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

In this paper, the detection and estimation of change points of local parameters are studied by means of localization procedures and rank statistics. These techniques are also applied to detection and estimation of the change points of scale parameters and that of location parameters of directional data. Change point problem arises in many fields and attracts the attention of many authors. The techniques employed to detect and estimate the change point can generally be classified into two categories: parametric, and nonparametric. Bayesian methods also plays a major role. In this paper, the authors concentrate their attention on the problem of detection of change points of location parameter by localization and rank statistics when data are large. Their method is different from, and has some advantages over, the existing methods, such as CUSUM (cumulative sum) and Csorgo and Horvath's non-sequential nonparametric AMOC (at most one change) procedures. First, localized procedures reduce computation. Second, these detecting and estimating procedures require no moment condition, instead it is only assumed that observed data come from a continuous distribution with a unique median.

There are no author-identified significant results in this report.

The fundamental objective is to determine the information available in ground-based millimeter-wave measurements of stratospheric constituent profiles, to identify the optimum method of retrieving this profile information, and to characterize the errors in the final result. A secondary objective is to produce retrieval software for operational use with Network for the Detection of Stratospheric Change (NDSC) measurements of O3, H2O, ClO, and perhaps N2O. Tests were performed on existing ozone retrieval programs in support of ongoing NDSC field measurements. The results show that if random spectral errors and retrieval bias errors are considered, accuracy of the retrieved profile is about 5 percent from 20-50 km, and about 10 percent from 50-60 km.

Assam is known for its extensive forest areas and availability of rich floras and faunas besides other valuable forest products. As per information available from the State Forest Department, the total area under forests in Assam was 26,781.91 Sq. km. at the end of March 2003, out of which 15,492.329 sq. km. was under Reserved Forests and 2,860.942 Sq. Km. under Protected Forest Area. Thus, the Forests area constitutes about 34.14 percent of the total geographical area of Assam. The forests products of the State mainly comprised of industrial wood, fuel wood, bamboo, stone, thatch, cane, sand etc. The State Government from its various Forest products earns a good amount of revenue. The importance of Forestry lies not only with its productivity but also in its capacity to prevent soil erosion. The State Government has therefore continued to impose ban on indiscriminate felling of trees in the State. The comparative study from Survey of India Topographical Map, 1974 and LISS-III 1D, 2005 of two districts of Assam namely Jorhat and Golaghat, it is very clear that there is substantial decrease in the Reserve Forest Area. Dayang Reserve forest is totally vanished by encroachment, same is the condition of Nambar, Holongapar and Disai Valley Reserve Forest where the crown coverage now is less than 40 percent of the total Reserve Forest Area. The total reserve forest area of Jorhat and Golaghat district during 1974 and 2005 is 458.8 sq km and 176.68 sq km respectively.

In this study the detection of the oil spill using synthetic aperture radar (SAR) imagery is considered. Detection of the oil spill is performed using change detection algorithms between imagery acquired at different times. The specific algorithms used are the correlation coefficient change statistic and the intensity ratio change statistic algorithms. Therefore these algorithms and the probabilistic selection of the threshold criteria is reviewed and discussed. A recently offered change detection method which depends on the idea of generating two different final change maps of two images in a sequence, is used. First final change map is obtained by cumulatively adding the sequences of change maps in such a manner that common change areas are excluded and uncommon change areas are included. The second final change map is obtained by comparing the first and the last images in the temporal sequence. This method requires at least three images to be employed and can be generalized to longer temporal image sequences. The purpose of this approach is to provide a double check mechanism to the conventional approach and thus to reduce the probability of false alarm and enhance the change detection. The algorithms mentioned are applied to a 2010 Gulf of Mexico oil spill imagery together with the method described. It is shown that intensity ratio change statistic is a better tool for identification of the changes due to the oil spill compared to the correlation coefficient change statistic. It is also shown that two final change map method can reduce the probability of false alarm. The data used in this study is acquired by the Japanese Aerospace Agency's Advanced Land Observing Satellite (ALOS) through Alaska SAR Facility (ASF) at the University of Alaska, Fairbanks, AK.

We propose a probabilistic formulation that enables sequential detection of multiple change points in a network setting. We present a class of sequential detection rules for certain functionals of change points (minimum among a subset), and prove their asymptotic optimality properties in terms of expected detection delay time. Drawing from graphical model formalism, the sequential detection rules can be implemented by a computationally efficient message-passing protocol which may scale up linearly in network size and in waiting time. The effectiveness of our inference algorithm is demonstrated by simulations.

We propose a novel and efficient method, that we shall call TopRank in the following paper, for detecting change-points in high-dimensional data. This issue is of growing concern to the network security community since network anomalies such as Denial of Service (DoS) attacks lead to changes in Internet traffic. Our method consists of a data reduction stage based on record filtering, followed by a nonparametric change-point detection test based on $U$-statistics. Using this approach, we can address massive data streams and perform anomaly detection and localization on the fly. We show how it applies to some real Internet traffic provided by France-T\'el\'ecom (a French Internet service provider) in the framework of the ANR-RNRT OSCAR project. This approach is very attractive since it benefits from a low computational load and is able to detect and localize several types of network anomalies. We also assess the performance of the TopRank algorithm using synthetic data and compare it with alternative approaches based on random aggregation.

Objectives during the past year were to (1) edit a special issue of the IEEE Journal of Oceanic Engineering devoted to the MBP program, (2) adapt custom sidescan sonar software used by the Hawaii Mapping Research Group (HMRG) for use with REMUS data, (3) carry on a time series of sidescan surveys off Kakaako (Oahu) around the Kilo Nalu Cabled Reef Observatory, and (4) use our sidescan system and ROV to look for unexploded ordnance in Hawaiian coastal waters.

Intermediate polars with known rates of spin period changes are not numerous because such tasks require measurements performed for a long time. To measure a spin period change, MU Cam is a good candidate because it has a spin oscillation with a large amplitude enabling measurements with high precision. Fortunately, in the past the spin period of MU Cam was measured with high precision. To measure the spin period anew, in 2014-2015 we performed extensive photometric observations of MU Cam, spanning a total duration of 208 h within 46 nights. We found that the spin, sideband and orbital periods are equal to 1187.16245+/-0.00047 s, 1276.3424+/-0.0022 s and 4.71942+/-0.00016 h, respectively. Comparing the measured spin period with the spin period of MU Cam in the past, we detected the spin period change with dP/dt=-(2.17+/-0.10) X 10^(-10). This rate of the spin period change was not stable and varied in a time scale of years. During four nights in 2014 April-May MU Cam was fainter than usual by 0.8 mag, and the amplitude of the sideband oscillation was five times larger, denoting significant fraction of disc-overflow accretion. The sideband oscillation showed a double-peaked pulse profile in the normal brightness state. When the star brightness was decreased by 0.8 mag, the sideband oscillation showed a single-peaked pulse profile. In contrast, the spin pulse, which was quasi-sinusoidal, remained remarkably stable both in profile and in amplitude. Moreover, the spin pulse was also remarkably stable in a time scale of years and even decades. MU Cam is of great interest because it represents a distinctive object with a large and unstable rate of the spin period change and exhibits a distinctive behaviour of the pulse profiles.

Intermediate polars with known rates of spin period changes are not numerous because such tasks require measurements performed for a long time. To measure a spin period change, MU Cam is a good candidate because it has a spin oscillation with a large amplitude enabling measurements with high precision. Fortunately, in the past the spin period of MU Cam was measured with high precision. To measure the spin period anew, in 2014-2015 we performed extensive photometric observations of MU Cam, spanning a total duration of 208 h within 46 nights. We found that the spin, sideband and orbital periods are equal to 1187.16245+/-0.00047 s, 1276.3424+/-0.0022 s and 4.71942+/-0.00016 h, respectively. Comparing the measured spin period with the spin period of MU Cam in the past, we detected the spin period change with dP/dt=-(2.17+/-0.10) X 10^(-10). This rate of the spin period change was not stable and varied in a time scale of years. During four nights in 2014 April-May MU Cam was fainter than usual by 0.8 mag, and the amplitude of the sideband oscillation was five times larger, denoting significant fraction of disc-overflow accretion. The sideband oscillation showed a double-peaked pulse profile in the normal brightness state. When the star brightness was decreased by 0.8 mag, the sideband oscillation showed a single-peaked pulse profile. In contrast, the spin pulse, which was quasi-sinusoidal, remained remarkably stable both in profile and in amplitude. Moreover, the spin pulse was also remarkably stable in a time scale of years and even decades. MU Cam is of great interest because it represents a distinctive object with a large and unstable rate of the spin period change and exhibits a distinctive behaviour of the pulse profiles.

Intermediate polars with known rates of spin period changes are not numerous because such tasks require measurements performed for a long time. To measure a spin period change, MU Cam is a good candidate because it has a spin oscillation with a large amplitude enabling measurements with high precision. Fortunately, in the past the spin period of MU Cam was measured with high precision. To measure the spin period anew, in 2014-2015 we performed extensive photometric observations of MU Cam, spanning a total duration of 208 h within 46 nights. We found that the spin, sideband and orbital periods are equal to 1187.16245+/-0.00047 s, 1276.3424+/-0.0022 s and 4.71942+/-0.00016 h, respectively. Comparing the measured spin period with the spin period of MU Cam in the past, we detected the spin period change with dP/dt=-(2.17+/-0.10) X 10^(-10). This rate of the spin period change was not stable and varied in a time scale of years. During four nights in 2014 April-May MU Cam was fainter than usual by 0.8 mag, and the amplitude of the sideband oscillation was five times larger, denoting significant fraction of disc-overflow accretion. The sideband oscillation showed a double-peaked pulse profile in the normal brightness state. When the star brightness was decreased by 0.8 mag, the sideband oscillation showed a single-peaked pulse profile. In contrast, the spin pulse, which was quasi-sinusoidal, remained remarkably stable both in profile and in amplitude. Moreover, the spin pulse was also remarkably stable in a time scale of years and even decades. MU Cam is of great interest because it represents a distinctive object with a large and unstable rate of the spin period change and exhibits a distinctive behaviour of the pulse profiles.

Change Detection Trend Prediction of Leisure Agriculture and Rural Tourism Based on High-Resolution Remote Sensing Image Classification Algorithm

Progress in monitoring the ClO content of the atmosphere at Mauna Kea, Hawaii and Scott Base, Antarctica as part of the Network for the Detection of Stratospheric Change (NDSC) is reported. ClO altitude profiles and the profile during spring 1992, 1994 and 1995 showing evidence for heterogeneous chemistry in the lower stratosphere are presented.

Change blindness results from the absence of attention from the source of a change. Unsurprisingly, change blindness worsens in dual task and high workload conditions. Such is the environment in which teams of operators in military command and control missions operate, yet little is known about change blindness susceptibility in these conditions. A flicker task required individuals and dyads to detect changes under high and low task difficulty. Reaction time, accuracy, eye movements, and three workload measures were examined. Scan path analyses showed that communicating dyads were more efficient at change detection than individuals and non-communicating dyads, but are just as blind to no change. Eye movement data also indicate differences in attention allocation and scan strategies among individuals, communicating, and non-communicating dyads. Implications for adaptive aids and tasks such as tactical command and control are discussed.

Tests for break points detection in the law of random vectors have been proposed in several papers. Nevertheless, they have often little powers for alternatives involving a change in the dependence between components of vectors. Specific tests for detection of a change in the copula of random vectors have been proposed in recent papers, but they do not allow to conclude of a change in the dependence structure without condition that the margins are constant. The goal of this article is to propose a test for detection of a break in the copula when a change in marginal distribution occurs at a known instant. The performances of this test are illustrated by Monte Carlo simulations.

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The author has identified the following significant results. In the first part of the investigation, spatial and spectral features were developed which were employed to automatically recognize terrain features through a clustering algorithm. In this part of the investigation, the size of the cell which is the number of digital picture elements used for computing the spatial and spectral features was varied. It was determined that the accuracy of terrain recognition decreases slowly as the cell size is reduced and coincides with increased cluster diffuseness. It was also proven that a cell size of 17 x 17 pixels when used with the clustering algorithm results in high recognition rates for major terrain classes. ERTS-1 data from five diverse geographic regions of the United States were processed through the clustering algorithm with 17 x 17 pixel cells. Simple land use maps were produced and the average terrain recognition accuracy was 82 percent.

Interconnection of distributed generation (DG) in distribution system will result in formation of islands in the event of loss of main supply. This scenario is harmful to the power system, hence quick detection is critical to halt the formation of islands. Among the common passive and active detection methods available, the hybrid detection method is identified as the most reliable method. This paper proposes a new hybrid method using the combination of passive and active technique which is the rate of change of frequency (ROCOF) and load impedance, respectively. The passive method works when the value of ROCOF exceeds the threshold value which is set at 0.3Hz/s. The active method works when it detects low value of ROCOF and immediately inject a pre-specified load into the system to increase the ROCOF value up to its threshold value. Simulation study on different case studies is carried out on distribution test system to evaluate the performance of the proposed method. Results show that this method is effective in detecting any events that could result in islanding.

Change detection (CD) provides information about the changes on earth’s surface over a period of time. Many algorithms have been proposed over the years for effective CD of satellite imagery. This paper presents the steps to preprocess the captured satellite images, which can be used to perform predictive analysis of earth’s surface by CD techniques. To design a highly effective system for CD, it is recommended that algorithm designers select efficient algorithms from any given application. Thus, this paper presents and investigates the review of most appropriate literature on CD, where CD techniques have been presented into three groups; i) thresholding, ii) clustering, and iii) deep learning. The first two categories mainly rely on the traditional machine learning, whereas the last one exploits the state-of-the art deep learning models. At the end, the standard methods are summarized based on advantages, limitation, and research gap.

The author has identified the following significant results. For the recognition of terrain types, spatial signatures are developed from the diffraction patterns of small areas of ERTS-1 images. This knowledge is exploited for the measurements of a small number of meaningful spatial features from the digital Fourier transforms of ERTS-1 image cells containing 32 x 32 picture elements. Using these spatial features and a heuristic algorithm, the terrain types in the vicinity of Phoenix, Arizona were recognized by the computer with a high accuracy. Then, the spatial features were combined with spectral features and using the maximum likelihood criterion the recognition accuracy of terrain types increased substantially. It was determined that the recognition accuracy with the maximum likelihood criterion depends on the statistics of the feature vectors. Nonlinear transformations of the feature vectors are required so that the terrain class statistics become approximately Gaussian. It was also determined that for a given geographic area the statistics of the classes remain invariable for a period of a month but vary substantially between seasons.

In the present experiment, we compared three types of computer-generated displays of battlefield information in order to address the possible influence of any of four potential causes of display-induced cognitive tunneling, which had been found in the Immersed display condition of Thomas, Wickens, & Merlo, 1999. First, it is possible that information within the initial 90 forward field of view (FFOV) of the 3-D egocentric view in the Immersed display suite acquired too much salience, and information outside the FFOV was overlooked. Second, it is possible that the Immersed participants were simply not using the manual panning function to view the entire 360 of the environment because of the information access cost associated with it, and thus never seeing information outside of the initial FFOV. Third, it is possible that there was simply too much information to store in working memory in the amount of time taken to complete each scene, which resulted in some relevant information never getting encoded. Finally, it is possible that Immersed participants were not accurately integrating information across the two views provided in the display. Participants viewed one of three types of displays of battlefield information and were asked to make spatial judgments, provide counts of visible enemy units, detect changes to these enemy units, and select paths through the environment. The Tethered display is equivalent to the one used in Thomas et al (1999). However, there were two versions of the Immersed display in this experiment; the first (self-pan) allowed participants to actively pan the environment (similar to the Immersed condition used by Thomas et al), while the second (auto-pan) automatically panned the environment as the participants observed passively.

In this study, the objective is to find changes in terrain geometry, which are encountered by a patrolling unmanned ground vehicle (UGV) from one patrol run to another. The UGV is equipped with a streaming imaging ladar (laser radar) that measures the distance to the nearest surface at an array of points spanning a prism-shaped field of view and with a navigation system that measures the location of the UGV in a world-fixed coordinate frame. The path followed by the UGV is approximately the same during each run. We are interested in the ability to detect true changes, to reject false alarms, and the range relative tothe UGV at which detections occur.

We propose a new computational approach for tracking and detecting statistically significant linguistic shifts in the meaning and usage of words. Such linguistic shifts are especially prevalent on the Internet, where the rapid exchange of ideas can quickly change a word's meaning. Our meta-analysis approach constructs property time series of word usage, and then uses statistically sound change point detection algorithms to identify significant linguistic shifts. We consider and analyze three approaches of increasing complexity to generate such linguistic property time series, the culmination of which uses distributional characteristics inferred from word co-occurrences. Using recently proposed deep neural language models, we first train vector representations of words for each time period. Second, we warp the vector spaces into one unified coordinate system. Finally, we construct a distance-based distributional time series for each word to track it's linguistic displacement over time. We demonstrate that our approach is scalable by tracking linguistic change across years of micro-blogging using Twitter, a decade of product reviews using a corpus of movie reviews from Amazon, and a century of written books using the Google Book-ngrams. Our analysis reveals interesting patterns of language usage change commensurate with each medium.

The goal of this project is to develop a computer-aided diagnosis (CAD) system for automatic interval change analysis of microcalcification clusters on mammograms. Based on our regional registration method and a search program cluster candidates were detected within the local area on the prior. The cluster on the current image is then paired with the candi-dates to form true (TP-TP) or false (TP-FP) pairs and a correspondence classifier is designed to reduce the (TP-FP). A temporal classifier (TC) based on current and prior information is used if a cluster is detected in the prior, and a current classifier (CurC) based on current information alone is used if no prior cluster is detected. For the TC an LDA, SVM and NN were used. 175 temporal pairs of mammograms were used for evaluation. The registration stage identified 85% (1491175) of the TP-TP pairs with 15 false matches within the 164 image pairs that had detected clusters. The TC based on LDA, SVM and NN achieved a test Az of 0.83, 0.82, 0.84, respectively, for the 164 pairs for classifying the clusters as malignant or benign. For the II clusters without detection on the prior, the test Az by the CurC was 0.72. Four radiologists participated in pilot observer study using our CAD. The average Az in estimating the likelihood of malignancy was 0.70 without CAD and improved to 0.77 with CAD(p=O.04).

This study focused on the development of a human performance model as a baseline performance capability for automatic change detection software for use in mine warfare. Through a series of survey images, operator performance was observed under a variety of sonar image conditions, including increasing clutter levels and changes in image altitude and orientation. While a rough model was developed utilizing only the physical attributes of the images, to obtain a close fit between the model and actual observations, the variability of personal proficiency was included in the final model. The inclusion of this parameter greatly improved model accuracy and highlights the need to better understand differences between operator performances in mine warfare planning.

Archetypal scenarios for change detection generally consider two images acquired through sensors of the same modality. However, in some specific cases such as emergency situations, the only images available may be those acquired through different kinds of sensors. More precisely, this paper addresses the problem of detecting changes between two multi-band optical images characterized by different spatial and spectral resolutions. This sensor dissimilarity introduces additional issues in the context of operational change detection. To alleviate these issues, classical change detection methods are applied after independent preprocessing steps (e.g., resampling) used to get the same spatial and spectral resolutions for the pair of observed images. Nevertheless, these preprocessing steps tend to throw away relevant information. Conversely, in this paper, we propose a method that more effectively uses the available information by modeling the two observed images as spatial and spectral versions of two (unobserved) latent images characterized by the same high spatial and high spectral resolutions. As they cover the same scene, these latent images are expected to be globally similar except for possible changes in sparse spatial locations. Thus, the change detection task is envisioned through a robust multi-band image fusion method which enforces the differences between the estimated latent images to be spatially sparse. This robust fusion problem is formulated as an inverse problem which is iteratively solved using an efficient block-coordinate descent algorithm. The proposed method is applied to real panchormatic/multispectral and hyperspectral images with simulated realistic changes. A comparison with state-of-the-art change detection methods evidences the accuracy of the proposed strategy.

Synthetic Aperture Radar (SAR) data enables direct observation of land surface at repetitive intervals and therefore allows temporal detection and monitoring of land changes. However, the problem of radar automatic change detection is made more dif􀅿cult, mainly with the presence of speckle noise. This paper presents a new method for SAR image change detection using the Dezert-Smarandache Theory (DSmT). First, a Gamma distribution function is used to characterize globally the radar texture data and allows mass assignment throw Kullback-Leibler distance. Then, local pixel measurements are introduced to re􀅿ne the mass attribution and take into account the context information. Finally, DSmT is carried out by comparing the modelling results between temporal images. The originality of the proposed method is on the one hand, the use of DSmT which achieve a plausible and paradoxical reasoning comparing to classical Dempster-Shafer Theory (DST). On the other hand, the given approach characterizes the radar texture data with a Gamma distribution which allows a better representation of the speckle. The radar texture is being usually modeled by a Gaussian model in previous DST and DSmT fusion works.

This article is from Frontiers in Psychology , volume 5 . Abstract Visual task performance is generally stronger in familiar environments. One reason for this familiarity benefit is that we learn where to direct our visual attention and effective attentional deployment enhances performance. Visual working memory plays a central role in supporting long-term memory guidance of visuospatial attention. We modified a change detection task to create a new paradigm for investigating long-term memory guidance of attention. During the training phase, subjects viewed images in a flicker paradigm and were asked to detect between one and three changes in the images. The test phase required subjects to detect a single change in a one-shot change detection task in which they held all possible locations of changes in visual working memory and deployed attention to those locations to determine if a change occurred. Subjects detected significantly more changes in images for which they had been trained to detect the changes, demonstrating that memory of the images guided subjects in deploying their attention. Moreover, capacity to detect changes was greater for images that had multiple changes during the training phase. In Experiment 2, we observed that capacity to detect changes for the 3-studied change condition increased significantly with more study exposures and capacity was significantly higher than 1, indicating that subjects were able to attend to more than one location. Together, these findings suggest memory and attentional systems interact via working memory such that long-term memory can be used to direct visual spatial attention to multiple locations based on previous experience.

Extensible markup Language (XML) and difference algorithms help mobile computing devices achieve interoperability with each other. XML provides applications a non-proprietary means of communicating among each other. Difference algorithms allow transferring only changes to files, not entire changed files. This research focuses on customizing current XML difference algorithms. The goal is to detect differences between two XML files that do not necessarily share the same structure. Small devices are generally incapable of presenting and processing parts of multi-media documents. They also have less memory and storage space than desktop units have. The sending device reduces transmission time by removing those contents, and other large embedded objects, from the data stream. The user can edit the streamlined file on the mobile device. Transmitting on the changes to this edited file conserves bandwidth, connection time, and battery-power. Integrating the transmitted changes back into the originating complex document becomes a fundamental requirement and a significant challenge.

במחקר זה אנו עוסקים בהשפעת חשיפה לסטטוס חברתי גבוה או נמוך על הערנות לשינויים. נחקור את ההשערה לפיה אדם בעל סטטוס חברתי גבוה ימשוך יותר קשב, ולכן המודעות לשינויים בהופעתו תהיה מוגברת.

במחקר זה אנו עוסקים בהשפעת חשיפה לסטטוס חברתי גבוה או נמוך על הערנות לשינויים. נחקור את ההשערה לפיה אדם בעל סטטוס חברתי גבוה ימשוך יותר קשב, ולכן המודעות לשינויים בהופעתו תהיה מוגברת.

Non-stationarity of the rate or variance of events is a well-known problem in the description and analysis of time series of events, such as neuronal spike trains. A multiple filter test (MFT) for homogeneity of the rate has been proposed earlier that detects change points on multiple time scales simultaneously. It is based on a filtered derivative approach, and the rejection threshold derives from a Gaussian limit process $L$ which is independent of the point process parameters. Here we extend the MFT to the analysis of variance homogeneity of life times. When the rate is constant, the MFT extends directly to the null hypothesis of constant variance and also to higher order moments under the assumption of homogeneity of lower order moments. In the presence of rate change points, we propose an adaptation of the test statistic. In order to ensure that rate changes are not falsely interpreted as variance changes, we propose to estimate the rate change points first, irrespective of potential variance changes, and incorporate these estimates in the statistical test for variance homogeneity. The resulting limit process shows slight deviations from $L$ that depend on unknown process parameters. We use simulations to show that these deviations tend to be small for a wide range of parameter values, suggesting that the use of $L$ does not considerably change the properties of the statistical test. Finally, the procedure is applied to empirical spike trains obtained from dopaminergic neurons in anaesthetized mice, indicating various profiles of rate and variance change points.

Alternative splicing of gene transcripts greatly expands the functional capacity of the genome, and certain splice isoforms may indicate specific disease states such as cancer. Splice junction microarrays interrogate thousands of splice junctions, but data analysis is difficult and error prone because of the increased complexity compared to differential gene expression analysis. We present Rank Change Detection (RCD) as a method to identify differential splicing events based upon a straightforward probabilistic model comparing the over- or underrepresentation of two or more competing isoforms. RCD has advantages over commonly used methods because it is robust to false positive errors due to nonlinear trends in microarray measurements. Further, RCD does not depend on prior knowledge of splice isoforms, yet it takes advantage of the inherent structure of mutually exclusive junctions, and it is conceptually generalizable to other types of splicing arrays or RNA-Seq. RCD specifically identifies the biologically important cases when a splice junction becomes more or less prevalent compared to other mutually exclusive junctions. The example data is from different cell lines of glioblastoma tumors assayed with Agilent microarrays.

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

This report contains no significant results.

There are no author-identified significant results in this report. This investigation is concerned with the development of automated interpretation techniques for the recognition and identification of earth resources. The resources will be identified by using both multispectral and spatial signatures. Ground truth data and aircraft underflight photography will be used to train the recognition algorithms. The data processed will be RBV and MSS images acquired by ERTS-1 satellite, over six test sites located in the vicinities of: Phoenix, Arizona; Weslaco, Texas; Cascade Mountains, Washington; New Orleans, Louisiana; Salt Lake City, Utah; and Salton Sea, California. The processed data will be thematic maps of resources consisting of annotated and outlined images. The seasonal changes of hydrologic and agricultural resources will also be identified. The results are expected to be applicable to a future automatic system of resource inventory and management.

We generalize the classic change-point problem to a "change-set" framework: a spatial Poisson process changes its intensity on an unobservable random set. Optimal detection of the set is defined by maximizing the expected value of a gain function. In the case that the unknown change-set is defined by a locally finite set of incomparable points, we present a sufficient condition for optimal detection of the set using multiparameter martingale techniques. Two examples are discussed.

An observer in motion often experiences motion-produced blur patterns when the relative angular velocities of the visual environment become appreciable. These blur patterns vary in form according to the type of motion producing them and thus are potentially important sources of visual orientation information. Unless an observer is looking straight to the side of his moving craft or perpendicularly to the path of a moving surface, typically the individual blur lines making up the pattern diverge or converge on his retina. Thus if the plane of motion changes, the divergence changes also and the divergence change becomes an important cue for orientation bearing information on motion parameters such as altitude change. This experiment measured human thresholds for divergence change in the form of sinusoidal expansion and contraction of downward-moving 16-line element patterns on the face of an oscilloscope. The objective was to determine whether blur pattern divergence change sensitivity was acute enough to be of any practical value in visual orientation using display information.

In the 1960s, Shiryaev developed a Bayesian theory of change-point detection in the i.i.d. case, which was generalized in the beginning of the 2000s by Tartakovsky and Veeravalli for general stochastic models assuming a certain stability of the log-likelihood ratio process. Hidden Markov models represent a wide class of stochastic processes that are very useful in a variety of applications. In this paper, we investigate the performance of the Bayesian Shiryaev change-point detection rule for hidden Markov models. We propose a set of regularity conditions under which the Shiryaev procedure is first-order asymptotically optimal in a Bayesian context, minimizing moments of the detection delay up to certain order asymptotically as the probability of false alarm goes to zero. The developed theory for hidden Markov models is based on Markov chain representation for the likelihood ratio and r-quick convergence for Markov random walks. In addition, applying Markov nonlinear renewal theory, we present a high-order asymptotic approximation for the expected delay to detection of the Shiryaev detection rule. Asymptotic properties of another popular change detection rule, the Shiryaev{Roberts rule, is studied as well. Some interesting examples are given for illustration.

Fundamental progress was made in the sequential and fixed sample detection and estimation of abrupt changes in stochastic systems and in the related problem of adaptive control of dynamical systems with time varying parameters. Advances were also made in recursive estimation and adaptive control of linear stochastic systems, optimal sequential testing of composite hypotheses, and regression analysis of censored failure time data.

In this project, we will use SFT coupled with computational modeling to examine the processing architecture and capacity of change detection decisions with varying numbers of distractors

A random sequence having two segments being the homogeneous Markov processes is registered. Each segment has his own transition probability law and the length of the segment is unknown and random. The transition probabilities of each process are known and a priori distribution of the disorder moment is given. The decision maker aim is to detect the moment of the transition probabilities change. The detection of the disorder rarely is precise. The decision maker accepts some deviation in estimation of the disorder moment. In the considered model the aim is to indicate the change point with fixed, bounded error with maximal probability. The case with various precision for over and under estimation of this point is analysed. The case when the disorder does not appears with positive probability is also included. The results insignificantly extends range of application, explain the structure of optimal detector in various circumstances and shows new details of the solution construction. The motivation for this investigation is the modelling of the attacks in the node of networks. The objectives is to detect one of the attack immediately or in very short time before or after it appearance with highest probability. The problem is reformulated to optimal stopping of the observed sequences. The detailed analysis of the problem is presented to show the form of optimal decision function.

In this second paper in the series, we investigate occurrence frequencies of apparent unipolar processes, cancellation, and emergence of patch structures in quiet regions. Apparent unipolar events are considerably more frequent than cancellation and emergence as per our definition, which is consistent with Lamb et al. (2013). Furthermore, we investigate the frequency distributions of changes in flux during apparent unipolar processes are and found that they concentrate around the detection limit of the analysis. Combining these findings with the results of our previous paper, Iida et al. (2012), that merging and splitting are more dominant than emergence and cancellation, these results support the understanding that apparent unipolar processes are actually interactions with and among patches below the detection limit and that there still are numerous flux interactions between the flux range in this analysis and below the detection limit. We also investigate occurrence frequency distributions of flux decrease during cancellation. We found a relatively strong dependence, 2.48$\pm$0:26 as a power-law index. This strong dependence on flux is consistent with the model, which is suggested in the previous paper.

Presently renewable energies have taken a special place in the world and most of the Distributed Generations (DGs) used in the interconnected power system are utilized, renewable energy resources. Due to the DG‟s advantages, including use of renewable energy such as, clean nature, does not pollute environment and having endless nature the use of these renewable resources to produce electrical energy in the world are increasing in day to day life. One problem with such Distributed generators is an unintentional islanding phenomenon. Islanding occurs when a Distributed Generation continues to energize an isolated part of a power system even after it was disconnected from the main grid, which is surrounded by unpowered lines. Since islanding can cause hazardous conditions for people and equipment which is connected to it. As per IEEE 1547 DG Interconnection standards, islanding should be quickly detected within 2 seconds, by protective relays and inverters that are part of the DG system. In this paper, a new passive method to identify islanding states has been proposed, based on the rate of change of frequency analysis (ROCOF) for a multilevel inverter based solar distributed generation systems. This method is efficient for both connecting DGs to the network with or without the Inverter. This method is more efficient than the existing methods and reducing the Non Detection Zone (NDZ), which is the disadvantage of existing passive methods and also clearly differentiating between the Islanding and Non-islanding events. The simulation results, which are carried on the MATLAB/Simulink environment shows the performance of the proposed method.

An investigation of five change detection methods used in synthetic aperture radar (SAR) is presented in this thesis. This investigation utilizes data gathered from the Air Force Research Laboratory (AFRL) Sensor Data Management System (SDMS) in order to compare the various change detection techniques. These change detection methods include the following: a) incoherent change detection (ICCD), b) coherent change detection (CCD), c) alternative coherent change detection (ACCD), d) log likelihood change statistic (LLCS), and e) a two-stage change detection, which involves a combination of ICCD and CCD. In addition, a new change detection method for comparison with these five basic methods is developed. This investigation reveals that the LLCS statistic is the most promising method for revealing changes within the SDMS dataset. Furthermore, the author's change detection method yields overall visual improvement in comparison to the two-stage change detection method.

This report presents test results from a demonstration of the Automated Change Detection and Classification (ACDC) system, developed by the Naval Research Laboratory (NRL) in support of sidescan sonar analysts at the Naval Oceanographic Office. NRL compared the computer-aided change detection capabilities of ACDC with traditional, manual methods of change detection during a naval mine warfare exercise in 2004. Two versions of ACDC (ACDC Lite and Medium) were tested to determine how well the system could aid analysts in detecting changes between newly collected sidescan sonar imagery and historical imagery. ACDC Lite enabled analysts to perform change detection significantly faster than manual methods (3.36 min. vs 2 hr) with identical results. ACDC Medium improved analysts' performance by matching one additional contact during change detection, in as little as 12.6 minutes. Follow-on work is suggested for further improving ACDC performance in support of mine countermeasures.

Evaluations of Long Term Resource Monitoring Program sampling designs for water quality, fish, aquatic vegetation, and macroinvertebrates were initiated in 1999 by analyzing data collected since 1992 in six trend analysis areas. Initial emphasis was placed on evaluating statistical power to detect change from one year or sampling interval to the next, and on determining what spatial, methodological, or target variable redundancies existed in the data Sets. Power to detect change was evaluated at halved, present, and doubled levels of effort. Power to detect change for different variables varied widely and was greatly influenced by sample size and for species by their frequency of occurrence. Power for detecting annual and seasonal changes in most water quality variables seems adequate. A doubling of effort would provide little increase in power, and some reduction or redistribution of effort may be possible.

This study looks at participant report of how many hours of sleep the night before testing as well as self-reported tiredness level in relation to processing during an auditory oddball task. The component elicited by the oddball task is the MMN and this will be examined in relation to measures of sleep/tiredness.

When driving, flying and even when walking one often can see motion-produced blur patterns that are caused by images of textures slipping over the retina and leaving trails of residual afterimages behind them which contain their motion histories. Other studies in this series on blur pattern parameters have shown that human observers can sensitively detect directional information within a blur pattern, for example when there is curvature change, or relative divergence of the individual blur lines. The purpose of this study was to determine whether the detection of blur pattern curvature change, studied in a previous experiment, would be enhanced if stationary reference lines were superimposed on the blur pattern. Reference lines were oriented in three ways, parallel, perpendicular, and at 45 deg to the direction of element flow. Patterns were viewed foveally or with peripheral vision.