WorldWideScience

Sample records for series change detection

  1. Lake Chapala change detection using time series

    Science.gov (United States)

    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.

  2. Unsupervised land cover change detection: meaningful sequential time series analysis

    CSIR Research Space (South Africa)

    Salmon, BP

    2011-06-01

    Full Text Available An automated land cover change detection method is proposed that uses coarse spatial resolution hyper-temporal earth observation satellite time series data. The study compared three different unsupervised clustering approaches that operate on short...

  3. Change detection in a series of Sentinel-1 SAR data

    DEFF Research Database (Denmark)

    Nielsen, Allan Aasbjerg; Conradsen, Knut; Skriver, Henning

    2017-01-01

    Based on an omnibus likelihood ratio test statistic for the equality of several variance-covariance matrices following the complex Wishart distribution with an associated p-value and a factorization of this test statistic, change analysis in a time series of seven multilook, dual polarization...

  4. SCALABLE TIME SERIES CHANGE DETECTION FOR BIOMASS MONITORING USING GAUSSIAN PROCESS

    Data.gov (United States)

    National Aeronautics and Space Administration — SCALABLE TIME SERIES CHANGE DETECTION FOR BIOMASS MONITORING USING GAUSSIAN PROCESS VARUN CHANDOLA AND RANGA RAJU VATSAVAI Abstract. Biomass monitoring,...

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

    Science.gov (United States)

    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.

  6. Change detection in a time series of polarimetric SAR images

    DEFF Research Database (Denmark)

    Skriver, Henning; Nielsen, Allan Aasbjerg; Conradsen, Knut

    A test statistic for the equality of two or several variance-covariance matrices following the real (as opposed to the complex) Wishart distribution with an associated probability of finding a smaller value of the test statistic is described in the literature [1]. In 2003 we introduced a test...... statistic for the equality of two variance-covariance matrices following the complex Wishart distribution with an associated probability measure [2]. In that paper we also demonstrated the use of the test statistic to change detection over time in both fully polarimetric and azimuthal symmetric SAR data...... positives (postulating a change when there actually is none) and/or false negatives (missing an actual change). Therefore we need to test for equality for all time points simultaneously. In this paper we demonstrate a new test statistic for the equality of several variance-covariance matrices from the real...

  7. A robust anomaly based change detection method for time-series remote sensing images

    Science.gov (United States)

    Shoujing, Yin; Qiao, Wang; Chuanqing, Wu; Xiaoling, Chen; Wandong, Ma; Huiqin, Mao

    2014-03-01

    Time-series remote sensing images record changes happening on the earth surface, which include not only abnormal changes like human activities and emergencies (e.g. fire, drought, insect pest etc.), but also changes caused by vegetation phenology and climate changes. Yet, challenges occur in analyzing global environment changes and even the internal forces. This paper proposes a robust Anomaly Based Change Detection method (ABCD) for time-series images analysis by detecting abnormal points in data sets, which do not need to follow a normal distribution. With ABCD we can detect when and where changes occur, which is the prerequisite condition of global change studies. ABCD was tested initially with 10-day SPOT VGT NDVI (Normalized Difference Vegetation Index) times series tracking land cover type changes, seasonality and noise, then validated to real data in a large area in Jiangxi, south of China. Initial results show that ABCD can precisely detect spatial and temporal changes from long time series images rapidly.

  8. Detecting land cover change using an extended Kalman filter on MODIS NDVI time-series data

    CSIR Research Space (South Africa)

    Kleynhans, W

    2011-05-01

    Full Text Available A method for detecting land cover change using NDVI time-series data derived from 500-m MODIS satellite data is proposed. The algorithm acts as a per-pixel change alarm and takes the NDVI time series of a 3 × 3 grid of MODIS pixels as the input...

  9. Change detection using landsat time series: A review of frequencies, preprocessing, algorithms, and applications

    Science.gov (United States)

    Zhu, Zhe

    2017-08-01

    The free and open access to all archived Landsat images in 2008 has completely changed the way of using Landsat data. Many novel change detection algorithms based on Landsat time series have been developed We present a comprehensive review of four important aspects of change detection studies based on Landsat time series, including frequencies, preprocessing, algorithms, and applications. We observed the trend that the more recent the study, the higher the frequency of Landsat time series used. We reviewed a series of image preprocessing steps, including atmospheric correction, cloud and cloud shadow detection, and composite/fusion/metrics techniques. We divided all change detection algorithms into six categories, including thresholding, differencing, segmentation, trajectory classification, statistical boundary, and regression. Within each category, six major characteristics of different algorithms, such as frequency, change index, univariate/multivariate, online/offline, abrupt/gradual change, and sub-pixel/pixel/spatial were analyzed. Moreover, some of the widely-used change detection algorithms were also discussed. Finally, we reviewed different change detection applications by dividing these applications into two categories, change target and change agent detection.

  10. a Landsat Time-Series Stacks Model for Detection of Cropland Change

    Science.gov (United States)

    Chen, J.; Chen, J.; Zhang, J.

    2017-09-01

    Global, timely, accurate and cost-effective cropland monitoring with a fine spatial resolution will dramatically improve our understanding of the effects of agriculture on greenhouse gases emissions, food safety, and human health. Time-series remote sensing imagery have been shown particularly potential to describe land cover dynamics. The traditional change detection techniques are often not capable of detecting land cover changes within time series that are severely influenced by seasonal difference, which are more likely to generate pseuso changes. Here,we introduced and tested LTSM ( Landsat time-series stacks model), an improved Continuous Change Detection and Classification (CCDC) proposed previously approach to extract spectral trajectories of land surface change using a dense Landsat time-series stacks (LTS). The method is expected to eliminate pseudo changes caused by phenology driven by seasonal patterns. The main idea of the method is that using all available Landsat 8 images within a year, LTSM consisting of two term harmonic function are estimated iteratively for each pixel in each spectral band .LTSM can defines change area by differencing the predicted and observed Landsat images. The LTSM approach was compared with change vector analysis (CVA) method. The results indicated that the LTSM method correctly detected the "true change" without overestimating the "false" one, while CVA pointed out "true change" pixels with a large number of "false changes". The detection of change areas achieved an overall accuracy of 92.37 %, with a kappa coefficient of 0.676.

  11. A Gaussian Process Based Online Change Detection Algorithm for Monitoring Periodic Time Series

    Energy Technology Data Exchange (ETDEWEB)

    Chandola, Varun [ORNL; Vatsavai, Raju [ORNL

    2011-01-01

    Online time series change detection is a critical component of many monitoring systems, such as space and air-borne remote sensing instruments, cardiac monitors, and network traffic profilers, which continuously analyze observations recorded by sensors. Data collected by such sensors typically has a periodic (seasonal) component. Most existing time series change detection methods are not directly applicable to handle such data, either because they are not designed to handle periodic time series or because they cannot operate in an online mode. We propose an online change detection algorithm which can handle periodic time series. The algorithm uses a Gaussian process based non-parametric time series prediction model and monitors the difference between the predictions and actual observations within a statistically principled control chart framework to identify changes. A key challenge in using Gaussian process in an online mode is the need to solve a large system of equations involving the associated covariance matrix which grows with every time step. The proposed algorithm exploits the special structure of the covariance matrix and can analyze a time series of length T in O(T^2) time while maintaining a O(T) memory footprint, compared to O(T^4) time and O(T^2) memory requirement of standard matrix manipulation methods. We experimentally demonstrate the superiority of the proposed algorithm over several existing time series change detection algorithms on a set of synthetic and real time series. Finally, we illustrate the effectiveness of the proposed algorithm for identifying land use land cover changes using Normalized Difference Vegetation Index (NDVI) data collected for an agricultural region in Iowa state, USA. Our algorithm is able to detect different types of changes in a NDVI validation data set (with ~80% accuracy) which occur due to crop type changes as well as disruptive changes (e.g., natural disasters).

  12. Trend analysis and change point detection of annual and seasonal temperature series in Peninsular Malaysia

    Science.gov (United States)

    Suhaila, Jamaludin; Yusop, Zulkifli

    2017-06-01

    Most of the trend analysis that has been conducted has not considered the existence of a change point in the time series analysis. If these occurred, then the trend analysis will not be able to detect an obvious increasing or decreasing trend over certain parts of the time series. Furthermore, the lack of discussion on the possible factors that influenced either the decreasing or the increasing trend in the series needs to be addressed in any trend analysis. Hence, this study proposes to investigate the trends, and change point detection of mean, maximum and minimum temperature series, both annually and seasonally in Peninsular Malaysia and determine the possible factors that could contribute to the significance trends. In this study, Pettitt and sequential Mann-Kendall (SQ-MK) tests were used to examine the occurrence of any abrupt climate changes in the independent series. The analyses of the abrupt changes in temperature series suggested that most of the change points in Peninsular Malaysia were detected during the years 1996, 1997 and 1998. These detection points captured by Pettitt and SQ-MK tests are possibly related to climatic factors, such as El Niño and La Niña events. The findings also showed that the majority of the significant change points that exist in the series are related to the significant trend of the stations. Significant increasing trends of annual and seasonal mean, maximum and minimum temperatures in Peninsular Malaysia were found with a range of 2-5 °C/100 years during the last 32 years. It was observed that the magnitudes of the increasing trend in minimum temperatures were larger than the maximum temperatures for most of the studied stations, particularly at the urban stations. These increases are suspected to be linked with the effect of urban heat island other than El Niño event.

  13. Detecting dynamical changes in time series by using the Jensen Shannon divergence

    Science.gov (United States)

    Mateos, D. M.; Riveaud, L. E.; Lamberti, P. W.

    2017-08-01

    Most of the time series in nature are a mixture of signals with deterministic and random dynamics. Thus the distinction between these two characteristics becomes important. Distinguishing between chaotic and aleatory signals is difficult because they have a common wide band power spectrum, a delta like autocorrelation function, and share other features as well. In general, signals are presented as continuous records and require to be discretized for being analyzed. In this work, we introduce different schemes for discretizing and for detecting dynamical changes in time series. One of the main motivations is to detect transitions between the chaotic and random regime. The tools here used here originate from the Information Theory. The schemes proposed are applied to simulated and real life signals, showing in all cases a high proficiency for detecting changes in the dynamics of the associated time series.

  14. Short-Term Change Detection in Wetlands Using Sentinel-1 Time Series

    DEFF Research Database (Denmark)

    Muro, Javier; Canty, Morton; Conradsen, Knut

    2016-01-01

    Automated monitoring systems that can capture wetlands’ high spatial and temporal variability are essential for their management. SAR-based change detection approaches offer a great opportunity to enhance our understanding of complex and dynamic ecosystems. We test a recently-developed time serie...... certain landscape changes are detected only by either the Landsat-based or the S1-omnibus method. The S1-omnibus method shows a great potential for an automated monitoring of short time changes and accurate delineation of areas of high variability and of slow and gradual changes....

  15. Trend Change Detection in NDVI Time Series: Effects of Inter-Annual Variability and Methodology

    Science.gov (United States)

    Forkel, Matthias; Carvalhais, Nuno; Verbesselt, Jan; Mahecha, Miguel D.; Neigh, Christopher S.R.; Reichstein, Markus

    2013-01-01

    Changing trends in ecosystem productivity can be quantified using satellite observations of Normalized Difference Vegetation Index (NDVI). However, the estimation of trends from NDVI time series differs substantially depending on analyzed satellite dataset, the corresponding spatiotemporal resolution, and the applied statistical method. Here we compare the performance of a wide range of trend estimation methods and demonstrate that performance decreases with increasing inter-annual variability in the NDVI time series. Trend slope estimates based on annual aggregated time series or based on a seasonal-trend model show better performances than methods that remove the seasonal cycle of the time series. A breakpoint detection analysis reveals that an overestimation of breakpoints in NDVI trends can result in wrong or even opposite trend estimates. Based on our results, we give practical recommendations for the application of trend methods on long-term NDVI time series. Particularly, we apply and compare different methods on NDVI time series in Alaska, where both greening and browning trends have been previously observed. Here, the multi-method uncertainty of NDVI trends is quantified through the application of the different trend estimation methods. Our results indicate that greening NDVI trends in Alaska are more spatially and temporally prevalent than browning trends. We also show that detected breakpoints in NDVI trends tend to coincide with large fires. Overall, our analyses demonstrate that seasonal trend methods need to be improved against inter-annual variability to quantify changing trends in ecosystem productivity with higher accuracy.

  16. Transforming the autocorrelation function of a time series to detect land cover change

    CSIR Research Space (South Africa)

    Salmon

    2015-07-01

    Full Text Available stream_source_info Salmon_2016.pdf.txt stream_content_type text/plain stream_size 1090 Content-Encoding ISO-8859-1 stream_name Salmon_2016.pdf.txt Content-Type text/plain; charset=ISO-8859-1 International Geoscience... and Remote Sensing Symposium (IEEE IGARSS), 10-15 July 2016, Beijing Transforming the autocorrelation function of a time series to detect land cover change Salmon, B.P., Kleynhans, W., Olivier, J.C. and Schwegmann, C.P. ABSTRACT Regional...

  17. Transfer Entropy Estimation and Directional Coupling Change Detection in Biomedical Time Series

    Directory of Open Access Journals (Sweden)

    Lee Joon

    2012-04-01

    Full Text Available Abstract Background The detection of change in magnitude of directional coupling between two non-linear time series is a common subject of interest in the biomedical domain, including studies involving the respiratory chemoreflex system. Although transfer entropy is a useful tool in this avenue, no study to date has investigated how different transfer entropy estimation methods perform in typical biomedical applications featuring small sample size and presence of outliers. Methods With respect to detection of increased coupling strength, we compared three transfer entropy estimation techniques using both simulated time series and respiratory recordings from lambs. The following estimation methods were analyzed: fixed-binning with ranking, kernel density estimation (KDE, and the Darbellay-Vajda (D-V adaptive partitioning algorithm extended to three dimensions. In the simulated experiment, sample size was varied from 50 to 200, while coupling strength was increased. In order to introduce outliers, the heavy-tailed Laplace distribution was utilized. In the lamb experiment, the objective was to detect increased respiratory-related chemosensitivity to O2 and CO2 induced by a drug, domperidone. Specifically, the separate influence of end-tidal PO2 and PCO2 on minute ventilation (V˙E before and after administration of domperidone was analyzed. Results In the simulation, KDE detected increased coupling strength at the lowest SNR among the three methods. In the lamb experiment, D-V partitioning resulted in the statistically strongest increase in transfer entropy post-domperidone for PO2→V˙E. In addition, D-V partitioning was the only method that could detect an increase in transfer entropy for PCO2→V˙E, in agreement with experimental findings. Conclusions Transfer entropy is capable of detecting directional coupling changes in non-linear biomedical time series analysis featuring a small number of observations and presence of outliers. The results

  18. Detection of land cover change using an Artificial Neural Network on a time-series of MODIS satellite data

    CSIR Research Space (South Africa)

    Olivier, JC

    2007-11-01

    Full Text Available An Artificial Neural Network (ANN) is proposed to detect human-induced land cover change using a sliding window through a time-series of Moderate Resolution Imaging Spectroradiometer (MODIS) satellite surface reflectance pixel values. Training...

  19. Using Page’s cumulative sum test on MODIS time series to detect land-cover changes

    CSIR Research Space (South Africa)

    Grobler, TL

    2012-01-01

    Full Text Available by natural vegetation using 500-m Moderate Resolution Imaging Spectroradiometer time-series satellite data. The method is a sequential per-pixel change alarm algorithm that can take into account positive detection delay, probability of detection, and false...

  20. Visualization of and Software for Omnibus Test Based Change Detected in a Time Series of Polarimetric SAR Data

    DEFF Research Database (Denmark)

    Nielsen, Allan Aasbjerg; Conradsen, Knut; Skriver, Henning

    2017-01-01

    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 occurs. Using airborne EMISAR and spaceborne RADARSAT-2 data this paper focuses on change detection based on the p-values, on visualization of change at pixel as well as segment level......, and on computer software....

  1. Quest for automated land cover change detection using satellite time series data

    CSIR Research Space (South Africa)

    Salmon, BP

    2009-07-01

    Full Text Available and surface climate in the next fifty years,” Global Change Biology, vol. 8, no. 5, pp. 438–458, May 2002. [3] J. A. Foley et al., “Global consequences of land use,” Science, vol. 309, no. 5734, pp. 570–574, July 2005. [4] R. S. Lunetta et al., “Land... (class 1). These four subsets were used to produce a confusion matrix to test if the operational MLP can detect change reliably in an automated fashion on subsets 1 and 2, while not falsely detecting change for subsets 3 and 4. This particular splic...

  2. Automated land cover change detection: the quest for meaningful high temporal time series extraction

    CSIR Research Space (South Africa)

    Salmon, BP

    2010-07-01

    Full Text Available and methodologies on sequential time series extracted from satellite data. 5. REFERENCES [1] R. S. DeFries, L. Bounoua, and G. J. Collatz, “Human modification of the landscape and surface climate in the next fifty years,” Global Change Biology, vol. 8, no. 5... was extracted for only the first two spectral bands from the 8-day composite MODIS MCD43A4 data set (tile H20V11) (year 2000–2008) as it was shown to have considerable class separation when the features are analyzed [6]. 2.3. Data sets: Validation...

  3. Automated Land Cover Change Detection and Mapping from Hidden Parameter Estimates of Normalized Difference Vegetation Index (NDVI) Time-Series

    Science.gov (United States)

    Chakraborty, S.; Banerjee, A.; Gupta, S. K. S.; Christensen, P. R.; Papandreou-Suppappola, A.

    2017-12-01

    Multitemporal observations acquired frequently by satellites with short revisit periods such as the Moderate Resolution Imaging Spectroradiometer (MODIS), is an important source for modeling land cover. Due to the inherent seasonality of the land cover, harmonic modeling reveals hidden state parameters characteristic to it, which is used in classifying different land cover types and in detecting changes due to natural or anthropogenic factors. In this work, we use an eight day MODIS composite to create a Normalized Difference Vegetation Index (NDVI) time-series of ten years. Improved hidden parameter estimates of the nonlinear harmonic NDVI model are obtained using the Particle Filter (PF), a sequential Monte Carlo estimator. The nonlinear estimation based on PF is shown to improve parameter estimation for different land cover types compared to existing techniques that use the Extended Kalman Filter (EKF), due to linearization of the harmonic model. As these parameters are representative of a given land cover, its applicability in near real-time detection of land cover change is also studied by formulating a metric that captures parameter deviation due to change. The detection methodology is evaluated by considering change as a rare class problem. This approach is shown to detect change with minimum delay. Additionally, the degree of change within the change perimeter is non-uniform. By clustering the deviation in parameters due to change, this spatial variation in change severity is effectively mapped and validated with high spatial resolution change maps of the given regions.

  4. Phenological change detection while accounting for abrupt and gradual trends in satellite image time series

    NARCIS (Netherlands)

    Verbesselt, J.; Hyndman, R.; Zeileis, A.; Culvenor, D.

    2010-01-01

    A challenge in phenology studies is understanding what constitutes phenological change amidst background variation. The majority of phenological studies have focused on extracting critical points in the seasonal growth cycle, without exploiting the full temporal detail. The high degree of

  5. An Unsupervised Change Detection Method Using Time-Series of PolSAR Images from Radarsat-2 and GaoFen-3.

    Science.gov (United States)

    Liu, Wensong; Yang, Jie; Zhao, Jinqi; Shi, Hongtao; Yang, Le

    2018-02-12

    The traditional unsupervised change detection methods based on the pixel level can only detect the changes between two different times with same sensor, and the results are easily affected by speckle noise. In this paper, a novel method is proposed to detect change based on time-series data from different sensors. Firstly, the overall difference image of the time-series PolSAR is calculated by omnibus test statistics, and difference images between any two images in different times are acquired by R j test statistics. Secondly, the difference images are segmented with a Generalized Statistical Region Merging (GSRM) algorithm which can suppress the effect of speckle noise. Generalized Gaussian Mixture Model (GGMM) is then used to obtain the time-series change detection maps in the final step of the proposed method. To verify the effectiveness of the proposed method, we carried out the experiment of change detection using time-series PolSAR images acquired by Radarsat-2 and Gaofen-3 over the city of Wuhan, in China. Results show that the proposed method can not only detect the time-series change from different sensors, but it can also better suppress the influence of speckle noise and improve the overall accuracy and Kappa coefficient.

  6. Change detection in a time series of polarimetric SAR data by an omnibus test statistic and its factorization (Conference Presentation)

    Science.gov (United States)

    Nielsen, Allan A.; Conradsen, Knut; Skriver, Henning

    2016-10-01

    ., Skriver, H., Nielsen, A. A., and Conradsen, K., "CFAR edge detector for polarimetric SAR images," IEEE Transactions on Geoscience and Remote Sensing 41(1): 20-32, 2003. [4] van Zyl, J. J. and Ulaby, F. T., "Scattering matrix representation for simple targets," in Radar Polarimetry for Geoscience Applications, Ulaby, F. T. and Elachi, C., eds., Artech, Norwood, MA (1990). [5] Canty, M. J., Image Analysis, Classification and Change Detection in Remote Sensing,with Algorithms for ENVI/IDL and Python, Taylor & Francis, CRC Press, third revised ed. (2014). [6] Nielsen, A. A., Conradsen, K., and Skriver, H., "Change detection in full and dual polarization, single- and multi-frequency SAR data," IEEE Journal of Selected Topics in Applied Earth Observations and Remote Sensing 8(8): 4041-4048, 2015. [7] Conradsen, K., Nielsen, A. A., and Skriver, H., "Determining the points of change in time series of polarimetric SAR data," IEEE Transactions on Geoscience and Remote Sensing 54(5), 3007-3024, 2016. [9] Christensen, E. L., Skou, N., Dall, J., Woelders, K., rgensen, J. H. J., Granholm, J., and Madsen, S. N., "EMISAR: An absolutely calibrated polarimetric L- and C-band SAR," IEEE Transactions on Geoscience and Remote Sensing 36: 1852-1865 (1998).

  7. Supervised / unsupervised change detection

    OpenAIRE

    de Alwis Pitts, Dilkushi; De Vecchi, Daniele; Harb, Mostapha; So, Emily; Dell'Acqua, Fabio

    2014-01-01

    The aim of this deliverable is to provide an overview of the state of the art in change detection techniques and a critique of what could be programmed to derive SENSUM products. It is the product of the collaboration between UCAM and EUCENTRE. The document includes as a necessary requirement a discussion about a proposed technique for co-registration. Since change detection techniques require an assessment of a series of images and the basic process involves comparing and contrasting the sim...

  8. Spatio-temporal approach to detecting land cover change using an extended kalman filter on modis time series data

    CSIR Research Space (South Africa)

    Kleynhans, W

    2010-01-01

    Full Text Available A method for detecting land cover change using NDVI timeseries data derived fromMODerate-resolution Imaging Spectroradiometer (MODIS) satellite data is proposed. The algorithm acts as a per pixel change alarm and takes as input the NDVI time...

  9. A New Approach to Performing Bundle Adjustment for Time Series UAV Images 3D Building Change Detection

    Directory of Open Access Journals (Sweden)

    Wenzhuo Li

    2017-06-01

    Full Text Available Successful change detection in multi-temporal images relies on high spatial co-registration accuracy. However, co-registration accuracy alone cannot meet the needs of change detection when using several ground control points to separately geo-reference multi-temporal images from unmanned aerial vehicles (UAVs. This letter reports on a new approach to perform bundle adjustment—named united bundle adjustment (UBA—to solve this co-registration problem for change detection in multi-temporal UAV images. In UBA, multi-temporal UAV images are matched with each other to construct a unified tie point net. One single bundle adjustment process is performed on the unified tie point net, placing every image into the same coordinate system and thus automatically accomplishing spatial co-registration. We then perform change detection using both orthophotos and three-dimensional height information derived from dense image matching techniques. Experimental results show that UBA co-registration accuracy is higher than the accuracy of commonly-used approaches for multi-temporal UAV images. Our proposed preprocessing method extends the capacities of consumer-level UAVs so they can eventually meet the growing need for automatic building change detection and dynamic monitoring using only RGB band images.

  10. Changing change detection

    DEFF Research Database (Denmark)

    Kyllingsbæk, Søren; Bundesen, Claus

    2009-01-01

    The change detection paradigm is a popular way of measuring visual short-term memory capacity. Using the paradigm, researchers have found evidence for a capacity of about four independent visual objects, confirming classic estimates that were based on the number of items that could be reported...

  11. An autocorrelation analysis approach to detecting land cover change using hyper-temporal time-series data

    CSIR Research Space (South Africa)

    Kleynhans, W

    2011-07-01

    Full Text Available Human settlement expansion is one of the most pervasive forms of land cover change in the Gauteng province of South Africa. A method for detecting new settlement developments in areas that are typically covered by natural vegetation using 500m MODIS...

  12. Land Cover Change Detection Using Autocorrelation Analysis on MODIS Time-Series Data: Detection of New Human Settlements in the Gauteng Province of South Africa

    CSIR Research Space (South Africa)

    Kleynhans, Waldo

    2012-01-01

    Full Text Available [5]. A land cover change was then simulated using no-change examples of typical natural vegetation and settlement time-se- ries data. Both the no-change and simulated change datasets are then used to determine a set of parameters in an off-line opti... be applied to any land cover change where the reflectance time-se- ries start and end class shows a significant change in the mean and/or amplitude. It should however be noted that when dealing with complex landscapes, the time-series in itself could have a...

  13. An omnibus likelihood test statistic and its factorization for change detection in time series of polarimetric SAR data

    DEFF Research Database (Denmark)

    Nielsen, Allan Aasbjerg; Conradsen, Knut; Skriver, Henning

    2016-01-01

    Based on an omnibus likelihood ratio test statistic for the equality of several variance-covariance matrices following the complex Wishart distribution with an associated p-value and a factorization of this test statistic, change analysis in a short sequence 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 L-band data but may be applied to Sentinel-1, Cosmo-SkyMed, TerraSAR-X, ALOS and RadarSat-2 or other dual- and quad...

  14. Change detection in a time series of polarimetric SAR data by an omnibus test statistic and its factorization

    DEFF Research Database (Denmark)

    Nielsen, Allan Aasbjerg; Conradsen, Knut; Skriver, Henning

    2016-01-01

    Based on an omnibus likelihood ratio test statistic for the equality of several variance-covariance matrices following the complex Wishart distribution with an associated p-value and a factorization of this test statistic, change analysis in a short sequence 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 L-band data but may be applied to Sentinel-1, Cosmo-SkyMed, TerraSAR-X, ALOS and RadarSat-2 or other dual- and quad...

  15. Outlier Detection in Structural Time Series Models

    DEFF Research Database (Denmark)

    Marczak, Martyna; Proietti, Tommaso

    investigate via Monte Carlo simulations how this approach performs for detecting additive outliers and level shifts in the analysis of nonstationary seasonal time series. The reference model is the basic structural model, featuring a local linear trend, possibly integrated of order two, stochastic seasonality......Structural change affects the estimation of economic signals, like the underlying growth rate or the seasonally adjusted series. An important issue, which has attracted a great deal of attention also in the seasonal adjustment literature, is its detection by an expert procedure. The general......–to–specific approach to the detection of structural change, currently implemented in Autometrics via indicator saturation, has proven to be both practical and effective in the context of stationary dynamic regression models and unit–root autoregressions. By focusing on impulse– and step–indicator saturation, we...

  16. Near Real Time Change-Point detection in Optical and Thermal Infrared Time Series Using Bayesian Inference over the Dry Chaco Forest

    Science.gov (United States)

    Barraza Bernadas, V.; Grings, F.; Roitberg, E.; Perna, P.; Karszenbaum, H.

    2017-12-01

    The Dry Chaco region (DCF) has the highest absolute deforestation rates of all Argentinian forests. The most recent report indicates a current deforestation rate of 200,000 Ha year-1. In order to better monitor this process, DCF was chosen to implement an early warning program for illegal deforestation. Although the area is intensively studied using medium resolution imagery (Landsat), the products obtained have a yearly pace and therefore unsuited for an early warning program. In this paper, we evaluated the performance of an online Bayesian change-point detection algorithm for MODIS Enhanced Vegetation Index (EVI) and Land Surface Temperature (LST) datasets. The goal was to to monitor the abrupt changes in vegetation dynamics associated with deforestation events. We tested this model by simulating 16-day EVI and 8-day LST time series with varying amounts of seasonality, noise, length of the time series and by adding abrupt changes with different magnitudes. This model was then tested on real satellite time series available through the Google Earth Engine, over a pilot area in DCF, where deforestation was common in the 2004-2016 period. A comparison with yearly benchmark products based on Landsat images is also presented (REDAF dataset). The results shows the advantages of using an automatic model to detect a changepoint in the time series than using only visual inspection techniques. Simulating time series with varying amounts of seasonality and noise, and by adding abrupt changes at different times and magnitudes, revealed that this model is robust against noise, and is not influenced by changes in amplitude of the seasonal component. Furthermore, the results compared favorably with REDAF dataset (near 65% of agreement). These results show the potential to combine LST and EVI to identify deforestation events. This work is being developed within the frame of the national Forest Law for the protection and sustainable development of Native Forest in Argentina in

  17. CHANGE DETECTION IN LAND-USE AND LAND-COVER DYNAMICS AT A REGIONAL SCALE FROM MODIS TIME-SERIES IMAGERY

    Directory of Open Access Journals (Sweden)

    Y. Setiawan

    2012-07-01

    Full Text Available Remote sensing has long been used as a means of detecting and classifying changes on the land. Analysis of multi-year time series of land surface attributes and their seasonal change indicates a complexity of land use land cover change (LULCC. This paper explores the temporal complexity of land change considering temporal vegetation dynamics, in other words, distinguishing the changes regarding to their properties in long-term image analysis. This study is based on the hypothesis that land cover might be dynamics; however, consistent land use has a typical, distinct and repeated temporal pattern of vegetation index inter-annually. Therefore, pixels represent a change when the inter-annual temporal dynamics is changed. We analysed the dynamics pattern of long-term image data of wavelet-filtered MODIS EVI from 2001 to 2007. The change of temporal vegetation dynamics was detected by differentiating distance between two successive annual EVI patterns. Moreover, we defined the type of changes using the clustering method, which were then validated by ground check points and secondary data sets.

  18. Detecting spatio-temporal changes in agricultural land use in Heilongjiang province, China using MODIS time-series data and a random forest regression model

    Science.gov (United States)

    Hu, Q.; Friedl, M. A.; Wu, W.

    2017-12-01

    Accurate and timely information regarding the spatial distribution of crop types and their changes is essential for acreage surveys, yield estimation, water management, and agricultural production decision-making. In recent years, increasing population, dietary shifts and climate change have driven drastic changes in China's agricultural land use. However, no maps are currently available that document the spatial and temporal patterns of these agricultural land use changes. Because of its short revisit period, rich spectral bands and global coverage, MODIS time series data has been shown to have great potential for detecting the seasonal dynamics of different crop types. However, its inherently coarse spatial resolution limits the accuracy with which crops can be identified from MODIS in regions with small fields or complex agricultural landscapes. To evaluate this more carefully and specifically understand the strengths and weaknesses of MODIS data for crop-type mapping, we used MODIS time-series imagery to map the sub-pixel fractional crop area for four major crop types (rice, corn, soybean and wheat) at 500-m spatial resolution for Heilongjiang province, one of the most important grain-production regions in China where recent agricultural land use change has been rapid and pronounced. To do this, a random forest regression (RF-g) model was constructed to estimate the percentage of each sub-pixel crop type in 2006, 2011 and 2016. Crop type maps generated through expert visual interpretation of high spatial resolution images (i.e., Landsat and SPOT data) were used to calibrate the regression model. Five different time series of vegetation indices (155 features) derived from different spectral channels of MODIS land surface reflectance (MOD09A1) data were used as candidate features for the RF-g model. An out-of-bag strategy and backward elimination approach was applied to select the optimal spectra-temporal feature subset for each crop type. The resulting crop maps

  19. Detection of Spatio-Temporal Changes of Norway Spruce Forest Stands in Ore Mountains Using Landsat Time Series and Airborne Hyperspectral Imagery

    Directory of Open Access Journals (Sweden)

    Jan Mišurec

    2016-01-01

    Full Text Available The study focuses on spatio-temporal changes in the physiological status of the Norway spruce forests located at the central and western parts of the Ore Mountains (northwestern part of the Czech Republic, which suffered from severe environmental pollution from the 1970s to the 1990s. The situation started improving after the pollution loads decreased significantly at the end of the 1990s. The general trends in forest recovery were studied using the tasseled cap transformation and disturbance index (DI extracted from the 1985–2015 time series of Landsat data. In addition, 16 vegetation indices (VIs extracted from airborne hyperspectral (HS data acquired in 1998 using the Advanced Solid-State Array Spectroradiometer (ASAS and in 2013 using the Airborne Prism Experiment (APEX were used to study changes in forest health. The forest health status analysis of HS image data was performed at two levels of spatial resolution; at a tree level (original 2.0 m spatial resolution, as well as at a forest stand level (generalized to 6.0 m spatial resolution. The temporal changes were studied primarily using the VOG1 vegetation index (VI as it was showing high and stable sensitivity to forest damage for both spatial resolutions considered. In 1998, significant differences between the moderately to heavily damaged (central Ore Mountains and initially damaged (western Ore Mountains stands were detected for all the VIs tested. In 2013, the stands in the central Ore Mountains exhibited VI values much closer to the global mean, indicating an improvement in their health status. This result fully confirms the finding of the Landsat time series analysis. The greatest difference in Disturbance Index (DI values between the central (1998: 0.37 and western Ore Mountains stands (1998: −1.21 could be seen at the end of the 1990s. Nonetheless, levelling of the physiological status of Norway spruce was observed for the central and western parts of the Ore Mountains in

  20. Detecting chaos in irregularly sampled time series.

    Science.gov (United States)

    Kulp, C W

    2013-09-01

    Recently, Wiebe and Virgin [Chaos 22, 013136 (2012)] developed an algorithm which detects chaos by analyzing a time series' power spectrum which is computed using the Discrete Fourier Transform (DFT). Their algorithm, like other time series characterization algorithms, requires that the time series be regularly sampled. Real-world data, however, are often irregularly sampled, thus, making the detection of chaotic behavior difficult or impossible with those methods. In this paper, a characterization algorithm is presented, which effectively detects chaos in irregularly sampled time series. The work presented here is a modification of Wiebe and Virgin's algorithm and uses the Lomb-Scargle Periodogram (LSP) to compute a series' power spectrum instead of the DFT. The DFT is not appropriate for irregularly sampled time series. However, the LSP is capable of computing the frequency content of irregularly sampled data. Furthermore, a new method of analyzing the power spectrum is developed, which can be useful for differentiating between chaotic and non-chaotic behavior. The new characterization algorithm is successfully applied to irregularly sampled data generated by a model as well as data consisting of observations of variable stars.

  1. Social Network Change Detection

    National Research Council Canada - National Science Library

    McCulloh, Ian A; Carley, Kathleen M

    2008-01-01

    ... between group members. The ability to systematically, statistically, effectively and efficiently detect these changes has the potential to enable the anticipation of change, provide early warning of change, and enable...

  2. Detecting gradual and abrupt changes in water quality time series in response to regional payment programs for watershed services in an agricultural area

    Science.gov (United States)

    He, Tian; Lu, Yan; Cui, Yanping; Luo, Yabo; Wang, Min; Meng, Wei; Zhang, Kaijie; Zhao, Feifei

    2015-06-01

    Market-based watershed protection instruments can effectively improve water quality at various catchment scales. Two payments for watershed services (PWS) programs for water quality improvement have been successively implemented in the Huai River catchment and its sub-watershed, the Shaying River catchment, in Henan Province since 2009. To detect changes in water quality in response to PWS schemes, nonparametric statistical approaches were used to analyze gradual and abrupt trends in water quality, focusing on chemical oxygen demand (COD) and ammonia-nitrogen (NH3-N) at 26 monitoring stations in the Huai River watershed during 2006-2013. The nonparametric Mann-Kendall test and the Theil-Sen estimator were used to identify trends and their magnitudes in weekly water quality observations and the Pettitt test was applied to change-point analysis of water quality time series. We found decreasing concentration trends in the weekly water quality data set in this catchment, with water quality at most stations affected by the PWS schemes. The COD and NH3-N concentrations decreased at 26 stations by an average of 0.05 mg/L wk and 0.01 mg/L wk, respectively, from 2006 to 2013. Meanwhile, the mean concentrations of COD and NH3-N decreased at the 26 stations by an average of 18.03 mg/L and 4.82 mg/L, respectively, after the abrupt change points of the time-series trends of these two pollutants. We also estimated annual reductions in COD and NH3-N for each station based on average flow observations using the Theil-Sen approach along with the resulting economic benefits from 2009 to 2010. The COD and NH3-N reductions were 14604.50 and 6213.25 t/y, respectively, in the Huai River catchment in Henan Province. The total economic benefits of reductions in these two pollutants were 769.71 million ¥ in 2009 and 2010, accounting for 0.08% and 0.06%, respectively, of the GDP in the entire Huai River watershed of Henan Province. These results provide new insights into the linkages

  3. Forecasting autoregressive time series under changing persistence

    DEFF Research Database (Denmark)

    Kruse, Robinson

    Changing persistence in time series models means that a structural change from nonstationarity to stationarity or vice versa occurs over time. Such a change has important implications for forecasting, as negligence may lead to inaccurate model predictions. This paper derives generally applicable...

  4. Detecting the hydrological impacts of forest cover change in tropical mountain areas: need for detrending time series of rainfall and streamflow data.

    Science.gov (United States)

    Molina, A.; Vanacker, V.; Brisson, E.; Balthazar, V.

    2012-04-01

    Interactions between human activities and the physical environment have increasingly transformed the hydrological functioning of Andean ecosystems. In these human-modified landscapes, land use/-cover change may have a profound effect on riverine water and sediment fluxes. The hydrological impacts of land use/-cover change are diverse, as changes in vegetation affect the various components of the hydrological cycle including evapotranspiration, infiltration and surface runoff. Quantitative data for tropical mountain regions are scarce, as few long time series on rainfall, water discharge and land use are available. Furthermore, time series of rainfall and streamflow data in tropical mountains are often highly influenced by large inter- and intra-annual variability. In this paper, we analyse the hydrological response to complex forest cover change for a catchment of 280 km2 located in the Ecuadorian Andes. Forest cover change in the Pangor catchment was reconstructed based on airphotos (1963, 1977), LANDSAT TM (1991) and ETM+ data (2001, 2009). From 1963, natural vegetation was converted to agricultural land and pine plantations: forests decreased by a factor 2, and paramo decreased by 20 km2 between 1963 and 2009. For this catchment, there exists an exceptionally long record of rainfall and streamflow data that dates back from the '70s till now, but large variability in hydrometeorological data exists that is partly related to ENSO events. Given the nonstationary and nonlinear character of the ENSO-related changes in rainfall, we used the Hilbert-Huang transformation to detrend the time series of the river flow data from inter- and intra-annual fluctuations in rainfall. After applying adaptive data analysis based on empirical model decomposition techniques, it becomes apparent that the long-term trend in streamflow is different from the long-term trend in rainfall data. While the streamflow data show a long-term decrease in monthly flow, the rainfall data have a

  5. Recurrence and symmetry of time series: Application to transition detection

    International Nuclear Information System (INIS)

    Girault, Jean-Marc

    2015-01-01

    Highlights: •A new theoretical framework based on the symmetry concept is proposed. •Four types of symmetry present in any time series were analyzed. •New descriptors make possible the analysis of regime changes in logistic systems. •Chaos–chaos, chaos–periodic, symmetry-breaking, symmetry-increasing bifurcations can be detected. -- Abstract: The study of transitions in low dimensional, nonlinear dynamical systems is a complex problem for which there is not yet a simple, global numerical method able to detect chaos–chaos, chaos–periodic bifurcations and symmetry-breaking, symmetry-increasing bifurcations. We present here for the first time a general framework focusing on the symmetry concept of time series that at the same time reveals new kinds of recurrence. We propose several numerical tools based on the symmetry concept allowing both the qualification and quantification of different kinds of possible symmetry. By using several examples based on periodic symmetrical time series and on logistic and cubic maps, we show that it is possible with simple numerical tools to detect a large number of bifurcations of chaos–chaos, chaos–periodic, broken symmetry and increased symmetry types

  6. Detecting nonlinear structure in time series

    International Nuclear Information System (INIS)

    Theiler, J.

    1991-01-01

    We describe an approach for evaluating the statistical significance of evidence for nonlinearity in a time series. The formal application of our method requires the careful statement of a null hypothesis which characterizes a candidate linear process, the generation of an ensemble of ''surrogate'' data sets which are similar to the original time series but consistent with the null hypothesis, and the computation of a discriminating statistic for the original and for each of the surrogate data sets. The idea is to test the original time series against the null hypothesis by checking whether the discriminating statistic computed for the original time series differs significantly from the statistics computed for each of the surrogate sets. While some data sets very cleanly exhibit low-dimensional chaos, there are many cases where the evidence is sketchy and difficult to evaluate. We hope to provide a framework within which such claims of nonlinearity can be evaluated. 5 refs., 4 figs

  7. Detecting mortality induced structural and functional changes in a pinon-juniper woodland using Landsat and RapidEye time series

    Science.gov (United States)

    Dan J. Krofcheck; Jan U. H. Eitel; Lee A. Vierling; Urs Schulthess; Timothy M. Hilton; Eva Dettweiler-Robinson; Rosemary Pendleton; Marcy E. Litvak

    2014-01-01

    Pinon-juniper (PJ) woodlands have recently undergone dramatic drought-induced mortality, triggering broad scale structural changes in this extensive Southwestern US biome. Given that climate projections for the region suggest widespread conifer mortality is likely to continue into the next century, it is critical to better understand how this climate-induced change in...

  8. Climate change and precipitation: Detecting changes Climate change and precipitation: Detecting changes

    International Nuclear Information System (INIS)

    Van Boxel, John H

    2001-01-01

    Precipitation is one of the most, if not the most important climate parameter In most studies on climate change the emphasis is on temperature and sea level rise. Often too little attention is given to precipitation. For a large part this is due to the large spatial en temporal variability of precipitation, which makes the detection of changes difficult. This paper describes methods to detect changes in precipitation. In order to arrive at statistically significant changes one must use long time series and spatial averages containing the information from several stations. In the Netherlands the average yearly precipitation increased by 11% during the 20th century .In the temperate latitudes on the Northern Hemisphere (40-60QN) the average increase was about 7% over the 20th century and the globally averaged precipitation increased by about 3%. During the 20th century 38% of the land surface of the earth became wetter, 42% experienced little change (less than 5% change) and 20% became dryer. More important than the average precipitation is the occurrence of extremes. In the Netherlands there is a tendency to more extreme precipitations, whereas the occurrence of relatively dry months has not changed. Also in many other countries increases in heavy precipitation events are observed. All climate models predict a further increase of mean global precipitation if the carbon dioxide concentration doubles. Nevertheless some areas get dryer, others have little change and consequently there are also areas where the increase is much more than the global average. On a regional scale however there are large differences between the models. Climate models do not yet provide adequate information on changes in extreme precipitations

  9. Change Detection in Social Networks

    National Research Council Canada - National Science Library

    McCulloh, Ian; Webb, Matthew; Graham, John; Carley, Kathleen; Horn, Daniel B

    2008-01-01

    .... This project proposes a new method for detecting change in social networks over time, by applying a cumulative sum statistical process control statistic to normally distributed network measures...

  10. Evolutionary Algorithms for the Detection of Structural Breaks in Time Series

    DEFF Research Database (Denmark)

    Doerr, Benjamin; Fischer, Paul; Hilbert, Astrid

    2013-01-01

    Detecting structural breaks is an essential task for the statistical analysis of time series, for example, for fitting parametric models to it. In short, structural breaks are points in time at which the behavior of the time series changes. Typically, no solid background knowledge of the time...

  11. Detecting change-points in extremes

    KAUST Repository

    Dupuis, D. J.

    2015-01-01

    Even though most work on change-point estimation focuses on changes in the mean, changes in the variance or in the tail distribution can lead to more extreme events. In this paper, we develop a new method of detecting and estimating the change-points in the tail of multiple time series data. In addition, we adapt existing tail change-point detection methods to our specific problem and conduct a thorough comparison of different methods in terms of performance on the estimation of change-points and computational time. We also examine three locations on the U.S. northeast coast and demonstrate that the methods are useful for identifying changes in seasonally extreme warm temperatures.

  12. Adaptive filtering and change detection

    CERN Document Server

    Gustafsson, Fredrik

    2003-01-01

    Adaptive filtering is a classical branch of digital signal processing (DSP). Industrial interest in adaptive filtering grows continuously with the increase in computer performance that allows ever more conplex algorithms to be run in real-time. Change detection is a type of adaptive filtering for non-stationary signals and is also the basic tool in fault detection and diagnosis. Often considered as separate subjects Adaptive Filtering and Change Detection bridges a gap in the literature with a unified treatment of these areas, emphasizing that change detection is a natural extensi

  13. Temporal changes and variability in temperature series over Peninsular Malaysia

    Science.gov (United States)

    Suhaila, Jamaludin

    2015-02-01

    With the current concern over climate change, the descriptions on how temperature series changed over time are very useful. Annual mean temperature has been analyzed for several stations over Peninsular Malaysia. Non-parametric statistical techniques such as Mann-Kendall test and Theil-Sen slope estimation are used primarily for assessing the significance and detection of trends, while a nonparametric Pettitt's test and sequential Mann-Kendall test are adopted to detect any abrupt climate change. Statistically significance increasing trends for annual mean temperature are detected for almost all studied stations with the magnitude of significant trend varied from 0.02°C to 0.05°C per year. The results shows that climate over Peninsular Malaysia is getting warmer than before. In addition, the results of the abrupt changes in temperature using Pettitt's and sequential Mann-Kendall test reveal the beginning of trends which can be related to El Nino episodes that occur in Malaysia. In general, the analysis results can help local stakeholders and water managers to understand the risks and vulnerabilities related to climate change in terms of mean events in the region.

  14. Drunk driving detection based on classification of multivariate time series.

    Science.gov (United States)

    Li, Zhenlong; Jin, Xue; Zhao, Xiaohua

    2015-09-01

    This paper addresses the problem of detecting drunk driving based on classification of multivariate time series. First, driving performance measures were collected from a test in a driving simulator located in the Traffic Research Center, Beijing University of Technology. Lateral position and steering angle were used to detect drunk driving. Second, multivariate time series analysis was performed to extract the features. A piecewise linear representation was used to represent multivariate time series. A bottom-up algorithm was then employed to separate multivariate time series. The slope and time interval of each segment were extracted as the features for classification. Third, a support vector machine classifier was used to classify driver's state into two classes (normal or drunk) according to the extracted features. The proposed approach achieved an accuracy of 80.0%. Drunk driving detection based on the analysis of multivariate time series is feasible and effective. The approach has implications for drunk driving detection. Copyright © 2015 Elsevier Ltd and National Safety Council. All rights reserved.

  15. DETECT: a MATLAB toolbox for event detection and identification in time series, with applications to artifact detection in EEG signals.

    Science.gov (United States)

    Lawhern, Vernon; Hairston, W David; Robbins, Kay

    2013-01-01

    Recent advances in sensor and recording technology have allowed scientists to acquire very large time-series datasets. Researchers often analyze these datasets in the context of events, which are intervals of time where the properties of the signal change relative to a baseline signal. We have developed DETECT, a MATLAB toolbox for detecting event time intervals in long, multi-channel time series. Our primary goal is to produce a toolbox that is simple for researchers to use, allowing them to quickly train a model on multiple classes of events, assess the accuracy of the model, and determine how closely the results agree with their own manual identification of events without requiring extensive programming knowledge or machine learning experience. As an illustration, we discuss application of the DETECT toolbox for detecting signal artifacts found in continuous multi-channel EEG recordings and show the functionality of the tools found in the toolbox. We also discuss the application of DETECT for identifying irregular heartbeat waveforms found in electrocardiogram (ECG) data as an additional illustration.

  16. DETECT: a MATLAB toolbox for event detection and identification in time series, with applications to artifact detection in EEG signals.

    Directory of Open Access Journals (Sweden)

    Vernon Lawhern

    Full Text Available Recent advances in sensor and recording technology have allowed scientists to acquire very large time-series datasets. Researchers often analyze these datasets in the context of events, which are intervals of time where the properties of the signal change relative to a baseline signal. We have developed DETECT, a MATLAB toolbox for detecting event time intervals in long, multi-channel time series. Our primary goal is to produce a toolbox that is simple for researchers to use, allowing them to quickly train a model on multiple classes of events, assess the accuracy of the model, and determine how closely the results agree with their own manual identification of events without requiring extensive programming knowledge or machine learning experience. As an illustration, we discuss application of the DETECT toolbox for detecting signal artifacts found in continuous multi-channel EEG recordings and show the functionality of the tools found in the toolbox. We also discuss the application of DETECT for identifying irregular heartbeat waveforms found in electrocardiogram (ECG data as an additional illustration.

  17. Change classification in SAR time series: a functional approach

    Science.gov (United States)

    Boldt, Markus; Thiele, Antje; Schulz, Karsten; Hinz, Stefan

    2017-10-01

    Change detection represents a broad field of research in SAR remote sensing, consisting of many different approaches. Besides the simple recognition of change areas, the analysis of type, category or class of the change areas is at least as important for creating a comprehensive result. Conventional strategies for change classification are based on supervised or unsupervised landuse / landcover classifications. The main drawback of such approaches is that the quality of the classification result directly depends on the selection of training and reference data. Additionally, supervised processing methods require an experienced operator who capably selects the training samples. This training step is not necessary when using unsupervised strategies, but nevertheless meaningful reference data must be available for identifying the resulting classes. Consequently, an experienced operator is indispensable. In this study, an innovative concept for the classification of changes in SAR time series data is proposed. Regarding the drawbacks of traditional strategies given above, it copes without using any training data. Moreover, the method can be applied by an operator, who does not have detailed knowledge about the available scenery yet. This knowledge is provided by the algorithm. The final step of the procedure, which main aspect is given by the iterative optimization of an initial class scheme with respect to the categorized change objects, is represented by the classification of these objects to the finally resulting classes. This assignment step is subject of this paper.

  18. A Suspicious Action Detection System Considering Time Series

    Science.gov (United States)

    Kozuka, Noriaki; Kimura, Koji; Hagiwara, Masafumi

    The paper proposes a new system that can detect suspicious actions such as a car break-in and surroundings in an open space parking, based on image processing. The proposed system focuses on three points of “order”, “time”, and “location” of human actions. The proposed system has the following features: it 1) deals time series data flow, 2) estimates human actions and the location, 3) extracts suspicious action detection rules automatically, 4) detects suspicious actions using the suspicious score. We carried out experiments using real image sequences. As a result, we obtained about 7.8% higher estimation rate than the conventional system.

  19. Time Series Outlier Detection Based on Sliding Window Prediction

    Directory of Open Access Journals (Sweden)

    Yufeng Yu

    2014-01-01

    Full Text Available In order to detect outliers in hydrological time series data for improving data quality and decision-making quality related to design, operation, and management of water resources, this research develops a time series outlier detection method for hydrologic data that can be used to identify data that deviate from historical patterns. The method first built a forecasting model on the history data and then used it to predict future values. Anomalies are assumed to take place if the observed values fall outside a given prediction confidence interval (PCI, which can be calculated by the predicted value and confidence coefficient. The use of PCI as threshold is mainly on the fact that it considers the uncertainty in the data series parameters in the forecasting model to address the suitable threshold selection problem. The method performs fast, incremental evaluation of data as it becomes available, scales to large quantities of data, and requires no preclassification of anomalies. Experiments with different hydrologic real-world time series showed that the proposed methods are fast and correctly identify abnormal data and can be used for hydrologic time series analysis.

  20. Detecting land cover change using a sliding window temporal autocorrelation approach

    CSIR Research Space (South Africa)

    Kleynhans, W

    2012-07-01

    Full Text Available There has been recent developments in the use of hypertemporal satellite time series data for land cover change detection and classification. Recently, an Autocorrelation function (ACF) change detection method was proposed to detect the development...

  1. Mackenzie River Delta morphological change based on Landsat time series

    Science.gov (United States)

    Vesakoski, Jenni-Mari; Alho, Petteri; Gustafsson, David; Arheimer, Berit; Isberg, Kristina

    2015-04-01

    Arctic rivers are sensitive and yet quite unexplored river systems to which the climate change will impact on. Research has not focused in detail on the fluvial geomorphology of the Arctic rivers mainly due to the remoteness and wideness of the watersheds, problems with data availability and difficult accessibility. Nowadays wide collaborative spatial databases in hydrology as well as extensive remote sensing datasets over the Arctic are available and they enable improved investigation of the Arctic watersheds. Thereby, it is also important to develop and improve methods that enable detecting the fluvio-morphological processes based on the available data. Furthermore, it is essential to reconstruct and improve the understanding of the past fluvial processes in order to better understand prevailing and future fluvial processes. In this study we sum up the fluvial geomorphological change in the Mackenzie River Delta during the last ~30 years. The Mackenzie River Delta (~13 000 km2) is situated in the North Western Territories, Canada where the Mackenzie River enters to the Beaufort Sea, Arctic Ocean near the city of Inuvik. Mackenzie River Delta is lake-rich, productive ecosystem and ecologically sensitive environment. Research objective is achieved through two sub-objectives: 1) Interpretation of the deltaic river channel planform change by applying Landsat time series. 2) Definition of the variables that have impacted the most on detected changes by applying statistics and long hydrological time series derived from Arctic-HYPE model (HYdrologic Predictions for Environment) developed by Swedish Meteorological and Hydrological Institute. According to our satellite interpretation, field observations and statistical analyses, notable spatio-temporal changes have occurred in the morphology of the river channel and delta during the past 30 years. For example, the channels have been developing in braiding and sinuosity. In addition, various linkages between the studied

  2. Detecting structural breaks in time series via genetic algorithms

    DEFF Research Database (Denmark)

    Doerr, Benjamin; Fischer, Paul; Hilbert, Astrid

    2016-01-01

    of the time series under consideration is available. Therefore, a black-box optimization approach is our method of choice for detecting structural breaks. We describe a genetic algorithm framework which easily adapts to a large number of statistical settings. To evaluate the usefulness of different crossover...... and mutation operations for this problem, we conduct extensive experiments to determine good choices for the parameters and operators of the genetic algorithm. One surprising observation is that use of uniform and one-point crossover together gave significantly better results than using either crossover...... operator alone. Moreover, we present a specific fitness function which exploits the sparse structure of the break points and which can be evaluated particularly efficiently. The experiments on artificial and real-world time series show that the resulting algorithm detects break points with high precision...

  3. Updating Landsat time series of surface-reflectance composites and forest change products with new observations

    Science.gov (United States)

    Hermosilla, Txomin; Wulder, Michael A.; White, Joanne C.; Coops, Nicholas C.; Hobart, Geordie W.

    2017-12-01

    The use of time series satellite data allows for the temporally dense, systematic, transparent, and synoptic capture of land dynamics over time. Subsequent to the opening of the Landsat archive, several time series approaches for characterizing landscape change have been developed, often representing a particular analytical time window. The information richness and widespread utility of these time series data have created a need to maintain the currency of time series information via the addition of new data, as it becomes available. When an existing time series is temporally extended, it is critical that previously generated change information remains consistent, thereby not altering reported change statistics or science outcomes based on that change information. In this research, we investigate the impacts and implications of adding additional years to an existing 29-year annual Landsat time series for forest change. To do so, we undertook a spatially explicit comparison of the 29 overlapping years of a time series representing 1984-2012, with a time series representing 1984-2016. Surface reflectance values, and presence, year, and type of change were compared. We found that the addition of years to extend the time series had minimal effect on the annual surface reflectance composites, with slight band-specific differences (r ≥ 0.1) in the final years of the original time series being updated. The area of stand replacing disturbances and determination of change year are virtually unchanged for the overlapping period between the two time-series products. Over the overlapping temporal period (1984-2012), the total area of change differs by 0.53%, equating to an annual difference in change area of 0.019%. Overall, the spatial and temporal agreement of the changes detected by both time series was 96%. Further, our findings suggest that the entire pre-existing historic time series does not need to be re-processed during the update process. Critically, given the time

  4. Forecasting Long Memory Series Subject to Structural Change

    DEFF Research Database (Denmark)

    Dias, Gustavo Fruet; Papailias, Fotis

    A two-stage forecasting approach for long memory time series is introduced. In the first step we estimate the fractional exponent and, applying the fractional differencing operator, we obtain the underlying weakly dependent series. In the second step, we perform the multi-step ahead forecasts...... for the weakly dependent series and obtain their long memory counterparts by applying the fractional cumulation operator. The methodology applies to stationary and nonstationary cases. Simulations and an application to seven time series provide evidence that the new methodology is more robust to structural...... change and yields good forecasting results....

  5. Perspective Effects during Reading: Evidence from Text Change-Detection

    Science.gov (United States)

    Bohan, Jason; Filik, Ruth

    2018-01-01

    We report two text change-detection studies in which we investigate the influence of reading perspective on text memory. In Experiment 1 participants read from the perspective of one of two characters in a series of short stories, and word changes were either semantically close or distant. Participants correctly reported more changes to…

  6. Land-cover change detection

    Science.gov (United States)

    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.

  7. Detection of cavity migration risks using radar interferometric time series

    Science.gov (United States)

    Chang, L.; Hanssen, R. F.

    2012-12-01

    , ERS-2, Envisat, and Radarsat-2, to investigate the dynamics (deformation) of the area. In particular we show, for the first time, shear-stress change distribution patterns within the structure of a building, over a period of close to 20 years. Time series analysis shows that deformation rates of ~4 mm/a could be detected for about 18 years, followed by a dramatic increase of up to 20 mm/a in the last period. These results imply that the driving mechanisms of the 2011 catastrophe have a very long lead time and are therefore likely due to a long-lasting gradual motion, such as the upward migration of a cavity. The analysis shows the collocation of the deformation location with relatively shallow near-horizontal mine shafts, suggesting that cavity migration has a high likelihood to be the driving mechanism of the collapse-sinkhole.

  8. On Radar Resolution in Coherent Change Detection.

    Energy Technology Data Exchange (ETDEWEB)

    Bickel, Douglas L. [Sandia National Lab. (SNL-NM), Albuquerque, NM (United States)

    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.

  9. The detection of local irreversibility in time series based on segmentation

    Science.gov (United States)

    Teng, Yue; Shang, Pengjian

    2018-06-01

    We propose a strategy for the detection of local irreversibility in stationary time series based on multiple scale. The detection is beneficial to evaluate the displacement of irreversibility toward local skewness. By means of this method, we can availably discuss the local irreversible fluctuations of time series as the scale changes. The method was applied to simulated nonlinear signals generated by the ARFIMA process and logistic map to show how the irreversibility functions react to the increasing of the multiple scale. The method was applied also to series of financial markets i.e., American, Chinese and European markets. The local irreversibility for different markets demonstrate distinct characteristics. Simulations and real data support the need of exploring local irreversibility.

  10. Detecting change in stochastic sound sequences.

    Directory of Open Access Journals (Sweden)

    Benjamin Skerritt-Davis

    2018-05-01

    Full Text Available Our ability to parse our acoustic environment relies on the brain's capacity to extract statistical regularities from surrounding sounds. Previous work in regularity extraction has predominantly focused on the brain's sensitivity to predictable patterns in sound sequences. However, natural sound environments are rarely completely predictable, often containing some level of randomness, yet the brain is able to effectively interpret its surroundings by extracting useful information from stochastic sounds. It has been previously shown that the brain is sensitive to the marginal lower-order statistics of sound sequences (i.e., mean and variance. In this work, we investigate the brain's sensitivity to higher-order statistics describing temporal dependencies between sound events through a series of change detection experiments, where listeners are asked to detect changes in randomness in the pitch of tone sequences. Behavioral data indicate listeners collect statistical estimates to process incoming sounds, and a perceptual model based on Bayesian inference shows a capacity in the brain to track higher-order statistics. Further analysis of individual subjects' behavior indicates an important role of perceptual constraints in listeners' ability to track these sensory statistics with high fidelity. In addition, the inference model facilitates analysis of neural electroencephalography (EEG responses, anchoring the analysis relative to the statistics of each stochastic stimulus. This reveals both a deviance response and a change-related disruption in phase of the stimulus-locked response that follow the higher-order statistics. These results shed light on the brain's ability to process stochastic sound sequences.

  11. Multisensor Fusion for Change Detection

    Science.gov (United States)

    Schenk, T.; Csatho, B.

    2005-12-01

    with detecting surface elevation changes on the Byrd Glacier, Antarctica, with aerial imagery from 1980s and ICESat laser altimetry data from 2003-05. Change detection from such disparate data sets is an intricate fusion problem, beginning with sensor alignment, and on to reasoning with spatial information as to where changes occurred and to what extent.

  12. Detecting and characterising ramp events in wind power time series

    International Nuclear Information System (INIS)

    Gallego, Cristóbal; Cuerva, Álvaro; Costa, Alexandre

    2014-01-01

    In order to implement accurate models for wind power ramp forecasting, ramps need to be previously characterised. This issue has been typically addressed by performing binary ramp/non-ramp classifications based on ad-hoc assessed thresholds. However, recent works question this approach. This paper presents the ramp function, an innovative wavelet- based tool which detects and characterises ramp events in wind power time series. The underlying idea is to assess a continuous index related to the ramp intensity at each time step, which is obtained by considering large power output gradients evaluated under different time scales (up to typical ramp durations). The ramp function overcomes some of the drawbacks shown by the aforementioned binary classification and permits forecasters to easily reveal specific features of the ramp behaviour observed at a wind farm. As an example, the daily profile of the ramp-up and ramp-down intensities are obtained for the case of a wind farm located in Spain

  13. Time-series models on somatic cell score improve detection of matistis

    DEFF Research Database (Denmark)

    Norberg, E; Korsgaard, I R; Sloth, K H M N

    2008-01-01

    In-line detection of mastitis using frequent milk sampling was studied in 241 cows in a Danish research herd. Somatic cell scores obtained at a daily basis were analyzed using a mixture of four time-series models. Probabilities were assigned to each model for the observations to belong to a normal...... "steady-state" development, change in "level", change of "slope" or "outlier". Mastitis was indicated from the sum of probabilities for the "level" and "slope" models. Time-series models were based on the Kalman filter. Reference data was obtained from veterinary assessment of health status combined...... with bacteriological findings. At a sensitivity of 90% the corresponding specificity was 68%, which increased to 83% using a one-step back smoothing. It is concluded that mixture models based on Kalman filters are efficient in handling in-line sensor data for detection of mastitis and may be useful for similar...

  14. Explicit behavioral detection of visual changes develops without their implicit neurophysiological detectability

    Directory of Open Access Journals (Sweden)

    Pessi eLyyra

    2012-03-01

    Full Text Available Change blindness is a failure of explicitly detecting changes between consecutively presented images when separated, e.g., by a brief blank screen. There is a growing body of evidence of implicit detection of even explicitly undetectable changes, pointing to the possibility of the implicit change detection as a prerequisite for its explicit counterpart. We recorded event-related potentials (ERPs of the electroencephalography in adults during an oddball-variant of change blindness flicker paradigm. In this variant, rare pictures with a change were interspersed with frequent pictures with no change. In separate stimulus blocks, the blank screen between the change and no-change picture was either of 100 ms or 500 ms in duration. In both stimulus conditions the participants eventually explicitly detect the changed pictures, the blank screen of the longer duration only requiring in average 10 % longer exposure to the picture series until the ability emerged. However, during the change blindness, ERPs were displaced towards negative polarity at 200–260 ms after the stimulus onset (visual mismatch negativity only with the blank screens of the shorter ISI. Our finding of ‘implicit change blindness’ for pictorial material that, nevertheless, successfully prepares the visual system for explicit change detection suggests that implicit change detection may not be a necessary condition for explicit change detection and that they may recruit at least partially distinct memory mechanisms.

  15. Detecting of forest afforestation and deforestation in Hainan Jianfengling Forest Park (China) using yearly Landsat time-series images

    Science.gov (United States)

    Jiao, Quanjun; Zhang, Xiao; Sun, Qi

    2018-03-01

    The availability of dense time series of Landsat images pro-vides a great chance to reconstruct forest disturbance and change history with high temporal resolution, medium spatial resolution and long period. This proposal aims to apply forest change detection method in Hainan Jianfengling Forest Park using yearly Landsat time-series images. A simple detection method from the dense time series Landsat NDVI images will be used to reconstruct forest change history (afforestation and deforestation). The mapping result showed a large decrease occurred in the extent of closed forest from 1980s to 1990s. From the beginning of the 21st century, we found an increase in forest areas with the implementation of forestry measures such as the prohibition of cutting and sealing in our study area. Our findings provide an effective approach for quickly detecting forest changes in tropical original forest, especially for afforestation and deforestation, and a comprehensive analysis tool for forest resource protection.

  16. Adaptively detecting changes in Autonomic Grid Computing

    KAUST Repository

    Zhang, Xiangliang; Germain, Cé cile; Sebag, Michè le

    2010-01-01

    Detecting the changes is the common issue in many application fields due to the non-stationary distribution of the applicative data, e.g., sensor network signals, web logs and gridrunning logs. Toward Autonomic Grid Computing, adaptively detecting

  17. Extreme Drought-induced Trend Changes in MODIS EVI Time Series in Yunnan, China

    International Nuclear Information System (INIS)

    Huang, Kaicheng; Zhou, Tao; Zhao, Xiang

    2014-01-01

    Extreme climatic events triggered by global climate change are expected to increase significantly hence research into vegetation response is crucial to evaluate environmental risk. Yunnan province, locating in southwest China, experienced an extreme drought event (from autumn of 2009 to spring of 2010), with the lowest percentage rainfall anomaly and the longest non-rain days in the past 50 years. This study aimed to explore the characteristics and differences in the response to drought of four land cover types in Yunnan province, including forest, grassland, shrub, and cropland during the period 2001-2011. We used remote sensing data, MODIS-derived EVI (Enhanced Vegetation Index) to study the vegetation responses to this extreme drought event. The EVI time series were decomposed into trend, seasonal and remainder components using BFAST (Breaks For Additive Seasonal and Trend) which accounts for seasonality and enables the detection of trend changes within the time series. The preliminary results showed that: (1) BFAST proved to be capable of detecting drought-induced trend changes in EVI time series. (2) Changes in the trend component over time consisted of both gradual and abrupt changes. (3) Different spatial patterns were found for abrupt and gradual changes. (4) Cropland exhibited an abrupt change, due to its sensitivity to severe drought, while the forest seemed least affected by the extreme drought

  18. Long Short-Term Memory Neural Networks for Online Disturbance Detection in Satellite Image Time Series

    Directory of Open Access Journals (Sweden)

    Yun-Long Kong

    2018-03-01

    Full Text Available A satellite image time series (SITS contains a significant amount of temporal information. By analysing this type of data, the pattern of the changes in the object of concern can be explored. The natural change in the Earth’s surface is relatively slow and exhibits a pronounced pattern. Some natural events (for example, fires, floods, plant diseases, and insect pests and human activities (for example, deforestation and urbanisation will disturb this pattern and cause a relatively profound change on the Earth’s surface. These events are usually referred to as disturbances. However, disturbances in ecosystems are not easy to detect from SITS data, because SITS contain combined information on disturbances, phenological variations and noise in remote sensing data. In this paper, a novel framework is proposed for online disturbance detection from SITS. The framework is based on long short-term memory (LSTM networks. First, LSTM networks are trained by historical SITS. The trained LSTM networks are then used to predict new time series data. Last, the predicted data are compared with real data, and the noticeable deviations reveal disturbances. Experimental results using 16-day compositions of the moderate resolution imaging spectroradiometer (MOD13Q1 illustrate the effectiveness and stability of the proposed approach for online disturbance detection.

  19. Producing Coordinate Time Series for Iraq's CORS Site for Detection Geophysical Phenomena

    Directory of Open Access Journals (Sweden)

    Oday Yaseen Mohamed Zeki Alhamadani

    2018-01-01

    Full Text Available Global Navigation Satellite Systems (GNSS have become an integral part of wide range of applications. One of these applications of GNSS is implementation of the cellular phone to locate the position of users and this technology has been employed in social media applications. Moreover, GNSS have been effectively employed in transportation, GIS, mobile satellite communications, and etc. On the other hand, the geomatics sciences use the GNSS for many practical and scientific applications such as surveying and mapping and monitoring, etc. In this study, the GNSS raw data of ISER CORS, which is located in the North of Iraq, are processed and analyzed to build up coordinate time series for the purpose of detection the Arabian tectonic plate motion over seven years and a half. Such coordinates time series have been produced very efficiently using GNSS Precise Point Positioning (PPP. The daily PPP results were processed, analyzed, and presented as coordinate time series using GPS Interactive Time Series Analysis. Furthermore, MATLAB (V.2013a is used in this study to computerize GITSA with Graphic User Interface (GUI. The objective of this study was to investigate both of the homogeneity and consistency of the Iraq CORSs GNSS raw data for detection any geophysical changes over long period of time. Additionally, this study aims to employ free online PPP services, such as CSRS_PPP software, for processing GNSS raw data for generation GNSS coordinate time series. The coordinate time series of ISER station showed a +20.9 mm per year, +27.2 mm per year, and -11.3 mm per year in the East, North, and up-down components, respectively. These findings showed a remarkable similarity with those obtained by long-term monitoring of Earth's crust deformation and movement based on global studies and this highlights the importance of using GNSS for monitoring the movement of tectonic plate motion based on CORS and online GNSS data processing services over long period of

  20. Fast Change Point Detection for Electricity Market Analysis

    Energy Technology Data Exchange (ETDEWEB)

    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.

  1. Prewhitening of hydroclimatic time series? Implications for inferred change and variability across time scales

    Science.gov (United States)

    Razavi, Saman; Vogel, Richard

    2018-02-01

    Prewhitening, the process of eliminating or reducing short-term stochastic persistence to enable detection of deterministic change, has been extensively applied to time series analysis of a range of geophysical variables. Despite the controversy around its utility, methodologies for prewhitening time series continue to be a critical feature of a variety of analyses including: trend detection of hydroclimatic variables and reconstruction of climate and/or hydrology through proxy records such as tree rings. With a focus on the latter, this paper presents a generalized approach to exploring the impact of a wide range of stochastic structures of short- and long-term persistence on the variability of hydroclimatic time series. Through this approach, we examine the impact of prewhitening on the inferred variability of time series across time scales. We document how a focus on prewhitened, residual time series can be misleading, as it can drastically distort (or remove) the structure of variability across time scales. Through examples with actual data, we show how such loss of information in prewhitened time series of tree rings (so-called "residual chronologies") can lead to the underestimation of extreme conditions in climate and hydrology, particularly droughts, reconstructed for centuries preceding the historical period.

  2. Early detection of metabolic and energy disorders by thermal time series stochastic complexity analysis

    Energy Technology Data Exchange (ETDEWEB)

    Lutaif, N.A. [Departamento de Clínica Médica, Faculdade de Ciências Médicas, Universidade Estadual de Campinas, Campinas, SP (Brazil); Palazzo, R. Jr [Departamento de Telemática, Faculdade de Engenharia Elétrica e Computação, Universidade Estadual de Campinas, Campinas, SP (Brazil); Gontijo, J.A.R. [Departamento de Clínica Médica, Faculdade de Ciências Médicas, Universidade Estadual de Campinas, Campinas, SP (Brazil)

    2014-01-17

    Maintenance of thermal homeostasis in rats fed a high-fat diet (HFD) is associated with changes in their thermal balance. The thermodynamic relationship between heat dissipation and energy storage is altered by the ingestion of high-energy diet content. Observation of thermal registers of core temperature behavior, in humans and rodents, permits identification of some characteristics of time series, such as autoreference and stationarity that fit adequately to a stochastic analysis. To identify this change, we used, for the first time, a stochastic autoregressive model, the concepts of which match those associated with physiological systems involved and applied in male HFD rats compared with their appropriate standard food intake age-matched male controls (n=7 per group). By analyzing a recorded temperature time series, we were able to identify when thermal homeostasis would be affected by a new diet. The autoregressive time series model (AR model) was used to predict the occurrence of thermal homeostasis, and this model proved to be very effective in distinguishing such a physiological disorder. Thus, we infer from the results of our study that maximum entropy distribution as a means for stochastic characterization of temperature time series registers may be established as an important and early tool to aid in the diagnosis and prevention of metabolic diseases due to their ability to detect small variations in thermal profile.

  3. Early detection of metabolic and energy disorders by thermal time series stochastic complexity analysis

    International Nuclear Information System (INIS)

    Lutaif, N.A.; Palazzo, R. Jr; Gontijo, J.A.R.

    2014-01-01

    Maintenance of thermal homeostasis in rats fed a high-fat diet (HFD) is associated with changes in their thermal balance. The thermodynamic relationship between heat dissipation and energy storage is altered by the ingestion of high-energy diet content. Observation of thermal registers of core temperature behavior, in humans and rodents, permits identification of some characteristics of time series, such as autoreference and stationarity that fit adequately to a stochastic analysis. To identify this change, we used, for the first time, a stochastic autoregressive model, the concepts of which match those associated with physiological systems involved and applied in male HFD rats compared with their appropriate standard food intake age-matched male controls (n=7 per group). By analyzing a recorded temperature time series, we were able to identify when thermal homeostasis would be affected by a new diet. The autoregressive time series model (AR model) was used to predict the occurrence of thermal homeostasis, and this model proved to be very effective in distinguishing such a physiological disorder. Thus, we infer from the results of our study that maximum entropy distribution as a means for stochastic characterization of temperature time series registers may be established as an important and early tool to aid in the diagnosis and prevention of metabolic diseases due to their ability to detect small variations in thermal profile

  4. Time series analysis for psychological research: examining and forecasting change.

    Science.gov (United States)

    Jebb, Andrew T; Tay, Louis; Wang, Wei; Huang, Qiming

    2015-01-01

    Psychological research has increasingly recognized the importance of integrating temporal dynamics into its theories, and innovations in longitudinal designs and analyses have allowed such theories to be formalized and tested. However, psychological researchers may be relatively unequipped to analyze such data, given its many characteristics and the general complexities involved in longitudinal modeling. The current paper introduces time series analysis to psychological research, an analytic domain that has been essential for understanding and predicting the behavior of variables across many diverse fields. First, the characteristics of time series data are discussed. Second, different time series modeling techniques are surveyed that can address various topics of interest to psychological researchers, including describing the pattern of change in a variable, modeling seasonal effects, assessing the immediate and long-term impact of a salient event, and forecasting future values. To illustrate these methods, an illustrative example based on online job search behavior is used throughout the paper, and a software tutorial in R for these analyses is provided in the Supplementary Materials.

  5. Time series analysis for psychological research: examining and forecasting change

    Science.gov (United States)

    Jebb, Andrew T.; Tay, Louis; Wang, Wei; Huang, Qiming

    2015-01-01

    Psychological research has increasingly recognized the importance of integrating temporal dynamics into its theories, and innovations in longitudinal designs and analyses have allowed such theories to be formalized and tested. However, psychological researchers may be relatively unequipped to analyze such data, given its many characteristics and the general complexities involved in longitudinal modeling. The current paper introduces time series analysis to psychological research, an analytic domain that has been essential for understanding and predicting the behavior of variables across many diverse fields. First, the characteristics of time series data are discussed. Second, different time series modeling techniques are surveyed that can address various topics of interest to psychological researchers, including describing the pattern of change in a variable, modeling seasonal effects, assessing the immediate and long-term impact of a salient event, and forecasting future values. To illustrate these methods, an illustrative example based on online job search behavior is used throughout the paper, and a software tutorial in R for these analyses is provided in the Supplementary Materials. PMID:26106341

  6. Detecting trends in forest disturbance and recovery using yearly Landsat time series: 1. LandTrendr — Temporal segmentation algorithms

    Science.gov (United States)

    Robert E. Kennedy; Zhiqiang Yang; Warren B. Cohen

    2010-01-01

    We introduce and test LandTrendr (Landsat-based detection of Trends in Disturbance and Recovery), a new approach to extract spectral trajectories of land surface change from yearly Landsat time-series stacks (LTS). The method brings together two themes in time-series analysis of LTS: capture of short-duration events and smoothing of long-term trends. Our strategy is...

  7. Detecting change-points in extremes

    KAUST Repository

    Dupuis, D. J.; Sun, Ying; Wang, Huixia Judy

    2015-01-01

    Even though most work on change-point estimation focuses on changes in the mean, changes in the variance or in the tail distribution can lead to more extreme events. In this paper, we develop a new method of detecting and estimating the change

  8. Time-series analysis of climatologic measurements: a method to distinguish future climatic changes

    International Nuclear Information System (INIS)

    Duband, D.

    1992-01-01

    Time-series analysis of climatic parameters as air temperature, rivers flow rate, lakes or seas level is an indispensable basis to detect a possible significant climatic change. These observations, when they are carefully analyzed and criticized, constitute the necessary reference for testing and validation numerical climatic models which try to simulate the physical and dynamical process of the ocean-atmosphere couple, taking continents into account. 32 refs., 13 figs

  9. Adaptively detecting changes in Autonomic Grid Computing

    KAUST Repository

    Zhang, Xiangliang

    2010-10-01

    Detecting the changes is the common issue in many application fields due to the non-stationary distribution of the applicative data, e.g., sensor network signals, web logs and gridrunning logs. Toward Autonomic Grid Computing, adaptively detecting the changes in a grid system can help to alarm the anomalies, clean the noises, and report the new patterns. In this paper, we proposed an approach of self-adaptive change detection based on the Page-Hinkley statistic test. It handles the non-stationary distribution without the assumption of data distribution and the empirical setting of parameters. We validate the approach on the EGEE streaming jobs, and report its better performance on achieving higher accuracy comparing to the other change detection methods. Meanwhile this change detection process could help to discover the device fault which was not claimed in the system logs. © 2010 IEEE.

  10. Correlation measure to detect time series distances, whence economy globalization

    Science.gov (United States)

    Miśkiewicz, Janusz; Ausloos, Marcel

    2008-11-01

    An instantaneous time series distance is defined through the equal time correlation coefficient. The idea is applied to the Gross Domestic Product (GDP) yearly increments of 21 rich countries between 1950 and 2005 in order to test the process of economic globalisation. Some data discussion is first presented to decide what (EKS, GK, or derived) GDP series should be studied. Distances are then calculated from the correlation coefficient values between pairs of series. The role of time averaging of the distances over finite size windows is discussed. Three network structures are next constructed based on the hierarchy of distances. It is shown that the mean distance between the most developed countries on several networks actually decreases in time, -which we consider as a proof of globalization. An empirical law is found for the evolution after 1990, similar to that found in flux creep. The optimal observation time window size is found ≃15 years.

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

    Science.gov (United States)

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

    2017-10-01

    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.

  12. Detection of bifurcations in noisy coupled systems from multiple time series

    Science.gov (United States)

    Williamson, Mark S.; Lenton, Timothy M.

    2015-03-01

    We generalize a method of detecting an approaching bifurcation in a time series of a noisy system from the special case of one dynamical variable to multiple dynamical variables. For a system described by a stochastic differential equation consisting of an autonomous deterministic part with one dynamical variable and an additive white noise term, small perturbations away from the system's fixed point will decay slower the closer the system is to a bifurcation. This phenomenon is known as critical slowing down and all such systems exhibit this decay-type behaviour. However, when the deterministic part has multiple coupled dynamical variables, the possible dynamics can be much richer, exhibiting oscillatory and chaotic behaviour. In our generalization to the multi-variable case, we find additional indicators to decay rate, such as frequency of oscillation. In the case of approaching a homoclinic bifurcation, there is no change in decay rate but there is a decrease in frequency of oscillations. The expanded method therefore adds extra tools to help detect and classify approaching bifurcations given multiple time series, where the underlying dynamics are not fully known. Our generalisation also allows bifurcation detection to be applied spatially if one treats each spatial location as a new dynamical variable. One may then determine the unstable spatial mode(s). This is also something that has not been possible with the single variable method. The method is applicable to any set of time series regardless of its origin, but may be particularly useful when anticipating abrupt changes in the multi-dimensional climate system.

  13. Detection of bifurcations in noisy coupled systems from multiple time series

    International Nuclear Information System (INIS)

    Williamson, Mark S.; Lenton, Timothy M.

    2015-01-01

    We generalize a method of detecting an approaching bifurcation in a time series of a noisy system from the special case of one dynamical variable to multiple dynamical variables. For a system described by a stochastic differential equation consisting of an autonomous deterministic part with one dynamical variable and an additive white noise term, small perturbations away from the system's fixed point will decay slower the closer the system is to a bifurcation. This phenomenon is known as critical slowing down and all such systems exhibit this decay-type behaviour. However, when the deterministic part has multiple coupled dynamical variables, the possible dynamics can be much richer, exhibiting oscillatory and chaotic behaviour. In our generalization to the multi-variable case, we find additional indicators to decay rate, such as frequency of oscillation. In the case of approaching a homoclinic bifurcation, there is no change in decay rate but there is a decrease in frequency of oscillations. The expanded method therefore adds extra tools to help detect and classify approaching bifurcations given multiple time series, where the underlying dynamics are not fully known. Our generalisation also allows bifurcation detection to be applied spatially if one treats each spatial location as a new dynamical variable. One may then determine the unstable spatial mode(s). This is also something that has not been possible with the single variable method. The method is applicable to any set of time series regardless of its origin, but may be particularly useful when anticipating abrupt changes in the multi-dimensional climate system

  14. Detection of bifurcations in noisy coupled systems from multiple time series

    Energy Technology Data Exchange (ETDEWEB)

    Williamson, Mark S., E-mail: m.s.williamson@exeter.ac.uk; Lenton, Timothy M. [Earth System Science Group, College of Life and Environmental Sciences, University of Exeter, Laver Building, North Park Road, Exeter EX4 4QE (United Kingdom)

    2015-03-15

    We generalize a method of detecting an approaching bifurcation in a time series of a noisy system from the special case of one dynamical variable to multiple dynamical variables. For a system described by a stochastic differential equation consisting of an autonomous deterministic part with one dynamical variable and an additive white noise term, small perturbations away from the system's fixed point will decay slower the closer the system is to a bifurcation. This phenomenon is known as critical slowing down and all such systems exhibit this decay-type behaviour. However, when the deterministic part has multiple coupled dynamical variables, the possible dynamics can be much richer, exhibiting oscillatory and chaotic behaviour. In our generalization to the multi-variable case, we find additional indicators to decay rate, such as frequency of oscillation. In the case of approaching a homoclinic bifurcation, there is no change in decay rate but there is a decrease in frequency of oscillations. The expanded method therefore adds extra tools to help detect and classify approaching bifurcations given multiple time series, where the underlying dynamics are not fully known. Our generalisation also allows bifurcation detection to be applied spatially if one treats each spatial location as a new dynamical variable. One may then determine the unstable spatial mode(s). This is also something that has not been possible with the single variable method. The method is applicable to any set of time series regardless of its origin, but may be particularly useful when anticipating abrupt changes in the multi-dimensional climate system.

  15. Outlier detection algorithms for least squares time series regression

    DEFF Research Database (Denmark)

    Johansen, Søren; Nielsen, Bent

    We review recent asymptotic results on some robust methods for multiple regression. The regressors include stationary and non-stationary time series as well as polynomial terms. The methods include the Huber-skip M-estimator, 1-step Huber-skip M-estimators, in particular the Impulse Indicator Sat...

  16. Multi-granular trend detection for time-series analysis

    NARCIS (Netherlands)

    van Goethem, A.I.; Staals, F.; Löffler, M.; Dykes, J.; Speckmann, B.

    2017-01-01

    Time series (such as stock prices) and ensembles (such as model runs for weather forecasts) are two important types of one-dimensional time-varying data. Such data is readily available in large quantities but visual analysis of the raw data quickly becomes infeasible, even for moderately sized data

  17. Detection of "noisy" chaos in a time series

    DEFF Research Database (Denmark)

    Chon, K H; Kanters, J K; Cohen, R J

    1997-01-01

    Time series from biological system often displays fluctuations in the measured variables. Much effort has been directed at determining whether this variability reflects deterministic chaos, or whether it is merely "noise". The output from most biological systems is probably the result of both...

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

    Science.gov (United States)

    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. © 2014 Society for Conservation Biology.

  19. Analysis and detection of climate change

    International Nuclear Information System (INIS)

    Thejll, P.; Stendel, M.

    2001-01-01

    The authors first discuss the concepts 'climate' and 'climate change detection', outlining the difficulties of the latter in terms of the properties of the former. In more detail they then discuss the analysis and detection, carried out at the Danish Climate Centre, of anthropogenic climate change and the nonanthropogenic changes regarding anthropogenic climate change the emphasis is on the improvement of global and regional climate models, and the reconstruction of past climates regarding non-anthropogenic changes the authors describe two case studies of potential solar influence on climate. (LN)

  20. Interpreting the change detection error matrix

    NARCIS (Netherlands)

    Oort, van P.A.J.

    2007-01-01

    Two different matrices are commonly reported in assessment of change detection accuracy: (1) single date error matrices and (2) binary change/no change error matrices. The third, less common form of reporting, is the transition error matrix. This paper discuses the relation between these matrices.

  1. Detecting switching and intermittent causalities in time series

    Science.gov (United States)

    Zanin, Massimiliano; Papo, David

    2017-04-01

    During the last decade, complex network representations have emerged as a powerful instrument for describing the cross-talk between different brain regions both at rest and as subjects are carrying out cognitive tasks, in healthy brains and neurological pathologies. The transient nature of such cross-talk has nevertheless by and large been neglected, mainly due to the inherent limitations of some metrics, e.g., causality ones, which require a long time series in order to yield statistically significant results. Here, we present a methodology to account for intermittent causal coupling in neural activity, based on the identification of non-overlapping windows within the original time series in which the causality is strongest. The result is a less coarse-grained assessment of the time-varying properties of brain interactions, which can be used to create a high temporal resolution time-varying network. We apply the proposed methodology to the analysis of the brain activity of control subjects and alcoholic patients performing an image recognition task. Our results show that short-lived, intermittent, local-scale causality is better at discriminating both groups than global network metrics. These results highlight the importance of the transient nature of brain activity, at least under some pathological conditions.

  2. Forest Disturbance Mapping Using Dense Synthetic Landsat/MODIS Time-Series and Permutation-Based Disturbance Index Detection

    Directory of Open Access Journals (Sweden)

    David Frantz

    2016-03-01

    Full Text Available Spatio-temporal information on process-based forest loss is essential for a wide range of applications. Despite remote sensing being the only feasible means of monitoring forest change at regional or greater scales, there is no retrospectively available remote sensor that meets the demand of monitoring forests with the required spatial detail and guaranteed high temporal frequency. As an alternative, we employed the Spatial and Temporal Adaptive Reflectance Fusion Model (STARFM to produce a dense synthetic time series by fusing Landsat and Moderate Resolution Imaging Spectroradiometer (MODIS nadir Bidirectional Reflectance Distribution Function (BRDF adjusted reflectance. Forest loss was detected by applying a multi-temporal disturbance detection approach implementing a Disturbance Index-based detection strategy. The detection thresholds were permutated with random numbers for the normal distribution in order to generate a multi-dimensional threshold confidence area. As a result, a more robust parameterization and a spatially more coherent detection could be achieved. (i The original Landsat time series; (ii synthetic time series; and a (iii combined hybrid approach were used to identify the timing and extent of disturbances. The identified clearings in the Landsat detection were verified using an annual woodland clearing dataset from Queensland’s Statewide Landcover and Trees Study. Disturbances caused by stand-replacing events were successfully identified. The increased temporal resolution of the synthetic time series indicated promising additional information on disturbance timing. The results of the hybrid detection unified the benefits of both approaches, i.e., the spatial quality and general accuracy of the Landsat detection and the increased temporal information of synthetic time series. Results indicated that a temporal improvement in the detection of the disturbance date could be achieved relative to the irregularly spaced Landsat

  3. Sensor for detecting changes in magnetic fields

    Science.gov (United States)

    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.

  4. Inhomogeneities detection in annual precipitation time series in Portugal using direct sequential simulation

    Science.gov (United States)

    Caineta, Júlio; Ribeiro, Sara; Costa, Ana Cristina; Henriques, Roberto; Soares, Amílcar

    2014-05-01

    Climate data homogenisation is of major importance in monitoring climate change, the validation of weather forecasting, general circulation and regional atmospheric models, modelling of erosion, drought monitoring, among other studies of hydrological and environmental impacts. This happens because non-climate factors can cause time series discontinuities which may hide the true climatic signal and patterns, thus potentially bias the conclusions of those studies. In the last two decades, many methods have been developed to identify and remove these inhomogeneities. One of those is based on geostatistical simulation (DSS - direct sequential simulation), where local probability density functions (pdf) are calculated at candidate monitoring stations, using spatial and temporal neighbouring observations, and then are used for detection of inhomogeneities. This approach has been previously applied to detect inhomogeneities in four precipitation series (wet day count) from a network with 66 monitoring stations located in the southern region of Portugal (1980-2001). This study revealed promising results and the potential advantages of geostatistical techniques for inhomogeneities detection in climate time series. This work extends the case study presented before and investigates the application of the geostatistical stochastic approach to ten precipitation series that were previously classified as inhomogeneous by one of six absolute homogeneity tests (Mann-Kendall test, Wald-Wolfowitz runs test, Von Neumann ratio test, Standard normal homogeneity test (SNHT) for a single break, Pettit test, and Buishand range test). Moreover, a sensibility analysis is implemented to investigate the number of simulated realisations that should be used to accurately infer the local pdfs. Accordingly, the number of simulations per iteration is increased from 50 to 500, which resulted in a more representative local pdf. A set of default and recommended settings is provided, which will help

  5. Time series data correction for the Chang'E-1 gamma-ray spectrometer

    International Nuclear Information System (INIS)

    Zhang Liyan; Zou Yongliao; Liu Jianzhong; Liu Jianjun; Shen Ji; Mu Lingli; Ren Xin; Wen Weibin; Li Chunlai

    2011-01-01

    The main goal of the gamma-ray spectrometer (GRS) onboard Chang'E-1 (CE-1) is to acquire global maps of elemental abundances and their distributions on the moon, since such maps will significantly improve our understanding of lunar formation and evolution. To derive the elemental maps and enable research on lunar formation and evolution, raw data that are received directly from the spacecraft must be converted into time series corrected gamma-ray spectra. The data correction procedures for the CE-1 GRS time series data are thoroughly described. The processing procedures to create the time series gamma-ray spectra described here include channel processing, optimal data selection, energy calibration, gain correction, dead time correction, geometric correction, orbit altitude normalization, eliminating unusable data and galactic cosmic ray correction. Finally, descriptions are also given on data measurement uncertainties, which will help the interested scientists to understand and estimate various uncertainties associated with the above data processing. (research papers)

  6. Intrusion Detection in NEAR System by Anti-denoising Traffic Data Series using Discrete Wavelet Transform

    Directory of Open Access Journals (Sweden)

    VANCEA, F.

    2014-11-01

    Full Text Available The paper presents two methods for detecting anomalies in data series derived from network traffic. Intrusion detection systems based on network traffic analysis are able to respond to incidents never seen before by detecting anomalies in data series extracted from the traffic. Some anomalies manifest themselves as pulses of various sizes and shapes, superimposed on series corresponding to normal traffic. In order to detect those impulses we propose two methods based on discrete wavelet transformation. Their effectiveness expressed in relative thresholds on pulse amplitude for no false negatives and no false positives is then evaluated against pulse duration and Hurst characteristic of original series. Different base functions are also evaluated for efficiency in the context of the proposed methods.

  7. Video change detection for fixed wing UAVs

    Science.gov (United States)

    Bartelsen, Jan; Müller, Thomas; Ring, Jochen; Mück, Klaus; Brüstle, Stefan; Erdnüß, Bastian; Lutz, Bastian; Herbst, Theresa

    2017-10-01

    In this paper we proceed the work of Bartelsen et al.1 We present the draft of a process chain for an image based change detection which is designed for videos acquired by fixed wing unmanned aerial vehicles (UAVs). From our point of view, automatic video change detection for aerial images can be useful to recognize functional activities which are typically caused by the deployment of improvised explosive devices (IEDs), e.g. excavations, skid marks, footprints, left-behind tooling equipment, and marker stones. Furthermore, in case of natural disasters, like flooding, imminent danger can be recognized quickly. Due to the necessary flight range, we concentrate on fixed wing UAVs. Automatic change detection can be reduced to a comparatively simple photogrammetric problem when the perspective change between the "before" and "after" image sets is kept as small as possible. Therefore, the aerial image acquisition demands a mission planning with a clear purpose including flight path and sensor configuration. While the latter can be enabled simply by a fixed and meaningful adjustment of the camera, ensuring a small perspective change for "before" and "after" videos acquired by fixed wing UAVs is a challenging problem. Concerning this matter, we have performed tests with an advanced commercial off the shelf (COTS) system which comprises a differential GPS and autopilot system estimating the repetition accuracy of its trajectory. Although several similar approaches have been presented,23 as far as we are able to judge, the limits for this important issue are not estimated so far. Furthermore, we design a process chain to enable the practical utilization of video change detection. It consists of a front-end of a database to handle large amounts of video data, an image processing and change detection implementation, and the visualization of the results. We apply our process chain on the real video data acquired by the advanced COTS fixed wing UAV and synthetic data. For the

  8. A Hybrid Change Detection Approach for Damage Detection and Recovery Monitoring

    Science.gov (United States)

    de Alwis Pitts, Dilkushi; Wieland, Marc; Wang, Shifeng; So, Emily; Pittore, Massimiliano

    2014-05-01

    Following a disaster, change detection via pre- and post-event very high resolution remote sensing images is an essential technique for damage assessment and recovery monitoring over large areas in complex urban environments. Most assessments to date focus on detection, destruction and recovery of man-made objects that facilitate shelter and accessibility, such as buildings, roads, bridges, etc., as indicators for assessment and better decision making. Moreover, many current change-detection mechanisms do not use all the data and knowledge which are often available for the pre-disaster state. Recognizing the continuous rather than dichotomous character of the data-rich/data-poor distinction permits the incorporation of ancillary data and existing knowledge into the processing flow. Such incorporation could improve the reliability of the results and thereby enhance the usability of robust methods for disaster management. This study proposes an application-specific and robust change detection method from multi-temporal very high resolution multi-spectral satellite images. This hybrid indicator-specific method uses readily available pre-disaster GIS data and integrates existing knowledge into the processing flow to optimize the change detection while offering the possibility to target specific types of changes to man-made objects. The indicator-specific information of the GIS objects is used as a series of masks to treat the GIS objects with similar characteristics similarly for better accuracy. The proposed approach is based on a fusion of a multi-index change detection method based on gradient, texture and edge similarity filters. The change detection index is flexible for disaster cases in which the pre-disaster and post-disaster images are not of the same resolution. The proposed automated method is evaluated with QuickBird and Ikonos datasets for abrupt changes soon after disaster. The method could also be extended in a semi-automated way for monitoring

  9. Afrikaans youth series books: exhibition of societal change | Snyman ...

    African Journals Online (AJOL)

    This article reports research on the history of the Afrikaans youth series book in the twentieth century. The polysystem theory of Evan-Zohar was taken as a theoretical basis for this study. An analysis of the publication history, as well as the content and settings of the researched series books for Afrikaans children, indicate ...

  10. Time series data correction for the Chang'E-1 gamma-ray spectrometer

    Institute of Scientific and Technical Information of China (English)

    Li-Yan Zhang; Yong-Liao Zou; Jian-Zhong Liu; Jian-Jun Liu; Ji Shen; Ling-Li Mu; Xin Ren; Wei-Bin Wen; Chun-Lai Li

    2011-01-01

    The main goal of the gamma-ray spectrometer (GRS) onboard Chang'E-l (CE-1) is to acquire global maps of elemental abundances and their distributions on the moon, since such maps will significantly improve our understanding of lunar formation and evolution. To derive the elemental maps and enable research on lunar formation and evolution, raw data that are received directly from the spacecraft must be converted into time series corrected gamma-ray spectra. The data correction procedures for the CE-1 GRS time series data are thoroughly described. The processing procedures to create the time series gamma-ray spectra described here include channel processing, optimal data selection, energy calibration, gain correction, dead time correction, geometric correction, orbit altitude normalization, eliminating unusable data and galactic cosmic ray correction. Finally, descriptions are also given on data measurement uncertainties, which will help the interested scientists to understand and estimate various uncertainties associated with the above data processing.

  11. Detection of additive outliers in seasonal time series

    DEFF Research Database (Denmark)

    Haldrup, Niels; Montañés, Antonio; Sansó, Andreu

    The detection and location of additive outliers in integrated variables has attracted much attention recently because such outliers tend to affect unit root inference among other things. Most of these procedures have been developed for non-seasonal processes. However, the presence of seasonality......) to deal with data sampled at a seasonal frequency and the size and power properties are discussed. We also show that the presence of periodic heteroscedasticity will inflate the size of the tests and hence will tend to identify an excessive number of outliers. A modified Perron-Rodriguez test which allows...... periodically varying variances is suggested and it is shown to have excellent properties in terms of both power and size...

  12. Use of the Box-Cox Transformation in Detecting Changepoints in Daily Precipitation Data Series

    Science.gov (United States)

    Wang, X. L.; Chen, H.; Wu, Y.; Pu, Q.

    2009-04-01

    This study integrates a Box-Cox power transformation procedure into two statistical tests for detecting changepoints in Gaussian data series, to make the changepoint detection methods applicable to non-Gaussian data series, such as daily precipitation amounts. The detection power aspects of transformed methods in a common trend two-phase regression setting are assessed by Monte Carlo simulations for data of a log-normal or Gamma distribution. The results show that the transformed methods have increased the power of detection, in comparison with the corresponding original (untransformed) methods. The transformed data much better approximate to a Gaussian distribution. As an example of application, the new methods are applied to a series of daily precipitation amounts recorded at a station in Canada, showing satisfactory detection power.

  13. Detection and Correction of Step Discontinuities in Kepler Flux Time Series

    Science.gov (United States)

    Kolodziejczak, J. J.; Morris, R. L.

    2011-01-01

    PDC 8.0 includes an implementation of a new algorithm to detect and correct step discontinuities appearing in roughly one of every 20 stellar light curves during a given quarter. The majority of such discontinuities are believed to result from high-energy particles (either cosmic or solar in origin) striking the photometer and causing permanent local changes (typically -0.5%) in quantum efficiency, though a partial exponential recovery is often observed [1]. Since these features, dubbed sudden pixel sensitivity dropouts (SPSDs), are uncorrelated across targets they cannot be properly accounted for by the current detrending algorithm. PDC detrending is based on the assumption that features in flux time series are due either to intrinsic stellar phenomena or to systematic errors and that systematics will exhibit measurable correlations across targets. SPSD events violate these assumptions and their successful removal not only rectifies the flux values of affected targets, but demonstrably improves the overall performance of PDC detrending [1].

  14. Nonlinear features identified by Volterra series for damage detection in a buckled beam

    Directory of Open Access Journals (Sweden)

    Shiki S. B.

    2014-01-01

    Full Text Available The present paper proposes a new index for damage detection based on nonlinear features extracted from prediction errors computed by multiple convolutions using the discrete-time Volterra series. A reference Volterra model is identified with data in the healthy condition and used for monitoring the system operating with linear or nonlinear behavior. When the system has some structural change, possibly associated with damage, the index metrics computed could give an alert to separate the linear and nonlinear contributions, besides provide a diagnostic about the structural state. To show the applicability of the method, an experimental test is performed using nonlinear vibration signals measured in a clamped buckled beam subject to different levels of force applied and with simulated damages through discontinuities inserted in the beam surface.

  15. Detecting Forest Disturbance Events from MODIS and Landsat Time Series for the Conterminous United States

    Science.gov (United States)

    Zhang, G.; Ganguly, S.; Saatchi, S. S.; Hagen, S. C.; Harris, N.; Yu, Y.; Nemani, R. R.

    2013-12-01

    Spatial and temporal patterns of forest disturbance and regrowth processes are key for understanding aboveground terrestrial vegetation biomass and carbon stocks at regional-to-continental scales. The NASA Carbon Monitoring System (CMS) program seeks key input datasets, especially information related to impacts due to natural/man-made disturbances in forested landscapes of Conterminous U.S. (CONUS), that would reduce uncertainties in current carbon stock estimation and emission models. This study provides a end-to-end forest disturbance detection framework based on pixel time series analysis from MODIS (Moderate Resolution Imaging Spectroradiometer) and Landsat surface spectral reflectance data. We applied the BFAST (Breaks for Additive Seasonal and Trend) algorithm to the Normalized Difference Vegetation Index (NDVI) data for the time period from 2000 to 2011. A harmonic seasonal model was implemented in BFAST to decompose the time series to seasonal and interannual trend components in order to detect abrupt changes in magnitude and direction of these components. To apply the BFAST for whole CONUS, we built a parallel computing setup for processing massive time-series data using the high performance computing facility of the NASA Earth Exchange (NEX). In the implementation process, we extracted the dominant deforestation events from the magnitude of abrupt changes in both seasonal and interannual components, and estimated dates for corresponding deforestation events. We estimated the recovery rate for deforested regions through regression models developed between NDVI values and time since disturbance for all pixels. A similar implementation of the BFAST algorithm was performed over selected Landsat scenes (all Landsat cloud free data was used to generate NDVI from atmospherically corrected spectral reflectances) to demonstrate the spatial coherence in retrieval layers between MODIS and Landsat. In future, the application of this largely parallel disturbance

  16. Detecting leaf pulvinar movements on NDVI time series of desert trees: a new approach for water stress detection.

    Directory of Open Access Journals (Sweden)

    Roberto O Chávez

    Full Text Available Heliotropic leaf movement or leaf 'solar tracking' occurs for a wide variety of plants, including many desert species and some crops. This has an important effect on the canopy spectral reflectance as measured from satellites. For this reason, monitoring systems based on spectral vegetation indices, such as the normalized difference vegetation index (NDVI, should account for heliotropic movements when evaluating the health condition of such species. In the hyper-arid Atacama Desert, Northern Chile, we studied seasonal and diurnal variations of MODIS and Landsat NDVI time series of plantation stands of the endemic species Prosopis tamarugo Phil., subject to different levels of groundwater depletion. As solar irradiation increased during the day and also during the summer, the paraheliotropic leaves of Tamarugo moved to an erectophile position (parallel to the sun rays making the NDVI signal to drop. This way, Tamarugo stands with no water stress showed a positive NDVI difference between morning and midday (ΔNDVI mo-mi and between winter and summer (ΔNDVI W-S. In this paper, we showed that the ΔNDVI mo-mi of Tamarugo stands can be detected using MODIS Terra and Aqua images, and the ΔNDVI W-S using Landsat or MODIS Terra images. Because pulvinar movement is triggered by changes in cell turgor, the effects of water stress caused by groundwater depletion can be assessed and monitored using ΔNDVI mo-mi and ΔNDVI W-S. For an 11-year time series without rainfall events, Landsat ΔNDVI W-S of Tamarugo stands showed a positive linear relationship with cumulative groundwater depletion. We conclude that both ΔNDVI mo-mi and ΔNDVI W-S have potential to detect early water stress of paraheliotropic vegetation.

  17. Detecting leaf pulvinar movements on NDVI time series of desert trees: a new approach for water stress detection.

    Science.gov (United States)

    Chávez, Roberto O; Clevers, Jan G P W; Verbesselt, Jan; Naulin, Paulette I; Herold, Martin

    2014-01-01

    Heliotropic leaf movement or leaf 'solar tracking' occurs for a wide variety of plants, including many desert species and some crops. This has an important effect on the canopy spectral reflectance as measured from satellites. For this reason, monitoring systems based on spectral vegetation indices, such as the normalized difference vegetation index (NDVI), should account for heliotropic movements when evaluating the health condition of such species. In the hyper-arid Atacama Desert, Northern Chile, we studied seasonal and diurnal variations of MODIS and Landsat NDVI time series of plantation stands of the endemic species Prosopis tamarugo Phil., subject to different levels of groundwater depletion. As solar irradiation increased during the day and also during the summer, the paraheliotropic leaves of Tamarugo moved to an erectophile position (parallel to the sun rays) making the NDVI signal to drop. This way, Tamarugo stands with no water stress showed a positive NDVI difference between morning and midday (ΔNDVI mo-mi) and between winter and summer (ΔNDVI W-S). In this paper, we showed that the ΔNDVI mo-mi of Tamarugo stands can be detected using MODIS Terra and Aqua images, and the ΔNDVI W-S using Landsat or MODIS Terra images. Because pulvinar movement is triggered by changes in cell turgor, the effects of water stress caused by groundwater depletion can be assessed and monitored using ΔNDVI mo-mi and ΔNDVI W-S. For an 11-year time series without rainfall events, Landsat ΔNDVI W-S of Tamarugo stands showed a positive linear relationship with cumulative groundwater depletion. We conclude that both ΔNDVI mo-mi and ΔNDVI W-S have potential to detect early water stress of paraheliotropic vegetation.

  18. Short-term change detection for UAV video

    Science.gov (United States)

    Saur, Günter; Krüger, Wolfgang

    2012-11-01

    In the last years, there has been an increased use of unmanned aerial vehicles (UAV) for video reconnaissance and surveillance. An important application in this context is change detection in UAV video data. Here we address short-term change detection, in which the time between observations ranges from several minutes to a few hours. We distinguish this task from video motion detection (shorter time scale) and from long-term change detection, based on time series of still images taken between several days, weeks, or even years. Examples for relevant changes we are looking for are recently parked or moved vehicles. As a pre-requisite, a precise image-to-image registration is needed. Images are selected on the basis of the geo-coordinates of the sensor's footprint and with respect to a certain minimal overlap. The automatic imagebased fine-registration adjusts the image pair to a common geometry by using a robust matching approach to handle outliers. 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 length of shadows, and compression or transmission artifacts. To detect changes in image pairs we analyzed image differencing, local image correlation, and a transformation-based approach (multivariate alteration detection). As input we used color and gradient magnitude images. To cope with local misalignment of image structures we extended the approaches by a local neighborhood search. The algorithms are applied to several examples covering both urban and rural scenes. The local neighborhood search in combination with intensity and gradient magnitude differencing clearly improved the results. Extended image differencing performed better than both the correlation based approach and the multivariate alternation detection. The algorithms are adapted to be used in semi-automatic workflows for the ABUL video exploitation system of Fraunhofer

  19. Time Series Discord Detection in Medical Data using a Parallel Relational Database [PowerPoint

    Energy Technology Data Exchange (ETDEWEB)

    Woodbridge, Diane; Wilson, Andrew T.; Rintoul, Mark Daniel; Goldstein, Richard H.

    2015-11-01

    Recent advances in sensor technology have made continuous real-time health monitoring available in both hospital and non-hospital settings. Since data collected from high frequency medical sensors includes a huge amount of data, storing and processing continuous medical data is an emerging big data area. Especially detecting anomaly in real time is important for patients’ emergency detection and prevention. A time series discord indicates a subsequence that has the maximum difference to the rest of the time series subsequences, meaning that it has abnormal or unusual data trends. In this study, we implemented two versions of time series discord detection algorithms on a high performance parallel database management system (DBMS) and applied them to 240 Hz waveform data collected from 9,723 patients. The initial brute force version of the discord detection algorithm takes each possible subsequence and calculates a distance to the nearest non-self match to find the biggest discords in time series. For the heuristic version of the algorithm, a combination of an array and a trie structure was applied to order time series data for enhancing time efficiency. The study results showed efficient data loading, decoding and discord searches in a large amount of data, benefiting from the time series discord detection algorithm and the architectural characteristics of the parallel DBMS including data compression, data pipe-lining, and task scheduling.

  20. Time Series Discord Detection in Medical Data using a Parallel Relational Database

    Energy Technology Data Exchange (ETDEWEB)

    Woodbridge, Diane; Rintoul, Mark Daniel; Wilson, Andrew T.; Goldstein, Richard

    2015-10-01

    Recent advances in sensor technology have made continuous real-time health monitoring available in both hospital and non-hospital settings. Since data collected from high frequency medical sensors includes a huge amount of data, storing and processing continuous medical data is an emerging big data area. Especially detecting anomaly in real time is important for patients’ emergency detection and prevention. A time series discord indicates a subsequence that has the maximum difference to the rest of the time series subsequences, meaning that it has abnormal or unusual data trends. In this study, we implemented two versions of time series discord detection algorithms on a high performance parallel database management system (DBMS) and applied them to 240 Hz waveform data collected from 9,723 patients. The initial brute force version of the discord detection algorithm takes each possible subsequence and calculates a distance to the nearest non-self match to find the biggest discords in time series. For the heuristic version of the algorithm, a combination of an array and a trie structure was applied to order time series data for enhancing time efficiency. The study results showed efficient data loading, decoding and discord searches in a large amount of data, benefiting from the time series discord detection algorithm and the architectural characteristics of the parallel DBMS including data compression, data pipe-lining, and task scheduling.

  1. Automated Change Detection for Synthetic Aperture Sonar

    Science.gov (United States)

    2014-01-01

    alerting to the presence of an acoustically chameleonic object. While the utility of exploiting changes in signal phase degrades over time, with time...pp. 643–656, October 2003. [7] D. Brie, M. Tomczak, H. Oehlmann, and A. Richard, “Gear crack detection by adaptive amplitude and phase demodulation

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

    Science.gov (United States)

    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.

  3. CHANGE DETECTION BASED ON PERSISTENT SCATTERER INTERFEROMETRY – A NEW METHOD OF MONITORING BUILDING CHANGES

    Directory of Open Access Journals (Sweden)

    C. H. Yang

    2016-06-01

    Full Text Available 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.

  4. Rapid Change Detection Algorithm for Disaster Management

    Science.gov (United States)

    Michel, U.; Thunig, H.; Ehlers, M.; Reinartz, P.

    2012-07-01

    This paper focuses on change detection applications in areas where catastrophic events took place which resulted in rapid destruction especially of manmade objects. Standard methods for automated change detection prove not to be sufficient; therefore a new method was developed and tested. The presented method allows a fast detection and visualization of change in areas of crisis or catastrophes. While often new methods of remote sensing are developed without user oriented aspects, organizations and authorities are not able to use these methods because of absence of remote sensing know how. Therefore a semi-automated procedure was developed. Within a transferable framework, the developed algorithm 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 Temporal Change Index (TCI) only panchromatic datasets are used to extract areas which are destroyed, areas which were not affected and in addition areas where rebuilding has already started.

  5. Total least squares for anomalous change detection

    Science.gov (United States)

    Theiler, James; Matsekh, Anna M.

    2010-04-01

    A family of subtraction-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 TLSQbased 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 special cases of it are equivalent to canonical correlation analysis and optimized covariance equalization. What whitened TLSQ offers is a generalization of these algorithms with the potential for better performance.

  6. CHANGE DETECTION VIA SELECTIVE GUIDED CONTRASTING FILTERS

    Directory of Open Access Journals (Sweden)

    Y. V. Vizilter

    2017-05-01

    Full Text Available Change detection scheme based on guided contrasting was previously proposed. Guided contrasting filter takes two images (test and sample as input and forms the output as filtered version of test image. Such filter preserves the similar details and smooths the non-similar details of test image with respect to sample image. Due to this the difference between test image and its filtered version (difference map could be a basis for robust change detection. Guided contrasting is performed in two steps: at the first step some smoothing operator (SO is applied for elimination of test image details; at the second step all matched details are restored with local contrast proportional to the value of some local similarity coefficient (LSC. The guided contrasting filter was proposed based on local average smoothing as SO and local linear correlation as LSC. In this paper we propose and implement new set of selective guided contrasting filters based on different combinations of various SO and thresholded LSC. Linear average and Gaussian smoothing, nonlinear median filtering, morphological opening and closing are considered as SO. Local linear correlation coefficient, morphological correlation coefficient (MCC, mutual information, mean square MCC and geometrical correlation coefficients are applied as LSC. Thresholding of LSC allows operating with non-normalized LSC and enhancing the selective properties of guided contrasting filters: details are either totally recovered or not recovered at all after the smoothing. These different guided contrasting filters are tested as a part of previously proposed change detection pipeline, which contains following stages: guided contrasting filtering on image pyramid, calculation of difference map, binarization, extraction of change proposals and testing change proposals using local MCC. Experiments on real and simulated image bases demonstrate the applicability of all proposed selective guided contrasting filters. All

  7. The influence of noise on nonlinear time series detection based on Volterra-Wiener-Korenberg model

    Energy Technology Data Exchange (ETDEWEB)

    Lei Min [State Key Laboratory of Vibration, Shock and Noise, Shanghai Jiao Tong University, Shanghai 200030 (China)], E-mail: leimin@sjtu.edu.cn; Meng Guang [State Key Laboratory of Vibration, Shock and Noise, Shanghai Jiao Tong University, Shanghai 200030 (China)

    2008-04-15

    This paper studies the influence of noises on Volterra-Wiener-Korenberg (VWK) nonlinear test model. Our numerical results reveal that different types of noises lead to different behavior of VWK model detection. For dynamic noise, it is difficult to distinguish chaos from nonchaotic but nonlinear determinism. For time series, measure noise has no impact on chaos determinism detection. This paper also discusses various behavior of VWK model detection with surrogate data for different noises.

  8. Detecting changes during pregnancy with Raman spectroscopy

    Science.gov (United States)

    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.

  9. Detecting evolutionary forces in language change.

    Science.gov (United States)

    Newberry, Mitchell G; Ahern, Christopher A; Clark, Robin; Plotkin, Joshua B

    2017-11-09

    Both language and genes evolve by transmission over generations with opportunity for differential replication of forms. The understanding that gene frequencies change at random by genetic drift, even in the absence of natural selection, was a seminal advance in evolutionary biology. Stochastic drift must also occur in language as a result of randomness in how linguistic forms are copied between speakers. Here we quantify the strength of selection relative to stochastic drift in language evolution. We use time series derived from large corpora of annotated texts dating from the 12th to 21st centuries to analyse three well-known grammatical changes in English: the regularization of past-tense verbs, the introduction of the periphrastic 'do', and variation in verbal negation. We reject stochastic drift in favour of selection in some cases but not in others. In particular, we infer selection towards the irregular forms of some past-tense verbs, which is likely driven by changing frequencies of rhyming patterns over time. We show that stochastic drift is stronger for rare words, which may explain why rare forms are more prone to replacement than common ones. This work provides a method for testing selective theories of language change against a null model and reveals an underappreciated role for stochasticity in language evolution.

  10. Perspectives on monitoring gradual change across the continuity of Landsat sensors using time-series data

    Science.gov (United States)

    Vogelmann, James; Gallant, Alisa L.; Shi, Hua; Zhu, Zhe

    2016-01-01

    There are many types of changes occurring over the Earth's landscapes that can be detected and monitored using Landsat data. Here we focus on monitoring “within-state,” gradual changes in vegetation in contrast with traditional monitoring of “abrupt” land-cover conversions. Gradual changes result from a variety of processes, such as vegetation growth and succession, damage from insects and disease, responses to shifts in climate, and other factors. Despite the prevalence of gradual changes across the landscape, they are largely ignored by the remote sensing community. Gradual changes are best characterized and monitored using time-series analysis, and with the successful launch of Landsat 8 we now have appreciable data continuity that extends the Landsat legacy across the previous 43 years. In this study, we conducted three related analyses: (1) comparison of spectral values acquired by Landsats 7 and 8, separated by eight days, to ensure compatibility for time-series evaluation; (2) tracking of multitemporal signatures for different change processes across Landsat 5, 7, and 8 sensors using anniversary-date imagery; and (3) tracking the same type of processes using all available acquisitions. In this investigation, we found that data representing natural vegetation from Landsats 5, 7, and 8 were comparable and did not indicate a need for major modification prior to use for long-term monitoring. Analyses using anniversary-date imagery can be very effective for assessing long term patterns and trends occurring across the landscape, and are especially good for providing insights regarding trends related to long-term and continuous trends of growth or decline. We found that use of all available data provided a much more comprehensive level of understanding of the trends occurring, providing information about rate, duration, and intra- and inter-annual variability that could not be readily gleaned from the anniversary date analyses. We observed that using all

  11. Brain correlates of automatic visual change detection.

    Science.gov (United States)

    Cléry, H; Andersson, F; Fonlupt, P; Gomot, M

    2013-07-15

    A number of studies support the presence of visual automatic detection of change, but little is known about the brain generators involved in such processing and about the modulation of brain activity according to the salience of the stimulus. The study presented here was designed to locate the brain activity elicited by unattended visual deviant and novel stimuli using fMRI. Seventeen adult participants were presented with a passive visual oddball sequence while performing a concurrent visual task. Variations in BOLD signal were observed in the modality-specific sensory cortex, but also in non-specific areas involved in preattentional processing of changing events. A degree-of-deviance effect was observed, since novel stimuli elicited more activity in the sensory occipital regions and at the medial frontal site than small changes. These findings could be compared to those obtained in the auditory modality and might suggest a "general" change detection process operating in several sensory modalities. Copyright © 2013 Elsevier Inc. All rights reserved.

  12. Removing Parallax-Induced False Changes in Change Detection

    Science.gov (United States)

    2014-03-27

    Teller , “Equation of state calculations by fast computing machines ,” The Journal of Chemical Physics, vol. 21, no. 6, pp. 1087–1092, 1953. [89] J. D’Errico...especially in the case of HS data, due to its hundreds of spectral channels. Therefore, there is a strong need for methodologies that enable automated ...researchers for many years due in large part to the number of applications across diverse disciplines. Automated image change detection (CD) is the

  13. Time series analysis of infrared satellite data for detecting thermal anomalies: a hybrid approach

    Science.gov (United States)

    Koeppen, W. C.; Pilger, E.; Wright, R.

    2011-07-01

    We developed and tested an automated algorithm that analyzes thermal infrared satellite time series data to detect and quantify the excess energy radiated from thermal anomalies such as active volcanoes. Our algorithm enhances the previously developed MODVOLC approach, a simple point operation, by adding a more complex time series component based on the methods of the Robust Satellite Techniques (RST) algorithm. Using test sites at Anatahan and Kīlauea volcanoes, the hybrid time series approach detected ~15% more thermal anomalies than MODVOLC with very few, if any, known false detections. We also tested gas flares in the Cantarell oil field in the Gulf of Mexico as an end-member scenario representing very persistent thermal anomalies. At Cantarell, the hybrid algorithm showed only a slight improvement, but it did identify flares that were undetected by MODVOLC. We estimate that at least 80 MODIS images for each calendar month are required to create good reference images necessary for the time series analysis of the hybrid algorithm. The improved performance of the new algorithm over MODVOLC will result in the detection of low temperature thermal anomalies that will be useful in improving our ability to document Earth's volcanic eruptions, as well as detecting low temperature thermal precursors to larger eruptions.

  14. Real-Time Detection of Application-Layer DDoS Attack Using Time Series Analysis

    Directory of Open Access Journals (Sweden)

    Tongguang Ni

    2013-01-01

    Full Text Available Distributed denial of service (DDoS attacks are one of the major threats to the current Internet, and application-layer DDoS attacks utilizing legitimate HTTP requests to overwhelm victim resources are more undetectable. Consequently, neither intrusion detection systems (IDS nor victim server can detect malicious packets. In this paper, a novel approach to detect application-layer DDoS attack is proposed based on entropy of HTTP GET requests per source IP address (HRPI. By approximating the adaptive autoregressive (AAR model, the HRPI time series is transformed into a multidimensional vector series. Then, a trained support vector machine (SVM classifier is applied to identify the attacks. The experiments with several databases are performed and results show that this approach can detect application-layer DDoS attacks effectively.

  15. Orthogonal series generalized likelihood ratio test for failure detection and isolation. [for aircraft control

    Science.gov (United States)

    Hall, Steven R.; Walker, Bruce K.

    1990-01-01

    A new failure detection and isolation algorithm for linear dynamic systems is presented. This algorithm, the Orthogonal Series Generalized Likelihood Ratio (OSGLR) test, is based on the assumption that the failure modes of interest can be represented by truncated series expansions. This assumption leads to a failure detection algorithm with several desirable properties. Computer simulation results are presented for the detection of the failures of actuators and sensors of a C-130 aircraft. The results show that the OSGLR test generally performs as well as the GLR test in terms of time to detect a failure and is more robust to failure mode uncertainty. However, the OSGLR test is also somewhat more sensitive to modeling errors than the GLR test.

  16. Deforestation: Can We Balance Resource Conservation with Economic Growth? Global Environmental Change Series.

    Science.gov (United States)

    Environmental Protection Agency, Washington, DC.

    This book is the second installment in the Global Environmental Change Series that links the ecology and biology of global environmental changes with insights and information from other disciplines. This series teaches students how to gather a wide range of information from pertinent areas of study and encourages them to develop their own opinions…

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

    Science.gov (United States)

    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. Using forbidden ordinal patterns to detect determinism in irregularly sampled time series.

    Science.gov (United States)

    Kulp, C W; Chobot, J M; Niskala, B J; Needhammer, C J

    2016-02-01

    It is known that when symbolizing a time series into ordinal patterns using the Bandt-Pompe (BP) methodology, there will be ordinal patterns called forbidden patterns that do not occur in a deterministic series. The existence of forbidden patterns can be used to identify deterministic dynamics. In this paper, the ability to use forbidden patterns to detect determinism in irregularly sampled time series is tested on data generated from a continuous model system. The study is done in three parts. First, the effects of sampling time on the number of forbidden patterns are studied on regularly sampled time series. The next two parts focus on two types of irregular-sampling, missing data and timing jitter. It is shown that forbidden patterns can be used to detect determinism in irregularly sampled time series for low degrees of sampling irregularity (as defined in the paper). In addition, comments are made about the appropriateness of using the BP methodology to symbolize irregularly sampled time series.

  19. Nationwide Hybrid Change Detection of Buildings

    Science.gov (United States)

    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.

  20. LANDSAT-8 OPERATIONAL LAND IMAGER CHANGE DETECTION ANALYSIS

    Directory of Open Access Journals (Sweden)

    W. Pervez

    2017-05-01

    Full Text Available This paper investigated the potential utility of Landsat-8 Operational Land Imager (OLI for change detection analysis and mapping application because of its superior technical design to previous Landsat series. The OLI SVM classified data was successfully classified with regard to all six test classes (i.e., bare land, built-up land, mixed trees, bushes, dam water and channel water. OLI support vector machine (SVM classified data for the four seasons (i.e., spring, autumn, winter, and summer was used to change detection results of six cases: (1 winter to spring which resulted reduction in dam water mapping and increases of bushes; (2 winter to summer which resulted reduction in dam water mapping and increase of vegetation; (3 winter to autumn which resulted increase in dam water mapping; (4 spring to summer which resulted reduction of vegetation and shallow water; (5 spring to autumn which resulted decrease of vegetation; and (6 summer to autumn which resulted increase of bushes and vegetation . OLI SVM classified data resulted higher overall accuracy and kappa coefficient and thus found suitable for change detection analysis.

  1. Formulating and testing a method for perturbing precipitation time series to reflect anticipated climatic changes

    DEFF Research Database (Denmark)

    Sørup, Hjalte Jomo Danielsen; Georgiadis, Stylianos; Gregersen, Ida Bülow

    2017-01-01

    Urban water infrastructure has very long planning horizons, and planning is thus very dependent on reliable estimates of the impacts of climate change. Many urban water systems are designed using time series with a high temporal resolution. To assess the impact of climate change on these systems......, similarly high-resolution precipitation time series for future climate are necessary. Climate models cannot at their current resolutions provide these time series at the relevant scales. Known methods for stochastic downscaling of climate change to urban hydrological scales have known shortcomings...... in constructing realistic climate-changed precipitation time series at the sub-hourly scale. In the present study we present a deterministic methodology to perturb historical precipitation time series at the minute scale to reflect non-linear expectations to climate change. The methodology shows good skill...

  2. The Changing Nature of Jobs: A Paraprofessional Time Series.

    Science.gov (United States)

    Johnson, Carol P.

    1996-01-01

    This study examined job descriptions (1975, 1981, 1990) of three paraprofessional jobs in an academic library technical services department at a small, private liberal arts college to determine changes occurring as a result of automation. It found no significant differences. Although changes were more idiosyncratic than expected, they may indicate…

  3. Analysis of rainfall and temperature time series to detect long-term ...

    Indian Academy of Sciences (India)

    67

    ABSTRACT. Arid and semiarid environments have been identified with locations prone to impacts of climate variability and change. Investigating long term trends is one way of tracing climate change impacts. This study investigates variability through annual and seasonal meteorological time series. Possible ...

  4. Active and Passive Remote Sensing Data Time Series for Flood Detection and Surface Water Mapping

    Science.gov (United States)

    Bioresita, Filsa; Puissant, Anne; Stumpf, André; Malet, Jean-Philippe

    2017-04-01

    As a consequence of environmental changes surface waters are undergoing changes in time and space. A better knowledge of the spatial and temporal distribution of surface waters resources becomes essential to support sustainable policies and development activities. Especially because surface waters, are not only a vital sweet water resource, but can also pose hazards to human settlements and infrastructures through flooding. Floods are a highly frequent disaster in the world and can caused huge material losses. Detecting and mapping their spatial distribution is fundamental to ascertain damages and for relief efforts. Spaceborne Synthetic Aperture Radar (SAR) is an effective way to monitor surface waters bodies over large areas since it provides excellent temporal coverage and, all-weather day-and-night imaging capabilities. However, emergent vegetation, trees, wind or flow turbulence can increase radar back-scatter returns and pose problems for the delineation of inundated areas. In such areas, passive remote sensing data can be used to identify vegetated areas and support the interpretation of SAR data. The availability of new Earth Observation products, for example Sentinel-1 (active) and Sentinel-2 (passive) imageries, with both high spatial and temporal resolution, have the potential to facilitate flood detection and monitoring of surface waters changes which are very dynamic in space and time. In this context, the research consists of two parts. In the first part, the objective is to propose generic and reproducible methodologies for the analysis of Sentinel-1 time series data for floods detection and surface waters mapping. The processing chain comprises a series of pre-processing steps and the statistical modeling of the pixel value distribution to produce probabilistic maps for the presence of surface waters. Images pre-processing for all Sentinel-1 images comprise the reduction SAR effect like orbit errors, speckle noise, and geometric effects. A modified

  5. Detecting nonlinearity in time series driven by non-Gaussian noise: the case of river flows

    Directory of Open Access Journals (Sweden)

    F. Laio

    2004-01-01

    Full Text Available Several methods exist for the detection of nonlinearity in univariate time series. In the present work we consider riverflow time series to infer the dynamical characteristics of the rainfall-runoff transformation. It is shown that the non-Gaussian nature of the driving force (rainfall can distort the results of such methods, in particular when surrogate data techniques are used. Deterministic versus stochastic (DVS plots, conditionally applied to the decay phases of the time series, are instead proved to be a suitable tool to detect nonlinearity in processes driven by non-Gaussian (Poissonian noise. An application to daily discharges from three Italian rivers provides important clues to the presence of nonlinearity in the rainfall-runoff transformation.

  6. Detecting settlement expansion using hyper-temporal SAR time-series

    CSIR Research Space (South Africa)

    Kleynhans, W

    2014-07-01

    Full Text Available The detection of new informal settlements in South Africa using time-series data derived from coarse resolution satellite imagery has recently been an active area of research. Most of the previous methods presented using hyper-temporal satellite...

  7. Assessment of MODIS NDVI time series data products for detecting forest defoliation by gypsy moth outbreaks

    Science.gov (United States)

    Joseph P. Spruce; Steven Sader; Robert E. Ryan; James Smoot; Philip Kuper; al. et.

    2011-01-01

    This paper discusses an assessment of Moderate Resolution Imaging Spectroradiometer (MODIS) time-series data products for detecting forest defoliation from European gypsy moth (Lymantria dispar). This paper describes an effort to aid the United States Department of Agriculture (USDA) Forest Service in developing and assessing MODIS-based gypsy moth defoliation...

  8. Accuracy and Sensitivity of a Method of Jump Detection, Evaluated by Simulated Time Series

    Czech Academy of Sciences Publication Activity Database

    Chapanov, Y.; Ron, Cyril; Vondrák, Jan

    2017-01-01

    Roč. 14, č. 1 (2017), s. 73-82 ISSN 1214-9705 R&D Projects: GA ČR GA13-15943S Institutional support: RVO:67985815 Keywords : time series * data jump detection * high-sensitive method Subject RIV: DE - Earth Magnetism, Geodesy, Geography OBOR OECD: Physical geography Impact factor: 0.699, year: 2016

  9. Detecting significant changes in protein abundance

    Directory of Open Access Journals (Sweden)

    Kai Kammers

    2015-06-01

    Full Text Available We review and demonstrate how an empirical Bayes method, shrinking a protein's sample variance towards a pooled estimate, leads to far more powerful and stable inference to detect significant changes in protein abundance compared to ordinary t-tests. Using examples from isobaric mass labelled proteomic experiments we show how to analyze data from multiple experiments simultaneously, and discuss the effects of missing data on the inference. We also present easy to use open source software for normalization of mass spectrometry data and inference based on moderated test statistics.

  10. ASM Lecture Series: Global Warming and Climate Change

    International Nuclear Information System (INIS)

    Rowland, F. S.

    2010-01-01

    The melting of ice and permafrost in the north polar region and the shrinking of the tropical glaciers are signals that global warming is no longer solely a warning about the future, but changes which have already arrived. The initial effects of this warming are noticeably present, and the concerns are now of substantial climate change in the near future. Modeling of the consequences on the future atmosphere from increased release of greenhouse gases and some of the possible consequences of climate change, such as rising sea levels and melting of the north polar ice, are discussed. (author)

  11. Glacier Volume Change Estimation Using Time Series of Improved Aster Dems

    Science.gov (United States)

    Girod, Luc; Nuth, Christopher; Kääb, Andreas

    2016-06-01

    Volume change data is critical to the understanding of glacier response to climate change. The Advanced Spaceborne Thermal Emission and Reflection Radiometer (ASTER) system embarked on the Terra (EOS AM-1) satellite has been a unique source of systematic stereoscopic images covering the whole globe at 15m resolution and at a consistent quality for over 15 years. While satellite stereo sensors with significantly improved radiometric and spatial resolution are available to date, the potential of ASTER data lies in its long consistent time series that is unrivaled, though not fully exploited for change analysis due to lack of data accuracy and precision. Here, we developed an improved method for ASTER DEM generation and implemented it in the open source photogrammetric library and software suite MicMac. The method relies on the computation of a rational polynomial coefficients (RPC) model and the detection and correction of cross-track sensor jitter in order to compute DEMs. ASTER data are strongly affected by attitude jitter, mainly of approximately 4 km and 30 km wavelength, and improving the generation of ASTER DEMs requires removal of this effect. Our sensor modeling does not require ground control points and allows thus potentially for the automatic processing of large data volumes. As a proof of concept, we chose a set of glaciers with reference DEMs available to assess the quality of our measurements. We use time series of ASTER scenes from which we extracted DEMs with a ground sampling distance of 15m. Our method directly measures and accounts for the cross-track component of jitter so that the resulting DEMs are not contaminated by this process. Since the along-track component of jitter has the same direction as the stereo parallaxes, the two cannot be separated and the elevations extracted are thus contaminated by along-track jitter. Initial tests reveal no clear relation between the cross-track and along-track components so that the latter seems not to be

  12. GLACIER VOLUME CHANGE ESTIMATION USING TIME SERIES OF IMPROVED ASTER DEMS

    Directory of Open Access Journals (Sweden)

    L. Girod

    2016-06-01

    Full Text Available Volume change data is critical to the understanding of glacier response to climate change. The Advanced Spaceborne Thermal Emission and Reflection Radiometer (ASTER system embarked on the Terra (EOS AM-1 satellite has been a unique source of systematic stereoscopic images covering the whole globe at 15m resolution and at a consistent quality for over 15 years. While satellite stereo sensors with significantly improved radiometric and spatial resolution are available to date, the potential of ASTER data lies in its long consistent time series that is unrivaled, though not fully exploited for change analysis due to lack of data accuracy and precision. Here, we developed an improved method for ASTER DEM generation and implemented it in the open source photogrammetric library and software suite MicMac. The method relies on the computation of a rational polynomial coefficients (RPC model and the detection and correction of cross-track sensor jitter in order to compute DEMs. ASTER data are strongly affected by attitude jitter, mainly of approximately 4 km and 30 km wavelength, and improving the generation of ASTER DEMs requires removal of this effect. Our sensor modeling does not require ground control points and allows thus potentially for the automatic processing of large data volumes. As a proof of concept, we chose a set of glaciers with reference DEMs available to assess the quality of our measurements. We use time series of ASTER scenes from which we extracted DEMs with a ground sampling distance of 15m. Our method directly measures and accounts for the cross-track component of jitter so that the resulting DEMs are not contaminated by this process. Since the along-track component of jitter has the same direction as the stereo parallaxes, the two cannot be separated and the elevations extracted are thus contaminated by along-track jitter. Initial tests reveal no clear relation between the cross-track and along-track components so that the latter

  13. Delayed Detection of Esophageal Intubation in Anesthesia Malpractice Claims: Brief Report of a Case Series.

    Science.gov (United States)

    Honardar, Marzieh R; Posner, Karen L; Domino, Karen B

    2017-12-01

    This retrospective case series analyzed 45 malpractice claims for delayed detection of esophageal intubation from the Anesthesia Closed Claims Project. Inclusion criteria were cases from 1995 to 2013, after adoption of identification of CO2 in expired gas to verify correct endotracheal tube position as a monitoring standard by the American Society of Anesthesiologists. Forty-nine percent (95% confidence interval 34%-64%) occurred in the operating room or other anesthesia location where CO2 detection equipment should have been available. The most common factors contributing to delayed detection were not using, ignoring, or misinterpreting CO2 readings. Misdiagnosis, as with bronchospasm, occurred in 33% (95% confidence interval 20%).

  14. Enhanced terahertz detection using multiple GaAs HEMTs connected in series

    KAUST Repository

    Elkhatib, Tamer A.; Veksler, Dmitry B.; Salama, Khaled N.; Zhang, Xi-C.; Shur, Michael S.

    2012-01-01

    We report here, for the first time, on enhanced nonresonant detection of terahertz radiation using multiple InGaAs/GaAs high-electron-mobility transistors (HEMTs) connected in series and biased by a direct drain current. A 1.63 THz (184 mum) response is proportional to the number of detecting transistors operating in saturation region at the same gate-source bias voltage. The experimental data are in agreement with the detection mechanism based on the rectification of overdamped plasma waves excited by radiation in channels of devices.

  15. Enhanced terahertz detection using multiple GaAs HEMTs connected in series

    KAUST Repository

    Elkhatib, Tamer A.

    2012-07-28

    We report here, for the first time, on enhanced nonresonant detection of terahertz radiation using multiple InGaAs/GaAs high-electron-mobility transistors (HEMTs) connected in series and biased by a direct drain current. A 1.63 THz (184 mum) response is proportional to the number of detecting transistors operating in saturation region at the same gate-source bias voltage. The experimental data are in agreement with the detection mechanism based on the rectification of overdamped plasma waves excited by radiation in channels of devices.

  16. Growth Curve Analysis and Change-Points Detection in Extremes

    KAUST Repository

    Meng, Rui

    2016-05-15

    The thesis consists of two coherent projects. The first project presents the results of evaluating salinity tolerance in barley using growth curve analysis where different growth trajectories are observed within barley families. The study of salinity tolerance in plants is crucial to understanding plant growth and productivity. Because fully-automated smarthouses with conveyor systems allow non-destructive and high-throughput phenotyping of large number of plants, it is now possible to apply advanced statistical tools to analyze daily measurements and to study salinity tolerance. To compare different growth patterns of barley variates, we use functional data analysis techniques to analyze the daily projected shoot areas. In particular, we apply the curve registration method to align all the curves from the same barley family in order to summarize the family-wise features. We also illustrate how to use statistical modeling to account for spatial variation in microclimate in smarthouses and for temporal variation across runs, which is crucial for identifying traits of the barley variates. In our analysis, we show that the concentrations of sodium and potassium in leaves are negatively correlated, and their interactions are associated with the degree of salinity tolerance. The second project studies change-points detection methods in extremes when multiple time series data are available. Motived by the scientific question of whether the chances to experience extreme weather are different in different seasons of a year, we develop a change-points detection model to study changes in extremes or in the tail of a distribution. Most of existing models identify seasons from multiple yearly time series assuming a season or a change-point location remains exactly the same across years. In this work, we propose a random effect model that allows the change-point to vary from year to year, following a given distribution. Both parametric and nonparametric methods are developed

  17. RECONSTRUCTION OF PRECIPITATION SERIES AND ANALYSIS OF CLIMATE CHANGE OVER PAST 500 YEARS IN NORTHERN CHINA

    Institute of Scientific and Technical Information of China (English)

    RONG Yan-shu; TU Qi-pu

    2005-01-01

    It is important and necessary to get a much longer precipitation series in order to research features of drought/flood and climate change.Based on dryness and wetness grades series of 18 stations in Northern China of 533 years from 1470 to 2002, the Moving Cumulative Frequency Method (MCFM) was developed, moving average precipitation series from 1499 to 2002 were reconstructed by testing three kinds of average precipitation, and the features of climate change and dry and wet periods were researched by using reconstructed precipitation series in the present paper.The results showed that there were good relationship between the reconstructed precipitation series and the observation precipitation series since 1954 and their relative root-mean-square error were below 1.89%, that the relation between reconstructed series and the dryness and wetness grades series were nonlinear and this nonlinear relation implied that reconstructed series were reliable and could became foundation data for researching evolution of the drought and flood.Analysis of climate change upon reconstructed precipitation series revealed that although drought intensity of recent dry period from middle 1970s of 20th century until early 21st century was not the strongest in historical climate of Northern China, intensity and duration of wet period was a great deal decreasing and shortening respectively, climate evolve to aridification situation in Northern China.

  18. Detecting determinism with improved sensitivity in time series: rank-based nonlinear predictability score.

    Science.gov (United States)

    Naro, Daniel; Rummel, Christian; Schindler, Kaspar; Andrzejak, Ralph G

    2014-09-01

    The rank-based nonlinear predictability score was recently introduced as a test for determinism in point processes. We here adapt this measure to time series sampled from time-continuous flows. We use noisy Lorenz signals to compare this approach against a classical amplitude-based nonlinear prediction error. Both measures show an almost identical robustness against Gaussian white noise. In contrast, when the amplitude distribution of the noise has a narrower central peak and heavier tails than the normal distribution, the rank-based nonlinear predictability score outperforms the amplitude-based nonlinear prediction error. For this type of noise, the nonlinear predictability score has a higher sensitivity for deterministic structure in noisy signals. It also yields a higher statistical power in a surrogate test of the null hypothesis of linear stochastic correlated signals. We show the high relevance of this improved performance in an application to electroencephalographic (EEG) recordings from epilepsy patients. Here the nonlinear predictability score again appears of higher sensitivity to nonrandomness. Importantly, it yields an improved contrast between signals recorded from brain areas where the first ictal EEG signal changes were detected (focal EEG signals) versus signals recorded from brain areas that were not involved at seizure onset (nonfocal EEG signals).

  19. Tuning the Voices of a Choir: Detecting Ecological Gradients in Time-Series Populations.

    Directory of Open Access Journals (Sweden)

    Allan Buras

    Full Text Available This paper introduces a new approach-the Principal Component Gradient Analysis (PCGA-to detect ecological gradients in time-series populations, i.e. several time-series originating from different individuals of a population. Detection of ecological gradients is of particular importance when dealing with time-series from heterogeneous populations which express differing trends. PCGA makes use of polar coordinates of loadings from the first two axes obtained by principal component analysis (PCA to define groups of similar trends. Based on the mean inter-series correlation (rbar the gain of increasing a common underlying signal by PCGA groups is quantified using Monte Carlo Simulations. In terms of validation PCGA is compared to three other existing approaches. Focusing on dendrochronological examples, PCGA is shown to correctly determine population gradients and in particular cases to be advantageous over other considered methods. Furthermore, PCGA groups in each example allowed for enhancing the strength of a common underlying signal and comparably well as hierarchical cluster analysis. Our results indicate that PCGA potentially allows for a better understanding of mechanisms causing time-series population gradients as well as objectively enhancing the performance of climate transfer functions in dendroclimatology. While our examples highlight the relevance of PCGA to the field of dendrochronology, we believe that also other disciplines working with data of comparable structure may benefit from PCGA.

  20. Tuning the Voices of a Choir: Detecting Ecological Gradients in Time-Series Populations.

    Science.gov (United States)

    Buras, Allan; van der Maaten-Theunissen, Marieke; van der Maaten, Ernst; Ahlgrimm, Svenja; Hermann, Philipp; Simard, Sonia; Heinrich, Ingo; Helle, Gerd; Unterseher, Martin; Schnittler, Martin; Eusemann, Pascal; Wilmking, Martin

    2016-01-01

    This paper introduces a new approach-the Principal Component Gradient Analysis (PCGA)-to detect ecological gradients in time-series populations, i.e. several time-series originating from different individuals of a population. Detection of ecological gradients is of particular importance when dealing with time-series from heterogeneous populations which express differing trends. PCGA makes use of polar coordinates of loadings from the first two axes obtained by principal component analysis (PCA) to define groups of similar trends. Based on the mean inter-series correlation (rbar) the gain of increasing a common underlying signal by PCGA groups is quantified using Monte Carlo Simulations. In terms of validation PCGA is compared to three other existing approaches. Focusing on dendrochronological examples, PCGA is shown to correctly determine population gradients and in particular cases to be advantageous over other considered methods. Furthermore, PCGA groups in each example allowed for enhancing the strength of a common underlying signal and comparably well as hierarchical cluster analysis. Our results indicate that PCGA potentially allows for a better understanding of mechanisms causing time-series population gradients as well as objectively enhancing the performance of climate transfer functions in dendroclimatology. While our examples highlight the relevance of PCGA to the field of dendrochronology, we believe that also other disciplines working with data of comparable structure may benefit from PCGA.

  1. NATIONWIDE HYBRID CHANGE DETECTION OF BUILDINGS

    Directory of Open Access Journals (Sweden)

    V. Hron

    2016-06-01

    Full Text Available 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.

  2. Responding to Social Change. Community Development Series, Volume 19.

    Science.gov (United States)

    Honikman, Basil, Ed.

    This book presents a spectrum of the environmental design and research issues of today, an introduction to the field as a whole, and an emphasis on the need for changes in attitudes and procedures in the disciplines. Contributions, multidisciplinary in approach, range from a diversity of perspectives, including urban planning, social and…

  3. Detectable perfusion changes in MAG3 studies

    International Nuclear Information System (INIS)

    Shuter, B.; Bernar, A.; Roach, P.

    1998-01-01

    Full text: The use of 120 MBq 99m Tc-MAG 3 instead of 600 MBq 99m Tc-DTPA in renal imaging has degraded the images obtained during the perfusion phase. An increase of the minimum detectable change (MDC) in blood flow (BF) would also be expected. In transplant patients, renal BF is an important factor in patient management and the MDC should be small to allow early detection of reduced perfusion. We determined the mean and coefficient of variation (CoV: standard deviation/mean) of three renal perfusion indices as a function of counts in the time-activity curves (TACs). Transplant patients were given a dose of about 300 MBq of 99m Tc-MAG3 and images acquired at 8 fps for 60s. TACs made up from 8, 4, 2 or I images per second allowed calculation of renal perfusion indices as if doses of 300, 150, 75 and 38 MBq had been administered. Perfusion indices based on area under the TACs up to the arterial peak (API), the maximum slopes of the TACs (SPI) and the maximum slope of renal TAC and height of arterial TAC (BPI) were calculated by our routine renal software package. As the administered dose decreased, the CoV rose for all indices, least for BPI and most for API. BPI CoV increased from ∼10% at 300 MBq to 20% at 75 MBq, but API CoV rose from 6% to 46%. Mean BPI was stable over the dose range, but mean API showed a systematic increase of about 50% over the 300 MBq result. We conclude that at 120 MBq the MDC (expressed as 2*CoV) in BF is 30-60%, whereas at 600 MBq it may be as low as 10%, allowing earlier confident detection of a change in BF. The BPI was the preferred perfusion index as its mean value changed little and it had the least CoV at lower activities. The data also imply that relative kidney perfusion in the one individual will be much less accurate with 120 MBq of MAG 3

  4. Detecting and Understanding Changing Arctic Carbon Emissions

    Science.gov (United States)

    Bruhwiler, L.

    2017-12-01

    Warming in the Arctic has proceeded faster than anyplace on Earth. Our current understanding of biogeochemistry suggests that we can expect feedbacks between climate and carbon in the Arctic. Changes in terrestrial fluxes of carbon can be expected as the Arctic warms, and the vast stores of organic carbon frozen in Arctic soils could be mobilized to the atmosphere, with possible significant impacts on global climate. Quantifying trends in Arctic carbon exchanges is important for policymaking because greater reductions in anthropogenic emissions may be required to meet climate goals. Observations of greenhouse gases in the Arctic and globally have been collected for several decades. Analysis of this data does not currently support significantly changed Arctic emissions of CH4, however it is difficult to detect changes in Arctic emissions because of transport from lower latitudes and large inter-annual variability. Unfortunately, current space-based remote sensing systems have limitations at Arctic latitudes. Modeling systems can help untangle the Arctic budget of greenhouse gases, but they are dependent on underlying prior fluxes, wetland distributions and global anthropogenic emissions. Also, atmospheric transport models may have significant biases and errors. For example, unrealistic near-surface stability can lead to underestimation of emissions in atmospheric inversions. We discuss our current understanding of the Arctic carbon budget from both top-down and bottom-up approaches. We show that current atmospheric inversions agree well on the CH4 budget. On the other hand, bottom-up models vary widely in their predictions of natural emissions, with some models predicting emissions too large to be accommodated by the budget implied by global observations. Large emissions from the shallow Arctic ocean are also inconsistent with atmospheric observations. We also discuss the sensitivity of the current atmospheric network to what is likely small, gradual increases in

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

    Science.gov (United States)

    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.

  6. Contamination Event Detection with Multivariate Time-Series Data in Agricultural Water Monitoring

    Directory of Open Access Journals (Sweden)

    Yingchi Mao

    2017-12-01

    Full Text Available Time series data of multiple water quality parameters are obtained from the water sensor networks deployed in the agricultural water supply network. The accurate and efficient detection and warning of contamination events to prevent pollution from spreading is one of the most important issues when pollution occurs. In order to comprehensively reduce the event detection deviation, a spatial–temporal-based event detection approach with multivariate time-series data for water quality monitoring (M-STED was proposed. The M-STED approach includes three parts. The first part is that M-STED adopts a Rule K algorithm to select backbone nodes as the nodes in the CDS, and forward the sensed data of multiple water parameters. The second part is to determine the state of each backbone node with back propagation neural network models and the sequential Bayesian analysis in the current timestamp. The third part is to establish a spatial model with Bayesian networks to estimate the state of the backbones in the next timestamp and trace the “outlier” node to its neighborhoods to detect a contamination event. The experimental results indicate that the average detection rate is more than 80% with M-STED and the false detection rate is lower than 9%, respectively. The M-STED approach can improve the rate of detection by about 40% and reduce the false alarm rate by about 45%, compared with the event detection with a single water parameter algorithm, S-STED. Moreover, the proposed M-STED can exhibit better performance in terms of detection delay and scalability.

  7. Historical Fire Detection of Tropical Forest from NDVI Time-series Data: Case Study on Jambi, Indonesia

    Directory of Open Access Journals (Sweden)

    Dyah R. Panuju

    2010-03-01

    Full Text Available In addition to forest encroachment, forest fire is a serious problem in Indonesia. Attempts at managing its widespread and frequent occurrence has led to intensive use of remote sensing data. Coarse resolution images have been employed to derive hot spots as an indicator of forest fire. However, most efforts to verify the hot spot data and to verify fire accidents have been restricted to the use of medium or high resolution data. At present, it is difficult to verify solely upon those data due to severe cloud cover and low revisit time. In this paper, we present a method to validate forest fire using NDVI time series data. With the freely available NDVI data from SPOT VEGETATION, we successfully detected changes in time series data which were associated with fire accidents.

  8. A Unified Framework for Estimating Minimum Detectable Effects for Comparative Short Interrupted Time Series Designs

    Science.gov (United States)

    Price, Cristofer; Unlu, Fatih

    2014-01-01

    The Comparative Short Interrupted Time Series (C-SITS) design is a frequently employed quasi-experimental method, in which the pre- and post-intervention changes observed in the outcome levels of a treatment group is compared with those of a comparison group where the difference between the former and the latter is attributed to the treatment. The…

  9. Towards Detection of Cutting in Hay Meadows by Using of NDVI and EVI Time Series

    Directory of Open Access Journals (Sweden)

    Andrej Halabuk

    2015-05-01

    Full Text Available The main requirement for preserving European hay meadows in good condition is through prerequisite cut management. However, monitoring these practices on a larger scale is very difficult. Our study analyses the use of MODIS vegetation indices products, namely EVI and NDVI, to discriminate cut and uncut meadows in Slovakia. We tested the added value of simple transformations of raw data series (seasonal statistics, first difference series, compared EVI and NDVI, and analyzed optimal periods, the number of scenes and the effect of smoothing on classification performance. The first difference series transformation saw substantial improvement in classification results. The best case NDVI series classification yielded overall accuracy of 85% with balanced rates of producer’s and user’s accuracies for both classes. EVI yielded slightly lower values, though not significantly different, although user accuracy of cut meadows achieved only 67%. Optimal periods for discriminating cut and uncut meadows lay between 16 May and 4 August, meaning only seven consecutive images are enough to accurately detect cutting in hay meadows. More importantly, the 16-day compositing period seemed to be enough for detection of cutting, which would be the time span that might be hopefully achieved by upcoming on-board HR sensors (e.g., Sentinel-2.

  10. Transportation system resilience, extreme weather and climate change : a thought leadership series

    Science.gov (United States)

    2014-09-01

    This report summarizes key findings from the Transportation System Resilience, Extreme Weather and Climate Change thought leadership series held at Volpe, the National Transportation Systems Center from fall 2013 to spring 2014.

  11. A Change of Heart: Case Series of Peripartum Cardiomyopathy

    Directory of Open Access Journals (Sweden)

    Sean Martin

    2013-01-01

    Full Text Available Peripartum cardiomyopathy (PPCM is an uncommon disease of pregnancy, occurring in about 1 in 2000 live births, and is characterized by the development of heart failure, due to left ventricular systolic dysfunction. It is associated with high rates of maternal and neonatal mortality. Cardiac disease is the leading cause of maternal death in the UK: PPCM accounts for about 17% of these. Clinical findings of decompensated heart failure (HF are often masked by the normal physiological changes seen in pregnancy making the diagnosis challenging. A high index of suspicion is essential—prompting referral for echocardiogram, which is crucial for diagnosis. Favourable prognosis is dependent on the early initiation of HF medications. Although full recovery occurs in around half of cases, left ventricular systolic dysfunction persists in a significant proportion of patients with PPCM and the risk of recurrence in subsequent pregnancies is high. The pathophysiology of PPCM is under intense research. We present four patients with PPCM and a review of the literature. Owing to the diagnostic challenge of PPCM and decompensated HF in pregnant mothers and its high mortality rate without treatment, prompt investigation and referral are key to improving maternal survival.

  12. Item Anomaly Detection Based on Dynamic Partition for Time Series in Recommender Systems.

    Science.gov (United States)

    Gao, Min; Tian, Renli; Wen, Junhao; Xiong, Qingyu; Ling, Bin; Yang, Linda

    2015-01-01

    In recent years, recommender systems have become an effective method to process information overload. However, recommendation technology still suffers from many problems. One of the problems is shilling attacks-attackers inject spam user profiles to disturb the list of recommendation items. There are two characteristics of all types of shilling attacks: 1) Item abnormality: The rating of target items is always maximum or minimum; and 2) Attack promptness: It takes only a very short period time to inject attack profiles. Some papers have proposed item anomaly detection methods based on these two characteristics, but their detection rate, false alarm rate, and universality need to be further improved. To solve these problems, this paper proposes an item anomaly detection method based on dynamic partitioning for time series. This method first dynamically partitions item-rating time series based on important points. Then, we use chi square distribution (χ2) to detect abnormal intervals. The experimental results on MovieLens 100K and 1M indicate that this approach has a high detection rate and a low false alarm rate and is stable toward different attack models and filler sizes.

  13. Item Anomaly Detection Based on Dynamic Partition for Time Series in Recommender Systems

    Science.gov (United States)

    Gao, Min; Tian, Renli; Wen, Junhao; Xiong, Qingyu; Ling, Bin; Yang, Linda

    2015-01-01

    In recent years, recommender systems have become an effective method to process information overload. However, recommendation technology still suffers from many problems. One of the problems is shilling attacks-attackers inject spam user profiles to disturb the list of recommendation items. There are two characteristics of all types of shilling attacks: 1) Item abnormality: The rating of target items is always maximum or minimum; and 2) Attack promptness: It takes only a very short period time to inject attack profiles. Some papers have proposed item anomaly detection methods based on these two characteristics, but their detection rate, false alarm rate, and universality need to be further improved. To solve these problems, this paper proposes an item anomaly detection method based on dynamic partitioning for time series. This method first dynamically partitions item-rating time series based on important points. Then, we use chi square distribution (χ2) to detect abnormal intervals. The experimental results on MovieLens 100K and 1M indicate that this approach has a high detection rate and a low false alarm rate and is stable toward different attack models and filler sizes. PMID:26267477

  14. Optimize the Coverage Probability of Prediction Interval for Anomaly Detection of Sensor-Based Monitoring Series

    Directory of Open Access Journals (Sweden)

    Jingyue Pang

    2018-03-01

    Full Text Available Effective anomaly detection of sensing data is essential for identifying potential system failures. Because they require no prior knowledge or accumulated labels, and provide uncertainty presentation, the probability prediction methods (e.g., Gaussian process regression (GPR and relevance vector machine (RVM are especially adaptable to perform anomaly detection for sensing series. Generally, one key parameter of prediction models is coverage probability (CP, which controls the judging threshold of the testing sample and is generally set to a default value (e.g., 90% or 95%. There are few criteria to determine the optimal CP for anomaly detection. Therefore, this paper designs a graphic indicator of the receiver operating characteristic curve of prediction interval (ROC-PI based on the definition of the ROC curve which can depict the trade-off between the PI width and PI coverage probability across a series of cut-off points. Furthermore, the Youden index is modified to assess the performance of different CPs, by the minimization of which the optimal CP is derived by the simulated annealing (SA algorithm. Experiments conducted on two simulation datasets demonstrate the validity of the proposed method. Especially, an actual case study on sensing series from an on-orbit satellite illustrates its significant performance in practical application.

  15. Shilling attack detection for recommender systems based on credibility of group users and rating time series.

    Science.gov (United States)

    Zhou, Wei; Wen, Junhao; Qu, Qiang; Zeng, Jun; Cheng, Tian

    2018-01-01

    Recommender systems are vulnerable to shilling attacks. Forged user-generated content data, such as user ratings and reviews, are used by attackers to manipulate recommendation rankings. Shilling attack detection in recommender systems is of great significance to maintain the fairness and sustainability of recommender systems. The current studies have problems in terms of the poor universality of algorithms, difficulty in selection of user profile attributes, and lack of an optimization mechanism. In this paper, a shilling behaviour detection structure based on abnormal group user findings and rating time series analysis is proposed. This paper adds to the current understanding in the field by studying the credibility evaluation model in-depth based on the rating prediction model to derive proximity-based predictions. A method for detecting suspicious ratings based on suspicious time windows and target item analysis is proposed. Suspicious rating time segments are determined by constructing a time series, and data streams of the rating items are examined and suspicious rating segments are checked. To analyse features of shilling attacks by a group user's credibility, an abnormal group user discovery method based on time series and time window is proposed. Standard testing datasets are used to verify the effect of the proposed method.

  16. Detecting method for crude oil price fluctuation mechanism under different periodic time series

    International Nuclear Information System (INIS)

    Gao, Xiangyun; Fang, Wei; An, Feng; Wang, Yue

    2017-01-01

    Highlights: • We proposed the concept of autoregressive modes to indicate the fluctuation patterns. • We constructed transmission networks for studying the fluctuation mechanism. • There are different fluctuation mechanism under different periodic time series. • Only a few types of autoregressive modes control the fluctuations in crude oil price. • There are cluster effects during the fluctuation mechanism of autoregressive modes. - Abstract: Current existing literatures can characterize the long-term fluctuation of crude oil price time series, however, it is difficult to detect the fluctuation mechanism specifically under short term. Because each fluctuation pattern for one short period contained in a long-term crude oil price time series have dynamic characteristics of diversity; in other words, there exhibit various fluctuation patterns in different short periods and transmit to each other, which reflects the reputedly complicate and chaotic oil market. Thus, we proposed an incorporated method to detect the fluctuation mechanism, which is the evolution of the different fluctuation patterns over time from the complex network perspective. We divided crude oil price time series into segments using sliding time windows, and defined autoregressive modes based on regression models to indicate the fluctuation patterns of each segment. Hence, the transmissions between different types of autoregressive modes over time form a transmission network that contains rich dynamic information. We then capture transmission characteristics of autoregressive modes under different periodic time series through the structure features of the transmission networks. The results indicate that there are various autoregressive modes with significantly different statistical characteristics under different periodic time series. However, only a few types of autoregressive modes and transmission patterns play a major role in the fluctuation mechanism of the crude oil price, and these

  17. Potential fire detection based on Kalman-driven change detection

    CSIR Research Space (South Africa)

    Van Den Bergh, F

    2009-07-01

    Full Text Available A new active fire event detection algorithm for data collected with the Spinning Enhanced Visible and Infrared Imager (SEVIRI) sensor, based on the extended Kalman filter, is introduced. Instead of using the observed temperatures of the spatial...

  18. RankExplorer: Visualization of Ranking Changes in Large Time Series Data.

    Science.gov (United States)

    Shi, Conglei; Cui, Weiwei; Liu, Shixia; Xu, Panpan; Chen, Wei; Qu, Huamin

    2012-12-01

    For many applications involving time series data, people are often interested in the changes of item values over time as well as their ranking changes. For example, people search many words via search engines like Google and Bing every day. Analysts are interested in both the absolute searching number for each word as well as their relative rankings. Both sets of statistics may change over time. For very large time series data with thousands of items, how to visually present ranking changes is an interesting challenge. In this paper, we propose RankExplorer, a novel visualization method based on ThemeRiver to reveal the ranking changes. Our method consists of four major components: 1) a segmentation method which partitions a large set of time series curves into a manageable number of ranking categories; 2) an extended ThemeRiver view with embedded color bars and changing glyphs to show the evolution of aggregation values related to each ranking category over time as well as the content changes in each ranking category; 3) a trend curve to show the degree of ranking changes over time; 4) rich user interactions to support interactive exploration of ranking changes. We have applied our method to some real time series data and the case studies demonstrate that our method can reveal the underlying patterns related to ranking changes which might otherwise be obscured in traditional visualizations.

  19. A novel Spatio-temporal change detection approach using hyper-temporal satellite data

    CSIR Research Space (South Africa)

    Kleynhans, W

    2014-07-01

    Full Text Available The use of hyper-temporal MODIS time-series data for the detection of land cover change in South Africa has been an active research area the last few year. This paper expands on previous studies that show that this type of data can be effectively...

  20. A Doubly Stochastic Change Point Detection Algorithm for Noisy Biological Signals

    Directory of Open Access Journals (Sweden)

    Nathan Gold

    2018-01-01

    Full Text Available Experimentally and clinically collected time series data are often contaminated with significant confounding noise, creating short, noisy time series. This noise, due to natural variability and measurement error, poses a challenge to conventional change point detection methods. We propose a novel and robust statistical method for change point detection for noisy biological time sequences. Our method is a significant improvement over traditional change point detection methods, which only examine a potential anomaly at a single time point. In contrast, our method considers all suspected anomaly points and considers the joint probability distribution of the number of change points and the elapsed time between two consecutive anomalies. We validate our method with three simulated time series, a widely accepted benchmark data set, two geological time series, a data set of ECG recordings, and a physiological data set of heart rate variability measurements of fetal sheep model of human labor, comparing it to three existing methods. Our method demonstrates significantly improved performance over the existing point-wise detection methods.

  1. Automated Bayesian model development for frequency detection in biological time series

    Directory of Open Access Journals (Sweden)

    Oldroyd Giles ED

    2011-06-01

    Full Text Available Abstract Background A first step in building a mathematical model of a biological system is often the analysis of the temporal behaviour of key quantities. Mathematical relationships between the time and frequency domain, such as Fourier Transforms and wavelets, are commonly used to extract information about the underlying signal from a given time series. This one-to-one mapping from time points to frequencies inherently assumes that both domains contain the complete knowledge of the system. However, for truncated, noisy time series with background trends this unique mapping breaks down and the question reduces to an inference problem of identifying the most probable frequencies. Results In this paper we build on the method of Bayesian Spectrum Analysis and demonstrate its advantages over conventional methods by applying it to a number of test cases, including two types of biological time series. Firstly, oscillations of calcium in plant root cells in response to microbial symbionts are non-stationary and noisy, posing challenges to data analysis. Secondly, circadian rhythms in gene expression measured over only two cycles highlights the problem of time series with limited length. The results show that the Bayesian frequency detection approach can provide useful results in specific areas where Fourier analysis can be uninformative or misleading. We demonstrate further benefits of the Bayesian approach for time series analysis, such as direct comparison of different hypotheses, inherent estimation of noise levels and parameter precision, and a flexible framework for modelling the data without pre-processing. Conclusions Modelling in systems biology often builds on the study of time-dependent phenomena. Fourier Transforms are a convenient tool for analysing the frequency domain of time series. However, there are well-known limitations of this method, such as the introduction of spurious frequencies when handling short and noisy time series, and

  2. Automated Bayesian model development for frequency detection in biological time series.

    Science.gov (United States)

    Granqvist, Emma; Oldroyd, Giles E D; Morris, Richard J

    2011-06-24

    A first step in building a mathematical model of a biological system is often the analysis of the temporal behaviour of key quantities. Mathematical relationships between the time and frequency domain, such as Fourier Transforms and wavelets, are commonly used to extract information about the underlying signal from a given time series. This one-to-one mapping from time points to frequencies inherently assumes that both domains contain the complete knowledge of the system. However, for truncated, noisy time series with background trends this unique mapping breaks down and the question reduces to an inference problem of identifying the most probable frequencies. In this paper we build on the method of Bayesian Spectrum Analysis and demonstrate its advantages over conventional methods by applying it to a number of test cases, including two types of biological time series. Firstly, oscillations of calcium in plant root cells in response to microbial symbionts are non-stationary and noisy, posing challenges to data analysis. Secondly, circadian rhythms in gene expression measured over only two cycles highlights the problem of time series with limited length. The results show that the Bayesian frequency detection approach can provide useful results in specific areas where Fourier analysis can be uninformative or misleading. We demonstrate further benefits of the Bayesian approach for time series analysis, such as direct comparison of different hypotheses, inherent estimation of noise levels and parameter precision, and a flexible framework for modelling the data without pre-processing. Modelling in systems biology often builds on the study of time-dependent phenomena. Fourier Transforms are a convenient tool for analysing the frequency domain of time series. However, there are well-known limitations of this method, such as the introduction of spurious frequencies when handling short and noisy time series, and the requirement for uniformly sampled data. Biological time

  3. Detecting a currency’s dominance using multivariate time series analysis

    Science.gov (United States)

    Syahidah Yusoff, Nur; Sharif, Shamshuritawati

    2017-09-01

    A currency exchange rate is the price of one country’s currency in terms of another country’s currency. There are four different prices; opening, closing, highest, and lowest can be achieved from daily trading activities. In the past, a lot of studies have been carried out by using closing price only. However, those four prices are interrelated to each other. Thus, the multivariate time series can provide more information than univariate time series. Therefore, the enthusiasm of this paper is to compare the results of two different approaches, which are mean vector and Escoufier’s RV coefficient in constructing similarity matrices of 20 world currencies. Consequently, both matrices are used to substitute the correlation matrix required by network topology. With the help of degree centrality measure, we can detect the currency’s dominance for both networks. The pros and cons for both approaches will be presented at the end of this paper.

  4. Probabilistic mapping of flood-induced backscatter changes in SAR time series

    Science.gov (United States)

    Schlaffer, Stefan; Chini, Marco; Giustarini, Laura; Matgen, Patrick

    2017-04-01

    The information content of flood extent maps can be increased considerably by including information on the uncertainty of the flood area delineation. This additional information can be of benefit in flood forecasting and monitoring. Furthermore, flood probability maps can be converted to binary maps showing flooded and non-flooded areas by applying a threshold probability value pF = 0.5. In this study, a probabilistic change detection approach for flood mapping based on synthetic aperture radar (SAR) time series is proposed. For this purpose, conditional probability density functions (PDFs) for land and open water surfaces were estimated from ENVISAT ASAR Wide Swath (WS) time series containing >600 images using a reference mask of permanent water bodies. A pixel-wise harmonic model was used to account for seasonality in backscatter from land areas caused by soil moisture and vegetation dynamics. The approach was evaluated for a large-scale flood event along the River Severn, United Kingdom. The retrieved flood probability maps were compared to a reference flood mask derived from high-resolution aerial imagery by means of reliability diagrams. The obtained performance measures indicate both high reliability and confidence although there was a slight under-estimation of the flood extent, which may in part be attributed to topographically induced radar shadows along the edges of the floodplain. Furthermore, the results highlight the importance of local incidence angle for the separability between flooded and non-flooded areas as specular reflection properties of open water surfaces increase with a more oblique viewing geometry.

  5. Orthogonal transformations for change detection, Matlab code

    DEFF Research Database (Denmark)

    2005-01-01

    Matlab code to do multivariate alteration detection (MAD) analysis, maximum autocorrelation factor (MAF) analysis, canonical correlation analysis (CCA) and principal component analysis (PCA) on image data.......Matlab code to do multivariate alteration detection (MAD) analysis, maximum autocorrelation factor (MAF) analysis, canonical correlation analysis (CCA) and principal component analysis (PCA) on image data....

  6. Modelling Changes in the Unconditional Variance of Long Stock Return Series

    DEFF Research Database (Denmark)

    Amado, Cristina; Teräsvirta, Timo

    In this paper we develop a testing and modelling procedure for describing the long-term volatility movements over very long return series. For the purpose, we assume that volatility is multiplicatively decomposed into a conditional and an unconditional component as in Amado and Teräsvirta (2011...... show that the long-memory property in volatility may be explained by ignored changes in the unconditional variance of the long series. Finally, based on a formal statistical test we find evidence of the superiority of volatility forecast accuracy of the new model over the GJR-GARCH model at all...... horizons for a subset of the long return series....

  7. Modelling changes in the unconditional variance of long stock return series

    DEFF Research Database (Denmark)

    Amado, Cristina; Teräsvirta, Timo

    2014-01-01

    In this paper we develop a testing and modelling procedure for describing the long-term volatility movements over very long daily return series. For this purpose we assume that volatility is multiplicatively decomposed into a conditional and an unconditional component as in Amado and Teräsvirta...... that the apparent long memory property in volatility may be interpreted as changes in the unconditional variance of the long series. Finally, based on a formal statistical test we find evidence of the superiority of volatility forecasting accuracy of the new model over the GJR-GARCH model at all horizons for eight...... subsets of the long return series....

  8. Return periods of losses associated with European windstorm series in a changing climate

    International Nuclear Information System (INIS)

    Karremann, Melanie K; Pinto, Joaquim G; Reyers, Mark; Klawa, Matthias

    2014-01-01

    Possible future changes of clustering and return periods (RPs) of European storm series with high potential losses are quantified. Historical storm series are identified using 40 winters of reanalysis. Time series of top events (1, 2 or 5 year return levels (RLs)) are used to assess RPs of storm series both empirically and theoretically. Additionally, 800 winters of general circulation model simulations for present (1960–2000) and future (2060–2100) climate conditions are investigated. Clustering is identified for most countries, and estimated RPs are similar for reanalysis and present day simulations. Future changes of RPs are estimated for fixed RLs and fixed loss index thresholds. For the former, shorter RPs are found for Western Europe, but changes are small and spatially heterogeneous. For the latter, which combines the effects of clustering and event ranking shifts, shorter RPs are found everywhere except for Mediterranean countries. These changes are generally not statistically significant between recent and future climate. However, the RPs for the fixed loss index approach are mostly beyond the range of pre-industrial natural climate variability. This is not true for fixed RLs. The quantification of losses associated with storm series permits a more adequate windstorm risk assessment in a changing climate. (letter)

  9. Long Series of GNSS Integrated Precipitable Water as a Climate Change Indicator

    Directory of Open Access Journals (Sweden)

    Kruczyk Michał

    2015-12-01

    Full Text Available This paper investigates information potential contained in tropospheric delay product for selected International GNSS Service (IGS stations in climatologic research. Long time series of daily averaged Integrated Precipitable Water (IPW can serve as climate indicator. The seasonal model of IPW change has been adjusted to the multi-year series (by the least square method. Author applied two modes: sinusoidal and composite (two or more oscillations. Even simple sinusoidal seasonal model (of daily IPW values series clearly represents diversity of world climates. Residuals in periods from 10 up to 17 years are searched for some long-term IPW trend – self-evident climate change indicator. Results are ambiguous: for some stations or periods IPW trends are quite clear, the following years (or the other station not visible. Method of fitting linear trend to IPW series does not influence considerably the value of linear trend. The results are mostly influenced by series length, completeness and data (e.g. meteorological quality. The longer and more homogenous IPW series, the better chance to estimate the magnitude of climatologic IPW changes.

  10. Minimal changes in health status questionnaires: distinction between minimally detectable change and minimally important change

    Directory of Open Access Journals (Sweden)

    Knol Dirk L

    2006-08-01

    Full Text Available Abstract Changes in scores on health status questionnaires are difficult to interpret. Several methods to determine minimally important changes (MICs have been proposed which can broadly be divided in distribution-based and anchor-based methods. Comparisons of these methods have led to insight into essential differences between these approaches. Some authors have tried to come to a uniform measure for the MIC, such as 0.5 standard deviation and the value of one standard error of measurement (SEM. Others have emphasized the diversity of MIC values, depending on the type of anchor, the definition of minimal importance on the anchor, and characteristics of the disease under study. A closer look makes clear that some distribution-based methods have been merely focused on minimally detectable changes. For assessing minimally important changes, anchor-based methods are preferred, as they include a definition of what is minimally important. Acknowledging the distinction between minimally detectable and minimally important changes is useful, not only to avoid confusion among MIC methods, but also to gain information on two important benchmarks on the scale of a health status measurement instrument. Appreciating the distinction, it becomes possible to judge whether the minimally detectable change of a measurement instrument is sufficiently small to detect minimally important changes.

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

    Science.gov (United States)

    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

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

    Science.gov (United States)

    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.

  13. Automatic Detection of Clouds and Shadows Using High Resolution Satellite Image Time Series

    Science.gov (United States)

    Champion, Nicolas

    2016-06-01

    Detecting clouds and their shadows is one of the primaries steps to perform when processing satellite images because they may alter the quality of some products such as large-area orthomosaics. The main goal of this paper is to present the automatic method developed at IGN-France for detecting clouds and shadows in a sequence of satellite images. In our work, surface reflectance orthoimages are used. They were processed from initial satellite images using a dedicated software. The cloud detection step consists of a region-growing algorithm. Seeds are firstly extracted. For that purpose and for each input ortho-image to process, we select the other ortho-images of the sequence that intersect it. The pixels of the input ortho-image are secondly labelled seeds if the difference of reflectance (in the blue channel) with overlapping ortho-images is bigger than a given threshold. Clouds are eventually delineated using a region-growing method based on a radiometric and homogeneity criterion. Regarding the shadow detection, our method is based on the idea that a shadow pixel is darker when comparing to the other images of the time series. The detection is basically composed of three steps. Firstly, we compute a synthetic ortho-image covering the whole study area. Its pixels have a value corresponding to the median value of all input reflectance ortho-images intersecting at that pixel location. Secondly, for each input ortho-image, a pixel is labelled shadows if the difference of reflectance (in the NIR channel) with the synthetic ortho-image is below a given threshold. Eventually, an optional region-growing step may be used to refine the results. Note that pixels labelled clouds during the cloud detection are not used for computing the median value in the first step; additionally, the NIR input data channel is used to perform the shadow detection, because it appeared to better discriminate shadow pixels. The method was tested on times series of Landsat 8 and Pl

  14. AUTOMATIC DETECTION OF CLOUDS AND SHADOWS USING HIGH RESOLUTION SATELLITE IMAGE TIME SERIES

    Directory of Open Access Journals (Sweden)

    N. Champion

    2016-06-01

    Full Text Available Detecting clouds and their shadows is one of the primaries steps to perform when processing satellite images because they may alter the quality of some products such as large-area orthomosaics. The main goal of this paper is to present the automatic method developed at IGN-France for detecting clouds and shadows in a sequence of satellite images. In our work, surface reflectance orthoimages are used. They were processed from initial satellite images using a dedicated software. The cloud detection step consists of a region-growing algorithm. Seeds are firstly extracted. For that purpose and for each input ortho-image to process, we select the other ortho-images of the sequence that intersect it. The pixels of the input ortho-image are secondly labelled seeds if the difference of reflectance (in the blue channel with overlapping ortho-images is bigger than a given threshold. Clouds are eventually delineated using a region-growing method based on a radiometric and homogeneity criterion. Regarding the shadow detection, our method is based on the idea that a shadow pixel is darker when comparing to the other images of the time series. The detection is basically composed of three steps. Firstly, we compute a synthetic ortho-image covering the whole study area. Its pixels have a value corresponding to the median value of all input reflectance ortho-images intersecting at that pixel location. Secondly, for each input ortho-image, a pixel is labelled shadows if the difference of reflectance (in the NIR channel with the synthetic ortho-image is below a given threshold. Eventually, an optional region-growing step may be used to refine the results. Note that pixels labelled clouds during the cloud detection are not used for computing the median value in the first step; additionally, the NIR input data channel is used to perform the shadow detection, because it appeared to better discriminate shadow pixels. The method was tested on times series of Landsat 8

  15. Epigenetic changes detected in micropropagated hop plants.

    Science.gov (United States)

    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.

  16. Activity Level Change Detection for Persistent Surveillance

    National Research Council Canada - National Science Library

    Liu, F; Bush, L. A

    2004-01-01

    .... Instead of traditional target tracking, this approach utilizes GMTI data as moving spots on the ground to estimate the level of activities and detect unusual activities such as military deployments...

  17. Detecting Holocene changes in thermohaline circulation

    OpenAIRE

    Keigwin, L. D.; Boyle, E. A.

    2000-01-01

    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. Change Detection in Naturalistic Pictures among Children with Autism

    Science.gov (United States)

    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,…

  19. Real-time change detection for countering improvised explosive devices

    NARCIS (Netherlands)

    Wouw, van de D.W.J.M.; Rens, van K.; Lint, van R.H.; Jaspers, Egbert; With, de P.H.N.; Loce, R.P.; Saber, E.

    2014-01-01

    We explore an automatic real-time change detection system to assist military personnel during transport and surveillance, by detection changes in the environment with respect to a previous operation. Such changes may indicate the presence of Improvised Explosive Devices (IEDs), which can then be

  20. The role of iconic memory in change-detection tasks.

    Science.gov (United States)

    Becker, M W; Pashler, H; Anstis, S M

    2000-01-01

    In three experiments, subjects attempted to detect the change of a single item in a visually presented array of items. Subjects' ability to detect a change was greatly reduced if a blank interstimulus interval (ISI) was inserted between the original array and an array in which one item had changed ('change blindness'). However, change detection improved when the location of the change was cued during the blank ISI. This suggests that people represent more information of a scene than change blindness might suggest. We test two possible hypotheses why, in the absence of a cue, this representation fails to produce good change detection. The first claims that the intervening events employed to create change blindness result in multiple neural transients which co-occur with the to-be-detected change. Poor detection rates occur because a serial search of all the transient locations is required to detect the change, during which time the representation of the original scene fades. The second claims that the occurrence of the second frame overwrites the representation of the first frame, unless that information is insulated against overwriting by attention. The results support the second hypothesis. We conclude that people may have a fairly rich visual representation of a scene while the scene is present, but fail to detect changes because they lack the ability to simultaneously represent two complete visual representations.

  1. Modeling climate change impacts on combined sewer overflow using synthetic precipitation time series.

    Science.gov (United States)

    Bendel, David; Beck, Ferdinand; Dittmer, Ulrich

    2013-01-01

    In the presented study climate change impacts on combined sewer overflows (CSOs) in Baden-Wuerttemberg, Southern Germany, were assessed based on continuous long-term rainfall-runoff simulations. As input data, synthetic rainfall time series were used. The applied precipitation generator NiedSim-Klima accounts for climate change effects on precipitation patterns. Time series for the past (1961-1990) and future (2041-2050) were generated for various locations. Comparing the simulated CSO activity of both periods we observe significantly higher overflow frequencies for the future. Changes in overflow volume and overflow duration depend on the type of overflow structure. Both values will increase at simple CSO structures that merely divide the flow, whereas they will decrease when the CSO structure is combined with a storage tank. However, there is a wide variation between the results of different precipitation time series (representative for different locations).

  2. Analysis of rhythmic variance - ANORVA. A new simple method for detecting rhythms in biological time series

    Directory of Open Access Journals (Sweden)

    Peter Celec

    2004-01-01

    Full Text Available Cyclic variations of variables are ubiquitous in biomedical science. A number of methods for detecting rhythms have been developed, but they are often difficult to interpret. A simple procedure for detecting cyclic variations in biological time series and quantification of their probability is presented here. Analysis of rhythmic variance (ANORVA is based on the premise that the variance in groups of data from rhythmic variables is low when a time distance of one period exists between the data entries. A detailed stepwise calculation is presented including data entry and preparation, variance calculating, and difference testing. An example for the application of the procedure is provided, and a real dataset of the number of papers published per day in January 2003 using selected keywords is compared to randomized datasets. Randomized datasets show no cyclic variations. The number of papers published daily, however, shows a clear and significant (p<0.03 circaseptan (period of 7 days rhythm, probably of social origin

  3. Sustained change blindness to incremental scene rotation: a dissociation between explicit change detection and visual memory.

    Science.gov (United States)

    Hollingworth, Andrew; Henderson, John M

    2004-07-01

    In a change detection paradigm, the global orientation of a natural scene was incrementally changed in 1 degree intervals. In Experiments 1 and 2, participants demonstrated sustained change blindness to incremental rotation, often coming to consider a significantly different scene viewpoint as an unchanged continuation of the original view. Experiment 3 showed that participants who failed to detect the incremental rotation nevertheless reliably detected a single-step rotation back to the initial view. Together, these results demonstrate an important dissociation between explicit change detection and visual memory. Following a change, visual memory is updated to reflect the changed state of the environment, even if the change was not detected.

  4. Discrete Data Qualification System and Method Comprising Noise Series Fault Detection

    Science.gov (United States)

    Fulton, Christopher; Wong, Edmond; Melcher, Kevin; Bickford, Randall

    2013-01-01

    A Sensor Data Qualification (SDQ) function has been developed that allows the onboard flight computers on NASA s launch vehicles to determine the validity of sensor data to ensure that critical safety and operational decisions are not based on faulty sensor data. This SDQ function includes a novel noise series fault detection algorithm for qualification of the output data from LO2 and LH2 low-level liquid sensors. These sensors are positioned in a launch vehicle s propellant tanks in order to detect propellant depletion during a rocket engine s boost operating phase. This detection capability can prevent the catastrophic situation where the engine operates without propellant. The output from each LO2 and LH2 low-level liquid sensor is a discrete valued signal that is expected to be in either of two states, depending on whether the sensor is immersed (wet) or exposed (dry). Conventional methods for sensor data qualification, such as threshold limit checking, are not effective for this type of signal due to its discrete binary-state nature. To address this data qualification challenge, a noise computation and evaluation method, also known as a noise fault detector, was developed to detect unreasonable statistical characteristics in the discrete data stream. The method operates on a time series of discrete data observations over a moving window of data points and performs a continuous examination of the resulting observation stream to identify the presence of anomalous characteristics. If the method determines the existence of anomalous results, the data from the sensor is disqualified for use by other monitoring or control functions.

  5. Burned area detection based on Landsat time series in savannas of southern Burkina Faso

    Science.gov (United States)

    Liu, Jinxiu; Heiskanen, Janne; Maeda, Eduardo Eiji; Pellikka, Petri K. E.

    2018-02-01

    West African savannas are subject to regular fires, which have impacts on vegetation structure, biodiversity and carbon balance. An efficient and accurate mapping of burned area associated with seasonal fires can greatly benefit decision making in land management. Since coarse resolution burned area products cannot meet the accuracy needed for fire management and climate modelling at local scales, the medium resolution Landsat data is a promising alternative for local scale studies. In this study, we developed an algorithm for continuous monitoring of annual burned areas using Landsat time series. The algorithm is based on burned pixel detection using harmonic model fitting with Landsat time series and breakpoint identification in the time series data. This approach was tested in a savanna area in southern Burkina Faso using 281 images acquired between October 2000 and April 2016. An overall accuracy of 79.2% was obtained with balanced omission and commission errors. This represents a significant improvement in comparison with MODIS burned area product (67.6%), which had more omission errors than commission errors, indicating underestimation of the total burned area. By observing the spatial distribution of burned areas, we found that the Landsat based method misclassified cropland and cloud shadows as burned areas due to the similar spectral response, and MODIS burned area product omitted small and fragmented burned areas. The proposed algorithm is flexible and robust against decreased data availability caused by clouds and Landsat 7 missing lines, therefore having a high potential for being applied in other landscapes in future studies.

  6. A graph-based approach to detect spatiotemporal dynamics in satellite image time series

    Science.gov (United States)

    Guttler, Fabio; Ienco, Dino; Nin, Jordi; Teisseire, Maguelonne; Poncelet, Pascal

    2017-08-01

    Enhancing the frequency of satellite acquisitions represents a key issue for Earth Observation community nowadays. Repeated observations are crucial for monitoring purposes, particularly when intra-annual process should be taken into account. Time series of images constitute a valuable source of information in these cases. The goal of this paper is to propose a new methodological framework to automatically detect and extract spatiotemporal information from satellite image time series (SITS). Existing methods dealing with such kind of data are usually classification-oriented and cannot provide information about evolutions and temporal behaviors. In this paper we propose a graph-based strategy that combines object-based image analysis (OBIA) with data mining techniques. Image objects computed at each individual timestamp are connected across the time series and generates a set of evolution graphs. Each evolution graph is associated to a particular area within the study site and stores information about its temporal evolution. Such information can be deeply explored at the evolution graph scale or used to compare the graphs and supply a general picture at the study site scale. We validated our framework on two study sites located in the South of France and involving different types of natural, semi-natural and agricultural areas. The results obtained from a Landsat SITS support the quality of the methodological approach and illustrate how the framework can be employed to extract and characterize spatiotemporal dynamics.

  7. Methods for Detecting Early Warnings of Critical Transitions in Time Series Illustrated Using Simulated Ecological Data

    Science.gov (United States)

    Dakos, Vasilis; Carpenter, Stephen R.; Brock, William A.; Ellison, Aaron M.; Guttal, Vishwesha; Ives, Anthony R.; Kéfi, Sonia; Livina, Valerie; Seekell, David A.; van Nes, Egbert H.; Scheffer, Marten

    2012-01-01

    Many dynamical systems, including lakes, organisms, ocean circulation patterns, or financial markets, are now thought to have tipping points where critical transitions to a contrasting state can happen. Because critical transitions can occur unexpectedly and are difficult to manage, there is a need for methods that can be used to identify when a critical transition is approaching. Recent theory shows that we can identify the proximity of a system to a critical transition using a variety of so-called ‘early warning signals’, and successful empirical examples suggest a potential for practical applicability. However, while the range of proposed methods for predicting critical transitions is rapidly expanding, opinions on their practical use differ widely, and there is no comparative study that tests the limitations of the different methods to identify approaching critical transitions using time-series data. Here, we summarize a range of currently available early warning methods and apply them to two simulated time series that are typical of systems undergoing a critical transition. In addition to a methodological guide, our work offers a practical toolbox that may be used in a wide range of fields to help detect early warning signals of critical transitions in time series data. PMID:22815897

  8. Illumination Invariant Change Detection (iicd): from Earth to Mars

    Science.gov (United States)

    Wan, X.; Liu, J.; Qin, M.; Li, S. Y.

    2018-04-01

    Multi-temporal Earth Observation and Mars orbital imagery data with frequent repeat coverage provide great capability for planetary surface change detection. When comparing two images taken at different times of day or in different seasons for change detection, the variation of topographic shades and shadows caused by the change of sunlight angle can be so significant that it overwhelms the real object and environmental changes, making automatic detection unreliable. An effective change detection algorithm therefore has to be robust to the illumination variation. This paper presents our research on developing and testing an Illumination Invariant Change Detection (IICD) method based on the robustness of phase correlation (PC) to the variation of solar illumination for image matching. The IICD is based on two key functions: i) initial change detection based on a saliency map derived from pixel-wise dense PC matching and ii) change quantization which combines change type identification, motion estimation and precise appearance change identification. Experiment using multi-temporal Landsat 7 ETM+ satellite images, Rapid eye satellite images and Mars HiRiSE images demonstrate that our frequency based image matching method can reach sub-pixel accuracy and thus the proposed IICD method can effectively detect and precisely segment large scale change such as landslide as well as small object change such as Mars rover, under daily and seasonal sunlight changes.

  9. Sensitive change detection for remote sensing monitoring of nuclear treaties

    DEFF Research Database (Denmark)

    Canty, Morton J.; Nielsen, Allan Aasbjerg; Schlittenhardt, Jörg

    2005-01-01

    change is a commonplace application in remote sensing, the detection of anthropogenic changes associated with nuclear activities, whether declared or clandestine, presents a difficult challenge. It is necessary to discriminate subtle, often weak signals of interest on a background of irrelevant...... in multispectral, bitemporal image data: New approaches to change detection studies, Remote Sens. Environ. 64(1), 1998, pp. 1--19. Nielsen, A. A., Iteratively re-weighted multivariate alteration detection in multi- and hyperspectral data, to be published....

  10. Kernel principal component analysis for change detection

    DEFF Research Database (Denmark)

    Nielsen, Allan Aasbjerg; Morton, J.C.

    2008-01-01

    region acquired at two different time points. If change over time does not dominate the scene, the projection of the original two bands onto the second eigenvector will show change over time. In this paper a kernel version of PCA is used to carry out the analysis. Unlike ordinary PCA, kernel PCA...... with a Gaussian kernel successfully finds the change observations in a case where nonlinearities are introduced artificially....

  11. Anomaly Detection in Smart Metering Infrastructure with the Use of Time Series Analysis

    Directory of Open Access Journals (Sweden)

    Tomasz Andrysiak

    2017-01-01

    Full Text Available The article presents solutions to anomaly detection in network traffic for critical smart metering infrastructure, realized with the use of radio sensory network. The structure of the examined smart meter network and the key security aspects which have influence on the correct performance of an advanced metering infrastructure (possibility of passive and active cyberattacks are described. An effective and quick anomaly detection method is proposed. At its initial stage, Cook’s distance was used for detection and elimination of outlier observations. So prepared data was used to estimate standard statistical models based on exponential smoothing, that is, Brown’s, Holt’s, and Winters’ models. To estimate possible fluctuations in forecasts of the implemented models, properly parameterized Bollinger Bands was used. Next, statistical relations between the estimated traffic model and its real variability were examined to detect abnormal behavior, which could indicate a cyberattack attempt. An update procedure of standard models in case there were significant real network traffic fluctuations was also proposed. The choice of optimal parameter values of statistical models was realized as forecast error minimization. The results confirmed efficiency of the presented method and accuracy of choice of the proper statistical model for the analyzed time series.

  12. A dual-process account of auditory change detection.

    Science.gov (United States)

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

    2010-08-01

    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 objects with those predicted by change-detection models based on signal detection theory (SDT) and high-threshold theory (HTT). Detected changes were not identified as accurately as predicted by models based on either theory, suggesting that some changes are detected by a process that does not support change identification. Undetected changes were identified as accurately as predicted by the HTT model but much less accurately than predicted by the SDT models. The process underlying change detection was investigated further by determining receiver-operating characteristics (ROCs). ROCs did not conform to those predicted by either a SDT or a HTT model but were well modeled by a dual-process that incorporated HTT and SDT components. The dual-process model also accurately predicted the rates at which detected and undetected changes were correctly identified.

  13. Detecting Inter-Annual Variations in the Phenology of Evergreen Conifers Using Long-Term MODIS Vegetation Index Time Series

    Directory of Open Access Journals (Sweden)

    Laura Ulsig

    2017-01-01

    Full Text Available Long-term observations of vegetation phenology can be used to monitor the response of terrestrial ecosystems to climate change. Satellite remote sensing provides the most efficient means to observe phenological events through time series analysis of vegetation indices such as the Normalized Difference Vegetation Index (NDVI. This study investigates the potential of a Photochemical Reflectance Index (PRI, which has been linked to vegetation light use efficiency, to improve the accuracy of MODIS-based estimates of phenology in an evergreen conifer forest. Timings of the start and end of the growing season (SGS and EGS were derived from a 13-year-long time series of PRI and NDVI based on a MAIAC (multi-angle implementation of atmospheric correction processed MODIS dataset and standard MODIS NDVI product data. The derived dates were validated with phenology estimates from ground-based flux tower measurements of ecosystem productivity. Significant correlations were found between the MAIAC time series and ground-estimated SGS (R2 = 0.36–0.8, which is remarkable since previous studies have found it difficult to observe inter-annual phenological variations in evergreen vegetation from satellite data. The considerably noisier NDVI product could not accurately predict SGS, and EGS could not be derived successfully from any of the time series. While the strongest relationship overall was found between SGS derived from the ground data and PRI, MAIAC NDVI exhibited high correlations with SGS more consistently (R2 > 0.6 in all cases. The results suggest that PRI can serve as an effective indicator of spring seasonal transitions, however, additional work is necessary to confirm the relationships observed and to further explore the usefulness of MODIS PRI for detecting phenology.

  14. Ability of Ultrasonography in Detection of Different Extremity Bone Fractures; a Case Series Study.

    Science.gov (United States)

    Bozorgi, Farzad; Shayesteh Azar, Massoud; Montazer, Seyed Hossein; Chabra, Aroona; Heidari, Seyed Farshad; Khalilian, Alireza

    2017-01-01

    Despite radiography being the gold standard in evaluation of orthopedic injuries, using bedside ultrasonography has several potential supremacies such as avoiding exposure to ionizing radiation, availability in pre-hospital settings, being extensively accessible, and ability to be used on the bedside. The aim of the present study is to evaluate the diagnostic accuracy of ultrasonography in detection of extremity bone fractures. This study is a case series study, which was prospectively conducted on multiple blunt trauma patients, who were 18 years old or older, had stable hemodynamic, Glasgow coma scale 15, and signs or symptoms of a possible extremity bone fracture. After initial assessment, ultrasonography of suspected bones was performed by a trained emergency medicine resident and prevalence of true positive and false negative findings were calculated compared to plain radiology. 108 patients with the mean age of 44.6 ± 20.4 years were studied (67.6% male). Analysis was done on 158 sites of fracture, which were confirmed with plain radiography. 91 (57.6%) cases were suspected to have upper extremity fracture(s) and 67 (42.4%) to have lower ones. The most frequent site of injuries were forearm (36.7%) in upper limbs and leg (27.8%) in lower limbs. Prevalence of true positive and false negative cases for fractures detected by ultrasonography were 59 (64.8%) and 32 (35.52%) for upper and 49 (73.1%) and 18 (26.9%) for lower extremities, respectively. In addition, prevalence of true positive and false negative detected cases for intra-articular fractures were 24 (48%) and 26 (52%), respectively. The present study shows the moderate sensitivity (68.3%) of ultrasonography in detection of different extremity bone fractures. Ultrasonography showed the best sensitivity in detection of femur (100%) and humerus (76.2%) fractures, respectively. It had low sensitivity in detection of in intra-articular fractures.

  15. OceanXtremes: Scalable Anomaly Detection in Oceanographic Time-Series

    Science.gov (United States)

    Wilson, B. D.; Armstrong, E. M.; Chin, T. M.; Gill, K. M.; Greguska, F. R., III; Huang, T.; Jacob, J. C.; Quach, N.

    2016-12-01

    The oceanographic community must meet the challenge to rapidly identify features and anomalies in complex and voluminous observations to further science and improve decision support. Given this data-intensive reality, we are developing an anomaly detection system, called OceanXtremes, powered by an intelligent, elastic Cloud-based analytic service backend that enables execution of domain-specific, multi-scale anomaly and feature detection algorithms across the entire archive of 15 to 30-year ocean science datasets.Our parallel analytics engine is extending the NEXUS system and exploits multiple open-source technologies: Apache Cassandra as a distributed spatial "tile" cache, Apache Spark for in-memory parallel computation, and Apache Solr for spatial search and storing pre-computed tile statistics and other metadata. OceanXtremes provides these key capabilities: Parallel generation (Spark on a compute cluster) of 15 to 30-year Ocean Climatologies (e.g. sea surface temperature or SST) in hours or overnight, using simple pixel averages or customizable Gaussian-weighted "smoothing" over latitude, longitude, and time; Parallel pre-computation, tiling, and caching of anomaly fields (daily variables minus a chosen climatology) with pre-computed tile statistics; Parallel detection (over the time-series of tiles) of anomalies or phenomena by regional area-averages exceeding a specified threshold (e.g. high SST in El Nino or SST "blob" regions), or more complex, custom data mining algorithms; Shared discovery and exploration of ocean phenomena and anomalies (facet search using Solr), along with unexpected correlations between key measured variables; Scalable execution for all capabilities on a hybrid Cloud, using our on-premise OpenStack Cloud cluster or at Amazon. The key idea is that the parallel data-mining operations will be run "near" the ocean data archives (a local "network" hop) so that we can efficiently access the thousands of files making up a three decade time-series

  16. Using space-time features to improve detection of forest disturbances from Landsat time series

    NARCIS (Netherlands)

    Hamunyela, E.; Reiche, J.; Verbesselt, J.; Herold, M.

    2017-01-01

    Current research on forest change monitoring using medium spatial resolution Landsat satellite data aims for accurate and timely detection of forest disturbances. However, producing forest disturbance maps that have both high spatial and temporal accuracy is still challenging because of the

  17. Grasp Preparation Improves Change Detection for Congruent Objects

    Science.gov (United States)

    Symes, Ed; Tucker, Mike; Ellis, Rob; Vainio, Lari; Ottoboni, Giovanni

    2008-01-01

    A series of experiments provided converging support for the hypothesis that action preparation biases selective attention to action-congruent object features. When visual transients are masked in so-called "change-blindness scenes," viewers are blind to substantial changes between 2 otherwise identical pictures that flick back and forth. The…

  18. Detecting Change-Point via Saddlepoint Approximations

    Institute of Scientific and Technical Information of China (English)

    Zhaoyuan LI; Maozai TIAN

    2017-01-01

    It's well-known that change-point problem is an important part of model statistical analysis.Most of the existing methods are not robust to criteria of the evaluation of change-point problem.In this article,we consider "mean-shift" problem in change-point studies.A quantile test of single quantile is proposed based on saddlepoint approximation method.In order to utilize the information at different quantile of the sequence,we further construct a "composite quantile test" to calculate the probability of every location of the sequence to be a change-point.The location of change-point can be pinpointed rather than estimated within a interval.The proposed tests make no assumptions about the functional forms of the sequence distribution and work sensitively on both large and small size samples,the case of change-point in the tails,and multiple change-points situation.The good performances of the tests are confirmed by simulations and real data analysis.The saddlepoint approximation based distribution of the test statistic that is developed in the paper is of independent interest and appealing.This finding may be of independent interest to the readers in this research area.

  19. Harmonic analysis of dense time series of landsat imagery for modeling change in forest conditions

    Science.gov (United States)

    Barry Tyler. Wilson

    2015-01-01

    This study examined the utility of dense time series of Landsat imagery for small area estimation and mapping of change in forest conditions over time. The study area was a region in north central Wisconsin for which Landsat 7 ETM+ imagery and field measurements from the Forest Inventory and Analysis program are available for the decade of 2003 to 2012. For the periods...

  20. Aircraft control surface failure detection and isolation using the OSGLR test. [orthogonal series generalized likelihood ratio

    Science.gov (United States)

    Bonnice, W. F.; Motyka, P.; Wagner, E.; Hall, S. R.

    1986-01-01

    The performance of the orthogonal series generalized likelihood ratio (OSGLR) test in detecting and isolating commercial aircraft control surface and actuator failures is evaluated. A modification to incorporate age-weighting which significantly reduces the sensitivity of the algorithm to modeling errors is presented. The steady-state implementation of the algorithm based on a single linear model valid for a cruise flight condition is tested using a nonlinear aircraft simulation. A number of off-nominal no-failure flight conditions including maneuvers, nonzero flap deflections, different turbulence levels and steady winds were tested. Based on the no-failure decision functions produced by off-nominal flight conditions, the failure detection and isolation performance at the nominal flight condition was determined. The extension of the algorithm to a wider flight envelope by scheduling on dynamic pressure and flap deflection is examined. Based on this testing, the OSGLR algorithm should be capable of detecting control surface failures that would affect the safe operation of a commercial aircraft. Isolation may be difficult if there are several surfaces which produce similar effects on the aircraft. Extending the algorithm over the entire operating envelope of a commercial aircraft appears feasible.

  1. Stochastic Change Detection based on an Active Fault Diagnosis Approach

    DEFF Research Database (Denmark)

    Poulsen, Niels Kjølstad; Niemann, Hans Henrik

    2007-01-01

    The focus in this paper is on stochastic change detection applied in connection with active fault diagnosis (AFD). An auxiliary input signal is applied in AFD. This signal injection in the system will in general allow to obtain a fast change detection/isolation by considering the output or an err...

  2. Unsupervised Speaker Change Detection for Broadcast News Segmentation

    DEFF Research Database (Denmark)

    Jørgensen, Kasper Winther; Mølgaard, Lasse Lohilahti; Hansen, Lars Kai

    2006-01-01

    This paper presents a speaker change detection system for news broadcast segmentation based on a vector quantization (VQ) approach. The system does not make any assumption about the number of speakers or speaker identity. The system uses mel frequency cepstral coefficients and change detection...

  3. Regularisation in multi- and hyperspectral remote sensing change detection

    DEFF Research Database (Denmark)

    Nielsen, Allan Aasbjerg

    2005-01-01

    Change detection methods for multi- and hypervariate data look for differences in data acquired over the same area at different points in time. These differences may be due to noise or differences in (atmospheric etc.) conditions at the two acquisition time points. To prevent a change detection m...

  4. Detection of Outliers and Imputing of Missing Values for Water Quality UV-VIS Absorbance Time Series

    Directory of Open Access Journals (Sweden)

    Leonardo Plazas-Nossa

    2017-01-01

    Full Text Available Context: The UV-Vis absorbance collection using online optical captors for water quality detection may yield outliers and/or missing values. Therefore, data pre-processing is a necessary pre-requisite to monitoring data processing. Thus, the aim of this study is to propose a method that detects and removes outliers as well as fills gaps in time series. Method: Outliers are detected using Winsorising procedure and the application of the Discrete Fourier Transform (DFT and the Inverse of Fast Fourier Transform (IFFT to complete the time series. Together, these tools were used to analyse a case study comprising three sites in Colombia ((i Bogotá D.C. Salitre-WWTP (Waste Water Treatment Plant, influent; (ii Bogotá D.C. Gibraltar Pumping Station (GPS; and, (iii Itagüí, San Fernando-WWTP, influent (Medellín metropolitan area analysed via UV-Vis (Ultraviolet and Visible spectra. Results: Outlier detection with the proposed method obtained promising results when window parameter values are small and self-similar, despite that the three time series exhibited different sizes and behaviours. The DFT allowed to process different length gaps having missing values. To assess the validity of the proposed method, continuous subsets (a section of the absorbance time series without outlier or missing values were removed from the original time series obtaining an average 12% error rate in the three testing time series. Conclusions: The application of the DFT and the IFFT, using the 10% most important harmonics of useful values, can be useful for its later use in different applications, specifically for time series of water quality and quantity in urban sewer systems. One potential application would be the analysis of dry weather interesting to rain events, a feat achieved by detecting values that correspond to unusual behaviour in a time series. Additionally, the result hints at the potential of the method in correcting other hydrologic time series.

  5. Unsupervised Condition Change Detection In Large Diesel Engines

    DEFF Research Database (Denmark)

    Pontoppidan, Niels Henrik; Larsen, Jan

    2003-01-01

    This paper presents a new method for unsupervised change detection which combines independent component modeling and probabilistic outlier etection. The method further provides a compact data representation, which is amenable to interpretation, i.e., the detected condition changes can be investig...... be investigated further. The method is successfully applied to unsupervised condition change detection in large diesel engines from acoustical emission sensor signal and compared to more classical techniques based on principal component analysis and Gaussian mixture models.......This paper presents a new method for unsupervised change detection which combines independent component modeling and probabilistic outlier etection. The method further provides a compact data representation, which is amenable to interpretation, i.e., the detected condition changes can...

  6. DynPeak: An Algorithm for Pulse Detection and Frequency Analysis in Hormonal Time Series

    Science.gov (United States)

    Vidal, Alexandre; Zhang, Qinghua; Médigue, Claire; Fabre, Stéphane; Clément, Frédérique

    2012-01-01

    The endocrine control of the reproductive function is often studied from the analysis of luteinizing hormone (LH) pulsatile secretion by the pituitary gland. Whereas measurements in the cavernous sinus cumulate anatomical and technical difficulties, LH levels can be easily assessed from jugular blood. However, plasma levels result from a convolution process due to clearance effects when LH enters the general circulation. Simultaneous measurements comparing LH levels in the cavernous sinus and jugular blood have revealed clear differences in the pulse shape, the amplitude and the baseline. Besides, experimental sampling occurs at a relatively low frequency (typically every 10 min) with respect to LH highest frequency release (one pulse per hour) and the resulting LH measurements are noised by both experimental and assay errors. As a result, the pattern of plasma LH may be not so clearly pulsatile. Yet, reliable information on the InterPulse Intervals (IPI) is a prerequisite to study precisely the steroid feedback exerted on the pituitary level. Hence, there is a real need for robust IPI detection algorithms. In this article, we present an algorithm for the monitoring of LH pulse frequency, basing ourselves both on the available endocrinological knowledge on LH pulse (shape and duration with respect to the frequency regime) and synthetic LH data generated by a simple model. We make use of synthetic data to make clear some basic notions underlying our algorithmic choices. We focus on explaining how the process of sampling affects drastically the original pattern of secretion, and especially the amplitude of the detectable pulses. We then describe the algorithm in details and perform it on different sets of both synthetic and experimental LH time series. We further comment on how to diagnose possible outliers from the series of IPIs which is the main output of the algorithm. PMID:22802933

  7. Saliency predicts change detection in pictures of natural scenes.

    Science.gov (United States)

    Wright, Michael J

    2005-01-01

    It has been proposed that the visual system encodes the salience of objects in the visual field in an explicit two-dimensional map that guides visual selective attention. Experiments were conducted to determine whether salience measurements applied to regions of pictures of outdoor scenes could predict the detection of changes in those regions. To obtain a quantitative measure of change detection, observers located changes in pairs of colour pictures presented across an interstimulus interval (ISI). Salience measurements were then obtained from different observers for image change regions using three independent methods, and all were positively correlated with change detection. Factor analysis extracted a single saliency factor that accounted for 62% of the variance contained in the four measures. Finally, estimates of the magnitude of the image change in each picture pair were obtained, using nine separate visual filters representing low-level vision features (luminance, colour, spatial frequency, orientation, edge density). None of the feature outputs was significantly associated with change detection or saliency. On the other hand it was shown that high-level (structural) properties of the changed region were related to saliency and to change detection: objects were more salient than shadows and more detectable when changed.

  8. Detecting Drought-Induced Tree Mortality in Sierra Nevada Forests with Time Series of Satellite Data

    Directory of Open Access Journals (Sweden)

    Sarah Byer

    2017-09-01

    Full Text Available A five-year drought in California led to a significant increase in tree mortality in the Sierra Nevada forests from 2012 to 2016. Landscape level monitoring of forest health and tree dieback is critical for vegetation and disaster management strategies. We examined the capability of multispectral imagery from the Moderate Resolution Imaging Spectroradiometer (MODIS in detecting and explaining the impacts of the recent severe drought in Sierra Nevada forests. Remote sensing metrics were developed to represent baseline forest health conditions and drought stress using time series of MODIS vegetation indices (VIs and a water index. We used Random Forest algorithms, trained with forest aerial detection surveys data, to detect tree mortality based on the remote sensing metrics and topographical variables. Map estimates of tree mortality demonstrated that our two-stage Random Forest models were capable of detecting the spatial patterns and severity of tree mortality, with an overall producer’s accuracy of 96.3% for the classification Random Forest (CRF and a RMSE of 7.19 dead trees per acre for the regression Random Forest (RRF. The overall omission errors of the CRF ranged from 19% for the severe mortality class to 27% for the low mortality class. Interpretations of the models revealed that forests with higher productivity preceding the onset of drought were more vulnerable to drought stress and, consequently, more likely to experience tree mortality. This method highlights the importance of incorporating baseline forest health data and measurements of drought stress in understanding forest response to severe drought.

  9. Detection of Hydrological changes of Wujiang River

    Science.gov (United States)

    Dong, L.; Chen, Y.

    2016-12-01

    In the century our earth experienced a rapid environment changes due to strong human activities, which impactedthe earth'shydrology and water resources systems negatively, and causedsevere problems to the society, such as increased flood and drought risk, water pollution and ecosystem degradation. Understanding the variations of hydrological characteristics has important meaning to solve the problem of hydrology and water resources and maintain sustainable development of river basin water resources.This paper takesWujiangriveras an example,which is a typical medium watershedaffected by human activities seriously in southern China.Using the methods of Mann-Kendall test and serial cluster analysis, this paper studies the characteristics and laws of historical hydrological process inWujiang river, detectsthe impact of changing environment to watershed hydrological processes, based on the observed hydrological data of 36 years from 1980 to 2015 in three representative hydrological stationsnamedFenshi,Chixi and Pingshi. The results show that the annual runoffandannual precipitation has some kind of changes.

  10. Satellite time-series data for vegetation phenology detection and environmental assessment in Southeast Asia

    Science.gov (United States)

    Suepa, Tanita

    The relationship between temporal and spatial data is considered the major advantage of remote sensing in research related to biophysical characteristics. With temporally formatted remote sensing products, it is possible to monitor environmental changes as well as global climate change through time and space by analyzing vegetation phenology. Although a number of different methods have been developed to determine the seasonal cycle using time series of vegetation indices, these methods were not designed to explore and monitor changes and trends of vegetation phenology in Southeast Asia (SEA). SEA is adversely affected by impacts of climate change, which causes considerable environmental problems, and the increase in agricultural land conversion and intensification also adds to those problems. Consequently, exploring and monitoring phenological change and environmental impacts are necessary for a better understanding of the ecosystem dynamics and environmental change in this region. This research aimed to investigate inter-annual variability of vegetation phenology and rainfall seasonality, analyze the possible drivers of phenological changes from both climatic and anthropogenic factors, assess the environmental impacts in agricultural areas, and develop an enhanced visualization method for phenological information dissemination. In this research, spatio-temporal patterns of vegetation phenology were analyzed by using MODIS-EVI time series data over the period of 2001-2010. Rainfall seasonality was derived from TRMM daily rainfall rate. Additionally, this research assessed environmental impacts of GHG emissions by using the environmental model (DNDC) to quantify emissions from rice fields in Thailand. Furthermore, a web mapping application was developed to present the output of phenological and environmental analysis with interactive functions. The results revealed that satellite time-series data provided a great opportunity to study regional vegetation variability

  11. Capturing Context-Related Change in Emotional Dynamics via Fixed Moderated Time Series Analysis.

    Science.gov (United States)

    Adolf, Janne K; Voelkle, Manuel C; Brose, Annette; Schmiedek, Florian

    2017-01-01

    Much of recent affect research relies on intensive longitudinal studies to assess daily emotional experiences. The resulting data are analyzed with dynamic models to capture regulatory processes involved in emotional functioning. Daily contexts, however, are commonly ignored. This may not only result in biased parameter estimates and wrong conclusions, but also ignores the opportunity to investigate contextual effects on emotional dynamics. With fixed moderated time series analysis, we present an approach that resolves this problem by estimating context-dependent change in dynamic parameters in single-subject time series models. The approach examines parameter changes of known shape and thus addresses the problem of observed intra-individual heterogeneity (e.g., changes in emotional dynamics due to observed changes in daily stress). In comparison to existing approaches to unobserved heterogeneity, model estimation is facilitated and different forms of change can readily be accommodated. We demonstrate the approach's viability given relatively short time series by means of a simulation study. In addition, we present an empirical application, targeting the joint dynamics of affect and stress and how these co-vary with daily events. We discuss potentials and limitations of the approach and close with an outlook on the broader implications for understanding emotional adaption and development.

  12. Land cover change detection in West Jilin using ETM+ images

    Institute of Scientific and Technical Information of China (English)

    Edward M.Osei,Jr.; ZHOU Yun-xuan

    2004-01-01

    In order to assess the information content and accuracy ofLandsat ETM+ digital images in land cover change detection,change-detection techniques of image differencing,normalized difference vegetation index,principal components analysis and tasseled-cap transformation were applied to yield 13 images. These images were thresholded into change and no change areas. The thresholded images were then checked in terms of various accuracies. The experiment results show that kappa coefficients of the 13 images range from 48.05 ~78.09. Different images do detect different types of changes. Images associated with changes in the near-infrared-reflectance or greenness detects crop-type changes and changes between vegetative and non-vegetative features. A unique means of using only Landsat imagery without reference data for the assessment of change in arid land are presented. Images of 12th June, 2000 and 2nd June, 2002 are used to validate the means. Analyses of standard accuracy and spatial agreement are performed to compare the new images (hereafter called "change images" ) representing the change between the two dates. Spatial agreement evaluates the conformity in the classified "change pixels" and "no-change pixels" at the same location on different change images and comprehensively examines the different techniques. This method would enable authorities to monitor land degradation efficiently and accurately.

  13. Detection of anthropogenic climate change in satellite records of ocean chlorophyll and productivity

    Directory of Open Access Journals (Sweden)

    S. A. Henson

    2010-02-01

    Full Text Available Global climate change is predicted to alter the ocean's biological productivity. But how will we recognise the impacts of climate change on ocean productivity? The most comprehensive information available on its global distribution comes from satellite ocean colour data. Now that over ten years of satellite-derived chlorophyll and productivity data have accumulated, can we begin to detect and attribute climate change-driven trends in productivity? Here we compare recent trends in satellite ocean colour data to longer-term time series from three biogeochemical models (GFDL, IPSL and NCAR. We find that detection of climate change-driven trends in the satellite data is confounded by the relatively short time series and large interannual and decadal variability in productivity. Thus, recent observed changes in chlorophyll, primary production and the size of the oligotrophic gyres cannot be unequivocally attributed to the impact of global climate change. Instead, our analyses suggest that a time series of ~40 years length is needed to distinguish a global warming trend from natural variability. In some regions, notably equatorial regions, detection times are predicted to be shorter (~20–30 years. Analysis of modelled chlorophyll and primary production from 2001–2100 suggests that, on average, the climate change-driven trend will not be unambiguously separable from decadal variability until ~2055. Because the magnitude of natural variability in chlorophyll and primary production is larger than, or similar to, the global warming trend, a consistent, decades-long data record must be established if the impact of climate change on ocean productivity is to be definitively detected.

  14. One new method for road data shape change detection

    Science.gov (United States)

    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.

  15. PhilDB: the time series database with built-in change logging

    Directory of Open Access Journals (Sweden)

    Andrew MacDonald

    2016-03-01

    Full Text Available PhilDB is an open-source time series database that supports storage of time series datasets that are dynamic; that is, it records updates to existing values in a log as they occur. PhilDB eases loading of data for the user by utilising an intelligent data write method. It preserves existing values during updates and abstracts the update complexity required to achieve logging of data value changes. It implements fast reads to make it practical to select data for analysis. Recent open-source systems have been developed to indefinitely store long-period high-resolution time series data without change logging. Unfortunately, such systems generally require a large initial installation investment before use because they are designed to operate over a cluster of servers to achieve high-performance writing of static data in real time. In essence, they have a ‘big data’ approach to storage and access. Other open-source projects for handling time series data that avoid the ‘big data’ approach are also relatively new and are complex or incomplete. None of these systems gracefully handle revision of existing data while tracking values that change. Unlike ‘big data’ solutions, PhilDB has been designed for single machine deployment on commodity hardware, reducing the barrier to deployment. PhilDB takes a unique approach to meta-data tracking; optional attribute attachment. This facilitates scaling the complexities of storing a wide variety of data. That is, it allows time series data to be loaded as time series instances with minimal initial meta-data, yet additional attributes can be created and attached to differentiate the time series instances when a wider variety of data is needed. PhilDB was written in Python, leveraging existing libraries. While some existing systems come close to meeting the needs PhilDB addresses, none cover all the needs at once. PhilDB was written to fill this gap in existing solutions. This paper explores existing time

  16. Learning a Transferable Change Rule from a Recurrent Neural Network for Land Cover Change Detection

    Directory of Open Access Journals (Sweden)

    Haobo Lyu

    2016-06-01

    Full Text Available When exploited in remote sensing analysis, a reliable change rule with transfer ability can detect changes accurately and be applied widely. However, in practice, the complexity of land cover changes makes it difficult to use only one change rule or change feature learned from a given multi-temporal dataset to detect any other new target images without applying other learning processes. In this study, we consider the design of an efficient change rule having transferability to detect both binary and multi-class changes. The proposed method relies on an improved Long Short-Term Memory (LSTM model to acquire and record the change information of long-term sequence remote sensing data. In particular, a core memory cell is utilized to learn the change rule from the information concerning binary changes or multi-class changes. Three gates are utilized to control the input, output and update of the LSTM model for optimization. In addition, the learned rule can be applied to detect changes and transfer the change rule from one learned image to another new target multi-temporal image. In this study, binary experiments, transfer experiments and multi-class change experiments are exploited to demonstrate the superiority of our method. Three contributions of this work can be summarized as follows: (1 the proposed method can learn an effective change rule to provide reliable change information for multi-temporal images; (2 the learned change rule has good transferability for detecting changes in new target images without any extra learning process, and the new target images should have a multi-spectral distribution similar to that of the training images; and (3 to the authors’ best knowledge, this is the first time that deep learning in recurrent neural networks is exploited for change detection. In addition, under the framework of the proposed method, changes can be detected under both binary detection and multi-class change detection.

  17. Detection of skeletal muscle metastases on initial staging of lung cancer: a retrospective case series.

    Science.gov (United States)

    Bocchino, Marialuisa; Valente, Tullio; Somma, Francesco; de Rosa, Ilaria; Bifulco, Marco; Rea, Gaetano

    2014-03-01

    Estimation of skeletal muscle metastases (SMMs) at the time of diagnosis and/or initial staging of lung cancer. Retrospective evaluation of clinical charts and imaging data suggestive of SMMs of patients with histology-proved lung cancer over a 5-year period. SMMs were identified in 46 out of 1,754 patients. Single and multiple (62.9% of cases) SMMs were detected by total body multi-detector computed tomography (MDCT). They were associated with poorly differentiated (43%) and advanced adenocarcinomas (52%) without clinically relevant symptoms and/or signs. Psoas and buttock muscles were most frequently involved (33.3%). MDCT findings consisted of well-defined homogeneously hyperdense oval masses (31%), lesions with ring-like enhancement and central hypoattenuation (68%), or large abscess-like necrotic lesions (24%). Sonography revealed well-defined hypoechoic masses (41.6%), ill-defined hypoechoic lesions (33.3%), or anechoic areas with a necrotic centre (25%). Positron emission tomography revealed that all SMMs were metabolically active. SMMs are uncommon but not negligible in lung cancer, with an estimated prevalence of 2.62% in our series. Although histology remains the recommended method, use of high-performance imaging techniques and increased clinical suspicion may improve their early detection. Efforts addressing their effect on the natural history of lung cancer are needed.

  18. Detection of Quasi-Periodic Pulsations in Solar EUV Time Series

    Science.gov (United States)

    Dominique, M.; Zhukov, A. N.; Dolla, L.; Inglis, A.; Lapenta, G.

    2018-04-01

    Quasi-periodic pulsations (QPPs) are intrinsically connected to the mechanism of solar flares. They are regularly observed in the impulsive phase of flares since the 1970s. In the past years, the studies of QPPs regained interest with the advent of a new generation of soft X-ray/extreme ultraviolet radiometers that pave the way for statistical surveys. Since the amplitude of QPPs in these wavelengths is rather small, detecting them implies that the overall trend of the time series needs to be removed before applying any Fourier or wavelet transform. This detrending process is known to produce artificial detection of periods that must then be distinguished from real ones. In this paper, we propose a set of criteria to help identify real periods and discard artifacts. We apply these criteria to data taken by the Extreme Ultraviolet Variability Experiment (EVE)/ESP onboard the Solar Dynamics Observatory (SDO) and the Large Yield Radiometer (LYRA) onboard the PRoject for On-Board Autonomy 2 (PROBA2) to search for QPPs in flares stronger than M5.0 that occurred during Solar Cycle 24.

  19. Detection of Botulinum Toxin Muscle Effect in Humans Using Magnetic Resonance Imaging: A Qualitative Case Series.

    Science.gov (United States)

    O'Dell, Michael W; Villanueva, Mark; Creelman, Carly; Telhan, Gaurav; Nestor, Jaclyn; Hentel, Keith D; Ballon, Douglas; Dyke, Jonathan P

    2017-12-01

    Although important for dosing and dilution, there are few data describing botulinum toxin (BT) movement in human muscle. To better understand BT movement within human muscle. Proof-of-concept study with descriptive case series. Outpatient academic practice. Five subjects with stroke who were BT naive with a mean age of 60.4 ± 14 years and time poststroke of 4.6 ± 3.7 years. Three standardized injections were given to the lateral gastrocnemius muscle (LGM): 2 contained 25 units (U) of onabotulinumtoxinA (Botox) in 0.25 mL of saline solution and the third 0.25 mL of saline solution only. The tibialis anterior muscle (TAM) was not injected in any subject. A leg magnetic resonance image was obtained at baseline, 2 months, and 3 months later with a 3.0 Tesla Siemens scanner. Three muscles, the LGM, lateral soleus muscle (LSM), and TAM, were manually outlined on the T2 mapping sequence at each time point. A histogram of T2 relaxation times (T2-RT) for all voxels at baseline was used to calculate a mean and standard deviation (SD) T2-RT for each muscle. Botulinum toxin muscle effect (BTME) at 2 months and 3 months was defined as a subject- and muscle-specific T2-RT voxel threshold ≥3 SD above the baseline mean at or near BT injection sites. BTME volume for each leg magnetic resonance imaging slice at 3 time points and 3 muscles for all subjects. One subject missed the 3-month scan, leaving 18 potential observations of BTME. Little to no BTME effect was seen in the noninjected TAM. A BTME was detected in the LGM in 13 of 18 possible observations, and no effect was detected in 5 observations. Possible BTME effect was seen in the LSM in 3 subjects due to either diffusion through fascia or needle misplacement. Volume of BTME, as defined here, appeared to be substantially greater than the 0.25-mL injection volume. This descriptive case series is among the first attempts to quantify BTME within human muscle. Our findings are preliminary and are limited by a few

  20. Bringing a Global Issue Closer to Home: The OSU Climate Change Webinar Series

    Science.gov (United States)

    Jentes Banicki, J.; Dierkes, C.

    2012-12-01

    When people think about the effects of climate change, many will still picture that iconic lone polar bear clinging to a shrinking iceberg in Antarctica. But many don't realize that the impacts that we will face here at home could also be severe, directly affecting the food we eat, the health we have, and the natural environments we appreciate. To help better explain and ultimately localize those impacts for Great Lakes residents, 10 departments within Ohio State University partnered in 2009 to create the Global Change, Local Impact webinar series. The monthly series brings in experts from around the Great Lakes region to discuss issues and impacts we will encounter regionally as our climate changes. Originally designed as a small series for Ohioans, the series has broadened to focus on Great Lakes-related issues, with more than 4,500 attendees representing 500 organizations in governmental agencies, academia, non-profit groups, private industry, and the legislature from around the country. Over the past two years, the OSU Climate Team expanded its educational reach by partnering with external groups like the Centers for Disease Control and Prevention, Great Lakes Regional Water Program, National Oceanic and Atmospheric Administration, and the Great Lakes Sea Grant Network to help deliver the most knowledgeable experts and resources for each Great Lake-focused climate topic and archive those resources on its www.changingclimate.osu.edu web site. As a result of these collaborative efforts, participants say the webinars are one of their primary resources for climate-related research information in the region, with 80-90% polled saying they use this information as an unbiased resource to help not only understand how climate change could affect local concerns like public health, agriculture, and infrastructure, but what they in their vocations and daily lives can do to prepare for it. For scientists and practitioners, this series serves as the perfect low carbon venue

  1. Detection and Attribution of Regional Climate Change

    Energy Technology Data Exchange (ETDEWEB)

    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.

  2. Adaptive 4d Psi-Based Change Detection

    Science.gov (United States)

    Yang, Chia-Hsiang; Soergel, Uwe

    2018-04-01

    In a previous work, we proposed a PSI-based 4D change detection to detect disappearing and emerging PS points (3D) along with their occurrence dates (1D). Such change points are usually caused by anthropic events, e.g., building constructions in cities. This method first divides an entire SAR image stack into several subsets by a set of break dates. The PS points, which are selected based on their temporal coherences before or after a break date, are regarded as change candidates. Change points are then extracted from these candidates according to their change indices, which are modelled from their temporal coherences of divided image subsets. Finally, we check the evolution of the change indices for each change point to detect the break date that this change occurred. The experiment validated both feasibility and applicability of our method. However, two questions still remain. First, selection of temporal coherence threshold associates with a trade-off between quality and quantity of PS points. This selection is also crucial for the amount of change points in a more complex way. Second, heuristic selection of change index thresholds brings vulnerability and causes loss of change points. In this study, we adapt our approach to identify change points based on statistical characteristics of change indices rather than thresholding. The experiment validates this adaptive approach and shows increase of change points compared with the old version. In addition, we also explore and discuss optimal selection of temporal coherence threshold.

  3. Land cover change mapping using MODIS time series to improve emissions inventories

    Science.gov (United States)

    López-Saldaña, Gerardo; Quaife, Tristan; Clifford, Debbie

    2016-04-01

    MELODIES is an FP7 funded project to develop innovative and sustainable services, based upon Open Data, for users in research, government, industry and the general public in a broad range of societal and environmental benefit areas. Understanding and quantifying land surface changes is necessary for estimating greenhouse gas and ammonia emissions, and for meeting air quality limits and targets. More sophisticated inventories methodologies for at least key emission source are needed due to policy-driven air quality directives. Quantifying land cover changes on an annual basis requires greater spatial and temporal disaggregation of input data. The main aim of this study is to develop a methodology for using Earth Observations (EO) to identify annual land surface changes that will improve emissions inventories from agriculture and land use/land use change and forestry (LULUCF) in the UK. First goal is to find the best sets of input features that describe accurately the surface dynamics. In order to identify annual and inter-annual land surface changes, a times series of surface reflectance was used to capture seasonal variability. Daily surface reflectance images from the Moderate Resolution Imaging Spectroradiometer (MODIS) at 500m resolution were used to invert a Bidirectional Reflectance Distribution Function (BRDF) model to create the seamless time series. Given the limited number of cloud-free observations, a BRDF climatology was used to constrain the model inversion and where no high-scientific quality observations were available at all, as a gap filler. The Land Cover Map 2007 (LC2007) produced by the Centre for Ecology & Hydrology (CEH) was used for training and testing purposes. A land cover product was created for 2003 to 2015 and a bayesian approach was created to identified land cover changes. We will present the results of the time series development and the first exercises when creating the land cover and land cover changes products.

  4. Comparison of feature extraction methods within a spatio-temporal land cover change detection framework

    CSIR Research Space (South Africa)

    Kleynhans, W

    2011-07-01

    Full Text Available OF FEATURE EXTRACTION METHODS WITHIN A SPATIO-TEMPORAL LAND COVER CHANGE DETECTION FRAMEWORK ??W. Kleynhans,, ??B.P. Salmon, ?J.C. Olivier, ?K.J. Wessels, ?F. van den Bergh ? Electrical, Electronic and Computer Engi- neering University of Pretoria, South... Bergh, and K. Steenkamp, ?Improving land cover class separation using an extended Kalman filter on MODIS NDVI time series data,? IEEE Geoscience and Remote Sensing Letters, vol. 7, no. 2, pp. 381?385, Apr. 2010. ...

  5. Geologic Carbon Sequestration: Mitigating Climate Change by Injecting CO2 Underground (LBNL Summer Lecture Series)

    Energy Technology Data Exchange (ETDEWEB)

    Oldenburg, Curtis M. [Lawrence Berkeley National Lab. (LBNL), Berkeley, CA (United States). Earth Sciences Division

    2009-07-21

    Summer Lecture Series 2009: Climate change provides strong motivation to reduce CO2 emissions from the burning of fossil fuels. Carbon dioxide capture and storage involves the capture, compression, and transport of CO2 to geologically favorable areas, where its injected into porous rock more than one kilometer underground for permanent storage. Oldenburg, who heads Berkeley Labs Geologic Carbon Sequestration Program, will focus on the challenges, opportunities, and research needs of this innovative technology.

  6. Determining the Points of Change in Time Series of Polarimetric SAR Data

    DEFF Research Database (Denmark)

    Conradsen, Knut; Nielsen, Allan Aasbjerg; Skriver, Henning

    2016-01-01

    We present the likelihood ratio test statistic for the homogeneity of several complex variance–covariance matrices that may be used in order to assess whether at least one change has taken place in a time series of SAR data. Furthermore, we give a factorization of this test statistic into a produ....... The pixelwise analyses are applied on homogeneous subareas covered with different vegetation types using the distribution of the observed p-values....

  7. Changes in time series of some climate elements in Estonia in early spring

    International Nuclear Information System (INIS)

    Russak, Viivi

    1999-01-01

    Systematic changes in some climate elements have become evident in Estonia during the latest decades; the greatest changes have been observed in March. Two possible causes may have led to the observed warming: changes in general atmospheric circulation and in radiation budget. Due to small solar evaluation and shortness of day the role of solar radiation in air temperature formation is not considerable in March. The absence of any significant trend in the time series of net long wave radiation in March seems to be caused by simultaneous and opposite influence of changes in downward and upward infrared radiation fluxes on temperature conditions near ground. In other seasons a significant increase in greenhouse effect has been established. Therefore, the changes in general atmospheric circulation should be considered the main reason of warming, while the role of solar radiation and greenhouse effect is not considerable in March

  8. Detecting and Attributing Health Burdens to Climate Change.

    Science.gov (United States)

    Ebi, Kristie L; Ogden, Nicholas H; Semenza, Jan C; Woodward, Alistair

    2017-08-07

    Detection and attribution of health impacts caused by climate change uses formal methods to determine a ) whether the occurrence of adverse health outcomes has changed, and b ) the extent to which that change could be attributed to climate change. There have been limited efforts to undertake detection and attribution analyses in health. Our goal was to show a range of approaches for conducting detection and attribution analyses. Case studies for heatwaves, Lyme disease in Canada, and Vibrio emergence in northern Europe highlight evidence that climate change is adversely affecting human health. Changes in rates and geographic distribution of adverse health outcomes were detected, and, in each instance, a proportion of the observed changes could, in our judgment, be attributed to changes in weather patterns associated with climate change. The results of detection and attribution studies can inform evidence-based risk management to reduce current, and plan for future, changes in health risks associated with climate change. Gaining a better understanding of the size, timing, and distribution of the climate change burden of disease and injury requires reliable long-term data sets, more knowledge about the factors that confound and modify the effects of climate on health, and refinement of analytic techniques for detection and attribution. At the same time, significant advances are possible in the absence of complete data and statistical certainty: there is a place for well-informed judgments, based on understanding of underlying processes and matching of patterns of health, climate, and other determinants of human well-being. https://doi.org/10.1289/EHP1509.

  9. Acoustic change detection algorithm using an FM radio

    Science.gov (United States)

    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.

  10. Detection of Greenhouse-Gas-Induced Climatic Change

    Energy Technology Data Exchange (ETDEWEB)

    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.

  11. On the pilot's behavior of detecting a system parameter change

    Science.gov (United States)

    Morizumi, N.; Kimura, H.

    1986-01-01

    The reaction of a human pilot, engaged in compensatory control, to a sudden change in the controlled element's characteristics is described. Taking the case where the change manifests itself as a variance change of the monitored signal, it is shown that the detection time, defined to be the time elapsed until the pilot detects the change, is related to the monitored signal and its derivative. Then, the detection behavior is modeled by an optimal controller, an optimal estimator, and a variance-ratio test mechanism that is performed for the monitored signal and its derivative. Results of a digital simulation show that the pilot's detection behavior can be well represented by the model proposed here.

  12. Analyzing temporal changes in maximum runoff volume series of the Danube River

    International Nuclear Information System (INIS)

    Halmova, Dana; Pekarova, Pavla; Onderka, Milan; Pekar, Jan

    2008-01-01

    Several hypotheses claim that more extremes in climatic and hydrologic phenomena are anticipated. In order to verify such hypotheses it is inevitable to examine the past periods by thoroughly analyzing historical data. In the present study, the annual maximum runoff volumes with t-day durations were calculated for a 130-year series of mean daily discharge of Danube River at Bratislava gauge (Slovakia). Statistical methods were used to clarify how the maximum runoff volumes of the Danube River changed over two historical periods (1876-1940 and 1941-2005). The conclusion is that the runoff volume regime during floods has not changed significantly during the last 130 years.

  13. Change detection in polarimetric SAR data over several time points

    DEFF Research Database (Denmark)

    Conradsen, Knut; Nielsen, Allan Aasbjerg; Skriver, Henning

    2014-01-01

    A test statistic for the equality of several variance-covariance matrices following the complex Wishart distribution is introduced. The test statistic is applied successfully to detect change in C-band EMISAR polarimetric SAR data over four time points.......A test statistic for the equality of several variance-covariance matrices following the complex Wishart distribution is introduced. The test statistic is applied successfully to detect change in C-band EMISAR polarimetric SAR data over four time points....

  14. Unsupervised Multi-Scale Change Detection from SAR Imagery for Monitoring Natural and Anthropogenic Disasters

    Science.gov (United States)

    Ajadi, Olaniyi A.

    Radar remote sensing can play a critical role in operational monitoring of natural and anthropogenic disasters. Despite its all-weather capabilities, and its high performance in mapping, and monitoring of change, the application of radar remote sensing in operational monitoring activities has been limited. This has largely been due to: (1) the historically high costs associated with obtaining radar data; (2) slow data processing, and delivery procedures; and (3) the limited temporal sampling that was provided by spaceborne radar-based satellites. Recent advances in the capabilities of spaceborne Synthetic Aperture Radar (SAR) sensors have developed an environment that now allows for SAR to make significant contributions to disaster monitoring. New SAR processing strategies that can take full advantage of these new sensor capabilities are currently being developed. Hence, with this PhD dissertation, I aim to: (i) investigate unsupervised change detection techniques that can reliably extract signatures from time series of SAR images, and provide the necessary flexibility for application to a variety of natural, and anthropogenic hazard situations; (ii) investigate effective methods to reduce the effects of speckle and other noise on change detection performance; (iii) automate change detection algorithms using probabilistic Bayesian inferencing; and (iv) ensure that the developed technology is applicable to current, and future SAR sensors to maximize temporal sampling of a hazardous event. This is achieved by developing new algorithms that rely on image amplitude information only, the sole image parameter that is available for every single SAR acquisition.. The motivation and implementation of the change detection concept are described in detail in Chapter 3. In the same chapter, I demonstrated the technique's performance using synthetic data as well as a real-data application to map wildfire progression. I applied Radiometric Terrain Correction (RTC) to the data to

  15. Annual land cover change mapping using MODIS time series to improve emissions inventories.

    Science.gov (United States)

    López Saldaña, G.; Quaife, T. L.; Clifford, D.

    2014-12-01

    Understanding and quantifying land surface changes is necessary for estimating greenhouse gas and ammonia emissions, and for meeting air quality limits and targets. More sophisticated inventories methodologies for at least key emission source are needed due to policy-driven air quality directives. Quantifying land cover changes on an annual basis requires greater spatial and temporal disaggregation of input data. The main aim of this study is to develop a methodology for using Earth Observations (EO) to identify annual land surface changes that will improve emissions inventories from agriculture and land use/land use change and forestry (LULUCF) in the UK. First goal is to find the best sets of input features that describe accurately the surface dynamics. In order to identify annual and inter-annual land surface changes, a times series of surface reflectance was used to capture seasonal variability. Daily surface reflectance images from the Moderate Resolution Imaging Spectroradiometer (MODIS) at 500m resolution were used to invert a Bidirectional Reflectance Distribution Function (BRDF) model to create the seamless time series. Given the limited number of cloud-free observations, a BRDF climatology was used to constrain the model inversion and where no high-scientific quality observations were available at all, as a gap filler. The Land Cover Map 2007 (LC2007) produced by the Centre for Ecology & Hydrology (CEH) was used for training and testing purposes. A prototype land cover product was created for 2006 to 2008. Several machine learning classifiers were tested as well as different sets of input features going from the BRDF parameters to spectral Albedo. We will present the results of the time series development and the first exercises when creating the prototype land cover product.

  16. Detection of density-dependent effects on caribou numbers from a series of census data

    Directory of Open Access Journals (Sweden)

    Francois Messier

    1991-10-01

    Full Text Available The main objective of this paper is to review and discuss the applicability of statistical procedures for the detection of density dependence based on a series of annual or multi-annual censuses. Regression models for which the statistic value under the null hypothesis of density independence is set a priori (slope = 0 or 1, generate spurious indications of density dependence. These tests are inappropriate because low sample sizes, high variance, and sampling error consistently bias the slope when applied to a finite number of population estimates. Two distribution-free tests are reviewed for which the rejection region for the hypothesis of density independence is derived intrinsically from the data through a computer-assisted permutation process. The "randomization test" gives the best results as the presence of a pronounced trend in the sequence of population estimates does not affect test results. The other non-parametric test, the "permutation test", gives reliable results only if the population fluctuates around a long-term equilibrium density. Both procedures are applied to three sets of data (Pukaskwa herd, Avalon herd, and a hypothetical example that represent quite divergent population trajectories over time.

  17. MODIS Time Series to Detect Anthropogenic Interventions and Degradation Processes in Tropical Pasture

    Directory of Open Access Journals (Sweden)

    Daniel Alves Aguiar

    2017-01-01

    Full Text Available The unavoidable diet change in emerging countries, projected for the coming years, will significantly increase the global consumption of animal protein. It is expected that Brazilian livestock production, responsible for close to 15% of global production, be prepared to answer to the increasing demand of beef. Consequently, the evaluation of pasture quality at regional scale is important to inform public policies towards a rational land use strategy directed to improve livestock productivity in the country. Our hypothesis is that MODIS images can be used to evaluate the processes of degradation, restoration and renovation of tropical pastures. To test this hypothesis, two field campaigns were performed covering a route of approximately 40,000 km through nine Brazilian states. To characterize the sampled pastures, biophysical parameters were measured and observations about the pastures, the adopted management and the landscape were collected. Each sampled pasture was evaluated using a time series of MODIS EVI2 images from 2000–2012, according to a new protocol based on seven phenological metrics, 14 Boolean criteria and two numerical criteria. The theoretical basis of this protocol was derived from interviews with producers and livestock experts during a third field campaign. The analysis of the MODIS EVI2 time series provided valuable historical information on the type of intervention and on the biological degradation process of the sampled pastures. Of the 782 pastures sampled, 26.6% experienced some type of intervention, 19.1% were under biological degradation, and 54.3% presented neither intervention nor trend of biomass decrease during the period analyzed.

  18. Automated baseline change detection phase I. Final report

    Energy Technology Data Exchange (ETDEWEB)

    NONE

    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.

  19. Automated baseline change detection phase I. Final report

    International Nuclear Information System (INIS)

    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 ampersand 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

  20. Shifts in Arctic phenology in response to climate and anthropogenic factors as detected from multiple satellite time series

    International Nuclear Information System (INIS)

    Zeng, Heqing; Jia, Gensuo; Forbes, Bruce C

    2013-01-01

    There is an urgent need to reduce the uncertainties in remotely sensed detection of phenological shifts of high latitude ecosystems in response to climate changes in past decades. In this study, vegetation phenology in western Arctic Russia (the Yamal Peninsula) was investigated by analyzing and comparing Normalized Difference Vegetation Index (NDVI) time series derived from the Advanced Very High Resolution Radiometer (AVHRR), the Moderate Resolution Imaging Spectroradiometer (MODIS), and SPOT-Vegetation (VGT) during the decade 2000–2010. The spatial patterns of key phenological parameters were highly heterogeneous along the latitudinal gradients based on multi-satellite data. There was earlier SOS (start of the growing season), later EOS (end of the growing season), longer LOS (length of the growing season), and greater MaxNDVI from north to south in the region. The results based on MODIS and VGT data showed similar trends in phenological changes from 2000 to 2010, while quite a different trend was found based on AVHRR data from 2000 to 2008. A significantly delayed EOS (p < 0.01), thus increasing the LOS, was found from AVHRR data, while no similar trends were detected from MODIS and VGT data. There were no obvious shifts in MaxNDVI during the last decade. MODIS and VGT data were considered to be preferred data for monitoring vegetation phenology in northern high latitudes. Temperature is still a key factor controlling spatial phenological gradients and variability, while anthropogenic factors (reindeer husbandry and resource exploitation) might explain the delayed SOS in southern Yamal. Continuous environmental damage could trigger a positive feedback to the delayed SOS. (letter)

  1. Emotion has no impact on attention in a change detection flicker task

    Directory of Open Access Journals (Sweden)

    Robert Colin Alan Bendall

    2015-10-01

    Full Text Available Past research provides conflicting findings regarding the influence of emotion on visual attention. Early studies suggested a broadening of attentional resources in relation to positive mood. However, more recent evidence indicates that positive emotions may not have a beneficial impact on attention, and that the relationship between emotion and attention may be mitigated by factors such as task demand or stimulus valence. The current study explored the effect of emotion on attention using the change detection flicker paradigm. Participants were induced into positive, neutral, and negative mood states and then completed a change detection task. A series of neutral scenes were presented and participants had to identify the location of a disappearing item in each scene. The change was made to the centre or the periphery of each scene and it was predicted that peripheral changes would be detected quicker in the positive mood condition and slower in the negative mood condition, compared to the neutral condition. In contrast to previous findings emotion had no influence on attention and whilst central changes were detected faster than peripheral changes, change blindness was not affected by mood. The findings suggest that the relationship between emotion and visual attention is influenced by the characteristics of a task, and any beneficial impact of positive emotion may be related to processing style rather than a broadening of attentional resources.

  2. Detection and Attribution of Anthropogenic Climate Change Impacts

    Science.gov (United States)

    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.

  3. MULTI-TEMPORAL CLASSIFICATION AND CHANGE DETECTION USING UAV IMAGES

    Directory of Open Access Journals (Sweden)

    S. Makuti

    2018-05-01

    Full Text Available In this paper different methodologies for the classification and change detection of UAV image blocks are explored. UAV is not only the cheapest platform for image acquisition but it is also the easiest platform to operate in repeated data collections over a changing area like a building construction site. Two change detection techniques have been evaluated in this study: the pre-classification and the post-classification algorithms. These methods are based on three main steps: feature extraction, classification and change detection. A set of state of the art features have been used in the tests: colour features (HSV, textural features (GLCM and 3D geometric features. For classification purposes Conditional Random Field (CRF has been used: the unary potential was determined using the Random Forest algorithm while the pairwise potential was defined by the fully connected CRF. In the performed tests, different feature configurations and settings have been considered to assess the performance of these methods in such challenging task. Experimental results showed that the post-classification approach outperforms the pre-classification change detection method. This was analysed using the overall accuracy, where by post classification have an accuracy of up to 62.6 % and the pre classification change detection have an accuracy of 46.5 %. These results represent a first useful indication for future works and developments.

  4. Automated Change Detection for Validation and Update of Geodata

    DEFF Research Database (Denmark)

    Olsen, Brian Pilemann; Knudsen, Thomas

    )is presented. Height information is used to determine the location of object which stands above terrain, and the CIR-Imagery is used to exclude vegetation, leading to a potential buildings mask. Comparing the existing objects in the map database with these extracted objects leads to a validation of the map...... to newer (raster based) remote sensing images in order to detect changes in objects. In this paper an automatic change detection method considering changes in the building theme and based on colourinfrared (CIR) aerial photographs in combination with height information (LIDAR, digital photogrammetry...

  5. Hardware accelerator design for change detection in smart camera

    Science.gov (United States)

    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.

  6. A procedure to derive intra-and inter-annual changes on vegetation from NDVI time series. A case study in Spain

    International Nuclear Information System (INIS)

    Gilabert, M. A; Martinez, B.; Melia, J.

    2009-01-01

    The objective of this work is to study the spatial patterns of vegetation activity over spain and its temporal variability throughout the period 1989-2002. A multi-resolution analysis (MRA) bases on the wavelet transform has been implemented on NDVI time series from the MEDOKADS database. The MRA decomposes the original signal as a sum of series associated with temporal scales. Specifically, the intra-annual series is processed to define several key features in relation with the vegetation penology. In contras, the inter-annual components of the signal is used to detect trends by means of a Mann-Kendall test and map the magnitude of the land-cover change. Finally, a comprehensive identification of the areas presenting a negative value of the magnitude of change is carried out to select those linked to land degradation processes. Results show a major presence of these areas the Southeast of Spain. (Author) 5 refs.

  7. A procedure to derive intra-and inter-annual changes on vegetation from NDVI time series. A case study in Spain

    Energy Technology Data Exchange (ETDEWEB)

    Gilabert, M. A; Martinez, B.; Melia, J.

    2009-07-01

    The objective of this work is to study the spatial patterns of vegetation activity over spain and its temporal variability throughout the period 1989-2002. A multi-resolution analysis (MRA) bases on the wavelet transform has been implemented on NDVI time series from the MEDOKADS database. The MRA decomposes the original signal as a sum of series associated with temporal scales. Specifically, the intra-annual series is processed to define several key features in relation with the vegetation penology. In contras, the inter-annual components of the signal is used to detect trends by means of a Mann-Kendall test and map the magnitude of the land-cover change. Finally, a comprehensive identification of the areas presenting a negative value of the magnitude of change is carried out to select those linked to land degradation processes. Results show a major presence of these areas the Southeast of Spain. (Author) 5 refs.

  8. Detecting geothermal anomalies and evaluating LST geothermal component by combining thermal remote sensing time series and land surface model data

    NARCIS (Netherlands)

    Romaguera, M.; Vaughan, R. G.; Ettema, J.; Izquierdo-Verdiguier, E.; Hecker, C. A.; van der Meer, F. D.

    This paper explores for the first time the possibilities to use two land surface temperature (LST) time series of different origins (geostationary Meteosat Second Generation satellite data and Noah land surface modelling, LSM), to detect geothermal anomalies and extract the geothermal component of

  9. Detecting geothermal anomalies and evaluating LST geothermal component by combining thermal remote sensing time series and land surface model data

    NARCIS (Netherlands)

    Romaguera, M.; Vaughan, R. G.; Ettema, J.; Izquierdo-Verdiguier, E.; Hecker, C. A.; van der Meer, F. D.

    2017-01-01

    This paper explores for the first time the possibilities to use two land surface temperature (LST) time series of different origins (geostationary Meteosat Second Generation satellite data and Noah land surface modelling, LSM), to detect geothermal anomalies and extract the geothermal component of

  10. No evidence for an item limit in change detection.

    Directory of Open Access Journals (Sweden)

    Shaiyan Keshvari

    Full Text Available Change detection is a classic paradigm that has been used for decades to argue that working memory can hold no more than a fixed number of items ("item-limit models". Recent findings force us to consider the alternative view that working memory is limited by the precision in stimulus encoding, with mean precision decreasing with increasing set size ("continuous-resource models". Most previous studies that used the change detection paradigm have ignored effects of limited encoding precision by using highly discriminable stimuli and only large changes. We conducted two change detection experiments (orientation and color in which change magnitudes were drawn from a wide range, including small changes. In a rigorous comparison of five models, we found no evidence of an item limit. Instead, human change detection performance was best explained by a continuous-resource model in which encoding precision is variable across items and trials even at a given set size. This model accounts for comparison errors in a principled, probabilistic manner. Our findings sharply challenge the theoretical basis for most neural studies of working memory capacity.

  11. Estimating Unbiased Land Cover Change Areas In The Colombian Amazon Using Landsat Time Series And Statistical Inference Methods

    Science.gov (United States)

    Arevalo, P. A.; Olofsson, P.; Woodcock, C. E.

    2017-12-01

    Unbiased estimation of the areas of conversion between land categories ("activity data") and their uncertainty is crucial for providing more robust calculations of carbon emissions to the atmosphere, as well as their removals. This is particularly important for the REDD+ mechanism of UNFCCC where an economic compensation is tied to the magnitude and direction of such fluxes. Dense time series of Landsat data and statistical protocols are becoming an integral part of forest monitoring efforts, but there are relatively few studies in the tropics focused on using these methods to advance operational MRV systems (Monitoring, Reporting and Verification). We present the results of a prototype methodology for continuous monitoring and unbiased estimation of activity data that is compliant with the IPCC Approach 3 for representation of land. We used a break detection algorithm (Continuous Change Detection and Classification, CCDC) to fit pixel-level temporal segments to time series of Landsat data in the Colombian Amazon. The segments were classified using a Random Forest classifier to obtain annual maps of land categories between 2001 and 2016. Using these maps, a biannual stratified sampling approach was implemented and unbiased stratified estimators constructed to calculate area estimates with confidence intervals for each of the stable and change classes. Our results provide evidence of a decrease in primary forest as a result of conversion to pastures, as well as increase in secondary forest as pastures are abandoned and the forest allowed to regenerate. Estimating areas of other land transitions proved challenging because of their very small mapped areas compared to stable classes like forest, which corresponds to almost 90% of the study area. Implications on remote sensing data processing, sample allocation and uncertainty reduction are also discussed.

  12. Cortical dynamics of visual change detection based on sensory memory.

    Science.gov (United States)

    Urakawa, Tomokazu; Inui, Koji; Yamashiro, Koya; Tanaka, Emi; Kakigi, Ryusuke

    2010-08-01

    Detecting a visual change was suggested to relate closely to the visual sensory memory formed by visual stimuli before the occurrence of the change, because change detection involves identifying a difference between ongoing and preceding sensory conditions. Previous neuroimaging studies showed that an abrupt visual change activates the middle occipital gyrus (MOG). However, it still remains to be elucidated whether the MOG is related to visual change detection based on sensory memory. Here we tried to settle this issue using a new method of stimulation with blue and red LEDs to emphasize a memory-based change detection process. There were two stimuli, a standard trial stimulus and a deviant trial stimulus. The former was a red light lasting 500 ms, and the latter was a red light lasting 250 ms immediately followed by a blue light lasting 250 ms. Effects of the trial-trial interval, 250 approximately 2000 ms, were investigated to know how cortical responses to the abrupt change (from red to blue) were affected by preceding conditions. The brain response to the deviant trial stimulus was recorded by magnetoencephalography. Results of a multi-dipole analysis showed that the activity in the MOG, peaking at around 150 ms after the change onset, decreased in amplitude as the interval increased, but the earlier activity in BA 17/18 was not affected by the interval. These results suggested that the MOG is an important cortical area relating to the sensory memory-based visual change-detecting system. Copyright 2010 Elsevier Inc. All rights reserved.

  13. Incorporating Spatio-temporal Phenological Variation in Detecting Exotic Saltcedar Using Landsat Time Series

    Science.gov (United States)

    Diao, C.; Wang, L.

    2017-12-01

    The invasion of exotic species compromises ecosystem functions and causes substantial economic losses at the global scale. Over the past century, non-native saltcedar has expanded into most riparian zones in southwestern United States and posed significant threats to the native biotic communities. Repeated monitoring of saltcedar distribution is essential for conservation agencies to locate highly susceptible areas and develop corresponding control strategies. Throughout the phenological cycle, the leaf senescence stage has been found to be the most crucial in spectrally detecting saltcedar. However, due to climate variability and anthropogenic forcing, the timing of saltcedar leaf senescence may vary over space and time. This spatial and inter-annual variation need to be accommodated to pinpoint the appropriate remotely sensed imagery for saltcedar mapping. The objective of this study was to develop a Landsat-based Multiyear Spectral Angle Clustering (MSAC) model to monitor the inter-annual leaf senescence of exotic saltcedar. At the Landsat scale, the time series analysis of vegetation phenology is usually limited by the temporal resolution of images. The MSAC model can overcome this limit and take advantage of the Landsat images from multiple years to compensate the lack of images in a single year. Results indicated the MSAC model provided a Landsat-based solution to capture the inter-annual leaf senescence of saltcedar. Compared to traditional NDVI-based phenological approaches, the proposed model achieved a more accurate classification results of saltcedar across years. The MSAC model provides unique opportunities to guide the selection of appropriate remotely sensed image for repetitive saltcedar mapping.

  14. Change of spatial information under rescaling: A case study using multi-resolution image series

    Science.gov (United States)

    Chen, Weirong; Henebry, Geoffrey M.

    Spatial structure in imagery depends on a complicated interaction between the observational regime and the types and arrangements of entities within the scene that the image portrays. Although block averaging of pixels has commonly been used to simulate coarser resolution imagery, relatively little attention has been focused on the effects of simple rescaling on spatial structure and the explanation and a possible solution to the problem. Yet, if there are significant differences in spatial variance between rescaled and observed images, it may affect the reliability of retrieved biogeophysical quantities. To investigate these issues, a nested series of high spatial resolution digital imagery was collected at a research site in eastern Nebraska in 2001. An airborne Kodak DCS420IR camera acquired imagery at three altitudes, yielding nominal spatial resolutions ranging from 0.187 m to 1 m. The red and near infrared (NIR) bands of the co-registered image series were normalized using pseudo-invariant features, and the normalized difference vegetation index (NDVI) was calculated. Plots of grain sorghum planted in orthogonal crop row orientations were extracted from the image series. The finest spatial resolution data were then rescaled by averaging blocks of pixels to produce a rescaled image series that closely matched the spatial resolution of the observed image series. Spatial structures of the observed and rescaled image series were characterized using semivariogram analysis. Results for NDVI and its component bands show, as expected, that decreasing spatial resolution leads to decreasing spatial variability and increasing spatial dependence. However, compared to the observed data, the rescaled images contain more persistent spatial structure that exhibits limited variation in both spatial dependence and spatial heterogeneity. Rescaling via simple block averaging fails to consider the effect of scene object shape and extent on spatial information. As the features

  15. Detection of greenhouse-gas-induced climatic change

    International Nuclear Information System (INIS)

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

    1992-01-01

    The aims of the US Department of Energy's Carbon Dioxide Research Program are to improve assessments of greenhouse-gas-induced climatic change and to define and reduce uncertainties through selected research. This project will address: The regional and seasonal details of the expected climatic changes; how rapidly will these changes occur; how and when will the climatic effects of CO 2 and other greenhouse gases be first detected; and the relationships between greenhouse-gas-induced climatic change and changes caused by other external and internal factors. The present project addresses all of these questions. Many of the diverse facets of greenhouse-gas-related climate research can be grouped under three interlinked subject areas: modeling, first detection and supporting data. This project will include the analysis of climate forcing factors, the development and refinement of transient response climate models, and the use of instrumental data in validating General Circulation Models (GCMs)

  16. Detection of increased series losses in PV arrays using Fuzzy Inference Systems

    DEFF Research Database (Denmark)

    Spataru, Sergiu; Sera, Dezso; Kerekes, Tamas

    2012-01-01

    There are well-defined methods to measure the (increased) series resistance of PV panels in controlled laboratory conditions. However, the presence of various irradiance levels and partial shadows, in case of an outdoor installation, may affect the series resistance estimation. This paper focuses...

  17. Towards a Framework for Change Detection in Data Sets

    Science.gov (United States)

    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.

  18. AN INVESTIGATION OF AUTOMATIC CHANGE DETECTION FOR TOPOGRAPHIC MAP UPDATING

    Directory of Open Access Journals (Sweden)

    P. Duncan

    2012-08-01

    Full Text Available Changes to the landscape are constantly occurring and it is essential for geospatial and mapping organisations that these changes are regularly detected and captured, so that map databases can be updated to reflect the current status of the landscape. The Chief Directorate of National Geospatial Information (CD: NGI, South Africa's national mapping agency, currently relies on manual methods of detecting changes and capturing these changes. These manual methods are time consuming and labour intensive, and rely on the skills and interpretation of the operator. It is therefore necessary to move towards more automated methods in the production process at CD: NGI. The aim of this research is to do an investigation into a methodology for automatic or semi-automatic change detection for the purpose of updating topographic databases. The method investigated for detecting changes is through image classification as well as spatial analysis and is focussed on urban landscapes. The major data input into this study is high resolution aerial imagery and existing topographic vector data. Initial results indicate the traditional pixel-based image classification approaches are unsatisfactory for large scale land-use mapping and that object-orientated approaches hold more promise. Even in the instance of object-oriented image classification generalization of techniques on a broad-scale has provided inconsistent results. A solution may lie with a hybrid approach of pixel and object-oriented techniques.

  19. Detection of Outliers and Imputing of Missing Values for Water Quality UV-VIS Absorbance Time Series

    OpenAIRE

    Plazas-Nossa, Leonardo; Ávila Angulo, Miguel Antonio; Torres, Andrés

    2017-01-01

    Context:The UV-Vis absorbance collection using online optical captors for water quality detection may yield outliers and/or missing values. Therefore, pre-processing to correct these anomalies is required to improve the analysis of monitoring data. The aim of this study is to propose a method to detect outliers as well as to fill-in the gaps in time series. Method:Outliers are detected using Winsorising procedure and the application of the Discrete Fourier Transform (DFT) and the Inverse of F...

  20. A travel time forecasting model based on change-point detection method

    Science.gov (United States)

    LI, Shupeng; GUANG, Xiaoping; QIAN, Yongsheng; ZENG, Junwei

    2017-06-01

    Travel time parameters obtained from road traffic sensors data play an important role in traffic management practice. A travel time forecasting model is proposed for urban road traffic sensors data based on the method of change-point detection in this paper. The first-order differential operation is used for preprocessing over the actual loop data; a change-point detection algorithm is designed to classify the sequence of large number of travel time data items into several patterns; then a travel time forecasting model is established based on autoregressive integrated moving average (ARIMA) model. By computer simulation, different control parameters are chosen for adaptive change point search for travel time series, which is divided into several sections of similar state.Then linear weight function is used to fit travel time sequence and to forecast travel time. The results show that the model has high accuracy in travel time forecasting.

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

    Science.gov (United States)

    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.

  2. Incrementally Detecting Change Types of Spatial Area Object: A Hierarchical Matching Method Considering Change Process

    Directory of Open Access Journals (Sweden)

    Yanhui Wang

    2018-01-01

    Full Text Available Detecting and extracting the change types of spatial area objects can track area objects’ spatiotemporal change pattern and provide the change backtracking mechanism for incrementally updating spatial datasets. To respond to the problems of high complexity of detection methods, high redundancy rate of detection factors, and the low automation degree during incrementally update process, we take into account the change process of area objects in an integrated way and propose a hierarchical matching method to detect the nine types of changes of area objects, while minimizing the complexity of the algorithm and the redundancy rate of detection factors. We illustrate in details the identification, extraction, and database entry of change types, and how we achieve a close connection and organic coupling of incremental information extraction and object type-of-change detection so as to characterize the whole change process. The experimental results show that this method can successfully detect incremental information about area objects in practical applications, with the overall accuracy reaching above 90%, which is much higher than the existing weighted matching method, making it quite feasible and applicable. It helps establish the corresponding relation between new-version and old-version objects, and facilitate the linked update processing and quality control of spatial data.

  3. Improving the measurement of longitudinal change in renal function: automated detection of changes in laboratory creatinine assay

    Directory of Open Access Journals (Sweden)

    Norman Poh

    2015-04-01

    Full Text Available IntroductionRenal function is reported using the estimates of glomerular filtration rate (eGFR. However, eGFR values are recorded without reference to the particular serum creatinine (SCr assays used to derive them, and newer assays were introduced at different time points across the laboratories in the United Kingdom. These changes may cause systematic bias in eGFR reported in routinely collected data, even though laboratory-reported eGFR values have a correction factor applied.DesignAn algorithm to detect changes in SCr that in turn affect eGFR calculation method was developed. It compares the mapping of SCr values on to eGFR values across a time series of paired eGFR and SCr measurements.SettingRoutinely collected primary care data from 20,000 people with the richest renal function data from the quality improvement in chronic kidney disease trial.ResultsThe algorithm identified a change in eGFR calculation method in 114 (90% of the 127 included practices. This change was identified in 4736 (23.7% patient time series analysed. This change in calibration method was found to cause a significant step change in the reported eGFR values, producing a systematic bias. The eGFR values could not be recalibrated by applying the Modification of Diet in Renal Disease equation to the laboratory reported SCr values.ConclusionsThis algorithm can identify laboratory changes in eGFR calculation methods and changes in SCr assay. Failure to account for these changes may misconstrue renal function changes over time. Researchers using routine eGFR data should account for these effects.  

  4. Using adversary text to detect adversary phase changes.

    Energy Technology Data Exchange (ETDEWEB)

    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.

  5. Landsat change detection can aid in water quality monitoring

    Science.gov (United States)

    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.

  6. Anterior prefrontal involvement in implicit contextual change detection

    Directory of Open Access Journals (Sweden)

    Stefan Pollmann

    2009-10-01

    Full Text Available Anterior prefrontal cortex is usually associated with high level executive functions. Here, we show that the frontal pole, specifically left lateral frontopolar cortex, is involved in signaling change in implicitly learned spatial contexts, in the absence of conscious change detection. In a variant of the contextual cueing paradigm, participants first learned implicitly contingencies between distractor contexts and target locations. After learning, repeated distractor contexts were paired with new target locations. Left lateral frontopolar (BA10 and superior frontal (BA9 cortices showed selective signal increase for this target location change in repeated displays in an event-related fMRI experiment, which was most pronounced in participants with high contextual facilitation before the change. The data support the view that left lateral frontopolar cortex is involved in signaling contextual change to posterior brain areas as a precondition for adaptive changes of attentional resource allocation. This signaling occurs in the absence of awareness of learned contingencies or contextual change.

  7. Detection of cardiac activity changes from human speech

    Science.gov (United States)

    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.

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

    Science.gov (United States)

    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.

  9. Pigeons (Columba livia) show change blindness in a color-change detection task.

    Science.gov (United States)

    Herbranson, Walter T; Jeffers, Jacob S

    2017-07-01

    Change blindness is a phenomenon whereby changes to a stimulus are more likely go unnoticed under certain circumstances. Pigeons learned a change detection task, in which they observed sequential stimulus displays consisting of individual colors back-projected onto three response keys. The color of one response key changed during each sequence and pecks to the key that displayed the change were reinforced. Pigeons showed a change blindness effect, in that change detection accuracy was worse when there was an inter-stimulus interval interrupting the transition between consecutive stimulus displays. Birds successfully transferred to stimulus displays involving novel colors, indicating that pigeons learned a general change detection rule. Furthermore, analysis of responses to specific color combinations showed that pigeons could detect changes involving both spectral and non-spectral colors and that accuracy was better for changes involving greater differences in wavelength. These results build upon previous investigations of change blindness in both humans and pigeons and suggest that change blindness may be a general consequence of selective visual attention relevant to multiple species and stimulus dimensions.

  10. Preparing Landsat Image Time Series (LITS for Monitoring Changes in Vegetation Phenology in Queensland, Australia

    Directory of Open Access Journals (Sweden)

    Santosh Bhandari

    2012-06-01

    Full Text Available Time series of images are required to extract and separate information on vegetation change due to phenological cycles, inter-annual climatic variability, and long-term trends. While images from the Landsat Thematic Mapper (TM sensor have the spatial and spectral characteristics suited for mapping a range of vegetation structural and compositional properties, its 16-day revisit period combined with cloud cover problems and seasonally limited latitudinal range, limit the availability of images at intervals and durations suitable for time series analysis of vegetation in many parts of the world. Landsat Image Time Series (LITS is defined here as a sequence of Landsat TM images with observations from every 16 days for a five-year period, commencing on July 2003, for a Eucalyptus woodland area in Queensland, Australia. Synthetic Landsat TM images were created using the Spatial and Temporal Adaptive Reflectance Fusion Model (STARFM algorithm for all dates when images were either unavailable or too cloudy. This was done using cloud-free scenes and a MODIS Nadir BRDF Adjusted Reflectance (NBAR product. The ability of the LITS to measure attributes of vegetation phenology was examined by: (1 assessing the accuracy of predicted image-derived Foliage Projective Cover (FPC estimates using ground-measured values; and (2 comparing the LITS-generated normalized difference vegetation index (NDVI and MODIS NDVI (MOD13Q1 time series. The predicted image-derived FPC products (value ranges from 0 to 100% had an RMSE of 5.6. Comparison between vegetation phenology parameters estimated from LITS-generated NDVI and MODIS NDVI showed no significant difference in trend and less than 16 days (equal to the composite period of the MODIS data used difference in key seasonal parameters, including start and end of season in most of the cases. In comparison to similar published work, this paper tested the STARFM algorithm in a new (broadleaf forest environment and also

  11. Neural correlates of change detection and change blindness in a working memory task.

    Science.gov (United States)

    Pessoa, Luiz; Ungerleider, Leslie G

    2004-05-01

    Detecting changes in an ever-changing environment is highly advantageous, and this ability may be critical for survival. In the present study, we investigated the neural substrates of change detection in the context of a visual working memory task. Subjects maintained a sample visual stimulus in short-term memory for 6 s, and were asked to indicate whether a subsequent, test stimulus matched or did not match the original sample. To study change detection largely uncontaminated by attentional state, we compared correct change and correct no-change trials at test. Our results revealed that correctly detecting a change was associated with activation of a network comprising parietal and frontal brain regions, as well as activation of the pulvinar, cerebellum, and inferior temporal gyrus. Moreover, incorrectly reporting a change when none occurred led to a very similar pattern of activations. Finally, few regions were differentially activated by trials in which a change occurred but subjects failed to detect it (change blindness). Thus, brain activation was correlated with a subject's report of a change, instead of correlated with the physical change per se. We propose that frontal and parietal regions, possibly assisted by the cerebellum and the pulvinar, might be involved in controlling the deployment of attention to the location of a change, thereby allowing further processing of the visual stimulus. Visual processing areas, such as the inferior temporal gyrus, may be the recipients of top-down feedback from fronto-parietal regions that control the reactive deployment of attention, and thus exhibit increased activation when a change is reported (irrespective of whether it occurred or not). Whereas reporting that a change occurred, be it correctly or incorrectly, was associated with strong activation in fronto-parietal sites, change blindness appears to involve very limited territories.

  12. Does facial processing prioritize change detection?: change blindness illustrates costs and benefits of holistic processing.

    Science.gov (United States)

    Wilford, Miko M; Wells, Gary L

    2010-11-01

    There is broad consensus among researchers both that faces are processed more holistically than other objects and that this type of processing is beneficial. We predicted that holistic processing of faces also involves a cost, namely, a diminished ability to localize change. This study (N = 150) utilized a modified change-blindness paradigm in which some trials involved a change in one feature of an image (nose, chin, mouth, hair, or eyes for faces; chimney, porch, window, roof, or door for houses), whereas other trials involved no change. People were better able to detect the occurrence of a change for faces than for houses, but were better able to localize which feature had changed for houses than for faces. Half the trials used inverted images, a manipulation that disrupts holistic processing. With inverted images, the critical interaction between image type (faces vs. houses) and task (change detection vs. change localization) disappeared. The results suggest that holistic processing reduces change-localization abilities.

  13. Detection of chaotic determinism in time series from randomly forced maps

    DEFF Research Database (Denmark)

    Chon, K H; Kanters, J K; Cohen, R J

    1997-01-01

    Time series from biological system often display fluctuations in the measured variables. Much effort has been directed at determining whether this variability reflects deterministic chaos, or whether it is merely "noise". Despite this effort, it has been difficult to establish the presence of cha...... series followed by an estimation of the characteristic exponents of the model over the observed probability distribution of states for the system. The method is tested by computer simulations, and applied to heart rate variability data....

  14. Nonlinear detection of disordered voice productions from short time series based on a Volterra-Wiener-Korenberg model

    Energy Technology Data Exchange (ETDEWEB)

    Zhang Yu, E-mail: yuzhang@xmu.edu.cn [Key Laboratory of Underwater Acoustic Communication and Marine Information Technology of the Ministry of Education, Xiamen University, Xiamen Fujian 361005 (China); Sprecher, Alicia J. [Department of Surgery, Division of Otolaryngology - Head and Neck Surgery, University of Wisconsin School of Medicine and Public Health, Madison, WI 53792-7375 (United States); Zhao Zongxi [Key Laboratory of Underwater Acoustic Communication and Marine Information Technology of the Ministry of Education, Xiamen University, Xiamen Fujian 361005 (China); Jiang, Jack J. [Department of Surgery, Division of Otolaryngology - Head and Neck Surgery, University of Wisconsin School of Medicine and Public Health, Madison, WI 53792-7375 (United States)

    2011-09-15

    Highlights: > The VWK method effectively detects the nonlinearity of a discrete map. > The method describes the chaotic time series of a biomechanical vocal fold model. > Nonlinearity in laryngeal pathology is detected from short and noisy time series. - Abstract: In this paper, we apply the Volterra-Wiener-Korenberg (VWK) model method to detect nonlinearity in disordered voice productions. The VWK method effectively describes the nonlinearity of a third-order nonlinear map. It allows for the analysis of short and noisy data sets. The extracted VWK model parameters show an agreement with the original nonlinear map parameters. Furthermore, the VWK mode method is applied to successfully assess the nonlinearity of a biomechanical voice production model simulating irregular vibratory dynamics of vocal folds with a unilateral vocal polyp. Finally, we show the clinical applicability of this nonlinear detection method to analyze the electroglottographic data generated by 14 patients with vocal nodules or polyps. The VWK model method shows potential in describing the nonlinearity inherent in disordered voice productions from short and noisy time series that are common in the clinical setting.

  15. Nonlinear detection of disordered voice productions from short time series based on a Volterra-Wiener-Korenberg model

    International Nuclear Information System (INIS)

    Zhang Yu; Sprecher, Alicia J.; Zhao Zongxi; Jiang, Jack J.

    2011-01-01

    Highlights: → The VWK method effectively detects the nonlinearity of a discrete map. → The method describes the chaotic time series of a biomechanical vocal fold model. → Nonlinearity in laryngeal pathology is detected from short and noisy time series. - Abstract: In this paper, we apply the Volterra-Wiener-Korenberg (VWK) model method to detect nonlinearity in disordered voice productions. The VWK method effectively describes the nonlinearity of a third-order nonlinear map. It allows for the analysis of short and noisy data sets. The extracted VWK model parameters show an agreement with the original nonlinear map parameters. Furthermore, the VWK mode method is applied to successfully assess the nonlinearity of a biomechanical voice production model simulating irregular vibratory dynamics of vocal folds with a unilateral vocal polyp. Finally, we show the clinical applicability of this nonlinear detection method to analyze the electroglottographic data generated by 14 patients with vocal nodules or polyps. The VWK model method shows potential in describing the nonlinearity inherent in disordered voice productions from short and noisy time series that are common in the clinical setting.

  16. Continuous Change Detection and Classification Using Hidden Markov Model: A Case Study for Monitoring Urban Encroachment onto Farmland in Beijing

    Directory of Open Access Journals (Sweden)

    Yuan Yuan

    2015-11-01

    Full Text Available In this paper, we propose a novel method to continuously monitor land cover change using satellite image time series, which can extract comprehensive change information including change time, location, and “from-to” information. This method is based on a hidden Markov model (HMM trained for each land cover class. Assuming a pixel’s initial class has been obtained, likelihoods of the corresponding model are calculated on incoming time series extracted with a temporal sliding window. By observing the likelihood change over the windows, land cover change can be precisely detected from the dramatic drop of likelihood. The established HMMs are then used for identifying the land cover class after the change. As a case study, the proposed method is applied to monitoring urban encroachment onto farmland in Beijing using 10-year MODIS time series from 2001 to 2010. The performance is evaluated on a validation set for different model structures and thresholds. Compared with other change detection methods, the proposed method shows superior change detection accuracy. In addition, it is also more computationally efficient.

  17. Blind spot detection & passive lane change assist systems

    NARCIS (Netherlands)

    Surovtcev, I.

    2015-01-01

    The project goal was design and implementation of proof-of-concept for two systems that aim to tackle the blind spot problem of for the commercial vehicles: Blind Spot Detection and Passive Lane Change Assist functions. The system implementation was done using Rapid Control Prototype (RCP) hardware.

  18. Efficient Incorporation of Markov Random Fields in Change Detection

    DEFF Research Database (Denmark)

    Aanæs, Henrik; Nielsen, Allan Aasbjerg; Carstensen, Jens Michael

    2009-01-01

    of noise, implying that the pixel-wise classifier is also noisy. There is thus a need for incorporating local homogeneity constraints into such a change detection framework. For this modelling task Markov Random Fields are suitable. Markov Random Fields have, however, previously been plagued by lack...

  19. Real-time change detection in data streams with FPGAs

    International Nuclear Information System (INIS)

    Vega, J.; Dormido-Canto, S.; Cruz, T.; Ruiz, M.; Barrera, E.; Castro, R.; Murari, A.; Ochando, M.

    2014-01-01

    Highlights: • Automatic recognition of changes in data streams of multidimensional signals. • Detection algorithm based on testing exchangeability on-line. • Real-time and off-line applicability. • Real-time implementation in FPGAs. - Abstract: The automatic recognition of changes in data streams is useful in both real-time and off-line data analyses. This article shows several effective change-detecting algorithms (based on martingales) and describes their real-time applicability in the data acquisition systems through the use of Field Programmable Gate Arrays (FPGA). The automatic event recognition system is absolutely general and it does not depend on either the particular event to detect or the specific data representation (waveforms, images or multidimensional signals). The developed approach provides good results for change detection in both the temporal evolution of profiles and the two-dimensional spatial distribution of volume emission intensity. The average computation time in the FPGA is 210 μs per profile

  20. Fundamental differences in change detection between vision and audition.

    Science.gov (United States)

    Demany, Laurent; Semal, Catherine; Cazalets, Jean-René; Pressnitzer, Daniel

    2010-06-01

    We compared auditory change detection to visual change detection using closely matched stimuli and tasks in the two modalities. On each trial, participants were presented with a test stimulus consisting of ten elements: pure tones with various frequencies for audition, or dots with various spatial positions for vision. The test stimulus was preceded or followed by a probe stimulus consisting of a single element, and two change-detection tasks were performed. In the "present/absent" task, the probe either matched one randomly selected element of the test stimulus or none of them; participants reported present or absent. In the "direction-judgment" task, the probe was always slightly shifted relative to one randomly selected element of the test stimulus; participants reported the direction of the shift. Whereas visual performance was systematically better in the present/absent task than in the direction-judgment task, the opposite was true for auditory performance. Moreover, whereas visual performance was strongly dependent on selective attention and on the time interval separating the probe from the test stimulus, this was not the case for auditory performance. Our results show that small auditory changes can be detected automatically across relatively long temporal gaps, using an implicit memory system that seems to have no similar counterpart in the visual domain.

  1. Reference chart for relative weight change to detect hypernatraemic dehydration

    NARCIS (Netherlands)

    Dommelen, P. van; Wouwe, J.P. van; Breuning-Boers, J.M.; Buuren, S. van; Verkerk, P.H.

    2007-01-01

    Objective: The validity of the rule of thumb that infants may have a weight loss of 10% in the first days after birth is unknown. We assessed the validity of this and other rules to detect breast-fed infants with hypernatraemic dehydration. Design: A reference chart for relative weight change was

  2. Consumer behaviour in district heating systems. Detecting changes

    Energy Technology Data Exchange (ETDEWEB)

    Jonsson, G.R. [University of Iceland (Iceland). Dept. of Mechanical and Industrial Engineering

    2002-10-01

    This paper focuses on methods or measures that can be used to detect changes in the consumer behavior regarding hot water use. This is done by estimating models that describe the average daily flow using several climate variables as input variables. (orig.)

  3. Scientific Uncertainties in Climate Change Detection and Attribution Studies

    Science.gov (United States)

    Santer, B. D.

    2017-12-01

    It has been claimed that the treatment and discussion of key uncertainties in climate science is "confined to hushed sidebar conversations at scientific conferences". This claim is demonstrably incorrect. Climate change detection and attribution studies routinely consider key uncertainties in observational climate data, as well as uncertainties in model-based estimates of natural variability and the "fingerprints" in response to different external forcings. The goal is to determine whether such uncertainties preclude robust identification of a human-caused climate change fingerprint. It is also routine to investigate the impact of applying different fingerprint identification strategies, and to assess how detection and attribution results are impacted by differences in the ability of current models to capture important aspects of present-day climate. The exploration of the uncertainties mentioned above will be illustrated using examples from detection and attribution studies with atmospheric temperature and moisture.

  4. Probabilistic BPRRC: Robust Change Detection against Illumination Changes and Background Movements

    Science.gov (United States)

    Yokoi, Kentaro

    This paper presents Probabilistic Bi-polar Radial Reach Correlation (PrBPRRC), a change detection method that is robust against illumination changes and background movements. Most of the traditional change detection methods are robust against either illumination changes or background movements; BPRRC is one of the illumination-robust change detection methods. We introduce a probabilistic background texture model into BPRRC and add the robustness against background movements including foreground invasions such as moving cars, walking people, swaying trees, and falling snow. We show the superiority of PrBPRRC in the environment with illumination changes and background movements by using three public datasets and one private dataset: ATON Highway data, Karlsruhe traffic sequence data, PETS 2007 data, and Walking-in-a-room data.

  5. Change Detection with Polarimetric SAR Imagery for Nuclear Verification

    International Nuclear Information System (INIS)

    Canty, M.

    2015-01-01

    This paper investigates the application of multivariate statistical change detection with high-resolution polarimetric SAR imagery acquired from commercial satellite platforms for observation and verification of nuclear activities. A prototype software tool comprising a processing chain starting from single look complex (SLC) multitemporal data through to change detection maps is presented. Multivariate change detection algorithms applied to polarimetric SAR data are not common. This is because, up until recently, not many researchers or practitioners have had access to polarimetric data. However with the advent of several spaceborne polarimetric SAR instruments such as the Japanese ALOS, the Canadian Radarsat-2, the German TerraSAR-X, the Italian COSMO-SkyMed missions and the European Sentinal SAR platform, the situation has greatly improved. There is now a rich source of weather-independent satellite radar data which can be exploited for Nuclear Safeguards purposes. The method will also work for univariate data, that is, it is also applicable to scalar or single polarimetric SAR data. The change detection procedure investigated here exploits the complex Wishart distribution of dual and quad polarimetric imagery in look-averaged covariance matrix format in order to define a per-pixel change/no-change hypothesis test. It includes approximations for the probability distribution of the test statistic, and so permits quantitative significance levels to be quoted for change pixels. The method has been demonstrated previously with polarimetric images from the airborne EMISAR sensor, but is applied here for the first time to satellite platforms. In addition, an improved multivariate method is used to estimate the so-called equivalent number of looks (ENL), which is a critical parameter of the hypothesis test. (author)

  6. Change-point analysis of geophysical time-series: application to landslide displacement rate (Séchilienne rock avalanche, France)

    Science.gov (United States)

    Amorese, D.; Grasso, J.-R.; Garambois, S.; Font, M.

    2018-05-01

    The rank-sum multiple change-point method is a robust statistical procedure designed to search for the optimal number and the location of change points in an arbitrary continue or discrete sequence of values. As such, this procedure can be used to analyse time-series data. Twelve years of robust data sets for the Séchilienne (French Alps) rockslide show a continuous increase in average displacement rate from 50 to 280 mm per month, in the 2004-2014 period, followed by a strong decrease back to 50 mm per month in the 2014-2015 period. When possible kinematic phases are tentatively suggested in previous studies, its solely rely on the basis of empirical threshold values. In this paper, we analyse how the use of a statistical algorithm for change-point detection helps to better understand time phases in landslide kinematics. First, we test the efficiency of the statistical algorithm on geophysical benchmark data, these data sets (stream flows and Northern Hemisphere temperatures) being already analysed by independent statistical tools. Second, we apply the method to 12-yr daily time-series of the Séchilienne landslide, for rainfall and displacement data, from 2003 December to 2015 December, in order to quantitatively extract changes in landslide kinematics. We find two strong significant discontinuities in the weekly cumulated rainfall values: an average rainfall rate increase is resolved in 2012 April and a decrease in 2014 August. Four robust changes are highlighted in the displacement time-series (2008 May, 2009 November-December-2010 January, 2012 September and 2014 March), the 2010 one being preceded by a significant but weak rainfall rate increase (in 2009 November). Accordingly, we are able to quantitatively define five kinematic stages for the Séchilienne rock avalanche during this period. The synchronization between the rainfall and displacement rate, only resolved at the end of 2009 and beginning of 2010, corresponds to a remarkable change (fourfold

  7. The fate of object memory traces under change detection and change blindness.

    Science.gov (United States)

    Busch, Niko A

    2013-07-03

    Observers often fail to detect substantial changes in a visual scene. This so-called change blindness is often taken as evidence that visual representations are sparse and volatile. This notion rests on the assumption that the failure to detect a change implies that representations of the changing objects are lost all together. However, recent evidence suggests that under change blindness, object memory representations may be formed and stored, but not retrieved. This study investigated the fate of object memory representations when changes go unnoticed. Participants were presented with scenes consisting of real world objects, one of which changed on each trial, while recording event-related potentials (ERPs). Participants were first asked to localize where the change had occurred. In an additional recognition task, participants then discriminated old objects, either from the pre-change or the post-change scene, from entirely new objects. Neural traces of object memories were studied by comparing ERPs for old and novel objects. Participants performed poorly in the detection task and often failed to recognize objects from the scene, especially pre-change objects. However, a robust old/novel effect was observed in the ERP, even when participants were change blind and did not recognize the old object. This implicit memory trace was found both for pre-change and post-change objects. These findings suggest that object memories are stored even under change blindness. Thus, visual representations may not be as sparse and volatile as previously thought. Rather, change blindness may point to a failure to retrieve and use these representations for change detection. Copyright © 2013 Elsevier B.V. All rights reserved.

  8. Drillstring Washout Diagnosis Using Friction Estimation and Statistical Change Detection

    DEFF Research Database (Denmark)

    Willersrud, Anders; Blanke, Mogens; Imsland, Lars

    2015-01-01

    In oil and gas drilling, corrosion or tensile stress can give small holes in the drillstring, which can cause leakage and prevent sufficient flow of drilling fluid. If such washout remains undetected and develops, the consequence can be a complete twist-off of the drillstring. Aiming at early...... washout diagnosis, this paper employs an adaptive observer to estimate friction parameters in the nonlinear pro- cess. Non-Gaussian noise is a nuisance in the parameter estimates, and dedicated generalized likelihood tests are developed to make efficient washout detection with the multivariate t...... -distribution encountered in data. Change detection methods are developed using logged sensor data from a horizontal 1400 m managed pressure drilling test rig. Detection scheme design is conducted using probabilities for false alarm and detection to determine thresholds in hypothesis tests. A multivariate...

  9. Probability of detection of clinical seizures using heart rate changes.

    Science.gov (United States)

    Osorio, Ivan; Manly, B F J

    2015-08-01

    Heart rate-based seizure detection is a viable complement or alternative to ECoG/EEG. This study investigates the role of various biological factors on the probability of clinical seizure detection using heart rate. Regression models were applied to 266 clinical seizures recorded from 72 subjects to investigate if factors such as age, gender, years with epilepsy, etiology, seizure site origin, seizure class, and data collection centers, among others, shape the probability of EKG-based seizure detection. Clinical seizure detection probability based on heart rate changes, is significantly (pprobability of detecting clinical seizures (>0.8 in the majority of subjects) using heart rate is highest for complex partial seizures, increases with a patient's years with epilepsy, is lower for females than for males and is unrelated to the side of hemisphere origin. Clinical seizure detection probability using heart rate is multi-factorially dependent and sufficiently high (>0.8) in most cases to be clinically useful. Knowledge of the role that these factors play in shaping said probability will enhance its applicability and usefulness. Heart rate is a reliable and practical signal for extra-cerebral detection of clinical seizures originating from or spreading to central autonomic network structures. Copyright © 2015 British Epilepsy Association. Published by Elsevier Ltd. All rights reserved.

  10. Water Feature Extraction and Change Detection Using Multitemporal Landsat Imagery

    Directory of Open Access Journals (Sweden)

    Komeil Rokni

    2014-05-01

    Full Text Available Lake Urmia is the 20th largest lake and the second largest hyper saline lake (before September 2010 in the world. It is also the largest inland body of salt water in the Middle East. Nevertheless, the lake has been in a critical situation in recent years due to decreasing surface water and increasing salinity. This study modeled the spatiotemporal changes of Lake Urmia in the period 2000–2013 using the multi-temporal Landsat 5-TM, 7-ETM+ and 8-OLI images. In doing so, the applicability of different satellite-derived indexes including Normalized Difference Water Index (NDWI, Modified NDWI (MNDWI, Normalized Difference Moisture Index (NDMI, Water Ratio Index (WRI, Normalized Difference Vegetation Index (NDVI, and Automated Water Extraction Index (AWEI were investigated for the extraction of surface water from Landsat data. Overall, the NDWI was found superior to other indexes and hence it was used to model the spatiotemporal changes of the lake. In addition, a new approach based on Principal Components of multi-temporal NDWI (NDWI-PCs was proposed and evaluated for surface water change detection. The results indicate an intense decreasing trend in Lake Urmia surface area in the period 2000–2013, especially between 2010 and 2013 when the lake lost about one third of its surface area compared to the year 2000. The results illustrate the effectiveness of the NDWI-PCs approach for surface water change detection, especially in detecting the changes between two and three different times, simultaneously.

  11. Detecting anthropogenic climate change with an optimal fingerprint method

    International Nuclear Information System (INIS)

    Hegerl, G.C.; Storch, H. von; Hasselmann, K.; Santer, B.D.; Jones, P.D.

    1994-01-01

    We propose a general fingerprint strategy to detect anthropogenic climate change and present application to near surface temperature trends. An expected time-space-variable pattern of anthropogenic climate change (the 'signal') is identified through application of an appropriate optimally matched space-time filter (the 'fingerprint') to the observations. The signal and the fingerprint are represented in a space with sufficient observed and simulated data. The signal pattern is derived from a model-generated prediction of anthropogenic climate change. Application of the fingerprint filter to the data yields a scalar detection variable. The statistically optimal fingerprint is obtained by weighting the model-predicted pattern towards low-noise directions. A combination of model output and observations is used to estimate the noise characteristics of the detection variable, arising from the natural variability of climate in the absence of external forcing. We test then the null hypothesis that the observed climate change is part of natural climate variability. We conclude that a statistically significant externally induced warming has been observed, with the caveat of a possibly inadequate estimate of the internal climate variability. In order to attribute this warming uniquely to anthropogenic greenhouse gas forcing, more information on the climate's response to other forcing mechanisms (e.g. changes in solar radiation, volcanic or anthropogenic aerosols) and their interaction is needed. (orig./KW)

  12. Region-Based Building Rooftop Extraction and Change Detection

    Science.gov (United States)

    Tian, J.; Metzlaff, L.; d'Angelo, P.; Reinartz, P.

    2017-09-01

    Automatic extraction of building changes is important for many applications like disaster monitoring and city planning. Although a lot of research work is available based on 2D as well as 3D data, an improvement in accuracy and efficiency is still needed. The introducing of digital surface models (DSMs) to building change detection has strongly improved the resulting accuracy. In this paper, a post-classification approach is proposed for building change detection using satellite stereo imagery. Firstly, DSMs are generated from satellite stereo imagery and further refined by using a segmentation result obtained from the Sobel gradients of the panchromatic image. Besides the refined DSMs, the panchromatic image and the pansharpened multispectral image are used as input features for mean-shift segmentation. The DSM is used to calculate the nDSM, out of which the initial building candidate regions are extracted. The candidate mask is further refined by morphological filtering and by excluding shadow regions. Following this, all segments that overlap with a building candidate region are determined. A building oriented segments merging procedure is introduced to generate a final building rooftop mask. As the last step, object based change detection is performed by directly comparing the building rooftops extracted from the pre- and after-event imagery and by fusing the change indicators with the roof-top region map. A quantitative and qualitative assessment of the proposed approach is provided by using WorldView-2 satellite data from Istanbul, Turkey.

  13. REGION-BASED BUILDING ROOFTOP EXTRACTION AND CHANGE DETECTION

    Directory of Open Access Journals (Sweden)

    J. Tian

    2017-09-01

    Full Text Available Automatic extraction of building changes is important for many applications like disaster monitoring and city planning. Although a lot of research work is available based on 2D as well as 3D data, an improvement in accuracy and efficiency is still needed. The introducing of digital surface models (DSMs to building change detection has strongly improved the resulting accuracy. In this paper, a post-classification approach is proposed for building change detection using satellite stereo imagery. Firstly, DSMs are generated from satellite stereo imagery and further refined by using a segmentation result obtained from the Sobel gradients of the panchromatic image. Besides the refined DSMs, the panchromatic image and the pansharpened multispectral image are used as input features for mean-shift segmentation. The DSM is used to calculate the nDSM, out of which the initial building candidate regions are extracted. The candidate mask is further refined by morphological filtering and by excluding shadow regions. Following this, all segments that overlap with a building candidate region are determined. A building oriented segments merging procedure is introduced to generate a final building rooftop mask. As the last step, object based change detection is performed by directly comparing the building rooftops extracted from the pre- and after-event imagery and by fusing the change indicators with the roof-top region map. A quantitative and qualitative assessment of the proposed approach is provided by using WorldView-2 satellite data from Istanbul, Turkey.

  14. Graph-based structural change detection for rotating machinery monitoring

    Science.gov (United States)

    Lu, Guoliang; Liu, Jie; Yan, Peng

    2018-01-01

    Detection of structural changes is critically important in operational monitoring of a rotating machine. This paper presents a novel framework for this purpose, where a graph model for data modeling is adopted to represent/capture statistical dynamics in machine operations. Meanwhile we develop a numerical method for computing temporal anomalies in the constructed graphs. The martingale-test method is employed for the change detection when making decisions on possible structural changes, where excellent performance is demonstrated outperforming exciting results such as the autoregressive-integrated-moving average (ARIMA) model. Comprehensive experimental results indicate good potentials of the proposed algorithm in various engineering applications. This work is an extension of a recent result (Lu et al., 2017).

  15. Vitreous changes after intravitreal bevacizumab monotherapy for retinopathy of prematurity: a case series.

    Science.gov (United States)

    Shoeibi, Nasser; Hosseini, Seyedeh Maryam; Banaee, Touka; Ansari-Astaneh, Mohammad-Reza; Abrishami, Majid; Ahmadieh, Hamid

    2018-01-01

    Reporting a special clinical finding after intravitreal bevacizumab monotherapy for retinopathy of prematurity. In a retrospective case series, the clinical courses of five premature infants with similar vitreous changes after a single dose of intravitreal bevacizumab (IVB) injection without additional laser therapy were reported. The mean post-conceptional age at IVB injection was 39.8 ± 2.2 (range 37-43) weeks. Localized vitreous syneresis and linear fibrotic vitreous condensation occurred 8.2 ± 2.3 weeks after IVB monotherapy in our patients (15.5% of injections). The mean last post injection visit was 61.6 ± 5.3 weeks (post-conceptional age). Further regression and complete retinal vascularization occurred in all patients. Thread-like vitreous condensation with localized vitreous liquefaction may be related to involutional ROP disease itself, combined to anti VEGF therapy and may be a predictor factor for further regression and retinal vascularization. The case series describes a successful response to anti-VEGF monotherapy with no further complications.

  16. Visualization of time series statistical data by shape analysis (GDP ratio changes among Asia countries)

    Science.gov (United States)

    Shirota, Yukari; Hashimoto, Takako; Fitri Sari, Riri

    2018-03-01

    It has been very significant to visualize time series big data. In the paper we shall discuss a new analysis method called “statistical shape analysis” or “geometry driven statistics” on time series statistical data in economics. In the paper, we analyse the agriculture, value added and industry, value added (percentage of GDP) changes from 2000 to 2010 in Asia. We handle the data as a set of landmarks on a two-dimensional image to see the deformation using the principal components. The point of the analysis method is the principal components of the given formation which are eigenvectors of its bending energy matrix. The local deformation can be expressed as the set of non-Affine transformations. The transformations give us information about the local differences between in 2000 and in 2010. Because the non-Affine transformation can be decomposed into a set of partial warps, we present the partial warps visually. The statistical shape analysis is widely used in biology but, in economics, no application can be found. In the paper, we investigate its potential to analyse the economic data.

  17. Change detection in polarimetric SAR images using complex Wishart distributed matrices

    DEFF Research Database (Denmark)

    Conradsen, Knut; Nielsen, Allan Aasbjerg; Skriver, Henning

    In surveillance it is important to be able to detect natural or man-made changes e.g. based on sequences of satellite or air borne images of the same area taken at different times. The mapping capability of synthetic aperture radar (SAR) is independent of e.g. cloud cover, and thus this technology...... scattering matrix, and after suitable preprocessing the outcome at each picture element (pixel) may be represented as a 3 by 3 Hermitian matrix following a complex Wishart distribution. One approach to solving the change detection problem based on SAR images is therefore to apply suitable statistical tests...... in the complex Wishart distribution. We propose a set-up for a systematic solution to the (practical) problems using the likelihood ratio test statistics. We show some examples based on a time series of images with 1024 by 1024 pixels....

  18. Enhanced plasma wave detection of terahertz radiation using multiple high electron-mobility transistors connected in series

    KAUST Repository

    Elkhatib, Tamer A.; Kachorovskiǐ, Valentin Yu; Stillman, William J.; Veksler, Dmitry B.; Salama, Khaled N.; Zhang, Xicheng; Shur, Michael S.

    2010-01-01

    We report on enhanced room-temperature detection of terahertz radiation by several connected field-effect transistors. For this enhanced nonresonant detection, we have designed, fabricated, and tested plasmonic structures consisting of multiple InGaAs/GaAs pseudomorphic high electron-mobility transistors connected in series. Results show a 1.63-THz response that is directly proportional to the number of detecting transistors biased by a direct drain current at the same gate-to-source bias voltages. The responsivity in the saturation regime was found to be 170 V/W with the noise equivalent power in the range of 10-7 W/Hz0.5. The experimental data are in agreement with the detection mechanism based on the rectification of overdamped plasma waves excited by terahertz radiation in the transistor channel. © 2010 IEEE.

  19. Enhanced plasma wave detection of terahertz radiation using multiple high electron-mobility transistors connected in series

    KAUST Repository

    Elkhatib, Tamer A.

    2010-02-01

    We report on enhanced room-temperature detection of terahertz radiation by several connected field-effect transistors. For this enhanced nonresonant detection, we have designed, fabricated, and tested plasmonic structures consisting of multiple InGaAs/GaAs pseudomorphic high electron-mobility transistors connected in series. Results show a 1.63-THz response that is directly proportional to the number of detecting transistors biased by a direct drain current at the same gate-to-source bias voltages. The responsivity in the saturation regime was found to be 170 V/W with the noise equivalent power in the range of 10-7 W/Hz0.5. The experimental data are in agreement with the detection mechanism based on the rectification of overdamped plasma waves excited by terahertz radiation in the transistor channel. © 2010 IEEE.

  20. MODVOLC2: A Hybrid Time Series Analysis for Detecting Thermal Anomalies Applied to Thermal Infrared Satellite Data

    Science.gov (United States)

    Koeppen, W. C.; Wright, R.; Pilger, E.

    2009-12-01

    We developed and tested a new, automated algorithm, MODVOLC2, which analyzes thermal infrared satellite time series data to detect and quantify the excess energy radiated from thermal anomalies such as active volcanoes, fires, and gas flares. MODVOLC2 combines two previously developed algorithms, a simple point operation algorithm (MODVOLC) and a more complex time series analysis (Robust AVHRR Techniques, or RAT) to overcome the limitations of using each approach alone. MODVOLC2 has four main steps: (1) it uses the original MODVOLC algorithm to process the satellite data on a pixel-by-pixel basis and remove thermal outliers, (2) it uses the remaining data to calculate reference and variability images for each calendar month, (3) it compares the original satellite data and any newly acquired data to the reference images normalized by their variability, and it detects pixels that fall outside the envelope of normal thermal behavior, (4) it adds any pixels detected by MODVOLC to those detected in the time series analysis. Using test sites at Anatahan and Kilauea volcanoes, we show that MODVOLC2 was able to detect ~15% more thermal anomalies than using MODVOLC alone, with very few, if any, known false detections. Using gas flares from the Cantarell oil field in the Gulf of Mexico, we show that MODVOLC2 provided results that were unattainable using a time series-only approach. Some thermal anomalies (e.g., Cantarell oil field flares) are so persistent that an additional, semi-automated 12-µm correction must be applied in order to correctly estimate both the number of anomalies and the total excess radiance being emitted by them. Although all available data should be included to make the best possible reference and variability images necessary for the MODVOLC2, we estimate that at least 80 images per calendar month are required to generate relatively good statistics from which to run MODVOLC2, a condition now globally met by a decade of MODIS observations. We also found

  1. Extracting Leading Nonlinear Modes of Changing Climate From Global SST Time Series

    Science.gov (United States)

    Mukhin, D.; Gavrilov, A.; Loskutov, E. M.; Feigin, A. M.; Kurths, J.

    2017-12-01

    Data-driven modeling of climate requires adequate principal variables extracted from observed high-dimensional data. For constructing such variables it is needed to find spatial-temporal patterns explaining a substantial part of the variability and comprising all dynamically related time series from the data. The difficulties of this task rise from the nonlinearity and non-stationarity of the climate dynamical system. The nonlinearity leads to insufficiency of linear methods of data decomposition for separating different processes entangled in the observed time series. On the other hand, various forcings, both anthropogenic and natural, make the dynamics non-stationary, and we should be able to describe the response of the system to such forcings in order to separate the modes explaining the internal variability. The method we present is aimed to overcome both these problems. The method is based on the Nonlinear Dynamical Mode (NDM) decomposition [1,2], but takes into account external forcing signals. An each mode depends on hidden, unknown a priori, time series which, together with external forcing time series, are mapped onto data space. Finding both the hidden signals and the mapping allows us to study the evolution of the modes' structure in changing external conditions and to compare the roles of the internal variability and forcing in the observed behavior. The method is used for extracting of the principal modes of SST variability on inter-annual and multidecadal time scales accounting the external forcings such as CO2, variations of the solar activity and volcanic activity. The structure of the revealed teleconnection patterns as well as their forecast under different CO2 emission scenarios are discussed.[1] Mukhin, D., Gavrilov, A., Feigin, A., Loskutov, E., & Kurths, J. (2015). Principal nonlinear dynamical modes of climate variability. Scientific Reports, 5, 15510. [2] Gavrilov, A., Mukhin, D., Loskutov, E., Volodin, E., Feigin, A., & Kurths, J. (2016

  2. Fragrance mix II in the baseline series contributes significantly to detection of fragrance allergy

    DEFF Research Database (Denmark)

    Heisterberg, Maria V; Andersen, Klaus E; Avnstorp, Christian

    2010-01-01

    Fragrance mix II (FM II) is a relatively new screening marker for fragrance contact allergy. It was introduced in the patch test baseline series in Denmark in 2005 and contains six different fragrance chemicals commonly present in cosmetic products and which are known allergens.......Fragrance mix II (FM II) is a relatively new screening marker for fragrance contact allergy. It was introduced in the patch test baseline series in Denmark in 2005 and contains six different fragrance chemicals commonly present in cosmetic products and which are known allergens....

  3. Fragrance mix II in the baseline series contributes significantly to detection of fragrance allergy

    DEFF Research Database (Denmark)

    Heisterberg, Maria S Vølund; Andersen, Klaus E.; Avnstorp, Christian

    2010-01-01

    Background: Fragrance mix II (FM II) is a relatively new screening marker for fragrance contact allergy. It was introduced in the patch test baseline series in Denmark in 2005 and contains six different fragrance chemicals commonly present in cosmetic products and which are known allergens. Aim......: To investigate the diagnostic contribution of including FM II in the baseline series by comparing it with other screening markers of fragrance allergy: fragrance mix I (FM I), Myroxylon pereirae and hydroxyisohexyl 3-cyclohexene carboxaldehyde (HICC). Method: Retrospective study of 12 302 patients consecutively...

  4. Breaks in MODIS time series portend vegetation change: verification using long-term data in an arid grassland ecosystem.

    Science.gov (United States)

    Browning, Dawn M; Maynard, Jonathan J; Karl, Jason W; Peters, Debra C

    2017-07-01

    breaks accompanied changes in the contribution to biomass by perennial and/or annual grasses. The BFAST method using satellite imagery proved useful for detecting previously reported ground-based changes in vegetation in this arid ecosystem. We demonstrate that time series analysis of NDVI data holds potential for monitoring landscape condition in arid ecosystems at the large spatial scales needed to differentiate responses to a changing climate from responses to seasonal variability in rainfall. © 2017 by the Ecological Society of America.

  5. How to determine life expectancy change of air pollution mortality: a time series study

    Directory of Open Access Journals (Sweden)

    Chau PYK

    2011-03-01

    Full Text Available Abstract Background Information on life expectancy (LE change is of great concern for policy makers, as evidenced by discussions of the "harvesting" (or "mortality displacement" issue, i.e. how large an LE loss corresponds to the mortality results of time series (TS studies. Whereas loss of LE attributable to chronic air pollution exposure can be determined from cohort studies, using life table methods, conventional TS studies have identified only deaths due to acute exposure, during the immediate past (typically the preceding one to five days, and they provide no information about the LE loss per death. Methods We show how to obtain information on population-average LE loss by extending the observation window (largest "lag" of TS to include a sufficient number of "impact coefficients" for past exposures ("lags". We test several methods for determining these coefficients. Once all of the coefficients have been determined, the LE change is calculated as time integral of the relative risk change after a permanent step change in exposure. Results The method is illustrated with results for daily data of non-accidental mortality from Hong Kong for 1985 - 2005, regressed against PM10 and SO2 with observation windows up to 5 years. The majority of the coefficients is statistically significant. The magnitude of the SO2 coefficients is comparable to those for PM10. But a window of 5 years is not sufficient and the results for LE change are only a lower bound; it is consistent with what is implied by other studies of long term impacts. Conclusions A TS analysis can determine the LE loss, but if the observation window is shorter than the relevant exposures one obtains only a lower bound.

  6. Global scene layout modulates contextual learning in change detection.

    Science.gov (United States)

    Conci, Markus; Müller, Hermann J

    2014-01-01

    Change in the visual scene often goes unnoticed - a phenomenon referred to as "change blindness." This study examined whether the hierarchical structure, i.e., the global-local layout of a scene can influence performance in a one-shot change detection paradigm. To this end, natural scenes of a laid breakfast table were presented, and observers were asked to locate the onset of a new local object. Importantly, the global structure of the scene was manipulated by varying the relations among objects in the scene layouts. The very same items were either presented as global-congruent (typical) layouts or as global-incongruent (random) arrangements. Change blindness was less severe for congruent than for incongruent displays, and this congruency benefit increased with the duration of the experiment. These findings show that global layouts are learned, supporting detection of local changes with enhanced efficiency. However, performance was not affected by scene congruency in a subsequent control experiment that required observers to localize a static discontinuity (i.e., an object that was missing from the repeated layouts). Our results thus show that learning of the global layout is particularly linked to the local objects. Taken together, our results reveal an effect of "global precedence" in natural scenes. We suggest that relational properties within the hierarchy of a natural scene are governed, in particular, by global image analysis, reducing change blindness for local objects through scene learning.

  7. Global scene layout modulates contextual learning in change detection

    Directory of Open Access Journals (Sweden)

    Markus eConci

    2014-02-01

    Full Text Available Change in the visual scene often goes unnoticed – a phenomenon referred to as ‘change blindness’. This study examined whether the hierarchical structure, i.e., the global-local layout of a scene can influence performance in a one-shot change detection paradigm. To this end, natural scenes of a laid breakfast table were presented, and observers were asked to locate the onset of a new local object. Importantly, the global structure of the scene was manipulated by varying the relations among objects in the scene layouts. The very same items were either presented as global-congruent (typical layouts or as global-incongruent (random arrangements. Change blindness was less severe for congruent than for incongruent displays, and this congruency benefit increased with the duration of the experiment. These findings show that global layouts are learned, supporting detection of local changes with enhanced efficiency. However, performance was not affected by scene congruency in a subsequent control experiment that required observers to localize a static discontinuity (i.e., an object that was missing from the repeated layouts. Our results thus show that learning of the global layout is particularly linked to the local objects. Taken together, our results reveal an effect of global precedence in natural scenes. We suggest that relational properties within the hierarchy of a natural scene are governed, in particular, by global image analysis, reducing change blindness for local objects through scene learning.

  8. Methodological examination of UAR-based change detection

    Energy Technology Data Exchange (ETDEWEB)

    Racz, A.; Kiss, S. [Hungarian Academy of Sciences, Budapest (Hungary). Central Research Inst. for Physics

    1995-12-01

    A methodological examination was performed in order to investigate the applicability of the combination of the well-known Univariate AutoRegressive model and the classical binary SPRT method. The signal was recorded by a vibration detector fixed at a white-noise excited fuel rod. During the experiments, the following abnormality (or minor changes) were simulated: loosening of the detector, changes in the underlying system (constraints and the environment), rod impact. The residual time series were generated by an UAR model while the hypothesis testing was performed by a binary SPRT applied for checking the variation of the variance of the residual. Although the results are very promising, few disturbing effects were recognized also, which are as yet unexplained, therefore more careful application of this familiar combination is required. (author).

  9. Methodological examination of UAR-based change detection

    International Nuclear Information System (INIS)

    Racz, A.; Kiss, S.

    1994-07-01

    A methodological examination was performed in order to investigate the applicability of the combination of the well-known Univariate Auto Regressive (UAR) model and the classical binary Sequential Probability Ratio Testing (SPRT) method. The signal was recorded by a vibration detector fixed at a white-noise excited fuel rod. During the experiments, the following abnormality (or minor changes) were simulated: loosening of the detector, changes in the underlying system (constraints and the environment), rod impact. The residual time series were generated by an UAR model while the hypothesis testing was performed by a binary SPRT applied for checking the variation of the variance of the residual. Although the results are very promising, few disturbing effects were recognized also, which seem to be unexplained yet, therefore they need more careful application of this familiar combination. (author) 14 refs.; 21 figs.; 3 tabs

  10. Detection of chaotic determinism in time series from randomly forced maps

    Science.gov (United States)

    Chon, K. H.; Kanters, J. K.; Cohen, R. J.; Holstein-Rathlou, N. H.

    1997-01-01

    Time series from biological system often display fluctuations in the measured variables. Much effort has been directed at determining whether this variability reflects deterministic chaos, or whether it is merely "noise". Despite this effort, it has been difficult to establish the presence of chaos in time series from biological sytems. The output from a biological system is probably the result of both its internal dynamics, and the input to the system from the surroundings. This implies that the system should be viewed as a mixed system with both stochastic and deterministic components. We present a method that appears to be useful in deciding whether determinism is present in a time series, and if this determinism has chaotic attributes, i.e., a positive characteristic exponent that leads to sensitivity to initial conditions. The method relies on fitting a nonlinear autoregressive model to the time series followed by an estimation of the characteristic exponents of the model over the observed probability distribution of states for the system. The method is tested by computer simulations, and applied to heart rate variability data.

  11. Statistical methods for change-point detection in surface temperature records

    Science.gov (United States)

    Pintar, A. L.; Possolo, A.; Zhang, N. F.

    2013-09-01

    We describe several statistical methods to detect possible change-points in a time series of values of surface temperature measured at a meteorological station, and to assess the statistical significance of such changes, taking into account the natural variability of the measured values, and the autocorrelations between them. These methods serve to determine whether the record may suffer from biases unrelated to the climate signal, hence whether there may be a need for adjustments as considered by M. J. Menne and C. N. Williams (2009) "Homogenization of Temperature Series via Pairwise Comparisons", Journal of Climate 22 (7), 1700-1717. We also review methods to characterize patterns of seasonality (seasonal decomposition using monthly medians or robust local regression), and explain the role they play in the imputation of missing values, and in enabling robust decompositions of the measured values into a seasonal component, a possible climate signal, and a station-specific remainder. The methods for change-point detection that we describe include statistical process control, wavelet multi-resolution analysis, adaptive weights smoothing, and a Bayesian procedure, all of which are applicable to single station records.

  12. Long-term Satellite NDVI Data Sets: Evaluating Their Ability to Detect Ecosystem Functional Changes in South America.

    Science.gov (United States)

    Baldi, Germán; Nosetto, Marcelo D; Aragón, Roxana; Aversa, Fernando; Paruelo, José M; Jobbágy, Esteban G

    2008-09-03

    In the last decades, South American ecosystems underwent important functional modifications due to climate alterations and direct human intervention on land use and land cover. Among remotely sensed data sets, NOAA-AVHRR "Normalized Difference Vegetation Index" (NDVI) represents one of the most powerful tools to evaluate these changes thanks to their extended temporal coverage. In this paper we explored the possibilities and limitations of three commonly used NOAA-AVHRR NDVI series (PAL, GIMMS and FASIR) to detect ecosystem functional changes in the South American continent. We performed pixel-based linear regressions for four NDVI variables (average annual, maximum annual, minimum annual and intra-annual coefficient of variation) for the 1982-1999 period and (1) analyzed the convergences and divergences of significant multi-annual trends identified across all series, (2) explored the degree of aggregation of the trends using the O-ring statistic, and (3) evaluated observed trends using independent information on ecosystem functional changes in five focal regions. Several differences arose in terms of the patterns of change (the sign, localization and total number of pixels with changes). FASIR presented the highest proportion of changing pixels (32.7%) and GIMMS the lowest (16.2%). PAL and FASIR data sets showed the highest agreement, with a convergence of detected trends on 71.2% of the pixels. Even though positive and negative changes showed substantial spatial aggregation, important differences in the scale of aggregation emerged among the series, with GIMMS showing the smaller scale (≤11 pixels). The independent evaluations suggest higher accuracy in the detection of ecosystem changes among PAL and FASIR series than with GIMMS, as they detected trends that match expected shifts. In fact, this last series eliminated most of the long term patterns over the continent. For example, in the "Eastern Paraguay" and "Uruguay River margins" focal regions, the

  13. Automatic change detection to facial expressions in adolescents

    DEFF Research Database (Denmark)

    Liu, Tongran; Xiao, Tong; Jiannong, Shi

    2016-01-01

    Adolescence is a critical period for the neurodevelopment of social-emotional processing, wherein the automatic detection of changes in facial expressions is crucial for the development of interpersonal communication. Two groups of participants (an adolescent group and an adult group) were...... in facial expressions between the two age groups. The current findings demonstrated that the adolescent group featured more negative vMMN amplitudes than the adult group in the fronto-central region during the 120–200 ms interval. During the time window of 370–450 ms, only the adult group showed better...... automatic processing on fearful faces than happy faces. The present study indicated that adolescent’s posses stronger automatic detection of changes in emotional expression relative to adults, and sheds light on the neurodevelopment of automatic processes concerning social-emotional information....

  14. Object-Based Change Detection in Urban Areas: The Effects of Segmentation Strategy, Scale, and Feature Space on Unsupervised Methods

    Directory of Open Access Journals (Sweden)

    Lei Ma

    2016-09-01

    Full Text Available Object-based change detection (OBCD has recently been receiving increasing attention as a result of rapid improvements in the resolution of remote sensing data. However, some OBCD issues relating to the segmentation of high-resolution images remain to be explored. For example, segmentation units derived using different segmentation strategies, segmentation scales, feature space, and change detection methods have rarely been assessed. In this study, we have tested four common unsupervised change detection methods using different segmentation strategies and a series of segmentation scale parameters on two WorldView-2 images of urban areas. We have also evaluated the effect of adding extra textural and Normalized Difference Vegetation Index (NDVI information instead of using only spectral information. Our results indicated that change detection methods performed better at a medium scale than at a fine scale where close to the pixel size. Multivariate Alteration Detection (MAD always outperformed the other methods tested, at the same confidence level. The overall accuracy appeared to benefit from using a two-date segmentation strategy rather than single-date segmentation. Adding textural and NDVI information appeared to reduce detection accuracy, but the magnitude of this reduction was not consistent across the different unsupervised methods and segmentation strategies. We conclude that a two-date segmentation strategy is useful for change detection in high-resolution imagery, but that the optimization of thresholds is critical for unsupervised change detection methods. Advanced methods need be explored that can take advantage of additional textural or other parameters.

  15. Segment-based change detection for polarimetric SAR data

    DEFF Research Database (Denmark)

    Skriver, Henning; Nielsen, Allan Aasbjerg; Conradsen, Knut

    2006-01-01

    that is needed compared to single polarisation SAR to provide reliable and robust detection of changes. Polarimetric SAR data will be available from satellites in the near future, e.g. the Japanese ALOS, the Canadian Radarsat-2 and the German TerraSAR-X. An appropriate way of representing multi-look fully...... be split into a number of smaller fields, a building may be removed from or added to some area, hedgerows may be removed/added or other type of vegetated areas may be partly removed or added. In this case, ambiguities may arise when segments have changed shape and extent from one image to another...

  16. A structural framework for anomalous change detection and characterization

    Energy Technology Data Exchange (ETDEWEB)

    Prasad, Lakshman [Los Alamos National Laboratory; Theiler, James P [Los Alamos National Laboratory

    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.

  17. Design of Passive Power Filter for Hybrid Series Active Power Filter using Estimation, Detection and Classification Method

    Science.gov (United States)

    Swain, Sushree Diptimayee; Ray, Pravat Kumar; Mohanty, K. B.

    2016-06-01

    This research paper discover the design of a shunt Passive Power Filter (PPF) in Hybrid Series Active Power Filter (HSAPF) that employs a novel analytic methodology which is superior than FFT analysis. This novel approach consists of the estimation, detection and classification of the signals. The proposed method is applied to estimate, detect and classify the power quality (PQ) disturbance such as harmonics. This proposed work deals with three methods: the harmonic detection through wavelet transform method, the harmonic estimation by Kalman Filter algorithm and harmonic classification by decision tree method. From different type of mother wavelets in wavelet transform method, the db8 is selected as suitable mother wavelet because of its potency on transient response and crouched oscillation at frequency domain. In harmonic compensation process, the detected harmonic is compensated through Hybrid Series Active Power Filter (HSAPF) based on Instantaneous Reactive Power Theory (IRPT). The efficacy of the proposed method is verified in MATLAB/SIMULINK domain and as well as with an experimental set up. The obtained results confirm the superiority of the proposed methodology than FFT analysis. This newly proposed PPF is used to make the conventional HSAPF more robust and stable.

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

    Science.gov (United States)

    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.

  19. Happy Face Superiority Effect in Change Detection Paradigm

    Directory of Open Access Journals (Sweden)

    Domagoj Švegar

    2013-09-01

    Full Text Available The aim of the present study was to investigate which affective component guides cognitive processing of emotional facial expressions. According to the threat hypothesis, processing of angry faces is prioritized by the human cognitive system, because rapid detection of threat has a large adaptive value. The negativity hypothesis presumes that distressing emotional experiences of other people attract attention, regardless of whether they represent danger or not. The emotionality hypothesis proposes that positive emotional facial expressions can capture attention as effective as negative ones, while the happy face superiority hypothesis predicts that happy faces are prioritized. In the present study, which was conducted on 24 participants, change detection paradigm was used, because that procedure enables insight into the later stage of information processing. The results obtained show that happy facial expressions are heavily prioritized by the human cognitive system. In explanation of these results, that clearly support the happy face superiority hypothesis, we propose that angry expressions are initially prioritized by our cognitive system, because we benefit from early detection of potential threat in the environment, but in later cognitive processing, happy expressions are given the priority, because smiling is a valuable mechanism for forming and maintaining cooperative relationships. Besides the theoretical relevance, the present study is also valuable methodologically, because we demonstrated that change detection paradigm can be efficiently used for the research of emotional facial expressions processing.

  20. Detecting changes in real-time data: a user's guide to optimal detection.

    Science.gov (United States)

    Johnson, P; Moriarty, J; Peskir, G

    2017-08-13

    The real-time detection of changes in a noisily observed signal is an important problem in applied science and engineering. The study of parametric optimal detection theory began in the 1930s, motivated by applications in production and defence. Today this theory, which aims to minimize a given measure of detection delay under accuracy constraints, finds applications in domains including radar, sonar, seismic activity, global positioning, psychological testing, quality control, communications and power systems engineering. This paper reviews developments in optimal detection theory and sequential analysis, including sequential hypothesis testing and change-point detection, in both Bayesian and classical (non-Bayesian) settings. For clarity of exposition, we work in discrete time and provide a brief discussion of the continuous time setting, including recent developments using stochastic calculus. Different measures of detection delay are presented, together with the corresponding optimal solutions. We emphasize the important role of the signal-to-noise ratio and discuss both the underlying assumptions and some typical applications for each formulation.This article is part of the themed issue 'Energy management: flexibility, risk and optimization'. © 2017 The Author(s).

  1. Applications of Graph-Theoretic Tests to Online Change Detection

    Science.gov (United States)

    2014-05-09

    stock broker decides to sell a majority of his positions due to a change in the markets; a child grabs a snack because he has become hungry; an alarm...detected and the time when a negative result will occur (i.e. machine death through mechanical failure) or a positive chance squandered (not buying ...and moving low pressure systems in the atmosphere, and these systems cause persistence to daily rainfall. The daily weather in this area is a

  2. Rainfall distribution and change detection across climatic zones in Nigeria

    OpenAIRE

    Stephen Bunmi Ogungbenro; Tobi Eniolu Morakinyo

    2014-01-01

    Nigerian agriculture is mainly rain-fed and basically dependent on the vagaries of weather especially rainfall. Nigeria today has about forty-four (44) weather observation stations which provide measurement of rainfall amount for different locations across the country. Hence, this study investigates change detection in rainfall pattern over each climatic zone of Nigeria. Data were collected for 90 years (1910–1999) period for all the weather observation stations in Nigeria, while a subdivisio...

  3. Pan-Arctic TV Series on Inuit wellness: a northern model of communication for social change?

    Science.gov (United States)

    Johnson, Rhonda; Morales, Robin; Leavitt, Doreen; Carry, Catherine; Kinnon, Dianne; Rideout, Denise; Clarida, Kath

    2011-06-01

    This paper provides highlights of a utilization-focused evaluation of a collaborative Pan-Arctic Inuit Wellness TV Series that was broadcast live in Alaska and Canada in May 2009. This International Polar Year (IPY) communication and outreach project intended to (1) share information on International Polar Year research progress, disseminate findings and explore questions with Inuit in Alaska, Canada and Greenland; (2) provide a forum for Inuit in Alaska, Canada and Greenland to showcase innovative health and wellness projects; (3) ensure Inuit youth and adult engagement throughout; and (4) document and reflect on the overall experience for the purposes of developing and "testing" a participatory communication model. Utilization-focused formative evaluation of the project, with a focus on overall objectives, key messages and lessons learned to facilitate program improvement. Participant observation, surveys, key informant interviews, document review and website tracking. Promising community programs related to 3 themes - men's wellness, maternity care and youth resilience - in diverse circumpolar regions were highlighted, as were current and stillevolving findings from ongoing Arctic research. Multiple media methods were used to effectively deliver and receive key messages determined by both community and academic experts. Local capacity and new regional networks were strengthened. Evidence-based resources for health education and community action were archived in digital formats (websites and DVDs), increasing accessibility to otherwise isolated individuals and remote communities. The Pan-Arctic Inuit Wellness TV Series was an innovative, multi-dimensional communication project that raised both interest and awareness about complex health conditions in the North and stimulated community dialogue and potential for increased collaborative action. Consistent with a communication for social change approach, the project created new networks, increased motivation to act

  4. Rapid detection of new and expanding human settlements in the Limpopo province of South Africa using a spatio-temporal change detection method

    Science.gov (United States)

    Kleynhans, W.; Salmon, B. P.; Wessels, K. J.; Olivier, J. C.

    2015-08-01

    Recent development has identified the benefits of using hyper-temporal satellite time series data for land cover change detection and classification in South Africa. In particular, the monitoring of human settlement expansion in the Limpopo province is of relevance as it is the one of the most pervasive forms of land-cover change in this province which covers an area of roughly 125 000 km2. In this paper, a spatio-temporal autocorrelation change detection (STACD) method is developed to improve the performance of a pixel based temporal Autocorrelation change detection (TACD) method previously proposed. The objective is to apply the algorithm to large areas to detect the conversion of natural vegetation to settlement which is then validated by an operator using additional data (such as high resolution imagery). Importantly, as the objective of the method is to indicate areas of potential change to operators for further analysis, a low false alarm rate is required while achieving an acceptable probability of detection. Results indicate that detection accuracies of 70% of new settlement instances are achievable at a false alarm rate of less than 1% with the STACD method, an improvement of up to 17% compared to the original TACD formulation.

  5. Gathering Time-Series Data for Evaluating Behavior-Change Campaigns in Developing Countries: Reactivity of Diaries and Interviews

    Science.gov (United States)

    Tobias, Robert; Inauen, Jennifer

    2010-01-01

    Gathering time-series data of behaviors and psychological variables is important to understand, guide, and evaluate behavior-change campaigns and other change processes. However, repeated measurement can affect the phenomena investigated, particularly frequent face-to-face interviews, which are often the only option in developing countries. This…

  6. Combining Different Conceptual Change Methods within Four-Step Constructivist Teaching Model: A Sample Teaching of Series and Parallel Circuits

    Science.gov (United States)

    Ipek, Hava; Calik, Muammer

    2008-01-01

    Based on students' alternative conceptions of the topics "electric circuits", "electric charge flows within an electric circuit", "how the brightness of bulbs and the resistance changes in series and parallel circuits", the current study aims to present a combination of different conceptual change methods within a four-step constructivist teaching…

  7. Ice Sheet Change Detection by Satellite Image Differencing

    Science.gov (United States)

    Bindschadler, Robert A.; Scambos, Ted A.; Choi, Hyeungu; Haran, Terry M.

    2010-01-01

    Differencing of digital satellite image pairs highlights subtle changes in near-identical scenes of Earth surfaces. Using the mathematical relationships relevant to photoclinometry, we examine the effectiveness of this method for the study of localized ice sheet surface topography changes using numerical experiments. We then test these results by differencing images of several regions in West Antarctica, including some where changes have previously been identified in altimeter profiles. The technique works well with coregistered images having low noise, high radiometric sensitivity, and near-identical solar illumination geometry. Clouds and frosts detract from resolving surface features. The ETM(plus) sensor on Landsat-7, ALI sensor on EO-1, and MODIS sensor on the Aqua and Terra satellite platforms all have potential for detecting localized topographic changes such as shifting dunes, surface inflation and deflation features associated with sub-glacial lake fill-drain events, or grounding line changes. Availability and frequency of MODIS images favor this sensor for wide application, and using it, we demonstrate both qualitative identification of changes in topography and quantitative mapping of slope and elevation changes.

  8. Robust and Accurate Anomaly Detection in ECG Artifacts Using Time Series Motif Discovery

    Science.gov (United States)

    Sivaraks, Haemwaan

    2015-01-01

    Electrocardiogram (ECG) anomaly detection is an important technique for detecting dissimilar heartbeats which helps identify abnormal ECGs before the diagnosis process. Currently available ECG anomaly detection methods, ranging from academic research to commercial ECG machines, still suffer from a high false alarm rate because these methods are not able to differentiate ECG artifacts from real ECG signal, especially, in ECG artifacts that are similar to ECG signals in terms of shape and/or frequency. The problem leads to high vigilance for physicians and misinterpretation risk for nonspecialists. Therefore, this work proposes a novel anomaly detection technique that is highly robust and accurate in the presence of ECG artifacts which can effectively reduce the false alarm rate. Expert knowledge from cardiologists and motif discovery technique is utilized in our design. In addition, every step of the algorithm conforms to the interpretation of cardiologists. Our method can be utilized to both single-lead ECGs and multilead ECGs. Our experiment results on real ECG datasets are interpreted and evaluated by cardiologists. Our proposed algorithm can mostly achieve 100% of accuracy on detection (AoD), sensitivity, specificity, and positive predictive value with 0% false alarm rate. The results demonstrate that our proposed method is highly accurate and robust to artifacts, compared with competitive anomaly detection methods. PMID:25688284

  9. Building Change Detection from Harvey using Unmanned Aerial System (UAS)

    Science.gov (United States)

    Chang, A.; Yeom, J.; Jung, J.; Choi, I.

    2017-12-01

    Unmanned Aerial System (UAS) is getting to be the most important technique in recent days since the fine spatial and high temporal resolution data previously unobtainable from traditional remote sensing platforms. Advanced UAS data can provide a great opportunity for disaster monitoring. Especially, building change detection is the one of the most important topics for damage assessment and recovery from disasters. This study is proposing a method to monitor building change with UAS data for Holiday Beach in Texas, where was directly hit by Harvey on 25 August 2017. This study adopted 3D change detection to monitor building damage and recovery levels with building height as well as natural color information. We used a rotorcraft UAS to collect RGB data twice on 9 September and 18 October 2017 after the hurricane. The UAS data was processed using Agisoft Photoscan Pro Software to generate super high resolution dataset including orthomosaic, DSM (Digital Surface Model), and 3D point cloud. We compared the processed dataset with an airborne image considerable as before-hurricane data, which was acquired on January 2016. Building damage and recovery levels were determined by height and color change. The result will show that UAS data is useful to assess building damage and recovery for affected area by the natural disaster such as Harvey.

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

    Energy Technology Data Exchange (ETDEWEB)

    Wohlberg, Brendt E [Los Alamos National Laboratory; Theiler, James P [Los Alamos National Laboratory

    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.

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

    International Nuclear Information System (INIS)

    Byler, E.

    1997-01-01

    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

  12. MODIS NDVI Change Detection Techniques and Products Used in the Near Real Time ForWarn System for Detecting, Monitoring, and Analyzing Regional Forest Disturbances

    Science.gov (United States)

    Spruce, Joseph P.; Hargrove, William; Gasser, Jerry; Smoot, James; Kuper, Philip D.

    2014-01-01

    This presentation discusses MODIS NDVI change detection methods and products used in the ForWarn Early Warning System (EWS) for near real time (NRT) recognition and tracking of regionally evident forest disturbances throughout the conterminous US (CONUS). The latter has provided NRT forest change products to the forest health protection community since 2010, using temporally processed MODIS Aqua and Terra NDVI time series data to currently compute and post 6 different forest change products for CONUS every 8 days. Multiple change products are required to improve detectability and to more fully assess the nature of apparent disturbances. Each type of forest change product reports per pixel percent change in NDVI for a given 24 day interval, comparing current versus a given historical baseline NDVI. EMODIS 7 day expedited MODIS MOD13 data are used to obtain current and historical NDVIs, respectively. Historical NDVI data is processed with Time Series Product Tool (TSPT); and 2) the Phenological Parameters Estimation Tool (PPET) software. While each change products employ maximum value compositing (MVC) of NDVI, the design of specific products primarily differs in terms of the historical baseline. The three main change products use either 1, 3, or all previous years of MVC NDVI as a baseline. Another product uses an Adaptive Length Compositing (ALC) version of MVC to derive an alternative current NDVI that is the freshest quality NDVI as opposed to merely the MVC NDVI across a 24 day time frame. The ALC approach can improve detection speed by 8 to 16 days. ForWarn also includes 2 change products that improve detectability of forest disturbances in lieu of climatic fluctuations, especially in the spring and fall. One compares current MVC NDVI to the zonal maximum under the curve NDVI per pheno-region cluster class, considering all previous years in the MODIS record. The other compares current maximum NDVI to the mean of maximum NDVI for all previous MODIS years.

  13. Detecting Inter-Annual Variations in the Phenology of Evergreen Conifers Using Long-Term MODIS Vegetation Index Time Series

    OpenAIRE

    Ulsig, Laura; Nichol, Caroline J.; Huemmrich, Karl F.; Landis, David R.; Middleton, Elizabeth M.; Lyapustin, Alexei I.; Mammarella, Ivan; Levula, Janne; Porcar-Castell, Albert

    2017-01-01

    Long-term observations of vegetation phenology can be used to monitor the response of terrestrial ecosystems to climate change. Satellite remote sensing provides the most efficient means to observe phenological events through time series analysis of vegetation indices such as the Normalized Difference Vegetation Index (NDVI). This study investigates the potential of a Photochemical Reflectance Index (PRI), which has been linked to vegetation light use efficiency, to improve the accuracy of MO...

  14. Detecting inter-annual variations in the phenology of evergreen conifers using long-term MODIS vegetation index time series.

    OpenAIRE

    Ulsig, Laura

    2016-01-01

    Long-term observations of vegetation phenology can be used to monitor the response of terrestrial ecosystems to climate change. Satellite remote sensing provides the most efficient means to observe phenological events through time series analysis of vegetation indices such as the Normalised Difference Vegetation Index (NDVI). This study investigates the potential of the Photochemical Reflectance Index (PRI), which has been linked to vegetation light use efficiency, to improve the accuracy of ...

  15. Detecting macroeconomic phases in the Dow Jones Industrial Average time series

    Science.gov (United States)

    Wong, Jian Cheng; Lian, Heng; Cheong, Siew Ann

    2009-11-01

    In this paper, we perform statistical segmentation and clustering analysis of the Dow Jones Industrial Average (DJI) time series between January 1997 and August 2008. Modeling the index movements and log-index movements as stationary Gaussian processes, we find a total of 116 and 119 statistically stationary segments respectively. These can then be grouped into between five and seven clusters, each representing a different macroeconomic phase. The macroeconomic phases are distinguished primarily by their volatilities. We find that the US economy, as measured by the DJI, spends most of its time in a low-volatility phase and a high-volatility phase. The former can be roughly associated with economic expansion, while the latter contains the economic contraction phase in the standard economic cycle. Both phases are interrupted by a moderate-volatility market correction phase, but extremely-high-volatility market crashes are found mostly within the high-volatility phase. From the temporal distribution of various phases, we see a high-volatility phase from mid-1998 to mid-2003, and another starting mid-2007 (the current global financial crisis). Transitions from the low-volatility phase to the high-volatility phase are preceded by a series of precursor shocks, whereas the transition from the high-volatility phase to the low-volatility phase is preceded by a series of inverted shocks. The time scale for both types of transitions is about a year. We also identify the July 1997 Asian Financial Crisis to be the trigger for the mid-1998 transition, and an unnamed May 2006 market event related to corrections in the Chinese markets to be the trigger for the mid-2007 transition.

  16. VEHICLE LOCALIZATION BY LIDAR POINT CORRELATION IMPROVED BY CHANGE DETECTION

    Directory of Open Access Journals (Sweden)

    A. Schlichting

    2016-06-01

    Full Text Available 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.

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

    Science.gov (United States)

    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.

  18. Non-linear laws of echoic memory and auditory change detection in humans

    OpenAIRE

    Inui, Koji; Urakawa, Tomokazu; Yamashiro, Koya; Otsuru, Naofumi; Nishihara, Makoto; Takeshima, Yasuyuki; Keceli, Sumru; Kakigi, Ryusuke

    2010-01-01

    Abstract Background The detection of any abrupt change in the environment is important to survival. Since memory of preceding sensory conditions is necessary for detecting changes, such a change-detection system relates closely to the memory system. Here we used an auditory change-related N1 subcomponent (change-N1) of event-related brain potentials to investigate cortical mechanisms underlying change detection and echoic memory. Results Change-N1 was elicited by a simple paradigm with two to...

  19. "All of Her Changes Have Made Me Think about My Changes": Fan Readings of Phyllis Reynolds Naylor's "Alice" Series

    Science.gov (United States)

    Heinecken, Dawn

    2013-01-01

    This essay follows the insights of reader response theory to examine how readers of Phyllis Reynolds Naylor's Alice McKinley series negotiate textual meaning and construct particular identities in relation to the series' controversial content. Ranking second on the American Library Association's top one hundred list of banned and challenged books…

  20. Forecasting long memory series subject to structural change: A two-stage approach

    DEFF Research Database (Denmark)

    Papailias, Fotis; Dias, Gustavo Fruet

    2015-01-01

    A two-stage forecasting approach for long memory time series is introduced. In the first step, we estimate the fractional exponent and, by applying the fractional differencing operator, obtain the underlying weakly dependent series. In the second step, we produce multi-step-ahead forecasts...... for the weakly dependent series and obtain their long memory counterparts by applying the fractional cumulation operator. The methodology applies to both stationary and nonstationary cases. Simulations and an application to seven time series provide evidence that the new methodology is more robust to structural...

  1. Detection of perturbation phases and developmental stages in organisms from DNA microarray time series data.

    Directory of Open Access Journals (Sweden)

    Marianne Rooman

    Full Text Available Available DNA microarray time series that record gene expression along the developmental stages of multicellular eukaryotes, or in unicellular organisms subject to external perturbations such as stress and diauxie, are analyzed. By pairwise comparison of the gene expression profiles on the basis of a translation-invariant and scale-invariant distance measure corresponding to least-rectangle regression, it is shown that peaks in the average distance values are noticeable and are localized around specific time points. These points systematically coincide with the transition points between developmental phases or just follow the external perturbations. This approach can thus be used to identify automatically, from microarray time series alone, the presence of external perturbations or the succession of developmental stages in arbitrary cell systems. Moreover, our results show that there is a striking similarity between the gene expression responses to these a priori very different phenomena. In contrast, the cell cycle does not involve a perturbation-like phase, but rather continuous gene expression remodeling. Similar analyses were conducted using three other standard distance measures, showing that the one we introduced was superior. Based on these findings, we set up an adapted clustering method that uses this distance measure and classifies the genes on the basis of their expression profiles within each developmental stage or between perturbation phases.

  2. Property-driven Urban Change in Post-Socialist Shanghai: Reading the Television Series Woju

    Directory of Open Access Journals (Sweden)

    Samuel Y. Liang

    2010-01-01

    Full Text Available In late 2009, the television series Woju (蜗居 received extremely high audience ratings in major Chinese cities. Its visual narratives engage the public and comment on social developments by presenting detailed pictures of urban change in Shanghai and the everyday lives of a range of urban characters who are involved in and affected by the urban-restructuring process and represent three distinct social groups: “white-collar” immigrants, low-income local residents, and powerful officials. By analysing the visual narratives of these characters, this article highlights the loss of the city’s historical identity and shows how the reorganization of urban space translates into a reallocation of resources, power and prestige among the social groups. The article also shows that Woju repre-sents a new development in literary and television production in the age of the Internet and globalization; its imaginative construct of the city was based on transnational and virtual rather than local and neighbourhood experience. This also testifies to the loss of the city’s established identity in cultural production.

  3. Change in dry matter and nutritive composition of Brachiaria humidicola grown in Ban Thon soil series

    Directory of Open Access Journals (Sweden)

    Jeerasak Chobtang

    2008-11-01

    Full Text Available This experiment was conducted to determine the change in dry matter and nutritive composition of Humidicola grass (Brachiaria humidicola grown in Ban Thon soil series (infertility soil as a function of growth age. One rai (0.16 ha of two-year-old pasture of fertilised Humidicola grass was uniformly cut and the regrowth samples were collected every twenty days. The samples were subjected to analysis for dry matter content and nutritive composition, i.e. crude protein, ash, calcium, phosphorus, neutral detergent fibre, acid detergent fibre, and acid detergent lignin. The results showed that while the yields of available forage and leaves increased curvilinearly (quadratic, p<0.05, the stem yield increased linearly (p<0.05 over sampling dates. The highest biomass accumulation rate was numerically observed between 40-60 days of regrowth. The concentrations of crude protein, ash, calcium and phosphorus decreased curvilinearly (quadratic, p<0.05 with advancing maturity and reached the lowest flat after 60 days of regrowth. The cell wall components, i.e. NDF, ADF and ADL, increased over the experimental period and reached the highest plateau at 40 days of regrowth. It was concluded that Humidicola grass should be grazed or preserved at the regrowth age of not over 60 days to maximise the utilisation of the grass.

  4. Developing Best Practices for Detecting Change at Marine Renewable Energy Sites

    Science.gov (United States)

    Linder, H. L.; Horne, J. K.

    2016-02-01

    In compliance with the National Environmental Policy Act (NEPA), an evaluation of environmental effects is mandatory for obtaining permits for any Marine Renewable Energy (MRE) project in the US. Evaluation includes an assessment of baseline conditions and on-going monitoring during operation to determine if biological conditions change relative to the baseline. Currently, there are no best practices for the analysis of MRE monitoring data. We have developed an approach to evaluate and recommend analytic models used to characterize and detect change in biological monitoring data. The approach includes six steps: review current MRE monitoring practices, identify candidate models to analyze data, fit models to a baseline dataset, develop simulated scenarios of change, evaluate model fit to simulated data, and produce recommendations on the choice of analytic model for monitoring data. An empirical data set from a proposed tidal turbine site at Admiralty Inlet, Puget Sound, Washington was used to conduct the model evaluation. Candidate models that were evaluated included: linear regression, time series, and nonparametric models. Model fit diagnostics Root-Mean-Square-Error and Mean-Absolute-Scaled-Error were used to measure accuracy of predicted values from each model. A power analysis was used to evaluate the ability of each model to measure and detect change from baseline conditions. As many of these models have yet to be applied in MRE monitoring studies, results of this evaluation will generate comprehensive guidelines on choice of model to detect change in environmental monitoring data from MRE sites. The creation of standardized guidelines for model selection enables accurate comparison of change between life stages of a MRE project, within life stages to meet real time regulatory requirements, and comparison of environmental changes among MRE sites.

  5. Time-Series Similarity Analysis of Satellite Derived Data to Understand Changes in Forest Biomass.

    Science.gov (United States)

    Singh, N.; Fritz, B.

    2017-12-01

    One of the goals of promoting bioenergy is reducing green-house gas emissions by replacing fossil fuels. However, there are concerns that carbon emissions due to changes in land use resulting from crop production for ethanol will negate the impact of biofuels on the environment. So, the current focus is to use lignocellulose feedstocks also referred to as second generation biofuels as the new source of bioenergy. Wood based pellets derived from the forests of southeastern United States are one such source which is being exported to Europe as a carbon-neutral fuel. These wood-pellets meet the EU standard for carbon emissions and are being used to replace coal for energy generation and heating. As a result US exports of wood-based pellets have increased from nearly zero to over 6 million metric tons over the past 8 years. Wood-based pellets are traditionally produced from softwood trees which have a relatively shorter life-cycle and propagate easily, and thus are expected to provide a sustainable source of wood chips used for pellet production. However, there are concerns that as the demand and price of wood pellets increases, lumber mills will seek wood chips from other sources as well, particularly from hardwood trees resulting in higher carbon emissions as well as loss of biodiversity. In this study we use annual stacks of normalized difference vegetation index (NDVI) data at a 16-day temporal resolution to monitor biomass around pellet mills in southeastern United States. We use a combination of time series similarity technique and supervised learning to understand if there have been significant changes in biomass around pellet mills in the southeastern US. We also demonstrate how our method can be used to monitor biomass over large geographic regions using phenological properties of growing vegetation.

  6. Sequential Change-Point Detection via Online Convex Optimization

    Directory of Open Access Journals (Sweden)

    Yang Cao

    2018-02-01

    Full Text Available Sequential change-point detection when the distribution parameters are unknown is a fundamental problem in statistics and machine learning. When the post-change parameters are unknown, we consider a set of detection procedures based on sequential likelihood ratios with non-anticipating estimators constructed using online convex optimization algorithms such as online mirror descent, which provides a more versatile approach to tackling complex situations where recursive maximum likelihood estimators cannot be found. When the underlying distributions belong to a exponential family and the estimators satisfy the logarithm regret property, we show that this approach is nearly second-order asymptotically optimal. This means that the upper bound for the false alarm rate of the algorithm (measured by the average-run-length meets the lower bound asymptotically up to a log-log factor when the threshold tends to infinity. Our proof is achieved by making a connection between sequential change-point and online convex optimization and leveraging the logarithmic regret bound property of online mirror descent algorithm. Numerical and real data examples validate our theory.

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

    Science.gov (United States)

    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.

  8. Early detection of structual changes in random signal

    International Nuclear Information System (INIS)

    Kuroda, Yoshiteru; Yokota, Katsuhiro

    1981-01-01

    Early detection of structual changes in observed random signal is very important from the point of system diagnosis. In this paper, the following procedures are applied to this problem and the results are compared. (1) auto-regressive model to random signal to calculate the prediction error, i.e., the defference between observed and predicted values. (2) auto-regressive method to caluculate the sum of the prediction error. (3) a method is based on AIC (Akaike Information Criterion). Simulation is made of these procedures, indicating their merits and demerits as a diagostic tools. (author)

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

    Science.gov (United States)

    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.

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

    Science.gov (United States)

    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.

  11. In-vitro studies of change in edge detection with changes in bone density

    International Nuclear Information System (INIS)

    Pocock, N.; Noakes, K.; Griffiths, M.

    1999-01-01

    Full text: Dual energy X-ray absorptiometry (DXA) requires edge detection software to identify the skeletal regions for quantitation of bone mineral density (BMD) and bone mineral content (BMC). As bone mass decreases, the detection of bone edges becomes more difficult and this potentially could cause errors in DXA estimations of areal BMD or BMC. To address this issue, we have used an in-vitro model to study the effects of 'bone loss' on calculated bone area, BMD and BMC. Multiple vertebral phantoms, of equal cross-sectional area but incrementally decreased areal BMD, were constructed using calcium sulphate hemihydrate. The weight of each phantom vertebra, measured accurately using an electronic balance, was used as an index of its true 'bone mass equivalent' (BME). The phantoms were scanned and analysed in the lumbar spine mode using a Lunar DPX-L (L) and Hologic QDR-1000 (H). The changes in BME were compared to changes in measured area, BMC and areal BMD. The results demonstrate that, in an in-vitro model, as bone mass decreases, measured bone area and consequently BMC will decrease as the edge detection algorithms have greater difficulty in detecting the true edges. In conclusion, in an in-vitro model, the DXA edge detection algorithms will underestimate bone area as bone mass decreases. This has potential implications for monitoring changes in bone mass in vivo

  12. Tool successfully detects changes in cathodic protection system

    Energy Technology Data Exchange (ETDEWEB)

    Anon.

    2011-05-15

    A new oil and gas industry tool has been developed to check if an operator's cathodic protection (CP) is effective. This inline inspection tool developed, by Baker Hughes, is called cathodic protection current measurement (CPCM). It measures how much CP current the pipeline is receiving and shows the direction of the current flowing back to the CP source. This system was used to successfully perform a full CP current inspection on a 43 mile-long pipeline in the Eastern United States. Tests identified that one rectifier was flowing current in the reverse direction from that expected and that a few areas had high current densities. The operator then changed the CP system to test the tool and results showed that the tool correctly detected the changes.

  13. Radiation detection using the color changes of lilac spodumene

    International Nuclear Information System (INIS)

    Oliveira, Raquel A.P.; Mello, Ana Carolina S.; Lima, Hestia R.B.R.; Campos, Simara Santos; Souza, Suzana O.

    2009-01-01

    The use of radiation in industrial processes currently offers several advantages in the field of sterilization of medical and pharmaceuticals products, the preservation of food, and a variety of other products widely used in modern society. A dosimetry of confidence is a key parameter for the quality assurance of radiation processing and the irradiated products. This work investigates dosimetric properties in natural spodumene, LiAlSi 2 O 6 , called kunzite, from Minas Gerais State, Brazil. After X irradiation on the samples in powder form was detected a change in color of the crystal where the dose received. This makes a possible viability of this material is applied in research on development of radiation detectors using the change in color of purple spodumene. (author)

  14. Molecular profiles of screen detected vs. symptomatic breast cancer and their impact on survival: results from a clinical series

    International Nuclear Information System (INIS)

    Crispo, Anna; Esposito, Emanuela; Amore, Alfonso; Di Bonito, Maurizio; Botti, Gerardo; Montella, Maurizio; Barba, Maddalena; D’Aiuto, Giuseppe; De Laurentiis, Michelino; Grimaldi, Maria; Rinaldo, Massimo; Caolo, Giuseppina; D’Aiuto, Massimiliano; Capasso, Immacolata

    2013-01-01

    Stage shift is widely considered a major determinant of the survival benefit conferred by breast cancer screening. However, factors and mechanisms underlying such a prognostic advantage need further clarification. We sought to compare the molecular characteristics of screen detected vs. symptomatic breast cancers and assess whether differences in tumour biology might translate into survival benefit. In a clinical series of 448 women with operable breast cancer, the Kaplan-Meier method and the log-rank test were used to estimate the likelihood of cancer recurrence and death. The Cox proportional hazard model was used for the multivariate analyses including mode of detection, age at diagnosis, tumour size, and lymph node status. These same models were applied to subgroups defined by molecular subtypes. Screen detected breast cancers tended to show more favourable clinicopathological features and survival outcomes compared to symptomatic cancers. The luminal A subtype was more common in women with mammography detected tumours than in symptomatic patients (68.5 vs. 59.0%, p=0.04). Data analysis across categories of molecular subtypes revealed significantly longer disease free and overall survival for screen detected cancers with a luminal A subtype only (p=0.01 and 0.02, respectively). For women with a luminal A subtype, the independent prognostic role of mode of detection on recurrence was confirmed in Cox proportional hazard models (p=0.03). An independent role of modality of detection on survival was also suggested (p=0.05). Molecular subtypes did not substantially explain the differences in survival outcomes between screened and symptomatic patients. However, our results suggest that molecular profiles might play a role in interpreting such differences at least partially. Further studies are warranted to reinterpret the efficacy of screening programmes in the light of tumour biology

  15. Comparison of Six Methods for the Detection of Causality in a Bivariate Time Series

    Czech Academy of Sciences Publication Activity Database

    Krakovská, A.; Jakubík, J.; Chvosteková, M.; Coufal, David; Jajcay, Nikola; Paluš, Milan

    2018-01-01

    Roč. 97, č. 4 (2018), č. článku 042207. ISSN 2470-0045 R&D Projects: GA MZd(CZ) NV15-33250A Institutional support: RVO:67985807 Keywords : comparative study * causality detection * bivariate models * Granger causality * transfer entropy * convergent cross mappings Impact factor: 2.366, year: 2016 https://journals.aps.org/pre/abstract/10.1103/PhysRevE.97.042207

  16. AUTOMATIC CLOUD DETECTION FROM MULTI-TEMPORAL SATELLITE IMAGES: TOWARDS THE USE OF PLÉIADES TIME SERIES

    Directory of Open Access Journals (Sweden)

    N. Champion

    2012-08-01

    Full Text Available Contrary to aerial images, satellite images are often affected by the presence of clouds. Identifying and removing these clouds is one of the primary steps to perform when processing satellite images, as they may alter subsequent procedures such as atmospheric corrections, DSM production or land cover classification. The main goal of this paper is to present the cloud detection approach, developed at the French Mapping agency. Our approach is based on the availability of multi-temporal satellite images (i.e. time series that generally contain between 5 and 10 images and is based on a region-growing procedure. Seeds (corresponding to clouds are firstly extracted through a pixel-to-pixel comparison between the images contained in time series (the presence of a cloud is here assumed to be related to a high variation of reflectance between two images. Clouds are then delineated finely using a dedicated region-growing algorithm. The method, originally designed for panchromatic SPOT5-HRS images, is tested in this paper using time series with 9 multi-temporal satellite images. Our preliminary experiments show the good performances of our method. In a near future, the method will be applied to Pléiades images, acquired during the in-flight commissioning phase of the satellite (launched at the end of 2011. In that context, this is a particular goal of this paper to show to which extent and in which way our method can be adapted to this kind of imagery.

  17. Task demands determine comparison strategy in whole probe change detection.

    Science.gov (United States)

    Udale, Rob; Farrell, Simon; Kent, Chris

    2018-05-01

    Detecting a change in our visual world requires a process that compares the external environment (test display) with the contents of memory (study display). We addressed the question of whether people strategically adapt the comparison process in response to different decision loads. Study displays of 3 colored items were presented, followed by 'whole-display' probes containing 3 colored shapes. Participants were asked to decide whether any probed items contained a new feature. In Experiments 1-4, irrelevant changes to the probed item's locations or feature bindings influenced memory performance, suggesting that participants employed a comparison process that relied on spatial locations. This finding occurred irrespective of whether participants were asked to decide about the whole display, or only a single cued item within the display. In Experiment 5, when the base-rate of changes in the nonprobed items increased (increasing the incentive to use the cue effectively), participants were not influenced by irrelevant changes in location or feature bindings. In addition, we observed individual differences in the use of spatial cues. These results suggest that participants can flexibly switch between spatial and nonspatial comparison strategies, depending on interactions between individual differences and task demand factors. These findings have implications for models of visual working memory that assume that the comparison between study and test obligatorily relies on accessing visual features via their binding to location. (PsycINFO Database Record (c) 2018 APA, all rights reserved).

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

    Energy Technology Data Exchange (ETDEWEB)

    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.

  19. Change Detection Algorithm for the Production of Land Cover Change Maps over the European Union Countries

    Directory of Open Access Journals (Sweden)

    Sebastian Aleksandrowicz

    2014-06-01

    Full Text Available Contemporary satellite Earth Observation systems provide growing amounts of very high spatial resolution data that can be used in various applications. An increasing number of sensors make it possible to monitor selected areas in great detail. However, in order to handle the volume of data, a high level of automation is required. The semi-automatic change detection methodology described in this paper was developed to annually update land cover maps prepared in the context of the Geoland2. The proposed algorithm was tailored to work with different very high spatial resolution images acquired over different European landscapes. The methodology is a fusion of various change detection methods ranging from: (1 layer arithmetic; (2 vegetation indices (NDVI differentiating; (3 texture calculation; and methods based on (4 canonical correlation analysis (multivariate alteration detection (MAD. User intervention during the production of the change map is limited to the selection of the input data, the size of initial segments and the threshold for texture classification (optionally. To achieve a high level of automation, statistical thresholds were applied in most of the processing steps. Tests showed an overall change recognition accuracy of 89%, and the change type classification methodology can accurately classify transitions between classes.

  20. Structural damage detection in wind turbine blades based on time series representations of dynamic responses

    Science.gov (United States)

    Hoell, Simon; Omenzetter, Piotr

    2015-03-01

    The development of large wind turbines that enable to harvest energy more efficiently is a consequence of the increasing demand for renewables in the world. To optimize the potential energy output, light and flexible wind turbine blades (WTBs) are designed. However, the higher flexibilities and lower buckling capacities adversely affect the long-term safety and reliability of WTBs, and thus the increased operation and maintenance costs reduce the expected revenue. Effective structural health monitoring techniques can help to counteract this by limiting inspection efforts and avoiding unplanned maintenance actions. Vibration-based methods deserve high attention due to the moderate instrumentation efforts and the applicability for in-service measurements. The present paper proposes the use of cross-correlations (CCs) of acceleration responses between sensors at different locations for structural damage detection in WTBs. CCs were in the past successfully applied for damage detection in numerical and experimental beam structures while utilizing only single lags between the signals. The present approach uses vectors of CC coefficients for multiple lags between measurements of two selected sensors taken from multiple possible combinations of sensors. To reduce the dimensionality of the damage sensitive feature (DSF) vectors, principal component analysis is performed. The optimal number of principal components (PCs) is chosen with respect to a statistical threshold. Finally, the detection phase uses the selected PCs of the healthy structure to calculate scores from a current DSF vector, where statistical hypothesis testing is performed for making a decision about the current structural state. The method is applied to laboratory experiments conducted on a small WTB with non-destructive damage scenarios.

  1. Sleep Spindle Detection and Prediction Using a Mixture of Time Series and Chaotic Features

    Directory of Open Access Journals (Sweden)

    Amin Hekmatmanesh

    2017-01-01

    Full Text Available It is well established that sleep spindles (bursts of oscillatory brain electrical activity are significant indicators of learning, memory and some disease states. Therefore, many attempts have been made to detect these hallmark patterns automatically. In this pilot investigation, we paid special attention to nonlinear chaotic features of EEG signals (in combination with linear features to investigate the detection and prediction of sleep spindles. These nonlinear features included: Higuchi's, Katz's and Sevcik's Fractal Dimensions, as well as the Largest Lyapunov Exponent and Kolmogorov's Entropy. It was shown that the intensity map of various nonlinear features derived from the constructive interference of spindle signals could improve the detection of the sleep spindles. It was also observed that the prediction of sleep spindles could be facilitated by means of the analysis of these maps. Two well-known classifiers, namely the Multi-Layer Perceptron (MLP and the K-Nearest Neighbor (KNN were used to distinguish between spindle and non-spindle patterns. The MLP classifier produced a~high discriminative capacity (accuracy = 94.93%, sensitivity = 94.31% and specificity = 95.28% with significant robustness (accuracy ranging from 91.33% to 94.93%, sensitivity varying from 91.20% to 94.31%, and specificity extending from 89.79% to 95.28% in separating spindles from non-spindles. This classifier also generated the best results in predicting sleep spindles based on chaotic features. In addition, the MLP was used to find out the best time window for predicting the sleep spindles, with the experimental results reaching 97.96% accuracy.

  2. Volumetric Forest Change Detection Through Vhr Satellite Imagery

    Science.gov (United States)

    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

  3. Multivariate Alteration Detection (MAD) and MAF Postprocessing in Multispectral, Bitemporal Image Data: New Approaches to Change Detection Studies

    DEFF Research Database (Denmark)

    Nielsen, Allan Aasbjerg; Conradsen, Knut; Simpson, James J.

    1998-01-01

    type analyses of simple difference images. Case studies with AHVRR and Landsat MSS data using simple linear stretching and masking of the change images show the usefulness of the new MAD and MAF/MAD change detection schemes. Ground truth observations confirm the detected changes. A simple simulation...

  4. Time series of GNSS-derived ionospheric maps to detect anomalies as possible precursors of high magnitude earthquakes

    Science.gov (United States)

    Barbarella, M.; De Giglio, M.; Galeandro, A.; Mancini, F.

    2012-04-01

    The modification of some atmospheric physical properties prior to a high magnitude earthquake has been recently debated within the Lithosphere-Atmosphere-Ionosphere (LAI) Coupling model. Among this variety of phenomena the ionization of air at the higher level of the atmosphere, called ionosphere, is investigated in this work. Such a ionization occurrences could be caused by possible leaking of gases from earth crust and their presence was detected around the time of high magnitude earthquakes by several authors. However, the spatial scale and temporal domain over which such a disturbances come into evidence is still a controversial item. Even thought the ionospheric activity could be investigated by different methodologies (satellite or terrestrial measurements), we selected the production of ionospheric maps by the analysis of GNSS (Global Navigation Satellite Data) data as possible way to detect anomalies prior of a seismic event over a wide area around the epicentre. It is well known that, in the GNSS sciences, the ionospheric activity could be probed by the analysis of refraction phenomena occurred on the dual frequency signals along the satellite to receiver path. The analysis of refraction phenomena affecting data acquired by the GNSS permanent trackers is able to produce daily to hourly maps representing the spatial distribution of the ionospheric Total Electron Content (TEC) as an index of the ionization degree in the upper atmosphere. The presence of large ionospheric anomalies could be therefore interpreted in the LAI Coupling model like a precursor signal of a strong earthquake, especially when the appearance of other different precursors (thermal anomalies and/or gas fluxes) could be detected. In this work, a six-month long series of ionospheric maps produced from GNSS data collected by a network of 49 GPS permanent stations distributed within an area around the city of L'Aquila (Abruzzi, Italy), where an earthquake (M = 6.3) occurred on April 6, 2009

  5. Prostate cancer detection from model-free T1-weighted time series and diffusion imaging

    Science.gov (United States)

    Haq, Nandinee F.; Kozlowski, Piotr; Jones, Edward C.; Chang, Silvia D.; Goldenberg, S. Larry; Moradi, Mehdi

    2015-03-01

    The combination of Dynamic Contrast Enhanced (DCE) images with diffusion MRI has shown great potential in prostate cancer detection. The parameterization of DCE images to generate cancer markers is traditionally performed based on pharmacokinetic modeling. However, pharmacokinetic models make simplistic assumptions about the tissue perfusion process, require the knowledge of contrast agent concentration in a major artery, and the modeling process is sensitive to noise and fitting instabilities. We address this issue by extracting features directly from the DCE T1-weighted time course without modeling. In this work, we employed a set of data-driven features generated by mapping the DCE T1 time course to its principal component space, along with diffusion MRI features to detect prostate cancer. The optimal set of DCE features is extracted with sparse regularized regression through a Least Absolute Shrinkage and Selection Operator (LASSO) model. We show that when our proposed features are used within the multiparametric MRI protocol to replace the pharmacokinetic parameters, the area under ROC curve is 0.91 for peripheral zone classification and 0.87 for whole gland classification. We were able to correctly classify 32 out of 35 peripheral tumor areas identified in the data when the proposed features were used with support vector machine classification. The proposed feature set was used to generate cancer likelihood maps for the prostate gland.

  6. Incremental Activation Detection for Real-Time fMRI Series Using Robust Kalman Filter

    Directory of Open Access Journals (Sweden)

    Liang Li

    2014-01-01

    Full Text Available Real-time functional magnetic resonance imaging (rt-fMRI is a technique that enables us to observe human brain activations in real time. However, some unexpected noises that emerged in fMRI data collecting, such as acute swallowing, head moving and human manipulations, will cause much confusion and unrobustness for the activation analysis. In this paper, a new activation detection method for rt-fMRI data is proposed based on robust Kalman filter. The idea is to add a variation to the extended kalman filter to handle the additional sparse measurement noise and a sparse noise term to the measurement update step. Hence, the robust Kalman filter is designed to improve the robustness for the outliers and can be computed separately for each voxel. The algorithm can compute activation maps on each scan within a repetition time, which meets the requirement for real-time analysis. Experimental results show that this new algorithm can bring out high performance in robustness and in real-time activation detection.

  7. Using a Conflict Map as an Instructional Tool To Change Student Conceptions in Simple Series Electric-Circuits.

    Science.gov (United States)

    Tsai, Chin-Chung

    2003-01-01

    Examines the effects of using a conflict map on 8th grade students' conceptual change and ideational networks about simple series electric circuits. Analyzes student interview data through a flow map method. Shows that the use of conflict maps could help students construct greater, richer, and more integrated ideational networks about electric…

  8. The use of a multilayer perceptron for detecting new human settlements from a time series of MODIS images

    CSIR Research Space (South Africa)

    Salmon, BP

    2011-12-01

    Full Text Available This paper presents a novel land cover change detection method that employs a sliding window over hyper-temporal multi-spectral images acquired from the 7 bands of the MODerate-resolution Imaging Spectroradiometer (MODIS) land surface reflectance...

  9. Examining change detection approaches for tropical mangrove monitoring

    Science.gov (United States)

    Myint, Soe W.; Franklin, Janet; Buenemann, Michaela; Kim, Won; Giri, Chandra

    2014-01-01

    This study evaluated the effectiveness of different band combinations and classifiers (unsupervised, supervised, object-oriented nearest neighbor, and object-oriented decision rule) for quantifying mangrove forest change using multitemporal Landsat data. A discriminant analysis using spectra of different vegetation types determined that bands 2 (0.52 to 0.6 μm), 5 (1.55 to 1.75 μm), and 7 (2.08 to 2.35 μm) were the most effective bands for differentiating mangrove forests from surrounding land cover types. A ranking of thirty-six change maps, produced by comparing the classification accuracy of twelve change detection approaches, was used. The object-based Nearest Neighbor classifier produced the highest mean overall accuracy (84 percent) regardless of band combinations. The automated decision rule-based approach (mean overall accuracy of 88 percent) as well as a composite of bands 2, 5, and 7 used with the unsupervised classifier and the same composite or all band difference with the object-oriented Nearest Neighbor classifier were the most effective approaches.

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

    Energy Technology Data Exchange (ETDEWEB)

    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.

  11. Early auditory change detection implicitly facilitated by ignored concurrent visual change during a Braille reading task.

    Science.gov (United States)

    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

  12. Detection and attribution of streamflow timing changes to climate change in the Western United States

    Science.gov (United States)

    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 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.

  13. Research on Healthy Anomaly Detection Model Based on Deep Learning from Multiple Time-Series Physiological Signals

    Directory of Open Access Journals (Sweden)

    Kai Wang

    2016-01-01

    Full Text Available Health is vital to every human being. To further improve its already respectable medical technology, the medical community is transitioning towards a proactive approach which anticipates and mitigates risks before getting ill. This approach requires measuring the physiological signals of human and analyzes these data at regular intervals. In this paper, we present a novel approach to apply deep learning in physiological signals analysis that allows doctor to identify latent risks. However, extracting high level information from physiological time-series data is a hard problem faced by the machine learning communities. Therefore, in this approach, we apply model based on convolutional neural network that can automatically learn features from raw physiological signals in an unsupervised manner and then based on the learned features use multivariate Gauss distribution anomaly detection method to detect anomaly data. Our experiment is shown to have a significant performance in physiological signals anomaly detection. So it is a promising tool for doctor to identify early signs of illness even if the criteria are unknown a priori.

  14. Influence of characteristics of time series on short-term forecasting error parameter changes in real time

    Science.gov (United States)

    Klevtsov, S. I.

    2018-05-01

    The impact of physical factors, such as temperature and others, leads to a change in the parameters of the technical object. Monitoring the change of parameters is necessary to prevent a dangerous situation. The control is carried out in real time. To predict the change in the parameter, a time series is used in this paper. Forecasting allows one to determine the possibility of a dangerous change in a parameter before the moment when this change occurs. The control system in this case has more time to prevent a dangerous situation. A simple time series was chosen. In this case, the algorithm is simple. The algorithm is executed in the microprocessor module in the background. The efficiency of using the time series is affected by its characteristics, which must be adjusted. In the work, the influence of these characteristics on the error of prediction of the controlled parameter was studied. This takes into account the behavior of the parameter. The values of the forecast lag are determined. The results of the research, in the case of their use, will improve the efficiency of monitoring the technical object during its operation.

  15. Theories of behaviour change synthesised into a set of theoretical groupings: introducing a thematic series on the theoretical domains framework.

    Science.gov (United States)

    Francis, Jill J; O'Connor, Denise; Curran, Janet

    2012-04-24

    Behaviour change is key to increasing the uptake of evidence into healthcare practice. Designing behaviour-change interventions first requires problem analysis, ideally informed by theory. Yet the large number of partly overlapping theories of behaviour makes it difficult to select the most appropriate theory. The need for an overarching theoretical framework of behaviour change was addressed in research in which 128 explanatory constructs from 33 theories of behaviour were identified and grouped. The resulting Theoretical Domains Framework (TDF) appears to be a helpful basis for investigating implementation problems. Research groups in several countries have conducted TDF-based studies. It seems timely to bring together the experience of these teams in a thematic series to demonstrate further applications and to report key developments. This overview article describes the TDF, provides a brief critique of the framework, and introduces this thematic series.In a brief review to assess the extent of TDF-based research, we identified 133 papers that cite the framework. Of these, 17 used the TDF as the basis for empirical studies to explore health professionals' behaviour. The identified papers provide evidence of the impact of the TDF on implementation research. Two major strengths of the framework are its theoretical coverage and its capacity to elicit beliefs that could signify key mediators of behaviour change. The TDF provides a useful conceptual basis for assessing implementation problems, designing interventions to enhance healthcare practice, and understanding behaviour-change processes. We discuss limitations and research challenges and introduce papers in this series.

  16. Uric acid, an important screening tool to detect inborn errors of metabolism: a case series.

    Science.gov (United States)

    Jasinge, Eresha; Kularatnam, Grace Angeline Malarnangai; Dilanthi, Hewa Warawitage; Vidanapathirana, Dinesha Maduri; Jayasena, Kandana Liyanage Subhashinie Priyadarshika Kapilani Menike; Chandrasiri, Nambage Dona Priyani Dhammika; Indika, Neluwa Liyanage Ruwan; Ratnayake, Pyara Dilani; Gunasekara, Vindya Nandani; Fairbanks, Lynette Dianne; Stiburkova, Blanka

    2017-09-06

    Uric acid is the metabolic end product of purine metabolism in humans. Altered serum and urine uric acid level (both above and below the reference ranges) is an indispensable marker in detecting rare inborn errors of metabolism. We describe different case scenarios of 4 Sri Lankan patients related to abnormal uric acid levels in blood and urine. CASE 1: A one-and-half-year-old boy was investigated for haematuria and a calculus in the bladder. Xanthine crystals were seen in microscopic examination of urine sediment. Low uric acid concentrations in serum and low urinary fractional excretion of uric acid associated with high urinary excretion of xanthine and hypoxanthine were compatible with xanthine oxidase deficiency. CASE 2: An 8-month-old boy presented with intractable seizures, feeding difficulties, screaming episodes, microcephaly, facial dysmorphism and severe neuro developmental delay. Low uric acid level in serum, low fractional excretion of uric acid and radiological findings were consistent with possible molybdenum cofactor deficiency. Diagnosis was confirmed by elevated levels of xanthine, hypoxanthine and sulfocysteine levels in urine. CASE 3: A 3-year-10-month-old boy presented with global developmental delay, failure to thrive, dystonia and self-destructive behaviour. High uric acid levels in serum, increased fractional excretion of uric acid and absent hypoxanthine-guanine phosphoribosyltransferase enzyme level confirmed the diagnosis of Lesch-Nyhan syndrome. CASE 4: A 9-year-old boy was investigated for lower abdominal pain, gross haematuria and right renal calculus. Low uric acid level in serum and increased fractional excretion of uric acid pointed towards hereditary renal hypouricaemia which was confirmed by genetic studies. Abnormal uric acid level in blood and urine is a valuable tool in screening for clinical conditions related to derangement of the nucleic acid metabolic pathway.

  17. Detection of temporal changes in insect body reflectance in response to killing agents.

    Directory of Open Access Journals (Sweden)

    Christian Nansen

    Full Text Available Computer vision and reflectance-based analyses are becoming increasingly important methods to quantify and characterize phenotypic responses by whole organisms to environmental factors. Here, we present the first study of how a non-destructive and completely non-invasive method, body reflectance profiling, can be used to detect and time stress responses in adult beetles. Based on high-resolution hyperspectral imaging, we acquired time series of average reflectance profiles (70 spectral bands from 434-876 nm from adults in two beetle species, maize weevils (Sitophilus zeamais and larger black flour beetles (Cynaus angustus. For each species, we acquired reflectance data from untreated controls and from individuals exposed continuously to killing agents (an insecticidal plant extract applied to maize kernels or entomopathogenic nematodes applied to soil applied at levels leading to ≈100% mortality. In maize weevils (exposed to hexanic plant extract, there was no significant effect of the on reflectance profiles acquired from adult beetles after 0 and 12 hours of exposure, but a significant treatment response in spectral bands from 434 to 550 nm was detected after 36 to 144 hours of exposure. In larger black flour beetles, there was no significant effect of exposure to entomopathogenic nematodes after 0 to 26 hours of exposure, but a significant response in spectral bands from 434-480 nm was detected after 45 and 69 hours of exposure. Spectral bands were used to develop reflectance-based classification models for each species, and independent validation of classification algorithms showed sensitivity (ability to positively detect terminal stress in beetles and specificity (ability to positively detect healthy beetles of about 90%. Significant changes in body reflectance occurred at exposure times, which coincided with published exposure times and known physiological responses to each killing agent. The results from this study underscore the

  18. Wide area change detection with satellite imagery for locating underground nuclear testing

    International Nuclear Information System (INIS)

    Canty, M.J.; Jasani, B.; Schlittenhardt, J.

    2001-01-01

    With the advent of high resolution optical imagery from commercial earth observation satellites, the use of remote sensing data for verification of nuclear non-proliferation agreements is becoming increasingly attractive. Non-governmental organizations are routinely publishing high-quality imagery of sensitive nuclear installations round the world, and international verification authorities, such as the International Atomic Energy Agency (IAEA) or the Comprehensive Nuclear-Test-Ban Treaty Organization (CTBTO), will also want to make use, directly or indirectly, of this additional open source of information. Exact location of the sites of underground nuclear explosions is a task eminently suited to satellite imagery. Here both moderate resolutions for detecting signals in very large testing ranges as well as high resolution images for exact interpretation play important roles. We describe in our paper a particularly sensitive change detection procedure for bitemporal, multispectral satellite imagery which can be used to locate the spall zone of underground nuclear explosions with commercial satellite imagery. The method is based on the multivariate alteration detection (MAD) technique of Nielsen et al. Linear combinations of the spectral channels in two images of the same scene are chosen so as to minimize their positive correlation. This leads to a series of difference images - the so-called MAD components - which are mutually orthogonal (uncorrelated) and ordered according to decreasing variance in their pixel intensities. Since interesting changes in man-made structures may contribute minimally to the overall variance (as the latter may be dominated for instance by seasonal vegetation differences) it is often the case that such changes turn up in a higher order MAD component. This is because they will be uncorrelated with seasonal vegetation changes, stochastic image noise or other major contributions to the overall change signal. This in fact is one of the

  19. A generalized Grubbs-Beck test statistic for detecting multiple potentially influential low outliers in flood series

    Science.gov (United States)

    Cohn, T.A.; England, J.F.; Berenbrock, C.E.; Mason, R.R.; Stedinger, J.R.; Lamontagne, J.R.

    2013-01-01

    he Grubbs-Beck test is recommended by the federal guidelines for detection of low outliers in flood flow frequency computation in the United States. This paper presents a generalization of the Grubbs-Beck test for normal data (similar to the Rosner (1983) test; see also Spencer and McCuen (1996)) that can provide a consistent standard for identifying multiple potentially influential low flows. In cases where low outliers have been identified, they can be represented as “less-than” values, and a frequency distribution can be developed using censored-data statistical techniques, such as the Expected Moments Algorithm. This approach can improve the fit of the right-hand tail of a frequency distribution and provide protection from lack-of-fit due to unimportant but potentially influential low flows (PILFs) in a flood series, thus making the flood frequency analysis procedure more robust.

  20. Updating National Topographic Data Base Using Change Detection Methods

    Science.gov (United States)

    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.

  1. Automatic detection of surface changes on Mars - a status report

    Science.gov (United States)

    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

  2. A case series of pediatric seymour fractures related to hoverboards: Increasing trend with changing lifestyle

    Directory of Open Access Journals (Sweden)

    A.E. Kattan

    2017-01-01

    Conclusion: We report a case series of a fracture pattern resulting from the improper/unsafe use of a hoverboard. This type of fracture occurs with a tremendous load of energy. Although improper use was a factor, design fault also plays a role in causing the injury. Parent awareness and supervision are important to prevent such injuries.

  3. Detecting Change in Landscape Greenness over Large Areas: An Example for New Mexico, USA

    Science.gov (United States)

    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...

  4. On spurious detection of linear response and misuse of the fluctuation-dissipation theorem in finite time series

    Science.gov (United States)

    Gottwald, Georg A.; Wormell, J. P.; Wouters, Jeroen

    2016-09-01

    Using a sensitive statistical test we determine whether or not one can detect the breakdown of linear response given observations of deterministic dynamical systems. A goodness-of-fit statistics is developed for a linear statistical model of the observations, based on results for central limit theorems for deterministic dynamical systems, and used to detect linear response breakdown. We apply the method to discrete maps which do not obey linear response and show that the successful detection of breakdown depends on the length of the time series, the magnitude of the perturbation and on the choice of the observable. We find that in order to reliably reject the assumption of linear response for typical observables sufficiently large data sets are needed. Even for simple systems such as the logistic map, one needs of the order of 106 observations to reliably detect the breakdown with a confidence level of 95 %; if less observations are available one may be falsely led to conclude that linear response theory is valid. The amount of data required is larger the smaller the applied perturbation. For judiciously chosen observables the necessary amount of data can be drastically reduced, but requires detailed a priori knowledge about the invariant measure which is typically not available for complex dynamical systems. Furthermore we explore the use of the fluctuation-dissipation theorem (FDT) in cases with limited data length or coarse-graining of observations. The FDT, if applied naively to a system without linear response, is shown to be very sensitive to the details of the sampling method, resulting in erroneous predictions of the response.

  5. A Systematic Comparison of Vertical GPS Time Series Calculated by Five Processing Centers for Detecting Climatic-Induced Crustal Movements

    Science.gov (United States)

    Setti Junior, P. D. T.; Wdowinski, S.

    2016-12-01

    Vertical crustal movements, as observed by continuous GPS measurements, are sensitive to load changes induced by atmospheric and hydrological processes, as lake level fluctuations, ice melt, groundwater depletion, or drought. These movements are often dominated by a seasonal signal but also by year-to-year changes, which reflect a long-term climatic signal. Recently, we developed a new technique that extracts the climatic signal by removing the seasonal signal from vertical GPS time series (Wdowinski et al., 2016). However, the method's results, which are the climatic signals, are very sensitive to the quality of the time series and the choice of reference frame (RF). In this study, we conduct a systematic comparison between eight vertical GPS time series calculated by five processing centers and evaluate their suitability to extract the climatic signal. We use the solutions produced by Central Washington University (CWU), New Mexico Institute of Technology (NMT), Nevada Geodetic Laboratory (NGL), Scripps Orbit and Permanent Array Center (SOPAC) and Jet Propulsion Laboratory (JPL), as well as combined solution calculated by the Plate Boundary Observatory (PBO) and GPS Explorer. We use the solutions constrained in the IGS08 RF and in the case of NGL, we also use the NA12 solutions. Three of the processing centers, CWU, NGL and JPL use the GIPSY software, whereas the other two, NMT and SOPAC, use GAMIT. Both combined solutions integrate between GIPSY and GAMIT solutions. We conducted our comparative analysis in two study areas, one in western US taking advantage of the two decades long time series of the Basin and Range network, and the other in eastern U.S. and Canada (Washington DC area, Newfoundland, and Ottawa area). Preliminary results suggest that the three GIPSY solutions (CWU, NGL and JPL) are more consistent between one another compared with the GAMIT solutions. The GIPSY solutions also yield climatic signal that is more consistent with regional climatic

  6. Detecting geothermal anomalies and evaluating LST geothermal component by combining thermal remote sensing time series and land surface model data

    Science.gov (United States)

    Romaguera, Mireia; Vaughan, R. Greg; Ettema, J.; Izquierdo-Verdiguier, E.; Hecker, C. A.; van der Meer, F.D.

    2018-01-01

    This paper explores for the first time the possibilities to use two land surface temperature (LST) time series of different origins (geostationary Meteosat Second Generation satellite data and Noah land surface modelling, LSM), to detect geothermal anomalies and extract the geothermal component of LST, the LSTgt. We hypothesize that in geothermal areas the LSM time series will underestimate the LST as compared to the remote sensing data, since the former does not account for the geothermal component in its model.In order to extract LSTgt, two approaches of different nature (physical based and data mining) were developed and tested in an area of about 560 × 560 km2 centered at the Kenyan Rift. Pre-dawn data in the study area during the first 45 days of 2012 were analyzed.The results show consistent spatial and temporal LSTgt patterns between the two approaches, and systematic differences of about 2 K. A geothermal area map from surface studies was used to assess LSTgt inside and outside the geothermal boundaries. Spatial means were found to be higher inside the geothermal limits, as well as the relative frequency of occurrence of high LSTgt. Results further show that areas with strong topography can result in anomalously high LSTgt values (false positives), which suggests the need for a slope and aspect correction in the inputs to achieve realistic results in those areas. The uncertainty analysis indicates that large uncertainties of the input parameters may limit detection of LSTgt anomalies. To validate the approaches, higher spatial resolution images from the Advanced Spaceborne Thermal Emission and Reflection Radiometer (ASTER) data over the Olkaria geothermal field were used. An established method to estimate radiant geothermal flux was applied providing values between 9 and 24 W/m2 in the geothermal area, which coincides with the LSTgt flux rates obtained with the proposed approaches.The proposed approaches are a first step in estimating LSTgt

  7. The Effect of Concurrent Music Reading and Performance on the Ability to Detect Tempo Change.

    Science.gov (United States)

    Ellis, Mark Carlton

    1989-01-01

    Measures the ability of three groups of musicians to detect tempo change while reading and performing music. Compares this ability with that of the same musicians to detect tempo change while listening only. Found that for all groups the ability to detect tempo changes was inhibited by the playing task, although to different degrees for each…

  8. Detecting and taking into account possible impacts of climate change on hydrological extremes

    International Nuclear Information System (INIS)

    Renard, B.

    2008-01-01

    Climate change is widely considered as a reality by scientists. Nevertheless, impacts on hydrological extremes are more difficult to observe and to forecast. The aim of this thesis is to answer the following questions: How to detect changes in hydro-climatic series? What are the observed changes for extreme discharges in France? How to take into account possible changes in frequency analysis? These objectives refer to both local and regional scales. This paper describes the developments related to the third question. In a first step, the concept of return period is revisited in a non-stationary context. Frequency analysis methods are then updated in order to account for evolutions in time. This is achieved by modelling trends affecting the distribution parameters. Parameter estimation uses the Bayesian formalism, which is a convenient tool for quantifying the uncertainty related to the stationarity hypothesis. This approach can be generalized at the regional scale, by means of non-stationary regional models. Such models are more general than the model underlying the index flood method. However, results of such a regional analysis are affected by the spatial dependence existing between studied sites. Impacts of this dependence on quantile estimates are highlighted, and a first approach is proposed in order to explicitly model spatial dependence. (author)

  9. Detecting Surface Changes from an Underground Explosion in Granite Using Unmanned Aerial System Photogrammetry

    Science.gov (United States)

    Schultz-Fellenz, Emily S.; Coppersmith, Ryan T.; Sussman, Aviva J.; Swanson, Erika M.; Cooley, James A.

    2017-08-01

    Efficient detection and high-fidelity quantification of surface changes resulting from underground activities are important national and global security efforts. In this investigation, a team performed field-based topographic characterization by gathering high-quality photographs at very low altitudes from an unmanned aerial system (UAS)-borne camera platform. The data collection occurred shortly before and after a controlled underground chemical explosion as part of the United States Department of Energy's Source Physics Experiments (SPE-5) series. The high-resolution overlapping photographs were used to create 3D photogrammetric models of the site, which then served to map changes in the landscape down to 1-cm-scale. Separate models were created for two areas, herein referred to as the test table grid region and the nearfield grid region. The test table grid includes the region within 40 m from surface ground zero, with photographs collected at a flight altitude of 8.5 m above ground level (AGL). The near-field grid area covered a broader area, 90-130 m from surface ground zero, and collected at a flight altitude of 22 m AGL. The photographs, processed using Agisoft Photoscan® in conjunction with 125 surveyed ground control point targets, yielded a 6-mm pixel-size digital elevation model (DEM) for the test table grid region. This provided the ≤3 cm resolution in the topographic data to map in fine detail a suite of features related to the underground explosion: uplift, subsidence, surface fractures, and morphological change detection. The near-field grid region data collection resulted in a 2-cm pixel-size DEM, enabling mapping of a broader range of features related to the explosion, including: uplift and subsidence, rock fall, and slope sloughing. This study represents one of the first works to constrain, both temporally and spatially, explosion-related surface damage using a UAS photogrammetric platform; these data will help to advance the science of

  10. Improvement of Alternative Crop Phenology Detection Algorithms using MODIS NDVI Time Series Data in US Corn Belt Region

    Science.gov (United States)

    Lee, J.; Kang, S.; Seo, B.; Lee, K.

    2017-12-01

    Predicting crop phenology is important for understanding of crop development and growth processes and improving the accuracy of crop model. Remote sensing offers a feasible tool for monitoring spatio-temporal patterns of crop phenology in region and continental scales. Various methods have been developed to determine the timing of crop phenological stages using spectral vegetation indices (i.e. NDVI and EVI) derived from satellite data. In our study, it was compared four alternative detection methods to identify crop phenological stages (i.e. the emergence and harvesting date) using high quality NDVI time series data derived from MODIS. Also we investigated factors associated with crop development rate. Temperature and photoperiod are the two main factors which would influence the crop's growth pattern expressed in the VI data. Only the effect of temperature on crop development rate was considered. The temperature response function in the Wang-Engel (WE) model was used, which simulates crop development using nonlinear models with response functions that range from zero to one. It has attempted at the state level over 14 years (2003-2016) in Iowa and Illinois state of USA, where the estimated phenology date by using four methods for both corn and soybean. Weekly crop progress reports produced by the USDA NASS were used to validate phenology detection algorithms effected by temperature. All methods showed substantial uncertainty but the threshold method showed relatively better agreement with the State-level data for soybean phenology.

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

    Science.gov (United States)

    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.

  12. UPDATING NATIONAL TOPOGRAPHIC DATA BASE USING CHANGE DETECTION METHODS

    Directory of Open Access Journals (Sweden)

    E. Keinan

    2016-06-01

    Full Text Available 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.

  13. Moment-to-moment changes in feeling moved match changes in closeness, tears, goosebumps, and warmth: time series analyses.

    Science.gov (United States)

    Schubert, Thomas W; Zickfeld, Janis H; Seibt, Beate; Fiske, Alan Page

    2018-02-01

    Feeling moved or touched can be accompanied by tears, goosebumps, and sensations of warmth in the centre of the chest. The experience has been described frequently, but psychological science knows little about it. We propose that labelling one's feeling as being moved or touched is a component of a social-relational emotion that we term kama muta (its Sanskrit label). We hypothesise that it is caused by appraising an intensification of communal sharing relations. Here, we test this by investigating people's moment-to-moment reports of feeling moved and touched while watching six short videos. We compare these to six other sets of participants' moment-to-moment responses watching the same videos: respectively, judgements of closeness (indexing communal sharing), reports of weeping, goosebumps, warmth in the centre of the chest, happiness, and sadness. Our eighth time series is expert ratings of communal sharing. Time series analyses show strong and consistent cross-correlations of feeling moved and touched and closeness with each other and with each of the three physiological variables and expert-rated communal sharing - but distinctiveness from happiness and sadness. These results support our model.

  14. Changing the science education paradigm: from teaching facts to engaging the intellect: Science Education Colloquia Series, Spring 2011.

    Science.gov (United States)

    Fischer, Caleb Nathaniel

    2011-09-01

    Dr. Jo Handelsman, Howard Hughes Medical Institute Professor in the Department of Molecular, Cellular and Developmental Biology at Yale University, is a long-time devotee of scientific teaching, receiving this year's Presidential Award for Science Mentoring. She gave a seminar entitled "What is Scientific Teaching? The Changing Landscape of Science Education" as a part of the Scientific Education Colloquia Series in spring 2011. After dissecting what is wrong with the status quo of American scientific education, several ideological and practical changes are proposed, including active learning, regular assessment, diversity, and mentorship. Copyright © 2011.

  15. Implementing Change from within Universities and Colleges: 10 Personal Accounts. Managing Innovation and Change in Universities and Colleges Series.

    Science.gov (United States)

    Slowey, Maria, Ed.

    This book presents the personal accounts and reflections of 10 individuals who were given leadership responsibility for the implementation of certain aspects of change in institutions of higher education in Great Britain. The contributions illustrate key dimensions of these changes--the development of strategies aimed at widening access, the…

  16. P2-18: Temporal and Featural Separation of Memory Items Play Little Role for VSTM-Based Change Detection

    Directory of Open Access Journals (Sweden)

    Dae-Gyu Kim

    2012-10-01

    Full Text Available Classic studies of visual short-term memory (VSTM found that presenting memory items either sequentially or simultaneously does not affect recognition accuracy of the remembered items. Other studies also suggest that capacity of VSTM benefits from formation of bound object-based representations leading to no cost of remembering multi-feature items. According to these ideas, we aimed to examine the role of temporal and featural separation of memory items in VSTM change detection, (1 if sample items are separated across different temporal moments and (2 if across different feature dimensions. In a series of change detection experiments, we asked participants to report a change between a sample and a test display with a brief delay in between. In experiment 1, the sample items were split into two sets with a different onset time. In experiment 2, the sample items were split across two different feature dimensions (e.g., half color and half orientation. The change detection accuracy in Experiment 1 showed no substantial drop when the memory items were separated into two onset groups compared to simultaneous onset. The accuracy did not drop either when the features of sample items were split across two different feature groups compared to when were not split. The results indicate that temporal and featural separation of VWM items does not play a significant role for VSTM-based change detection.

  17. Statistics for Time-Series Spatial Data: Applying Survival Analysis to Study Land-Use Change

    Science.gov (United States)

    Wang, Ninghua Nathan

    2013-01-01

    Traditional spatial analysis and data mining methods fall short of extracting temporal information from data. This inability makes their use difficult to study changes and the associated mechanisms of many geographic phenomena of interest, for example, land-use. On the other hand, the growing availability of land-change data over multiple time…

  18. Coherent Change Detection with COSMO SkyMed Data-experimental Results

    OpenAIRE

    A. Mishra; D. Chaudhuri; C. Bhattacharya; Y.S. Rao

    2013-01-01

    Change detection is a technique in which we try to find changes between two acquisitions. These acquisitions can be from different platforms and sensors. Acquisition from satellite using synthetic aperture radar (SAR) is of immense interest to military applications. Satellite has the ability to peep into the enemy territory while SAR has the capability of day and night operations, being an active sensor. Coherent change detection (CCD) can be used to detect minute changes between two images. ...

  19. EGFR status in oral squamous cell carcinoma: comparing immunohistochemistry, FISH and CISH detection in a case series study.

    Science.gov (United States)

    Bernardes, Vanessa Fátima; Gleber-Netto, Frederico Omar; Sousa, Sílvia Ferreira de; Rocha, Rafael Malagoli; Aguiar, Maria Cássia Ferreira de

    2013-01-28

    To compare the immunohistochemistry (IHC) expression of epidermal growth factor receptor (EGFR) in oral squamous cell carcinomas (OSCC) with the gene amplification evaluated by fluorescence in situ hybridization (FISH) and chromogenic in situ hybridization (CISH) and their association with clinicopathological parameters. Additionally, we tested the sensibility and specificity of CISH in comparison with FISH. Case series study Oral surgery and pathology department in a school of dentistry. 52 patients with histopathological diagnosis of OSCC. Tumour tissue samples from 52 patients with OSCC were evaluated by IHC, FISH and CISH using tissue microarray technology. Clinicopathological data from all patients were collected. EGFR+ rates were 53.8% (28/52) by IHC, 5.8% (3/52) by CISH and 15.4% (8/52) by FISH. Amplification detected by CISH and FISH with IHC negative occurred in 3.8% (2/52), and one case (1.9%) showed amplification detected by CISH and FISH and protein overexpression concomitantly. There were 9.6% FISH+ cases with IHC and CISH negative rates and 6/8 (75%) FISH+ and also EGFR+ cases; however, an association between protein expression and gene amplification was not found for both techniques. IHC and FISH rates were not associated with clinicopathological features. CISH+ rates were associated with T3-T4 status. Compared with FISH assay, CISH reached a sensitivity of 37.5% and specificity of 100%. There is no association between EGFR expression and gene amplification in OSCC when the IHC is driven to external epitopes of the protein. Although CISH demonstrates specificity, technical problems may influence sensibility when compared with FISH.

  20. Ultra-wideband pose detection system for boom-type roadheader based on Caffery transform and Taylor series expansion

    Science.gov (United States)

    Fu, Shichen; Li, Yiming; Zhang, Minjun; Zong, Kai; Cheng, Long; Wu, Miao

    2018-01-01

    To realize unmanned pose detection of a coalmine boom-type roadheader, an ultra-wideband (UWB) pose detection system (UPDS) for a roadheader is designed, which consists of four UWB positioning base stations and three roadheader positioning nodes. The positioning base stations are used in turn to locate the positioning nodes of the roadheader fuselage. Using 12 sets of distance measurement information, a time-of-arrival (TOA) positioning model is established to calculate the 3D coordinates of three positioning nodes of the roadheader fuselage, and the three attitude angles (heading, pitch, and roll angles) of the roadheader fuselage are solved. A range accuracy experiment of a UWB P440 module was carried out in a narrow and closed tunnel, and the experiment data show that the mean error and standard deviation of the module can reach below 2 cm. Based on the TOA positioning model of the UPDS, we propose a fusion-positioning algorithm based on a Caffery transform and Taylor series expansion (CTFPA). We derived the complete calculation process, designed a flowchart, and carried out a simulation of CTFPA in MATLAB, comparing 1000 simulated positioning nodes of CTFPA and the Caffery positioning algorithm (CPA) for a 95 m long tunnel. The positioning error field of the tunnel was established, and the influence of the spatial variation on the positioning accuracy of CPA and CTFPA was analysed. The simulation results show that, compared with CPA, the positioning accuracy of CTFPA is clearly improved, and the accuracy of each axis can reach more than 5 mm. The accuracy of the X-axis is higher than that of the Y- and Z-axes. In section X-Y of the tunnel, the root mean square error (RMSE) contours of CTFPA are clear and orderly, and with an increase in the measuring distance, RMSE increases linearly. In section X-Z, the RMSE contours are concentric circles, and the variation ratio is nonlinear.

  1. Individual tree crown modeling and change detection from airborne lidar data

    NARCIS (Netherlands)

    Xiao, W.; Xu, Sudan; Oude Elberink, S.J.; Vosselman, G.

    2016-01-01

    Light detection and ranging (lidar) provides a promising way of detecting changes of trees in three-dimensional (3-D) because laser beams can penetrate through the foliage and therefore provide full coverage of trees. The aim is to detect changes in trees in urban areas using multitemporal airborne

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

    Directory of Open Access Journals (Sweden)

    Yuan Xu

    2015-01-01

    Full Text Available 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.

  3. Emergence timescales for detection of anthropogenic climate change in US tropical cyclone loss data

    International Nuclear Information System (INIS)

    Crompton, Ryan P; McAneney, K John; Pielke, Roger A Jr

    2011-01-01

    Recent reviews have concluded that efforts to date have yet to detect or attribute an anthropogenic climate change influence on Atlantic tropical cyclone (of at least tropical storm strength) behaviour and concomitant damage. However, the possibility of identifying such influence in the future cannot be ruled out. Using projections of future tropical cyclone activity from a recent prominent study we estimate the time that it would take for anthropogenic signals to emerge in a time series of normalized US tropical cyclone losses. Depending on the global climate model(s) underpinning the projection, emergence timescales range between 120 and 550 years, reflecting a large uncertainty. It takes 260 years for an 18-model ensemble-based signal to emerge. Consequently, under the projections examined here, the detection or attribution of an anthropogenic signal in tropical cyclone loss data is extremely unlikely to occur over periods of several decades (and even longer). This caution extends more generally to global weather-related natural disaster losses.

  4. Optimising 4-D surface change detection: an approach for capturing rockfall magnitude-frequency

    Science.gov (United States)

    Williams, Jack G.; Rosser, Nick J.; Hardy, Richard J.; Brain, Matthew J.; Afana, Ashraf A.

    2018-02-01

    We present a monitoring technique tailored to analysing change from near-continuously collected, high-resolution 3-D data. Our aim is to fully characterise geomorphological change typified by an event magnitude-frequency relationship that adheres to an inverse power law or similar. While recent advances in monitoring have enabled changes in volume across more than 7 orders of magnitude to be captured, event frequency is commonly assumed to be interchangeable with the time-averaged event numbers between successive surveys. Where events coincide, or coalesce, or where the mechanisms driving change are not spatially independent, apparent event frequency must be partially determined by survey interval.The data reported have been obtained from a permanently installed terrestrial laser scanner, which permits an increased frequency of surveys. Surveying from a single position raises challenges, given the single viewpoint onto a complex surface and the need for computational efficiency associated with handling a large time series of 3-D data. A workflow is presented that optimises the detection of change by filtering and aligning scans to improve repeatability. An adaptation of the M3C2 algorithm is used to detect 3-D change to overcome data inconsistencies between scans. Individual rockfall geometries are then extracted and the associated volumetric errors modelled. The utility of this approach is demonstrated using a dataset of ˜ 9 × 103 surveys acquired at ˜ 1 h intervals over 10 months. The magnitude-frequency distribution of rockfall volumes generated is shown to be sensitive to monitoring frequency. Using a 1 h interval between surveys, rather than 30 days, the volume contribution from small (< 0.1 m3) rockfalls increases from 67 to 98 % of the total, and the number of individual rockfalls observed increases by over 3 orders of magnitude. High-frequency monitoring therefore holds considerable implications for magnitude-frequency derivatives, such as hazard return

  5. Optimising 4-D surface change detection: an approach for capturing rockfall magnitude–frequency

    Directory of Open Access Journals (Sweden)

    J. G. Williams

    2018-02-01

    Full Text Available We present a monitoring technique tailored to analysing change from near-continuously collected, high-resolution 3-D data. Our aim is to fully characterise geomorphological change typified by an event magnitude–frequency relationship that adheres to an inverse power law or similar. While recent advances in monitoring have enabled changes in volume across more than 7 orders of magnitude to be captured, event frequency is commonly assumed to be interchangeable with the time-averaged event numbers between successive surveys. Where events coincide, or coalesce, or where the mechanisms driving change are not spatially independent, apparent event frequency must be partially determined by survey interval.The data reported have been obtained from a permanently installed terrestrial laser scanner, which permits an increased frequency of surveys. Surveying from a single position raises challenges, given the single viewpoint onto a complex surface and the need for computational efficiency associated with handling a large time series of 3-D data. A workflow is presented that optimises the detection of change by filtering and aligning scans to improve repeatability. An adaptation of the M3C2 algorithm is used to detect 3-D change to overcome data inconsistencies between scans. Individual rockfall geometries are then extracted and the associated volumetric errors modelled. The utility of this approach is demonstrated using a dataset of  ∼  9  ×  103 surveys acquired at  ∼  1 h intervals over 10 months. The magnitude–frequency distribution of rockfall volumes generated is shown to be sensitive to monitoring frequency. Using a 1 h interval between surveys, rather than 30 days, the volume contribution from small (< 0.1 m3 rockfalls increases from 67 to 98 % of the total, and the number of individual rockfalls observed increases by over 3 orders of magnitude. High-frequency monitoring therefore holds considerable

  6. Change detection in urban and rural driving scenes: Effects of target type and safety relevance on change blindness.

    Science.gov (United States)

    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). Copyright © 2017 Elsevier Ltd. All rights reserved.

  7. Self-adaptive change detection in streaming data with non-stationary distribution

    KAUST Repository

    Zhang, Xiangliang; Wang, Wei

    2010-01-01

    Non-stationary distribution, in which the data distribution evolves over time, is a common issue in many application fields, e.g., intrusion detection and grid computing. Detecting the changes in massive streaming data with a non

  8. Change Detection Algorithms for Information Assurance of Computer Networks

    National Research Council Canada - National Science Library

    Cardenas, Alvaro A

    2002-01-01

    .... In this thesis, the author will focus on the detection of three attack scenarios: the spreading of active worms throughout the Internet, distributed denial of service attacks, and routing attacks to wireless ad hoc networks...

  9. Evaluation of functional changes in the evolutionary stages of Parkinson's disease: a case series

    Directory of Open Access Journals (Sweden)

    Natália Feitoza do Nascimento

    Full Text Available Abstract Introduction : Parkinson's disease is one of the fastest growing neurological disorders in the world and is considered to be predominantly a motor disorder, classified as neurodegenerative, chronic and extrapyramidal, characterized by tremors, muscular rigidity, postural balance deficit and bradykinesia. Objective : To analyze the functional changes presented by Parkinson's disease patients in the different evolutionary stages of the disease. Methods : The sample included 30 patients of both genders, aged between 51 and 75 years, with clinical diagnoses of Parkinson's disease in different evolutionary stages, undergoing physiotherapy in the Pernambuco Parkinson Association. The patients were evaluated through the Hoehn and Yahr Staging Scale and the Unified Parkinson's Disease Rating Scale (UPDRS. Results : The results highlighted an increase in the presence and severity of the changes presented by the Parkinson's disease patients, according to the evolution of the stages of the disease, especially considering aspects relating to motor activity and activities of daily living, including changes related to speech, swallowing, the ability to get dressed, personal hygiene, turning in bed and arising from a chair, occurrence of falls, posture, gait, postural stability and the presence of bradykinesia/hypokinesia. Conclusion : It is believed that the classification of the evolutionary stage of patients through the Hoehn and Yahr Scale and the registration of functional change through the UPDRS are useful for the development of the physiotherapy treatment plan.

  10. Ripe for Change: Garden-Based Learning in Schools. Harvard Education Letter Impact Series

    Science.gov (United States)

    Hirschi, Jane S.

    2015-01-01

    "Ripe for Change: Garden-Based Learning in Schools" takes a big-picture view of the school garden movement and the state of garden-based learning in public K--8 education. The book frames the garden movement for educators and shows how school gardens have the potential to be a significant resource for teaching and learning. In this…

  11. Mapping change of older forest with nearest-neighbor imputation and Landsat time-series

    Science.gov (United States)

    Janet L. Ohmann; Matthew J. Gregory; Heather M. Roberts; Warren B. Cohen; Robert E. Kennedy; Zhiqiang. Yang

    2012-01-01

    The Northwest Forest Plan (NWFP), which aims to conserve late-successional and old-growth forests (older forests) and associated species, established new policies on federal lands in the Pacific Northwest USA. As part of monitoring for the NWFP, we tested nearest-neighbor imputation for mapping change in older forest, defined by threshold values for forest attributes...

  12. Faces of Change. Visual Evidence: An Instructional Approach. Instructor's Notes: Film/Essay Series.

    Science.gov (United States)

    Miller, Norman N.

    Designed for use with the multidisciplinary film project, "Faces of Change, Five Rural Societies in Transition" for the college social studies curriculum, this manual contains an overview of the material and its underlying philosophy and suggests teaching strategies. The first section discusses the overall approach, the use of films in…

  13. Data mining in time series databases

    CERN Document Server

    Kandel, Abraham; Bunke, Horst

    2004-01-01

    Adding the time dimension to real-world databases produces Time SeriesDatabases (TSDB) and introduces new aspects and difficulties to datamining and knowledge discovery. This book covers the state-of-the-artmethodology for mining time series databases. The novel data miningmethods presented in the book include techniques for efficientsegmentation, indexing, and classification of noisy and dynamic timeseries. A graph-based method for anomaly detection in time series isdescribed and the book also studies the implications of a novel andpotentially useful representation of time series as strings. Theproblem of detecting changes in data mining models that are inducedfrom temporal databases is additionally discussed.

  14. 55-year (1960-2015) spatiotemporal shoreline change analysis using historical DISP and Landsat time series data in Shanghai

    Science.gov (United States)

    Qiao, Gang; Mi, Huan; Wang, Weian; Tong, Xiaohua; Li, Zhongbin; Li, Tan; Liu, Shijie; Hong, Yang

    2018-06-01

    Shoreline change has been an increasing concern for low-lying and vulnerable coastal zones worldwide, especially in estuarine delta regions, which generally have significant economic development, large human settlements and infrastructures. Thus, long time-series shoreline change data are useful for understanding how shorelines respond to natural and anthropogenic activities, as well as for providing greater insights into coastal protection and sustainable development in the future. For the first time, this study analyzes 55 years of spatiotemporal shoreline changes in Shanghai, China, by integrating the historical Declassified Intelligence Satellite Photography (DISP) and Landsat time series data at five-year intervals from 1960 to 2015. Twelve shorelines were interpreted from DISP and Landsat images. The spatiotemporal changes in the shorelines were explored at five-year intervals within the study period for the Shanghai mainland and islands. The results indicate that shorelines in Shanghai accreted significantly over the last 55 years, but different accretion patterns were observed in Chongming Dongtan. The rate of shoreline change varied in different areas, and the most noticeable expansions were Chongming Beitan, Chongming Dongtan, Hengsha Dongtan, and Nanhuizui. The length of the entire shoreline increased by 25.7% from 472.6 km in 1960 to 594.2 km in 2015. Due to the shoreline changes, the Shanghai area expanded by 1,192.5 km2 by 2015, which was an increase of 19.9% relative to its 1960 area. The Digital Shoreline Analysis System (DSAS) was used to compute rate-of-change statistics. Between 1960 and 2015, 10.6% of the total transects exceeded 3 km of Net Shoreline Movement (NSM), with a maximum value of approximately 20 km at eastern Hengsha Island. The average Weighted Linear Regression Rate (WLR) of the Shanghai shoreline was 52.2 m/yr from 1960 to 2015; there was 94.1% accretion, 3.1% erosion, and 2.8% with no significant change. In addition, the driving

  15. A hybrid symplectic principal component analysis and central tendency measure method for detection of determinism in noisy time series with application to mechanomyography.

    Science.gov (United States)

    Xie, Hong-Bo; Dokos, Socrates

    2013-06-01

    We present a hybrid symplectic geometry and central tendency measure (CTM) method for detection of determinism in noisy time series. CTM is effective for detecting determinism in short time series and has been applied in many areas of nonlinear analysis. However, its performance significantly degrades in the presence of strong noise. In order to circumvent this difficulty, we propose to use symplectic principal component analysis (SPCA), a new chaotic signal de-noising method, as the first step to recover the system dynamics. CTM is then applied to determine whether the time series arises from a stochastic process or has a deterministic component. Results from numerical experiments, ranging from six benchmark deterministic models to 1/f noise, suggest that the hybrid method can significantly improve detection of determinism in noisy time series by about 20 dB when the data are contaminated by Gaussian noise. Furthermore, we apply our algorithm to study the mechanomyographic (MMG) signals arising from contraction of human skeletal muscle. Results obtained from the hybrid symplectic principal component analysis and central tendency measure demonstrate that the skeletal muscle motor unit dynamics can indeed be deterministic, in agreement with previous studies. However, the conventional CTM method was not able to definitely detect the underlying deterministic dynamics. This result on MMG signal analysis is helpful in understanding neuromuscular control mechanisms and developing MMG-based engineering control applications.

  16. Detection Capability Evaluation on Chang'e-5 Lunar Mineralogical Spectrometer (LMS)

    Science.gov (United States)

    Liu, Bin; Ren, Xin; Yan, Wei; Xu, Xuesen; Cai, Tingni; Liu, Dawei; Liu, Jianjun; Li, Chunlai

    2016-04-01

    The Chang'e-5 (CE-5) lunar sample return mission is scheduled to launch in 2017 to bring back lunar regolith and drill samples. The Chang'e-5 Lunar Mineralogical Spectrometer (LMS), as one of the three sets of scientific payload installed on the lander, is used to collect in-situ spectrum and analyze the mineralogical composition of the sampling site. It can also help to select the sampling site , and to compare the measured laboratory spectrum of returned sample with in-situ data. LMS employs acousto-optic tunable filters (AOTFs) and is composed of a VIS/NIR module (0.48μm-1.45μm) and an IR module (1.4μm -3.2μm). It has spectral resolution ranging from 3 to 25 nm, with a field of view (FOV) of 4.24°×4.24°. Unlike Chang'e-3 VIS/NIR Imaging Spectrometer (VNIS), the spectral coverage of LMS is extended from 2.4μm to 3.2μm, which has capability to identify H2O/OH absorption features around 2.7μm. An aluminum plate and an Infragold plate are fixed in the dust cover, being used as calibration targets in the VIS/NIR and IR spectral range respectively when the dust cover is open. Before launch, a ground verification test of LMS needs to be conducted in order to: 1) test and verify the detection capability of LMS through evaluation on the quality of image and spectral data collected for the simulated lunar samples; and 2) evaluate the accuracy of data processing methods by the simulation of instrument working on the moon. The ground verification test will be conducted both in the lab and field. The spectra of simulated lunar regolith/mineral samples will be collected simultaneously by the LMS and two calibrated spectrometers: a FTIR spectrometer (Model 102F) and an ASD FieldSpec 4 Hi-Res spectrometer. In this study, the results of the LMS ground verification test will be reported including the evaluation on the LMS spectral and image data quality, mineral identification and inversion ability, accuracy of calibration and geometric positioning .

  17. Feature learning and change feature classification based on deep learning for ternary change detection in SAR images

    Science.gov (United States)

    Gong, Maoguo; Yang, Hailun; Zhang, Puzhao

    2017-07-01

    Ternary change detection aims to detect changes and group the changes into positive change and negative change. It is of great significance in the joint interpretation of spatial-temporal synthetic aperture radar images. In this study, sparse autoencoder, convolutional neural networks (CNN) and unsupervised clustering are combined to solve ternary change detection problem without any supervison. Firstly, sparse autoencoder is used to transform log-ratio difference image into a suitable feature space for extracting key changes and suppressing outliers and noise. And then the learned features are clustered into three classes, which are taken as the pseudo labels for training a CNN model as change feature classifier. The reliable training samples for CNN are selected from the feature maps learned by sparse autoencoder with certain selection rules. Having training samples and the corresponding pseudo labels, the CNN model can be trained by using back propagation with stochastic gradient descent. During its training procedure, CNN is driven to learn the concept of change, and more powerful model is established to distinguish different types of changes. Unlike the traditional methods, the proposed framework integrates the merits of sparse autoencoder and CNN to learn more robust difference representations and the concept of change for ternary change detection. Experimental results on real datasets validate the effectiveness and superiority of the proposed framework.

  18. Imaging Fracture Networks Using Angled Crosshole Seismic Logging and Change Detection Techniques

    Science.gov (United States)

    Knox, H. A.; Grubelich, M. C.; Preston, L. A.; Knox, J. M.; King, D. K.

    2015-12-01

    We present results from a SubTER funded series of cross borehole geophysical imaging efforts designed to characterize fracture zones generated with an alternative stimulation method, which is being developed for Enhanced Geothermal Systems (EGS). One important characteristic of this stimulation method is that each detonation will produce multiple fractures without damaging the wellbore. To date, we have collected six full data sets with ~30k source-receiver pairs each for the purposes of high-resolution cross borehole seismic tomographic imaging. The first set of data serves as the baseline measurement (i.e. un-stimulated), three sets evaluate material changes after fracture emplacement and/or enhancement, and two sets are used for evaluation of pick error and seismic velocity changes attributable to changing environmental factors (i.e. saturation due to rain/snowfall in the shallow subsurface). Each of the six datasets has been evaluated for data quality and first arrivals have been picked on nearly 200k waveforms in the target area. Each set of data is then inverted using a Vidale-Hole finite-difference 3-D eikonal solver in two ways: 1) allowing for iterative ray tracing and 2) with fixed ray paths determined from the test performed before the fracture stimulation of interest. Utilizing these two methods allows us to compare and contrast the results from two commonly used change detection techniques. Sandia National Laboratories is a multi-program laboratory managed and operated by Sandia Corporation, a wholly owned subsidiary of Lockheed Martin Corporation, for the U.S. Department of Energy's National Nuclear Security Administration under contract DE-AC04-94AL85000.

  19. Techniques for land use change detection using Landsat imagery

    Science.gov (United States)

    Angelici, G. L.; Bryant, N. A.; Friedman, S. Z.

    1977-01-01

    A variety of procedures were developed for the delineation of areas of land use change using Landsat Multispectral Scanner data and the generation of statistics revealing the nature of the changes involved (i.e., number of acres changed from rural to urban). Techniques of the Image Based Information System were utilized in all stages of the procedure, from logging the Landsat data and registering two frames of imagery, to extracting the changed areas and printing tabulations of land use change in acres. Two alternative methods of delineating land use change are presented while enumerating the steps of the entire process. The Houston, Texas urban area, and the Orlando, Florida urban area, are used as illustrative examples of various procedures.

  20. Diagnosis of UAV Pitot Tube Defects Using Statistical Change Detection

    DEFF Research Database (Denmark)

    Hansen, Søren; Blanke, Mogens; Adrian, Jens

    2010-01-01

    Unmanned Aerial Vehicles need a large degree of tolerance to faults. One of the most important steps towards this is the ability to detect and isolate faults in sensors and actuators in real time and make remedial actions to avoid that faults develop to failure. This paper analyses the possibilit......Unmanned Aerial Vehicles need a large degree of tolerance to faults. One of the most important steps towards this is the ability to detect and isolate faults in sensors and actuators in real time and make remedial actions to avoid that faults develop to failure. This paper analyses...... the possibilities of detecting faults in the pitot tube of a small unmanned aerial vehicle, a fault that easily causes a crash if not diagnosed and handled in time. Using as redundant information the velocity measured from an onboard GPS receiver, the air-speed estimated from engine throttle and the pitot tube...

  1. Comparison of computed tomography and radiography for detecting changes induced by malignant nasal neoplasia in dogs

    International Nuclear Information System (INIS)

    Park, R.D.; Beck, E.R.; LeCouteur, R.A.

    1992-01-01

    The ability of computed tomography and radiography to detect changes associated with nasal neoplasia was compared in dogs. Eighteen areas or anatomic structures were evaluated in 21 dogs for changes indicative of neoplasia. Computed tomography was superior (P < or = 0.05) to radiography for detecting changes in 14 of 18 areas. Radiography was not superior for detecting changes in any structure or area. Computed tomography reveals vital information not always detected radiographically to assist in providing a prognosis and in planning treatment for nasal neoplasms in dogs

  2. Detecting CO/sub 2/-induced climatic change

    Energy Technology Data Exchange (ETDEWEB)

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

    1981-07-16

    Although it is widely believed that increasing atmospheric CO/sub 2/ levels will cause noticeable global warming, the effects are not yet detectable, possibly because of the 'noise' of natural climatic variability. An examination of the spatial and seasonal distribution of signal-to-noise ratio shows that the highest values occur in summer and annual mean surface temperatures averaged over the Northern Hemisphere or over mid-latitudes. The spatial and seasonal characteristics of the early twentieth century warming were similar to those expected from increasing CO/sub 2/ based on an equilibrium response model. This similarity may hinder the early detection of CO/sub 2/ effects on climate.

  3. Visual long-term memory and change blindness: Different effects of pre- and post-change information on one-shot change detection using meaningless geometric objects.

    Science.gov (United States)

    Nishiyama, Megumi; Kawaguchi, Jun

    2014-11-01

    To clarify the relationship between visual long-term memory (VLTM) and online visual processing, we investigated whether and how VLTM involuntarily affects the performance of a one-shot change detection task using images consisting of six meaningless geometric objects. In the study phase, participants observed pre-change (Experiment 1), post-change (Experiment 2), or both pre- and post-change (Experiment 3) images appearing in the subsequent change detection phase. In the change detection phase, one object always changed between pre- and post-change images and participants reported which object was changed. Results showed that VLTM of pre-change images enhanced the performance of change detection, while that of post-change images decreased accuracy. Prior exposure to both pre- and post-change images did not influence performance. These results indicate that pre-change information plays an important role in change detection, and that information in VLTM related to the current task does not always have a positive effect on performance. Copyright © 2014 Elsevier Inc. All rights reserved.

  4. Daily Changes in Composition and Diversity of the Intestinal Microbiota in Patients with Anorexia Nervosa: A Series of Three Cases.

    Science.gov (United States)

    Kleiman, Susan C; Glenny, Elaine M; Bulik-Sullivan, Emily C; Huh, Eun Young; Tsilimigras, Matthew C B; Fodor, Anthony A; Bulik, Cynthia M; Carroll, Ian M

    2017-09-01

    Anorexia nervosa, a severe psychiatric illness, is associated with an intestinal microbial dysbiosis. Individual microbial signatures dominate in healthy samples, even over time and under controlled conditions, but whether microbial markers of the disorder overcome inter-individual variation during the acute stage of illness or renourishment is unknown. We characterized daily changes in the intestinal microbiota in three acutely ill patients with anorexia nervosa over the entire course of hospital-based renourishment and found significant, patient-specific changes in microbial composition and diversity. This preliminary case series suggests that even in a state of pathology, individual microbial signatures persist in accounting for the majority of intestinal microbial variation. Copyright © 2017 John Wiley & Sons, Ltd and Eating Disorders Association. Copyright © 2017 John Wiley & Sons, Ltd and Eating Disorders Association.

  5. Detecting change in processes using comparative trace clustering

    NARCIS (Netherlands)

    Hompes, B.F.A.; Buijs, J.C.A.M.; van der Aalst, W.M.P.; Dixit, P.M.; Buurman, J.

    2015-01-01

    Real-life business processes are complex and show a high degree of variability. Additionally, due to changing conditions and circumstances, these processes continuously evolve over time. For example, in the healthcare domain, advances in medicine trigger changes in diagnoses and treatment processes.

  6. Trend analysis and change point detection of annual and seasonal ...

    Indian Academy of Sciences (India)

    elevation ranges from 0 m in the coastal areas of the. Persian Gulf to over ... been explained by Kang and Yusof (2012); Dhorde ...... J L 2004 Detection of statistically significant trends in ... Sun H, Chen Y, Li W, Li F, Chen Y, Hao X and Yang Y.

  7. Detecting Hacked Twitter Accounts based on Behavioural Change

    NARCIS (Netherlands)

    Nauta, Meike; Habib, Mena Badieh; van Keulen, Maurice

    Social media accounts are valuable for hackers for spreading phishing links, malware and spam. Furthermore, some people deliberately hack an acquaintance to damage his or her image. This paper describes a classification for detecting hacked Twitter accounts. The model is mainly based on features

  8. Recent changes in phenology over the northern high latitudes detected from multi-satellite data

    International Nuclear Information System (INIS)

    Zeng Heqing; Jia Gensuo; Epstein, Howard

    2011-01-01

    Phenology of vegetation is a sensitive and valuable indicator of the dynamic responses of terrestrial ecosystems to climate change. Therefore, to better understand and predict ecosystems dynamics, it is important to reduce uncertainties in detecting phenological changes. Here, changes in phenology over the past several decades across the northern high-latitude region (≥60°N) were examined by calibrating and analyzing time series of the Moderate Resolution Imaging Spectroradiometer (MODIS) and the Advanced Very High Resolution Radiometer (AVHRR). Over the past decade (2000–10), an expanded length of the growing season (LOS) was detected by MODIS, largely due to an earlier start of the growing season (SOS) by 4.7 days per decade and a delayed end of the growing season (EOS) by 1.6 days per decade over the northern high latitudes. There were significant differences between North America and Eurasia in phenology from 2000 to 2010 based on MODIS data (SOS: df = 21, F = 49.02, p < 0.0001; EOS: df = 21, F = 49.25, p < 0.0001; LOS: df = 21, F = 79.40, p < 0.0001). In northern America, SOS advanced by 11.5 days per decade, and EOS was delayed by 2.2 days per decade. In Eurasia, SOS advanced by 2.7 days per decade, and EOS was delayed by 3.5 days per decade. SOS has likely advanced due to the warming Arctic during April and May. Our results suggest that in recent decades the longer vegetation growing seasons can be attributed to more advanced SOS rather than delayed EOS. AVHRR detected longer LOS over the past three decades, largely related to delayed EOS rather than advanced SOS. These two datasets are significantly different in key phenological parameters (SOS: df = 17, F = 14.63, p = 0.0015; EOS: df = 17, F = 38.69, p < 0.0001; LOS: df = 17, F = 16.47, p = 0.0009) from 2000 to 2008 over the northern high latitudes. Thus, further inter-calibration between the sensors is needed to resolve the inconsistency and to better understand long-term trends of vegetation growth

  9. Communities and change : number 8 of a series of papers on energy and the offshore

    International Nuclear Information System (INIS)

    Anon

    1998-01-01

    The issue of how communities change when faced with the sudden influx of new industries and major capital investments are discussed, focusing on the benefits and drawbacks of Nova Scotia's offshore oil and gas developments, especially those experienced in the the five counties of southwest Nova Scotia with close connections to Georges Bank. The prospect of offshore oil and gas is clearly controversial in the area. One of the concerns is that transient construction workers will create problems for local communities. Other concerns are that the beauty of the coastline will be spoiled, the way of life changed forever, communities will be split into haves and have-nots, and fears that the petroleum industry will drive hard bargains and cheat people. On the positive side, opportunities for job creation go a long way to dampen concerns. The need to consider lessons that can be learned from other communities with offshore oil and gas experience, including the experiences of the Scottish North Sea developments, Newfoundland's Hibernia development, the Gulf Coast of the United States, and Nova Scotia's Sable Offshore Energy Project are stressed as a way to cushion the impact of disruption. 9 refs

  10. Changes in ecosystem service values in Zhoushan Island using remote sensing time series data

    Science.gov (United States)

    Zhang, Xiaoping; Qin, Yanpei; Lv, Ying; Zhen, Guangwei; Gong, Fang; Li, Chaokui

    2017-10-01

    The largest inhabited island, Zhoushan Island, is the center of economy, culture, shipping, and fishing in the Zhoushan Archipelago New Area. Its coastal wetland and tidal flats offer significant ecological services including floodwater storage, wildlife habitat, and buffers against tidal surges. Yet, large-scale land reclamation and new land development may dramatically change ecosystem services. In this research, we assess changes in ecosystem service values in Zhoushan Island during 1990-2000-2011. Three LANDSAT TM and/or ETM data sets were used to determine the spatial pattern of land use, and previously published value coefficients were used to calculate the ecosystem service values delivered by each land category. The results show that total value of ecosystem services in Zhoushan Island declined by 11% from 2920.07 billion Yuan to 2609.77 billion Yuan per year between 1990 and 2011. This decrease is largely attributable to the 51% loss of tidal flats. The combined ecosystem service values of woodland, paddy land and tidal flats were over 90% of the total values. The result indicates that future land-use policy should pay attention to the conservation of these ecosystems over uncontrolled reclamation and coastal industrial development, and that further coastal reclamation should be on rigorous environmental impact analyses.

  11. PRESENTATION ON--LAND-COVER CHANGE DETECTION USING MULTI-TEMPORAL MODIS NDVI DATA

    Science.gov (United States)

    Monitoring the locations and distributions of land-cover changes is important for establishing linkages between policy decisions, regulatory actions and subsequent landuse activities. Past efforts incorporating two-date change detection using moderate resolution data (e.g., Lands...

  12. Kernel based orthogonalization for change detection in hyperspectral images

    DEFF Research Database (Denmark)

    Nielsen, Allan Aasbjerg

    function and all quantities needed in the analysis are expressed in terms of this kernel function. This means that we need not know the nonlinear mappings explicitly. Kernel PCA and MNF analyses handle nonlinearities by implicitly transforming data into high (even infinite) dimensional feature space via...... analysis all 126 spectral bands of the HyMap are included. Changes on the ground are most likely due to harvest having taken place between the two acquisitions and solar effects (both solar elevation and azimuth have changed). Both types of kernel analysis emphasize change and unlike kernel PCA, kernel MNF...

  13. OBJECT-ORIENTED CHANGE DETECTION BASED ON MULTI-SCALE APPROACH

    Directory of Open Access Journals (Sweden)

    Y. Jia

    2016-06-01

    Full Text Available 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. Changing Series: Narrative Models and the Role of the Viewer in Contemporary Television Seriality

    Directory of Open Access Journals (Sweden)

    Veronica Innocenti

    2015-04-01

    Full Text Available In the past few years television serial narratives have gone through some severe changes in the way they are organized from a narrative point of view, as well as in the way they are perceived by the audiences. Our article originates from the necessity of investigating the products of this new wave of serial production, with the purpose of focusing on narrative formulas and on the relationships that serial products establish with their users. Vast serialized narratives are not just texts anymore, they have become complex universes of meaning that last for long time and that have a strong influence on the audiences. The article aims to investigate serial narratives as usable objects, that are not just seen by the public, but that are part of a complex experience.

  15. Coherent Change Detection: Theoretical Description and Experimental Results

    Science.gov (United States)

    2006-08-01

    scene changes created using a rotary hoe and lawn mower . In the first collection the repeat pass delay is 24 hours and for a false alarm rate of 1 in 20...the rotary hoe and lawn mower . . . . . . . . . . . . . . . . . 65 35 Intensity SAR image of the scene used for repeat pass interferometry experi...ments. Superimposed on the image is a schematic showing the scene changes carried out with the rotary hoe and lawn mower . . . . . . . . . . . . . . . . . 66

  16. The Distributed Biological Observatory (DBO)-A Change Detection Array in the Pacific Arctic Sector

    Science.gov (United States)

    Grebmeier, J. M.; Moore, S. E.; Cooper, L. W.; Frey, K. E.; Pickart, R. S.

    2011-12-01

    The Pacific sector of the Arctic Ocean is experiencing major reductions in seasonal sea ice extent and increases in sea surface temperatures. One of the key uncertainties in this region is how the marine ecosystem will respond to seasonal shifts in the timing of spring sea ice retreat and/or delays in fall sea ice formation. Variations in upper ocean water hydrography, planktonic production, pelagic-benthic coupling and sediment carbon cycling are all influenced by sea ice and temperature changes. Climate changes are likely to result in shifts in species composition and abundance, northward range expansions, and changes in lower trophic level productivity that can directly cascade and affect the life cycles of higher trophic level organisms. Several regionally critical marine sites in the Pacific Arctic sector that have very high biomass and are focused foraging points for apex predators have been re-occupied during multiple international cruises. The data documenting the importance of these ecosystem "hotspots" provide a growing marine time-series from the northern Bering Sea to Barrow Canyon at the boundary of the Chukchi and Beaufort seas. Results from these studies show spatial changes in carbon production and export to the sediments as indicated by infaunal community composition and biomass, shifts in sediment grain size on a S-to-N latitudinal gradient, and range extensions for lower trophic levels and further northward migration of higher trophic organisms, such as gray whales. There is also direct evidence of negative impacts on ice dependent species, such as walrus and polar bears. To more systematically track the broad biological response to sea ice retreat and associated environmental change, an international consortium of scientists are developing a "Distributed Biological Observatory" (DBO) that includes selected biological measurements at multiple trophic levels. The DBO currently focuses on five regional biological "hotspot" locations along a

  17. Recent Structural Change in Remote Sensing Data Time Series Linked to Farm Management in Horn of Africa (1999-2009)

    Science.gov (United States)

    Crisci, A.; Vignaroli, P.; Genesio, L.; Grasso, V.; Bacci, M.; Tarchiani, V.; Capecchi, V.

    2011-01-01

    the relationship between two time series (Maraun and Kurths, 2004). This kind of multi-scale temporal investigation provides an explanation of break detected in time series, confirming or not their climatic linkage; results of the analysis are shown. Finally, in order to support the dissemination and sharing of information, interactive vegetation maps have been implemented with Google Earth mash-up. The maturity of Web-based GIS enables the generation of thematic maps dynamically and efficiently, with a thin/thick client or hybrid architectures. This could be a great support for the understanding environmental phenomena.

  18. Using Land Surface Phenology to Detect Land Use Change in the Northern Great Plains

    Science.gov (United States)

    Nguyen, L. H.; Henebry, G. M.

    2017-12-01

    The Northern Great Plains of the US have been undergoing many types of land cover / land use change over the past two decades, including expansion of irrigation, conversion of grassland to cropland, biofuels production, urbanization, and fossil fuel mining. Much of the literature on these changes has relied on post-classification change detection based on a limited number of observations per year. Here we demonstrate an approach to characterize land dynamics through land surface phenology (LSP) by synergistic use of image time series at two scales. Our study areas include regions of interest (ROIs) across the Northern Great Plains located within Landsat path overlap zones to boost the number of valid observations (free of clouds or snow) each year. We first compute accumulated growing degree-days (AGDD) from MODIS 8-day composites of land surface temperature (MOD11A2 and MYD11A2). Using Landsat Collection 1 surface reflectance-derived vegetation indices (NDVI, EVI), we then fit at each pixel a downward convex quadratic model linking the vegetation index to each year's progression of AGDD. This quadratic equation exhibits linearity in a mathematical sense; thus, the fitted models can be linearly mixed and unmixed using a set of LSP endmembers (defined by the fitted parameter coefficients of the quadratic model) that represent "pure" land cover types with distinct seasonal patterns found within the region, such as winter wheat, spring wheat, maize, soybean, sunflower, hay/pasture/grassland, developed/built-up, among others. Information about land cover corresponding to each endmember are provided by the NLCD (National Land Cover Dataset) and CDL (Cropland Data Layer). We use linear unmixing to estimate the likely proportion of each LSP endmember within particular areas stratified by latitude. By tracking the proportions over the 2001-2011 period, we can quantify various types of land transitions in the Northern Great Plains.

  19. Time series changes of MR/PET image of brain glucose metabolism in healthy subjects and alzheimer disease patients

    International Nuclear Information System (INIS)

    Tarusawa, Ayaka; Nihei, Mitsuyo; Tanaka, Mika; Fukami, Tadanori; Yuasa, Tetsuya; Wu, Jin; Kawasaki, Keiichi; Ishiwata, Kiichi; Ishii, Kenji

    2010-01-01

    Combination of morphological information by MRI and functional one by positron emission tomography (PET) was applied to quantitative evaluation of brain regional glucose metabolism in healthy subjects (HS) and Alzheimer disease patients (AD) and their individual aging changes were elucidated for ultimate purpose of computer-aided diagnosis. Subjects were: 5 AD patients (3M/2F, av. age 77.27 y), 14 ε4-carrying HS (EHS, 4M/10F, 71.3y) and 24 non-ε4-carrying HS (NEHS, 4M/20F, 70.21), where ε4 (apolipoprotein E type 4 gene allele)-carrying HS were reported to be prone to early AD and to tend to give increased brain atrophy incidence. Acquisitions of T1-weighted 3D MR and PET images were in 256 x 256 x(88-104) and x (90-100) voxels, respectively, with digitization level 16 bits, and were repeated 3 times in the time series of 21-38 months. Segmentation was performed with the MR imaging software SPM8 (Statistic Parametric Mapping: Metalab) to specify the regions of white/gray matters and cerebrospinal fluid (CSF). The binary MR and registered PET images were fused for comparison of glucose metabolism by SUVs (standardized uptake values) in gray matter of the three subject groups. Findings were: SUV in AD was markedly reduced; average time series changes per year were 0.11% in AD, -2.63% in EHS and 1.48% in NEHS; and statistical significance of the changes was between AD and NEHS, and between EHS and NEHS. Glucose metabolism by MR/PET can be thus used for a distinction of ε4-carrier and non-carrier in HS. (T.T.)

  20. Antibiotics in the treatment of patients with lower back pain associated with Modic changes: a case series.

    Science.gov (United States)

    Gupta, Gaurav; Jarzem, Peter; Meredith, Sean; Radhakrishna, Mohan; Besemann, Markus; Elgueta, Maria Francisca; Charghi, Roshanak; Chankowsky, Jeffrey

    2017-01-01

    To determine the clinical effect of antibiotic treatment for patients with low back pain and Modic 1 changes. This is a retrospective case series of patients treated at the Canadian Forces Health Services Centre in Ottawa and the McGill University Health Centre. Where available, pain, functional, and imaging outcomes in 11 patients treated between 2013 and 2015 were analyzed to determine effect of antibiotic treatment for patients with low back pain and associated Modic 1 changes on magnetic resonance imaging. Conservatively, only 3 of 11 patients met the criteria for improvement for pain and/or function. While a larger proportion improved in the long term, outcomes were not thought to be temporally attributable to antibiotic treatment, as in most cases, ongoing therapy, medications, and/or injections were required. There did not appear to be a correlation between clinical improvement and associated end plate volume involvement for Modic changes. Antibiotics for the treatment of low back pain in the context of Modic changes on MRI did not generally provide significant improvement in pain and function for patients in this small cohort. Despite early excitement regarding this treatment, further research is required.

  1. Detecting and Reacting to Change: The Effect of Exposure to Narrow Categorizations

    Science.gov (United States)

    Chakravarti, Amitav; Fang, Christina; Shapira, Zur

    2011-01-01

    The ability to detect a change, to accurately assess the magnitude of the change, and to react to that change in a commensurate fashion are of critical importance in many decision domains. Thus, it is important to understand the factors that systematically affect people's reactions to change. In this article we document a novel effect: Decision…

  2. Quantile index for gradual and abrupt change detection from CFB boiler sensor data in online settings

    NARCIS (Netherlands)

    Maslov, A.; Pechenizkiy, M.; Kärkkäinen, T.; Tähtinen, M.

    2012-01-01

    In this paper we consider the problem of online detection of gradual and abrupt changes in sensor data having high levels of noise and outliers. We propose a simple heuristic method based on the Quantile Index (QI) and study how robust this method is for detecting both gradual and abrupt changes

  3. Graph-Theoretic Statistical Methods for Detecting and Localizing Distributional Change in Multivariate Data

    Science.gov (United States)

    2015-06-01

    context of regression. Tran, Gaber , and Sattler (2014) describe recent change-detection efforts as applied to streaming data. -2 -1 0 1 2 3 4 -2 -1 0 1 Y...human monitors: A signal detection analysis. Human-Computer Interaction, 1(1), 49–75. Tran, D. H., Gaber , M. M., & Sattler, K. U. (2014). Change

  4. Detection of retinal changes from illumination normalized fundus images using convolutional neural networks

    NARCIS (Netherlands)

    Adal, K.M.; Van Etten, Peter G.; Martinez, Jose P; Rouwen, Kenneth; Vermeer, K.A.; van Vliet, L.J.; Armato, Samuel G.; Petrick, Nicholas A.

    2017-01-01

    Automated detection and quantification of spatio-temporal retinal changes is an important step to objectively assess disease progression and treatment effects for dynamic retinal diseases such as diabetic retinopathy (DR). However, detecting retinal changes caused by early DR lesions such as

  5. Change-Point Detection Method for Clinical Decision Support System Rule Monitoring.

    Science.gov (United States)

    Liu, Siqi; Wright, Adam; Hauskrecht, Milos

    2017-06-01

    A clinical decision support system (CDSS) and its components can malfunction due to various reasons. Monitoring the system and detecting its malfunctions can help one to avoid any potential mistakes and associated costs. In this paper, we investigate the problem of detecting changes in the CDSS operation, in particular its monitoring and alerting subsystem, by monitoring its rule firing counts. The detection should be performed online, that is whenever a new datum arrives, we want to have a score indicating how likely there is a change in the system. We develop a new method based on Seasonal-Trend decomposition and likelihood ratio statistics to detect the changes. Experiments on real and simulated data show that our method has a lower delay in detection compared with existing change-point detection methods.

  6. Linear and kernel methods for multivariate change detection

    DEFF Research Database (Denmark)

    Canty, Morton J.; Nielsen, Allan Aasbjerg

    2012-01-01

    ), as well as maximum autocorrelation factor (MAF) and minimum noise fraction (MNF) analyses of IR-MAD images, both linear and kernel-based (nonlinear), may further enhance change signals relative to no-change background. IDL (Interactive Data Language) implementations of IR-MAD, automatic radiometric...... normalization, and kernel PCA/MAF/MNF transformations are presented that function as transparent and fully integrated extensions of the ENVI remote sensing image analysis environment. The train/test approach to kernel PCA is evaluated against a Hebbian learning procedure. Matlab code is also available...... that allows fast data exploration and experimentation with smaller datasets. New, multiresolution versions of IR-MAD that accelerate convergence and that further reduce no-change background noise are introduced. Computationally expensive matrix diagonalization and kernel image projections are programmed...

  7. Auditory cortical change detection in adults with Asperger syndrome.

    Science.gov (United States)

    Lepistö, Tuulia; Nieminen-von Wendt, Taina; von Wendt, Lennart; Näätänen, Risto; Kujala, Teija

    2007-03-06

    The present study investigated whether auditory deficits reported in children with Asperger syndrome (AS) are also present in adulthood. To this end, event-related potentials (ERPs) were recorded from adults with AS for duration, pitch, and phonetic changes in vowels, and for acoustically matched non-speech stimuli. These subjects had enhanced mismatch negativity (MMN) amplitudes particularly for pitch and duration deviants, indicating enhanced sound-discrimination abilities. Furthermore, as reflected by the P3a, their involuntary orienting was enhanced for changes in non-speech sounds, but tended to be deficient for changes in speech sounds. The results are consistent with those reported earlier in children with AS, except for the duration-MMN, which was diminished in children and enhanced in adults.

  8. Changes According to Incubation Periods in Some Microbiological Characteristics at Soil Samples of Some Soil Series from the Gelemen Agricultural Administration

    OpenAIRE

    KARA, Emine Erman

    1998-01-01

    Changes according to incubation periods in some microbiological characteristics at soil samples of soil series from Gelemen Agricultural Administraction were investigated in this study. The results show that bacteria, actinomycet had values in the first periods of incubation (30ºC and field capacity) and in the following periods increased. However, fungus population changed depending upon series properties and reached maximum values 24th and 32th days after the beginning of incubation. During...

  9. Limited scleroderma and early detection of visceral changes

    International Nuclear Information System (INIS)

    Kalyuzhnaya, L.D.; Potsibina, V.V.; Stychinskaya, L.P.; Turik, N.V.

    1989-01-01

    The state of liver, kidneys, osteoarticular apparatus at the early stages of development of limited scleroderma and with the exclusion of visceral changes on the basis of clinical-laboratory studies is investigated. 11 patients with scleroderma in the age of 7-18 years were examined. Osteoscintigraphy with 99m TC-phosphone and dynamic scintigraphy of kidneys without additional introduction of RF, and hepatocholecyctoscintigraphy with 99m tc-HIPA of the patients were realized. The conclusion is made that radionuclide investigation methods permit to reveal various visceral changes, which are not recognizable by clinical methods

  10. Nonexplicit change detection in complex dynamic settings: what eye movements reveal.

    Science.gov (United States)

    Vachon, François; Vallières, Benoît R; Jones, Dylan M; Tremblay, Sébastien

    2012-12-01

    We employed a computer-controlled command-and-control (C2) simulation and recorded eye movements to examine the extent and nature of the inability to detect critical changes in dynamic displays when change detection is implicit (i.e., requires no explicit report) to the operator's task. Change blindness-the failure to notice significant changes to a visual scene-may have dire consequences on performance in C2 and surveillance operations. Participants performed a radar-based risk-assessment task involving multiple subtasks. Although participants were not required to explicitly report critical changes to the operational display, change detection was critical in informing decision making. Participants' eye movements were used as an index of visual attention across the display. Nonfixated (i.e., unattended) changes were more likely to be missed than were fixated (i.e., attended) changes, supporting the idea that focused attention is necessary for conscious change detection. The finding of significant pupil dilation for changes undetected but fixated suggests that attended changes can nonetheless be missed because of a failure of attentional processes. Change blindness in complex dynamic displays takes the form of failures in establishing task-appropriate patterns of attentional allocation. These findings have implications in the design of change-detection support tools for dynamic displays and work procedure in C2 and surveillance.

  11. The Japanese attitude towards nuclear power generation. Changes as seen through time series

    International Nuclear Information System (INIS)

    Kitada, Atsuko; Hayashi, Chikio

    1999-01-01

    This study is intended to determine people's attitudes toward nuclear power generation, shedding light on the changed and unchanged structures of attitudes by comparing data on nuclear power generation for 1993 and 1998. Although some nuclear facility accidents occurred during the last five years, public attitudes toward nuclear power generation remain almost the same. For the utilization of nuclear power generation, there was a slight increase in passive affirmation. The percentage of active affirmation was less than 10 percent, but if passive affirmation is included a high percentage exceeding 70 percent acknowledged the utilization of nuclear power. It was found that people's attitudes toward the utilization of nuclear power became slightly more positive in 1998 than in 1993. The difference was found in the general measure of attitudes based on many questions about nuclear power generation, and in the importance and the utility of nuclear power generation including the purpose of nuclear power generation. People are not conscious of the anxiety about nuclear power generation in ordinary life. However, when people were made to think about nuclear power generation, the degree of anxiety increases even if provided with data that prove its safety. On the other hand, it was revealed that the degree of anxiety about nuclear facility accidents remains the same in the last five years, that is, it has not increased, although a growing interest in the disposal and treatment of radioactive wastes was seen. As a result of a comparison of the structure of attitudes, based on the study by Hayashi 1994, it was found that the group that had no interest in nuclear power generation offered the most noticeable features in answering pattern in both 1993 and 1998. Moreover, it was found also that the latter group of respondents were characterized by a little opportunity to have information. A similarity in the relationship between people's attitudes toward nuclear power generation

  12. Attentional capture by irrelevant transients leads to perceptual errors in a competitive change detection task

    Directory of Open Access Journals (Sweden)

    Daniel eSchneider

    2012-05-01

    Full Text Available Theories on visual change detection imply that attention is a necessary but not sufficient prerequisite for aware perception. Misguidance of attention due to salient irrelevant distractors can therefore lead to severe deficits in change detection. The present study investigates the mechanisms behind such perceptual errors and their relation to error processing on higher cognitive levels. Participants had to detect a luminance change that occasionally occurred simultaneously with an irrelevant orientation change in the opposite hemi-field (conflict condition. By analyzing event-related potentials in the EEG separately in those error prone conflict trials for correct and erroneous change detection, we demonstrate that only correct change detection was associated with the allocation of attention to the relevant luminance change. Erroneous change detection was associated with an initial capture of attention towards the irrelevant orientation change in the N1 time window and a lack of subsequent target selection processes (N2pc. Errors were additionally accompanied by an increase of the fronto-central N2 and a kind of error negativity (Ne or ERN, which, however, peaked prior to the response. These results suggest that a strong perceptual conflict by salient distractors can disrupt the further processing of relevant information and thus affect its aware perception. Yet, it does not impair higher cognitive processes for conflict and error detection, indicating that these processes are independent from awareness.

  13. Detecting Forest Cover and Ecosystem Service Change Using ...

    African Journals Online (AJOL)

    user

    Mpigi, than in Butambala by 5.99%, disturbed forest was 3%, farm land ... climate change impacts on ecosystem services requires more attention and ... While these conceptual models usually assume relatively a causal-effect ... images with relatively low cloud cover or free-cloud imagery during the time period of interest.

  14. Detecting changes in insect herbivore communities along a pollution gradient

    International Nuclear Information System (INIS)

    Eatough Jones, Michele; Paine, Timothy D.

    2006-01-01

    The forests surrounding the urban areas of the Los Angeles basin are impacted by ozone and nitrogen pollutants arising from urban areas. We examined changes in the herbivore communities of three prominent plant species (ponderosa pine, California black oak and bracken fern) at six sites along an air pollution gradient. Insects were extracted from foliage samples collected in spring, as foliage reached full expansion. Community differences were evaluated using total herbivore abundance, richness, Shannon-Weiner diversity, and discriminant function analysis. Even without conspicuous changes in total numbers, diversity or richness of herbivores, herbivore groups showed patterns of change that followed the air pollution gradient that were apparent through discriminant function analysis. For bracken fern and oak, chewing insects were more dominant at high pollution sites. Oak herbivore communities showed the strongest effect. These changes in herbivore communities may affect nutrient cycling in forest systems. - Differences in insect herbivore communities were associated with an ambient air pollution gradient in the mixed conifer forest outside the Los Angeles area

  15. Detecting areal changes in tidal flats after sea dike construction ...

    Indian Academy of Sciences (India)

    The main objective of this study was to estimate changes in the area of tidal flats that occurred after sea dike construction on the western coast of South Korea using Landsat-TM images. Applying the ISODATA method of unsupervised classification for Landsat-TM images, the tidal flats were identified, and the resulting areas ...

  16. Detecting changes in insect herbivore communities along a pollution gradient

    Energy Technology Data Exchange (ETDEWEB)

    Eatough Jones, Michele [Department of Entomology, University of California Riverside, Riverside, CA 92521 (United States)]. E-mail: michele.eatough@ucr.edu; Paine, Timothy D. [Department of Entomology, University of California Riverside, Riverside, CA 92521 (United States)]. E-mail: timothy.paine@ucr.edu

    2006-10-15

    The forests surrounding the urban areas of the Los Angeles basin are impacted by ozone and nitrogen pollutants arising from urban areas. We examined changes in the herbivore communities of three prominent plant species (ponderosa pine, California black oak and bracken fern) at six sites along an air pollution gradient. Insects were extracted from foliage samples collected in spring, as foliage reached full expansion. Community differences were evaluated using total herbivore abundance, richness, Shannon-Weiner diversity, and discriminant function analysis. Even without conspicuous changes in total numbers, diversity or richness of herbivores, herbivore groups showed patterns of change that followed the air pollution gradient that were apparent through discriminant function analysis. For bracken fern and oak, chewing insects were more dominant at high pollution sites. Oak herbivore communities showed the strongest effect. These changes in herbivore communities may affect nutrient cycling in forest systems. - Differences in insect herbivore communities were associated with an ambient air pollution gradient in the mixed conifer forest outside the Los Angeles area.

  17. Ontology Versioning and Change Detection on the Web

    NARCIS (Netherlands)

    Klein, Michel; Fensel, Dieter; Kiryakov, Atanas; Ognyanov, Damyan

    2002-01-01

    To effectively use ontologies on the Web, it is essential that changes in ontologies are managed well. This paper analyzes the topic of ontology versioning in the context of the Web by looking at the characteristics of the version relation between ontologies and at the identification of online

  18. A New Statistic for Detection of Aberrant Answer Changes

    Science.gov (United States)

    Sinharay, Sandip; Duong, Minh Q.; Wood, Scott W.

    2017-01-01

    As noted by Fremer and Olson, analysis of answer changes is often used to investigate testing irregularities because the analysis is readily performed and has proven its value in practice. Researchers such as Belov, Sinharay and Johnson, van der Linden and Jeon, van der Linden and Lewis, and Wollack, Cohen, and Eckerly have suggested several…

  19. Detecting forest cover and ecosystem service change using ...

    African Journals Online (AJOL)

    Natural forests in Uganda have experienced both spatial and temporal modifications from different drivers which need to be monitored to assess the impacts of such changes on ecosystems and prevent related risks of reduction in ecosystem service benefits. Ground investigations may be complex because of dual ...

  20. Detecting geomorphic processes and change with high resolution topographic data

    Science.gov (United States)

    Mudd, Simon; Hurst, Martin; Grieve, Stuart; Clubb, Fiona; Milodowski, David; Attal, Mikael

    2016-04-01

    The first global topographic dataset was released in 1996, with 1 km grid spacing. It is astonishing that in only 20 years we now have access to tens of thousands of square kilometres of LiDAR data at point densities greater than 5 points per square meter. This data represents a treasure trove of information that our geomorphic predecessors could only dream of. But what are we to do with this data? Here we explore the potential of high resolution topographic data to dig deeper into geomorphic processes across a wider range of landscapes and using much larger spatial coverage than previously possible. We show how this data can be used to constrain sediment flux relationships using relief and hillslope length, and how this data can be used to detect landscape transience. We show how the nonlinear sediment flux law, proposed for upland, soil mantled landscapes by Roering et al. (1999) is consistent with a number of topographic tests. This flux law allows us to predict how landscapes will respond to tectonic forcing, and we show how these predictions can be used to detect erosion rate perturbations across a range of tectonic settings.

  1. Upper gastrointestinal series after Roux-en-Y gastric bypass for morbid obesity: effectiveness in leakage detection. a systematic review of the literature.

    Science.gov (United States)

    Quartararo, Giovanni; Facchiano, Enrico; Scaringi, Stefano; Liscia, Gadiel; Lucchese, Marcello

    2014-07-01

    The aim of this study is to evaluate the results of routine and selective postoperative upper gastrointestinal series (UGIS) after Roux-en-Y gastric bypass (RYGB) for morbid obesity in different published series to assessing its utility and cost-effectiveness. A search in PubMed's MEDLINE was performed for English-spoken articles published from January 2002 to December 2012. Keywords used were upper GI series, RYGB, and obesity. Only cases of anastomotic leaks were considered. A total of 22 studies have been evaluated, 15 recommended a selective use of postoperative UGIS. No differences in leakage detection or in clinical benefit between routine and selective approaches were found. Tachycardia and respiratory distress represent the best criteria to perform UGIS for early diagnosis of anastomotic leak after a RYGB.

  2. Using non-time-series to determine supply elasticity: how far do prices change the Hubbert curve?

    International Nuclear Information System (INIS)

    Reynolds, D.B.

    2002-01-01

    An important concern of OPEC's work is to be able to understand how much supply of oil exists in different countries, in order to help better conserve oil. This paper extends M. King Hubbert's oil production and discovery forecasting model (Hubbert, 1962), using a non-time-series cumulative discovery and production quadratic Hubbert curve and structural shift variables to model technology and regulation changes. The model can be used to determine better world oil supplies. Price is tested, to see how powerful it is for increasing or decreasing oil supply. Using a trend of cumulative production, instead of time, will help to better fix the supply elasticity with respect to price, which is shown to be very inelastic. An interesting question is whether cumulative discovery or production constitutes an I(2) variable. This paper explains that they are not I(2) variables. (Author)

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

    Science.gov (United States)

    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

  4. Change Detection and Sustainable Policies of Mangrove Forests

    DEFF Research Database (Denmark)

    Malik, Abdul

    Deforestation and degradation of mangrove forests have become one of the main issues for coastal ecosystems in Indonesia and elsewhere in Southeast Asia. Over the past decades, over-exploitation of timber, firewood, charcoal production, housing materials, and commercial logging and conversion...... into other forms of land use such as agriculture, settlement, mining, and especially aquaculture have led to a reduction in the extent of mangrove forests and their biodiversity, which has had significant effects on local communities. This thesis addresses mangrove forest change over the past 33 years...... and the environmental and socioeconomic consequences of the observed changes for communities living around mangrove areas. In this connection, the effects of mangrove exploitation on biodiversity and ecosystem services, including forestry and fishery products, are explored. Finally, the total economic value...

  5. Linear and kernel methods for multi- and hypervariate change detection

    DEFF Research Database (Denmark)

    Nielsen, Allan Aasbjerg; Canty, Morton J.

    2010-01-01

    . Principal component analysis (PCA) as well as maximum autocorrelation factor (MAF) and minimum noise fraction (MNF) analyses of IR-MAD images, both linear and kernel-based (which are nonlinear), may further enhance change signals relative to no-change background. The kernel versions are based on a dual...... formulation, also termed Q-mode analysis, in which the data enter into the analysis via inner products in the Gram matrix only. In the kernel version the inner products of the original data are replaced by inner products between nonlinear mappings into higher dimensional feature space. Via kernel substitution......, also known as the kernel trick, these inner products between the mappings are in turn replaced by a kernel function and all quantities needed in the analysis are expressed in terms of the kernel function. This means that we need not know the nonlinear mappings explicitly. Kernel principal component...

  6. A New Study of Two Divergence Metrics for Change Detection in Data Streams

    KAUST Repository

    Qahtan, Abdulhakim Ali Ali; Wang, Suojin; Carroll, Raymond; Zhang, Xiangliang

    2014-01-01

    Streaming data are dynamic in nature with frequent changes. To detect such changes, most methods measure the difference between the data distributions in a current time window and a reference window. Divergence metrics and density estimation are required to measure the difference between the data distributions. Our study shows that the Kullback-Leibler (KL) divergence, the most popular metric for comparing distributions, fails to detect certain changes due to its asymmetric property and its dependence on the variance of the data. We thus consider two metrics for detecting changes in univariate data streams: a symmetric KL-divergence and a divergence metric measuring the intersection area of two distributions. The experimental results show that these two metrics lead to more accurate results in change detection than baseline methods such as Change Finder and using conventional KL-divergence.

  7. A New Study of Two Divergence Metrics for Change Detection in Data Streams

    KAUST Repository

    Qahtan, Abdulhakim Ali Ali

    2014-08-01

    Streaming data are dynamic in nature with frequent changes. To detect such changes, most methods measure the difference between the data distributions in a current time window and a reference window. Divergence metrics and density estimation are required to measure the difference between the data distributions. Our study shows that the Kullback-Leibler (KL) divergence, the most popular metric for comparing distributions, fails to detect certain changes due to its asymmetric property and its dependence on the variance of the data. We thus consider two metrics for detecting changes in univariate data streams: a symmetric KL-divergence and a divergence metric measuring the intersection area of two distributions. The experimental results show that these two metrics lead to more accurate results in change detection than baseline methods such as Change Finder and using conventional KL-divergence.

  8. Detecting climate-change responses of plants and soil organic matter using isotopomers

    Science.gov (United States)

    Schleucher, Jürgen; Ehlers, Ina; Segura, Javier; Haei, Mahsa; Augusti, Angela; Köhler, Iris; Zuidema, Pieter; Nilsson, Mats; Öquist, Mats

    2015-04-01

    Responses of vegetation and soils to environmental changes will strongly influence future climate, and responses on century time scales are most important for feedbacks on the carbon cycle, climate models, prediction of crop productivity, and for adaptation to climate change. That plants respond to increasing CO2 on century time scales has been proven by changes in stomatal index, but very little is known beyond this. In soil, the complexity of soil organic matter (SOM) has hampered a sufficient understanding of the temperature sensitivity of SOM turnover. Here we present new stable isotope methodology that allows detecting shifts in metabolism on long time scales, and elucidating SOM turnover on the molecular level. Compound-specific isotope analysis measures isotope ratios of defined metabolites, but as average of the entire molecule. Here we demonstrate how much more detailed information can be obtained from analyses of intramolecular distributions of stable isotopes, so-called isotopomer abundances. As key tool, we use nuclear magnetic resonance (NMR) spectroscopy, which allows detecting isotope abundance with intramolecular resolution and without risk for isotope fractionation during analysis. Enzyme isotope fractionations create non-random isotopomer patterns in biochemical metabolites. At natural isotope abundance, these patterns continuously store metabolic information. We present a strategy how these patterns can be used as to extract signals on plant physiology, climate variables, and their interactions. Applied in retrospective analyses to herbarium samples and tree-ring series, we detect century-time-scale metabolic changes in response to increasing atmospheric CO2, with no evidence for acclimatory reactions by the plants. In trees, the increase in photosynthesis expected from increasing CO2 ("CO2 fertilization) was diminished by increasing temperatures, which resolves the discrepancy between expected increases in photosynthesis and commonly observed

  9. The Distributed Biological Observatory (DBO): A Change Detection Array in the Pacific Arctic Region

    Science.gov (United States)

    Grebmeier, J. M.; Moore, S. E.; Cooper, L. W.; Frey, K. E.; Pickart, R. S.

    2012-12-01

    The Pacific region of the Arctic Ocean is experiencing major reductions in seasonal sea ice extent and increases in sea surface temperatures. One of the key uncertainties in this region is how the marine ecosystem will respond to seasonal shifts in the timing of spring sea ice retreat and/or delays in fall sea ice formation. Climate changes are likely to result in shifts in species composition and abundance, northward range expansions, and changes in lower trophic level productivity that can directly cascade and affect the life cycles of higher trophic level organisms. The developing Distributed Biological Observatory (DBO) is composed of focused biological and oceanographic sampling at biological "hot spot" sites for lower and higher trophic organisms on a latitudinal S-to-N array. The DBO is being developed by an international consortium of scientists in the Pacific Arctic as a change detection array to systematically track the broad biological response to sea ice retreat and associated environmental change. Coordinated ship-based observations over various seasons, together with satellite and mooring data collections at the designated sites, can provide an early detection system for biological and ecosystem response to climate warming. The data documenting the importance of these ecosystem "hotspots" provide a growing marine time-series from the northern Bering Sea to Barrow Canyon at the boundary of the Chukchi and Beaufort seas. Results from these studies show spatial changes in carbon production and export to the sediments as indicated by infaunal community composition and biomass, shifts in sediment grain size on a S-to-N latitudinal gradient, and range extensions for lower trophic levels and further northward migration of higher trophic organisms, such as gray whales. There is also direct evidence of negative impacts on ice dependent species, such as walrus and polar bears. As a ramp up to a fully operational observatory, hydrographic transects and select

  10. Detecting discontinuities in GNSS coordinate time series with STARS: case study, the Bologna and Medicina GPS sites

    Science.gov (United States)

    Bruni, S.; Zerbini, Susanna; Raicich, F.; Errico, M.; Santi, E.

    2014-12-01

    Global navigation satellite systems (GNSS) data are a fundamental source of information for achieving a better understanding of geophysical and climate-related phenomena. However, discontinuities in the coordinate time series might be a severe limiting factor for the reliable estimate of long-term trends. A methodological approach has been adapted from Rodionov (Geophys Res Lett 31:L09204, 2004; Geophys Res Lett 31:L12707, 2006) and from Rodionov and Overland (J Marine Sci 62:328-332, 2005) to identify both the epoch of occurrence and the magnitude of jumps corrupting GNSS data sets without any a priori information on these quantities. The procedure is based on the Sequential t test Analysis of Regime Shifts (STARS) (Rodionov in Geophys Res Lett 31:L09204, 2004). The method has been tested against a synthetic data set characterized by typical features exhibited by real GNSS time series, such as linear trend, seasonal cycle, jumps, missing epochs and a combination of white and flicker noise. The results show that the offsets identified by the algorithm are split into 48 % of true-positive, 28 % of false-positive and 24 % of false-negative events. The procedure has then been applied to GPS coordinate time series of stations located in the southeastern Po Plain, in Italy. The series span more than 15 years and are affected by offsets of different nature. The methodology proves to be effective, as confirmed by the comparison between the corrected GPS time series and those obtained by other observation techniques.

  11. Information Foraging and Change Detection for Automated Science Exploration

    Science.gov (United States)

    Furlong, P. Michael; Dille, Michael

    2016-01-01

    This paper presents a new algorithm for autonomous on-line exploration in unknown environments. The objective is to free remote scientists from possibly-infeasible extensive preliminary site investigation prior to sending robotic agents. We simulate a common exploration task for an autonomous robot sampling the environment at various locations and compare performance against simpler control strategies. An extension is proposed and evaluated that further permits operation in the presence of environmental variability in which the robot encounters a change in the distribution underlying sampling targets. Experimental results indicate a strong improvement in performance across varied parameter choices for the scenario.

  12. Detecting Arctic Climate Change Using Koeppen Climate Classification

    Energy Technology Data Exchange (ETDEWEB)

    Wang, M. [Joint Institute for the Study of Atmosphere and Oceans, University of Washington, Seattle, Washington (United States); Overland, J.E. [NOAA/Pacific Marine Environmental Laboratory, Sand Point Way NE, Seattle, Washington (United States)

    2004-11-01

    Ecological impacts of the recent warming trend in the Arctic are already noted as changes in tree line and a decrease in tundra area with the replacement of ground cover by shrubs in northern Alaska and several locations in northern Eurasia. The potential impact of vegetation changes to feedbacks on the atmospheric climate system is substantial because of the large land area impacted and the multi-year persistence of the vegetation cover. Satellite NDVI estimates beginning in 1981 and the Koeppen climate classification, which relates surface types to monthly mean air temperatures from 1901 onward, track these changes on an Arctic-wide basis. Temperature fields from the NCEP/NCAR reanalysis and CRU analysis serve as proxy for vegetation cover over the century. A downward trend in the coverage of tundra group for the first 40 yr of the twentieth century was followed by two increases during 1940s and early 1960s, and then a rapid decrease in the last 20 yr. The decrease of tundra group in the 1920-40 period was localized, mostly over Scandinavia; whereas the decrease since 1990 is primarily pan-Arctic, but largest in NW Canada, and eastern and coastal Siberia. The decrease in inferred tundra coverage from 1980 to 2000 was 1.4 x 106 km{sup 2}, or about a 20% reduction in tundra area based on the CRU analyses. This rate of decrease is confirmed by the NDVI data. These tundra group changes in the last 20 yr are accompanied by increase in the area of both the boreal and temperate groups. During the tundra group decrease in the first half of the century boreal group area also decreased while temperate group area increased. The calculated minimum coverage of tundra group from both the Koeppen classification and NDVI indicates that the impact of warming on the spatial coverage of the tundra group in the 1990s is the strongest in the century, and will have multi-decadal consequences for the Arctic.

  13. In search of functional association from time-series microarray data based on the change trend and level of gene expression

    Directory of Open Access Journals (Sweden)

    Zeng An-Ping

    2006-02-01

    Full Text Available Abstract Background The increasing availability of time-series expression data opens up new possibilities to study functional linkages of genes. Present methods used to infer functional linkages between genes from expression data are mainly based on a point-to-point comparison. Change trends between consecutive time points in time-series data have been so far not well explored. Results In this work we present a new method based on extracting main features of the change trend and level of gene expression between consecutive time points. The method, termed as trend correlation (TC, includes two major steps: 1, calculating a maximal local alignment of change trend score by dynamic programming and a change trend correlation coefficient between the maximal matched change levels of each gene pair; 2, inferring relationships of gene pairs based on two statistical extraction procedures. The new method considers time shifts and inverted relationships in a similar way as the local clustering (LC method but the latter is merely based on a point-to-point comparison. The TC method is demonstrated with data from yeast cell cycle and compared with the LC method and the widely used Pearson correlation coefficient (PCC based clustering method. The biological significance of the gene pairs is examined with several large-scale yeast databases. Although the TC method predicts an overall lower number of gene pairs than the other two methods at a same p-value threshold, the additional number of gene pairs inferred by the TC method is considerable: e.g. 20.5% compared with the LC method and 49.6% with the PCC method for a p-value threshold of 2.7E-3. Moreover, the percentage of the inferred gene pairs consistent with databases by our method is generally higher than the LC method and similar to the PCC method. A significant number of the gene pairs only inferred by the TC method are process-identity or function-similarity pairs or have well-documented biological

  14. Supporting dynamic change detection: using the right tool for the task.

    Science.gov (United States)

    Vallières, Benoît R; Hodgetts, Helen M; Vachon, François; Tremblay, Sébastien

    2016-01-01

    Detecting task-relevant changes in a visual scene is necessary for successfully monitoring and managing dynamic command and control situations. Change blindness-the failure to notice visual changes-is an important source of human error. Change History EXplicit (CHEX) is a tool developed to aid change detection and maintain situation awareness; and in the current study we test the generality of its ability to facilitate the detection of changes when this subtask is embedded within a broader dynamic decision-making task. A multitasking air-warfare simulation required participants to perform radar-based subtasks, for which change detection was a necessary aspect of the higher-order goal of protecting one's own ship. In this task, however, CHEX rendered the operator even more vulnerable to attentional failures in change detection and increased perceived workload. Such support was only effective when participants performed a change detection task without concurrent subtasks. Results are interpreted in terms of the NSEEV model of attention behavior (Steelman, McCarley, & Wickens, Hum. Factors 53:142-153, 2011; J. Exp. Psychol. Appl. 19:403-419, 2013), and suggest that decision aids for use in multitasking contexts must be designed to fit within the available workload capacity of the user so that they may truly augment cognition.

  15. Change Detection of High-Resolution Remote Sensing Images Based on Adaptive Fusion of Multiple Features

    Science.gov (United States)

    Wang, G. H.; Wang, H. B.; Fan, W. F.; Liu, Y.; Chen, C.

    2018-04-01

    In view of the traditional change detection algorithm mainly depends on the spectral information image spot, failed to effectively mining and fusion of multi-image feature detection advantage, the article borrows the ideas of object oriented analysis proposed a multi feature fusion of remote sensing image change detection algorithm. First by the multi-scale segmentation of image objects based; then calculate the various objects of color histogram and linear gradient histogram; utilizes the color distance and edge line feature distance between EMD statistical operator in different periods of the object, using the adaptive weighted method, the color feature distance and edge in a straight line distance of combination is constructed object heterogeneity. Finally, the curvature histogram analysis image spot change detection results. The experimental results show that the method can fully fuse the color and edge line features, thus improving the accuracy of the change detection.

  16. Spatiotemporal Change Detection Using Landsat Imagery: the Case Study of Karacabey Flooded Forest, Bursa, Turkey

    Science.gov (United States)

    Akay, A. E.; Gencal, B.; Taş, İ.

    2017-11-01

    This short paper aims to detect spatiotemporal detection of land use/land cover change within Karacabey Flooded Forest region. Change detection analysis applied to Landsat 5 TM images representing July 2000 and a Landsat 8 OLI representing June 2017. Various image processing tools were implemented using ERDAS 9.2, ArcGIS 10.4.1, and ENVI programs to conduct spatiotemporal change detection over these two images such as band selection, corrections, subset, classification, recoding, accuracy assessment, and change detection analysis. Image classification revealed that there are five significant land use/land cover types, including forest, flooded forest, swamp, water, and other lands (i.e. agriculture, sand, roads, settlement, and open areas). The results indicated that there was increase in flooded forest, water, and other lands, while the cover of forest and swamp decreased.

  17. Inland wetland change detection using aircraft MSS [multispectral scanner] data

    International Nuclear Information System (INIS)

    Jensen, J.R.; Ramsey, E.W.; Mackey, H.E. Jr.; Sharitz, R.R.; Christensen, E.J.

    1986-01-01

    Nontidal wetlands in a portion of the Savannah River swamp forest affected by reactor cooling water discharges were mapped using March 31, 1981 and April 29, 1985 high-resolution aircraft multispectral scanner (MSS) data. Due to the inherent distortion in the aircraft MSS data and the complex spectral characteristics of the wetland vegetation, it was necessary to implement multiple techniques in the registration and classification of the MSS imagery of the Pen Branch Delta on each date. In particular, it was necessary to use a piecewise-linear registration process over relatively small regions to perform image-to-image registration. When performing unsupervised classification, an iterative ''cluster busting'' technique was used, which simplified the cluster labeling process. These procedures allowed important wetland vegetation categories to be identified on each date. The multiple-date classification maps were then evaluated using a post-classification comparison technique yielding change classes that were of value in determining the extent of inland wetland change in this region

  18. Proliferative changes in nonpalpable breast lesions detected by mammography

    International Nuclear Information System (INIS)

    Vega, A.; Delgado, A.; Ortega, E.; Garijo, F.; Mosquera, J.; Sogo, C.; Alvarez, A.

    2000-01-01

    To analyze retrospectively the radiological findings in nonpalpable breast lesions detected by mammography that lead to the performance of surgical biopsy, resulting in a histological diagnosis of proliferative breast disease with and without atypia. From two Spanish hospitals, 421 women with 429 biopsies indicative of the presence of proliferative breast disease with and without atypia were selected out of a total of 1252 surgical biopsies in nonpalpable lesions that proved to be benign. Age, personal and familial history of breast cancer, reason for requesting the mammography and radiological findings that had indicated the need for surgical biopsy were recorded for each patient. The diagnosis was proliferative breast disease (epithelial hyperplasia) in 347 women with 354 biopsies and atypical hyperplasia in the remaining 74 women with 75 biopsies, representing 28% and 6%, respectively, of the 1252 biopsies of lesions found to be benign. In 221 of the 354 cases of epithelial hyperplasia (62%) and 45 of the 75 cases of atypical hyperplasia (60%), the presence of calcifications was the most common radiological findings leading to biopsy (p<0.05). Parenchymal distortion, with or without calcifications, was the second most common radiological sign. The histological study revealed a close relationship between these proliferative events and radial scars. Calcifications are the radiological finding that most frequently indicate the need for surgical biopsy in nonpalpable lesions that results in a diagnosis of proliferative breast disease with and without atypia. (Author) 12 refs

  19. Bivariate Genomic Footprinting Detects Changes in Transcription Factor Activity

    Directory of Open Access Journals (Sweden)

    Songjoon Baek

    2017-05-01

    Full Text Available In response to activating signals, transcription factors (TFs bind DNA and regulate gene expression. TF binding can be measured by protection of the bound sequence from DNase digestion (i.e., footprint. Here, we report that 80% of TF binding motifs do not show a measurable footprint, partly because of a variable cleavage pattern within the motif sequence. To more faithfully portray the effect of TFs on chromatin, we developed an algorithm that captures two TF-dependent effects on chromatin accessibility: footprinting and motif-flanking accessibility. The algorithm, termed bivariate genomic footprinting (BaGFoot, efficiently detects TF activity. BaGFoot is robust to different accessibility assays (DNase-seq, ATAC-seq, all examined peak-calling programs, and a variety of cut bias correction approaches. BaGFoot reliably predicts TF binding and provides valuable information regarding the TFs affecting chromatin accessibility in various biological systems and following various biological events, including in cases where an absolute footprint cannot be determined.

  20. Detecting change in dynamic process systems with immunocomputing

    Energy Technology Data Exchange (ETDEWEB)

    Yang, X.; Aldrich, C.; Maree, C. [University of Stellenbosch, Stellenbosch (South Africa). Dept. of Process Engineering

    2007-02-15

    The natural immune system is an adaptive distributed pattern recognition system with several functional components designed for recognition, memory acquisition, diversity and self-regulation. In artificial immune systems, some of these characteristics are exploited in order to design computational systems capable of detecting novel patterns or the anomalous behaviour of a system in some sense. Despite their obvious promise in the application of fault diagnostic systems in process engineering, their potential remains largely unexplored in this regard. In this paper, the application of real-valued negative selection algorithms to simulated and real-world systems is considered. These algorithms deal with the self-nonself discrimination problem in immunocomputing, where normal process behaviour is coded as the self and any deviations from normal behaviour is encoded as nonself. The case studies have indicated that immunocomputing based on negative selection can provide competitive options for fault diagnosis in nonlinear process systems, but further work is required on large systems characterized by many variables.