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...

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...

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.

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...

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.

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.

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

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.

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.

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

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.

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...

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

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...

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

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.

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.

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.

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...

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.

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

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.

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.

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

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…

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.

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...

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.

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.

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.

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.

A Hybrid Change Detection Approach for Damage Detection and Recovery Monitoring

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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

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

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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.

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

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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

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

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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.

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

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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.

Short-term change detection for UAV video

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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

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

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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.

Time Series Outlier Detection Based on Sliding Window Prediction

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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.

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.

Mackenzie River Delta morphological change based on Landsat time series

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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

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).

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

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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

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.

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

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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.

LANDSAT-8 OPERATIONAL LAND IMAGER CHANGE DETECTION ANALYSIS

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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.

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...

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

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.

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...

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

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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

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

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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

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...

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

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

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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.

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.

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...

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.

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...

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.

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

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.

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.

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

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....

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.

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...

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

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...

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...

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.

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.

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

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

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.

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

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)

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...

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.

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.

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.

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.

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.

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.

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...

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

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.

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.

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.

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....

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.

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.

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.

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.

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

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.

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

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

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...

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.

ESTIMATING RELIABILITY OF DISTURBANCES IN SATELLITE TIME SERIES DATA BASED ON STATISTICAL ANALYSIS

Directory of Open Access Journals (Sweden)

Z.-G. Zhou

2016-06-01

Full Text Available Normally, the status of land cover is inherently dynamic and changing continuously on temporal scale. However, disturbances or abnormal changes of land cover — caused by such as forest fire, flood, deforestation, and plant diseases — occur worldwide at unknown times and locations. Timely detection and characterization of these disturbances is of importance for land cover monitoring. Recently, many time-series-analysis methods have been developed for near real-time or online disturbance detection, using satellite image time series. However, the detection results were only labelled with “Change/ No change” by most of the present methods, while few methods focus on estimating reliability (or confidence level of the detected disturbances in image time series. To this end, this paper propose a statistical analysis method for estimating reliability of disturbances in new available remote sensing image time series, through analysis of full temporal information laid in time series data. The method consists of three main steps. (1 Segmenting and modelling of historical time series data based on Breaks for Additive Seasonal and Trend (BFAST. (2 Forecasting and detecting disturbances in new time series data. (3 Estimating reliability of each detected disturbance using statistical analysis based on Confidence Interval (CI and Confidence Levels (CL. The method was validated by estimating reliability of disturbance regions caused by a recent severe flooding occurred around the border of Russia and China. Results demonstrated that the method can estimate reliability of disturbances detected in satellite image with estimation error less than 5% and overall accuracy up to 90%.

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.

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.

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.

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.

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.

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.

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

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].

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 ...

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...

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)

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.  

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

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.

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.

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

Segmentation of time series with long-range fractal correlations

Science.gov (United States)

Bernaola-Galván, P.; Oliver, J.L.; Hackenberg, M.; Coronado, A.V.; Ivanov, P.Ch.; Carpena, P.

2012-01-01

Segmentation is a standard method of data analysis to identify change-points dividing a nonstationary time series into homogeneous segments. However, for long-range fractal correlated series, most of the segmentation techniques detect spurious change-points which are simply due to the heterogeneities induced by the correlations and not to real nonstationarities. To avoid this oversegmentation, we present a segmentation algorithm which takes as a reference for homogeneity, instead of a random i.i.d. series, a correlated series modeled by a fractional noise with the same degree of correlations as the series to be segmented. We apply our algorithm to artificial series with long-range correlations and show that it systematically detects only the change-points produced by real nonstationarities and not those created by the correlations of the signal. Further, we apply the method to the sequence of the long arm of human chromosome 21, which is known to have long-range fractal correlations. We obtain only three segments that clearly correspond to the three regions of different G + C composition revealed by means of a multi-scale wavelet plot. Similar results have been obtained when segmenting all human chromosome sequences, showing the existence of previously unknown huge compositional superstructures in the human genome. PMID:23645997

Segmentation of time series with long-range fractal correlations.

Science.gov (United States)

Bernaola-Galván, P; Oliver, J L; Hackenberg, M; Coronado, A V; Ivanov, P Ch; Carpena, P

2012-06-01

Segmentation is a standard method of data analysis to identify change-points dividing a nonstationary time series into homogeneous segments. However, for long-range fractal correlated series, most of the segmentation techniques detect spurious change-points which are simply due to the heterogeneities induced by the correlations and not to real nonstationarities. To avoid this oversegmentation, we present a segmentation algorithm which takes as a reference for homogeneity, instead of a random i.i.d. series, a correlated series modeled by a fractional noise with the same degree of correlations as the series to be segmented. We apply our algorithm to artificial series with long-range correlations and show that it systematically detects only the change-points produced by real nonstationarities and not those created by the correlations of the signal. Further, we apply the method to the sequence of the long arm of human chromosome 21, which is known to have long-range fractal correlations. We obtain only three segments that clearly correspond to the three regions of different G + C composition revealed by means of a multi-scale wavelet plot. Similar results have been obtained when segmenting all human chromosome sequences, showing the existence of previously unknown huge compositional superstructures in the human genome.

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.

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.

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

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...

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...

Homogenization of Portuguese long-term temperature data series: Lisbon, Coimbra and Porto

Directory of Open Access Journals (Sweden)

A. L. Morozova

2012-12-01

Full Text Available Three long-term temperature data series measured in Portugal were studied to detect and correct non-climatic homogeneity breaks and are now available for future studies of climate variability.

Series of monthly minimum (T_min and maximum (T_max temperatures measured in the three Portuguese meteorological stations of Lisbon (from 1856 to 2008, Coimbra (from 1865 to 2005 and Porto (from 1888 to 2001 were studied to detect and correct non-climatic breaks. These series, together with monthly series of average temperature (T_aver and temperature range (DTR derived from them, were tested in order to detect breaks, using firstly metadata, secondly a visual analysis, and thirdly four widely used homogeneity tests: von Neumann ratio test, Buishand test, standard normal homogeneity test, and Pettitt test. The homogeneity tests were used in absolute (using temperature series themselves and relative (using sea-surface temperature anomalies series obtained from HadISST2.0.0.0 close to the Portuguese coast or already corrected temperature series as reference series modes. We considered the T_min, T_max and DTR series as most informative for the detection of breaks due to the fact that T_min and T_max could respond differently to changes in position of a thermometer or other changes in the instrument's environment; T_aver series have been used mainly as control.

The homogeneity tests showed strong inhomogeneity of the original data series, which could have both internal climatic and non-climatic origins. Breaks that were identified by the last three mentioned homogeneity tests were compared with available metadata containing data such as instrument changes, changes in station location and environment, observation procedures, etc. Significant breaks (significance 95% or more that coincided with known dates of

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.

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…

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.

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.

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

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

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.

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.

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.

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.

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.

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.

Homogenising time series: beliefs, dogmas and facts

Science.gov (United States)

Domonkos, P.

2011-06-01

In the recent decades various homogenisation methods have been developed, but the real effects of their application on time series are still not known sufficiently. The ongoing COST action HOME (COST ES0601) is devoted to reveal the real impacts of homogenisation methods more detailed and with higher confidence than earlier. As a part of the COST activity, a benchmark dataset was built whose characteristics approach well the characteristics of real networks of observed time series. This dataset offers much better opportunity than ever before to test the wide variety of homogenisation methods, and analyse the real effects of selected theoretical recommendations. Empirical results show that real observed time series usually include several inhomogeneities of different sizes. Small inhomogeneities often have similar statistical characteristics than natural changes caused by climatic variability, thus the pure application of the classic theory that change-points of observed time series can be found and corrected one-by-one is impossible. However, after homogenisation the linear trends, seasonal changes and long-term fluctuations of time series are usually much closer to the reality than in raw time series. Some problems around detecting multiple structures of inhomogeneities, as well as that of time series comparisons within homogenisation procedures are discussed briefly in the study.

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)

Non-parametric characterization of long-term rainfall time series

Science.gov (United States)

Tiwari, Harinarayan; Pandey, Brij Kishor

2018-03-01

The statistical study of rainfall time series is one of the approaches for efficient hydrological system design. Identifying, and characterizing long-term rainfall time series could aid in improving hydrological systems forecasting. In the present study, eventual statistics was applied for the long-term (1851-2006) rainfall time series under seven meteorological regions of India. Linear trend analysis was carried out using Mann-Kendall test for the observed rainfall series. The observed trend using the above-mentioned approach has been ascertained using the innovative trend analysis method. Innovative trend analysis has been found to be a strong tool to detect the general trend of rainfall time series. Sequential Mann-Kendall test has also been carried out to examine nonlinear trends of the series. The partial sum of cumulative deviation test is also found to be suitable to detect the nonlinear trend. Innovative trend analysis, sequential Mann-Kendall test and partial cumulative deviation test have potential to detect the general as well as nonlinear trend for the rainfall time series. Annual rainfall analysis suggests that the maximum changes in mean rainfall is 11.53% for West Peninsular India, whereas the maximum fall in mean rainfall is 7.8% for the North Mountainous Indian region. The innovative trend analysis method is also capable of finding the number of change point available in the time series. Additionally, we have performed von Neumann ratio test and cumulative deviation test to estimate the departure from homogeneity. Singular spectrum analysis has been applied in this study to evaluate the order of departure from homogeneity in the rainfall time series. Monsoon season (JS) of North Mountainous India and West Peninsular India zones has higher departure from homogeneity and singular spectrum analysis shows the results to be in coherence with the same.

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).

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.

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. ...

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.

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.

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.

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.

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....

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....

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

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

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...

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

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.

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

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

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.

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.

Identification of flood-rich and flood-poor periods in flood series

Science.gov (United States)

Mediero, Luis; Santillán, David; Garrote, Luis

2015-04-01

Recently, a general concern about non-stationarity of flood series has arisen, as changes in catchment response can be driven by several factors, such as climatic and land-use changes. Several studies to detect trends in flood series at either national or trans-national scales have been conducted. Trends are usually detected by the Mann-Kendall test. However, the results of this test depend on the starting and ending year of the series, which can lead to different results in terms of the period considered. The results can be conditioned to flood-poor and flood-rich periods located at the beginning or end of the series. A methodology to identify statistically significant flood-rich and flood-poor periods is developed, based on the comparison between the expected sampling variability of floods when stationarity is assumed and the observed variability of floods in a given series. The methodology is applied to a set of long series of annual maximum floods, peaks over threshold and counts of annual occurrences in peaks over threshold series observed in Spain in the period 1942-2009. Mediero et al. (2014) found a general decreasing trend in flood series in some parts of Spain that could be caused by a flood-rich period observed in 1950-1970, placed at the beginning of the flood series. The results of this study support the findings of Mediero et al. (2014), as a flood-rich period in 1950-1970 was identified in most of the selected sites. References: Mediero, L., Santillán, D., Garrote, L., Granados, A. Detection and attribution of trends in magnitude, frequency and timing of floods in Spain, Journal of Hydrology, 517, 1072-1088, 2014.

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.

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.

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.

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.

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.

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.

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.

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.

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

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.

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…

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

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

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.

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.

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

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.

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.

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

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.

Dimension Reduction of Multi-Spectral Satellite Image Time Series to Improve Deforestation Monitoring

Directory of Open Access Journals (Sweden)

Meng Lu

2017-10-01

Full Text Available In recent years, sequential tests for detecting structural changes in time series have been adapted for deforestation monitoring using satellite data. The input time series of such sequential tests is typically a vegetation index (e.g., NDVI, which uses two or three bands and ignores all other bands. Being limited to a vegetation index will not benefit from the richer spectral information provided by newly launched satellites and will bring two bottle-necks for deforestation monitoring. Firstly, it is hard to select a suitable vegetation index a priori. Secondly, a single vegetation index is typically affected by seasonal signals, noise and other natural dynamics, which decrease its power for deforestation detection. A novel multispectral time series change monitoring method that combines dimension reduction methods with a sequential hypothesis test is proposed to address these limitations. For each location, the proposed method automatically chooses a “suitable” index for deforestation monitoring. To demonstrate our approach, we implemented it in two study areas: a dry tropical forest in Bolivia (time series length: 444 with strong seasonality and a moist tropical forest in Brazil (time series length: 225 with almost no seasonality. Our method significantly improves accuracy in the presence of strong seasonality, in particular the temporal lag between disturbance and its detection.

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...

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.

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.

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

Multi-temporal change image inference towards false alarms reduction for an operational photogrammetric rockfall detection system

Science.gov (United States)

Partsinevelos, Panagiotis; Kallimani, Christina; Tripolitsiotis, Achilleas

2015-06-01

Rockfall incidents affect civil security and hamper the sustainable growth of hard to access mountainous areas due to casualties, injuries and infrastructure loss. Rockfall occurrences cannot be easily prevented, whereas previous studies for rockfall multiple sensor early detection systems have focused on large scale incidents. However, even a single rock may cause the loss of a human life along transportation routes thus, it is highly important to establish methods for the early detection of small-scale rockfall incidents. Terrestrial photogrammetric techniques are prone to a series of errors leading to false alarm incidents, including vegetation, wind, and non relevant change in the scene under consideration. In this study, photogrammetric monitoring of rockfall prone slopes is established and the resulting multi-temporal change imagery is processed in order to minimize false alarm incidents. Integration of remote sensing imagery analysis techniques is hereby applied to enhance early detection of a rockfall. Experimental data demonstrated that an operational system able to identify a 10-cm rock movement within a 10% false alarm rate is technically feasible.

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.

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.

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.

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...

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.

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.

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

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.

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.

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.

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.

Monitoring Farmland Loss Caused by Urbanization in Beijing from Modis Time Series Using Hierarchical Hidden Markov Model

Science.gov (United States)

Yuan, Y.; Meng, Y.; Chen, Y. X.; Jiang, C.; Yue, A. Z.

2018-04-01

In this study, we proposed a method to map urban encroachment onto farmland using satellite image time series (SITS) based on the hierarchical hidden Markov model (HHMM). In this method, the farmland change process is decomposed into three hierarchical levels, i.e., the land cover level, the vegetation phenology level, and the SITS level. Then a three-level HHMM is constructed to model the multi-level semantic structure of farmland change process. Once the HHMM is established, a change from farmland to built-up could be detected by inferring the underlying state sequence that is most likely to generate the input time series. The performance of the method is evaluated on MODIS time series in Beijing. Results on both simulated and real datasets demonstrate that our method improves the change detection accuracy compared with the HMM-based method.

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

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%).

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...

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.

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.

Applications of multiscale change point detections to monthly stream flow and rainfall in Xijiang River in southern China, part I: correlation and variance

Science.gov (United States)

Zhu, Yuxiang; Jiang, Jianmin; Huang, Changxing; Chen, Yongqin David; Zhang, Qiang

2018-04-01

This article, as part I, introduces three algorithms and applies them to both series of the monthly stream flow and rainfall in Xijiang River, southern China. The three algorithms include (1) normalization of probability distribution, (2) scanning U test for change points in correlation between two time series, and (3) scanning F-test for change points in variances. The normalization algorithm adopts the quantile method to normalize data from a non-normal into the normal probability distribution. The scanning U test and F-test have three common features: grafting the classical statistics onto the wavelet algorithm, adding corrections for independence into each statistic criteria at given confidence respectively, and being almost objective and automatic detection on multiscale time scales. In addition, the coherency analyses between two series are also carried out for changes in variance. The application results show that the changes of the monthly discharge are still controlled by natural precipitation variations in Xijiang's fluvial system. Human activities disturbed the ecological balance perhaps in certain content and in shorter spells but did not violate the natural relationships of correlation and variance changes so far.

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.

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

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.

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.

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

A PCA-Based Change Detection Framework for Multidimensional Data Streams

KAUST Repository

Qahtan, Abdulhakim Ali Ali

2015-08-10

Detecting changes in multidimensional data streams is an important and challenging task. In unsupervised change detection, changes are usually detected by comparing the distribution in a current (test) window with a reference window. It is thus essential to design divergence metrics and density estimators for comparing the data distributions, which are mostly done for univariate data. Detecting changes in multidimensional data streams brings difficulties to the density estimation and comparisons. In this paper, we propose a framework for detecting changes in multidimensional data streams based on principal component analysis, which is used for projecting data into a lower dimensional space, thus facilitating density estimation and change-score calculations. The proposed framework also has advantages over existing approaches by reducing computational costs with an efficient density estimator, promoting the change-score calculation by introducing effective divergence metrics, and by minimizing the efforts required from users on the threshold parameter setting by using the Page-Hinkley test. The evaluation results on synthetic and real data show that our framework outperforms two baseline methods in terms of both detection accuracy and computational costs.

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).

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

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.

Quality Control Procedure Based on Partitioning of NMR Time Series

Directory of Open Access Journals (Sweden)

Michał Staniszewski

2018-03-01

Full Text Available The quality of the magnetic resonance spectroscopy (MRS depends on the stability of magnetic resonance (MR system performance and optimal hardware functioning, which ensure adequate levels of signal-to-noise ratios (SNR as well as good spectral resolution and minimal artifacts in the spectral data. MRS quality control (QC protocols and methodologies are based on phantom measurements that are repeated regularly. In this work, a signal partitioning algorithm based on a dynamic programming (DP method for QC assessment of the spectral data is described. The proposed algorithm allows detection of the change points—the abrupt variations in the time series data. The proposed QC method was tested using the simulated and real phantom data. Simulated data were randomly generated time series distorted by white noise. The real data were taken from the phantom quality control studies of the MRS scanner collected for four and a half years and analyzed by LCModel software. Along with the proposed algorithm, performance of various literature methods was evaluated for the predefined number of change points based on the error values calculated by subtracting the mean values calculated for the periods between the change-points from the original data points. The time series were checked using external software, a set of external methods and the proposed tool, and the obtained results were comparable. The application of dynamic programming in the analysis of the phantom MRS data is a novel approach to QC. The obtained results confirm that the presented change-point-detection tool can be used either for independent analysis of MRS time series (or any other or as a part of quality control.

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.

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.

Multi-complexity ensemble measures for gait time series analysis: application to diagnostics, monitoring and biometrics.

Science.gov (United States)

Gavrishchaka, Valeriy; Senyukova, Olga; Davis, Kristina

2015-01-01

Previously, we have proposed to use complementary complexity measures discovered by boosting-like ensemble learning for the enhancement of quantitative indicators dealing with necessarily short physiological time series. We have confirmed robustness of such multi-complexity measures for heart rate variability analysis with the emphasis on detection of emerging and intermittent cardiac abnormalities. Recently, we presented preliminary results suggesting that such ensemble-based approach could be also effective in discovering universal meta-indicators for early detection and convenient monitoring of neurological abnormalities using gait time series. Here, we argue and demonstrate that these multi-complexity ensemble measures for gait time series analysis could have significantly wider application scope ranging from diagnostics and early detection of physiological regime change to gait-based biometrics applications.

A KST framework for correlation network construction from time series signals

Science.gov (United States)

Qi, Jin-Peng; Gu, Quan; Zhu, Ying; Zhang, Ping

2018-04-01

A KST (Kolmogorov-Smirnov test and T statistic) method is used for construction of a correlation network based on the fluctuation of each time series within the multivariate time signals. In this method, each time series is divided equally into multiple segments, and the maximal data fluctuation in each segment is calculated by a KST change detection procedure. Connections between each time series are derived from the data fluctuation matrix, and are used for construction of the fluctuation correlation network (FCN). The method was tested with synthetic simulations and the result was compared with those from using KS or T only for detection of data fluctuation. The novelty of this study is that the correlation analyses was based on the data fluctuation in each segment of each time series rather than on the original time signals, which would be more meaningful for many real world applications and for analysis of large-scale time signals where prior knowledge is uncertain.

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)

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,…

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.

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.

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. ...

Trend analysis using non-stationary time series clustering based on the finite element method

Science.gov (United States)

Gorji Sefidmazgi, M.; Sayemuzzaman, M.; Homaifar, A.; Jha, M. K.; Liess, S.

2014-05-01

In order to analyze low-frequency variability of climate, it is useful to model the climatic time series with multiple linear trends and locate the times of significant changes. In this paper, we have used non-stationary time series clustering to find change points in the trends. Clustering in a multi-dimensional non-stationary time series is challenging, since the problem is mathematically ill-posed. Clustering based on the finite element method (FEM) is one of the methods that can analyze multidimensional time series. One important attribute of this method is that it is not dependent on any statistical assumption and does not need local stationarity in the time series. In this paper, it is shown how the FEM-clustering method can be used to locate change points in the trend of temperature time series from in situ observations. This method is applied to the temperature time series of North Carolina (NC) and the results represent region-specific climate variability despite higher frequency harmonics in climatic time series. Next, we investigated the relationship between the climatic indices with the clusters/trends detected based on this clustering method. It appears that the natural variability of climate change in NC during 1950-2009 can be explained mostly by AMO and solar activity.

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.

Predicting long-term catchment nutrient export: the use of nonlinear time series models

Science.gov (United States)

Valent, Peter; Howden, Nicholas J. K.; Szolgay, Jan; Komornikova, Magda

2010-05-01

After the Second World War the nitrate concentrations in European water bodies changed significantly as the result of increased nitrogen fertilizer use and changes in land use. However, in the last decades, as a consequence of the implementation of nitrate-reducing measures in Europe, the nitrate concentrations in water bodies slowly decrease. This causes that the mean and variance of the observed time series also changes with time (nonstationarity and heteroscedascity). In order to detect changes and properly describe the behaviour of such time series by time series analysis, linear models (such as autoregressive (AR), moving average (MA) and autoregressive moving average models (ARMA)), are no more suitable. Time series with sudden changes in statistical characteristics can cause various problems in the calibration of traditional water quality models and thus give biased predictions. Proper statistical analysis of these non-stationary and heteroscedastic time series with the aim of detecting and subsequently explaining the variations in their statistical characteristics requires the use of nonlinear time series models. This information can be then used to improve the model building and calibration of conceptual water quality model or to select right calibration periods in order to produce reliable predictions. The objective of this contribution is to analyze two long time series of nitrate concentrations of the rivers Ouse and Stour with advanced nonlinear statistical modelling techniques and compare their performance with traditional linear models of the ARMA class in order to identify changes in the time series characteristics. The time series were analysed with nonlinear models with multiple regimes represented by self-exciting threshold autoregressive (SETAR) and Markov-switching models (MSW). The analysis showed that, based on the value of residual sum of squares (RSS) in both datasets, SETAR and MSW models described the time-series better than models of the

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...

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).

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.

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.

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.

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.

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.

HESS Opinions "More efforts and scientific rigour are needed to attribute trends in flood time series"

Directory of Open Access Journals (Sweden)

Y. Hundecha

2012-05-01

Full Text Available The question whether the magnitude and frequency of floods have changed due to climate change or other drivers of change is of high interest. The number of flood trend studies is rapidly rising. When changes are detected, many studies link the identified change to the underlying causes, i.e. they attribute the changes in flood behaviour to certain drivers of change. We propose a hypothesis testing framework for trend attribution which consists of essential ingredients for a sound attribution: evidence of consistency, evidence of inconsistency, and provision of confidence statement. Further, we evaluate the current state-of-the-art of flood trend attribution. We assess how selected recent studies approach the attribution problem, and to which extent their attribution statements seem defendable. In our opinion, the current state of flood trend attribution is poor. Attribution statements are mostly based on qualitative reasoning or even speculation. Typically, the focus of flood trend studies is the detection of change, i.e. the statistical analysis of time series, and attribution is regarded as an appendix: (1 flood time series are analysed by means of trend tests, (2 if a significant change is detected, a hypothesis on the cause of change is given, and (3 explanations or published studies are sought which support the hypothesis. We believe that we need a change in perspective and more scientific rigour: detection should be seen as an integral part of the more challenging attribution problem, and detection and attribution should be placed in a sound hypothesis testing framework.

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...

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.

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

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...

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

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.

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.

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.

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....

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.

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.

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.

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.

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.

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.

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

Long time series

DEFF Research Database (Denmark)

Hisdal, H.; Holmqvist, E.; Hyvärinen, V.

Awareness that emission of greenhouse gases will raise the global temperature and change the climate has led to studies trying to identify such changes in long-term climate and hydrologic time series. This report, written by the......Awareness that emission of greenhouse gases will raise the global temperature and change the climate has led to studies trying to identify such changes in long-term climate and hydrologic time series. This report, written by the...

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)

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.

Observing climate change trends in ocean biogeochemistry: when and where.

Science.gov (United States)

Henson, Stephanie A; Beaulieu, Claudie; Lampitt, Richard

2016-04-01

Understanding the influence of anthropogenic forcing on the marine biosphere is a high priority. Climate change-driven trends need to be accurately assessed and detected in a timely manner. As part of the effort towards detection of long-term trends, a network of ocean observatories and time series stations provide high quality data for a number of key parameters, such as pH, oxygen concentration or primary production (PP). Here, we use an ensemble of global coupled climate models to assess the temporal and spatial scales over which observations of eight biogeochemically relevant variables must be made to robustly detect a long-term trend. We find that, as a global average, continuous time series are required for between 14 (pH) and 32 (PP) years to distinguish a climate change trend from natural variability. Regional differences are extensive, with low latitudes and the Arctic generally needing shorter time series (ocean surface. Our results present a quantitative framework for assessing the adequacy of current and future ocean observing networks for detection and monitoring of climate change-driven responses in the marine ecosystem. © 2016 The Authors. Global Change Biology Published by John Wiley & Sons Ltd.

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.

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.

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.

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.

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.

TOWARDS CHANGE DETECTION IN URBAN AREA BY SAR INTERFEROMETRY AND RADARGRAMMETRY

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C. Dubois

2013-04-01

Full Text Available Change detection in urban area is an active topic in remote sensing. However, well-dealt subject in optical remote sensing, this research topic is still at an early stage and needs deeper investigations and improvement in what concerns SAR and InSAR remote sensing. Due to their weather and daylight-independency, SAR sensors allow an all-time observation of the earth. This is determining in cases where rapid change detection is required after a natural – or technological – disaster. Due to the high resolution that can be achieved, the new generation of space-borne radar sensors opens up new perspectives for analysing buildings in urban areas. Moreover, due to their short revisiting cycle, they give rise to monitoring and change detection applications. In this paper, we present a concept for change detection in urban area at building level, relying only on SAR- and InSAR data. In this approach, interferometric and radargrammetric SAR data are merged in order to detect changes. Here, we present the overall workflow, the test area, the required data as well as first findings on the best-suited stereo-configurations for change detection.

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.

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

Mapping Clearances in Tropical Dry Forests Using Breakpoints, Trend, and Seasonal Components from MODIS Time Series: Does Forest Type Matter?

NARCIS (Netherlands)

Grogan, Kenneth; Pflugmacher, Dirk; Hostert, Patrick; Verbesselt, Jan; Fensholt, Rasmus

2016-01-01

Tropical environments present a unique challenge for optical time series analysis, primarily owing to fragmented data availability, persistent cloud cover and atmospheric aerosols. Additionally, little is known of whether the performance of time series change detection is affected by diverse forest

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

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)

Changing scenes: memory for naturalistic events following change blindness.

Science.gov (United States)

Mäntylä, Timo; Sundström, Anna

2004-11-01

Research on scene perception indicates that viewers often fail to detect large changes to scene regions when these changes occur during a visual disruption such as a saccade or a movie cut. In two experiments, we examined whether this relative inability to detect changes would produce systematic biases in event memory. In Experiment 1, participants decided whether two successively presented images were the same or different, followed by a memory task, in which they recalled the content of the viewed scene. In Experiment 2, participants viewed a short video, in which an actor carried out a series of daily activities, and central scenes' attributes were changed during a movie cut. A high degree of change blindness was observed in both experiments, and these effects were related to scene complexity (Experiment 1) and level of retrieval support (Experiment 2). Most important, participants reported the changed, rather than the initial, event attributes following a failure in change detection. These findings suggest that attentional limitations during encoding contribute to biases in episodic memory.

Detecting Brain State Changes via Fiber-Centered Functional Connectivity Analysis

Science.gov (United States)

Li, Xiang; Lim, Chulwoo; Li, Kaiming; Guo, Lei; Liu, Tianming

2013-01-01

Diffusion tensor imaging (DTI) and functional magnetic resonance imaging (fMRI) have been widely used to study structural and functional brain connectivity in recent years. A common assumption used in many previous functional brain connectivity studies is the temporal stationarity. However, accumulating literature evidence has suggested that functional brain connectivity is under temporal dynamic changes in different time scales. In this paper, a novel and intuitive approach is proposed to model and detect dynamic changes of functional brain states based on multimodal fMRI/DTI data. The basic idea is that functional connectivity patterns of all fiber-connected cortical voxels are concatenated into a descriptive functional feature vector to represent the brain’s state, and the temporal change points of brain states are decided by detecting the abrupt changes of the functional vector patterns via the sliding window approach. Our extensive experimental results have shown that meaningful brain state change points can be detected in task-based fMRI/DTI, resting state fMRI/DTI, and natural stimulus fMRI/DTI data sets. Particularly, the detected change points of functional brain states in task-based fMRI corresponded well to the external stimulus paradigm administered to the participating subjects, thus partially validating the proposed brain state change detection approach. The work in this paper provides novel perspective on the dynamic behaviors of functional brain connectivity and offers a starting point for future elucidation of the complex patterns of functional brain interactions and dynamics. PMID:22941508

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.

Self-adaptive change detection in streaming data with non-stationary distribution

KAUST Repository

Zhang, Xiangliang

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-stationary distribution helps to alarm the anomalies, to clean the noises, and to report the new patterns. In this paper, we employ a novel approach for detecting changes in streaming data with the purpose of improving the quality of modeling the data streams. Through observing the outliers, this approach of change detection uses a weighted standard deviation to monitor the evolution of the distribution of data streams. A cumulative statistical test, Page-Hinkley, is employed to collect the evidence of changes in distribution. The parameter used for reporting the changes is self-adaptively adjusted according to the distribution of data streams, rather than set by a fixed empirical value. The self-adaptability of the novel approach enhances the effectiveness of modeling data streams by timely catching the changes of distributions. We validated the approach on an online clustering framework with a benchmark KDDcup 1999 intrusion detection data set as well as with a real-world grid data set. The validation results demonstrate its better performance on achieving higher accuracy and lower percentage of outliers comparing to the other change detection approaches. © 2010 Springer-Verlag.

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.

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

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...

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

Quantifying Forest Spatial Pattern Trends at Multiple Extents: An Approach to Detect Significant Changes at Different Scales

Directory of Open Access Journals (Sweden)

Ludovico Frate

2014-09-01

Full Text Available We propose a procedure to detect significant changes in forest spatial patterns and relevant scales. Our approach consists of four sequential steps. First, based on a series of multi-temporal forest maps, a set of geographic windows of increasing extents are extracted. Second, for each extent and date, specific stochastic simulations that replicate real-world spatial pattern characteristics are run. Third, by computing pattern metrics on both simulated and real maps, their empirical distributions and confidence intervals are derived. Finally, multi-temporal scalograms are built for each metric. Based on cover maps (1954, 2011 with a resolution of 10 m we analyze forest pattern changes in a central Apennines (Italy reserve at multiple spatial extents (128, 256 and 512 pixels. We identify three types of multi-temporal scalograms, depending on pattern metric behaviors, describing different dynamics of natural reforestation process. The statistical distribution and variability of pattern metrics at multiple extents offers a new and powerful tool to detect forest variations over time. Similar procedures can (i help to identify significant changes in spatial patterns and provide the bases to relate them to landscape processes; (ii minimize the bias when comparing pattern metrics at a single extent and (iii be extended to other landscapes and scales.

Automated detection of multiple sclerosis lesions in serial brain MRI

International Nuclear Information System (INIS)

Llado, Xavier; Ganiler, Onur; Oliver, Arnau; Marti, Robert; Freixenet, Jordi; Valls, Laia; Vilanova, Joan C.; Ramio-Torrenta, Lluis; Rovira, Alex

2012-01-01

Multiple sclerosis (MS) is a serious disease typically occurring in the brain whose diagnosis and efficacy of treatment monitoring are vital. Magnetic resonance imaging (MRI) is frequently used in serial brain imaging due to the rich and detailed information provided. Time-series analysis of images is widely used for MS diagnosis and patient follow-up. However, conventional manual methods are time-consuming, subjective, and error-prone. Thus, the development of automated techniques for the detection and quantification of MS lesions is a major challenge. This paper presents an up-to-date review of the approaches which deal with the time-series analysis of brain MRI for detecting active MS lesions and quantifying lesion load change. We provide a comprehensive reference source for researchers in which several approaches to change detection and quantification of MS lesions are investigated and classified. We also analyze the results provided by the approaches, discuss open problems, and point out possible future trends. Lesion detection approaches are required for the detection of static lesions and for diagnostic purposes, while either quantification of detected lesions or change detection algorithms are needed to follow up MS patients. However, there is not yet a single approach that can emerge as a standard for the clinical practice, automatically providing an accurate MS lesion evolution quantification. Future trends will focus on combining the lesion detection in single studies with the analysis of the change detection in serial MRI. (orig.)

Automated detection of multiple sclerosis lesions in serial brain MRI

Energy Technology Data Exchange (ETDEWEB)

Llado, Xavier; Ganiler, Onur; Oliver, Arnau; Marti, Robert; Freixenet, Jordi [University of Girona, Computer Vision and Robotics Group, Girona (Spain); Valls, Laia [Dr. Josep Trueta University Hospital, Department of Radiology, Girona (Spain); Vilanova, Joan C. [Girona Magnetic Resonance Center, Girona (Spain); Ramio-Torrenta, Lluis [Dr. Josep Trueta University Hospital, Institut d' Investigacio Biomedica de Girona, Multiple Sclerosis and Neuroimmunology Unit, Girona (Spain); Rovira, Alex [Vall d' Hebron University Hospital, Magnetic Resonance Unit, Department of Radiology, Barcelona (Spain)

2012-08-15

Multiple sclerosis (MS) is a serious disease typically occurring in the brain whose diagnosis and efficacy of treatment monitoring are vital. Magnetic resonance imaging (MRI) is frequently used in serial brain imaging due to the rich and detailed information provided. Time-series analysis of images is widely used for MS diagnosis and patient follow-up. However, conventional manual methods are time-consuming, subjective, and error-prone. Thus, the development of automated techniques for the detection and quantification of MS lesions is a major challenge. This paper presents an up-to-date review of the approaches which deal with the time-series analysis of brain MRI for detecting active MS lesions and quantifying lesion load change. We provide a comprehensive reference source for researchers in which several approaches to change detection and quantification of MS lesions are investigated and classified. We also analyze the results provided by the approaches, discuss open problems, and point out possible future trends. Lesion detection approaches are required for the detection of static lesions and for diagnostic purposes, while either quantification of detected lesions or change detection algorithms are needed to follow up MS patients. However, there is not yet a single approach that can emerge as a standard for the clinical practice, automatically providing an accurate MS lesion evolution quantification. Future trends will focus on combining the lesion detection in single studies with the analysis of the change detection in serial MRI. (orig.)

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.

Topological data analysis of financial time series: Landscapes of crashes

Science.gov (United States)

Gidea, Marian; Katz, Yuri

2018-02-01

We explore the evolution of daily returns of four major US stock market indices during the technology crash of 2000, and the financial crisis of 2007-2009. Our methodology is based on topological data analysis (TDA). We use persistence homology to detect and quantify topological patterns that appear in multidimensional time series. Using a sliding window, we extract time-dependent point cloud data sets, to which we associate a topological space. We detect transient loops that appear in this space, and we measure their persistence. This is encoded in real-valued functions referred to as a 'persistence landscapes'. We quantify the temporal changes in persistence landscapes via their Lp-norms. We test this procedure on multidimensional time series generated by various non-linear and non-equilibrium models. We find that, in the vicinity of financial meltdowns, the Lp-norms exhibit strong growth prior to the primary peak, which ascends during a crash. Remarkably, the average spectral density at low frequencies of the time series of Lp-norms of the persistence landscapes demonstrates a strong rising trend for 250 trading days prior to either dotcom crash on 03/10/2000, or to the Lehman bankruptcy on 09/15/2008. Our study suggests that TDA provides a new type of econometric analysis, which complements the standard statistical measures. The method can be used to detect early warning signals of imminent market crashes. We believe that this approach can be used beyond the analysis of financial time series presented here.

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)

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.

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...

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.

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.

Change detection of medical images using dictionary learning techniques and PCA

Science.gov (United States)

Nika, Varvara; Babyn, Paul; Zhu, Hongmei

2014-03-01

Automatic change detection methods for identifying the changes of serial MR images taken at different times are of great interest to radiologists. The majority of existing change detection methods in medical imaging, and those of brain images in particular, include many preprocessing steps and rely mostly on statistical analysis of MRI scans. Although most methods utilize registration software, tissue classification remains a difficult and overwhelming task. Recently, dictionary learning techniques are used in many areas of image processing, such as image surveillance, face recognition, remote sensing, and medical imaging. In this paper we present the Eigen-Block Change Detection algorithm (EigenBlockCD). It performs local registration and identifies the changes between consecutive MR images of the brain. Blocks of pixels from baseline scan are used to train local dictionaries that are then used to detect changes in the follow-up scan. We use PCA to reduce the dimensionality of the local dictionaries and the redundancy of data. Choosing the appropriate distance measure significantly affects the performance of our algorithm. We examine the differences between L1 and L2 norms as two possible similarity measures in the EigenBlockCD. We show the advantages of L2 norm over L1 norm theoretically and numerically. We also demonstrate the performance of the EigenBlockCD algorithm for detecting changes of MR images and compare our results with those provided in recent literature. Experimental results with both simulated and real MRI scans show that the EigenBlockCD outperforms the previous methods. It detects clinical changes while ignoring the changes due to patient's position and other acquisition artifacts.

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...

A pdf-Free Change Detection Test Based on Density Difference Estimation.

Science.gov (United States)

Bu, Li; Alippi, Cesare; Zhao, Dongbin

2018-02-01

The ability to detect online changes in stationarity or time variance in a data stream is a hot research topic with striking implications. In this paper, we propose a novel probability density function-free change detection test, which is based on the least squares density-difference estimation method and operates online on multidimensional inputs. The test does not require any assumption about the underlying data distribution, and is able to operate immediately after having been configured by adopting a reservoir sampling mechanism. Thresholds requested to detect a change are automatically derived once a false positive rate is set by the application designer. Comprehensive experiments validate the effectiveness in detection of the proposed method both in terms of detection promptness and accuracy.

Region-Based Building Rooftop Extraction and Change Detection

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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.

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.

Optimising 4-D surface change detection: an approach for capturing rockfall magnitude–frequency

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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

Optimising 4-D surface change detection: an approach for capturing rockfall magnitude-frequency

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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

Temporal Forest Change Detection and Forest Health Assessment using Remote Sensing

International Nuclear Information System (INIS)

Ya'acob, Norsuzila; Azize, Aziean Binti Mohd; Mahmon, Nur Anis; Yusof, Azita Laily; Azmi, Nor Farhana; Mustafa, Norfazira

2014-01-01

This paper presents the detection of Angsi and Berembun Reserve Forest change for years 1996 and 2013. Forest is an important part of our ecosystem. The main function is to absorb carbon oxide and produce oxygen in their cycle of photosynthesis to maintain a balance and healthy atmosphere. However, forest changes as time changes. Some changes are necessary as to give way for economic growth. Nevertheless, it is important to monitor forest change so that deforestation and development can be planned and the balance of ecosystem is still preserved. It is important because there are number of unfavorable effects of deforestation that include environmental and economic such as erosion of soil, loss of biodiversity and climate change. The forest change detection can be studied with reference of several satellite images using remote sensing application. Forest change detection is best done with remote sensing due to large and remote study area. The objective of this project is to detect forest change over time and to compare forest health indicated by Normalized Difference Vegetation Index (NDVI) using remote sensing and image processing. The forest under study shows depletion of forest area by 12% and 100% increment of deforestation activities. The NDVI value which is associated with the forest health also shows 13% of reduction

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....

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

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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

Change Detection Analysis of Water Pollution in Coimbatore Region using Different Color Models

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Jiji, G. Wiselin; Devi, R. Naveena

2017-12-01

The data acquired through remote sensing satellites furnish facts about the land and water at varying resolutions and has been widely used for several change detection studies. Apart from the existence of many change detection methodologies and techniques, emergence of new ones continues to subsist. Existing change detection techniques exploit images that are either in gray scale or RGB color model. In this paper we introduced color models for performing change detection for water pollution. Here the polluted lakes are classified and post-classification change detection techniques are applied to RGB images and results obtained are analysed for changes to exist or not. Furthermore RGB images obtained after classification when converted to any of the two color models YCbCr and YIQ is found to produce the same results as that of the RGB model images. Thus it can be concluded that other color models like YCbCr, YIQ can be used as substitution to RGB color model for analysing change detection with regard to water pollution.

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).

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

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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.

Detecting temporal changes in acoustic scenes: The variable benefit of selective attention.

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Demany, Laurent; Bayle, Yann; Puginier, Emilie; Semal, Catherine

2017-09-01

Four experiments investigated change detection in acoustic scenes consisting of a sum of five amplitude-modulated pure tones. As the tones were about 0.7 octave apart and were amplitude-modulated with different frequencies (in the range 2-32 Hz), they were perceived as separate streams. Listeners had to detect a change in the frequency (experiments 1 and 2) or the shape (experiments 3 and 4) of the modulation of one of the five tones, in the presence of an informative cue orienting selective attention either before the scene (pre-cue) or after it (post-cue). The changes left intensity unchanged and were not detectable in the spectral (tonotopic) domain. Performance was much better with pre-cues than with post-cues. Thus, change deafness was manifest in the absence of an appropriate focusing of attention when the change occurred, even though the streams and the changes to be detected were acoustically very simple (in contrast to the conditions used in previous demonstrations of change deafness). In one case, the results were consistent with a model based on the assumption that change detection was possible if and only if attention was endogenously focused on a single tone. However, it was also found that changes resulting in a steepening of amplitude rises were to some extent able to draw attention exogenously. Change detection was not markedly facilitated when the change produced a discontinuity in the modulation domain, contrary to what could be expected from the perspective of predictive coding. Copyright © 2017 Elsevier B.V. All rights reserved.

Detection of Changes in Ground-Level Ozone Concentrations via Entropy

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Yuehua Wu

2015-04-01

Full Text Available Ground-level ozone concentration is a key indicator of air quality. Theremay exist sudden changes in ozone concentration data over a long time horizon, which may be caused by the implementation of government regulations and policies, such as establishing exhaust emission limits for on-road vehicles. To monitor and assess the efficacy of these policies, we propose a methodology for detecting changes in ground-level ozone concentrations, which consists of three major steps: data transformation, simultaneous autoregressive modelling and change-point detection on the estimated entropy. To show the effectiveness of the proposed methodology, the methodology is applied to detect changes in ground-level ozone concentration data collected in the Toronto region of Canada between June and September for the years from 1988 to 2009. The proposed methodology is also applicable to other climate data.

Synthetic generation of myocardial blood-oxygen-level-dependent MRI time series via structural sparse decomposition modeling.

Science.gov (United States)

Rusu, Cristian; Morisi, Rita; Boschetto, Davide; Dharmakumar, Rohan; Tsaftaris, Sotirios A

2014-07-01

This paper aims to identify approaches that generate appropriate synthetic data (computer generated) for cardiac phase-resolved blood-oxygen-level-dependent (CP-BOLD) MRI. CP-BOLD MRI is a new contrast agent- and stress-free approach for examining changes in myocardial oxygenation in response to coronary artery disease. However, since signal intensity changes are subtle, rapid visualization is not possible with the naked eye. Quantifying and visualizing the extent of disease relies on myocardial segmentation and registration to isolate the myocardium and establish temporal correspondences and ischemia detection algorithms to identify temporal differences in BOLD signal intensity patterns. If transmurality of the defect is of interest pixel-level analysis is necessary and thus a higher precision in registration is required. Such precision is currently not available affecting the design and performance of the ischemia detection algorithms. In this work, to enable algorithmic developments of ischemia detection irrespective to registration accuracy, we propose an approach that generates synthetic pixel-level myocardial time series. We do this by 1) modeling the temporal changes in BOLD signal intensity based on sparse multi-component dictionary learning, whereby segmentally derived myocardial time series are extracted from canine experimental data to learn the model; and 2) demonstrating the resemblance between real and synthetic time series for validation purposes. We envision that the proposed approach has the capacity to accelerate development of tools for ischemia detection while markedly reducing experimental costs so that cardiac BOLD MRI can be rapidly translated into the clinical arena for the noninvasive assessment of ischemic heart disease.

Automatic detection of lexical change: an auditory event-related potential study.

Science.gov (United States)

Muller-Gass, Alexandra; Roye, Anja; Kirmse, Ursula; Saupe, Katja; Jacobsen, Thomas; Schröger, Erich

2007-10-29

We investigated the detection of rare task-irrelevant changes in the lexical status of speech stimuli. Participants performed a nonlinguistic task on word and pseudoword stimuli that occurred, in separate conditions, rarely or frequently. Task performance for pseudowords was deteriorated relative to words, suggesting unintentional lexical analysis. Furthermore, rare word and pseudoword changes had a similar effect on the event-related potentials, starting as early as 165 ms. This is the first demonstration of the automatic detection of change in lexical status that is not based on a co-occurring acoustic change. We propose that, following lexical analysis of the incoming stimuli, a mental representation of the lexical regularity is formed and used as a template against which lexical change can be detected.

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.

Change detection in multi-temporal dual polarization Sentinel-1 data

DEFF Research Database (Denmark)

Nielsen, Allan Aasbjerg; Canty, Morton J.; 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 19 multilook, dual polarization Sentin...

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.

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.

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.

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

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

An evaluation of dynamic mutuality measurements and methods in cyclic time series

Science.gov (United States)

Xia, Xiaohua; Huang, Guitian; Duan, Na

2010-12-01

Several measurements and techniques have been developed to detect dynamic mutuality and synchronicity of time series in econometrics. This study aims to compare the performances of five methods, i.e., linear regression, dynamic correlation, Markov switching models, concordance index and recurrence quantification analysis, through numerical simulations. We evaluate the abilities of these methods to capture structure changing and cyclicity in time series and the findings of this paper would offer guidance to both academic and empirical researchers. Illustration examples are also provided to demonstrate the subtle differences of these techniques.

Detecting Abrupt Change of Streamflow at Lintong Station of Wei River

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Jingjing Fan

2013-01-01

Full Text Available According to abrupt diagnosis of runoff, two methods, that is, moving approximate entropy and moving permutation entropy, are used to analyse the abrupt year of the daily river runoff from 1961 to 2006 at Lintong station of Wei River in Loess Plateau. The runoff series are divided into 4 stages. With the analysis of hydrological characters of different stages, we find that there are abrupt changes at the three years 1972, 1983, and 2002. The result shows that moving approximate entropy and moving permutation entropy methods are useful tools for abrupt diagnosis of runoff. The attribution of abrupt change at the Lintong runoff series is primarily due to the reduced precipitation, increased water conservancy project, increased water consumption of industry and agriculture, significantly decreased groundwater table, and increased evaporation.

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.

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…

CEST ANALYSIS: AUTOMATED CHANGE DETECTION FROM VERY-HIGH-RESOLUTION REMOTE SENSING IMAGES

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M. Ehlers

2012-08-01

Full Text Available A fast detection, visualization and assessment of change in areas of crisis or catastrophes are important requirements for coordination and planning of help. Through the availability of new satellites and/or airborne sensors with very high spatial resolutions (e.g., WorldView, GeoEye new remote sensing data are available for a better detection, delineation and visualization of change. For automated change detection, a large number of algorithms has been proposed and developed. From previous studies, however, it is evident that to-date no single algorithm has the potential for being a reliable change detector for all possible scenarios. This paper introduces the Combined Edge Segment Texture (CEST analysis, a decision-tree based cooperative suite of algorithms for automated change detection that is especially designed for the generation of new satellites with very high spatial resolution. The method incorporates frequency based filtering, texture analysis, and image segmentation techniques. For the frequency analysis, different band pass filters can be applied to identify the relevant frequency information for change detection. After transforming the multitemporal images via a fast Fourier transform (FFT and applying the most suitable band pass filter, different methods are available to extract changed structures: differencing and correlation in the frequency domain and correlation and edge detection in the spatial domain. Best results are obtained using edge extraction. For the texture analysis, different 'Haralick' parameters can be calculated (e.g., energy, correlation, contrast, inverse distance moment with 'energy' so far providing the most accurate results. These algorithms are combined with a prior segmentation of the image data as well as with morphological operations for a final binary change result. A rule-based combination (CEST of the change algorithms is applied to calculate the probability of change for a particular location. CEST

Damage Detection In Laboratory Concrete Beams

DEFF Research Database (Denmark)

Brincker, Rune; Andersen, Palle; Kirkegaard, Poul Henning

1995-01-01

The aim of the investigation reported in this paper is to clarify to what extent damages in reinforced concrete can be detected by estimating changes in the vibrational properties. A series of damages were introduced by applying static load cycles of increasing magnitude to two concrete beams...

Damage Detection in Laboratory Concrete Beams

DEFF Research Database (Denmark)

Brincker, Rune; Andersen, P.; Kirkegaard, Poul Henning

The aim of the investigation reported in this paper is to clarify to what extent damages in reinforced concrete can be detected by estimating changes in the vibrational properties. A series of damages were introduced by applying static load cycles of increasing magnitude to two concrete beams...

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

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.

Simulation of TanDEM-X interferograms for urban change detection

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Welte, Amelie; Hammer, Horst; Thiele, Antje; Hinz, Stefan

2017-10-01

Damage detection after natural disasters is one of the remote sensing tasks in which Synthetic Aperture Radar (SAR) sensors play an important role. Since SAR is an active sensor, it can record images at all times of day and in all weather conditions, making it ideally suited for this task. While with the newer generation of SAR satellites such as TerraSAR-X or COSMOSkyMed amplitude change detection has become possible even for urban areas, interferometric phase change detection has not been published widely. This is mainly because of the long revisit times of common SAR sensors leading to temporal decorrelation. This situation has changed dramatically with the advent of the TanDEM-X constellation, which can create single-pass interferograms from space at very high resolutions, avoiding temporal decorrelation almost completely. In this paper the basic structures that are present for any building in InSAR phases, i.e. layover, shadow, and roof areas, are examined. Approaches for their extraction from TanDEM-X interferograms are developed using simulated SAR interferograms. The extracted features of the building signature will in the future be used for urban change detection in real TanDEM-X High Resolution Spotlight interferograms.

Stochastic change detection in uncertain nonlinear systems using reduced-order models: classification

International Nuclear Information System (INIS)

Yun, Hae-Bum; Masri, Sami F

2009-01-01

A reliable structural health monitoring methodology (SHM) is proposed to detect relatively small changes in uncertain nonlinear systems. A total of 4000 physical tests were performed using a complex nonlinear magneto-rheological (MR) damper. With the effective (or 'genuine') changes and uncertainties in the system characteristics of the semi-active MR damper, which were precisely controlled with known means and standard deviation of the input current, the tested MR damper was identified with the restoring force method (RFM), a non-parametric system identification method involving two-dimensional orthogonal polynomials. Using the identified RFM coefficients, both supervised and unsupervised pattern recognition techniques (including support vector classification and k-means clustering) were employed to detect system changes in the MR damper. The classification results showed that the identified coefficients with orthogonal basis function can be used as reliable indicators for detecting (small) changes, interpreting the physical meaning of the detected changes without a priori knowledge of the monitored system and quantifying the uncertainty bounds of the detected changes. The classification errors were analyzed using the standard detection theory to evaluate the performance of the developed SHM methodology. An optimal classifier design procedure was also proposed and evaluated to minimize type II (or 'missed') errors

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…

Adaptive Change Detection for Long-Term Machinery Monitoring Using Incremental Sliding-Window

Science.gov (United States)

Wang, Teng; Lu, Guo-Liang; Liu, Jie; Yan, Peng

2017-11-01

Detection of structural changes from an operational process is a major goal in machine condition monitoring. Existing methods for this purpose are mainly based on retrospective analysis, resulting in a large detection delay that limits their usages in real applications. This paper presents a new adaptive real-time change detection algorithm, an extension of the recent research by combining with an incremental sliding-window strategy, to handle the multi-change detection in long-term monitoring of machine operations. In particular, in the framework, Hilbert space embedding of distribution is used to map the original data into the Re-producing Kernel Hilbert Space (RKHS) for change detection; then, a new adaptive threshold strategy can be developed when making change decision, in which a global factor (used to control the coarse-to-fine level of detection) is introduced to replace the fixed value of threshold. Through experiments on a range of real testing data which was collected from an experimental rotating machinery system, the excellent detection performances of the algorithm for engineering applications were demonstrated. Compared with state-of-the-art methods, the proposed algorithm can be more suitable for long-term machinery condition monitoring without any manual re-calibration, thus is promising in modern industries.

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

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

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).

Detection and attribution of climate change at regional scale: case study of Karkheh river basin in the west of Iran

Science.gov (United States)

Zohrabi, Narges; Goodarzi, Elahe; Massah Bavani, Alireza; Najafi, Husain

2017-11-01

This research aims at providing a statistical framework for detection and attribution of climate variability and change at regional scale when at least 30 years of observation data are available. While extensive research has been done on detecting significant observed trends in hydroclimate variables and attribution to anthropogenic greenhouse gas emissions in large continents, less attention has been paid for regional scale analysis. The latter is mainly important for adaptation to climate change in different sectors including but not limited to energy, agriculture, and water resources planning and management, and it is still an open discussion in many countries including the West Asian ones. In the absence of regional climate models, an informative framework is suggested providing useful insights for policymakers. It benefits from general flexibility, not being computationally expensive, and applying several trend tests to analyze temporal variations in temperature and precipitation (gradual and step changes). The framework is implemented for a very important river basin in the west of Iran. In general, some increasing and decreasing trends of the interannual precipitation and temperature have been detected. For precipitation annual time series, a reducing step was seen around 1996 compared with the gradual change in most of the stations, which have not experience a dramatical change. The range of natural forcing is found to be ±76 % for precipitation and ±1.4 °C for temperature considering a two-dimensional diagram of precipitation and temperature anomalies from 1000-year control run of global climate model (GCM). Findings out of applying the proposed framework may provide useful insights into how to approach structural and non-structural climate change adaptation strategies from central governments.

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

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

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)

The Feasibility Evaluation of Land Use Change Detection Using GAOFEN-3 Data

Science.gov (United States)

Huang, G.; Sun, Y.; Zhao, Z.

2018-04-01

GaoFen-3 (GF-3) satellite, is the first C band and multi-polarimetric Synthetic Aperture Radar (SAR) satellite in China. In order to explore the feasibility of GF-3 satellite in remote sensing interpretation and land-use remote sensing change detection, taking Guangzhou, China as a study area, the full polarimetric image of GF-3 satellite with 8 m resolution of two temporal as the data source. Firstly, the image is pre-processed by orthorectification, image registration and mosaic, and the land-use remote sensing digital orthophoto map (DOM) in 2017 is made according to the each county. Then the classification analysis and judgment of ground objects on the image are carried out by means of ArcGIS combining with the auxiliary data and using artificial visual interpretation, to determine the area of changes and the category of change objects. According to the unified change information extraction principle to extract change areas. Finally, the change detection results are compared with 3 m resolution TerraSAR-X data and 2 m resolution multi-spectral image, and the accuracy is evaluated. Experimental results show that the accuracy of the GF-3 data is over 75 % in detecting the change of ground objects, and the detection capability of new filling soil is better than that of TerraSAR-X data, verify the detection and monitoring capability of GF-3 data to the change information extraction, also, it shows that GF-3 can provide effective data support for the remote sensing detection of land resources.

Robust Automated Image Co-Registration of Optical Multi-Sensor Time Series Data: Database Generation for Multi-Temporal Landslide Detection

Directory of Open Access Journals (Sweden)

Robert Behling

2014-03-01

Full Text Available Reliable multi-temporal landslide detection over longer periods of time requires multi-sensor time series data characterized by high internal geometric stability, as well as high relative and absolute accuracy. For this purpose, a new methodology for fully automated co-registration has been developed allowing efficient and robust spatial alignment of standard orthorectified data products originating from a multitude of optical satellite remote sensing data of varying spatial resolution. Correlation-based co-registration uses world-wide available terrain corrected Landsat Level 1T time series data as the spatial reference, ensuring global applicability. The developed approach has been applied to a multi-sensor time series of 592 remote sensing datasets covering an approximately 12,000 km2 area in Southern Kyrgyzstan (Central Asia strongly affected by landslides. The database contains images acquired during the last 26 years by Landsat (ETM, ASTER, SPOT and RapidEye sensors. Analysis of the spatial shifts obtained from co-registration has revealed sensor-specific alignments ranging between 5 m and more than 400 m. Overall accuracy assessment of these alignments has resulted in a high relative image-to-image accuracy of 17 m (RMSE and a high absolute accuracy of 23 m (RMSE for the whole co-registered database, making it suitable for multi-temporal landslide detection at a regional scale in Southern Kyrgyzstan.

Multiscale-Driven approach to detecting change in Synthetic Aperture Radar (SAR) imagery

Science.gov (United States)

Gens, R.; Hogenson, K.; Ajadi, O. A.; Meyer, F. J.; Myers, A.; Logan, T. A.; Arnoult, K., Jr.

2017-12-01

Detecting changes between Synthetic Aperture Radar (SAR) images can be a useful but challenging exercise. SAR with its all-weather capabilities can be an important resource in identifying and estimating the expanse of events such as flooding, river ice breakup, earthquake damage, oil spills, and forest growth, as it can overcome shortcomings of optical methods related to cloud cover. However, detecting change in SAR imagery can be impeded by many factors including speckle, complex scattering responses, low temporal sampling, and difficulty delineating boundaries. In this presentation we use a change detection method based on a multiscale-driven approach. By using information at different resolution levels, we attempt to obtain more accurate change detection maps in both heterogeneous and homogeneous regions. Integrated within the processing flow are processes that 1) improve classification performance by combining Expectation-Maximization algorithms with mathematical morphology, 2) achieve high accuracy in preserving boundaries using measurement level fusion techniques, and 3) combine modern non-local filtering and 2D-discrete stationary wavelet transform to provide robustness against noise. This multiscale-driven approach to change detection has recently been incorporated into the Alaska Satellite Facility (ASF) Hybrid Pluggable Processing Pipeline (HyP3) using radiometrically terrain corrected SAR images. Examples primarily from natural hazards are presented to illustrate the capabilities and limitations of the change detection method.

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.

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

Science.gov (United States)

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

2014-11-01

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

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

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.

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

Change detection and change monitoring of natural and man-made features in multispectral and hyperspectral satellite imagery

Science.gov (United States)

Moody, Daniela Irina

2018-04-17

An approach for land cover classification, seasonal and yearly change detection and monitoring, and identification of changes in man-made features may use a clustering of sparse approximations (CoSA) on sparse representations in learned dictionaries. A Hebbian learning rule may be used to build multispectral or hyperspectral, multiresolution dictionaries that are adapted to regional satellite image data. Sparse image representations of pixel patches over the learned dictionaries may be used to perform unsupervised k-means clustering into land cover categories. The clustering process behaves as a classifier in detecting real variability. This approach may combine spectral and spatial textural characteristics to detect geologic, vegetative, hydrologic, and man-made features, as well as changes in these features over time.

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

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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.

The reliability and internal consistency of one-shot and flicker change detection for measuring individual differences in visual working memory capacity.

Science.gov (United States)

Pailian, Hrag; Halberda, Justin

2015-04-01

We investigated the psychometric properties of the one-shot change detection task for estimating visual working memory (VWM) storage capacity-and also introduced and tested an alternative flicker change detection task for estimating these limits. In three experiments, we found that the one-shot whole-display task returns estimates of VWM storage capacity (K) that are unreliable across set sizes-suggesting that the whole-display task is measuring different things at different set sizes. In two additional experiments, we found that the one-shot single-probe variant shows improvements in the reliability and consistency of K estimates. In another additional experiment, we found that a one-shot whole-display-with-click task (requiring target localization) also showed improvements in reliability and consistency. The latter results suggest that the one-shot task can return reliable and consistent estimates of VWM storage capacity (K), and they highlight the possibility that the requirement to localize the changed target is what engenders this enhancement. Through a final series of four experiments, we introduced and tested an alternative flicker change detection method that also requires the observer to localize the changing target and that generates, from response times, an estimate of VWM storage capacity (K). We found that estimates of K from the flicker task correlated with estimates from the traditional one-shot task and also had high reliability and consistency. We highlight the flicker method's ability to estimate executive functions as well as VWM storage capacity, and discuss the potential for measuring multiple abilities with the one-shot and flicker tasks.

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

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

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.

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.

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.

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.

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.

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.

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

Science.gov (United States)

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

2010-07-03

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. Change-N1 was elicited by a simple paradigm with two tones, a standard followed by a deviant, while subjects watched a silent movie. The amplitude of change-N1 elicited by a fixed sound pressure deviance (70 dB vs. 75 dB) was negatively correlated with the logarithm of the interval between the standard sound and deviant sound (1, 10, 100, or 1000 ms), while positively correlated with the logarithm of the duration of the standard sound (25, 100, 500, or 1000 ms). The amplitude of change-N1 elicited by a deviance in sound pressure, sound frequency, and sound location was correlated with the logarithm of the magnitude of physical differences between the standard and deviant sounds. The present findings suggest that temporal representation of echoic memory is non-linear and Weber-Fechner law holds for the automatic cortical response to sound changes within a suprathreshold range. Since the present results show that the behavior of echoic memory can be understood through change-N1, change-N1 would be a useful tool to investigate memory systems.

Off-line tracking of series parameters in distribution systems using AMI data

Energy Technology Data Exchange (ETDEWEB)

Williams, Tess L.; Sun, Yannan; Schneider, Kevin

2016-05-01

Electric distribution systems have historically lacked measurement points, and equipment is often operated to its failure point, resulting in customer outages. The widespread deployment of sensors at the distribution level is enabling observability. This paper presents an off-line parameter value tracking procedure that takes advantage of the increasing number of measurement devices being deployed at the distribution level to estimate changes in series impedance parameter values over time. The tracking of parameter values enables non-diurnal and non-seasonal change to be flagged for investigation. The presented method uses an unbalanced Distribution System State Estimation (DSSE) and a measurement residual-based parameter estimation procedure. Measurement residuals from multiple measurement snapshots are combined in order to increase the effective local redundancy and improve the robustness of the calculations in the presence of measurement noise. Data from devices on the primary distribution system and from customer meters, via an AMI system, form the input data set. Results of simulations on the IEEE 13-Node Test Feeder are presented to illustrate the proposed approach applied to changes in series impedance parameters. A 5% change in series resistance elements can be detected in the presence of 2% measurement error when combining less than 1 day of measurement snapshots into a single estimate.

Change detection in polarimetric SAR data over several time points

DEFF Research Database (Denmark)

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