Consumer behaviour in district heating systems. Detecting changes

Energy Technology Data Exchange (ETDEWEB)

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

2002-10-01

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

Transistor-based particle detection systems and methods

Science.gov (United States)

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

2015-06-09

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

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

Development of lane change aid system; Lane change aid system no kaihatsu

Energy Technology Data Exchange (ETDEWEB)

Uemura, H; Yoshida, T; Butsuen, T [Mazda Motor Corp., Hiroshima (Japan); Sakai, N; Nakano, T; Nakamoto, T

1997-10-01

We have developed a lane change aid system, which detects vehicles behind in adjacent lanes with CCD images, shows the driver the information of vehicle location with a head-up display, and warns him/her if necessary. By comparing the brightness on two CCD images, distance distribution along several CCD lines is measured. To assure the effective vehicle detection on public roads, we have developed a detection algorithm which distinguishes vehicles and guard-rails by observing the patterns of measured distances. We will show the system configuration, detection logic, and the system evaluation on public road. 5 refs., 12 figs.

Using Stable Isotopes to Detect Land Use Change and Nitrogen Sources in Aquatic Systems

Energy Technology Data Exchange (ETDEWEB)

Rogers, K. M. [National Isotope Center, GNS Science, Lower Hutt (New Zealand)

2013-05-15

Changing land use is one of the primary causes of increased sedimentation and nutrient levels in aquatic systems, resulting in contamination and reduction of biodiversity. Detecting and quantifying these inputs is the first step towards remediation, and enabling targeted reductions of transport processes into waterways from human impacted land surfaces. More recently, stable isotope analyses are being used as detection and quantification tools in aquatic environments. Carbon ({delta}{sup 13}C) and nitrogen ({delta}{sup 15}N) isotopes of sediments, as well as algae and invertebrates from aquatic systems can be used as proxies to record both short and long term environmental change. Excess nitrogen (or nitrogen-compounds) derived from urbanization, industry, forestry, farming and agriculture, increase the bioavailability of nitrogen to aquatic organisms, changing their natural {delta}15N isotopic signatures. Allochthonous (terrestrial) input from soil destabilization and human activity in surrounding catchments changes {delta}{sup 13}C isotopic compositions and increases the C:N ratio of sediments. Heavy metal and other organic pollutants can also be used to indicate urbanization and industrial contamination. The combined use of carbon and nitrogen isotopes, C:N ratios and heavy metals are powerful environmental monitoring tools, which are useful indicators of source and transport pathways of terrestrial derived material and anthropogenic pollutants into streams, rivers and estuaries. (author)

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.

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

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.

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.

Portable reconfigurable detection and assessment system

International Nuclear Information System (INIS)

Blattman, D.A.

1991-01-01

Rapidly changing geopolitical issues throughout the world have made the ability to effectively respond to political, military, terrorist and peace-keeping requirements increasingly important. Recent Middle East events indicate a continuing escalation in these activities. These activities are defining the requirements for a rapidly deployable, portable, real-time detection and assessment operational security system that is reconfigurable to site specific threats. This paper describes such a system Mobile Operational Detection and Assessment system (MODAS); a commercially-off-the shelf (COTS) integrated and reconfigurable hardware/software system solution for the ever-changing geopolitical security issues of the Nineties

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

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...

Compensated intruder-detection systems

Science.gov (United States)

McNeilly, David R.; Miller, William R.

1984-01-01

Intruder-detection systems in which intruder-induced signals are transmitted through a medium also receive spurious signals induced by changes in a climatic condition affecting the medium. To combat this, signals received from the detection medium are converted to a first signal. The system also provides a reference signal proportional to climate-induced changes in the medium. The first signal and the reference signal are combined for generating therefrom an output signal which is insensitive to the climatic changes in the medium. An alarm is energized if the output signal exceeds a preselected value. In one embodiment, an acoustic cable is coupled to a fence to generate a first electrical signal proportional to movements thereof. False alarms resulting from wind-induced movements of the fence (detection medium) are eliminated by providing an anemometer-driven voltage generator to provide a reference voltage proportional to the velocity of wind incident on the fence. An analog divider receives the first electrical signal and the reference signal as its numerator and denominator inputs, respectively, and generates therefrom an output signal which is insensitive to the wind-induced movements in the fence.

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.

Detecting change in dynamic process systems with immunocomputing

Energy Technology Data Exchange (ETDEWEB)

Yang, X.; Aldrich, C.; Maree, C. [University of Stellenbosch, Stellenbosch (South Africa). Dept. of Process Engineering

2007-02-15

The natural immune system is an adaptive distributed pattern recognition system with several functional components designed for recognition, memory acquisition, diversity and self-regulation. In artificial immune systems, some of these characteristics are exploited in order to design computational systems capable of detecting novel patterns or the anomalous behaviour of a system in some sense. Despite their obvious promise in the application of fault diagnostic systems in process engineering, their potential remains largely unexplored in this regard. In this paper, the application of real-valued negative selection algorithms to simulated and real-world systems is considered. These algorithms deal with the self-nonself discrimination problem in immunocomputing, where normal process behaviour is coded as the self and any deviations from normal behaviour is encoded as nonself. The case studies have indicated that immunocomputing based on negative selection can provide competitive options for fault diagnosis in nonlinear process systems, but further work is required on large systems characterized by many variables.

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).

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...

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

Science.gov (United States)

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

1990-01-01

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

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

Research in Model-Based Change Detection and Site Model Updating

National Research Council Canada - National Science Library

Nevatia, R

1998-01-01

.... Some of these techniques also are applicable to automatic site modeling and some of our change detection techniques may apply to detection of larger mobile objects, such as airplanes. We have implemented an interactive modeling system that works in conjunction with our automatic system to minimize the need for tedious interaction.

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...

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.

Happy Face Superiority Effect in Change Detection Paradigm

Directory of Open Access Journals (Sweden)

Domagoj Švegar

2013-09-01

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

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

Directory of Open Access Journals (Sweden)

Takeshima Yasuyuki

2010-07-01

Full Text Available 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 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. Conclusions 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.

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

International Nuclear Information System (INIS)

Byler, E.

1997-01-01

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

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.

Comparing apples and oranges: fold-change detection of multiple simultaneous inputs.

Directory of Open Access Journals (Sweden)

Yuval Hart

Full Text Available Sensory systems often detect multiple types of inputs. For example, a receptor in a cell-signaling system often binds multiple kinds of ligands, and sensory neurons can respond to different types of stimuli. How do sensory systems compare these different kinds of signals? Here, we consider this question in a class of sensory systems - including bacterial chemotaxis- which have a property known as fold-change detection: their output dynamics, including amplitude and response time, depends only on the relative changes in signal, rather than absolute changes, over a range of several decades of signal. We analyze how fold-change detection systems respond to multiple signals, using mathematical models. Suppose that a step of fold F1 is made in input 1, together with a step of F2 in input 2. What total response does the system provide? We show that when both input signals impact the same receptor with equal number of binding sites, the integrated response is multiplicative: the response dynamics depend only on the product of the two fold changes, F1F2. When the inputs bind the same receptor with different number of sites n1 and n2, the dynamics depend on a product of power laws, [Formula: see text]. Thus, two input signals which vary over time in an inverse way can lead to no response. When the two inputs affect two different receptors, other types of integration may be found and generally the system is not constrained to respond according to the product of the fold-change of each signal. These predictions can be readily tested experimentally, by providing cells with two simultaneously varying input signals. The present study suggests how cells can compare apples and oranges, namely by comparing each to its own background level, and then multiplying these two fold-changes.

Evaluation of video detection systems, volume 1 : effects of configuration changes in the performance of video detection systems.

Science.gov (United States)

2009-10-01

The effects of modifying the configuration of three video detection (VD) systems (Iteris, Autoscope, and Peek) : are evaluated in daytime and nighttime conditions. Four types of errors were used: false, missed, stuck-on, and : dropped calls. The thre...

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...

Statistical fault detection in photovoltaic systems

KAUST Repository

Garoudja, Elyes

2017-05-08

Faults in photovoltaic (PV) systems, which can result in energy loss, system shutdown or even serious safety breaches, are often difficult to avoid. Fault detection in such systems is imperative to improve their reliability, productivity, safety and efficiency. Here, an innovative model-based fault-detection approach for early detection of shading of PV modules and faults on the direct current (DC) side of PV systems is proposed. This approach combines the flexibility, and simplicity of a one-diode model with the extended capacity of an exponentially weighted moving average (EWMA) control chart to detect incipient changes in a PV system. The one-diode model, which is easily calibrated due to its limited calibration parameters, is used to predict the healthy PV array\\'s maximum power coordinates of current, voltage and power using measured temperatures and irradiances. Residuals, which capture the difference between the measurements and the predictions of the one-diode model, are generated and used as fault indicators. Then, the EWMA monitoring chart is applied on the uncorrelated residuals obtained from the one-diode model to detect and identify the type of fault. Actual data from the grid-connected PV system installed at the Renewable Energy Development Center, Algeria, are used to assess the performance of the proposed approach. Results show that the proposed approach successfully monitors the DC side of PV systems and detects temporary shading.

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.

Stability of detecting system using NaI(Tl)

International Nuclear Information System (INIS)

Zhuo Yunshang; Lei Zhangyun; Zen Yu; Gong Hua

1996-01-01

A detecting system using NaI(Tl) is widely used in research and industry of nuclear science and other fields. For providing the high accuracy and working well under inclement environment, the stability of detecting system using NaI(Tl) is very important. The variation of environment temperature, the change of counting rate and long time continuous working of detector will cause un-negligible effect on the measurement. Three approaches were used. They are: 1) temperature control (It makes the effect of the variation of environment temperature on the measurement negligible.); 2) spectrum stabilizing (It adjust the peak position of the spectrum when the counting rate changes.); and 3) auto-checking and adjusting (It adjusts the drift of the NaI(Tl) detecting system when it works continuously)

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

DEFF Research Database (Denmark)

Muro, Javier; Canty, Morton; Conradsen, Knut

2016-01-01

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

Automated system for crack detection using infrared thermograph

International Nuclear Information System (INIS)

Starman, Stanislav

2009-01-01

The objective of this study was the development of the automated system for crack detection on square steel bars used in the automotive industry for axle and shaft construction. The automated system for thermographic crack detection uses brief pulsed eddy currents to heat steel components under inspection. Cracks, if present, will disturb the current flow and so generate changes in the temperature profile in the crack area. These changes of temperature are visualized using an infrared camera. The image acquired by the infrared camera is evaluated through an image processing system. The advantages afforded by the system are its inspection time, its excellent flaw detection sensitivity and its ability to detect hidden, subsurface cracks. The automated system consists of four IR cameras (each side of steel bar is evaluated at a time), coil, high frequency generator and control place with computers. The system is a part of the inspection line where the subsurface and surface cracks are searched. If the crack is present, the cracked place is automatically marked. The components without cracks are then deposited apart from defective blocks. The system is fully automated and its ability is to evaluate four meter blocks within 20 seconds. This is the real reason for using this system in real industrial applications. (author)

Changes are detected - cameras and video systems are monitoring the plant site, only rarely giving false alarm

International Nuclear Information System (INIS)

Zeissler, H.

1988-01-01

The main purpose of automatic data acquisition and processing for monitoring goals is to relieve the security personnel from monotonous observation tasks. The novel video systems can be programmed to detect moving target alarm signals, or accept alarm-suppressing image changes. This allows an intelligent alarm evaluation for physical protection in industry, differentiating between real and false alarm signals. (orig.) [de

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.

Use of change detection in assessing development plans - A Philippine example. [aircraft/Landsat remote sensing information system for regional planning

Science.gov (United States)

Coiner, J. C.; Bruce, R. C.

1978-01-01

An aircraft/Landsat change-detection study conducted 1948-1972 on Marinduque Province, Republic of the Philippines, is discussed, and a procedure using both remote sensing and information systems for collection, spatial analysis, and display of periodic data is described. Each of the 4,008 25-hectare cells representing Marinduque were observed, and changes in and between variables were measured and tested using nonparametric statistics to determine the effect of specific land cover changes. Procedures using Landsat data to obtain a more continuous updating of the data base are considered. The system permits storage and comparison of historical and current data.

Development of a recombinant DNA assay system for the detection of genetic change in astronauts' cells

International Nuclear Information System (INIS)

Atchley, S.V.; Chen, D.J.C.; Strniste, G.F.; Walters, R.A.; Moyzis, R.K.

1984-01-01

We are developing a new recombinant DNA system for the detection and measurement of genetic change in humans caused by exposure to low level ionizing radiation. A unique feature of the method is the use of cloned repetitive DNA probes to assay human DNA for structural changes during or after irradiation. Repetitive sequences exist in different families. Collectively they constitute over 25% of the DNA in a human cell. Repeat families have between 10 and 500,000 members. We have constructed repetitive DNA sequence libraries using recombinant DNA techniques. From these libraries we have isolated and characterized individual repeats comprising 75 to 90% of the mass of human repetitive DNA. Repeats used in our assay system exist in tandem arrays in the genome. Perturbation of these sequences in a cell, followed by detection with a repeat probe, produces a new, multimeric ''ladder'' pattern on an autoradiogram. The repeat probe used in our initial study is complementary to 1% of human DNA. Therefore, the sensitivity of this method is several orders of magnitude better than existing assays. Preliminary evidence from human skin cells exposed to acute, low-dose x-ray treatments indicates that DNA is affected at a dose as low as 5R. The radiation doses used in this system are well within the range of doses received by astronauts during spaceflight missions. Due to its small material requirements, this technique could easily be adapted for use in space. 16 refs., 1 fig

A Hybrid Change Detection Approach for Damage Detection and Recovery Monitoring

Science.gov (United States)

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

2014-05-01

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

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.

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.

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.

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

Detecting Output Pressure Change of Positive-Displacement Pump by Phase Trajectory Method

Directory of Open Access Journals (Sweden)

Jerzy Stojek

2010-06-01

Full Text Available The monitoring of hydraulic system condition change during its exploitation ran its complex problem. The main task is to identifyearly phase damage of hydraulic system elements (pumps, valves, ect. in order to take decision which can avoid hydraulic system breakdown. This paper presents the possibility of phase trajectories use in detecting output pressure change of hydraulic system causedby positive-displacement pump wear.

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.

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)

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

Science.gov (United States)

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

2016-07-01

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

Structural material anomaly detection system using water chemistry data

International Nuclear Information System (INIS)

Asakura, Yamato; Nagase, Makoto; Uchida, Shunsuke; Ohsumi, Katsumi.

1992-01-01

The concept of an advanced water chemistry diagnosis system for detection of anomalies and preventive maintenance of system components is proposed and put into a concrete form. Using the analogy to a medical inspection system, analyses of water chemistry change will make it possible to detect symptoms of anomalies in system components. Then, correlations between water chemistry change and anomaly occurrence in the components of the BWR primary cooling system are analyzed theoretically. These fragmentary correlations are organized and reduced to an algorithm for the on-line diagnosis system using on-line monitoring data, pH and conductivity. By using actual plant data, the on-line diagnosis model system is verified to be applicable for early and automatic finding of the anomaly cause and for timely supply of much diagnostic information to plant operators. (author)

Multisensor Fusion for Change Detection

Science.gov (United States)

Schenk, T.; Csatho, B.

2005-12-01

Combining sensors that record different properties of a 3-D scene leads to complementary and redundant information. If fused properly, a more robust and complete scene description becomes available. Moreover, fusion facilitates automatic procedures for object reconstruction and modeling. For example, aerial imaging sensors, hyperspectral scanning systems, and airborne laser scanning systems generate complementary data. We describe how data from these sensors can be fused for such diverse applications as mapping surface erosion and landslides, reconstructing urban scenes, monitoring urban land use and urban sprawl, and deriving velocities and surface changes of glaciers and ice sheets. An absolute prerequisite for successful fusion is a rigorous co-registration of the sensors involved. We establish a common 3-D reference frame by using sensor invariant features. Such features are caused by the same object space phenomena and are extracted in multiple steps from the individual sensors. After extracting, segmenting and grouping the features into more abstract entities, we discuss ways on how to automatically establish correspondences. This is followed by a brief description of rigorous mathematical models suitable to deal with linear and area features. In contrast to traditional, point-based registration methods, lineal and areal features lend themselves to a more robust and more accurate registration. More important, the chances to automate the registration process increases significantly. The result of the co-registration of the sensors is a unique transformation between the individual sensors and the object space. This makes spatial reasoning of extracted information more versatile; reasoning can be performed in sensor space or in 3-D space where domain knowledge about features and objects constrains reasoning processes, reduces the search space, and helps to make the problem well-posed. We demonstrate the feasibility of the proposed multisensor fusion approach

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.

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.

Contribution to the detection of changes in multi-modal 3D MRI sequences

International Nuclear Information System (INIS)

Bosc, Marcel

2003-01-01

This research thesis reports the study of automatic techniques for the detection of changes in image sequences of brain magnetic resonance imagery (MRI), and more particularly the study of localised intensity changes occurring during pathological evolutions such as evolutions of lesions into multiple sclerosis. Thus, this work focused on the development of image processing tools allowing to decide whether changes are statistically significant or not. The author developed automatic techniques of identification and correction of the main artefacts (position, deformations, intensity variation, and so on), and proposes an original technique for cortex segmentation which introduced anatomic information for an improved automatic detection. The developed change detection system has been assessed within the frame of the study of the evolution of lesions of multiple sclerosis. Performance have been determined on a large number of multi-modal images, and the automatic system has shown better performance than a human expert [fr

Damage Detection and Verification System (DDVS) for In-Situ Health Monitoring

Science.gov (United States)

Williams, Martha K.; Lewis, Mark; Szafran, J.; Shelton, C.; Ludwig, L.; Gibson, T.; Lane, J.; Trautwein, T.

2015-01-01

Project presentation for Game Changing Program Smart Book Release. Detection and Verification System (DDVS) expands the Flat Surface Damage Detection System (FSDDS) sensory panels damage detection capabilities and includes an autonomous inspection capability utilizing cameras and dynamic computer vision algorithms to verify system health. Objectives of this formulation task are to establish the concept of operations, formulate the system requirements for a potential ISS flight experiment, and develop a preliminary design of an autonomous inspection capability system that will be demonstrated as a proof-of-concept ground based damage detection and inspection system.

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.

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...

Obstacle detection system for underground mining vehicles

Energy Technology Data Exchange (ETDEWEB)

Cohen, P.; Polotski, V.; Piotte, M.; Melamed, F. [Ecole Polytechnique de Montreal, Montreal, PQ (Canada)

1998-01-01

A device for detecting obstacles by autonomous vehicles navigating in mine drifts is described. The device is based upon structured lighting and the extraction of relevant features from images of obstacles. The system uses image profile changes, ground and wall irregularities, disturbances of the vehicle`s trajectory, and impaired visibility to detect obstacles, rather than explicit three-dimensional scene reconstruction. 7 refs., 5 figs.

Sensor and methods of detecting target materials and situations in closed systems

Energy Technology Data Exchange (ETDEWEB)

Mee, David K.; Ripley, Edward B.; Nienstedt, Zachary C.; Nienstedt, Alex W.; Howell, Jr., Layton N.

2018-03-13

Disclosed is a passive, in-situ pressure sensor. The sensor includes a sensing element having a ferromagnetic metal and a tension inducing mechanism coupled to the ferromagnetic metal. The tension inducing mechanism is operable to change a tensile stress upon the ferromagnetic metal based on a change in pressure in the sensing element. Changes in pressure are detected based on changes in the magnetic switching characteristics of the ferromagnetic metal when subjected to an alternating magnetic field caused by the change in the tensile stress. The sensing element is embeddable in a closed system for detecting pressure changes without the need for any penetrations of the system for power or data acquisition by detecting changes in the magnetic switching characteristics of the ferromagnetic metal caused by the tensile stress.

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.

Farmers' preferences for automatic lameness-detection systems in dairy cattle.

Science.gov (United States)

Van De Gucht, T; Saeys, W; Van Nuffel, A; Pluym, L; Piccart, K; Lauwers, L; Vangeyte, J; Van Weyenberg, S

2017-07-01

As lameness is a major health problem in dairy herds, a lot of attention goes to the development of automated lameness-detection systems. Few systems have made it to the market, as most are currently still in development. To get these systems ready for practice, developers need to define which system characteristics are important for the farmers as end users. In this study, farmers' preferences for the different characteristics of proposed lameness-detection systems were investigated. In addition, the influence of sociodemographic and farm characteristics on farmers' preferences was assessed. The third aim was to find out if preferences change after the farmer receives extra information on lameness and its consequences. Therefore, a discrete choice experiment was designed with 3 alternative lameness-detection systems: a system attached to the cow, a walkover system, and a camera system. Each system was defined by 4 characteristics: the percentage missed lame cows, the percentage false alarms, the system cost, and the ability to indicate which leg is lame. The choice experiment was embedded in an online survey. After answering general questions and choosing their preferred option in 4 choice sets, extra information on lameness was provided. Consecutively, farmers were shown a second block of 4 choice sets. Results from 135 responses showed that farmers' preferences were influenced by the 4 system characteristics. The importance a farmer attaches to lameness, the interval between calving and first insemination, and the presence of an estrus-detection system contributed significantly to the value a farmer attaches to lameness-detection systems. Farmers who already use an estrus detection system were more willing to use automatic detection systems instead of visual lameness detection. Similarly, farmers who achieve shorter intervals between calving and first insemination and farmers who find lameness highly important had a higher tendency to choose for automatic

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.

Edge detection techniques for iris recognition system

International Nuclear Information System (INIS)

Tania, U T; Motakabber, S M A; Ibrahimy, M I

2013-01-01

Nowadays security and authentication are the major parts of our daily life. Iris is one of the most reliable organ or part of human body which can be used for identification and authentication purpose. To develop an iris authentication algorithm for personal identification, this paper examines two edge detection techniques for iris recognition system. Between the Sobel and the Canny edge detection techniques, the experimental result shows that the Canny's technique has better ability to detect points in a digital image where image gray level changes even at slow rate

SQL injection detection system

OpenAIRE

Vargonas, Vytautas

2017-01-01

SQL injection detection system Programmers do not always ensure security of developed systems. That is why it is important to look for solutions outside being reliant on developers. In this work SQL injection detection system is proposed. The system analyzes HTTP request parameters and detects intrusions. It is based on unsupervised machine learning. Trained by regular request data system detects outlier user parameters. Since training is not reliant on previous knowledge of SQL injections, t...

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

Energy Technology Data Exchange (ETDEWEB)

Chandola, Varun [ORNL; Vatsavai, Raju [ORNL

2011-01-01

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

Single-trial lie detection using a combined fNIRS-polygraph system

Science.gov (United States)

Bhutta, M. Raheel; Hong, Melissa J.; Kim, Yun-Hee; Hong, Keum-Shik

2015-01-01

Deception is a human behavior that many people experience in daily life. It involves complex neuronal activities in addition to several physiological changes in the body. A polygraph, which can measure some of the physiological responses from the body, has been widely employed in lie-detection. Many researchers, however, believe that lie detection can become more precise if the neuronal changes that occur in the process of deception can be isolated and measured. In this study, we combine both measures (i.e., physiological and neuronal changes) for enhanced lie-detection. Specifically, to investigate the deception-related hemodynamic response, functional near-infrared spectroscopy (fNIRS) is applied at the prefrontal cortex besides a commercially available polygraph system. A mock crime scenario with a single-trial stimulus is set up as a deception protocol. The acquired data are classified into “true” and “lie” classes based on the fNIRS-based hemoglobin-concentration changes and polygraph-based physiological signal changes. Linear discriminant analysis is utilized as a classifier. The results indicate that the combined fNIRS-polygraph system delivers much higher classification accuracy than that of a singular system. This study demonstrates a plausible solution toward single-trial lie-detection by combining fNIRS and the polygraph. PMID:26082733

Single-trial lie detection using a combined fNIRS-polygraph system

Directory of Open Access Journals (Sweden)

M. Raheel eBhutta

2015-06-01

Full Text Available Deception is a human behavior that many people experience in daily life. It involves complex neuronal activities in addition to several physiological changes in the body. A polygraph, which can measure some of the physiological responses from the body, has been widely employed in lie-detection. Many researchers, however, believe that lie detection can become more precise if the neuronal changes that occur in the process of deception can be isolated and measured. In this study, we combine both measures (i.e., physiological and neuronal changes for enhanced lie-detection. Specifically, to investigate the deception-related hemodynamic response, functional near-infrared spectroscopy (fNIRS is applied at the prefrontal cortex besides a commercially available polygraph system. A mock crime scenario with a single-trial stimulus is set up as a deception protocol. The acquired data are classified into true and lie classes based on the fNIRS-based hemoglobin-concentration changes and polygraph-based physiological signal changes. Linear discriminant analysis is utilized as a classifier. The results indicate that the combined fNIRS-polygraph system delivers much higher classification accuracy than that of a singular system. This study demonstrates a plausible solution toward single-trial lie-detection by combining fNIRS and the polygraph.

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.

Applications of Graph-Theoretic Tests to Online Change Detection

Science.gov (United States)

2014-05-09

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

Developing nucleic acid-based electrical detection systems

Directory of Open Access Journals (Sweden)

Gabig-Ciminska Magdalena

2006-03-01

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

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

Directory of Open Access Journals (Sweden)

Sebastian Aleksandrowicz

2014-06-01

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

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.

Ionization detection system for aerosols

International Nuclear Information System (INIS)

Jacobs, M.E.

1977-01-01

This invention relates to an improved smoke-detection system of the ionization-chamber type. In the preferred embodiment, the system utilizes a conventional detector head comprising a measuring ionization chamber, a reference ionization chamber, and a normally non-conductive gas triode for discharging when a threshold concentration of airborne particulates is present in the measuring chamber. The improved system utilizes a measuring ionization chamber which is modified to minimize false alarms and reductions in sensitivity resulting from changes in ambient temperature. In the preferred form of the modification, an annular radiation shield is mounted about the usual radiation source provided to effect ionization in the measuring chamber. The shield is supported by a bimetallic strip which flexes in response to changes in ambient temperature, moving the shield relative to the source so as to vary the radiative area of the source in a manner offsetting temperature-induced variations in the sensitivity of the chamber. 8 claims, 7 figures

Geospatial Image Stream Processing: Models, techniques, and applications in remote sensing change detection

Science.gov (United States)

Rueda-Velasquez, Carlos Alberto

Detection of changes in environmental phenomena using remotely sensed data is a major requirement in the Earth sciences, especially in natural disaster related scenarios where real-time detection plays a crucial role in the saving of human lives and the preservation of natural resources. Although various approaches formulated to model multidimensional data can in principle be applied to the inherent complexity of remotely sensed geospatial data, there are still challenging peculiarities that demand a precise characterization in the context of change detection, particularly in scenarios of fast changes. In the same vein, geospatial image streams do not fit appropriately in the standard Data Stream Management System (DSMS) approach because these systems mainly deal with tuple-based streams. Recognizing the necessity for a systematic effort to address the above issues, the work presented in this thesis is a concrete step toward the foundation and construction of an integrated Geospatial Image Stream Processing framework, GISP. First, we present a data and metadata model for remotely sensed image streams. We introduce a precise characterization of images and image streams in the context of remotely sensed geospatial data. On this foundation, we define spatially-aware temporal operators with a consistent semantics for change analysis tasks. We address the change detection problem in settings where multiple image stream sources are available, and thus we introduce an architectural design for the processing of geospatial image streams from multiple sources. With the aim of targeting collaborative scientific environments, we construct a realization of our architecture based on Kepler, a robust and widely used scientific workflow management system, as the underlying computational support; and open data and Web interface standards, as a means to facilitate the interoperability of GISP instances with other processing infrastructures and client applications. We demonstrate our

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.

Using a forehead reflectance pulse oximeter to detect changes in sympathetic tone.

Science.gov (United States)

Wendelken, Suzanne M; McGrath, Susan P; Akay, Metin; Blike, George T

2004-01-01

The extreme conditions of combat and multi-casualty rescue often make field triage difficult and put the medic or first responder at risk. In an effort to improve field triage, we have developed an automated remote triage system called ARTEMIS (automated remote triage and emergency management information system) for use in the battlefield or disaster zone. Common to field injuries is a sudden change in arterial pressure resulting from massive blood loss or shock. In effort to stabilize the arterial pressure, the sympathetic system is strongly activated and sympathetic tone is increased. This preliminary research seeks to empirically demonstrate that a forehead reflectance pulse oximeter is a viable sensor for detecting sudden changes in sympathetic tone. We performed the classic supine-standing experiment and collected the raw waveform, the photoplethysmogram (PPG), continuously using a forehead reflectance pulse oximeter. The resulting waveform was processed in Matlab using various spectral analysis techniques (FFT and AR). Our preliminary results show that a relative ratio analysis (low frequency power/high frequency power) for both the raw PPG signal and its derived pulse statistics (height, beat-to-beat interval) is a useful technique for detecting change in sympathetic tone resulting from positional change.

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.

Landslide Change Detection Based on Multi-Temporal Airborne LiDAR-Derived DEMs

Directory of Open Access Journals (Sweden)

Omar E. Mora

2018-01-01

Full Text Available Remote sensing technologies have seen extraordinary improvements in both spatial resolution and accuracy recently. In particular, airborne laser scanning systems can now provide data for surface modeling with unprecedented resolution and accuracy, which can effectively support the detection of sub-meter surface features, vital for landslide mapping. Also, the easy repeatability of data acquisition offers the opportunity to monitor temporal surface changes, which are essential to identifying developing or active slides. Specific methods are needed to detect and map surface changes due to landslide activities. In this paper, we present a methodology that is based on fusing probabilistic change detection and landslide surface feature extraction utilizing multi-temporal Light Detection and Ranging (LiDAR derived Digital Elevation Models (DEMs to map surface changes demonstrating landslide activity. The proposed method was tested in an area with numerous slides ranging from 200 m2 to 27,000 m2 in area under low vegetation and tree cover, Zanesville, Ohio, USA. The surface changes observed are probabilistically evaluated to determine the likelihood of the changes being landslide activity related. Next, based on surface features, a Support Vector Machine (SVM quantifies and maps the topographic signatures of landslides in the entire area. Finally, these two processes are fused to detect landslide prone changes. The results demonstrate that 53 out of 80 inventory mapped landslides were identified using this method. Additionally, some areas that were not mapped in the inventory map displayed changes that are likely to be developing landslides.

[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

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.

Intrusion detection system elements

International Nuclear Information System (INIS)

Eaton, M.J.; Mangan, D.L.

1980-09-01

This report highlights elements required for an intrusion detection system and discusses problems which can be encountered in attempting to make the elements effective. Topics discussed include: sensors, both for exterior detection and interior detection; alarm assessment systems, with the discussion focused on video assessment; and alarm reporting systems, including alarm communication systems and dislay/console considerations. Guidance on careful planning and design of a new or to-be-improved system is presented

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 thermocouple malfunction in the Beacon system

International Nuclear Information System (INIS)

Morita, T.; Heibel, M.D.; Congedo, T.V.

1992-01-01

The BEACON system uses Core Exit Thermocouples (T/C) extensively for continuous radial power distribution monitoring. The T/C's are used to adjust the reference power distribution generated by the BEACON system to match the current radial power distribution. T/C reliability, repeatability, and relative accuracy have been very satisfactory. However, it is very important to detect any T/C malfunctions during operation, since a T/C signal change caused by an undetected malfunction can lead to serious errors in the radial power distribution developed by BEACON. A simple procedure has been developed which is capable of discriminating between changes in T/C signals caused by actual changes in reactor conditions and signal changes caused by T/C malfunctions

Detecting and Understanding Changing Arctic Carbon Emissions

Science.gov (United States)

Bruhwiler, L.

2017-12-01

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

Study on Method of Geohazard Change Detection Based on Integrating Remote Sensing and GIS

International Nuclear Information System (INIS)

Zhao, Zhenzhen; Yan, Qin; Liu, Zhengjun; Luo, Chengfeng

2014-01-01

Following a comprehensive literature review, this paper looks at analysis of geohazard using remote sensing information. This paper compares the basic types and methods of change detection, explores the basic principle of common methods and makes an respective analysis of the characteristics and shortcomings of the commonly used methods in the application of geohazard. Using the earthquake in JieGu as a case study, this paper proposes a geohazard change detection method integrating RS and GIS. When detecting the pre-earthquake and post-earthquake remote sensing images at different phases, it is crucial to set an appropriate threshold. The method adopts a self-adapting determination algorithm for threshold. We select a training region which is obtained after pixel information comparison and set a threshold value. The threshold value separates the changed pixel maximum. Then we apply the threshold value to the entire image, which could also make change detection accuracy maximum. Finally, we output the result to the GIS system to make change analysis. The experimental results show that this method of geohazard change detection based on integrating remote sensing and GIS information has higher accuracy with obvious advantages compared with the traditional methods

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.

Automatic detection system for multiple region of interest registration to account for posture changes in head and neck radiotherapy

Science.gov (United States)

Mencarelli, A.; van Beek, S.; Zijp, L. J.; Rasch, C.; van Herk, M.; Sonke, J.-J.

2014-04-01

Despite immobilization of head and neck (H and N) cancer patients, considerable posture changes occur over the course of radiotherapy (RT). To account for the posture changes, we previously implemented a multiple regions of interest (mROIs) registration system tailored to the H and N region for image-guided RT correction strategies. This paper is focused on the automatic segmentation of the ROIs in the H and N region. We developed a fast and robust automatic detection system suitable for an online image-guided application and quantified its performance. The system was developed to segment nine high contrast structures from the planning CT including cervical vertebrae, mandible, hyoid, manubrium of sternum, larynx and occipital bone. It generates nine 3D rectangular-shaped ROIs and informs the user in case of ambiguities. Two observers evaluated the robustness of the segmentation on 188 H and N cancer patients. Bland-Altman analysis was applied to a sub-group of 50 patients to compare the registration results using only the automatically generated ROIs and those manually set by two independent experts. Finally the time performance and workload were evaluated. Automatic detection of individual anatomical ROIs had a success rate of 97%/53% with/without user notifications respectively. Following the notifications, for 38% of the patients one or more structures were manually adjusted. The processing time was on average 5 s. The limits of agreement between the local registrations of manually and automatically set ROIs was comprised between ±1.4 mm, except for the manubrium of sternum (-1.71 mm and 1.67 mm), and were similar to the limits agreement between the two experts. The workload to place the nine ROIs was reduced from 141 s (±20 s) by the manual procedure to 59 s (±17 s) using the automatic method. An efficient detection system to segment multiple ROIs was developed for Cone-Beam CT image-guided applications in the H and N region and is clinically implemented in

Nucleic acid detection system and method for detecting influenza

Science.gov (United States)

Cai, Hong; Song, Jian

2015-03-17

The invention provides a rapid, sensitive and specific nucleic acid detection system which utilizes isothermal nucleic acid amplification in combination with a lateral flow chromatographic device, or DNA dipstick, for DNA-hybridization detection. The system of the invention requires no complex instrumentation or electronic hardware, and provides a low cost nucleic acid detection system suitable for highly sensitive pathogen detection. Hybridization to single-stranded DNA amplification products using the system of the invention provides a sensitive and specific means by which assays can be multiplexed for the detection of multiple target sequences.

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.

π0 detection system

International Nuclear Information System (INIS)

Suzuki, Yoichiro

1977-01-01

A π-zero meson detection system used for the measurement of charge exchange reaction is described. The detection of π-zero is made by observing the coincidence events of two gamma-ray emission following the decay of π-zero meson. The angles of the emitted gamma-rays are detected with a wire spark chamber system, and the energies of the gamma-rays are measured with hodoscope type lead glass Cherenkov counters. In front of the π-zero counter system, a lead converter is set, and the incident gamma-rays convert to electron positron pairs, which can be detected with the wire spark chambers. The system is a multi-track detection system. The high voltage pulser of the wire spark chamber system is a charge line thyratron pulser, and the chamber itself is a transmission line type. Read-out can be made by a mag-line system. Wave forms and efficiencies were measured. The three-track efficiency was about 90% by the condenser method and 95% by the charge line method. (Kato, T.)

Portable modular detection system

Science.gov (United States)

Brennan, James S [Rodeo, CA; Singh, Anup [Danville, CA; Throckmorton, Daniel J [Tracy, CA; Stamps, James F [Livermore, CA

2009-10-13

Disclosed herein are portable and modular detection devices and systems for detecting electromagnetic radiation, such as fluorescence, from an analyte which comprises at least one optical element removably attached to at least one alignment rail. Also disclosed are modular detection devices and systems having an integrated lock-in amplifier and spatial filter and assay methods using the portable and modular detection devices.

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.

Microcontroller based driver alertness detection systems to detect drowsiness

Science.gov (United States)

Adenin, Hasibah; Zahari, Rahimi; Lim, Tiong Hoo

2018-04-01

The advancement of embedded system for detecting and preventing drowsiness in a vehicle is a major challenge for road traffic accident systems. To prevent drowsiness while driving, it is necessary to have an alert system that can detect a decline in driver concentration and send a signal to the driver. Studies have shown that traffc accidents usually occur when the driver is distracted while driving. In this paper, we have reviewed a number of detection systems to monitor the concentration of a car driver and propose a portable Driver Alertness Detection System (DADS) to determine the level of concentration of the driver based on pixelated coloration detection technique using facial recognition. A portable camera will be placed at the front visor to capture facial expression and the eye activities. We evaluate DADS using 26 participants and have achieved 100% detection rate with good lighting condition and a low detection rate at night.

Video-based real-time on-street parking occupancy detection system

Science.gov (United States)

Bulan, Orhan; Loce, Robert P.; Wu, Wencheng; Wang, YaoRong; Bernal, Edgar A.; Fan, Zhigang

2013-10-01

Urban parking management is receiving significant attention due to its potential to reduce traffic congestion, fuel consumption, and emissions. Real-time parking occupancy detection is a critical component of on-street parking management systems, where occupancy information is relayed to drivers via smart phone apps, radio, Internet, on-road signs, or global positioning system auxiliary signals. Video-based parking occupancy detection systems can provide a cost-effective solution to the sensing task while providing additional functionality for traffic law enforcement and surveillance. We present a video-based on-street parking occupancy detection system that can operate in real time. Our system accounts for the inherent challenges that exist in on-street parking settings, including illumination changes, rain, shadows, occlusions, and camera motion. Our method utilizes several components from video processing and computer vision for motion detection, background subtraction, and vehicle detection. We also present three traffic law enforcement applications: parking angle violation detection, parking boundary violation detection, and exclusion zone violation detection, which can be integrated into the parking occupancy cameras as a value-added option. Our experimental results show that the proposed parking occupancy detection method performs in real-time at 5 frames/s and achieves better than 90% detection accuracy across several days of videos captured in a busy street block under various weather conditions such as sunny, cloudy, and rainy, among others.

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.

Traffic Congestion Detection System through Connected Vehicles and Big Data

Directory of Open Access Journals (Sweden)

Néstor Cárdenas-Benítez

2016-04-01

Full Text Available This article discusses the simulation and evaluation of a traffic congestion detection system which combines inter-vehicular communications, fixed roadside infrastructure and infrastructure-to-infrastructure connectivity and big data. The system discussed in this article permits drivers to identify traffic congestion and change their routes accordingly, thus reducing the total emissions of CO2 and decreasing travel time. This system monitors, processes and stores large amounts of data, which can detect traffic congestion in a precise way by means of a series of algorithms that reduces localized vehicular emission by rerouting vehicles. To simulate and evaluate the proposed system, a big data cluster was developed based on Cassandra, which was used in tandem with the OMNeT++ discreet event network simulator, coupled with the SUMO (Simulation of Urban MObility traffic simulator and the Veins vehicular network framework. The results validate the efficiency of the traffic detection system and its positive impact in detecting, reporting and rerouting traffic when traffic events occur.

Traffic Congestion Detection System through Connected Vehicles and Big Data.

Science.gov (United States)

Cárdenas-Benítez, Néstor; Aquino-Santos, Raúl; Magaña-Espinoza, Pedro; Aguilar-Velazco, José; Edwards-Block, Arthur; Medina Cass, Aldo

2016-04-28

This article discusses the simulation and evaluation of a traffic congestion detection system which combines inter-vehicular communications, fixed roadside infrastructure and infrastructure-to-infrastructure connectivity and big data. The system discussed in this article permits drivers to identify traffic congestion and change their routes accordingly, thus reducing the total emissions of CO₂ and decreasing travel time. This system monitors, processes and stores large amounts of data, which can detect traffic congestion in a precise way by means of a series of algorithms that reduces localized vehicular emission by rerouting vehicles. To simulate and evaluate the proposed system, a big data cluster was developed based on Cassandra, which was used in tandem with the OMNeT++ discreet event network simulator, coupled with the SUMO (Simulation of Urban MObility) traffic simulator and the Veins vehicular network framework. The results validate the efficiency of the traffic detection system and its positive impact in detecting, reporting and rerouting traffic when traffic events occur.

Generalized Detectability for Discrete Event Systems

Science.gov (United States)

Shu, Shaolong; Lin, Feng

2011-01-01

In our previous work, we investigated detectability of discrete event systems, which is defined as the ability to determine the current and subsequent states of a system based on observation. For different applications, we defined four types of detectabilities: (weak) detectability, strong detectability, (weak) periodic detectability, and strong periodic detectability. In this paper, we extend our results in three aspects. (1) We extend detectability from deterministic systems to nondeterministic systems. Such a generalization is necessary because there are many systems that need to be modeled as nondeterministic discrete event systems. (2) We develop polynomial algorithms to check strong detectability. The previous algorithms are based on observer whose construction is of exponential complexity, while the new algorithms are based on a new automaton called detector. (3) We extend detectability to D-detectability. While detectability requires determining the exact state of a system, D-detectability relaxes this requirement by asking only to distinguish certain pairs of states. With these extensions, the theory on detectability of discrete event systems becomes more applicable in solving many practical problems. PMID:21691432

Beauty hinders attention switch in change detection: the role of facial attractiveness and distinctiveness.

Directory of Open Access Journals (Sweden)

Wenfeng Chen

Full Text Available BACKGROUND: Recent research has shown that the presence of a task-irrelevant attractive face can induce a transient diversion of attention from a perceptual task that requires covert deployment of attention to one of the two locations. However, it is not known whether this spontaneous appraisal for facial beauty also modulates attention in change detection among multiple locations, where a slower, and more controlled search process is simultaneously affected by the magnitude of a change and the facial distinctiveness. Using the flicker paradigm, this study examines how spontaneous appraisal for facial beauty affects the detection of identity change among multiple faces. METHODOLOGY/PRINCIPAL FINDINGS: Participants viewed a display consisting of two alternating frames of four faces separated by a blank frame. In half of the trials, one of the faces (target face changed to a different person. The task of the participant was to indicate whether a change of face identity had occurred. The results showed that (1 observers were less efficient at detecting identity change among multiple attractive faces relative to unattractive faces when the target and distractor faces were not highly distinctive from one another; and (2 it is difficult to detect a change if the new face is similar to the old. CONCLUSIONS/SIGNIFICANCE: The findings suggest that attractive faces may interfere with the attention-switch process in change detection. The results also show that attention in change detection was strongly modulated by physical similarity between the alternating faces. Although facial beauty is a powerful stimulus that has well-demonstrated priority, its influence on change detection is easily superseded by low-level image similarity. The visual system appears to take a different approach to facial beauty when a task requires resource-demanding feature comparisons.

Beauty hinders attention switch in change detection: the role of facial attractiveness and distinctiveness.

Science.gov (United States)

Chen, Wenfeng; Liu, Chang Hong; Nakabayashi, Kazuyo

2012-01-01

Recent research has shown that the presence of a task-irrelevant attractive face can induce a transient diversion of attention from a perceptual task that requires covert deployment of attention to one of the two locations. However, it is not known whether this spontaneous appraisal for facial beauty also modulates attention in change detection among multiple locations, where a slower, and more controlled search process is simultaneously affected by the magnitude of a change and the facial distinctiveness. Using the flicker paradigm, this study examines how spontaneous appraisal for facial beauty affects the detection of identity change among multiple faces. Participants viewed a display consisting of two alternating frames of four faces separated by a blank frame. In half of the trials, one of the faces (target face) changed to a different person. The task of the participant was to indicate whether a change of face identity had occurred. The results showed that (1) observers were less efficient at detecting identity change among multiple attractive faces relative to unattractive faces when the target and distractor faces were not highly distinctive from one another; and (2) it is difficult to detect a change if the new face is similar to the old. The findings suggest that attractive faces may interfere with the attention-switch process in change detection. The results also show that attention in change detection was strongly modulated by physical similarity between the alternating faces. Although facial beauty is a powerful stimulus that has well-demonstrated priority, its influence on change detection is easily superseded by low-level image similarity. The visual system appears to take a different approach to facial beauty when a task requires resource-demanding feature comparisons.

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.

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.

Interoperable cross-domain semantic and geospatial framework for automatic change detection

Science.gov (United States)

Kuo, Chiao-Ling; Hong, Jung-Hong

2016-01-01

With the increasingly diverse types of geospatial data established over the last few decades, semantic interoperability in integrated applications has attracted much interest in the field of Geographic Information System (GIS). This paper proposes a new strategy and framework to process cross-domain geodata at the semantic level. This framework leverages the semantic equivalence of concepts between domains through bridge ontology and facilitates the integrated use of different domain data, which has been long considered as an essential superiority of GIS, but is impeded by the lack of understanding about the semantics implicitly hidden in the data. We choose the task of change detection to demonstrate how the introduction of ontology concept can effectively make the integration possible. We analyze the common properties of geodata and change detection factors, then construct rules and summarize possible change scenario for making final decisions. The use of topographic map data to detect changes in land use shows promising success, as far as the improvement of efficiency and level of automation is concerned. We believe the ontology-oriented approach will enable a new way for data integration across different domains from the perspective of semantic interoperability, and even open a new dimensionality for the future GIS.

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.

Optical coherence tomography for the structural changes detection in aging skin

Science.gov (United States)

Cheng, Chih-Ming; Chang, Yu-Fen; Chiang, Hung-Chih; Chang, Chir-Weei

2018-01-01

Optical coherence tomography (OCT) technique is an extremely powerful tool to detect numerous ophthalmological disorders, such as retinal disorder, and can be applied on other fields. Thus, many OCT systems are developed. For assessment of the skin textures, a cross-sectional (B-scan) spectra domain OCT system is better than an en-face one. However, this kind of commercial OCT system is not available. We designed a brand-new probe of commercial OCT system for evaluating skin texture without destroying the original instrument and it can be restored in 5 minutes. This modification of OCT system retains the advantages of commercial instrument, such as reliable, stable, and safe. Furthermore, the structural changes in aging skin are easily obtained by means of our probe, including larger pores, thinning of the dermis, collagen volume loss, vessel atrophy and flattening of dermal-epidermal junction. We can use this OCT technique in the field of cosmetic medicine such as detecting the skin textures and skin care product effect followup.

Interior intrusion detection systems

Energy Technology Data Exchange (ETDEWEB)

Rodriguez, J.R.; Matter, J.C. (Sandia National Labs., Albuquerque, NM (United States)); Dry, B. (BE, Inc., Barnwell, SC (United States))

1991-10-01

The purpose of this NUREG is to present technical information that should be useful to NRC licensees in designing interior intrusion detection systems. Interior intrusion sensors are discussed according to their primary application: boundary-penetration detection, volumetric detection, and point protection. Information necessary for implementation of an effective interior intrusion detection system is presented, including principles of operation, performance characteristics and guidelines for design, procurement, installation, testing, and maintenance. A glossary of sensor data terms is included. 36 figs., 6 tabs.

Interior intrusion detection systems

International Nuclear Information System (INIS)

Rodriguez, J.R.; Matter, J.C.; Dry, B.

1991-10-01

The purpose of this NUREG is to present technical information that should be useful to NRC licensees in designing interior intrusion detection systems. Interior intrusion sensors are discussed according to their primary application: boundary-penetration detection, volumetric detection, and point protection. Information necessary for implementation of an effective interior intrusion detection system is presented, including principles of operation, performance characteristics and guidelines for design, procurement, installation, testing, and maintenance. A glossary of sensor data terms is included. 36 figs., 6 tabs

Real-time DNA Amplification and Detection System Based on a CMOS Image Sensor.

Science.gov (United States)

Wang, Tiantian; Devadhasan, Jasmine Pramila; Lee, Do Young; Kim, Sanghyo

2016-01-01

In the present study, we developed a polypropylene well-integrated complementary metal oxide semiconductor (CMOS) platform to perform the loop mediated isothermal amplification (LAMP) technique for real-time DNA amplification and detection simultaneously. An amplification-coupled detection system directly measures the photon number changes based on the generation of magnesium pyrophosphate and color changes. The photon number decreases during the amplification process. The CMOS image sensor observes the photons and converts into digital units with the aid of an analog-to-digital converter (ADC). In addition, UV-spectral studies, optical color intensity detection, pH analysis, and electrophoresis detection were carried out to prove the efficiency of the CMOS sensor based the LAMP system. Moreover, Clostridium perfringens was utilized as proof-of-concept detection for the new system. We anticipate that this CMOS image sensor-based LAMP method will enable the creation of cost-effective, label-free, optical, real-time and portable molecular diagnostic devices.

Early detection of structual changes in random signal

International Nuclear Information System (INIS)

Kuroda, Yoshiteru; Yokota, Katsuhiro

1981-01-01

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

An Approach for Unsupervised Change Detection in Multitemporal VHR Images Acquired by Different Multispectral Sensors

Directory of Open Access Journals (Sweden)

Yady Tatiana Solano-Correa

2018-03-01

Full Text Available This paper proposes an approach for the detection of changes in multitemporal Very High Resolution (VHR optical images acquired by different multispectral sensors. The proposed approach, which is inspired by a recent framework developed to support the design of change-detection systems for single-sensor VHR remote sensing images, addresses and integrates in the general approach a strategy to effectively deal with multisensor information, i.e., to perform change detection between VHR images acquired by different multispectral sensors on two dates. This is achieved by the definition of procedures for the homogenization of radiometric, spectral and geometric image properties. These procedures map images into a common feature space where the information acquired by different multispectral sensors becomes comparable across time. Although the approach is general, here we optimize it for the detection of changes in vegetation and urban areas by employing features based on linear transformations (Tasseled Caps and Orthogonal Equations, which are shown to be effective for representing the multisensor information in a homogeneous physical way irrespectively of the considered sensor. Experiments on multitemporal images acquired by different VHR satellite systems (i.e., QuickBird, WorldView-2 and GeoEye-1 confirm the effectiveness of the proposed approach.

Single particle detecting telescope system

International Nuclear Information System (INIS)

Yamamoto, I.; Tomiyama, T.; Iga, Y.; Komatsubara, T.; Kanada, M.; Yamashita, Y.; Wada, T.; Furukawa, S.

1981-01-01

We constructed the single particle detecting telescope system for detecting a fractionally charged particle. The telescope consists of position detecting counters, wall-less multi-cell chambers, single detecting circuits and microcomputer system as data I/0 processor. Especially, a frequency of double particle is compared the case of the single particle detecting with the case of an ordinary measurement

A Portable Impedance Immunosensing System for Rapid Detection of Salmonella Typhimurium.

Science.gov (United States)

Wen, Tao; Wang, Ronghui; Sotero, America; Li, Yanbin

2017-08-28

Salmonella Typhimurium is one of the most dangerous foodborne pathogens and poses a significant threat to human health. The objective of this study was to develop a portable impedance immunosensing system for rapid and sensitive detection of S . Typhimurium in poultry. The developed portable impedance immunosensing system consisted of a gold interdigitated array microelectrode (IDAM), a signal acquisitive interface and a laptop computer with LabVIEW software. The IDAM was first functionalized with 16-Mercaptohexadecanoic acid, and streptavidin was immobilized onto the electrode surface through covalent bonding. Then, biotin-labelled S . Typhimurium -antibody was immobilized onto the IDAM surface. Samples were dropped on the surface of the IDAM and the S . Typhimurium cells in the samples were captured by the antibody on the IDAM. This resulted in impedance changes that were measured and displayed with the LabVIEW software. An equivalent circuit of the immunosensor demonstrated that the largest change in impedance was due to the electron-transfer resistance. The equivalent circuit showed an increase of 35% for the electron-transfer resistance value compared to the negative control. The calibration result indicated that the portable impedance immunosensing system could be used to measure the standard impedance elements, and it had a maximum error of measurement of approximately 13%. For pure culture detection, the system had a linear relationship between the impedance change and the logarithmic value of S . Typhimurium cells ranging from 76 to 7.6 × 10⁶ CFU (colony-forming unit) (50 μL) -1 . The immunosensor also had a correlation coefficient of 0.98, and a high specificity for detection of S . Typhimurium cells with a limit of detection (LOD) of 10² CFU (50 μL) -1 . The detection time from the moment a sample was introduced to the display of the results was 1 h. To conclude, the portable impedance immunosensing system for detection of S . Typhimurium achieved

A Portable Impedance Immunosensing System for Rapid Detection of Salmonella Typhimurium

Directory of Open Access Journals (Sweden)

Tao Wen

2017-08-01

Full Text Available Salmonella Typhimurium is one of the most dangerous foodborne pathogens and poses a significant threat to human health. The objective of this study was to develop a portable impedance immunosensing system for rapid and sensitive detection of S. Typhimurium in poultry. The developed portable impedance immunosensing system consisted of a gold interdigitated array microelectrode (IDAM, a signal acquisitive interface and a laptop computer with LabVIEW software. The IDAM was first functionalized with 16-Mercaptohexadecanoic acid, and streptavidin was immobilized onto the electrode surface through covalent bonding. Then, biotin-labelled S. Typhimurium-antibody was immobilized onto the IDAM surface. Samples were dropped on the surface of the IDAM and the S. Typhimurium cells in the samples were captured by the antibody on the IDAM. This resulted in impedance changes that were measured and displayed with the LabVIEW software. An equivalent circuit of the immunosensor demonstrated that the largest change in impedance was due to the electron-transfer resistance. The equivalent circuit showed an increase of 35% for the electron-transfer resistance value compared to the negative control. The calibration result indicated that the portable impedance immunosensing system could be used to measure the standard impedance elements, and it had a maximum error of measurement of approximately 13%. For pure culture detection, the system had a linear relationship between the impedance change and the logarithmic value of S. Typhimurium cells ranging from 76 to 7.6 × 106 CFU (colony-forming unit (50 μL−1. The immunosensor also had a correlation coefficient of 0.98, and a high specificity for detection of S. Typhimurium cells with a limit of detection (LOD of 102 CFU (50 μL−1. The detection time from the moment a sample was introduced to the display of the results was 1 h. To conclude, the portable impedance immunosensing system for detection of S. Typhimurium

Rapid deployment intrusion detection system

International Nuclear Information System (INIS)

Graham, R.H.

1997-01-01

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

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,…

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. ...

An environmental monitoring detection system

International Nuclear Information System (INIS)

Leli Yuniarsari; Istofa; Sukandar

2015-01-01

Is part of radiation detection of the nuclear facilities engineering activities within nuclear facilities. The system comprised of gamma-ray radiation detector and weather detection which includes anemometer to detect the wind direction and speed, as well as rain gauge to measure the rainfall in a period of time. Data acquisition of the output is processed by Arduino Uno system which transformed the data into a particular standard and then displayed online in the website. The radiation detection system uses gamma-ray detector of NaI(Tl) and GM which convert the radiation detected into electric pulse to be fed into a pre-amp and amplifier and modified into square pulse. The weather detection system on the other hand works based on switch principle. For example, the wind with a certain speed could turn on a switch in the system and produce a voltage or pulse which can be measured. This value will then be interpreted as the wind direction and speed. Likewise for the rainfall gauge, the volume of water entering the bucket will turn the switch on, at the same time producing 1 pulse. The result of the experiment shows that for radiation detection system the output is a square pulse 4 volts by using detector NaI(Tl) and 4.4 volts by using detector GM. For weather detection system, basically was able to detect the wind direction, wind speed and rainfall just to find out further research is needed accuracy and the results compared with the standard tools available in BMKG. (author)

A Design of Portable Pesticide Residue Detection System Based on the Enzyme Electrode

Directory of Open Access Journals (Sweden)

Xia SUN

2013-03-01

Full Text Available In this paper, a portable detection system was designed based on amperometric acetylcholinesterase biosensor for rapidly detecting pesticide residues in fruits and vegetables. There were potentiostat, three electrode system, differential amplification circuit and double integral analog to digital (A/D circuit modules in this system. The measurement principle of this system was depended on the weak current from enzyme catalyzing substrate in acetylcholinesterase biosensor for detecting pesticide residues. The weak current generated by the enzyme biosensor was changed into 0-5 V standard voltage signal by this system as an output signal. The proposed system was investigated with eight kinds of standard pesticide of different concentrations, the results showed that the detection limits were all lower than 10 ng/kg. Thus, a new effective home-made system of detecting pesticide residues with portable, easy-to-use, fast response was developed. The pesticide residues rapid detection system can collect the weak current signal generated by electrochemical reaction and on-site detect the concentration of pesticide residues in real fruits and vegetables samples.

Change detection in the dynamics of an intracellular protein synthesis model using nonlinear Kalman filtering.

Science.gov (United States)

Rigatos, Gerasimos G; Rigatou, Efthymia G; Djida, Jean Daniel

2015-10-01

A method for early diagnosis of parametric changes in intracellular protein synthesis models (e.g. the p53 protein - mdm2 inhibitor model) is developed with the use of a nonlinear Kalman Filtering approach (Derivative-free nonlinear Kalman Filter) and of statistical change detection methods. The intracellular protein synthesis dynamic model is described by a set of coupled nonlinear differential equations. It is shown that such a dynamical system satisfies differential flatness properties and this allows to transform it, through a change of variables (diffeomorphism), to the so-called linear canonical form. For the linearized equivalent of the dynamical system, state estimation can be performed using the Kalman Filter recursion. Moreover, by applying an inverse transformation based on the previous diffeomorphism it becomes also possible to obtain estimates of the state variables of the initial nonlinear model. By comparing the output of the Kalman Filter (which is assumed to correspond to the undistorted dynamical model) with measurements obtained from the monitored protein synthesis system, a sequence of differences (residuals) is obtained. The statistical processing of the residuals with the use of x2 change detection tests, can provide indication within specific confidence intervals about parametric changes in the considered biological system and consequently indications about the appearance of specific diseases (e.g. malignancies).

Study on Leak Detection of the Pipeline System by Acoustic Emission

International Nuclear Information System (INIS)

Yoon, D. J.; Kim, C. J.

1987-01-01

Leak detection testing for the pipeline system was performed by the acoustic emission method. It was found that the detected signal spectrum was influenced by the frequency response of sensors and pressure changes. AE parameters and frequency spectrum distributions were used to analyze the leak signals. The slope rise time of AE parameters were the important factors for distinguishing leak signals. The amplitude of leak signal was more affected by the changes of leak, rate and pressure than those of leak type

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.

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.

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.

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

System Detects Vibrational Instabilities

Science.gov (United States)

Bozeman, Richard J., Jr.

1990-01-01

Sustained vibrations at two critical frequencies trigger diagnostic response or shutdown. Vibration-analyzing electronic system detects instabilities of combustion in rocket engine. Controls pulse-mode firing of engine and identifies vibrations above threshold amplitude at 5.9 and/or 12kHz. Adapted to other detection and/or control schemes involving simultaneous real-time detection of signals above or below preset amplitudes at two or more specified frequencies. Potential applications include rotating machinery and encoders and decoders in security systems.

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 Blindness Phenomena for Virtual Reality Display Systems.

Science.gov (United States)

Steinicke, Frank; Bruder, Gerd; Hinrichs, Klaus; Willemsen, Pete

2011-09-01

In visual perception, change blindness describes the phenomenon that persons viewing a visual scene may apparently fail to detect significant changes in that scene. These phenomena have been observed in both computer-generated imagery and real-world scenes. Several studies have demonstrated that change blindness effects occur primarily during visual disruptions such as blinks or saccadic eye movements. However, until now the influence of stereoscopic vision on change blindness has not been studied thoroughly in the context of visual perception research. In this paper, we introduce change blindness techniques for stereoscopic virtual reality (VR) systems, providing the ability to substantially modify a virtual scene in a manner that is difficult for observers to perceive. We evaluate techniques for semiimmersive VR systems, i.e., a passive and active stereoscopic projection system as well as an immersive VR system, i.e., a head-mounted display, and compare the results to those of monoscopic viewing conditions. For stereoscopic viewing conditions, we found that change blindness phenomena occur with the same magnitude as in monoscopic viewing conditions. Furthermore, we have evaluated the potential of the presented techniques for allowing abrupt, and yet significant, changes of a stereoscopically displayed virtual reality environment.

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

Proactive interference does not meaningfully distort visual working memory capacity estimates in the canonical change detection task

Directory of Open Access Journals (Sweden)

Po-Han eLin

2012-02-01

Full Text Available The change detection task has become a standard method for estimating the storage capacity of visual working memory. Most researchers assume that this task isolates the properties of an active short-term storage system that can be dissociated from long-term memory systems. However, long-term memory storage may influence performance on this task. In particular, memory traces from previous trials may create proactive interference that sometimes leads to errors, thereby reducing estimated capacity. Consequently, the capacity of visual working memory may be higher than is usually thought, and correlations between capacity and other measures of cognition may reflect individual differences in proactive interference rather than individual differences in the capacity of the short-term storage system. Indeed, previous research has shown that change detection performance can be influenced by proactive interference under some conditions. The purpose of the present study was to determine whether the canonical version of the change detection task—in which the to-be-remembered information consists of simple, briefly presented features—is influenced by proactive interference. Two experiments were conducted using methods that ordinarily produce substantial evidence of proactive interference, but no proactive interference was observed. Thus, the canonical version of the change detection task can be used to assess visual working memory capacity with no meaningful influence of proactive interference.

Proactive interference does not meaningfully distort visual working memory capacity estimates in the canonical change detection task.

Science.gov (United States)

Lin, Po-Han; Luck, Steven J

2012-01-01

The change detection task has become a standard method for estimating the storage capacity of visual working memory. Most researchers assume that this task isolates the properties of an active short-term storage system that can be dissociated from long-term memory systems. However, long-term memory storage may influence performance on this task. In particular, memory traces from previous trials may create proactive interference that sometimes leads to errors, thereby reducing estimated capacity. Consequently, the capacity of visual working memory may be higher than is usually thought, and correlations between capacity and other measures of cognition may reflect individual differences in proactive interference rather than individual differences in the capacity of the short-term storage system. Indeed, previous research has shown that change detection performance can be influenced by proactive interference under some conditions. The purpose of the present study was to determine whether the canonical version of the change detection task - in which the to-be-remembered information consists of simple, briefly presented features - is influenced by proactive interference. Two experiments were conducted using methods that ordinarily produce substantial evidence of proactive interference, but no proactive interference was observed. Thus, the canonical version of the change detection task can be used to assess visual working memory capacity with no meaningful influence of proactive interference.

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.

Study on APD real time compensation methods of laser Detection system

International Nuclear Information System (INIS)

Feng Ying; Zhang He; Zhang Xiangjin; Liu Kun

2011-01-01

their operating principles. The constant false alarm rate compensation can't detect the pulse signal which comes randomly. Therefore real-time performance can't be realized. The noise compensation can meet the request of real-time performance. If it is used in the environment where background light is intense or changes acutely, there is a better effect. The temperature compensation can also achieve the real-time performance request. If it is used in the environment where temperature changes acutely, there is also a better effect. Aim at such problems, this paper presents that different APD real-time compensations should be adopt to adapt to different environments. The exiting temperature compensation adjusts output voltage by using variable resistance to regulate input voltage. Its structure is complex; the real-time performance is worse. In order to remedy these defects, a real-time temperature compensation which is based on switch on-off time of switching power supply is designed. Its feasibility and operating stability are confirmed by plate making and experiment. At last, the comparison experiments between the real-time noise compensation and the real-time temperature compensation is carried out in the environments where temperature is almost invariant and background light acutely changes from5lux to150lux . The result shows that the operating effect of the real-time noise compensation is better here, the noise minifies to a sixth of original noise. The comparison experiments between the real-time noise compensation and the real-time temperature compensation is carried out in darkroom where background light is 5lux and temperature almost rapidly changes from -20 deg. C to 80 deg. C. The result shows that the operating effect of the real-time temperature compensation is better here, the noise minifies to a seventh of original noise. Moreover, these methods can be applied to other type detection systems of weak photoelectric signal; they have high actual application

Study on APD real time compensation methods of laser Detection system

Energy Technology Data Exchange (ETDEWEB)

Feng Ying; Zhang He; Zhang Xiangjin; Liu Kun, E-mail: fy_caimi@163.com [ZNDY of Ministerial Key Laboratory, Nanjing University of Science and Technology, Nanjing 210094 (China)

2011-02-01

by analyzing their operating principles. The constant false alarm rate compensation can't detect the pulse signal which comes randomly. Therefore real-time performance can't be realized. The noise compensation can meet the request of real-time performance. If it is used in the environment where background light is intense or changes acutely, there is a better effect. The temperature compensation can also achieve the real-time performance request. If it is used in the environment where temperature changes acutely, there is also a better effect. Aim at such problems, this paper presents that different APD real-time compensations should be adopt to adapt to different environments. The exiting temperature compensation adjusts output voltage by using variable resistance to regulate input voltage. Its structure is complex; the real-time performance is worse. In order to remedy these defects, a real-time temperature compensation which is based on switch on-off time of switching power supply is designed. Its feasibility and operating stability are confirmed by plate making and experiment. At last, the comparison experiments between the real-time noise compensation and the real-time temperature compensation is carried out in the environments where temperature is almost invariant and background light acutely changes from5lux to150lux . The result shows that the operating effect of the real-time noise compensation is better here, the noise minifies to a sixth of original noise. The comparison experiments between the real-time noise compensation and the real-time temperature compensation is carried out in darkroom where background light is 5lux and temperature almost rapidly changes from -20 deg. C to 80 deg. C. The result shows that the operating effect of the real-time temperature compensation is better here, the noise minifies to a seventh of original noise. Moreover, these methods can be applied to other type detection systems of weak photoelectric signal; they

Study on APD real time compensation methods of laser Detection system

Science.gov (United States)

Ying, Feng; He, Zhang; Xiangjin, Zhang; Kun, Liu

2011-02-01

their operating principles. The constant false alarm rate compensation can't detect the pulse signal which comes randomly. Therefore real-time performance can't be realized. The noise compensation can meet the request of real-time performance. If it is used in the environment where background light is intense or changes acutely, there is a better effect. The temperature compensation can also achieve the real-time performance request. If it is used in the environment where temperature changes acutely, there is also a better effect. Aim at such problems, this paper presents that different APD real-time compensations should be adopt to adapt to different environments. The exiting temperature compensation adjusts output voltage by using variable resistance to regulate input voltage. Its structure is complex; the real-time performance is worse. In order to remedy these defects, a real-time temperature compensation which is based on switch on-off time of switching power supply is designed. Its feasibility and operating stability are confirmed by plate making and experiment. At last, the comparison experiments between the real-time noise compensation and the real-time temperature compensation is carried out in the environments where temperature is almost invariant and background light acutely changes from5lux to150lux . The result shows that the operating effect of the real-time noise compensation is better here, the noise minifies to a sixth of original noise. The comparison experiments between the real-time noise compensation and the real-time temperature compensation is carried out in darkroom where background light is 5lux and temperature almost rapidly changes from -20°C to 80°C. The result shows that the operating effect of the real-time temperature compensation is better here, the noise minifies to a seventh of original noise. Moreover, these methods can be applied to other type detection systems of weak photoelectric signal; they have high actual application value.

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....

3D change detection at street level using mobile laser scanning point clouds and terrestrial images

Science.gov (United States)

Qin, Rongjun; Gruen, Armin

2014-04-01

Automatic change detection and geo-database updating in the urban environment are difficult tasks. There has been much research on detecting changes with satellite and aerial images, but studies have rarely been performed at the street level, which is complex in its 3D geometry. Contemporary geo-databases include 3D street-level objects, which demand frequent data updating. Terrestrial images provides rich texture information for change detection, but the change detection with terrestrial images from different epochs sometimes faces problems with illumination changes, perspective distortions and unreliable 3D geometry caused by the lack of performance of automatic image matchers, while mobile laser scanning (MLS) data acquired from different epochs provides accurate 3D geometry for change detection, but is very expensive for periodical acquisition. This paper proposes a new method for change detection at street level by using combination of MLS point clouds and terrestrial images: the accurate but expensive MLS data acquired from an early epoch serves as the reference, and terrestrial images or photogrammetric images captured from an image-based mobile mapping system (MMS) at a later epoch are used to detect the geometrical changes between different epochs. The method will automatically mark the possible changes in each view, which provides a cost-efficient method for frequent data updating. The methodology is divided into several steps. In the first step, the point clouds are recorded by the MLS system and processed, with data cleaned and classified by semi-automatic means. In the second step, terrestrial images or mobile mapping images at a later epoch are taken and registered to the point cloud, and then point clouds are projected on each image by a weighted window based z-buffering method for view dependent 2D triangulation. In the next step, stereo pairs of the terrestrial images are rectified and re-projected between each other to check the geometrical

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.

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.

Particle detection systems and methods

Science.gov (United States)

Morris, Christopher L.; Makela, Mark F.

2010-05-11

Techniques, apparatus and systems for detecting particles such as muons and neutrons. In one implementation, a particle detection system employs a plurality of drift cells, which can be for example sealed gas-filled drift tubes, arranged on sides of a volume to be scanned to track incoming and outgoing charged particles, such as cosmic ray-produced muons. The drift cells can include a neutron sensitive medium to enable concurrent counting of neutrons. The system can selectively detect devices or materials, such as iron, lead, gold, uranium, plutonium, and/or tungsten, occupying the volume from multiple scattering of the charged particles passing through the volume and can concurrently detect any unshielded neutron sources occupying the volume from neutrons emitted therefrom. If necessary, the drift cells can be used to also detect gamma rays. The system can be employed to inspect occupied vehicles at border crossings for nuclear threat objects.

Ferret Workflow Anomaly Detection System

National Research Council Canada - National Science Library

Smith, Timothy J; Bryant, Stephany

2005-01-01

The Ferret workflow anomaly detection system project 2003-2004 has provided validation and anomaly detection in accredited workflows in secure knowledge management systems through the use of continuous, automated audits...

Hierarchical 2.5D scene alignment for change detection with large viewpoint differences

NARCIS (Netherlands)

van de Wouw, D.; Dubbelman, G.; de With, P.H.N.

2016-01-01

Change detection from mobile platforms is a relevant topic in the field of intelligent vehicles and has many applications, such as countering improvised explosive devices (C-IED). Existing real-time C-IED systems are not robust against large viewpoint differences, which are unavoidable under

Solar system fault detection

Science.gov (United States)

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

1984-05-14

A fault detecting apparatus and method are provided for use with an active solar system. The apparatus provides an indication as to whether one or more predetermined faults have occurred in the solar system. The apparatus includes a plurality of sensors, each sensor being used in determining whether a predetermined condition is present. The outputs of the sensors are combined in a pre-established manner in accordance with the kind of predetermined faults to be detected. Indicators communicate with the outputs generated by combining the sensor outputs to give the user of the solar system and the apparatus an indication as to whether a predetermined fault has occurred. Upon detection and indication of any predetermined fault, the user can take appropriate corrective action so that the overall reliability and efficiency of the active solar system are increased.

Leak detection system with distributed microprocessor in the primary containment vessel

International Nuclear Information System (INIS)

Inahara, K.; Yoshioka, K.; Tomizawa, T.

1980-01-01

Responding to the demand for greater improvements of the safety monitoring system, less public radiation exposure, and increase of plant availability, measuring and control systems in nuclear power plants have undergone many improvements. Leak detection systems are also required to give earlier warning, additional accuracy, and continuous monitoring function. This paper describes the drywell sump leakage detection system utilizing a distributed microprocessor, which is a successful application owing to its versatile function and ease of installation. The microprocessor performs various functions such as a rate of level change computation, conversion to leakage flow rate, initiation of alarm, and sump pump control. This system has already been applied to three operating BWR plants that demonstrate its efficiency. (auth)

Nuclear fuel element leak detection system

International Nuclear Information System (INIS)

John, C.D. Jr.

1978-01-01

Disclosed is a leak detection system integral with a wall of a building used to fabricate nuclear fuel elements for detecting radiation leakage from the nuclear fuel elements as the fuel elements exit the building. The leak detecting system comprises a shielded compartment constructed to withstand environmental hazards extending into a similarly constructed building and having sealed doors on both ends along with leak detecting apparatus connected to the compartment. The leak detecting system provides a system for removing a nuclear fuel element from its fabrication building while testing for radiation leaks in the fuel element

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.

Traffic Congestion Detection System through Connected Vehicles and Big Data

OpenAIRE

Néstor Cárdenas-Benítez; Raúl Aquino-Santos; Pedro Magaña-Espinoza; José Aguilar-Velazco; Arthur Edwards-Block; Aldo Medina Cass

2016-01-01

This article discusses the simulation and evaluation of a traffic congestion detection system which combines inter-vehicular communications, fixed roadside infrastructure and infrastructure-to-infrastructure connectivity and big data. The system discussed in this article permits drivers to identify traffic congestion and change their routes accordingly, thus reducing the total emissions of CO2 and decreasing travel time. This system monitors, processes and stores large amounts of data, which ...

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.

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...

Development of contaminant detection system based on ultra-low field SQUID-NMR/MRI

International Nuclear Information System (INIS)

Tsunaki, S; Yamamoto, M; Hatta, J; Hatsukade, Y; Tanaka, S

2014-01-01

We have developed an ultra-low field (ULF) NMR/MRI system using an HTS-rf-SQUID and evaluated performance of the system as a contaminant detection system for foods and drinks. In this work, we measured 1D MRIs from water samples with or without various contaminants, such as aluminum and glass balls using the system. In the 1D MRIs, changes of the MRI spectra were detected, corresponding to positions of the contaminants. We measured 2D MRIs from food samples with and without a hole. In the 2D MRIs, the hole position in the sample was well visualized. These results show that the feasibility of the system to detect and localize contaminants in foods and drinks.

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.

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

Directory of Open Access Journals (Sweden)

C. H. Yang

2016-06-01

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

Continuous Fraud Detection in Enterprise Systems through Audit Trail Analysis

Directory of Open Access Journals (Sweden)

Peter J. Best

2009-03-01

Full Text Available Enterprise systems, real time recording and real time reporting pose new and significant challenges to the accounting and auditing professions. This includes developing methods and tools for continuous assurance and fraud detection. In this paper we propose a methodology for continuous fraud detection that exploits security audit logs, changes in master records and accounting audit trails in enterprise systems. The steps in this process are: (1 threat monitoring-surveillance of security audit logs for ‘red flags’, (2 automated extraction and analysis of data from audit trails, and (3 using forensic investigation techniques to determine whether a fraud has actually occurred. We demonstrate how mySAP, an enterprise system, can be used for audit trail analysis in detecting financial frauds; afterwards we use a case study of a suspected fraud to illustrate how to implement the methodology.

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.

46 CFR 108.405 - Fire detection system.

Science.gov (United States)

2010-10-01

... 46 Shipping 4 2010-10-01 2010-10-01 false Fire detection system. 108.405 Section 108.405 Shipping... EQUIPMENT Fire Extinguishing Systems § 108.405 Fire detection system. (a) Each fire detection system and each smoke detection system on a unit must— (1) Be approved by the Commandant; and (2) Have a visual...

Proximity detection system underground

Energy Technology Data Exchange (ETDEWEB)

Denis Kent [Mine Site Technologies (Australia)

2008-04-15

Mine Site Technologies (MST) with the support ACARP and Xstrata Coal NSW, as well as assistance from Centennial Coal, has developed a Proximity Detection System to proof of concept stage as per plan. The basic aim of the project was to develop a system to reduce the risk of the people coming into contact with vehicles in an uncontrolled manner (i.e. being 'run over'). The potential to extend the developed technology into other areas, such as controls for vehicle-vehicle collisions and restricting access of vehicle or people into certain zones (e.g. non FLP vehicles into Hazardous Zones/ERZ) was also assessed. The project leveraged off MST's existing Intellectual Property and experience gained with our ImPact TRACKER tagging technology, allowing the development to be fast tracked. The basic concept developed uses active RFID Tags worn by miners underground to be detected by vehicle mounted Readers. These Readers in turn provide outputs that can be used to alert a driver (e.g. by light and/or audible alarm) that a person (Tag) approaching within their vicinity. The prototype/test kit developed proved the concept and technology, the four main components being: Active RFID Tags to send out signals for detection by vehicle mounted receivers; Receiver electronics to detect RFID Tags approaching within the vicinity of the unit to create a long range detection system (60 m to 120 m); A transmitting/exciter device to enable inner detection zone (within 5 m to 20 m); and A software/hardware device to process & log incoming Tags reads and create certain outputs. Tests undertaken in the laboratory and at a number of mine sites, confirmed the technology path taken could form the basis of a reliable Proximity Detection/Alert System.

TOWARDS CHANGE DETECTION IN URBAN AREA BY SAR INTERFEROMETRY AND RADARGRAMMETRY

Directory of Open Access Journals (Sweden)

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

Detection and Location of Structural Degradation in Mechanical Systems

International Nuclear Information System (INIS)

Blakeman, E.D.; Damiano, B.; Phillips, L.D.

1999-01-01

The investigation of a diagnostic method for detecting and locating the source of structural degradation in a mechanical system is described in this paper. The diagnostic method uses a mathematical model of the mechanical system to determine relationships between system parameters and measurable spectral features. These relationships are incorporated into a neural network, which associates measured spectral features with system parameters. Condition diagnosis is performed by presenting the neural network with measured spectral features and comparing the system parameters estimated by the neural network to previously estimated values. Changes in the estimated system parameters indicate the location and severity of degradation in the mechanical system

Automatic detection of adverse events to predict drug label changes using text and data mining techniques.

Science.gov (United States)

Gurulingappa, Harsha; Toldo, Luca; Rajput, Abdul Mateen; Kors, Jan A; Taweel, Adel; Tayrouz, Yorki

2013-11-01

The aim of this study was to assess the impact of automatically detected adverse event signals from text and open-source data on the prediction of drug label changes. Open-source adverse effect data were collected from FAERS, Yellow Cards and SIDER databases. A shallow linguistic relation extraction system (JSRE) was applied for extraction of adverse effects from MEDLINE case reports. Statistical approach was applied on the extracted datasets for signal detection and subsequent prediction of label changes issued for 29 drugs by the UK Regulatory Authority in 2009. 76% of drug label changes were automatically predicted. Out of these, 6% of drug label changes were detected only by text mining. JSRE enabled precise identification of four adverse drug events from MEDLINE that were undetectable otherwise. Changes in drug labels can be predicted automatically using data and text mining techniques. Text mining technology is mature and well-placed to support the pharmacovigilance tasks. Copyright © 2013 John Wiley & Sons, Ltd.

Testing the robustness of the anthropogenic climate change detection statements using different empirical models

KAUST Repository

Imbers, J.; Lopez, A.; Huntingford, C.; Allen, M. R.

2013-01-01

This paper aims to test the robustness of the detection and attribution of anthropogenic climate change using four different empirical models that were previously developed to explain the observed global mean temperature changes over the last few decades. These studies postulated that the main drivers of these changes included not only the usual natural forcings, such as solar and volcanic, and anthropogenic forcings, such as greenhouse gases and sulfates, but also other known Earth system oscillations such as El Niño Southern Oscillation (ENSO) or the Atlantic Multidecadal Oscillation (AMO). In this paper, we consider these signals, or forced responses, and test whether or not the anthropogenic signal can be robustly detected under different assumptions for the internal variability of the climate system. We assume that the internal variability of the global mean surface temperature can be described by simple stochastic models that explore a wide range of plausible temporal autocorrelations, ranging from short memory processes exemplified by an AR(1) model to long memory processes, represented by a fractional differenced model. In all instances, we conclude that human-induced changes to atmospheric gas composition is affecting global mean surface temperature changes. ©2013. American Geophysical Union. All Rights Reserved.

Testing the robustness of the anthropogenic climate change detection statements using different empirical models

KAUST Repository

Imbers, J.

2013-04-27

This paper aims to test the robustness of the detection and attribution of anthropogenic climate change using four different empirical models that were previously developed to explain the observed global mean temperature changes over the last few decades. These studies postulated that the main drivers of these changes included not only the usual natural forcings, such as solar and volcanic, and anthropogenic forcings, such as greenhouse gases and sulfates, but also other known Earth system oscillations such as El Niño Southern Oscillation (ENSO) or the Atlantic Multidecadal Oscillation (AMO). In this paper, we consider these signals, or forced responses, and test whether or not the anthropogenic signal can be robustly detected under different assumptions for the internal variability of the climate system. We assume that the internal variability of the global mean surface temperature can be described by simple stochastic models that explore a wide range of plausible temporal autocorrelations, ranging from short memory processes exemplified by an AR(1) model to long memory processes, represented by a fractional differenced model. In all instances, we conclude that human-induced changes to atmospheric gas composition is affecting global mean surface temperature changes. ©2013. American Geophysical Union. All Rights Reserved.

29 CFR 1910.164 - Fire detection systems.

Science.gov (United States)

2010-07-01

... 29 Labor 5 2010-07-01 2010-07-01 false Fire detection systems. 1910.164 Section 1910.164 Labor... detection systems. (a) Scope and application. This section applies to all automatic fire detection systems... detection systems and components to normal operating condition as promptly as possible after each test or...

Systemically induced changes in skeletal structure

International Nuclear Information System (INIS)

Van Derslice, R.

1989-01-01

The number of systemic designs leading the radiographically detectable bone and joint changes is legion. Some disorders (notably the arthridites), while qualifying as diseases with protean systemic manifestations are discussed elsewhere in this book. Other disorders (e.g., achondroplasia), while not representing diseases per se, are included in this chapter for the sake of completeness and due to their relative frequency and their interesting radiographic characteristics. The author focuses on a number of selected conditions, some commonly seen and some relatively infrequently encountered. They are classified into he following categories: endocrinologic; metabolic; hematologic and reticuloendothelial; and dysplastic

Hybrid online sensor error detection and functional redundancy for systems with time-varying parameters.

Science.gov (United States)

Feng, Jianyuan; Turksoy, Kamuran; Samadi, Sediqeh; Hajizadeh, Iman; Littlejohn, Elizabeth; Cinar, Ali

2017-12-01

Supervision and control systems rely on signals from sensors to receive information to monitor the operation of a system and adjust manipulated variables to achieve the control objective. However, sensor performance is often limited by their working conditions and sensors may also be subjected to interference by other devices. Many different types of sensor errors such as outliers, missing values, drifts and corruption with noise may occur during process operation. A hybrid online sensor error detection and functional redundancy system is developed to detect errors in online signals, and replace erroneous or missing values detected with model-based estimates. The proposed hybrid system relies on two techniques, an outlier-robust Kalman filter (ORKF) and a locally-weighted partial least squares (LW-PLS) regression model, which leverage the advantages of automatic measurement error elimination with ORKF and data-driven prediction with LW-PLS. The system includes a nominal angle analysis (NAA) method to distinguish between signal faults and large changes in sensor values caused by real dynamic changes in process operation. The performance of the system is illustrated with clinical data continuous glucose monitoring (CGM) sensors from people with type 1 diabetes. More than 50,000 CGM sensor errors were added to original CGM signals from 25 clinical experiments, then the performance of error detection and functional redundancy algorithms were analyzed. The results indicate that the proposed system can successfully detect most of the erroneous signals and substitute them with reasonable estimated values computed by functional redundancy system.

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

Physics-Based Predictions for Coherent Change Detection Using X-Band Synthetic Aperture Radar

Directory of Open Access Journals (Sweden)

Mark Preiss

2005-12-01

Full Text Available A theoretical model is developed to describe the interferometric coherency between pairs of SAR images of rough soil surfaces. The model is derived using a dyadic form for surface reflectivity in the Kirchhoff approximation. This permits the combination of Kirchhoff theory and spotlight synthetic aperture radar (SAR image formation theory. The resulting model is used to describe the interferometric coherency between pairs of SAR images of rough soil surfaces. The theoretical model is applied to SAR images formed before and after surface changes observed by a repeat-pass SAR system. The change in surface associated with a tyre track following vehicle passage is modelled and SAR coherency estimates are obtained. Predicted coherency distributions for both the change and no-change scenarios are used to estimate receiver operator curves for the detection of the changes using a high-resolution, X-band SAR system.

Algorithms and data structures for automated change detection and classification of sidescan sonar imagery

Science.gov (United States)

Gendron, Marlin Lee

During Mine Warfare (MIW) operations, MIW analysts perform change detection by visually comparing historical sidescan sonar imagery (SSI) collected by a sidescan sonar with recently collected SSI in an attempt to identify objects (which might be explosive mines) placed at sea since the last time the area was surveyed. This dissertation presents a data structure and three algorithms, developed by the author, that are part of an automated change detection and classification (ACDC) system. MIW analysts at the Naval Oceanographic Office, to reduce the amount of time to perform change detection, are currently using ACDC. The dissertation introductory chapter gives background information on change detection, ACDC, and describes how SSI is produced from raw sonar data. Chapter 2 presents the author's Geospatial Bitmap (GB) data structure, which is capable of storing information geographically and is utilized by the three algorithms. This chapter shows that a GB data structure used in a polygon-smoothing algorithm ran between 1.3--48.4x faster than a sparse matrix data structure. Chapter 3 describes the GB clustering algorithm, which is the author's repeatable, order-independent method for clustering. Results from tests performed in this chapter show that the time to cluster a set of points is not affected by the distribution or the order of the points. In Chapter 4, the author presents his real-time computer-aided detection (CAD) algorithm that automatically detects mine-like objects on the seafloor in SSI. The author ran his GB-based CAD algorithm on real SSI data, and results of these tests indicate that his real-time CAD algorithm performs comparably to or better than other non-real-time CAD algorithms. The author presents his computer-aided search (CAS) algorithm in Chapter 5. CAS helps MIW analysts locate mine-like features that are geospatially close to previously detected features. A comparison between the CAS and a great circle distance algorithm shows that the

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

Science.gov (United States)

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

2017-09-01

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

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.

On resilience studies of system detection and recovery techniques against stealthy insider attacks

Science.gov (United States)

Wei, Sixiao; Zhang, Hanlin; Chen, Genshe; Shen, Dan; Yu, Wei; Pham, Khanh D.; Blasch, Erik P.; Cruz, Jose B.

2016-05-01

With the explosive growth of network technologies, insider attacks have become a major concern to business operations that largely rely on computer networks. To better detect insider attacks that marginally manipulate network traffic over time, and to recover the system from attacks, in this paper we implement a temporal-based detection scheme using the sequential hypothesis testing technique. Two hypothetical states are considered: the null hypothesis that the collected information is from benign historical traffic and the alternative hypothesis that the network is under attack. The objective of such a detection scheme is to recognize the change within the shortest time by comparing the two defined hypotheses. In addition, once the attack is detected, a server migration-based system recovery scheme can be triggered to recover the system to the state prior to the attack. To understand mitigation of insider attacks, a multi-functional web display of the detection analysis was developed for real-time analytic. Experiments using real-world traffic traces evaluate the effectiveness of Detection System and Recovery (DeSyAR) scheme. The evaluation data validates the detection scheme based on sequential hypothesis testing and the server migration-based system recovery scheme can perform well in effectively detecting insider attacks and recovering the system under attack.

Islanding detection technique using wavelet energy in grid-connected PV system

Science.gov (United States)

Kim, Il Song

2016-08-01

This paper proposes a new islanding detection method using wavelet energy in a grid-connected photovoltaic system. The method detects spectral changes in the higher-frequency components of the point of common coupling voltage and obtains wavelet coefficients by multilevel wavelet analysis. The autocorrelation of the wavelet coefficients can clearly identify islanding detection, even in the variations of the grid voltage harmonics during normal operating conditions. The advantage of the proposed method is that it can detect islanding condition the conventional under voltage/over voltage/under frequency/over frequency methods fail to detect. The theoretical method to obtain wavelet energies is evolved and verified by the experimental result.

Real-Time Model-Based Leak-Through Detection within Cryogenic Flow Systems

Science.gov (United States)

Walker, M.; Figueroa, F.

2015-01-01

The timely detection of leaks within cryogenic fuel replenishment systems is of significant importance to operators on account of the safety and economic impacts associated with material loss and operational inefficiencies. Associated loss in control of pressure also effects the stability and ability to control the phase of cryogenic fluids during replenishment operations. Current research dedicated to providing Prognostics and Health Management (PHM) coverage of such cryogenic replenishment systems has focused on the detection of leaks to atmosphere involving relatively simple model-based diagnostic approaches that, while effective, are unable to isolate the fault to specific piping system components. The authors have extended this research to focus on the detection of leaks through closed valves that are intended to isolate sections of the piping system from the flow and pressurization of cryogenic fluids. The described approach employs model-based detection of leak-through conditions based on correlations of pressure changes across isolation valves and attempts to isolate the faults to specific valves. Implementation of this capability is enabled by knowledge and information embedded in the domain model of the system. The approach has been used effectively to detect such leak-through faults during cryogenic operational testing at the Cryogenic Testbed at NASA's Kennedy Space Center.

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.

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

Directory of Open Access Journals (Sweden)

Wenzhuo Li

2017-06-01

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

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

Research on photodiode detector-based spatial transient light detection and processing system

Science.gov (United States)

Liu, Meiying; Wang, Hu; Liu, Yang; Zhao, Hui; Nan, Meng

2016-10-01

In order to realize real-time signal identification and processing of spatial transient light, the features and the energy of the captured target light signal are first described and quantitatively calculated. Considering that the transient light signal has random occurrence, a short duration and an evident beginning and ending, a photodiode detector based spatial transient light detection and processing system is proposed and designed in this paper. This system has a large field of view and is used to realize non-imaging energy detection of random, transient and weak point target under complex background of spatial environment. Weak signal extraction under strong background is difficult. In this paper, considering that the background signal changes slowly and the target signal changes quickly, filter is adopted for signal's background subtraction. A variable speed sampling is realized by the way of sampling data points with a gradually increased interval. The two dilemmas that real-time processing of large amount of data and power consumption required by the large amount of data needed to be stored are solved. The test results with self-made simulative signal demonstrate the effectiveness of the design scheme. The practical system could be operated reliably. The detection and processing of the target signal under the strong sunlight background was realized. The results indicate that the system can realize real-time detection of target signal's characteristic waveform and monitor the system working parameters. The prototype design could be used in a variety of engineering applications.

Fluorescence detection system for microfluidic droplets

Science.gov (United States)

Chen, Binyu; Han, Xiaoming; Su, Zhen; Liu, Quanjun

2018-05-01

In microfluidic detection technology, because of the universality of optical methods in laboratory, optical detection is an attractive solution for microfluidic chip laboratory equipment. In addition, the equipment with high stability and low cost can be realized by integrating appropriate optical detection technology on the chip. This paper reports a detection system for microfluidic droplets. Photomultiplier tubes (PMT) is used as a detection device to improve the sensitivity of detection. This system improves the signal to noise ratio by software filtering and spatial filter. The fluorescence intensity is proportional to the concentration of the fluorescence and intensity of the laser. The fluorescence micro droplets of different concentrations can be distinguished by this system.

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

Idaho Explosives Detection System

International Nuclear Information System (INIS)

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

2005-01-01

The Idaho Explosives Detection System was developed at the Idaho National Laboratory (INL) to respond to threats imposed by delivery trucks potentially carrying explosives into military bases. A full-scale prototype system has been built and is currently undergoing testing. The system consists of two racks, one on each side of a subject vehicle. Each rack includes a neutron generator and an array of NaI detectors. The two neutron generators are pulsed and synchronized. A laptop computer controls the entire system. The control software is easily operable by minimally trained staff. The system was developed to detect explosives in a medium size truck within a 5-min measurement time. System performance was successfully demonstrated with explosives at the INL in June 2004 and at Andrews Air Force Base in July 2004

Idaho Explosives Detection System

Energy Technology Data Exchange (ETDEWEB)

Reber, Edward L. [Idaho National Laboratory, 2525 N. Freemont Ave., Idaho Falls, ID 83415-2114 (United States)]. E-mail: reber@inel.gov; Blackwood, Larry G. [Idaho National Laboratory, 2525 N. Freemont Ave., Idaho Falls, ID 83415-2114 (United States); Edwards, Andrew J. [Idaho National Laboratory, 2525 N. Freemont Ave., Idaho Falls, ID 83415-2114 (United States); Jewell, J. Keith [Idaho National Laboratory, 2525 N. Freemont Ave., Idaho Falls, ID 83415-2114 (United States); Rohde, Kenneth W. [Idaho National Laboratory, 2525 N. Freemont Ave., Idaho Falls, ID 83415-2114 (United States); Seabury, Edward H. [Idaho National Laboratory, 2525 N. Freemont Ave., Idaho Falls, ID 83415-2114 (United States); Klinger, Jeffery B. [Idaho National Laboratory, 2525 N. Freemont Ave., Idaho Falls, ID 83415-2114 (United States)

2005-12-15

The Idaho Explosives Detection System was developed at the Idaho National Laboratory (INL) to respond to threats imposed by delivery trucks potentially carrying explosives into military bases. A full-scale prototype system has been built and is currently undergoing testing. The system consists of two racks, one on each side of a subject vehicle. Each rack includes a neutron generator and an array of NaI detectors. The two neutron generators are pulsed and synchronized. A laptop computer controls the entire system. The control software is easily operable by minimally trained staff. The system was developed to detect explosives in a medium size truck within a 5-min measurement time. System performance was successfully demonstrated with explosives at the INL in June 2004 and at Andrews Air Force Base in July 2004.

Damage Detection and Deteriorating Structural Systems

DEFF Research Database (Denmark)

Long, Lijia; Thöns, Sebastian; Döhler, Michael

2017-01-01

This paper addresses the quantification of the value of damage detection system and algorithm information on the basis of Value of Information (VoI) analysis to enhance the benefit of damage detection information by providing the basis for its optimization before it is performed and implemented....... The approach of the quantification the value of damage detection information builds upon the Bayesian decision theory facilitating the utilization of damage detection performance models, which describe the information and its precision on structural system level, facilitating actions to ensure the structural...... detection information is determined utilizing Bayesian updating. The damage detection performance is described with the probability of indication for different component and system damage states taking into account type 1 and type 2 errors. The value of damage detection information is then calculated...

Sensitivity and Dynamic Range Considerations for Homodyne Detection Systems

DEFF Research Database (Denmark)

Jaggard, Dwight L.; King, Ray J

1973-01-01

The effects of modulation frequency, RF reference power, and external bias upon the sensitivity and dynamic range of microwave homodyne detection systems was measured for point contact diodes and low l/f noise Schottky and backward diodes. The measurements were made at 4.89 GHz using a signal...... to noise ratio of 3 dB and a detection system bandwidth of 10 Hz. Maximum sensitivities of -135, -150, and -145 dBm, and dynamic ranges of 92, 110, and 124 dB were measured for the point contact, Schottky, and backward diodes at modulation frequencies of 30, 30, and 3 kHz, respectively. It was found...... that the level of RF reference signal needed to obtain the maximum sensitivity was equal to or somewhat above the point where the diode changes from square law to linear detection. The results are significant in that previously reported homodyne sensitivities (not necessarily maximum) were on the order of -90...

Gas detection system

International Nuclear Information System (INIS)

Allan, C.J.; Bayly, J.G.

1975-01-01

The gas detection system provides for the effective detection of gas leaks over a large area. It includes a laser which has a laser line corresponding to an absorption line of the gas to be detected. A He-Xe laser scans a number of retroreflectors which are strategically located around a D 2 O plant to detect H 2 S leaks. The reflected beam is focused by a telescope, filtered, and passed into an infrared detector. The laser may be made to emit two frequencies, one of which corresponds with an H 2 S absorption line; or it may be modulated on and off the H 2 S absorption line. The relative amplitude of the absorbed light will be a measure of the H 2 S present

Semiconductor radiation detection systems

CERN Document Server

2010-01-01

Covers research in semiconductor detector and integrated circuit design in the context of medical imaging using ionizing radiation. This book explores other applications of semiconductor radiation detection systems in security applications such as luggage scanning, dirty bomb detection and border control.

LANDSAT-8 OPERATIONAL LAND IMAGER CHANGE DETECTION ANALYSIS

Directory of Open Access Journals (Sweden)

W. Pervez

2017-05-01

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

The rf control and detection system for PACO the parametric converter detector

CERN Document Server

Bernard, P; Parodi, R; Picasso, Emilio; Bernard, Ph.

2000-01-01

In this technical note the rf control and detection system for a detector of small harmonic displacements based on two coupled microwave cavities (PACO) is presented. The basic idea underlying this detector is the principle of parametric power conversion between two resonant modes of the system, stimulated by the (small) harmonic modulation of one system parameter. In this experiment we change the cavity length applying an harmonic voltage to a piezo-electric crystal. The system can achieve a great sensitivity to small harmonic displacements and can be an interesting candidate for the detection of small, mechanically coupled, interactions (e.g. high frequency gravitational waves).

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)

Real-time petroleum spill detection system

International Nuclear Information System (INIS)

Dakin, D.T.

2001-01-01

A real-time autonomous oil and fuel spill detection system has been developed to rapidly detect of a wide range of petroleum products floating on, or suspended in water. The system consists of an array of spill detection buoys distributed within the area to be monitored. The buoys are composed of a float and a multispectral fluorometer, which looks up through the top 5 cm of water to detect floating and suspended petroleum products. The buoys communicate to a base station computer that controls the sampling of the buoys and analyses the data from each buoy to determine if a spill has occurred. If statistically significant background petroleum levels are detected, the system raises an oil spill alarm. The system is useful because early detection of a marine oil spill allows for faster containment, thereby minimizing the contaminated area and reducing cleanup costs. This paper also provided test results for biofouling, various petroleum product detection, water turbidity and wave tolerance. The technology has been successfully demonstrated. The UV light source keeps the optic window free from biofouling, and the electronics are fully submerged so there is no risk that the unit could ignite the vapours of a potential oil spill. The system can also tolerate moderately turbid waters and can therefore be used in many rivers, harbours, water intakes and sumps. The system can detect petroleum products with an average thickness of less than 3 micrometers floating on the water surface. 3 refs., 15 figs

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.

A polarization system for persistent chemical detection

Science.gov (United States)

Craven-Jones, Julia; Appelhans, Leah; Couphos, Eric; Embree, Todd; Finnegan, Patrick; Goldstein, Dennis; Karelitz, David; LaCasse, Charles; Luk, Ting S.; Mahamat, Adoum; Massey, Lee; Tanbakuchi, Anthony; Washburn, Cody; Vigil, Steven

2015-09-01

We report on the development of a prototype polarization tag based system for detecting chemical vapors. The system primarily consists of two components, a chemically sensitive tag that experiences a change in its optical polarization properties when exposed to a specific chemical of interest, and an optical imaging polarimeter that is used to measure the polarization properties of the tags. Although the system concept could be extended to other chemicals, for the initial system prototype presented here the tags were developed to be sensitive to hydrogen fluoride (HF) vapors. HF is used in many industrial processes but is highly toxic and thus monitoring for its presence and concentration is often of interest for personnel and environmental safety. The tags are periodic multilayer structures that are produced using standard photolithographic processes. The polarimetric imager has been designed to measure the degree of linear polarization reflected from the tags in the short wave infrared. By monitoring the change in the reflected polarization signature from the tags, the polarimeter can be used to determine if the tag was exposed to HF gas. In this paper, a review of the system development effort and preliminary test results are presented and discussed, as well as our plan for future work.

46 CFR 108.411 - Smoke detection system.

Science.gov (United States)

2010-10-01

... 46 Shipping 4 2010-10-01 2010-10-01 false Smoke detection system. 108.411 Section 108.411 Shipping... EQUIPMENT Fire Extinguishing Systems § 108.411 Smoke detection system. Each smoke accumulator in a smoke detection system must be located on the overhead of the compartment protected by the system in a location...

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.

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.

Focal-plane change triggered video compression for low-power vision sensor systems.

Directory of Open Access Journals (Sweden)

Yu M Chi

Full Text Available Video sensors with embedded compression offer significant energy savings in transmission but incur energy losses in the complexity of the encoder. Energy efficient video compression architectures for CMOS image sensors with focal-plane change detection are presented and analyzed. The compression architectures use pixel-level computational circuits to minimize energy usage by selectively processing only pixels which generate significant temporal intensity changes. Using the temporal intensity change detection to gate the operation of a differential DCT based encoder achieves nearly identical image quality to traditional systems (4dB decrease in PSNR while reducing the amount of data that is processed by 67% and reducing overall power consumption reduction of 51%. These typical energy savings, resulting from the sparsity of motion activity in the visual scene, demonstrate the utility of focal-plane change triggered compression to surveillance vision systems.

Integrated multisensor perimeter detection systems

Science.gov (United States)

Kent, P. J.; Fretwell, P.; Barrett, D. J.; Faulkner, D. A.

2007-10-01

The report describes the results of a multi-year programme of research aimed at the development of an integrated multi-sensor perimeter detection system capable of being deployed at an operational site. The research was driven by end user requirements in protective security, particularly in threat detection and assessment, where effective capability was either not available or prohibitively expensive. Novel video analytics have been designed to provide robust detection of pedestrians in clutter while new radar detection and tracking algorithms provide wide area day/night surveillance. A modular integrated architecture based on commercially available components has been developed. A graphical user interface allows intuitive interaction and visualisation with the sensors. The fusion of video, radar and other sensor data provides the basis of a threat detection capability for real life conditions. The system was designed to be modular and extendable in order to accommodate future and legacy surveillance sensors. The current sensor mix includes stereoscopic video cameras, mmWave ground movement radar, CCTV and a commercially available perimeter detection cable. The paper outlines the development of the system and describes the lessons learnt after deployment in a pilot trial.

Region-Based Building Rooftop Extraction and Change Detection

Science.gov (United States)

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

2017-09-01

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

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.

Leak detection system for a high temperature fluid pipe

International Nuclear Information System (INIS)

Puyal, C.; Meuwisse, C.

1989-01-01

The leak detection system is made by a cable with at least two isolated electrical conductors, close to the wall of the pipe. The material of the cable is chosen so as to change its electrical characteristics if a leak causes heating of the cable. A detector at one end of the cable can measure the modifications of the electrical characteristics [fr

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.

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

Apriori-based network intrusion detection system

International Nuclear Information System (INIS)

Wang Wenjin; Liu Junrong; Liu Baoxu

2012-01-01

With the development of network communication technology, more and more social activities run by Internet. In the meantime, the network information security is getting increasingly serious. Intrusion Detection System (IDS) has greatly improved the general security level of whole network. But there are still many problem exists in current IDS, e.g. high leak rate detection/false alarm rates and feature library need frequently upgrade. This paper presents an association-rule based IDS. This system can detect unknown attack by generate rules from training data. Experiment in last chapter proved the system has great accuracy on unknown attack detection. (authors)

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

Science.gov (United States)

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

2010-12-01

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

Homodyne detection of holographic memory systems

Science.gov (United States)

Urness, Adam C.; Wilson, William L.; Ayres, Mark R.

2014-09-01

We present a homodyne detection system implemented for a page-wise holographic memory architecture. Homodyne detection by holographic memory systems enables phase quadrature multiplexing (doubling address space), and lower exposure times (increasing read transfer rates). It also enables phase modulation, which improves signal-to-noise ratio (SNR) to further increase data capacity. We believe this is the first experimental demonstration of homodyne detection for a page-wise holographic memory system suitable for a commercial design.

Soft-Fault Detection Technologies Developed for Electrical Power Systems

Science.gov (United States)

Button, Robert M.

2004-01-01

The NASA Glenn Research Center, partner universities, and defense contractors are working to develop intelligent power management and distribution (PMAD) technologies for future spacecraft and launch vehicles. The goals are to provide higher performance (efficiency, transient response, and stability), higher fault tolerance, and higher reliability through the application of digital control and communication technologies. It is also expected that these technologies will eventually reduce the design, development, manufacturing, and integration costs for large, electrical power systems for space vehicles. The main focus of this research has been to incorporate digital control, communications, and intelligent algorithms into power electronic devices such as direct-current to direct-current (dc-dc) converters and protective switchgear. These technologies, in turn, will enable revolutionary changes in the way electrical power systems are designed, developed, configured, and integrated in aerospace vehicles and satellites. Initial successes in integrating modern, digital controllers have proven that transient response performance can be improved using advanced nonlinear control algorithms. One technology being developed includes the detection of "soft faults," those not typically covered by current systems in use today. Soft faults include arcing faults, corona discharge faults, and undetected leakage currents. Using digital control and advanced signal analysis algorithms, we have shown that it is possible to reliably detect arcing faults in high-voltage dc power distribution systems (see the preceding photograph). Another research effort has shown that low-level leakage faults and cable degradation can be detected by analyzing power system parameters over time. This additional fault detection capability will result in higher reliability for long-lived power systems such as reusable launch vehicles and space exploration missions.

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

Science.gov (United States)

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.

Gas Flow Detection System

Science.gov (United States)

Moss, Thomas; Ihlefeld, Curtis; Slack, Barry

2010-01-01

This system provides a portable means to detect gas flow through a thin-walled tube without breaking into the tubing system. The flow detection system was specifically designed to detect flow through two parallel branches of a manifold with only one inlet and outlet, and is a means for verifying a space shuttle program requirement that saves time and reduces the risk of flight hardware damage compared to the current means of requirement verification. The prototype Purge Vent and Drain Window Cavity Conditioning System (PVD WCCS) Flow Detection System consists of a heater and a temperature-sensing thermistor attached to a piece of Velcro to be attached to each branch of a WCCS manifold for the duration of the requirement verification test. The heaters and thermistors are connected to a shielded cable and then to an electronics enclosure, which contains the power supplies, relays, and circuit board to provide power, signal conditioning, and control. The electronics enclosure is then connected to a commercial data acquisition box to provide analog to digital conversion as well as digital control. This data acquisition box is then connected to a commercial laptop running a custom application created using National Instruments LabVIEW. The operation of the PVD WCCS Flow Detection System consists of first attaching a heater/thermistor assembly to each of the two branches of one manifold while there is no flow through the manifold. Next, the software application running on the laptop is used to turn on the heaters and to monitor the manifold branch temperatures. When the system has reached thermal equilibrium, the software application s graphical user interface (GUI) will indicate that the branch temperatures are stable. The operator can then physically open the flow control valve to initiate the test flow of gaseous nitrogen (GN2) through the manifold. Next, the software user interface will be monitored for stable temperature indications when the system is again at

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

Science.gov (United States)

Wilford, Miko M; Wells, Gary L

2010-11-01

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

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

Science.gov (United States)

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.

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

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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.

A Hybrid Islanding Detection Technique Using Average Rate of Voltage Change and Real Power Shift

DEFF Research Database (Denmark)

Mahat, Pukar; Chen, Zhe; Bak-Jensen, Birgitte

2009-01-01

The mainly used islanding detection techniques may be classified as active and passive techniques. Passive techniques don't perturb the system but they have larger nondetection znes, whereas active techniques have smaller nondetection zones but they perturb the system. In this paper, a new hybrid...... technique is proposed to solve this problem. An average rate of voltage change (passive technique) has been used to initiate a real power shift (active technique), which changes the eal power of distributed generation (DG), when the passive technique cannot have a clear discrimination between islanding...

Lake Chapala change detection using time series

Science.gov (United States)

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

2008-10-01

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

Embedded Systems - Missile Detection/Interception

Directory of Open Access Journals (Sweden)

Luis Cintron

2010-01-01

Full Text Available Missile defense systems are often related to major military resources aimed at shielding a specific region from incoming attacks. They are intended to detect, track, intercept, and destruct incoming enemy missiles. These systems vary in cost, efficiency, dependability, and technology. In present times, the possession of these types of systems is associated with large capacity military countries. Demonstrated here are the mathematical techniques behind missile systems which calculate trajectories of incoming missiles and potential intercept positions after initial missile detection. This procedure involved the use of vector-valued functions, systems of equations, and knowledge of projectile motion concepts.

JACoW Model learning algorithms for anomaly detection in CERN control systems

CERN Document Server

Tilaro, Filippo; Gonzalez-Berges, Manuel; Roshchin, Mikhail; Varela, Fernando

2018-01-01

The CERN automation infrastructure consists of over 600 heterogeneous industrial control systems with around 45 million deployed sensors, actuators and control objects. Therefore, it is evident that the monitoring of such huge system represents a challenging and complex task. This paper describes three different mathematical approaches that have been designed and developed to detect anomalies in any of the CERN control systems. Specifically, one of these algorithms is purely based on expert knowledge; the other two mine the historical generated data to create a simple model of the system; this model is then used to detect faulty sensors measurements. The presented methods can be categorized as dynamic unsupervised anomaly detection; “dynamic” since the behaviour of the system and the evolution of its attributes are observed and changing in time. They are “unsupervised” because we are trying to predict faulty events without examples in the data history. So, the described strategies involve monitoring t...

AN INVESTIGATION OF AUTOMATIC CHANGE DETECTION FOR TOPOGRAPHIC MAP UPDATING

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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.

Hybrid Intrusion Detection System for DDoS Attacks

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Özge Cepheli

2016-01-01

Full Text Available Distributed denial-of-service (DDoS attacks are one of the major threats and possibly the hardest security problem for today’s Internet. In this paper we propose a hybrid detection system, referred to as hybrid intrusion detection system (H-IDS, for detection of DDoS attacks. Our proposed detection system makes use of both anomaly-based and signature-based detection methods separately but in an integrated fashion and combines the outcomes of both detectors to enhance the overall detection accuracy. We apply two distinct datasets to our proposed system in order to test the detection performance of H-IDS and conclude that the proposed hybrid system gives better results than the systems based on nonhybrid detection.

Review of current neutron detection systems for emergency response

Science.gov (United States)

Mukhopadhyay, Sanjoy; Maurer, Richard; Guss, Paul; Kruschwitz, Craig

2014-09-01

Neutron detectors are used in a myriad of applications—from safeguarding special nuclear materials (SNM) to determining lattice spacing in soft materials. The transformational changes taking place in neutron detection and imaging techniques in the last few years are largely being driven by the global shortage of helium-3 (3He). This article reviews the status of neutron sensors used specifically for SNM detection in radiological emergency response. These neutron detectors must be highly efficient, be rugged, have fast electronics to measure neutron multiplicity, and be capable of measuring direction of the neutron sources and possibly image them with high spatial resolution. Neutron detection is an indirect physical process: neutrons react with nuclei in materials to initiate the release of one or more charged particles that produce electric signals that can be processed by the detection system. Therefore, neutron detection requires conversion materials as active elements of the detection system; these materials may include boron-10 (10B), lithium-6 (6Li), and gadollinium-157 (157Gd), to name a few, but the number of materials available for neutron detection is limited. However, in recent years, pulse-shape-discriminating plastic scintillators, scintillators made of helium-4 (4He) under high pressure, pillar and trench semiconductor diodes, and exotic semiconductor neutron detectors made from uranium oxide and other materials have widely expanded the parameter space in neutron detection methodology. In this article we will pay special attention to semiconductor-based neutron sensors. Modern microfabricated nanotubes covered inside with neutron converter materials and with very high aspect ratios for better charge transport will be discussed.

Flat Surface Damage Detection System (FSDDS)

Science.gov (United States)

Williams, Martha; Lewis, Mark; Gibson, Tracy; Lane, John; Medelius, Pedro; Snyder, Sarah; Ciarlariello, Dan; Parks, Steve; Carrejo, Danny; Rojdev, Kristina

2013-01-01

The Flat Surface Damage Detection system (FSDDS} is a sensory system that is capable of detecting impact damages to surfaces utilizing a novel sensor system. This system will provide the ability to monitor the integrity of an inflatable habitat during in situ system health monitoring. The system consists of three main custom designed subsystems: the multi-layer sensing panel, the embedded monitoring system, and the graphical user interface (GUI). The GUI LABVIEW software uses a custom developed damage detection algorithm to determine the damage location based on the sequence of broken sensing lines. It estimates the damage size, the maximum depth, and plots the damage location on a graph. Successfully demonstrated as a stand alone technology during 2011 D-RATS. Software modification also allowed for communication with HDU avionics crew display which was demonstrated remotely (KSC to JSC} during 2012 integration testing. Integrated FSDDS system and stand alone multi-panel systems were demonstrated remotely and at JSC, Mission Operations Test using Space Network Research Federation (SNRF} network in 2012. FY13, FSDDS multi-panel integration with JSC and SNRF network Technology can allow for integration with other complementary damage detection systems.

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.

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

Science.gov (United States)

Zhu, Zhe

2017-08-01

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

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.

Stochastic adaptation and fold-change detection: from single-cell to population behavior

Directory of Open Access Journals (Sweden)

Leier André

2011-02-01

Full Text Available Abstract Background In cell signaling terminology, adaptation refers to a system's capability of returning to its equilibrium upon a transient response. To achieve this, a network has to be both sensitive and precise. Namely, the system must display a significant output response upon stimulation, and later on return to pre-stimulation levels. If the system settles at the exact same equilibrium, adaptation is said to be 'perfect'. Examples of adaptation mechanisms include temperature regulation, calcium regulation and bacterial chemotaxis. Results We present models of the simplest adaptation architecture, a two-state protein system, in a stochastic setting. Furthermore, we consider differences between individual and collective adaptive behavior, and show how our system displays fold-change detection properties. Our analysis and simulations highlight why adaptation needs to be understood in terms of probability, and not in strict numbers of molecules. Most importantly, selection of appropriate parameters in this simple linear setting may yield populations of cells displaying adaptation, while single cells do not. Conclusions Single cell behavior cannot be inferred from population measurements and, sometimes, collective behavior cannot be determined from the individuals. By consequence, adaptation can many times be considered a purely emergent property of the collective system. This is a clear example where biological ergodicity cannot be assumed, just as is also the case when cell replication rates are not homogeneous, or depend on the cell state. Our analysis shows, for the first time, how ergodicity cannot be taken for granted in simple linear examples either. The latter holds even when cells are considered isolated and devoid of replication capabilities (cell-cycle arrested. We also show how a simple linear adaptation scheme displays fold-change detection properties, and how rupture of ergodicity prevails in scenarios where transitions between

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.

Structural Damage Detection Using Changes in Natural Frequencies: Theory and Applications

Science.gov (United States)

He, K.; Zhu, W. D.

2011-07-01

A vibration-based method that uses changes in natural frequencies of a structure to detect damage has advantages over conventional nondestructive tests in detecting various types of damage, including loosening of bolted joints, using minimum measurement data. Two major challenges associated with applications of the vibration-based damage detection method to engineering structures are addressed: accurate modeling of structures and the development of a robust inverse algorithm to detect damage, which are defined as the forward and inverse problems, respectively. To resolve the forward problem, new physics-based finite element modeling techniques are developed for fillets in thin-walled beams and for bolted joints, so that complex structures can be accurately modeled with a reasonable model size. To resolve the inverse problem, a logistical function transformation is introduced to convert the constrained optimization problem to an unconstrained one, and a robust iterative algorithm using a trust-region method, called the Levenberg-Marquardt method, is developed to accurately detect the locations and extent of damage. The new methodology can ensure global convergence of the iterative algorithm in solving under-determined system equations and deal with damage detection problems with relatively large modeling error and measurement noise. The vibration-based damage detection method is applied to various structures including lightning masts, a space frame structure and one of its components, and a pipeline. The exact locations and extent of damage can be detected in the numerical simulation where there is no modeling error and measurement noise. The locations and extent of damage can be successfully detected in experimental damage detection.

Structural Damage Detection Using Changes in Natural Frequencies: Theory and Applications

International Nuclear Information System (INIS)

He, K; Zhu, W D

2011-01-01

A vibration-based method that uses changes in natural frequencies of a structure to detect damage has advantages over conventional nondestructive tests in detecting various types of damage, including loosening of bolted joints, using minimum measurement data. Two major challenges associated with applications of the vibration-based damage detection method to engineering structures are addressed: accurate modeling of structures and the development of a robust inverse algorithm to detect damage, which are defined as the forward and inverse problems, respectively. To resolve the forward problem, new physics-based finite element modeling techniques are developed for fillets in thin-walled beams and for bolted joints, so that complex structures can be accurately modeled with a reasonable model size. To resolve the inverse problem, a logistical function transformation is introduced to convert the constrained optimization problem to an unconstrained one, and a robust iterative algorithm using a trust-region method, called the Levenberg-Marquardt method, is developed to accurately detect the locations and extent of damage. The new methodology can ensure global convergence of the iterative algorithm in solving under-determined system equations and deal with damage detection problems with relatively large modeling error and measurement noise. The vibration-based damage detection method is applied to various structures including lightning masts, a space frame structure and one of its components, and a pipeline. The exact locations and extent of damage can be detected in the numerical simulation where there is no modeling error and measurement noise. The locations and extent of damage can be successfully detected in experimental damage detection.

Detection technique of targets for missile defense system

Science.gov (United States)

Guo, Hua-ling; Deng, Jia-hao; Cai, Ke-rong

2009-11-01

Ballistic missile defense system (BMDS) is a weapon system for intercepting enemy ballistic missiles. It includes ballistic-missile warning system, target discrimination system, anti-ballistic-missile guidance systems, and command-control communication system. Infrared imaging detection and laser imaging detection are widely used in BMDS for surveillance, target detection, target tracking, and target discrimination. Based on a comprehensive review of the application of target-detection techniques in the missile defense system, including infrared focal plane arrays (IRFPA), ground-based radar detection technology, 3-dimensional imaging laser radar with a photon counting avalanche photodiode (APD) arrays and microchip laser, this paper focuses on the infrared and laser imaging detection techniques in missile defense system, as well as the trends for their future development.

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

Directory of Open Access Journals (Sweden)

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

Field test of a leak detection system: planning, execution and results

Energy Technology Data Exchange (ETDEWEB)

Sampaio da Silva; Daniel; Melo Filho, Silvio A.; Niehues de Farias, Mauro; Pacheco, Anderson [Petrobas Transporte SA - TRANSPETRO, (Brazil)

2010-07-01

The OLAPA pipeline (Brazil) with its 97.6 km in length crosses difficult environment with a combination of mountain and dense forest. The non-detection of leaks in this kind of pipeline would have serious consequences. This pipeline was chosen for testing the performance of a new TRANSPETRO leak detection system. This paper reports the testing process of the new leakage detection method carried out on the OLAPA pipeline. TRANSPETRO decided to test the leakwarn system which is a computational pipeline monitoring (CPM) method which and can be integrated into their SCADA system. The CPM uses the mass balance principle with line pack change to analyze the pipeline operational parameters. The tests consisted of comparing the theoretical results with in-field results of alarm times obtained from controlled removal of product, simulating a real leak. Three leaks were tested in different states of operation and size of leak. It was found that the results were compatible with the expected alarm time.

Improvement of the Raman detection system for pesticide residues on/in fruits and vegetables

Science.gov (United States)

Li, Yan; Peng, Yankun; Zhai, Chen; Chao, Kuanglin; Qin, Jianwei

2017-05-01

Pesticide residue is one of the major challenges to fruits safety, while the traditional detection methods of pesticide residue on fruits and vegetables can't afford the demand of rapid detection in actual production because of timeconsuming. Thus rapid identification and detection methods for pesticide residue are urgently needed at present. While most Raman detection systems in the market are spot detection systems, which limits the range of application. In the study, our lab develops a Raman detection system to achieve area-scan thorough the self-developed spot detection Raman system with a control software and two devices. In the system, the scanning area is composed of many scanning spots, which means every spot needs to be detected and more time will be taken than area-scan Raman system. But lower detection limit will be achieved in this method. And some detection device is needed towards fruits and vegetables in different shape. Two detection devices are developed to detect spherical fruits and leaf vegetables. During the detection, the device will make spherical fruit rotate along its axis of symmetry, and leaf vegetables will be pressed in the test surface smoothly. The detection probe will be set to keep a proper distance to the surface of fruits and vegetables. It should make sure the laser shins on the surface of spherical fruit vertically. And two software are used to detect spherical fruits and leaf vegetables will be integrated to one, which make the operator easier to switch. Accordingly two detection devices for spherical fruits and leaf vegetables will also be portable devices to make it easier to change. In the study, a new way is developed to achieve area-scan result by spot-scan Raman detection system.

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

Science.gov (United States)

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.

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).

A Sensitivity Analysis of a Computer Model-Based Leak Detection System for Oil Pipelines

Directory of Open Access Journals (Sweden)

Zhe Lu

2017-08-01

Full Text Available Improving leak detection capability to eliminate undetected releases is an area of focus for the energy pipeline industry, and the pipeline companies are working to improve existing methods for monitoring their pipelines. Computer model-based leak detection methods that detect leaks by analyzing the pipeline hydraulic state have been widely employed in the industry, but their effectiveness in practical applications is often challenged by real-world uncertainties. This study quantitatively assessed the effects of uncertainties on leak detectability of a commonly used real-time transient model-based leak detection system. Uncertainties in fluid properties, field sensors, and the data acquisition system were evaluated. Errors were introduced into the input variables of the leak detection system individually and collectively, and the changes in leak detectability caused by the uncertainties were quantified using simulated leaks. This study provides valuable quantitative results contributing towards a better understanding of how real-world uncertainties affect leak detection. A general ranking of the importance of the uncertainty sources was obtained: from high to low it is time skew, bulk modulus error, viscosity error, and polling time. It was also shown that inertia-dominated pipeline systems were less sensitive to uncertainties compared to friction-dominated systems.

Near-real-time radiography detects 0.1% changes in areal density with 1-millimeter spatial resolution

International Nuclear Information System (INIS)

Stupin, D.M.

1987-06-01

Using digital subtraction radiography, the author detects an 0.1% change in areal density in a phantom. Areal density is the product rho x, where rho is the material density and x is the material thickness. Therefore, it is possible to detect an 0.1% difference in either density or thickness in unknown samples. A special x-ray television camera detects the areal density change on the phantom. In a difference image, formed by subtracting the 128-television-frame averages of the phantom image from the phantom-and-step image, the step is resolved with a 1-mm spatial resolution. Surprisingly, crossed 2-μm-diam tungsten wires that overlie the phantom are also detected. This procedure takes a few seconds. The performance of any digital imaging x-ray system will improve by using the averaging and digital subtraction techniques. 8 refs., 6 figs

POST-DISASTER DAMAGE ASSESSMENT THROUGH COHERENT CHANGE DETECTION ON SAR IMAGERY

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L. Guida

2018-04-01

Full Text Available Damage assessment is a fundamental step to support emergency response and recovery activities in a post-earthquake scenario. In recent years, UAVs and satellite optical imagery was applied to assess major structural damages before technicians could reach the areas affected by the earthquake. However, bad weather conditions may harm the quality of these optical assessments, thus limiting the practical applicability of these techniques. In this paper, the application of Synthetic Aperture Radar (SAR imagery is investigated and a novel approach to SAR-based damage assessment is presented. Coherent Change Detection (CCD algorithms on multiple interferometrically pre-processed SAR images of the area affected by the seismic event are exploited to automatically detect potential damages to buildings and other physical structures. As a case study, the 2016 Central Italy earthquake involving the cities of Amatrice and Accumoli was selected. The main contribution of the research outlined above is the integration of a complex process, requiring the coordination of a variety of methods and tools, into a unitary framework, which allows end-to-end application of the approach from SAR data pre-processing to result visualization in a Geographic Information System (GIS. A prototype of this pipeline was implemented, and the outcomes of this methodology were validated through an extended comparison with traditional damage assessment maps, created through photo-interpretation of high resolution aerial imagery. The results indicate that the proposed methodology is able to perform damage detection with a good level of accuracy, as most of the detected points of change are concentrated around highly damaged buildings.

Post-Disaster Damage Assessment Through Coherent Change Detection on SAR Imagery

Science.gov (United States)

Guida, L.; Boccardo, P.; Donevski, I.; Lo Schiavo, L.; Molinari, M. E.; Monti-Guarnieri, A.; Oxoli, D.; Brovelli, M. A.

2018-04-01

Damage assessment is a fundamental step to support emergency response and recovery activities in a post-earthquake scenario. In recent years, UAVs and satellite optical imagery was applied to assess major structural damages before technicians could reach the areas affected by the earthquake. However, bad weather conditions may harm the quality of these optical assessments, thus limiting the practical applicability of these techniques. In this paper, the application of Synthetic Aperture Radar (SAR) imagery is investigated and a novel approach to SAR-based damage assessment is presented. Coherent Change Detection (CCD) algorithms on multiple interferometrically pre-processed SAR images of the area affected by the seismic event are exploited to automatically detect potential damages to buildings and other physical structures. As a case study, the 2016 Central Italy earthquake involving the cities of Amatrice and Accumoli was selected. The main contribution of the research outlined above is the integration of a complex process, requiring the coordination of a variety of methods and tools, into a unitary framework, which allows end-to-end application of the approach from SAR data pre-processing to result visualization in a Geographic Information System (GIS). A prototype of this pipeline was implemented, and the outcomes of this methodology were validated through an extended comparison with traditional damage assessment maps, created through photo-interpretation of high resolution aerial imagery. The results indicate that the proposed methodology is able to perform damage detection with a good level of accuracy, as most of the detected points of change are concentrated around highly damaged buildings.

Ferromagnetic Objects Magnetovision Detection System.

Science.gov (United States)

Nowicki, Michał; Szewczyk, Roman

2013-12-02

This paper presents the application of a weak magnetic fields magnetovision scanning system for detection of dangerous ferromagnetic objects. A measurement system was developed and built to study the magnetic field vector distributions. The measurements of the Earth's field distortions caused by various ferromagnetic objects were carried out. The ability for passive detection of hidden or buried dangerous objects and the determination of their location was demonstrated.

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…

Deep Recurrent Neural Networks for seizure detection and early seizure detection systems

Energy Technology Data Exchange (ETDEWEB)

Talathi, S. S. [Lawrence Livermore National Lab. (LLNL), Livermore, CA (United States)

2017-06-05

Epilepsy is common neurological diseases, affecting about 0.6-0.8 % of world population. Epileptic patients suffer from chronic unprovoked seizures, which can result in broad spectrum of debilitating medical and social consequences. Since seizures, in general, occur infrequently and are unpredictable, automated seizure detection systems are recommended to screen for seizures during long-term electroencephalogram (EEG) recordings. In addition, systems for early seizure detection can lead to the development of new types of intervention systems that are designed to control or shorten the duration of seizure events. In this article, we investigate the utility of recurrent neural networks (RNNs) in designing seizure detection and early seizure detection systems. We propose a deep learning framework via the use of Gated Recurrent Unit (GRU) RNNs for seizure detection. We use publicly available data in order to evaluate our method and demonstrate very promising evaluation results with overall accuracy close to 100 %. We also systematically investigate the application of our method for early seizure warning systems. Our method can detect about 98% of seizure events within the first 5 seconds of the overall epileptic seizure duration.

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

Science.gov (United States)

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

2014-03-01

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

Failure to detect seasonal changes in the song system nuclei of the black-capped chickadee (Poecile atricapillus).

Science.gov (United States)

Smulders, T V; Lisi, M D; Tricomi, E; Otter, K A; Chruszcz, B; Ratcliffe, L M; DeVoogd, T J

2006-08-01

Most temperate songbird species sing seasonally, and the brain areas involved in producing song (the song system) vary in size alongside the changes in behavior. Black-capped chickadees (Poecile atricapillus) also sing seasonally, and we find that there are changes in the stereotypy and the length of the fee-bee song from the nonbreeding to the breeding season. Yet despite these changes, we fail to find any evidence of seasonal changes in the song system. The song system of males is larger than that of females, as is typical in songbirds, but the ratio between the sexes is small compared to other species. We suggest three hypotheses to explain our failure to find seasonal variation in the chickadee song system.

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.

Near-infrared laser, time domain, breast tumour detection system

International Nuclear Information System (INIS)

Joblin, A.J.

1996-01-01

image resolution using this technique is poor, being worse that S mm, with currently available time resolved detection system capabilities. However it was also found that tumour bodies smaller in size than the system resolution parameter produced a measurable change in the signal, which could be used to detect small tumour bodies. So while high resolution images of the breast are not possible, simple measurements to detect the presence of breast disease may be possible. This will require further work to allow the signal from breast disease to be distinguished without error from the expected variations in signal due to the variability in normal breast characteristics

Improved biosensor-based detection system

DEFF Research Database (Denmark)

2015-01-01

Described is a new biosensor-based detection system for effector compounds, useful for in vivo applications in e.g. screening and selecting of cells which produce a small molecule effector compound or which take up a small molecule effector compound from its environment. The detection system...... comprises a protein or RNA-based biosensor for the effector compound which indirectly regulates the expression of a reporter gene via two hybrid proteins, providing for fewer false signals or less 'noise', tuning of sensitivity or other advantages over conventional systems where the biosensor directly...

VEHICLE LOCALIZATION BY LIDAR POINT CORRELATION IMPROVED BY CHANGE DETECTION

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A. Schlichting

2016-06-01

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

Vehicle Localization by LIDAR Point Correlation Improved by Change Detection

Science.gov (United States)

Schlichting, A.; Brenner, C.

2016-06-01

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

Ferromagnetic Objects Magnetovision Detection System

Directory of Open Access Journals (Sweden)

Michał Nowicki

2013-12-01

Full Text Available This paper presents the application of a weak magnetic fields magnetovision scanning system for detection of dangerous ferromagnetic objects. A measurement system was developed and built to study the magnetic field vector distributions. The measurements of the Earth’s field distortions caused by various ferromagnetic objects were carried out. The ability for passive detection of hidden or buried dangerous objects and the determination of their location was demonstrated.

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.

Fusion of Heterogeneous Intrusion Detection Systems for Network Attack Detection

Directory of Open Access Journals (Sweden)

Jayakumar Kaliappan

2015-01-01

Full Text Available An intrusion detection system (IDS helps to identify different types of attacks in general, and the detection rate will be higher for some specific category of attacks. This paper is designed on the idea that each IDS is efficient in detecting a specific type of attack. In proposed Multiple IDS Unit (MIU, there are five IDS units, and each IDS follows a unique algorithm to detect attacks. The feature selection is done with the help of genetic algorithm. The selected features of the input traffic are passed on to the MIU for processing. The decision from each IDS is termed as local decision. The fusion unit inside the MIU processes all the local decisions with the help of majority voting rule and makes the final decision. The proposed system shows a very good improvement in detection rate and reduces the false alarm rate.

Anomaly-based intrusion detection for SCADA systems

International Nuclear Information System (INIS)

Yang, D.; Usynin, A.; Hines, J. W.

2006-01-01

Most critical infrastructure such as chemical processing plants, electrical generation and distribution networks, and gas distribution is monitored and controlled by Supervisory Control and Data Acquisition Systems (SCADA. These systems have been the focus of increased security and there are concerns that they could be the target of international terrorists. With the constantly growing number of internet related computer attacks, there is evidence that our critical infrastructure may also be vulnerable. Researchers estimate that malicious online actions may cause $75 billion at 2007. One of the interesting countermeasures for enhancing information system security is called intrusion detection. This paper will briefly discuss the history of research in intrusion detection techniques and introduce the two basic detection approaches: signature detection and anomaly detection. Finally, it presents the application of techniques developed for monitoring critical process systems, such as nuclear power plants, to anomaly intrusion detection. The method uses an auto-associative kernel regression (AAKR) model coupled with the statistical probability ratio test (SPRT) and applied to a simulated SCADA system. The results show that these methods can be generally used to detect a variety of common attacks. (authors)

[Wavelength Selection in Hemolytic Evaluation Systems with Spectrophotometry Detection].

Science.gov (United States)

Zhang, Hong; Su, Baochang; Ye, Xunda; Luo, Man

2016-04-01

Spectrophotometry is a simple hemolytic evaluation method commonly used in new drugs,biomedical materials and blood products.It is for the quantitative analysis of the characteristic absorption peaks of hemoglobin.Therefore,it is essential to select the correct detection wavelength when the evaluation system has influences on the conformation of hemoglobin.Based on the study of changes in the characteristic peaks over time of the hemolysis supernatant in four systems,namely,cell culture medium,phosphate buffered saline(PBS),physiological saline and banked blood preservation solution,using continuous wavelength scanning,the selections of detection wavelength were proposed as follows.In the cell culture medium system,the wavelength of 415 nm should be selected within 4h;,near 408 nm should be selected within 4~72h.In PBS system,within 4h,541 nm,577nm or 415 nm should be selected;4~72h,541 nm,577nm or near 406 nm should be selected.In physiological saline system,within 4h,414 nm should be selected;4~72h,near 405 nm should be selected;within 12 h,541nm or 577 nm could also be selected.In banked blood preservation solution system,within 72 h,415nm,540 nm or 576 nm should be selected.

Minimum detectable gas concentration performance evaluation method for gas leak infrared imaging detection systems.

Science.gov (United States)

Zhang, Xu; Jin, Weiqi; Li, Jiakun; Wang, Xia; Li, Shuo

2017-04-01

Thermal imaging technology is an effective means of detecting hazardous gas leaks. Much attention has been paid to evaluation of the performance of gas leak infrared imaging detection systems due to several potential applications. The minimum resolvable temperature difference (MRTD) and the minimum detectable temperature difference (MDTD) are commonly used as the main indicators of thermal imaging system performance. This paper establishes a minimum detectable gas concentration (MDGC) performance evaluation model based on the definition and derivation of MDTD. We proposed the direct calculation and equivalent calculation method of MDGC based on the MDTD measurement system. We build an experimental MDGC measurement system, which indicates the MDGC model can describe the detection performance of a thermal imaging system to typical gases. The direct calculation, equivalent calculation, and direct measurement results are consistent. The MDGC and the minimum resolvable gas concentration (MRGC) model can effectively describe the performance of "detection" and "spatial detail resolution" of thermal imaging systems to gas leak, respectively, and constitute the main performance indicators of gas leak detection systems.

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.

The design method and research status of vehicle detection system based on geomagnetic detection principle

Science.gov (United States)

Lin, Y. H.; Bai, R.; Qian, Z. H.

2018-03-01

Vehicle detection systems are applied to obtain real-time information of vehicles, realize traffic control and reduce traffic pressure. This paper reviews geomagnetic sensors as well as the research status of the vehicle detection system. Presented in the paper are also our work on the vehicle detection system, including detection algorithms and experimental results. It is found that the GMR based vehicle detection system has a detection accuracy up to 98% with a high potential for application in the road traffic control area.

Survey of pedestrian detection for advanced driver assistance systems.

Science.gov (United States)

Gerónimo, David; López, Antonio M; Sappa, Angel D; Graf, Thorsten

2010-07-01

Advanced driver assistance systems (ADASs), and particularly pedestrian protection systems (PPSs), have become an active research area aimed at improving traffic safety. The major challenge of PPSs is the development of reliable on-board pedestrian detection systems. Due to the varying appearance of pedestrians (e.g., different clothes, changing size, aspect ratio, and dynamic shape) and the unstructured environment, it is very difficult to cope with the demanded robustness of this kind of system. Two problems arising in this research area are the lack of public benchmarks and the difficulty in reproducing many of the proposed methods, which makes it difficult to compare the approaches. As a result, surveying the literature by enumerating the proposals one--after-another is not the most useful way to provide a comparative point of view. Accordingly, we present a more convenient strategy to survey the different approaches. We divide the problem of detecting pedestrians from images into different processing steps, each with attached responsibilities. Then, the different proposed methods are analyzed and classified with respect to each processing stage, favoring a comparative viewpoint. Finally, discussion of the important topics is presented, putting special emphasis on the future needs and challenges.

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.

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

An analysis of student privacy rights in the use of plagiarism detection systems.

Science.gov (United States)

Brinkman, Bo

2013-09-01

Plagiarism detection services are a powerful tool to help encourage academic integrity. Adoption of these services has proven to be controversial due to ethical concerns about students' rights. Central to these concerns is the fact that most such systems make permanent archives of student work to be re-used in plagiarism detection. This computerization and automation of plagiarism detection is changing the relationships of trust and responsibility between students, educators, educational institutions, and private corporations. Educators must respect student privacy rights when implementing such systems. Student work is personal information, not the property of the educator or institution. The student has the right to be fully informed about how plagiarism detection works, and the fact that their work will be permanently archived as a result. Furthermore, plagiarism detection should not be used if the permanent archiving of a student's work may expose him or her to future harm.

Detection of system changes due to damage using a tuned hyperchaotic probe

International Nuclear Information System (INIS)

Torkamani, S; Butcher, E A; Todd, M D; Park, G

2011-01-01

This study explores the use of a hyperchaotic signal as an excitation to probe a system for dynamic changes induced by damage events. In chaotic interrogation a deterministic chaotic input (rather than the more commonly employed stochastic white noise input) is applied to the structure and the dynamic response is mined for features derived from its state space reconstruction. The steady-state chaotic excitation is tuned to excite the structure in a way that optimal sensitivity to dimensionality changes in the response may be observed, resulting in damage-sensitive features extracted from the resulting attractors. The enhanced technique proposed in this paper explores a hyperchaotic excitation, which is fundamentally new in its use as an excitation. Hyperchaotic oscillators have at least two Lyapunov exponents, in contrast to simple chaotic oscillators. By using the Kaplan–Yorke conjecture and performing a parametric investigation, the steady-state hyperchaotic excitation is tuned to excite the structure in such a way that the optimal (as will be defined) dimensionality of the steady-state response is achieved. A feature called the 'average local attractor variance ratio' (ALAVR), which is based on attractor geometry, is used to compare the geometry of a baseline attractor to a test attractor. The enhanced technique is applied to analytically and experimentally analyze the response of an eight-degree-of-freedom system to the hyperchaotic excitation for the purpose of damage assessment. A comparison between the results obtained from current hyperchaotic excitation versus a chaotic excitation highlights the higher damage sensitivity in the system response to the hyperchaotic excitation

Network Intrusion Detection System using Apache Storm

Directory of Open Access Journals (Sweden)

Muhammad Asif Manzoor

2017-06-01

Full Text Available Network security implements various strategies for the identification and prevention of security breaches. Network intrusion detection is a critical component of network management for security, quality of service and other purposes. These systems allow early detection of network intrusion and malicious activities; so that the Network Security infrastructure can react to mitigate these threats. Various systems are proposed to enhance the network security. We are proposing to use anomaly based network intrusion detection system in this work. Anomaly based intrusion detection system can identify the new network threats. We also propose to use Real-time Big Data Stream Processing Framework, Apache Storm, for the implementation of network intrusion detection system. Apache Storm can help to manage the network traffic which is generated at enormous speed and size and the network traffic speed and size is constantly increasing. We have used Support Vector Machine in this work. We use Knowledge Discovery and Data Mining 1999 (KDD’99 dataset to test and evaluate our proposed solution.

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.

Road Anomalies Detection System Evaluation.

Science.gov (United States)

Silva, Nuno; Shah, Vaibhav; Soares, João; Rodrigues, Helena

2018-06-21

Anomalies on road pavement cause discomfort to drivers and passengers, and may cause mechanical failure or even accidents. Governments spend millions of Euros every year on road maintenance, often causing traffic jams and congestion on urban roads on a daily basis. This paper analyses the difference between the deployment of a road anomalies detection and identification system in a “conditioned” and a real world setup, where the system performed worse compared to the “conditioned” setup. It also presents a system performance analysis based on the analysis of the training data sets; on the analysis of the attributes complexity, through the application of PCA techniques; and on the analysis of the attributes in the context of each anomaly type, using acceleration standard deviation attributes to observe how different anomalies classes are distributed in the Cartesian coordinates system. Overall, in this paper, we describe the main insights on road anomalies detection challenges to support the design and deployment of a new iteration of our system towards the deployment of a road anomaly detection service to provide information about roads condition to drivers and government entities.

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.

Change detection in polarimetric SAR data over several time points

DEFF Research Database (Denmark)

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

2014-01-01

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

Remote detection system

International Nuclear Information System (INIS)

Nixon, K.V.; France, S.W.; Garcia, C.; Hastings, R.D.

1981-05-01

A newly designed remote detection system has been developed at Los Alamos that allows the collection of high-resolution gamma-ray spectra and neutron data from a remote location. The system consists of the remote unit and a command unit. The remote unit collects data in a potentially hostile environment while the operator controls the unit by either radio or wire link from a safe position. Both units are battery powered and are housed in metal carrying cases

Symmetrized local co-registration optimization for anomalous change detection

Energy Technology Data Exchange (ETDEWEB)

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

2009-01-01

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

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.

NATIONWIDE HYBRID CHANGE DETECTION OF BUILDINGS

Directory of Open Access Journals (Sweden)

V. Hron

2016-06-01

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

Automatic change detection to facial expressions in adolescents

DEFF Research Database (Denmark)

Liu, Tongran; Xiao, Tong; Jiannong, Shi

2016-01-01

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

Detection of morphological changes in cliff face surrounding a waterfall using terrestrial laser scanning and unmanned aerial system

Science.gov (United States)

Hayakawa, Yuichi S.; Obanawa, Hiroyuki

2015-04-01

Waterfall or bedrock knickpoint appears as an erosional front in bedrock rivers forming deep v-shaped valley downstream. Following the rapid fluvial erosion of waterfall, rockfalls and gravita-tional collapses often occur in surrounding steep cliffs. Although morphological changes of such steep cliffs are sometimes visually observed, quantitative and precise measurements of their spatio-temporal distribution have been limited due to the difficulties in direct access to such cliffs if with classical measurement methods. However, for the clarification of geomorphological processes oc-curring in the cliffs, multi-temporal mapping of the cliff face at a high resolution is necessary. Re-mote sensing approaches are therefore suitable for the topographic measurements and detection of changes in such inaccessible cliffs. To achieve accurate topographic mapping of cliffs around a wa-terfall, here we perform multi-temporal terrestrial laser scanning (TLS), as well as structure-from-motion multi-view stereo (SfM-MVS) photogrammetry based on unmanned aerial system (UAS). The study site is Kegon Falls in central Japan, having a vertical drop of surface water from top of its overhanging cliff, as well as groundwater outflows from its lower portions. The bedrock is composed of alternate layers of andesite lava and conglomerates. Minor rockfalls in the cliffs are often ob-served by local people. The latest major rockfall occurred in 1986, causing ca. 8-m upstream propa-gation of the waterfall lip. This provides a good opportunity to examine the changes in the surround-ing cliffs following the waterfall recession. Multi-time point clouds were obtained by TLS measure-ment over years, and the three-dimensional changes of the rock surface were detected, uncovering the locus of small rockfalls and gully developments. Erosion seems particularly frequent in relatively weak the conglomerates layer, whereas small rockfalls seems to have occurred in the andesite layers. Also, shadows in the

Pothole Detection System Using a Black-box Camera

Directory of Open Access Journals (Sweden)

Youngtae Jo

2015-11-01

Full Text Available Aging roads and poor road-maintenance systems result a large number of potholes, whose numbers increase over time. Potholes jeopardize road safety and transportation efficiency. Moreover, they are often a contributing factor to car accidents. To address the problems associated with potholes, the locations and size of potholes must be determined quickly. Sophisticated road-maintenance strategies can be developed using a pothole database, which requires a specific pothole-detection system that can collect pothole information at low cost and over a wide area. However, pothole repair has long relied on manual detection efforts. Recent automatic detection systems, such as those based on vibrations or laser scanning, are insufficient to detect potholes correctly and inexpensively owing to the unstable detection of vibration-based methods and high costs of laser scanning-based methods. Thus, in this paper, we introduce a new pothole-detection system using a commercial black-box camera. The proposed system detects potholes over a wide area and at low cost. We have developed a novel pothole-detection algorithm specifically designed to work with the embedded computing environments of black-box cameras. Experimental results are presented with our proposed system, showing that potholes can be detected accurately in real-time.

A comprehensive change detection method for updating the National Land Cover Database to circa 2011

Science.gov (United States)

Jin, Suming; Yang, Limin; Danielson, Patrick; Homer, Collin G.; Fry, Joyce; Xian, George

2013-01-01

The importance of characterizing, quantifying, and monitoring land cover, land use, and their changes has been widely recognized by global and environmental change studies. Since the early 1990s, three U.S. National Land Cover Database (NLCD) products (circa 1992, 2001, and 2006) have been released as free downloads for users. The NLCD 2006 also provides land cover change products between 2001 and 2006. To continue providing updated national land cover and change datasets, a new initiative in developing NLCD 2011 is currently underway. We present a new Comprehensive Change Detection Method (CCDM) designed as a key component for the development of NLCD 2011 and the research results from two exemplar studies. The CCDM integrates spectral-based change detection algorithms including a Multi-Index Integrated Change Analysis (MIICA) model and a novel change model called Zone, which extracts change information from two Landsat image pairs. The MIICA model is the core module of the change detection strategy and uses four spectral indices (CV, RCVMAX, dNBR, and dNDVI) to obtain the changes that occurred between two image dates. The CCDM also includes a knowledge-based system, which uses critical information on historical and current land cover conditions and trends and the likelihood of land cover change, to combine the changes from MIICA and Zone. For NLCD 2011, the improved and enhanced change products obtained from the CCDM provide critical information on location, magnitude, and direction of potential change areas and serve as a basis for further characterizing land cover changes for the nation. An accuracy assessment from the two study areas show 100% agreement between CCDM mapped no-change class with reference dataset, and 18% and 82% disagreement for the change class for WRS path/row p22r39 and p33r33, respectively. The strength of the CCDM is that the method is simple, easy to operate, widely applicable, and capable of capturing a variety of natural and

Learning Change from Synthetic Aperture Radar Images: Performance Evaluation of a Support Vector Machine to Detect Earthquake and Tsunami-Induced Changes

Directory of Open Access Journals (Sweden)

Marc Wieland

2016-09-01

Full Text Available This study evaluates the performance of a Support Vector Machine (SVM classifier to learn and detect changes in single- and multi-temporal X- and L-band Synthetic Aperture Radar (SAR images under varying conditions. The purpose is to provide guidance on how to train a powerful learning machine for change detection in SAR images and to contribute to a better understanding of potentials and limitations of supervised change detection approaches. This becomes particularly important on the background of a rapidly growing demand for SAR change detection to support rapid situation awareness in case of natural disasters. The application environment of this study thus focuses on detecting changes caused by the 2011 Tohoku earthquake and tsunami disaster, where single polarized TerraSAR-X and ALOS PALSAR intensity images are used as input. An unprecedented reference dataset of more than 18,000 buildings that have been visually inspected by local authorities for damages after the disaster forms a solid statistical population for the performance experiments. Several critical choices commonly made during the training stage of a learning machine are being assessed for their influence on the change detection performance, including sampling approach, location and number of training samples, classification scheme, change feature space and the acquisition dates of the satellite images. Furthermore, the proposed machine learning approach is compared with the widely used change image thresholding. The study concludes that a well-trained and tuned SVM can provide highly accurate change detections that outperform change image thresholding. While good performance is achieved in the binary change detection case, a distinction between multiple change classes in terms of damage grades leads to poor performance in the tested experimental setting. The major drawback of a machine learning approach is related to the high costs of training. The outcomes of this study, however

The Network for the Detection of Atmospheric Composition Change (NDACC): history, status and perspectives

Science.gov (United States)

De Mazière, Martine; Thompson, Anne M.; Kurylo, Michael J.; Wild, Jeannette D.; Bernhard, Germar; Blumenstock, Thomas; Braathen, Geir O.; Hannigan, James W.; Lambert, Jean-Christopher; Leblanc, Thierry; McGee, Thomas J.; Nedoluha, Gerald; Petropavlovskikh, Irina; Seckmeyer, Gunther; Simon, Paul C.; Steinbrecht, Wolfgang; Strahan, Susan E.

2018-04-01

The Network for the Detection of Atmospheric Composition Change (NDACC) is an international global network of more than 90 stations making high-quality measurements of atmospheric composition that began official operations in 1991 after 5 years of planning. Apart from sonde measurements, all measurements in the network are performed by ground-based remote-sensing techniques. Originally named the Network for the Detection of Stratospheric Change (NDSC), the name of the network was changed to NDACC in 2005 to better reflect the expanded scope of its measurements. The primary goal of NDACC is to establish long-term databases for detecting changes and trends in the chemical and physical state of the atmosphere (mesosphere, stratosphere, and troposphere) and to assess the coupling of such changes with climate and air quality. NDACC's origins, station locations, organizational structure, and data archiving are described. NDACC is structured around categories of ground-based observational techniques (sonde, lidar, microwave radiometers, Fourier-transform infrared, UV-visible DOAS (differential optical absorption spectroscopy)-type, and Dobson-Brewer spectrometers, as well as spectral UV radiometers), timely cross-cutting themes (ozone, water vapour, measurement strategies, cross-network data integration), satellite measurement systems, and theory and analyses. Participation in NDACC requires compliance with strict measurement and data protocols to ensure that the network data are of high and consistent quality. To widen its scope, NDACC has established formal collaborative agreements with eight other cooperating networks and Global Atmosphere Watch (GAW). A brief history is provided, major accomplishments of NDACC during its first 25 years of operation are reviewed, and a forward-looking perspective is presented.

Sequential Change-Point Detection via Online Convex Optimization

Directory of Open Access Journals (Sweden)

Yang Cao

2018-02-01

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

Detecting Change in Landscape Greenness over Large Areas: An Example for New Mexico, USA

Science.gov (United States)

Monitoring and quantifying changes in vegetation cover over large areas using remote sensing can potentially detect large-scale, slow changes (e.g., climate change), as well as more local and rapid changes (e.g., fire, land development). A useful indicator for detecting change i...

Feasibility study of Self Powered Neutron Detectors in Fast Reactors for detecting local change in neutron flux distribution

International Nuclear Information System (INIS)

Jammes, Christian; Filliatre, Philippe; Verma, Vasudha; Hellesen, Carl; Jacobsson Svard, Staffan

2015-01-01

Neutron flux monitoring system forms an integral part of the design of a Generation IV sodium cooled fast reactor system. Diverse possibilities of detector systems installation have to be investigated with respect to practicality and feasibility according to the detection parameters. In this paper, we demonstrate the feasibility of using self powered neutron detectors as in-core detectors in fast reactors for detecting local change in neutron flux distribution. We show that the gamma contribution from fission products decay in the fuel and activation of structural materials is very small compared to the fission gammas. Thus, it is possible for the in-core SPND signal to follow changes in local neutron flux as they are proportional to each other. This implies that the signal from an in-core SPND can provide dynamic information on the neutron flux perturbations occurring inside the reactor core. (authors)

Feasibility study of Self Powered Neutron Detectors in Fast Reactors for detecting local change in neutron flux distribution

Energy Technology Data Exchange (ETDEWEB)

Jammes, Christian; Filliatre, Philippe [CEA, DEN, DER, Instrumentation Sensors and Dosimetry Laboratory, Cadarache, F-13108 St Paul-Lez-Durance, (France); Verma, Vasudha; Hellesen, Carl; Jacobsson Svard, Staffan [Division of Applied Nuclear Physics, Uppsala University, SE-75120 Uppsala, (Sweden)

2015-07-01

Neutron flux monitoring system forms an integral part of the design of a Generation IV sodium cooled fast reactor system. Diverse possibilities of detector systems installation have to be investigated with respect to practicality and feasibility according to the detection parameters. In this paper, we demonstrate the feasibility of using self powered neutron detectors as in-core detectors in fast reactors for detecting local change in neutron flux distribution. We show that the gamma contribution from fission products decay in the fuel and activation of structural materials is very small compared to the fission gammas. Thus, it is possible for the in-core SPND signal to follow changes in local neutron flux as they are proportional to each other. This implies that the signal from an in-core SPND can provide dynamic information on the neutron flux perturbations occurring inside the reactor core. (authors)

Morphometric Change Detection of Lake Hawassa in the Ethiopian Rift Valley

Directory of Open Access Journals (Sweden)

Yonas Abebe

2018-05-01

Full Text Available The Ethiopian Rift Valley lakes have been subjected to environmental and ecological changes due to recent development endeavors and natural phenomena, which are visible in the alterations to the quality and quantity of the water resources. Monitoring lakes for temporal and spatial alterations has become a valuable indicator of environmental change. In this regard, hydrographic information has a paramount importance. The first extensive hydrographic survey of Lake Hawassa was conducted in 1999. In this study, a bathymetric map was prepared using advances in global positioning systems, portable sonar sounder technology, geostatistics, remote sensing and geographic information system (GIS software analysis tools with the aim of detecting morphometric changes. Results showed that the surface area of Lake Hawassa increased by 7.5% in 1999 and 3.2% in 2011 from that of 1985. Water volume decreased by 17% between 1999 and 2011. Silt accumulated over more than 50% of the bed surface has caused a 4% loss of the lake’s storage capacity. The sedimentation patterns identified may have been strongly impacted by anthropogenic activities including urbanization and farming practices located on the northern, eastern and western sides of the lake watershed. The study demonstrated this geostatistical modeling approach to be a rapid and cost-effective method for bathymetric mapping.

A universal DNA-based protein detection system.

Science.gov (United States)

Tran, Thua N N; Cui, Jinhui; Hartman, Mark R; Peng, Songming; Funabashi, Hisakage; Duan, Faping; Yang, Dayong; March, John C; Lis, John T; Cui, Haixin; Luo, Dan

2013-09-25

Protein immune detection requires secondary antibodies which must be carefully selected in order to avoid interspecies cross-reactivity, and is therefore restricted by the limited availability of primary/secondary antibody pairs. Here we present a versatile DNA-based protein detection system using a universal adapter to interface between IgG antibodies and DNA-modified reporter molecules. As a demonstration of this capability, we successfully used DNA nano-barcodes, quantum dots, and horseradish peroxidase enzyme to detect multiple proteins using our DNA-based labeling system. Our system not only eliminates secondary antibodies but also serves as a novel method platform for protein detection with modularity, high capacity, and multiplexed capability.

The quench detection system of Wendelstein 7-X

International Nuclear Information System (INIS)

Birus, Dietrich; Schneider, Matthias; Rummel, Thomas; Fricke, Marko

2011-01-01

The Quench Detection System of Wendelstein W7-X has been developed, pretested and manufactured during the last four years. This safety subsystem of the superconducting magnet power supply will guarantee the safe operating of the whole magnet system. The main targets of the Quench Detection System are the complete data acquisition of all the voltages along the superconducting components, i.e. non planar and planar coils, and bus bars, the evaluation of this data and the control of the magnet system safety discharges. The Quench Detection System is generating control commands for the magnet power supply control system and the electrical status of the superconducting components of W7-X. The Quench Detection System consists of nearly 580 Quench Detection Units (QDU) located in 10 QD-subsystems, 8 racks in each, one host system and two special interfaces for evaluation of the quench control commands and the failure signals. The operating software suite of the QD System allows the configuration, the operation and the maintenance of the whole system.

Smart sensor systems for outdoor intrusion detection

International Nuclear Information System (INIS)

Lynn, J.K.

1988-01-01

A major improvement in outdoor perimeter security system probability of detection (PD) and reduction in false alarm rate (FAR) and nuisance alarm rate (NAR) may be obtained by analyzing the indications immediately preceding an event which might be interpreted as an intrusion. Existing systems go into alarm after crossing a threshold. Very slow changes, which accumulate until the threshold is reached, may be assessed falsely as an intrusion. A hierarchial program has begun at Stellar to develop a modular, expandable Smart Sensor system which may be interfaced to most types of sensor and alarm reporting systems. A major upgrade to the SSI Test Site is in progress so that intrusions may be simulated in a controlled and repeatable manner. A test platform is being constructed which will operate in conduction with a mobile instrumentation center with CCTVB, lighting control, weather and data monitoring and remote control of the test platform and intrusion simulators. Additional testing was contracted with an independent test facility to assess the effects of severe winter weather conditions

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

Energy Technology Data Exchange (ETDEWEB)

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

2014-09-01

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

A new islanding detection technique for multiple mini hydro based on rate of change of reactive power and load connecting strategy

International Nuclear Information System (INIS)

Laghari, J.A.; Mokhlis, H.; Bakar, A.H.A.; Karimi, M.

2013-01-01

Highlights: • The requirement of DG interconnection with existing power system is discussed. • Various islanding detection techniques are discussed with their merits and demerits. • New islanding detection strategy is proposed for multiple mini hydro type DGs. • The proposed strategy is based on dq/dt and load connecting strategy. • The effectiveness of strategy is verified on various other cases. - Abstract: The interconnection of distributed generation (DG) into distribution networks is undergoing a rapid global expansion. It enhances the system’s reliability, while simultaneously reduces pollution problems related to the generation of electrical power. To fully utilize the benefits of DGs, certain technical issues need to be addressed. One of the most important issues in this context is islanding detection. This paper presents a new islanding detection technique that is suitable for multiple mini-hydro type DG units. The proposed strategy is based on the rate of change of reactive power and load connecting strategy to detect islanding within the system. For a large power mismatch, islanding is detected by rate of change of reactive power only. However, for a close power mismatch, the rate of change of reactive power initiates a load connecting strategy, which in turn alters the load on the distribution network. This load variation in the distribution network causes a variation in the rate of change of reactive power, which is utilized to distinguish islanding and other events. The simulation results show that the proposed strategy is effective in detecting islanding occurrence in a distribution network

46 CFR 154.1350 - Flammable gas detection system.

Science.gov (United States)

2010-10-01

... 46 Shipping 5 2010-10-01 2010-10-01 false Flammable gas detection system. 154.1350 Section 154... Equipment Instrumentation § 154.1350 Flammable gas detection system. (a) The vessel must have a fixed flammable gas detection system that has sampling points in: (1) Each cargo pump room; (2) Each cargo...

Field validation of protocols developed to evaluate in-line mastitis detection systems.

Science.gov (United States)

Kamphuis, C; Dela Rue, B T; Eastwood, C R

2016-02-01

This paper reports on a field validation of previously developed protocols for evaluating the performance of in-line mastitis-detection systems. The protocols outlined 2 requirements of these systems: (1) to detect cows with clinical mastitis (CM) promptly and accurately to enable timely and appropriate treatment and (2) to identify cows with high somatic cell count (SCC) to manage bulk milk SCC levels. Gold standard measures, evaluation tests, performance measures, and performance targets were proposed. The current study validated the protocols on commercial dairy farms with automated in-line mastitis-detection systems using both electrical conductivity (EC) and SCC sensor systems that both monitor at whole-udder level. The protocol for requirement 1 was applied on 3 commercial farms. For requirement 2, the protocol was applied on 6 farms; 3 of them had low bulk milk SCC (128×10(3) cells/mL) and were the same farms as used for field evaluation of requirement 1. Three farms with high bulk milk SCC (270×10(3) cells/mL) were additionally enrolled. The field evaluation methodology and results were presented at a workshop including representation from 7 international suppliers of in-line mastitis-detection systems. Feedback was sought on the acceptance of standardized performance evaluation protocols and recommended refinements to the protocols. Although the methodology for requirement 1 was relatively labor intensive and required organizational skills over an extended period, no major issues were encountered during the field validation of both protocols. The validation, thus, proved the protocols to be practical. Also, no changes to the data collection process were recommended by the technology supplier representatives. However, 4 recommendations were made to refine the protocols: inclusion of an additional analysis that ignores small (low-density) clot observations in the definition of CM, extension of the time window from 4 to 5 milkings for timely alerts for CM

Building change detection via a combination of CNNs using only RGB aerial imageries

Science.gov (United States)

Nemoto, Keisuke; Hamaguchi, Ryuhei; Sato, Masakazu; Fujita, Aito; Imaizumi, Tomoyuki; Hikosaka, Shuhei

2017-10-01

Building change information extracted from remote sensing imageries is important for various applications such as urban management and marketing planning. The goal of this work is to develop a methodology for automatically capturing building changes from remote sensing imageries. Recent studies have addressed this goal by exploiting 3-D information as a proxy for building height. In contrast, because in practice it is expensive or impossible to prepare 3-D information, we do not rely on 3-D data but focus on using only RGB aerial imageries. Instead, we employ deep convolutional neural networks (CNNs) to extract effective features, and improve change detection accuracy in RGB remote sensing imageries. We consider two aspects of building change detection, building detection and subsequent change detection. Our proposed methodology was tested on several areas, which has some differences such as dominant building characteristics and varying brightness values. On all over the tested areas, the proposed method provides good results for changed objects, with recall values over 75 % with a strict overlap requirement of over 50% in intersection-over-union (IoU). When the IoU threshold was relaxed to over 10%, resulting recall values were over 81%. We conclude that use of CNNs enables accurate detection of building changes without employing 3-D information.

Ten Years of Land Cover Change on the California Coast Detected using Landsat Satellite Image Analysis

Science.gov (United States)

Potter, Christopher S.

2013-01-01

Landsat satellite imagery was analyzed to generate a detailed record of 10 years of vegetation disturbance and regrowth for Pacific coastal areas of Marin and San Francisco Counties. The Landsat Ecosystem Disturbance Adaptive Processing System (LEDAPS) methodology, a transformation of Tasseled-Cap data space, was applied to detected changes in perennial coastal shrubland, woodland, and forest cover from 1999 to 2009. Results showed several principal points of interest, within which extensive contiguous areas of similar LEDAPS vegetation change (either disturbed or restored) were detected. Regrowth areas were delineated as burned forest areas in the Point Reyes National Seashore (PRNS) from the 1995 Vision Fire. LEDAPS-detected disturbance patterns on Inverness Ridge, PRNS in areas observed with dieback of tanoak and bay laurel trees was consistent with defoliation by sudden oak death (Phytophthora ramorum). LEDAPS regrowth pixels were detected over much of the predominantly grassland/herbaceous cover of the Olema Valley ranchland near PRNS. Extensive restoration of perennial vegetation cover on Crissy Field, Baker Beach and Lobos Creek dunes in San Francisco was identified. Based on these examples, the LEDAPS methodology will be capable of fulfilling much of the need for continual, low-cost monitoring of emerging changes to coastal ecosystems.

Tape Cassette Bacteria Detection System

Science.gov (United States)

1973-01-01

The design, fabrication, and testing of an automatic bacteria detection system with a zero-g capability and based on the filter-capsule approach is described. This system is intended for monitoring the sterility of regenerated water in a spacecraft. The principle of detection is based on measuring the increase in chemiluminescence produced by the action of bacterial porphyrins (i.e., catalase, cytochromes, etc.) on a luminol-hydrogen peroxide mixture. Since viable as well as nonviable organisms initiate this luminescence, viable organisms are detected by comparing the signal of an incubated water sample with an unincubated control. Higher signals for the former indicate the presence of viable organisms. System features include disposable sealed sterile capsules, each containing a filter membrane, for processing discrete water samples and a tape transport for moving these capsules through a processing sequence which involves sample concentration, nutrient addition, incubation, a 4 Molar Urea wash and reaction with luminol-hydrogen peroxide in front of a photomultiplier tube. Liquids are introduced by means of a syringe needle which pierces a rubber septum contained in the wall of the capsule. Detection thresholds obtained with this unit towards E. coli and S. marcescens assuming a 400 ml water sample are indicated.

Detection of kinetic change points in piece-wise linear single molecule motion

Science.gov (United States)

Hill, Flynn R.; van Oijen, Antoine M.; Duderstadt, Karl E.

2018-03-01

Single-molecule approaches present a powerful way to obtain detailed kinetic information at the molecular level. However, the identification of small rate changes is often hindered by the considerable noise present in such single-molecule kinetic data. We present a general method to detect such kinetic change points in trajectories of motion of processive single molecules having Gaussian noise, with a minimum number of parameters and without the need of an assumed kinetic model beyond piece-wise linearity of motion. Kinetic change points are detected using a likelihood ratio test in which the probability of no change is compared to the probability of a change occurring, given the experimental noise. A predetermined confidence interval minimizes the occurrence of false detections. Applying the method recursively to all sub-regions of a single molecule trajectory ensures that all kinetic change points are located. The algorithm presented allows rigorous and quantitative determination of kinetic change points in noisy single molecule observations without the need for filtering or binning, which reduce temporal resolution and obscure dynamics. The statistical framework for the approach and implementation details are discussed. The detection power of the algorithm is assessed using simulations with both single kinetic changes and multiple kinetic changes that typically arise in observations of single-molecule DNA-replication reactions. Implementations of the algorithm are provided in ImageJ plugin format written in Java and in the Julia language for numeric computing, with accompanying Jupyter Notebooks to allow reproduction of the analysis presented here.

Fully automatic AI-based leak detection system

Energy Technology Data Exchange (ETDEWEB)

Tylman, Wojciech; Kolczynski, Jakub [Dept. of Microelectronics and Computer Science, Technical University of Lodz in Poland, ul. Wolczanska 221/223, Lodz (Poland); Anders, George J. [Kinectrics Inc., 800 Kipling Ave., Toronto, Ontario M8Z 6C4 (Canada)

2010-09-15

This paper presents a fully automatic system intended to detect leaks of dielectric fluid in underground high-pressure, fluid-filled (HPFF) cables. The system combines a number of artificial intelligence (AI) and data processing techniques to achieve high detection capabilities for various rates of leaks, including leaks as small as 15 l per hour. The system achieves this level of precision mainly thanks to a novel auto-tuning procedure, enabling learning of the Bayesian network - the decision-making component of the system - using simulated leaks of various rates. Significant new developments extending the capabilities of the original leak detection system described in and form the basis of this paper. Tests conducted on the real-life HPFF cable system in New York City are also discussed. (author)

A stereo vision-based obstacle detection system in vehicles

Science.gov (United States)

Huh, Kunsoo; Park, Jaehak; Hwang, Junyeon; Hong, Daegun

2008-02-01

Obstacle detection is a crucial issue for driver assistance systems as well as for autonomous vehicle guidance function and it has to be performed with high reliability to avoid any potential collision with the front vehicle. The vision-based obstacle detection systems are regarded promising for this purpose because they require little infrastructure on a highway. However, the feasibility of these systems in passenger car requires accurate and robust sensing performance. In this paper, an obstacle detection system using stereo vision sensors is developed. This system utilizes feature matching, epipoplar constraint and feature aggregation in order to robustly detect the initial corresponding pairs. After the initial detection, the system executes the tracking algorithm for the obstacles. The proposed system can detect a front obstacle, a leading vehicle and a vehicle cutting into the lane. Then, the position parameters of the obstacles and leading vehicles can be obtained. The proposed obstacle detection system is implemented on a passenger car and its performance is verified experimentally.

Rapid Land Cover Map Updates Using Change Detection and Robust Random Forest Classifiers

Directory of Open Access Journals (Sweden)

Konrad J. Wessels

2016-10-01

Full Text Available The paper evaluated the Landsat Automated Land Cover Update Mapping (LALCUM system designed to rapidly update a land cover map to a desired nominal year using a pre-existing reference land cover map. The system uses the Iteratively Reweighted Multivariate Alteration Detection (IRMAD to identify areas of change and no change. The system then automatically generates large amounts of training samples (n > 1 million in the no-change areas as input to an optimized Random Forest classifier. Experiments were conducted in the KwaZulu-Natal Province of South Africa using a reference land cover map from 2008, a change mask between 2008 and 2011 and Landsat ETM+ data for 2011. The entire system took 9.5 h to process. We expected that the use of the change mask would improve classification accuracy by reducing the number of mislabeled training data caused by land cover change between 2008 and 2011. However, this was not the case due to exceptional robustness of Random Forest classifier to mislabeled training samples. The system achieved an overall accuracy of 65%–67% using 22 detailed classes and 72%–74% using 12 aggregated national classes. “Water”, “Plantations”, “Plantations—clearfelled”, “Orchards—trees”, “Sugarcane”, “Built-up/dense settlement”, “Cultivation—Irrigated” and “Forest (indigenous” had user’s accuracies above 70%. Other detailed classes (e.g., “Low density settlements”, “Mines and Quarries”, and “Cultivation, subsistence, drylands” which are required for operational, provincial-scale land use planning and are usually mapped using manual image interpretation, could not be mapped using Landsat spectral data alone. However, the system was able to map the 12 national classes, at a sufficiently high level of accuracy for national scale land cover monitoring. This update approach and the highly automated, scalable LALCUM system can improve the efficiency and update rate of regional land

Change detection in full and dual polarization sar data and the complex wishart distribution

DEFF Research Database (Denmark)

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

A test statistic for equality of two complex variance-covariance matrices following the complex Wishart distribution with an associated probability of observing a smaller value of the test statistic is sketched. We demonstrate the use of the test statistic and the associated probability measure f...... for change detection in both full and dual polarimetry synthetic aperture radar (SAR) data collected by the Danish EMISAR system....

Automatic Emergence Detection in Complex Systems

Directory of Open Access Journals (Sweden)

Eugene Santos

2017-01-01

Full Text Available Complex systems consist of multiple interacting subsystems, whose nonlinear interactions can result in unanticipated (emergent system events. Extant systems analysis approaches fail to detect such emergent properties, since they analyze each subsystem separately and arrive at decisions typically through linear aggregations of individual analysis results. In this paper, we propose a quantitative definition of emergence for complex systems. We also propose a framework to detect emergent properties given observations of its subsystems. This framework, based on a probabilistic graphical model called Bayesian Knowledge Bases (BKBs, learns individual subsystem dynamics from data, probabilistically and structurally fuses said dynamics into a single complex system dynamics, and detects emergent properties. Fusion is the central element of our approach to account for situations when a common variable may have different probabilistic distributions in different subsystems. We evaluate our detection performance against a baseline approach (Bayesian Network ensemble on synthetic testbeds from UCI datasets. To do so, we also introduce a method to simulate and a metric to measure discrepancies that occur with shared/common variables. Experiments demonstrate that our framework outperforms the baseline. In addition, we demonstrate that this framework has uniform polynomial time complexity across all three learning, fusion, and reasoning procedures.

Development of the environmental neutron detection system

International Nuclear Information System (INIS)

Kume, Kyo

2002-03-01

Environmental neutron detection system was proposed and developed. The main goal of this system was set to detect fast and thermal neutrons with the identical detectors setup without degraders. This system consists of a 10 B doped liquid scintillator for n detection and CsI scintillators for simultaneous γ emission from 10 B doped in the liquid scintillator after the n capture reaction. The first setup was optimized for the thermal n detection, while the second setup was for the fast n detection. It was shown that the thermal n flux was obtained in the first setup by using the method of the γ coincidence method with the help of the Monte Carlo calculation. The second setup was designed to improve the detection efficiency for the fast n, and was shown qualitatively that both the pulse shape discrimination and the coincidence methods are efficient. There will be more improvements, particularly for the quantitative discussion. (author)

Detection of contamination of municipal water distribution systems

Science.gov (United States)

Cooper, John F [Oakland, CA

2012-01-17

A system for the detection of contaminates of a fluid in a conduit. The conduit is part of a fluid distribution system. A chemical or biological sensor array is connected to the conduit. The sensor array produces an acoustic signal burst in the fluid upon detection of contaminates in the fluid. A supervisory control system connected to the fluid and operatively connected to the fluid distribution system signals the fluid distribution system upon detection of contaminates in the fluid.

A DoS/DDoS Attack Detection System Using Chi-Square Statistic Approach

Directory of Open Access Journals (Sweden)

Fang-Yie Leu

2010-04-01

Full Text Available Nowadays, users can easily access and download network attack tools, which often provide friendly interfaces and easily operated features, from the Internet. Therefore, even a naive hacker can also launch a large scale DoS or DDoS attack to prevent a system, i.e., the victim, from providing Internet services. In this paper, we propose an agent based intrusion detection architecture, which is a distributed detection system, to detect DoS/DDoS attacks by invoking a statistic approach that compares source IP addresses' normal and current packet statistics to discriminate whether there is a DoS/DDoS attack. It first collects all resource IPs' packet statistics so as to create their normal packet distribution. Once some IPs' current packet distribution suddenly changes, very often it is an attack. Experimental results show that this approach can effectively detect DoS/DDoS attacks.

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

Land-Cover Change Detection Using Multi-Temporal MODIS NDVI Imagery

Science.gov (United States)

Monitoring the locations and distributions of land-cover change is important for establishing linkages between policy decisions, regulatory actions and subsequent land-use activities. Past studies incorporating two-date change detection using Landsat data have tended to be perfor...

An FPGA-Based People Detection System

Directory of Open Access Journals (Sweden)

James J. Clark

2005-05-01

Full Text Available This paper presents an FPGA-based system for detecting people from video. The system is designed to use JPEG-compressed frames from a network camera. Unlike previous approaches that use techniques such as background subtraction and motion detection, we use a machine-learning-based approach to train an accurate detector. We address the hardware design challenges involved in implementing such a detector, along with JPEG decompression, on an FPGA. We also present an algorithm that efficiently combines JPEG decompression with the detection process. This algorithm carries out the inverse DCT step of JPEG decompression only partially. Therefore, it is computationally more efficient and simpler to implement, and it takes up less space on the chip than the full inverse DCT algorithm. The system is demonstrated on an automated video surveillance application and the performance of both hardware and software implementations is analyzed. The results show that the system can detect people accurately at a rate of about 2.5 frames per second on a Virtex-II 2V1000 using a MicroBlaze processor running at 75 MHz, communicating with dedicated hardware over FSL links.

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.

46 CFR 108.413 - Fusible element fire detection system.

Science.gov (United States)

2010-10-01

... 46 Shipping 4 2010-10-01 2010-10-01 false Fusible element fire detection system. 108.413 Section... UNITS DESIGN AND EQUIPMENT Fire Extinguishing Systems § 108.413 Fusible element fire detection system. (a) A fusible element fire detection system may be installed. (b) The arrangements for the system...

Image Fusion-Based Land Cover Change Detection Using Multi-Temporal High-Resolution Satellite Images

Directory of Open Access Journals (Sweden)

Biao Wang

2017-08-01

Full Text Available Change detection is usually treated as a problem of explicitly detecting land cover transitions in satellite images obtained at different times, and helps with emergency response and government management. This study presents an unsupervised change detection method based on the image fusion of multi-temporal images. The main objective of this study is to improve the accuracy of unsupervised change detection from high-resolution multi-temporal images. Our method effectively reduces change detection errors, since spatial displacement and spectral differences between multi-temporal images are evaluated. To this end, a total of four cross-fused images are generated with multi-temporal images, and the iteratively reweighted multivariate alteration detection (IR-MAD method—a measure for the spectral distortion of change information—is applied to the fused images. In this experiment, the land cover change maps were extracted using multi-temporal IKONOS-2, WorldView-3, and GF-1 satellite images. The effectiveness of the proposed method compared with other unsupervised change detection methods is demonstrated through experimentation. The proposed method achieved an overall accuracy of 80.51% and 97.87% for cases 1 and 2, respectively. Moreover, the proposed method performed better when differentiating the water area from the vegetation area compared to the existing change detection methods. Although the water area beneath moderate and sparse vegetation canopy was captured, vegetation cover and paved regions of the water body were the main sources of omission error, and commission errors occurred primarily in pixels of mixed land use and along the water body edge. Nevertheless, the proposed method, in conjunction with high-resolution satellite imagery, offers a robust and flexible approach to land cover change mapping that requires no ancillary data for rapid implementation.

Automatic detection of health changes using statistical process control techniques on measured transfer times of elderly.

Science.gov (United States)

Baldewijns, Greet; Luca, Stijn; Nagels, William; Vanrumste, Bart; Croonenborghs, Tom

2015-01-01

It has been shown that gait speed and transfer times are good measures of functional ability in elderly. However, data currently acquired by systems that measure either gait speed or transfer times in the homes of elderly people require manual reviewing by healthcare workers. This reviewing process is time-consuming. To alleviate this burden, this paper proposes the use of statistical process control methods to automatically detect both positive and negative changes in transfer times. Three SPC techniques: tabular CUSUM, standardized CUSUM and EWMA, known for their ability to detect small shifts in the data, are evaluated on simulated transfer times. This analysis shows that EWMA is the best-suited method with a detection accuracy of 82% and an average detection time of 9.64 days.

The Network for the Detection of Atmospheric Composition Change (NDACC: history, status and perspectives

Directory of Open Access Journals (Sweden)

M. De Mazière

2018-04-01

Full Text Available The Network for the Detection of Atmospheric Composition Change (NDACC is an international global network of more than 90 stations making high-quality measurements of atmospheric composition that began official operations in 1991 after 5 years of planning. Apart from sonde measurements, all measurements in the network are performed by ground-based remote-sensing techniques. Originally named the Network for the Detection of Stratospheric Change (NDSC, the name of the network was changed to NDACC in 2005 to better reflect the expanded scope of its measurements. The primary goal of NDACC is to establish long-term databases for detecting changes and trends in the chemical and physical state of the atmosphere (mesosphere, stratosphere, and troposphere and to assess the coupling of such changes with climate and air quality. NDACC's origins, station locations, organizational structure, and data archiving are described. NDACC is structured around categories of ground-based observational techniques (sonde, lidar, microwave radiometers, Fourier-transform infrared, UV-visible DOAS (differential optical absorption spectroscopy-type, and Dobson–Brewer spectrometers, as well as spectral UV radiometers, timely cross-cutting themes (ozone, water vapour, measurement strategies, cross-network data integration, satellite measurement systems, and theory and analyses. Participation in NDACC requires compliance with strict measurement and data protocols to ensure that the network data are of high and consistent quality. To widen its scope, NDACC has established formal collaborative agreements with eight other cooperating networks and Global Atmosphere Watch (GAW. A brief history is provided, major accomplishments of NDACC during its first 25 years of operation are reviewed, and a forward-looking perspective is presented.

Nuclear detection systems in traffic

International Nuclear Information System (INIS)

Farkas, T.; Pernicka, L.; Svec, A.

2005-01-01

Illicit trafficking in nuclear materials (nuclear criminality) has become a problem, due to the circulation of a high number of radioactive sources caused by the changes of the organisational infrastructures to supervise these material within the successor states of the former Soviet Union. Aim of this paper is to point out the technical requirements and the practicability of an useful monitoring system at preselected traffic check points (railway and highway border crossings, industrial sites entry gates, international airports). The ITRAP lab test was designed to work as strict benchmark to qualify border monitoring systems 67 with very low false alarm rates, in addition the minimum sensitivity to give an alarm has been defined for fix-installed systems, pocket type and hand held instruments. For the neutron tests a special prepared Californium source ( 252 Cf) was used to simulate the weapons plutonium. The source is shielded against gamma radiation, use a moderator and provides the required neutron rate of 20000 n/s at 2 rn distance. To test the false alarm rate (rate of false positive ) the same test facility , under the same background conditions, was used but without a radioactive test source. The ITRAP lab tests for the fix-installed systems started at May 1998 and first results were given in September 1998. Only 2 of 14 fix-installed monitoring systems could fulfil the minimum requirement for neutron detection. 7 of 14 fix-installed monitoring systems (50%) passed the ITRAP lab test. The analytical method developed and used for certification of installed radiation monitors in the Slovak Institute of Metrology consists in measurement of radiation activity of selected radionuclide in defined conditions. (authors)

Fault detection and diagnosis in nonlinear systems a differential and algebraic viewpoint

CERN Document Server

Martinez-Guerra, Rafael

2014-01-01

The high reliability required in industrial processes has created the necessity of detecting abnormal conditions, called faults, while processes are operating. The term fault generically refers to any type of process degradation, or degradation in equipment performance because of changes in the process's physical characteristics, process inputs or environmental conditions. This book is about the fundamentals of fault detection and diagnosis in a variety of nonlinear systems which are represented by ordinary differential equations. The fault detection problem is approached from a differential algebraic viewpoint, using residual generators based upon high-gain nonlinear auxiliary systems (‘observers’). A prominent role is played by the type of mathematical tools that will be used, requiring knowledge of differential algebra and differential equations. Specific theorems tailored to the needs of the problem-solving procedures are developed and proved. Applications to real-world problems, both with constant an...

Ultrasonic leak detection

International Nuclear Information System (INIS)

Murphy, R.V.

1977-01-01

A scanning ultrasonic microphone was used to detect the presence and locate the sources of hydraulic noises in piping systems in a reactor environment. The intensity changes of the noises correspond to changes of flow conditions within the system caused by throttled valves, flow rate changes, and leaks. (author)

Lung nodule detection on chest CT: evaluation of a computer-aided detection (CAD) system

International Nuclear Information System (INIS)

Lee, In Jae; Gamsu, Gordon; Czum, Julianna; Johnson, Rebecca; Chakrapani, Sanjay; Wu, Ning

2005-01-01

To evaluate the capacity of a computer-aided detection (CAD) system to detect lung nodules in clinical chest CT. A total of 210 consecutive clinical chest CT scans and their reports were reviewed by two chest radiologists and 70 were selected (33 without nodules and 37 with 1-6 nodules, 4-15.4 mm in diameter). The CAD system (ImageChecker CT LN-1000) developed by R2 Technology, Inc. (Sunnyvale, CA) was used. Its algorithm was designed to detect nodules with a diameter of 4-20 mm. The two chest radiologists working with the CAD system detected a total of 78 nodules. These 78 nodules form the database for this study. Four independent observers interpreted the studies with and without the CAD system. The detection rates of the four independent observers without CAD were 81% (63/78), 85% (66/78), 83% (65/78), and 83% (65/78), respectively. With CAD their rates were 87% (68/78), 85% (66/78), 86% (67/78), and 85% (66/78), respectively. The differences between these two sets of detection rates did not reach statistical significance. In addition, CAD detected eight nodules that were not mentioned in the original clinical radiology reports. The CAD system produced 1.56 false-positive nodules per CT study. The four test observers had 0, 0.1, 0.17, and 0.26 false-positive results per study without CAD and 0.07, 0.2, 0.23, and 0.39 with CAD, respectively. The CAD system can assist radiologists in detecting pulmonary nodules in chest CT, but with a potential increase in their false positive rates. Technological improvements to the system could increase the sensitivity and specificity for the detection of pulmonary nodules and reduce these false-positive results