situations on patrol. In order to explore this potential, the scene of interest is recorded first on a reference tour representing the 'everything okay' situation. On further patrols changes are detected with respect to the reference in a two step processing scheme. In the first step, an image retrieval is done to find the reference images that are closest to the current camera image on patrol. This is done efficiently based on precalculated image-to-image registrations of the reference by optimizing image overlap in a local reference search (after a global search when that is needed). In the second step, a robust spatio-temporal change detection is performed that widely compensates 3-D parallax according to variations of the camera position. Various results document the performance of the presented approach.

  11. Detecting Change in Landscape Greenness over Large Areas: An Example for New Mexico, USA

    EPA Science Inventory

    Monitoring and quantifying changes in vegetation cover over large areas using remote sensing can potentially detect large-scale, slow changes (e.g., climate change), as well as more local and rapid changes (e.g., fire, land development). A useful indicator for detecting change i...

  12. Analyzing the auditory scene: neurophysiologic evidence of a dissociation between detection of regularity and detection of change.

    PubMed

    Pannese, Alessia; Herrmann, Christoph S; Sussman, Elyse

    2015-05-01

    Detecting regularity and change in the environment is crucial for survival, as it enables making predictions about the world and informing goal-directed behavior. In the auditory modality, the detection of regularity involves segregating incoming sounds into distinct perceptual objects (stream segregation). The detection of change from this within-stream regularity is associated with the mismatch negativity, a component of auditory event-related brain potentials (ERPs). A central unanswered question is how the detection of regularity and the detection of change are interrelated, and whether attention affects the former, the latter, or both. Here we show that the detection of regularity and the detection of change can be empirically dissociated, and that attention modulates the detection of change without precluding the detection of regularity, and the perceptual organization of the auditory background into distinct streams. By applying frequency spectra analysis on the EEG of subjects engaged in a selective listening task, we found distinct peaks of ERP synchronization, corresponding to the rhythm of the frequency streams, independently of whether the stream was attended or ignored. Our results provide direct neurophysiological evidence of regularity detection in the auditory background, and show that it can occur independently of change detection and in the absence of attention.

  13. Optimal Parameter Exploration for Online Change-Point Detection in Activity Monitoring Using Genetic Algorithms

    PubMed Central

    Khan, Naveed; McClean, Sally; Zhang, Shuai; Nugent, Chris

    2016-01-01

    In recent years, smart phones with inbuilt sensors have become popular devices to facilitate activity recognition. The sensors capture a large amount of data, containing meaningful events, in a short period of time. The change points in this data are used to specify transitions to distinct events and can be used in various scenarios such as identifying change in a patient’s vital signs in the medical domain or requesting activity labels for generating real-world labeled activity datasets. Our work focuses on change-point detection to identify a transition from one activity to another. Within this paper, we extend our previous work on multivariate exponentially weighted moving average (MEWMA) algorithm by using a genetic algorithm (GA) to identify the optimal set of parameters for online change-point detection. The proposed technique finds the maximum accuracy and F_measure by optimizing the different parameters of the MEWMA, which subsequently identifies the exact location of the change point from an existing activity to a new one. Optimal parameter selection facilitates an algorithm to detect accurate change points and minimize false alarms. Results have been evaluated based on two real datasets of accelerometer data collected from a set of different activities from two users, with a high degree of accuracy from 99.4% to 99.8% and F_measure of up to 66.7%. PMID:27792177

  14. Automatic detection of surface changes on Mars - a status report

    NASA Astrophysics Data System (ADS)

    Sidiropoulos, Panagiotis; Muller, Jan-Peter

    2016-10-01

    Orbiter missions have acquired approximately 500,000 high-resolution visible images of the Martian surface, covering an area approximately 6 times larger than the overall area of Mars. This data abundance allows the scientific community to examine the Martian surface thoroughly and potentially make exciting new discoveries. However, the increased data volume, as well as its complexity, generate problems at the data processing stages, which are mainly related to a number of unresolved issues that batch-mode planetary data processing presents. As a matter of fact, the scientific community is currently struggling to scale the common ("one-at-a-time" processing of incoming products by expert scientists) paradigm to tackle the large volumes of input data. Moreover, expert scientists are more or less forced to use complex software in order to extract input information for their research from raw data, even though they are not data scientists themselves.Our work within the STFC and EU FP7 i-Mars projects aims at developing automated software that will process all of the acquired data, leaving domain expert planetary scientists to focus on their final analysis and interpretation. Moreover, after completing the development of a fully automated pipeline that processes automatically the co-registration of high-resolution NASA images to ESA/DLR HRSC baseline, our main goal has shifted to the automated detection of surface changes on Mars. In particular, we are developing a pipeline that uses as an input multi-instrument image pairs, which are processed by an automated pipeline, in order to identify changes that are correlated with Mars surface dynamic phenomena. The pipeline has currently been tested in anger on 8,000 co-registered images and by the time of DPS/EPSC we expect to have processed many tens of thousands of image pairs, producing a set of change detection results, a subset of which will be shown in the presentation.The research leading to these results has received

  15. Comparison of multiple methods for detecting changes in urban areas in TerraSAR-X data

    NASA Astrophysics Data System (ADS)

    Hammer, Horst; Dubois, Clémence; Boldt, Markus; Kuny, Silvia; Cadario, Erich; Thiele, Antje

    2016-10-01

    The current generation of SAR satellites such as TerraSAR-X, TanDEM-X and COSMO-SkyMed provide resolutions below one meter, permitting the detailed analysis of urban areas while covering large zones. Furthermore, as they are deployable independently of daylight and weather, such remote sensing SAR data are particularly popular for purposes such as rapid damage assessment at building level after a natural disaster. The purpose of our study is the investigation of techniques for the detection of changes based on one pre-event and one post-event SAR amplitude image. We provide a comparison of several methods for detecting changes in urban areas. Especially, changes at building locations are looked for. We analyzed two areas affected differently in detail. First, a suburban area of Paris, France, was considered due to changes caused by an urbanization project. Here, we have two TanDEM-X acquisitions available, before (November 4, 2012) and after (May 10, 2013) the changes. Second, we investigated changes that happened in Kathmandu, Nepal, after the April 25, 2015 earthquake. For this analysis, we have two TerraSAR-X acquisitions, one before (October 13, 2013) and one immediately after (April 27, 2015) the earthquake. Both areas differ by the building types, the image resolution and the available reference, which makes it an interesting challenge. In this paper, we compare six different methods for change detection. The investigated methods contain both standard criteria such as Log Ratio, Kullback-Leibler and the Difference of Entropies detector, and methods developed by the authors such as a Log Ratio combined with an Alternating Sequential Filter. All change detection results are presented and discussed by considering the available ground truth.

  16. A voxel-based morphometric magnetic resonance imaging study of the brain detects age-related gray matter volume changes in healthy subjects of 21–45 years old

    PubMed Central

    El-Beltagi, Ahmed; Cherian, Jigi; Gejo, Grace; Al-Jazzaf, Abrar; Ismail, Mohammad

    2015-01-01

    Previous and more recent work of analyzing structural changes in the brain suggest that certain brain regions such as the frontal lobe are among the brain regions profoundly affected by the aging process across males and females. Also, a unified model of structural changes in a normally aging brain is still lacking. The present study investigated age-related structural brain changes in gray matter from young to early middle-age adulthood for males and females. Magnetic resonance images of 215 normal and healthy participants between the ages of 21–45 years were acquired. Changes in gray matter were assessed using voxel-based morphometry and gray matter volumetric analysis. The results showed significant decrease in gray matter volume between the youngest and oldest groups in the following brain regions: frontal, temporal, and parietal lobes. Grey matter loss in the frontal lobe was among the most widespread of all brain regions across the comparison groups that showed significant age-related changes in grey matter for both males and females. This work provides a unique pattern of age-related decline of normal and healthy adult males and females that can aid in the future development of a unified model of normal brain aging. PMID:26306927

  17. Detecting meaningful body composition changes in athletes using dual-energy x-ray absorptiometry.

    PubMed

    Colyer, Steffi L; Roberts, Simon P; Robinson, Jonathan B; Thompson, Dylan; Stokes, Keith A; Bilzon, James L J; Salo, Aki I T

    2016-04-01

    Dual-energy x-ray absorptiometry (DXA) imaging is considered to provide a valid and reliable estimation of body composition when stringent scanning protocols are adopted. However, applied practitioners are not always able to achieve this level of control and the subsequent impact on measurement precision is not always taken into account when evaluating longitudinal body composition changes. The primary aim of this study was to establish the reliability of DXA in an applied elite sport setting to investigate whether real body composition changes can be detected. Additionally, the performance implications of these changes during the training year were investigated. Forty-eight well-trained athletes (from four diverse sports) underwent two DXA scans using a 'real-world' approach (with limited pre-scan controls), typically within 48 h, to quantify typical error of measurement (TEM). Twenty-five athletes underwent further scans, before and after specific training and competition blocks. 'True' body composition changes were evaluated using 2  ×  TEM thresholds. Twelve bob skeleton athletes also performed countermovement jump and leg press tests at each time point. Many 'true' body composition changes were detected and coincided with the primary training emphases (e.g. lean mass gains during hypertrophy-based training). Clear relationships (r  ±  90% CI) were observed between performance changes (countermovement jump and leg press) and changes in lean mass (0.53  ±  0.26 and 0.35  ±  0.28, respectively) and fat mass (-0.44  ±  0.27 and  -0.37  ±  0.28, respectively). DXA was able to detect real body composition changes without the use of stringent scanning controls. Associations between changes in body composition and performance demonstrated the potential influence of these changes on strength and power indices.

  18. Comic image understanding based on polygon detection

    NASA Astrophysics Data System (ADS)

    Li, Luyuan; Wang, Yongtao; Tang, Zhi; Liu, Dong

    2013-01-01

    Comic image understanding aims to automatically decompose scanned comic page images into storyboards and then identify the reading order of them, which is the key technique to produce digital comic documents that are suitable for reading on mobile devices. In this paper, we propose a novel comic image understanding method based on polygon detection. First, we segment a comic page images into storyboards by finding the polygonal enclosing box of each storyboard. Then, each storyboard can be represented by a polygon, and the reading order of them is determined by analyzing the relative geometric relationship between each pair of polygons. The proposed method is tested on 2000 comic images from ten printed comic series, and the experimental results demonstrate that it works well on different types of comic images.

  19. Smell Detection Agent Based Optimization Algorithm

    NASA Astrophysics Data System (ADS)

    Vinod Chandra, S. S.

    2016-09-01

    In this paper, a novel nature-inspired optimization algorithm has been employed and the trained behaviour of dogs in detecting smell trails is adapted into computational agents for problem solving. The algorithm involves creation of a surface with smell trails and subsequent iteration of the agents in resolving a path. This algorithm can be applied in different computational constraints that incorporate path-based problems. Implementation of the algorithm can be treated as a shortest path problem for a variety of datasets. The simulated agents have been used to evolve the shortest path between two nodes in a graph. This algorithm is useful to solve NP-hard problems that are related to path discovery. This algorithm is also useful to solve many practical optimization problems. The extensive derivation of the algorithm can be enabled to solve shortest path problems.

  20. Detecting Soft Errors in Stencil based Computations

    SciTech Connect

    Sharma, V.; Gopalkrishnan, G.; Bronevetsky, G.

    2015-05-06

    Given the growing emphasis on system resilience, it is important to develop software-level error detectors that help trap hardware-level faults with reasonable accuracy while minimizing false alarms as well as the performance overhead introduced. We present a technique that approaches this idea by taking stencil computations as our target, and synthesizing detectors based on machine learning. In particular, we employ linear regression to generate computationally inexpensive models which form the basis for error detection. Our technique has been incorporated into a new open-source library called SORREL. In addition to reporting encouraging experimental results, we demonstrate techniques that help reduce the size of training data. We also discuss the efficacy of various detectors synthesized, as well as our future plans.

  1. SuBSENSE: a universal change detection method with local adaptive sensitivity.

    PubMed

    St-Charles, Pierre-Luc; Bilodeau, Guillaume-Alexandre; Bergevin, Robert

    2015-01-01

    Foreground/background segmentation via change detection in video sequences is often used as a stepping stone in high-level analytics and applications. Despite the wide variety of methods that have been proposed for this problem, none has been able to fully address the complex nature of dynamic scenes in real surveillance tasks. In this paper, we present a universal pixel-level segmentation method that relies on spatiotemporal binary features as well as color information to detect changes. This allows camouflaged foreground objects to be detected more easily while most illumination variations are ignored. Besides, instead of using manually set, frame-wide constants to dictate model sensitivity and adaptation speed, we use pixel-level feedback loops to dynamically adjust our method's internal parameters without user intervention. These adjustments are based on the continuous monitoring of model fidelity and local segmentation noise levels. This new approach enables us to outperform all 32 previously tested state-of-the-art methods on the 2012 and 2014 versions of the ChangeDetection.net dataset in terms of overall F-Measure. The use of local binary image descriptors for pixel-level modeling also facilitates high-speed parallel implementations: our own version, which used no low-level or architecture-specific instruction, reached real-time processing speed on a midlevel desktop CPU. A complete C++ implementation based on OpenCV is available online.

  2. Hybrid flagellar motor/MEMS based TNT detection system

    NASA Astrophysics Data System (ADS)

    Kim, Jin-Woo; Tung, Steve

    2006-05-01

    Effective and rapid detection of nitroaromatic explosive compounds, especially trinitrotoluene (TNT), is very important to homeland security as well as to environmental monitoring of contaminants in soil and water, and landmine detection. In this research, we explore a novel nanoscale flagellar motor based TNT detection system (nFMTNT). The nFMTNT is a bio-hybrid MEMS system which combines genetically engineered flagellar motors and MEMS devices. The system consists of three major components: (1) a non-pathogenic, genetically modified Escherichia coli strain KAF95 with a rotating flagellar filament, (2) a microchannel with tethered cells, and (3) a sub-micron bead attached to a rotating flagellar filament. The operational principle of nFMTNT is based on detecting the change in the rotational behavior of the nanoscale flagellar filament in the presence of TNT. The rotational behavior of flagellar filaments of E. coli KAF95 was shown to be extremely sensitive to the presence of nitrate or nitrite. Normally, the flagellar filaments were locked in to rotate in the counterclockwise direction. However, when a nitrate or nitrite was present in the immediate environment, the filaments cease to rotate. Our results indicate that the threshold concentrations required for this response were 10 -4 M for nitrate and 10 -3 M for nitrite. This is equivalent to around 10 pg of nitrate and 100 pg of nitrite, based on the dimension of the MEMS-based reaction system used for the experiment (400 μm × 100 μm × 40 μm). These detection limits can be even lower when the size of the system is reduced.

  3. Rapid systematic assessment of the detection and attribution of regional anthropogenic climate change

    NASA Astrophysics Data System (ADS)

    Stone, Dáithí A.; Hansen, Gerrit

    2016-09-01

    Despite being a well-established research field, the detection and attribution of observed climate change to anthropogenic forcing is not yet provided as a climate service. One reason for this is the lack of a methodology for performing tailored detection and attribution assessments on a rapid time scale. Here we develop such an approach, based on the translation of quantitative analysis into the "confidence" language employed in recent Assessment Reports of the Intergovernmental Panel on Climate Change. While its systematic nature necessarily ignores some nuances examined in detailed expert assessments, the approach nevertheless goes beyond most detection and attribution studies in considering contributors to building confidence such as errors in observational data products arising from sparse monitoring networks. When compared against recent expert assessments, the results of this approach closely match those of the existing assessments. Where there are small discrepancies, these variously reflect ambiguities in the details of what is being assessed, reveal nuances or limitations of the expert assessments, or indicate limitations of the accuracy of the sort of systematic approach employed here. Deployment of the method on 116 regional assessments of recent temperature and precipitation changes indicates that existing rules of thumb concerning the detectability of climate change ignore the full range of sources of uncertainty, most particularly the importance of adequate observational monitoring.

  4. Investigations in vegetation map rectification, and the remotely sensed detection and measurement of natural vegetation changes

    NASA Astrophysics Data System (ADS)

    Walker, Richard Eugene

    2000-10-01

    As projected climate changes loom, the monitoring of the response of natural vegetation becomes important for both science and management. Successful monitoring requires good baseline information and vegetation change detection techniques. The research reported here involved three main tasks: (1) digital geometric rectification of a detailed historic vegetation map; (2) an analysis of high spatial resolution airborne remote sensing data for tree mortality; and (3) the development of a Landsat Thematic Mapper-based vegetation change detection procedure. These studies focused on the Sierra Nevada of California, and in particular Yosemite National Park. The Vegetation Type Maps (VTMs) (Wieslander 1935) represent some of the finest maps of their kind in the world, and cover more than 40% of California. Yosemite National Park was mapped using these techniques in the late 1930s. Geometric inaccuracies in the 19th century USGS basemaps were mitigated using newly available GIS and remote sensing tools, enabling the rectified VTMs, to be integrated into the National Park's vegetation monitoring work. In 1992, several transects of a four-band high spatial resolution airborne scanner (ADAR) were taken of mid-elevation forests in the southern Sierra Nevada, to evaluate their use for tree mortality monitoring. This analysis highlighted the difficulty of using single-date imagery for monitoring vegetation changes, but showed (1) the best measure of tree mortality (when compared with field data) was found using solely the red wavelength band; and (2) the metric most highly correlated with the field data was relative canopy mortality (%), not absolute area (ha). Lastly, based upon Principal Components Analysis, I developed an algorithm for separating spectral changes resulting from vegetation changes on the ground from other changes present but not of concern to monitoring. Three vectors were derived using 6 of 7 Thematic Mapper bands. Although few published change detection

  5. Detection of biological thiols based on a colorimetric method*

    PubMed Central

    Xu, Yuan-yuan; Sun, Yang-yang; Zhang, Yu-juan; Lu, Chen-he; Miao, Jin-feng

    2016-01-01

    Biological thiols (biothiols), an important kind of functional biomolecules, such as cysteine (Cys) and glutathione (GSH), play vital roles in maintaining the stability of the intracellular environment. In past decades, studies have demonstrated that metabolic disorder of biothiols is related to many serious disease processes and will lead to extreme damage in human and numerous animals. We carried out a series of experiments to detect biothiols in biosamples, including bovine plasma and cell lysates of seven different cell lines based on a simple colorimetric method. In a typical test, the color of the test solution could gradually change from blue to colorless after the addition of biothiols. Based on the color change displayed, experimental results reveal that the percentage of biothiols in the embryonic fibroblast cell line is significantly higher than those in the other six cell lines, which provides the basis for the following biothiols-related study. PMID:27704750

  6. A hybrid change detection analysis using high-resolution remote sensing image

    NASA Astrophysics Data System (ADS)

    Xu, Q. Q.; Liu, Z. J.; Yang, M. Z.; Ren, H. C.; Song, C.; Li, F. F.

    2016-11-01

    In order to reduce noise and improve the accuracy of the final change results, in this paper, we presented a hybrid change detection method based on combining pixel- and object-schemes, Firstly, the method obtained the orthogonal difference images using the pixel-based iteratively reweighted multivariate alteration detection (IR-MAD) algorithm, additionally in the process of iterative weighting, we applied the regularized scheme to stable the generalized characteristic equation for the multispectral data. Consequently, image segmentation algorithm was used to extract the image objects where the changes occurred. Finally, object-based classification method was applied to determinate the types of changes. In order to validate the effectiveness and feasibility of the proposed approach, a simple case was done by using the Horgos Port local multi-temporal and multispectral high-resolution image data in Xinjiang. Compared to the pixel-level IR-MAD, the experimental results showed that the overall accuracy has been improved, moreover successfully reduced noise and pseudo small changes in the final result.

  7. APTAMER-BASED SERRS SENSOR FOR THROMBIN DETECTION

    SciTech Connect

    Cho, H; Baker, B R; Wachsmann-Hogiu, S; Pagba, C V; Laurence, T A; Lane, S M; Lee, L P; Tok, J B

    2008-07-02

    We describe an aptamer-based Surface Enhanced Resonance Raman Scattering (SERRS) sensor with high sensitivity, specificity, and stability for the detection of a coagulation protein, human a-thrombin. The sensor achieves high sensitivity and a limit of detection of 100 pM by monitoring the SERRS signal change upon the single step of thrombin binding to immobilized thrombin binding aptamer. The selectivity of the sensor is demonstrated by the specific discrimination of thrombin from other protein analytes. The specific recognition and binding of thrombin by the thrombin binding aptamer is essential to the mechanism of the aptamer-based sensor, as shown through measurements using negative control oligonucleotides. In addition, the sensor can detect 1 nM thrombin in the presence of complex biofluids, such as 10% fetal calf serum, demonstrating that the immobilized, 5{prime}-capped, 3{prime}-capped aptamer is sufficiently robust for clinical diagnostic applications. Furthermore, the proposed sensor may be implemented for multiplexed detection using different aptamer-Raman probe complexes.

  8. Integrated impedance and guided wave based damage detection

    NASA Astrophysics Data System (ADS)

    An, Yun-Kyu; Sohn, Hoon

    2012-04-01

    Recently, impedance and guided wave based damage detection techniques have been widely used for structural health monitoring (SHM) and Nondestructive testing (NDT) due to their sensitivity to small structural changes. Each of these techniques has its own technical merits, making them complementary to each other. For example, the guided wave technique typically has a larger sensing range than the impedance technique while the latter has better applicability to more complex structures. In this study, a new damage detection technique, which is named as integrated impedance and guided wave (IIG) based damage detection, is developed by utilizing impedance and guided wave signals simultaneously obtained from surface-mounted piezoelectric transducers (PZTs) to enhance the performance and reliability of damage diagnosis especially under varying temperature conditions. The proposed IIG technique first divides the measured impedance signal into two parts: passive impedance only sensitive to temperature variation and active impedance closely related to the mechanical property of the host structure. Then, the temperature effects on the active impedance and guided wave signals are minimized using the passive impedance. Finally, improved damage diagnosis is performed using both impedance and guided wave signals. The applicability of the proposed IIG technique to the detection of (1) bolt loosening in a steel lap joint, (2) a notch in an aluminum specimen with a complex geometry and (3) delamination in a composite wing mock-up specimen with stringers is experimentally investigated under varying temperatures.

  9. Change detection from very high resolution satellite time series with variable off-nadir angle

    NASA Astrophysics Data System (ADS)

    Barazzetti, Luigi; Brumana, Raffaella; Cuca, Branka; Previtali, Mattia

    2015-06-01

    Very high resolution (VHR) satellite images have the potential for revealing changes occurred overtime with a superior level of detail. However, their use for metric purposes requires accurate geo-localization with ancillary DEMs and GCPs to achieve sub-pixel terrain correction, in order to obtain images useful for mapping applications. Change detection with a time series of VHS images is not a simple task because images acquired with different off-nadir angles have a lack of pixel-to-pixel image correspondence, even after accurate geo-correction. This paper presents a procedure for automatic change detection able to deal with variable off-nadir angles. The case study concerns the identification of damaged buildings from pre- and post-event images acquired on the historic center of L'Aquila (Italy), which was struck by an earthquake in April 2009. The developed procedure is a multi-step approach where (i) classes are assigned to both images via object-based classification, (ii) an initial alignment is provided with an automated tile-based rubber sheeting interpolation on the extracted layers, and (iii) change detection is carried out removing residual mis-registration issues resulting in elongated features close to building edges. The method is fully automated except for some thresholds that can be interactively set to improve the visualization of the damaged buildings. The experimental results proved that damages can be automatically found without additional information, such as digital surface models, SAR data, or thematic vector layers.

  10. A paper-based platform for detection of viral RNA.

    PubMed

    Zhang, Daohong; Broyles, David; Hunt, Eric A; Dikici, Emre; Daunert, Sylvia; Deo, Sapna K

    2017-02-27

    Viral detection presents a host of challenges for even the most sensitive analytical techniques, and the complexity of common detection platforms typically preclude portability. With these considerations in mind, we designed a paper microzone plate-based virus detection system for the detection of viral genetic material that can be performed with simple instruments. The sensing system can detect viral cDNA reverse-transcribed from total RNA extraction by utilizing a biotinylated capture probe and an Alexa Fluor® 647-labeled reporter probe. The biotinylated capture probe was linked to the paper surface via NeutrAvidin® that was physically adsorbed on the paper. After addition of reverse-transcribed sample and reporter probe in sequence, the reverse-transcribed target captured the reporter probe and tethered it to the capture probe in a bridged format. Fluorescence intensity was imaged using a Western blot imaging system, and higher target concentration was visible by the increased emission intensity from Alexa Fluor® 647. By utilizing paper, this detection setup could also serve as a sample concentration method via evaporation, which could remarkably lower the detection limit if needed. This detection platform used Epstein-Barr virus (EBV) RNA as a proof-of-concept by sensing cDNA resulting from reverse transcription and can be further expanded as a general method for other pathogens. EBV is a well-known human tumor virus, which has also recently been linked to the development of cervical cancer. The assay was accomplished within two hours including the room-temperature RNA extraction and reverse transcription steps. Also, this paper microzone plate-based platform can potentially be applicable for the development of point-of-care (POC) detection kits or devices due to its robust design, convenient interface, and easy portability. The experiment could be stopped after each step, and continued at a later time. The shelf-life of the modified paper plate setup was at

  11. A novel technique for unsupervised change detection in multitemporal SAR Images

    NASA Astrophysics Data System (ADS)

    Bazi, Y.; Bruzzone, L.; Melgani, F.

    The detection of changes that occur on the Earth surface by using multitemporal remote sensing images is one of the most important applications of the remote sensing technology. This depends on the fact that the knowledge of the dynamics of either natural resources or man-made structures is a valuable source of information in decision-making. In this context, optical remote sensing sensors have been used for addressing change-detection applications for many years. Unlike the optical sensors, images acquired by synthetic aperture radar (SAR) have been less exploited in the context of change detection. This is explained by the fact that SAR images suffer from the presence of the speckle noise that renders their analysis complex. However, the use of SAR sensors in change detection is attractive from the operational view-point, since they present the advantage to be independent on atmospheric and sunlight conditions. In the context of multitemporal SAR image analysis, the problem of change detection has been addressed with focus on different aspects, which include the choice of the comparison operator, the image despeckling and the optimal threshold selection. Despite some interesting works have been proposed in the literature, the main problem still open with SAR data is the lack of accurate and reliable methods capable to perform unsupervised change detection in a completely automatic way. In this paper, we propose to face the aforementioned issue by developing an automatic and unsupervised change-detection method specifically oriented to the analysis of multitemporal single-channel single-polarization SAR images. Such a method is based on three main steps: 1) controlled pre-processing based on adaptive filtering (despeckling); 2) comparison of a pair of multitemporal images according to a log-ratio operator; 3) automatic analysis of the log-ratio image. The first step aims at reducing the speckle noise in a controlled way in order to maximize the discrimination

  12. Detection of anthropogenic influence on multi-decadal changes in ocean stratification

    NASA Astrophysics Data System (ADS)

    Andrews, Oliver; Le Quéré, Corinne

    2016-04-01

    Signals of anthropogenic climate change have been identified in the ocean system using established detection and attribution methods to examine historical records of ocean temperature, salinity and dissolved oxygen concentration. Strengthened stratification of the upper ocean is another likely consequence of climate-driven warming and freshening of near surface waters. However, whilst qualitative links have been made between climate forcing and observed and projected future ocean stratification, the relative contribution of natural and anthropogenic processes remains uncertain. Elevated density stratification reduces physical exchange between the surface and interior ocean, impacting upon ventilation processes and biogeochemical cycling. Here, we combine recent temperature and salinity measurements to assess the extent to which large-scale changes in ocean stratification between the 1960s and 2000s can be attributed to anthropogenic climate change using a suite of coupled climate model simulations. Applying formal, regression-based fingerprinting methods we show that external climate forcing has had a detectable influence on observed changes in density stratification and that these changes cannot be explained by climate variability or natural external factors such as volcanism or solar output. Our study indicates that human influence has already significantly altered the density structure of the upper ocean. We discuss the implications and potential for detecting the variability and trends in carbon and oxygen storage in the ocean and in heat uptake efficiency.

  13. Scalable time series change detection for biomass monitoring using gaussian process

    SciTech Connect

    Chandola, Varun; Vatsavai, Raju

    2010-01-01

    Biomass monitoring, specifically detecting changes in the biomass or vegetation of a geographical region, is vital for studying the carbon cycle of the system and has significant implications in the context of understanding climate change and its impacts. Recently, several time series change detection methods have been proposed to identify land cover changes in temporal profiles (time series) of vegetation collected using remote sensing instruments. In this paper, we adapt Gaussian process regression to detect changes in such time series in an online fashion. While Gaussian process (GP) has been widely used as a kernel based learning method for regression and classification, their applicability to massive spatio-temporal data sets, such as remote sensing data, has been limited owing to the high computational costs involved. In this paper we address the scalability aspect of GP based time series change detection. Specifically, we exploit the special structure of the covariance matrix generated for GP analysis to come up with methods that can efficiently estimate the hyper-parameters associated with GP as well as identify changes in the time series while requiring a memory footprint which is linear in the size of input data, as compared to traditional method which involves solving a linear system of equations for the Choleksy decomposition of the quadratic sized covariance matrix. Experimental results show that our proposed method achieves significant speedups, as high as 1000, when processing long time series, while maintaining a small memory footprint. To further improve the computational complexity of the proposed method, we provide a parallel version which can concurrently process multiple input time series using the same set of hyper-parameters. The parallel version exploits the natural parallelization potential of the serial algorithm and is shown to perform significantly better than the serial version, with speedups as high as 10. Finally, we demonstrate the

  14. Representational pseudoneglect for detecting changes to Rey-Osterrieth figures.

    PubMed

    Aniulis, Ellie; Churches, Owen; Thomas, Nicole A; Nicholls, Michael E R

    2016-11-01

    When dividing attention between the left and right sides of physical space, most individuals pay slightly more attention to the left side. This phenomenon, known as pseudoneglect, may also occur for the left and right sides of mental representations of stimuli. Representational pseudoneglect has been shown for the recall of real-world scenes and for simple, briefly presented stimuli. The current study sought to investigate the effect of exposure duration and complexity using adaptations of the Rey-Osterrieth figures. Undergraduates (n = 97) were shown a stimulus for 20 s and asked to remember it. Participants were then shown a probe and indicated whether it was the same or different. Results showed that, irrespective of whether an element was added or subtracted, changes on the left side of the remembered image were better detected. These results are consistent with representational pseudoneglect and demonstrate that this effect occurs for complex stimuli when presented for an extended period of time. Representation neglect is therefore unlikely to be the result of an initial saccade to the left-but could be related to the formation or recall of the representation.

  15. QCM-based aptamer selection and detection of Salmonella typhimurium.

    PubMed

    Wang, Lijun; Wang, Ronghui; Chen, Fang; Jiang, Tieshan; Wang, Hong; Slavik, Michael; Wei, Hua; Li, Yanbin

    2017-04-15

    In this study, quartz crystal microbalance (QCM) was used to select aptamers against Salmonella typhimurium. To increase the success rate of Systematic Evolution of Ligands Exponential Enrichment (SELEX), the affinity of DNA pool in each round was simultaneously tracked using QCM in order to avoid the loss of high-quality aptamers. When the frequency change reached a maximum value after several rounds of selection and counter-selection, the candidate pool was cloned and sequenced. Out of three aptamer candidates, aptamer B5 showed high specificity and binding affinity with dissociation constant (Kd value) of 58.5nM, and was chosen for further studies. Subsequently, a QCM-based aptasensor was developed to detect S. typhimurium. This aptasensor was able to detect 10(3)CFU/mL of S. typhimurium with less than 1h. This study demonstrated QCM-based selection could be more effective selection of aptamers and QCM-based aptasensor could be more sensitive in detecting S. typhimurium.

  16. Managing Change Toward Site-Based Management.

    ERIC Educational Resources Information Center

    Terry, Paul M.

    The successful implementation of site-based management (SBM) requires time and changes in the role expectations among all members of the school community. Because needs and achievement levels differ among schools, there is no one best way to implement SBM. Schools are better off developing their own self-starting, working models. This paper…

  17. Cellular telephone-based radiation detection instrument

    DOEpatents

    Craig, William W.; Labov, Simon E.

    2011-06-14

    A network of radiation detection instruments, each having a small solid state radiation sensor module integrated into a cellular phone for providing radiation detection data and analysis directly to a user. The sensor module includes a solid-state crystal bonded to an ASIC readout providing a low cost, low power, light weight compact instrument to detect and measure radiation energies in the local ambient radiation field. In particular, the photon energy, time of event, and location of the detection instrument at the time of detection is recorded for real time transmission to a central data collection/analysis system. The collected data from the entire network of radiation detection instruments are combined by intelligent correlation/analysis algorithms which map the background radiation and detect, identify and track radiation anomalies in the region.

  18. Detection of greenhouse-gas-induced climatic change. Progress report, July 1, 1994--July 31, 1995

    SciTech Connect

    Jones, P.D.; Wigley, T.M.L.

    1995-07-21

    The objective of this research is to assembly and analyze instrumental climate data and to develop and apply climate models as a basis for detecting greenhouse-gas-induced climatic change, and validation of General Circulation Models. In addition to changes due to variations in anthropogenic forcing, including greenhouse gas and aerosol concentration changes, the global climate system exhibits a high degree of internally-generated and externally-forced natural variability. To detect the anthropogenic effect, its signal must be isolated from the ``noise`` of this natural climatic variability. A high quality, spatially extensive data base is required to define the noise and its spatial characteristics. To facilitate this, available land and marine data bases will be updated and expanded. The data will be analyzed to determine the potential effects on climate of greenhouse gas and aerosol concentration changes and other factors. Analyses will be guided by a variety of models, from simple energy balance climate models to coupled atmosphere ocean General Circulation Models. These analyses are oriented towards obtaining early evidence of anthropogenic climatic change that would lead either to confirmation, rejection or modification of model projections, and towards the statistical validation of General Circulation Model control runs and perturbation experiments.

  19. Investigation of environmental change pattern in Japan: A study on change detection of land cover in Tokyo districts using multi-dates LANDSAT CCT

    NASA Technical Reports Server (NTRS)

    Maruyasu, T.; Murai, S. (Principal Investigator)

    1976-01-01

    The author has identified the following significant results. The software program, which enables the geographically corrected LANDSAT digital data base, was developed. The data base could provide land use planners with land cover information and the environmental change pattern. Land cover was evaluated by the color representation for ratio of three primary components, water vegetation, and nonorganic matter. Software was also developed for the change detection within multidates LANDSAT MSS data.

  20. Ten Years of Land Cover Change on the California Coast Detected using Landsat Satellite Image Analysis

    NASA Technical Reports Server (NTRS)

    Potter, Christopher S.

    2013-01-01

    Landsat satellite imagery was analyzed to generate a detailed record of 10 years of vegetation disturbance and regrowth for Pacific coastal areas of Marin and San Francisco Counties. The Landsat Ecosystem Disturbance Adaptive Processing System (LEDAPS) methodology, a transformation of Tasseled-Cap data space, was applied to detected changes in perennial coastal shrubland, woodland, and forest cover from 1999 to 2009. Results showed several principal points of interest, within which extensive contiguous areas of similar LEDAPS vegetation change (either disturbed or restored) were detected. Regrowth areas were delineated as burned forest areas in the Point Reyes National Seashore (PRNS) from the 1995 Vision Fire. LEDAPS-detected disturbance patterns on Inverness Ridge, PRNS in areas observed with dieback of tanoak and bay laurel trees was consistent with defoliation by sudden oak death (Phytophthora ramorum). LEDAPS regrowth pixels were detected over much of the predominantly grassland/herbaceous cover of the Olema Valley ranchland near PRNS. Extensive restoration of perennial vegetation cover on Crissy Field, Baker Beach and Lobos Creek dunes in San Francisco was identified. Based on these examples, the LEDAPS methodology will be capable of fulfilling much of the need for continual, low-cost monitoring of emerging changes to coastal ecosystems.

  1. Monitoring and change detection of Wadden Sea areas using Lidar data

    NASA Astrophysics Data System (ADS)

    Schmidt, A.; Soergel, U.

    2013-10-01

    In coastal areas morphological changes of various kinds are caused by tidal flows, storms, climate change, and human activities. For these reasons a recurrent monitoring becomes necessary in order to detect undesired changes at early stages enabling rapid countermeasures to mitigate or minimize potential harm or hazard. The morphology of the terrain can be represented by highly precise digital terrain models (DTM). Airborne lidar (light detection and ranging) has become a standard method for DTM generation in coastal zones like Wadden Sea areas. In comparison to echo sounding systems, lidar is feasible for data acquisition of large areas. However, only the eulittoral zone can be covered by standard laser because the near-infrared laser pulses are not able to penetrate water which remains, for example, in some tidal channels even during low tide. In the framework of a German research project, we analyse the spatial and temporal variability of Wadden Sea areas in the North Sea. For a systematic monitoring and the detection of morphological changes we compare terrain models of two different epochs in order to determine height differences which can be caused by natural influences or human activities. We focus especially on the analysis of morphological changes near to tidal channels. In order to detect changes we compare the location of edges derived from each DTM based on the gray values' gradients. Our results for a test site in the German Wadden Sea show height differences up to 1 m due to the shifting of tidal channels and relocations of the channels up to 55 m within a period of two years.

  2. Detection of Salmonella typhimurium using phage-based magnetostrictive sensor

    NASA Astrophysics Data System (ADS)

    Lakshmanan, Ramji S.; Hu, Jing; Guntupalli, Rajesh; Wan, Jiehui; Huang, Shichu; Yang, Hong; Petrenko, Valery A.; Barbaree, James M.; Chin, Bryan A.

    2006-05-01

    This article presents a contactless, remote sensing Salmonella typhimurium sensor based on the principle of magnetostriction. Magnetostrictive materials have been used widely for various types of sensor systems. In this work, the use of a magnetostrictive material for the detection of Salmonella typhimurium has been established. The mass of the bacteria attached to the sensor causes changes in the resonance frequency of the sensor. Filamentous bacteriophage was used as a probe order to ensure specific and selective binding of the bacteria onto the sensor surface. Thus changes in response of the sensor due to the mass added onto the sensor caused by specific attachment of bacteria can be monitored in absence of any contact to the sensor. The response of the sensor due to increasing concentrations (from 5x101 to 5x10 8 cfu/ml) of the bacteria was studied. A reduction in the physical dimensions enhances the sensitivity of these sensors and hence different dimensions of the sensor ribbons were studied. For a 2mm x 0.1mm x 0.02mm the detection limit was observed to be of the order of 10 4 cfu/mL and for a sensor of 1mm x 0.2mm x 0.02mm a reduced detection limit of 10 3 cfu/mL was achieved.

  3. Detection of Clinically Relevant Exonic Copy-Number Changes by Array CGH

    PubMed Central

    Boone, Philip M.; Bacino, Carlos A.; Shaw, Chad A.; Eng, Patricia A.; Hixson, Patricia M.; Pursley, Amber N.; Kang, Sung-Hae L.; Yang, Yaping; Wiszniewska, Joanna; Nowakowska, Beata A.; Gaudio, Daniela del; Xia, Zhilian; Simpson-Patel, Gayle; Immken, LaDonna L.; Gibson, James B.; Tsai, Anne C.-H.; Bowers, Jennifer A.; Reimschisel, Tyler E.; Schaaf, Christian P.; Potocki, Lorraine; Scaglia, Fernando; Gambin, Tomasz; Sykulski, Maciej; Bartnik, Magdalena; Derwinska, Katarzyna; Wisniowiecka-Kowalnik, Barbara; Lalani, Seema R.; Probst, Frank J.; Bi, Weimin; Beaudet, Arthur L.; Patel, Ankita; Lupski, James R.; Cheung, Sau Wai; Stankiewicz, Pawel

    2011-01-01

    Array comparative genomic hybridization (aCGH) is a powerful tool for the molecular elucidation and diagnosis of disorders resulting from genomic copy-number variation (CNV). However, intragenic deletions or duplications—those including genomic intervals of a size smaller than a gene—have remained beyond the detection limit of most clinical aCGH analyses. Increasing array probe number improves genomic resolution, although higher cost may limit implementation, and enhanced detection of benign CNV can confound clinical interpretation. We designed an array with exonic coverage of selected disease and candidate genes and used it clinically to identify losses or gains throughout the genome involving at least one exon and as small as several hundred base pairs in size. In some patients, the detected copy-number change occurs within a gene known to be causative of the observed clinical phenotype, demonstrating the ability of this array to detect clinically relevant CNVs with subkilobase resolution. In summary, we demonstrate the utility of a custom-designed, exon-targeted oligonucleotide array to detect intragenic copy-number changes in patients with various clinical phenotypes. PMID:20848651

  4. Lagrangian based methods for coherent structure detection

    SciTech Connect

    Allshouse, Michael R.; Peacock, Thomas

    2015-09-15

    There has been a proliferation in the development of Lagrangian analytical methods for detecting coherent structures in fluid flow transport, yielding a variety of qualitatively different approaches. We present a review of four approaches and demonstrate the utility of these methods via their application to the same sample analytic model, the canonical double-gyre flow, highlighting the pros and cons of each approach. Two of the methods, the geometric and probabilistic approaches, are well established and require velocity field data over the time interval of interest to identify particularly important material lines and surfaces, and influential regions, respectively. The other two approaches, implementing tools from cluster and braid theory, seek coherent structures based on limited trajectory data, attempting to partition the flow transport into distinct regions. All four of these approaches share the common trait that they are objective methods, meaning that their results do not depend on the frame of reference used. For each method, we also present a number of example applications ranging from blood flow and chemical reactions to ocean and atmospheric flows.

  5. Detecting and Quantifying Forest Change: The Potential of Existing C- and X-Band Radar Datasets.

    PubMed

    Tanase, Mihai A; Ismail, Ismail; Lowell, Kim; Karyanto, Oka; Santoro, Maurizio

    2015-01-01

    This paper evaluates the opportunity provided by global interferometric radar datasets for monitoring deforestation, degradation and forest regrowth in tropical and semi-arid environments. The paper describes an easy to implement method for detecting forest spatial changes and estimating their magnitude. The datasets were acquired within space-borne high spatial resolutions radar missions at near-global scales thus being significant for monitoring systems developed under the United Framework Convention on Climate Change (UNFCCC). The approach presented in this paper was tested in two areas located in Indonesia and Australia. Forest change estimation was based on differences between a reference dataset acquired in February 2000 by the Shuttle Radar Topography Mission (SRTM) and TanDEM-X mission (TDM) datasets acquired in 2011 and 2013. The synergy between SRTM and TDM datasets allowed not only identifying changes in forest extent but also estimating their magnitude with respect to the reference through variations in forest height.

  6. Obtaining Accurate Change Detection Results from High-Resolution Satellite Sensors

    NASA Technical Reports Server (NTRS)

    Bryant, N.; Bunch, W.; Fretz, R.; Kim, P.; Logan, T.; Smyth, M.; Zobrist, A.

    2012-01-01

    Multi-date acquisitions of high-resolution imaging satellites (e.g. GeoEye and WorldView), can display local changes of current economic interest. However, their large data volume precludes effective manual analysis, requiring image co-registration followed by image-to-image change detection, preferably with minimal analyst attention. We have recently developed an automatic change detection procedure that minimizes false-positives. The processing steps include: (a) Conversion of both the pre- and post- images to reflectance values (this step is of critical importance when different sensors are involved); reflectance values can be either top-of-atmosphere units or have full aerosol optical depth calibration applied using bi-directional reflectance knowledge. (b) Panchromatic band image-to-image co-registration, using an orthorectified base reference image (e.g. Digital Orthophoto Quadrangle) and a digital elevation model; this step can be improved if a stereo-pair of images have been acquired on one of the image dates. (c) Pan-sharpening of the multispectral data to assure recognition of change objects at the highest resolution. (d) Characterization of multispectral data in the post-image ( i.e. the background) using unsupervised cluster analysis. (e) Band ratio selection in the post-image to separate surface materials of interest from the background. (f) Preparing a pre-to-post change image. (g) Identifying locations where change has occurred involving materials of interest.

  7. Protein Conformational Changes Are Detected and Resolved Site Specifically by Second-Harmonic Generation.

    PubMed

    Moree, Ben; Connell, Katelyn; Mortensen, Richard B; Liu, C Tony; Benkovic, Stephen J; Salafsky, Joshua

    2015-08-18

    We present here a straightforward, broadly applicable technique for real-time detection and measurement of protein conformational changes in solution. This method is based on tethering proteins labeled with a second-harmonic generation (SHG) active dye to supported lipid bilayers. We demonstrate our method by measuring the conformational changes that occur upon ligand binding with three well-characterized proteins labeled at lysine residues: calmodulin (CaM), maltose-binding protein (MBP), and dihydrofolate reductase (DHFR). We also create a single-site cysteine mutant of DHFR engineered within the Met20 catalytic loop region and study the protein's structural motion at this site. Using published x-ray crystal structures, we show that the changes in the SHG signals upon ligand binding are the result of structural motions that occur at the labeled sites between the apo and ligand-bound forms of the proteins, which are easily distinguished from each other. In addition, we demonstrate that different magnitudes of the SHG signal changes are due to different and specific ligand-induced conformational changes. Taken together, these data illustrate the potential of the SHG approach for detecting and measuring protein conformational changes for a wide range of biological applications.

  8. Protein Conformational Changes Are Detected and Resolved Site Specifically by Second-Harmonic Generation

    PubMed Central

    Moree, Ben; Connell, Katelyn; Mortensen, Richard B.; Liu, C. Tony; Benkovic, Stephen J.; Salafsky, Joshua

    2015-01-01

    We present here a straightforward, broadly applicable technique for real-time detection and measurement of protein conformational changes in solution. This method is based on tethering proteins labeled with a second-harmonic generation (SHG) active dye to supported lipid bilayers. We demonstrate our method by measuring the conformational changes that occur upon ligand binding with three well-characterized proteins labeled at lysine residues: calmodulin (CaM), maltose-binding protein (MBP), and dihydrofolate reductase (DHFR). We also create a single-site cysteine mutant of DHFR engineered within the Met20 catalytic loop region and study the protein’s structural motion at this site. Using published x-ray crystal structures, we show that the changes in the SHG signals upon ligand binding are the result of structural motions that occur at the labeled sites between the apo and ligand-bound forms of the proteins, which are easily distinguished from each other. In addition, we demonstrate that different magnitudes of the SHG signal changes are due to different and specific ligand-induced conformational changes. Taken together, these data illustrate the potential of the SHG approach for detecting and measuring protein conformational changes for a wide range of biological applications. PMID:26287632

  9. Reliability and Minimum Detectable Change of Temporal-Spatial, Kinematic, and Dynamic Stability Measures during Perturbed Gait

    PubMed Central

    2015-01-01

    Temporal-spatial, kinematic variability, and dynamic stability measures collected during perturbation-based assessment paradigms are often used to identify dysfunction associated with gait instability. However, it remains unclear which measures are most reliable for detecting and tracking responses to perturbations. This study systematically determined the between-session reliability and minimum detectable change values of temporal-spatial, kinematic variability, and dynamic stability measures during three types of perturbed gait. Twenty young healthy adults completed two identical testing sessions two weeks apart, comprised of an unperturbed and three perturbed (cognitive, physical, and visual) walking conditions in a virtual reality environment. Within each session, perturbation responses were compared to unperturbed walking using paired t-tests. Between-session reliability and minimum detectable change values were also calculated for each measure and condition. All temporal-spatial, kinematic variability and dynamic stability measures demonstrated fair to excellent between-session reliability. Minimal detectable change values, normalized to mean values ranged from 1–50%. Step width mean and variability measures demonstrated the greatest response to perturbations with excellent between-session reliability and low minimum detectable change values. Orbital stability measures demonstrated specificity to perturbation direction and sensitivity with excellent between-session reliability and low minimum detectable change values. We observed substantially greater between-session reliability and lower minimum detectable change values for local stability measures than previously described which may be the result of averaging across trials within a session and using velocity versus acceleration data for reconstruction of state spaces. Across all perturbation types, temporal-spatial, orbital and local measures were the most reliable measures with the lowest minimum

  10. Ground-Based Detection of Exoatmospheric Calcium

    NASA Astrophysics Data System (ADS)

    Rojo, Patricio M.; Astudillo-Defru, Nicola

    2014-11-01

    Data acquired with HDS@Subaru for HD209458b is re-analyzed. A new pipeline performs an automated search for the exoatmospheric presence of several elements without any a-priori assumptions on its existence or strength. We analyzed thousands of lines in the full spectral range of this optical echelle spectrograph using a robust method to correct for the telluric contamination. We recover previous detections of Sodium and Halpha, and present the first strong detection of Calcium in an Extrasolar Atmosphere as well as the tentative detection of other elements. The Calcium detection is in disagreement with theoretical thermal-equilibrium models.

  11. Unsupervised change detection in a particular vegetation land cover type using spectral angle mapper

    NASA Astrophysics Data System (ADS)

    Renza, Diego; Martinez, Estibaliz; Molina, Iñigo; Ballesteros L., Dora M.

    2017-04-01

    This paper presents a new unsupervised change detection methodology for multispectral images applied to specific land covers. The proposed method involves comparing each image against a reference spectrum, where the reference spectrum is obtained from the spectral signature of the type of coverage you want to detect. In this case the method has been tested using multispectral images (SPOT5) of the community of Madrid (Spain), and multispectral images (Quickbird) of an area over Indonesia that was impacted by the December 26, 2004 tsunami; here, the tests have focused on the detection of changes in vegetation. The image comparison is obtained by applying Spectral Angle Mapper between the reference spectrum and each multitemporal image. Then, a threshold to produce a single image of change is applied, which corresponds to the vegetation zones. The results for each multitemporal image are combined through an exclusive or (XOR) operation that selects vegetation zones that have changed over time. Finally, the derived results were compared against a supervised method based on classification with the Support Vector Machine. Furthermore, the NDVI-differencing and the Spectral Angle Mapper techniques were selected as unsupervised methods for comparison purposes. The main novelty of the method consists in the detection of changes in a specific land cover type (vegetation), therefore, for comparison purposes, the best scenario is to compare it with methods that aim to detect changes in a specific land cover type (vegetation). This is the main reason to select NDVI-based method and the post-classification method (SVM implemented in a standard software tool). To evaluate the improvements using a reference spectrum vector, the results are compared with the basic-SAM method. In SPOT5 image, the overall accuracy was 99.36% and the κ index was 90.11%; in Quickbird image, the overall accuracy was 97.5% and the κ index was 82.16%. Finally, the precision results of the method are

  12. Contour-based automatic crater recognition using digital elevation models from Chang'E missions

    NASA Astrophysics Data System (ADS)

    Zuo, Wei; Zhang, Zhoubin; Li, Chunlai; Wang, Rongwu; Yu, Linjie; Geng, Liang

    2016-12-01

    In order to provide fundamental information for exploration and related scientific research on the Moon and other planets, we propose a new automatic method to recognize craters on the lunar surface based on contour data extracted from a digital elevation model (DEM). Through DEM and image processing, this method can be used to reconstruct contour surfaces, extract and combine contour lines, set the characteristic parameters of crater morphology, and establish a crater pattern recognition program. The method has been tested and verified with DEM data from Chang'E-1 (CE-1) and Chang'E-2 (CE-2), showing a strong crater recognition ability with high detection rate, high robustness, and good adaptation to recognize various craters with different diameter and morphology. The method has been used to identify craters with high precision and accuracy on the Moon. The results meet requirements for supporting exploration and related scientific research for the Moon and planets.

  13. Aptamer-based Electrochemical Biosensor for Interferon Gamma Detection

    PubMed Central

    Liu, Ying; Tuleouva, Nazgul; Ramanculov, Erlan; Revzin, Alexander

    2010-01-01

    In this paper, we describe the development of an electrochemical DNA aptamer-based biosensor for detection of IFN-γ. A DNA hairpin containing IFN-γ-binding aptamer was thiolated, conjugated with Methylene Blue (MB) redox tag and immobilized on a gold electrode by self-assembly. Binding of IFN-γ caused the aptamer hairpin to unfold, pushing MB redox molecules away from the electrode and decreasing electron-transfer efficiency. The change in redox current was quantified using Square Wave Voltammetry (SWV) and was found to be highly sensitive to IFN-γ concentration. The limit of detection for optimized biosensor was 0.06 nM with linear response extending to 10 nM. This aptasensor was specific to IFN-γ in the presence of overabundant serum proteins. Importantly, the same aptasensor could be regenerated by disrupting aptamer-IFN-γ complex in urea buffer and re-used multiple times. Unlike standard sandwich immunoassays, the aptasensor described here allowed to detect IFN-γ binding directly without the need for multiple washing steps and reagents. An electrochemical biosensor for simple and sensitive detection of IFN-γ demonstrated in this paper will have future applications in immunology, cancer research and infectious disease monitoring. PMID:20815336

  14. Liquid crystal based biosensors for bile acid detection

    NASA Astrophysics Data System (ADS)

    He, Sihui; Liang, Wenlang; Tanner, Colleen; Fang, Jiyu; Wu, Shin-Tson

    2013-03-01

    The concentration level of bile acids is a useful indicator for early diagnosis of liver diseases. The prevalent measurement method in detecting bile acids is the chromatography coupled with mass spectrometry, which is precise yet expensive. Here we present a biosensor platform based on liquid crystal (LC) films for the detection of cholic acid (CA). This platform has the advantage of low cost, label-free, solution phase detection and simple analysis. In this platform, LC film of 4-Cyano-4'-pentylbiphenyl (5CB) was hosted by a copper grid supported with a polyimide-coated glass substrate. By immersing into sodium dodecyl sulfate (SDS) solution, the LC film was coated with SDS which induced a homeotropic anchoring of 5CB. Addition of CA introduced competitive adsorption between CA and SDS at the interface, triggering a transition from homeotropic to homogeneous anchoring. The detection limit can be tuned by changing the pH value of the solution from 12uM to 170uM.

  15. Quantification of Forecasting and Change-Point Detection Methods for Predictive Maintenance

    DTIC Science & Technology

    2015-08-19

    Final 3. DATES COVERED (From - To) 22 Jul 2014 – 21 Jul 2015 4. TITLE AND SUBTITLE Quantification of forecasting and change -point...disadvantages of change detection techniques using Singular Spectral Transform (SST) and Autoregressive Integrated Moving Average (ARIMA) applied to equipment...are used to evaluate the capability of detection of both methods for several types of changes . SST was applied to change detection in rotating

  16. Climate Change detection using GPS RO and CMIP5 data

    NASA Astrophysics Data System (ADS)

    Molodtsov, S.; Kirilenko, A.; Olsen, D.

    2013-12-01

    General circulation models' (GCMs) response to the increase of atmospheric concentration of greenhouse gases (GHG) due to anthropogenic emissions is evident under variety of forcing scenarios. However the investigation of the anthropogenic signal in the real climate system is challenging as it requires long term high accuracy global coverage data. GPS Radio Occultation (GPS RO) technique becomes close to meeting all of these requirements, becoming the benchmark for climate data. We implemented the analysis for upper troposphere - lower stratosphere (UTLS) region between 50°N and 50°S latitudes, where the GPS RO data has higher quality. For observations, we used vertical profiles of temperature and/or geopotential heights from 2001-2008 CHAMP and 2006-2011 COSMIC occultation. GCM trends of the response patterns to the external forcings of respective climate variables were extracted from CMIP 5 GCM runs under representative concentration pathways (RCP) 8.5 and historical climate scenarios, for the same period. Natural variability was calculated using CMIP 5 GCM runs under preindustrial control scenario. The GPS RO data was downloaded from CDAAC website, GCM CMIP 5 data was obtained from Earth System Grid web page. We used optimal fingerprinting method which is generalized multivariate regression adjusted for climate change detection studies. Optimal fingerprinting allows estimating of the climate signal separately from natural climate variability. Following this methodology we applied Empirical Orthogonal Functions to filter observed and modeled data in order to decrease impact of natural variability and maximize the signal to noise ratio. Temperature and geopotential height trends in the simulated and observed climate datasets show similar features of warming of the upper troposphere and cooling of the lower stratosphere in the tropics region. Using optimal fingerprinting we found that the anthropogenic signal emerges from natural variability in GPS RO temperature

  17. Automated detection of slum area change in Hyderabad, India using multitemporal satellite imagery

    NASA Astrophysics Data System (ADS)

    Kit, Oleksandr; Lüdeke, Matthias

    2013-09-01

    This paper presents an approach to automated identification of slum area change patterns in Hyderabad, India, using multi-year and multi-sensor very high resolution satellite imagery. It relies upon a lacunarity-based slum detection algorithm, combined with Canny- and LSD-based imagery pre-processing routines. This method outputs plausible and spatially explicit slum locations for the whole urban agglomeration of Hyderabad in years 2003 and 2010. The results indicate a considerable growth of area occupied by slums between these years and allow identification of trends in slum development in this urban agglomeration.

  18. Signal Detection Theory-Based Information Processing for the Detection of Breast Cancer at Microwave Frequencies

    DTIC Science & Technology

    2002-08-01

    the measurement noise, as well as the physical model of the forward scattered electric field. The Bayesian algorithms for the Uncertain Permittivity...received at multiple sensors. In this research project a tissue- model -based signal-detection theory approach for the detection of mammary tumors in the...oriented information processors. In this research project a tissue- model - based signal detection theory approach for the detection of mammary tumors in the

  19. Photonic crystal waveguide-based biosensor for detection of diseases

    NASA Astrophysics Data System (ADS)

    Chopra, Harshita; Kaler, Rajinder S.; Painam, Balveer

    2016-07-01

    A biosensor is a device that is used to detect the analytes or molecules of a sample by means of a binding mechanism. A two-dimensional photonic crystal waveguide-based biosensor is designed with a diamond-shaped ring resonator and two waveguides: a bus waveguide and a drop waveguide. The sensing mechanism is based on change in refractive index of the analytes, leading to a shift in the peak resonant wavelength. This mechanism can be used in the field of biomedical treatment where different body fluids such as blood, tears, saliva, or urine can be used as the analyte in which different components of the fluid can be detected. It can also be used to differentiate between the cell lines of a normal and an unhealthy human being. Average value of quality factor for this device comes out to be 1082.2063. For different analytes used, the device exhibits enhanced sensitivity and, hence, it is useful for the detection of diseases.

  20. Dependence-Based Anomaly Detection Methodologies

    DTIC Science & Technology

    2012-08-16

    tricks the user to enter their Netflix login. Detecting it is out of our scope and requires site authentication (i.e., certification verification... Netflix login. Detecting it is out of our scope and requires site authentication (i.e., certification verification) and user education. The preliminary

  1. Nuclear based techniques for detection of contraband

    SciTech Connect

    Gozani, T.

    1993-12-31

    The detection of contraband such as explosives and drugs concealed in luggage or other container can be quite difficult. Nuclear techniques offer capabilities which are essential to having effective detection devices. This report describes the features of various nuclear techniques and instrumentation.

  2. Change detection in urban and rural driving scenes: Effects of target type and safety relevance on change blindness.

    PubMed

    Beanland, Vanessa; Filtness, Ashleigh J; Jeans, Rhiannon

    2017-03-01

    The ability to detect changes is crucial for safe driving. Previous research has demonstrated that drivers often experience change blindness, which refers to failed or delayed change detection. The current study explored how susceptibility to change blindness varies as a function of the driving environment, type of object changed, and safety relevance of the change. Twenty-six fully-licenced drivers completed a driving-related change detection task. Changes occurred to seven target objects (road signs, cars, motorcycles, traffic lights, pedestrians, animals, or roadside trees) across two environments (urban or rural). The contextual safety relevance of the change was systematically manipulated within each object category, ranging from high safety relevance (i.e., requiring a response by the driver) to low safety relevance (i.e., requiring no response). When viewing rural scenes, compared with urban scenes, participants were significantly faster and more accurate at detecting changes, and were less susceptible to "looked-but-failed-to-see" errors. Interestingly, safety relevance of the change differentially affected performance in urban and rural environments. In urban scenes, participants were more efficient at detecting changes with higher safety relevance, whereas in rural scenes the effect of safety relevance has marginal to no effect on change detection. Finally, even after accounting for safety relevance, change blindness varied significantly between target types. Overall the results suggest that drivers are less susceptible to change blindness for objects that are likely to change or move (e.g., traffic lights vs. road signs), and for moving objects that pose greater danger (e.g., wild animals vs. pedestrians).

  3. A New Metric for Detecting Change in Slowly Evolving Brain Tumors: Validation in Meningioma Patients

    PubMed Central

    Pohl, Kilian M.; Konukoglu, Ender; Novellas, Sebastian; Ayache, Nicholas; Fedorov, Andriy; Talos, Ion-Florin; Golby, Alexandra; Wells, William M.; Kikinis, Ron; Black, Peter M.

    2011-01-01

    Background Change detection is a critical component in the diagnosis and monitoring of many slowly evolving pathologies. Objective This article describes a semi-automatic monitoring approach using longitudinal medical images. We test the method on brain scans of meningioma patients, which experts found difficult to monitor as the tumor evolution is very slow and may be obscured by artifacts related to image acquisition. Methods We describe a semi-automatic procedure targeted towards identifying difficult-to-detect changes in brain tumor imaging. The tool combines input from a medical expert with state-of-the-art technology. The software is easy to calibrate and, in less than five minutes, returns the total volume of tumor change in mm3. We test the method on post-gadolinium, T1-weighted Magnetic Resonance Images of ten meningioma patients and compare our results to experts’ findings. We also perform benchmark testing with synthetic data. Results Our experiments indicated that experts’ visual inspections are not sensitive enough to detect subtle growth. Measurements based on experts’ manual segmentations were highly accurate but also labor intensive. The accuracy of our approach was comparable to the experts’ results. However, our approach required far less user input and generated more consistent measurements. Conclusion The sensitivity of experts’ visual inspection is often too low to detect subtle growth of meningiomas from longitudinal scans. Measurements based on experts’ segmentation are highly accurate but generally too labor intensive for standard clinical settings. We described an alternative metric that provides accurate and robust measurements of subtle tumor changes while requiring a minimal amount of user input. PMID:21206318

  4. Signal subspace change detection in averaged multi-look SAR imagery

    NASA Astrophysics Data System (ADS)

    Ranney, Kenneth; Soumekh, Mehrdad

    2005-05-01

    Modern Synthetic Aperture Radar (SAR) signal processing algorithms could retrieve accurate and subtle information regarding a scene that is being interrogated by an airborne radar system. An important reconnaissance problem that is being studied via the use of SAR systems and their sophisticated signal processing methods involves detecting changes in an imaged scene. In these problems, the user interrogates a scene with a SAR system at two different time points (e.g. different days); the resultant two SAR databases that we refer to as reference and test data, are used to determine where targets have entered or left the imaged scene between the two data acquisitions. For instance, X band SAR systems have the potential to become a potent tool to determine whether mines have been recently placed in an area. This paper describes an algorithm for detecting changes in averaged multi-look SAR imagery. Averaged multi-look SAR images are preferable to full aperture SAR reconstructions when the imaging algorithm is approximation based (e.g. polar format processing), or motion data are not accurate over a long full aperture. We study the application of a SAR detection method, known as Signal Subspace Processing, that is based on the principles of 2D adaptive filtering. We identify the change detection problem as a binary hypothesis-testing problem, and identify an error signal and its normalized version to determine whether i) there is no change in the imaged scene; or ii) a target has been added to the imaged scene. A statistical analysis of the error signal is provided to show its properties and merits. Results are provided for data collected by an X band SAR platform and processed to form non-coherently look-averaged SAR images.

  5. Laser-based Sensors for Chemical Detection

    SciTech Connect

    Myers, Tanya L.; Phillips, Mark C.; Taubman, Matthew S.; Bernacki, Bruce E.; Schiffern, John T.; Cannon, Bret D.

    2010-05-10

    Stand-off detection of hazardous materials ensures that the responder is located at a safe distance from the suspected source. Remote detection and identification of hazardous materials can be accomplished using a highly sensitive and portable device, at significant distances downwind from the source or the threat. Optical sensing methods, in particular infrared absorption spectroscopy combined with quantum cascade lasers (QCLs), are highly suited for the detection of chemical substances since they enable rapid detection and are amenable for autonomous operation in a compact and rugged package. This talk will discuss the sensor systems developed at Pacific Northwest National Laboratory and will discuss the progress to reduce the size and power while maintaining sensitivity to enable stand-off detection of multiple chemicals.

  6. Electrophysiological Correlates of Automatic Visual Change Detection in School-Age Children

    ERIC Educational Resources Information Center

    Clery, Helen; Roux, Sylvie; Besle, Julien; Giard, Marie-Helene; Bruneau, Nicole; Gomot, Marie

    2012-01-01

    Automatic stimulus-change detection is usually investigated in the auditory modality by studying Mismatch Negativity (MMN). Although the change-detection process occurs in all sensory modalities, little is known about visual deviance detection, particularly regarding the development of this brain function throughout childhood. The aim of the…

  7. Validity, responsiveness, minimal detectable change, and minimal clinically important change of Pediatric Balance Scale in children with cerebral palsy.

    PubMed

    Chen, Chia-ling; Shen, I-hsuan; Chen, Chung-yao; Wu, Ching-yi; Liu, Wen-yu; Chung, Chia-ying

    2013-03-01

    This study examined criterion-related validity and clinimetric properties of the pediatric balance scale (PBS) in children with cerebral palsy (CP). Forty-five children with CP (age range: 19-77 months) and their parents participated in this study. At baseline and at follow up, Pearson correlation coefficients were used to determine criterion-related validity by analyzing the correlation between the PBS, including PBS-static, PBS-dynamic, and PBS-total, and criterion measures, including the Gross Motor Function Measure-66 items (GMFM-66) and Functional Independence Measures for Children (WeeFIM). Responsiveness was examined by paired t test and by standardized response mean (SRM). The minimal detectable change (MDC) was analyzed at the 90% confidence level, and the minimal clinically important differences (MCID) was estimated by anchor-based and distribution-based approaches. The PBS with GMFM-66 and WeeFIM showed fair-to-excellent concurrent validity at pretreatment and follow up and predictive validity. The SRM values of all PBS scales were 0.75. For the PBS-static, PBS-dynamic, and PBS-total, the MDC(90) values were 0.79, 0.96, and 1.59, and the MCID ranges were 1.47-2.92, 2.23-2.92, and 3.66-5.83, respectively. Improvement of at least MDC values on the PBS can be considered a true change, not measurement error. A mean change must exceed the MCID range on PBS to be considered clinically important change