change detection based: Topics by Science.gov

Sample records for change detection based

Change detection from remotely sensed images: From pixel-based to object-based approaches

NASA Astrophysics Data System (ADS)

Hussain, Masroor; Chen, Dongmei; Cheng, Angela; Wei, Hui; Stanley, David

2013-06-01

The appetite for up-to-date information about earth's surface is ever increasing, as such information provides a base for a large number of applications, including local, regional and global resources monitoring, land-cover and land-use change monitoring, and environmental studies. The data from remote sensing satellites provide opportunities to acquire information about land at varying resolutions and has been widely used for change detection studies. A large number of change detection methodologies and techniques, utilizing remotely sensed data, have been developed, and newer techniques are still emerging. This paper begins with a discussion of the traditionally pixel-based and (mostly) statistics-oriented change detection techniques which focus mainly on the spectral values and mostly ignore the spatial context. This is succeeded by a review of object-based change detection techniques. Finally there is a brief discussion of spatial data mining techniques in image processing and change detection from remote sensing data. The merits and issues of different techniques are compared. The importance of the exponential increase in the image data volume and multiple sensors and associated challenges on the development of change detection techniques are highlighted. With the wide use of very-high-resolution (VHR) remotely sensed images, object-based methods and data mining techniques may have more potential in change detection.
Illumination Invariant Change Detection (iicd): from Earth to Mars

NASA Astrophysics Data System (ADS)

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

DOEpatents

Paglieroni, David W.

2016-06-07

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

DOEpatents

Paglieroni, David W.; Beer, Reginald N.

2016-01-19

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

NASA Astrophysics Data System (ADS)

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

2016-10-01

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

NASA Astrophysics Data System (ADS)

Sha'abani, M. N. A. H.; Adan, N. F.; Sabani, M. S. M.; Abdullah, F.; Nadira, J. H. S.; Yasin, M. S. M.

2017-09-01

This paper investigates a habituation based approach in detecting visual changes using video surveillance systems in a passive environment. Various techniques have been introduced for dynamic environment such as motion detection, object classification and behaviour analysis. However, in a passive environment, most of the scenes recorded by the surveillance system are normal. Therefore, implementing a complex analysis all the time in the passive environment resulting on computationally expensive, especially when using a high video resolution. Thus, a mechanism of attention is required, where the system only responds to an abnormal event. This paper proposed a novelty detection mechanism in detecting visual changes and a habituation based approach to measure the level of novelty. The objective of the paper is to investigate the feasibility of the habituation based approach in detecting visual changes. Experiment results show that the approach are able to accurately detect the presence of novelty as deviations from the learned knowledge.
Change detection and classification in brain MR images using change vector analysis.

PubMed

Simões, Rita; Slump, Cornelis

2011-01-01

The automatic detection of longitudinal changes in brain images is valuable in the assessment of disease evolution and treatment efficacy. Most existing change detection methods that are currently used in clinical research to monitor patients suffering from neurodegenerative diseases--such as Alzheimer's--focus on large-scale brain deformations. However, such patients often have other brain impairments, such as infarcts, white matter lesions and hemorrhages, which are typically overlooked by the deformation-based methods. Other unsupervised change detection algorithms have been proposed to detect tissue intensity changes. The outcome of these methods is typically a binary change map, which identifies changed brain regions. However, understanding what types of changes these regions underwent is likely to provide equally important information about lesion evolution. In this paper, we present an unsupervised 3D change detection method based on Change Vector Analysis. We compute and automatically threshold the Generalized Likelihood Ratio map to obtain a binary change map. Subsequently, we perform histogram-based clustering to classify the change vectors. We obtain a Kappa Index of 0.82 using various types of simulated lesions. The classification error is 2%. Finally, we are able to detect and discriminate both small changes and ventricle expansions in datasets from Mild Cognitive Impairment patients.
A dual-process account of auditory change detection.

PubMed

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

NASA Astrophysics Data System (ADS)

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.
A Deep Convolutional Coupling Network for Change Detection Based on Heterogeneous Optical and Radar Images.

PubMed

Liu, Jia; Gong, Maoguo; Qin, Kai; Zhang, Puzhao

2018-03-01

We propose an unsupervised deep convolutional coupling network for change detection based on two heterogeneous images acquired by optical sensors and radars on different dates. Most existing change detection methods are based on homogeneous images. Due to the complementary properties of optical and radar sensors, there is an increasing interest in change detection based on heterogeneous images. The proposed network is symmetric with each side consisting of one convolutional layer and several coupling layers. The two input images connected with the two sides of the network, respectively, are transformed into a feature space where their feature representations become more consistent. In this feature space, the different map is calculated, which then leads to the ultimate detection map by applying a thresholding algorithm. The network parameters are learned by optimizing a coupling function. The learning process is unsupervised, which is different from most existing change detection methods based on heterogeneous images. Experimental results on both homogenous and heterogeneous images demonstrate the promising performance of the proposed network compared with several existing approaches.
Modeling Patterns of Activities using Activity Curves

PubMed Central

Dawadi, Prafulla N.; Cook, Diane J.; Schmitter-Edgecombe, Maureen

2016-01-01

Pervasive computing offers an unprecedented opportunity to unobtrusively monitor behavior and use the large amount of collected data to perform analysis of activity-based behavioral patterns. In this paper, we introduce the notion of an activity curve, which represents an abstraction of an individual’s normal daily routine based on automatically-recognized activities. We propose methods to detect changes in behavioral routines by comparing activity curves and use these changes to analyze the possibility of changes in cognitive or physical health. We demonstrate our model and evaluate our change detection approach using a longitudinal smart home sensor dataset collected from 18 smart homes with older adult residents. Finally, we demonstrate how big data-based pervasive analytics such as activity curve-based change detection can be used to perform functional health assessment. Our evaluation indicates that correlations do exist between behavior and health changes and that these changes can be automatically detected using smart homes, machine learning, and big data-based pervasive analytics. PMID:27346990
Modeling Patterns of Activities using Activity Curves.

PubMed

Dawadi, Prafulla N; Cook, Diane J; Schmitter-Edgecombe, Maureen

2016-06-01

Pervasive computing offers an unprecedented opportunity to unobtrusively monitor behavior and use the large amount of collected data to perform analysis of activity-based behavioral patterns. In this paper, we introduce the notion of an activity curve , which represents an abstraction of an individual's normal daily routine based on automatically-recognized activities. We propose methods to detect changes in behavioral routines by comparing activity curves and use these changes to analyze the possibility of changes in cognitive or physical health. We demonstrate our model and evaluate our change detection approach using a longitudinal smart home sensor dataset collected from 18 smart homes with older adult residents. Finally, we demonstrate how big data-based pervasive analytics such as activity curve-based change detection can be used to perform functional health assessment. Our evaluation indicates that correlations do exist between behavior and health changes and that these changes can be automatically detected using smart homes, machine learning, and big data-based pervasive analytics.
Transistor-based particle detection systems and methods

DOEpatents

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

DOE Office of Scientific and Technical Information (OSTI.GOV)

Beer, N. Reginald; Paglieroni, David W.

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

NASA Astrophysics Data System (ADS)

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

2016-10-01

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

NASA Astrophysics Data System (ADS)

Wang, Xiao; Gao, Feng; Dong, Junyu; Qi, Qiang

2018-04-01

Synthetic aperture radar (SAR) image is independent on atmospheric conditions, and it is the ideal image source for change detection. Existing methods directly analysis all the regions in the speckle noise contaminated difference image. The performance of these methods is easily affected by small noisy regions. In this paper, we proposed a novel change detection framework for saliency-guided change detection based on pattern and intensity distinctiveness analysis. The saliency analysis step can remove small noisy regions, and therefore makes the proposed method more robust to the speckle noise. In the proposed method, the log-ratio operator is first utilized to obtain a difference image (DI). Then, the saliency detection method based on pattern and intensity distinctiveness analysis is utilized to obtain the changed region candidates. Finally, principal component analysis and k-means clustering are employed to analysis pixels in the changed region candidates. Thus, the final change map can be obtained by classifying these pixels into changed or unchanged class. The experiment results on two real SAR images datasets have demonstrated the effectiveness of the proposed method.
Hardware accelerator design for change detection in smart camera

NASA Astrophysics Data System (ADS)

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

2011-10-01

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

PubMed Central

Lu, Dengsheng; Batistella, Mateus; Moran, Emilio

2009-01-01

Traditional change detection approaches have been proven to be difficult in detecting vegetation changes in the moist tropical regions with multitemporal images. This paper explores the integration of Landsat Thematic Mapper (TM) and SPOT High Resolution Geometric (HRG) instrument data for vegetation change detection in the Brazilian Amazon. A principal component analysis was used to integrate TM and HRG panchromatic data. Vegetation change/non-change was detected with the image differencing approach based on the TM and HRG fused image and the corresponding TM image. A rule-based approach was used to classify the TM and HRG multispectral images into thematic maps with three coarse land-cover classes: forest, non-forest vegetation, and non-vegetation lands. A hybrid approach combining image differencing and post-classification comparison was used to detect vegetation change trajectories. This research indicates promising vegetation change techniques, especially for vegetation gain and loss, even if very limited reference data are available. PMID:19789721
A pdf-Free Change Detection Test Based on Density Difference Estimation.

PubMed

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.
One new method for road data shape change detection

NASA Astrophysics Data System (ADS)

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

2009-10-01

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

Consistent and powerful non-Euclidean graph-based change-point test with applications to segmenting random interfered video data.

PubMed

Shi, Xiaoping; Wu, Yuehua; Rao, Calyampudi Radhakrishna

2018-06-05

The change-point detection has been carried out in terms of the Euclidean minimum spanning tree (MST) and shortest Hamiltonian path (SHP), with successful applications in the determination of authorship of a classic novel, the detection of change in a network over time, the detection of cell divisions, etc. However, these Euclidean graph-based tests may fail if a dataset contains random interferences. To solve this problem, we present a powerful non-Euclidean SHP-based test, which is consistent and distribution-free. The simulation shows that the test is more powerful than both Euclidean MST- and SHP-based tests and the non-Euclidean MST-based test. Its applicability in detecting both landing and departure times in video data of bees' flower visits is illustrated.
A service relation model for web-based land cover change detection

NASA Astrophysics Data System (ADS)

Xing, Huaqiao; Chen, Jun; Wu, Hao; Zhang, Jun; Li, Songnian; Liu, Boyu

2017-10-01

Change detection with remotely sensed imagery is a critical step in land cover monitoring and updating. Although a variety of algorithms or models have been developed, none of them can be universal for all cases. The selection of appropriate algorithms and construction of processing workflows depend largely on the expertise of experts about the "algorithm-data" relations among change detection algorithms and the imagery data used. This paper presents a service relation model for land cover change detection by integrating the experts' knowledge about the "algorithm-data" relations into the web-based geo-processing. The "algorithm-data" relations are mapped into a set of web service relations with the analysis of functional and non-functional service semantics. These service relations are further classified into three different levels, i.e., interface, behavior and execution levels. A service relation model is then established using the Object and Relation Diagram (ORD) approach to represent the multi-granularity services and their relations for change detection. A set of semantic matching rules are built and used for deriving on-demand change detection service chains from the service relation model. A web-based prototype system is developed in .NET development environment, which encapsulates nine change detection and pre-processing algorithms and represents their service relations as an ORD. Three test areas from Shandong and Hebei provinces, China with different imagery conditions are selected for online change detection experiments, and the results indicate that on-demand service chains can be generated according to different users' demands.
Sensor and methods of detecting target materials and situations in closed systems

DOEpatents

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.
Change Detection of High-Resolution Remote Sensing Images Based on Adaptive Fusion of Multiple Features

NASA Astrophysics Data System (ADS)

Wang, G. H.; Wang, H. B.; Fan, W. F.; Liu, Y.; Chen, C.

2018-04-01

In view of the traditional change detection algorithm mainly depends on the spectral information image spot, failed to effectively mining and fusion of multi-image feature detection advantage, the article borrows the ideas of object oriented analysis proposed a multi feature fusion of remote sensing image change detection algorithm. First by the multi-scale segmentation of image objects based; then calculate the various objects of color histogram and linear gradient histogram; utilizes the color distance and edge line feature distance between EMD statistical operator in different periods of the object, using the adaptive weighted method, the color feature distance and edge in a straight line distance of combination is constructed object heterogeneity. Finally, the curvature histogram analysis image spot change detection results. The experimental results show that the method can fully fuse the color and edge line features, thus improving the accuracy of the change detection.
A scale-invariant change detection method for land use/cover change research

NASA Astrophysics Data System (ADS)

Xing, Jin; Sieber, Renee; Caelli, Terrence

2018-07-01

Land Use/Cover Change (LUCC) detection relies increasingly on comparing remote sensing images with different spatial and spectral scales. Based on scale-invariant image analysis algorithms in computer vision, we propose a scale-invariant LUCC detection method to identify changes from scale heterogeneous images. This method is composed of an entropy-based spatial decomposition, two scale-invariant feature extraction methods, Maximally Stable Extremal Region (MSER) and Scale-Invariant Feature Transformation (SIFT) algorithms, a spatial regression voting method to integrate MSER and SIFT results, a Markov Random Field-based smoothing method, and a support vector machine classification method to assign LUCC labels. We test the scale invariance of our new method with a LUCC case study in Montreal, Canada, 2005-2012. We found that the scale-invariant LUCC detection method provides similar accuracy compared with the resampling-based approach but this method avoids the LUCC distortion incurred by resampling.
Automatic detection of Floating Ice at Antarctic Continental Margin from Remotely Sensed Image with Object-oriented Matching

NASA Astrophysics Data System (ADS)

Zhao, Z.

2011-12-01

Changes in ice sheet and floating ices around that have great significance for global change research. In the context of global warming, rapidly changing of Antarctic continental margin, caving of ice shelves, movement of iceberg are all closely related to climate change and ocean circulation. Using automatic change detection technology to rapid positioning the melting Region of Polar ice sheet and the location of ice drift would not only strong support for Global Change Research but also lay the foundation for establishing early warning mechanism for melting of the polar ice and Ice displacement. This paper proposed an automatic change detection method using object-based segmentation technology. The process includes three parts: ice extraction using image segmentation, object-baed ice tracking, change detection based on similarity matching. An approach based on similarity matching of eigenvector is proposed in this paper, which used area, perimeter, Hausdorff distance, contour, shape and other information of each ice-object. Different time of LANDSAT ETM+ data, Chinese environment disaster satellite HJ1B date, MODIS 1B date are used to detect changes of Floating ice at Antarctic continental margin respectively. We select different time of ETM+ data(January 7, 2003 and January 16, 2003) with the area around Antarctic continental margin near the Lazarev Bay, which is from 70.27454853 degrees south latitude, longitude 12.38573410 degrees to 71.44474167 degrees south latitude, longitude 10.39252222 degrees,included 11628 sq km of Antarctic continental margin area, as a sample. Then we can obtain the area of floating ices reduced 371km2, and the number of them reduced 402 during the time. In addition, the changes of all the floating ices around the margin region of Antarctic within 1200 km are detected using MODIS 1B data. During the time from January 1, 2008 to January 7, 2008, the floating ice area decreased by 21644732 km2, and the number of them reduced by 83080. The results show that the object-based information extraction algorithm can obtain more precise details of a single object, while the change detection method based on similarity matching can effectively tracking the change of floating ice.
Fetal heart rate deceleration detection using a discrete cosine transform implementation of singular spectrum analysis.

PubMed

Warrick, P A; Precup, D; Hamilton, E F; Kearney, R E

2007-01-01

To develop a singular-spectrum analysis (SSA) based change-point detection algorithm applicable to fetal heart rate (FHR) monitoring to improve the detection of deceleration events. We present a method for decomposing a signal into near-orthogonal components via the discrete cosine transform (DCT) and apply this in a novel online manner to change-point detection based on SSA. The SSA technique forms models of the underlying signal that can be compared over time; models that are sufficiently different indicate signal change points. To adapt the algorithm to deceleration detection where many successive similar change events can occur, we modify the standard SSA algorithm to hold the reference model constant under such conditions, an approach that we term "base-hold SSA". The algorithm is applied to a database of 15 FHR tracings that have been preprocessed to locate candidate decelerations and is compared to the markings of an expert obstetrician. Of the 528 true and 1285 false decelerations presented to the algorithm, the base-hold approach improved on standard SSA, reducing the number of missed decelerations from 64 to 49 (21.9%) while maintaining the same reduction in false-positives (278). The standard SSA assumption that changes are infrequent does not apply to FHR analysis where decelerations can occur successively and in close proximity; our base-hold SSA modification improves detection of these types of event series.
Adaptive 4d Psi-Based Change Detection

NASA Astrophysics Data System (ADS)

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.
The characteristics and interpretability of land surface change and implications for project design

USGS Publications Warehouse

Sohl, Terry L.; Gallant, Alisa L.; Loveland, Thomas R.

2004-01-01

The need for comprehensive, accurate information on land-cover change has never been greater. While remotely sensed imagery affords the opportunity to provide information on land-cover change over large geographic expanses at a relatively low cost, the characteristics of land-surface change bring into question the suitability of many commonly used methodologies. Algorithm-based methodologies to detect change generally cannot provide the same level of accuracy as the analyses done by human interpreters. Results from the Land Cover Trends project, a cooperative venture that includes the U.S. Geological Survey, Environmental Protection Agency, and National Aeronautics and Space Administration, have shown that land-cover conversion is a relatively rare event, occurs locally in small patches, varies geographically and temporally, and is spectrally ambiguous. Based on these characteristics of change and the type of information required, manual interpretation was selected as the primary means of detecting change in the Land Cover Trends project. Mixtures of algorithm-based detection and manual interpretation may often prove to be the most feasible and appropriate design for change-detection applications. Serious examination of the expected characteristics and measurability of change must be considered during the design and implementation phase of any change analysis project.
Enhanced change detection index for disaster response, recovery assessment and monitoring of buildings and critical facilities-A case study for Muzzaffarabad, Pakistan

NASA Astrophysics Data System (ADS)

de Alwis Pitts, Dilkushi A.; So, Emily

2017-12-01

The availability of Very High Resolution (VHR) optical sensors and a growing image archive that is frequently updated, allows the use of change detection in post-disaster recovery and monitoring for robust and rapid results. The proposed semi-automated GIS object-based method uses readily available pre-disaster GIS data and adds existing knowledge into the processing to enhance change detection. It also allows targeting specific types of changes pertaining to similar man-made objects such as buildings and critical facilities. The change detection method is based on pre/post normalized index, gradient of intensity, texture and edge similarity filters within the object and a set of training data. More emphasis is put on the building edges to capture the structural damage in quantifying change after disaster. Once the change is quantified, based on training data, the method can be used automatically to detect change in order to observe recovery over time in potentially large areas. Analysis over time can also contribute to obtaining a full picture of the recovery and development after disaster, thereby giving managers a better understanding of productive management and recovery practices. The recovery and monitoring can be analyzed using the index in zones extending from to epicentre of disaster or administrative boundaries over time.
Detecting Chemically Modified DNA Bases Using Surface Enhanced Raman Spectroscopy

PubMed Central

Barhoumi, Aoune; Halas, Naomi J.

2013-01-01

Post-translational modifications of DNA- changes in the chemical structure of individual bases that occur without changes in the DNA sequence- are known to alter gene expression. They are believed to result in frequently deleterious phenotypic changes, such as cancer. Methylation of adenine, methylation and hydroxymethylation of cytosine, and guanine oxidation are the primary DNA base modifications identified to date. Here we show it is possible to use surface enhanced Raman spectroscopy (SERS) to detect these primary DNA base modifications. SERS detection of modified DNA bases is label-free and requires minimal additional sample preparation, reducing the possibility of additional chemical modifications induced prior to measurement. This approach shows the feasibility of DNA base modification assessment as a potentially routine analysis that may be further developed for clinical diagnostics. PMID:24427449
Detecting Chemically Modified DNA Bases Using Surface Enhanced Raman Spectroscopy.

PubMed

Barhoumi, Aoune; Halas, Naomi J

2011-12-15

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

NASA Astrophysics Data System (ADS)

Shi, Aiye; Wang, Chao; Shen, Shaohong; Huang, Fengchen; Ma, Zhenli

2016-10-01

Chi-squared transform (CST), as a statistical method, can describe the difference degree between vectors. The CST-based methods operate directly on information stored in the difference image and are simple and effective methods for detecting changes in remotely sensed images that have been registered and aligned. However, the technique does not take spatial information into consideration, which leads to much noise in the result of change detection. An improved unsupervised change detection method is proposed based on spatial constraint CST (SCCST) in combination with a Markov random field (MRF) model. First, the mean and variance matrix of the difference image of bitemporal images are estimated by an iterative trimming method. In each iteration, spatial information is injected to reduce scattered changed points (also known as "salt and pepper" noise). To determine the key parameter confidence level in the SCCST method, a pseudotraining dataset is constructed to estimate the optimal value. Then, the result of SCCST, as an initial solution of change detection, is further improved by the MRF model. The experiments on simulated and real multitemporal and multispectral images indicate that the proposed method performs well in comprehensive indices compared with other methods.
Region-based automatic building and forest change detection on Cartosat-1 stereo imagery

NASA Astrophysics Data System (ADS)

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

2013-05-01

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

NASA Astrophysics Data System (ADS)

Awrangjeb, M.; Fraser, C. S.; Lu, G.

2015-08-01

Building data are one of the important data types in a topographic database. Building change detection after a period of time is necessary for many applications, such as identification of informal settlements. Based on the detected changes, the database has to be updated to ensure its usefulness. This paper proposes an improved building detection technique, which is a prerequisite for many building change detection techniques. The improved technique examines the gap between neighbouring buildings in the building mask in order to avoid under segmentation errors. Then, a new building change detection technique from LIDAR point cloud data is proposed. Buildings which are totally new or demolished are directly added to the change detection output. However, for demolished or extended building parts, a connected component analysis algorithm is applied and for each connected component its area, width and height are estimated in order to ascertain if it can be considered as a demolished or new building part. Finally, a graphical user interface (GUI) has been developed to update detected changes to the existing building map. Experimental results show that the improved building detection technique can offer not only higher performance in terms of completeness and correctness, but also a lower number of undersegmentation errors as compared to its original counterpart. The proposed change detection technique produces no omission errors and thus it can be exploited for enhanced automated building information updating within a topographic database. Using the developed GUI, the user can quickly examine each suggested change and indicate his/her decision with a minimum number of mouse clicks.
Cest Analysis: Automated Change Detection from Very-High Remote Sensing Images

NASA Astrophysics Data System (ADS)

Ehlers, M.; Klonus, S.; Jarmer, T.; Sofina, N.; Michel, U.; Reinartz, P.; Sirmacek, B.

2012-08-01

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 was tested with high-resolution satellite images of the crisis areas of Darfur (Sudan). CEST results are compared with a number of standard algorithms for automated change detection such as image difference, image ratioe, principal component analysis, delta cue technique and post classification change detection. The new combined method shows superior results averaging between 45% and 15% improvement in accuracy.
Short-term change detection for UAV video

NASA Astrophysics Data System (ADS)

Saur, Günter; Krüger, Wolfgang

2012-11-01

In the last years, there has been an increased use of unmanned aerial vehicles (UAV) for video reconnaissance and surveillance. An important application in this context is change detection in UAV video data. Here we address short-term change detection, in which the time between observations ranges from several minutes to a few hours. We distinguish this task from video motion detection (shorter time scale) and from long-term change detection, based on time series of still images taken between several days, weeks, or even years. Examples for relevant changes we are looking for are recently parked or moved vehicles. As a pre-requisite, a precise image-to-image registration is needed. Images are selected on the basis of the geo-coordinates of the sensor's footprint and with respect to a certain minimal overlap. The automatic imagebased fine-registration adjusts the image pair to a common geometry by using a robust matching approach to handle outliers. The change detection algorithm has to distinguish between relevant and non-relevant changes. Examples for non-relevant changes are stereo disparity at 3D structures of the scene, changed length of shadows, and compression or transmission artifacts. To detect changes in image pairs we analyzed image differencing, local image correlation, and a transformation-based approach (multivariate alteration detection). As input we used color and gradient magnitude images. To cope with local misalignment of image structures we extended the approaches by a local neighborhood search. The algorithms are applied to several examples covering both urban and rural scenes. The local neighborhood search in combination with intensity and gradient magnitude differencing clearly improved the results. Extended image differencing performed better than both the correlation based approach and the multivariate alternation detection. The algorithms are adapted to be used in semi-automatic workflows for the ABUL video exploitation system of Fraunhofer IOSB, see Heinze et. al. 2010.1 In a further step we plan to incorporate more information from the video sequences to the change detection input images, e.g., by image enhancement or by along-track stereo which are available in the ABUL system.
Determining root correspondence between previously and newly detected objects

DOEpatents

Paglieroni, David W.; Beer, N Reginald

2014-06-17

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

PubMed Central

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
Metamaterial Absorber Based Multifunctional Sensor Application

NASA Astrophysics Data System (ADS)

Ozer, Z.; Mamedov, A. M.; Ozbay, E.

2017-02-01

In this study metamaterial based (MA) absorber sensor, integrated with an X-band waveguide, is numerically and experimentally suggested for important application including pressure, density sensing and marble type detecting applications based on rectangular split ring resonator, sensor layer and absorber layer that measures of changing in the dielectric constant and/or the thickness of a sensor layer. Changing of physical, chemical or biological parameters in the sensor layer can be detected by measuring the resonant frequency shifting of metamaterial absorber based sensor. Suggested MA based absorber sensor can be used for medical, biological, agricultural and chemical detecting applications in microwave frequency band. We compare the simulation and experimentally obtained results from the fabricated sample which are good agreement. Simulation results show that the proposed structure can detect the changing of the refractive indexes of different materials via special resonance frequencies, thus it could be said that the MA-based sensors have high sensitivity. Additionally due to the simple and tiny structures it could be adapted to other electronic devices in different sizes.

Experiments in Coherent Change Detection for Synthetic Aperture Sonar

DTIC Science & Technology

2010-06-01

data from synthetic aperture sonars mounted on autonomous undersea ve- hicles and actively navigated tow bodies. A noncoherent example carried out...III of this paper describe approaches for au- tomatic change detection and introduces CCD. Section IV pro- vides an example of noncoherent change...registration insufficiently robust to support correlation-based change detection (whether cohe- rent or noncoherent ). Fig. 6. Baseline (a) and
SWCD: a sliding window and self-regulated learning-based background updating method for change detection in videos

NASA Astrophysics Data System (ADS)

Işık, Şahin; Özkan, Kemal; Günal, Serkan; Gerek, Ömer Nezih

2018-03-01

Change detection with background subtraction process remains to be an unresolved issue and attracts research interest due to challenges encountered on static and dynamic scenes. The key challenge is about how to update dynamically changing backgrounds from frames with an adaptive and self-regulated feedback mechanism. In order to achieve this, we present an effective change detection algorithm for pixelwise changes. A sliding window approach combined with dynamic control of update parameters is introduced for updating background frames, which we called sliding window-based change detection. Comprehensive experiments on related test videos show that the integrated algorithm yields good objective and subjective performance by overcoming illumination variations, camera jitters, and intermittent object motions. It is argued that the obtained method makes a fair alternative in most types of foreground extraction scenarios; unlike case-specific methods, which normally fail for their nonconsidered scenarios.
An Unsupervised Change Detection Method Using Time-Series of PolSAR Images from Radarsat-2 and GaoFen-3.

PubMed

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

2018-02-12

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

PubMed Central

Karlsson, Lena

2010-01-01

A conservative, noninvasive or minimally invasive approach to clinical management of dental caries requires diagnostic techniques capable of detecting and quantifying lesions at an early stage, when progression can be arrested or reversed. Objective evidence of initiation of the disease can be detected in the form of distinct changes in the optical properties of the affected tooth structure. Caries detection methods based on changes in a specific optical property are collectively referred to as optically based methods. This paper presents a simple overview of the feasibility of three such technologies for quantitative or semiquantitative assessment of caries lesions. Two of the techniques are well-established: quantitative light-induced fluorescence, which is used primarily in caries research, and laser-induced fluorescence, a commercially available method used in clinical dental practice. The third technique, based on near-infrared transillumination of dental enamel is in the developmental stages. PMID:20454579
Pressure sensor

DOE Office of Scientific and Technical Information (OSTI.GOV)

Mee, David K.; Ripley, Edward B.; Nienstedt, Zachary C.

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 needmore » 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.« less
Detecting and Attributing Health Burdens to Climate Change.

PubMed

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

USGS Publications Warehouse

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

2003-01-01

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

NASA Astrophysics Data System (ADS)

Gond, Laetitia; Monnin, David; Schneider, Armin

2012-06-01

The detection and matching of robust features in images is an important step in many computer vision applications. In this paper, the importance of the keypoint detection algorithms and their inherent parameters in the particular context of an image-based change detection system for IED detection is studied. Through extensive application-oriented experiments, we draw an evaluation and comparison of the most popular feature detectors proposed by the computer vision community. We analyze how to automatically adjust these algorithms to changing imaging conditions and suggest improvements in order to achieve more exibility and robustness in their practical implementation.
a Method of Time-Series Change Detection Using Full Polsar Images from Different Sensors

NASA Astrophysics Data System (ADS)

Liu, W.; Yang, J.; Zhao, J.; Shi, H.; Yang, L.

2018-04-01

Most of the existing change detection methods using full polarimetric synthetic aperture radar (PolSAR) are limited to detecting change between two points in time. In this paper, a novel method was proposed to detect the change based on time-series data from different sensors. Firstly, the overall difference image of a time-series PolSAR was calculated by ominous statistic test. Secondly, difference images between any two images in different times ware acquired by Rj statistic test. Generalized Gaussian mixture model (GGMM) was used to obtain time-series change detection maps in the last step for the proposed method. To verify the effectiveness of the proposed method, we carried out the experiment of change detection by using the time-series PolSAR images acquired by Radarsat-2 and Gaofen-3 over the city of Wuhan, in China. Results show that the proposed method can detect the time-series change from different sensors.
Efficient ensemble system based on the copper binding motif for highly sensitive and selective detection of cyanide ions in 100% aqueous solutions by fluorescent and colorimetric changes.

PubMed

Jung, Kwan Ho; Lee, Keun-Hyeung

2015-09-15

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

EPA Pesticide Factsheets

Research on early warning indicators has generally focused on assessing temporal transitions with limited application of these methods to detecting spatial regimes. Traditional spatial boundary detection procedures that result in ecoregion maps are typically based on ecological potential (i.e. potential vegetation), and often fail to account for ongoing changes due to stressors such as land use change and climate change and their effects on plant and animal communities. We use Fisher information, an information theory based method, on both terrestrial and aquatic animal data (US Breeding Bird Survey and marine zooplankton) to identify ecological boundaries, and compare our results to traditional early warning indicators, conventional ecoregion maps, and multivariate analysis such as nMDS (non-metric Multidimensional Scaling) and cluster analysis. We successfully detect spatial regimes and transitions in both terrestrial and aquatic systems using Fisher information. Furthermore, Fisher information provided explicit spatial information about community change that is absent from other multivariate approaches. Our results suggest that defining spatial regimes based on animal communities may better reflect ecological reality than do traditional ecoregion maps, especially in our current era of rapid and unpredictable ecological change. Use an information theory based method to identify ecological boundaries and compare our results to traditional early warning
Trends and shifts in streamflow in Hawaii, 1913-2008

USGS Publications Warehouse

Bassiouni, Maoya; Oki, Delwyn S.

2013-01-01

This study addresses a need to document changes in streamflow and base flow (groundwater discharge to streams) in Hawai'i during the past century. Statistically significant long-term (1913-2008) downward trends were detected (using the nonparametric Mann-Kendall test) in low-streamflow and base-flow records. These long-term downward trends are likely related to a statistically significant downward shift around 1943 detected (using the nonparametric Pettitt test) in index records of streamflow and base flow. The downward shift corresponds to a decrease of 22% in median streamflow and a decrease of 23% in median base flow between the periods 1913-1943 and 1943-2008. The shift coincides with other local and regional factors, including a change from a positive to a negative phase in the Pacific Decadal Oscillation, shifts in the direction of the trade winds over Hawai'i, and a reforestation programme. The detected shift and long-term trends reflect region-wide changes in climatic and land-cover factors. A weak pattern of downward trends in base flows during the period 1943-2008 may indicate a continued decrease in base flows after the 1943 shift. Downward trends were detected more commonly in base-flow records than in high-streamflow, peak-flow, and rainfall records. The decrease in base flow is likely related to a decrease in groundwater storage and recharge and therefore is a valuable indicator of decreasing water availability and watershed vulnerability to hydrologic changes. Whether the downward trends will continue is largely uncertain given the uncertainty in climate-change projections and watershed responses to changes.
Subsurface event detection and classification using Wireless Signal Networks.

PubMed

Yoon, Suk-Un; Ghazanfari, Ehsan; Cheng, Liang; Pamukcu, Sibel; Suleiman, Muhannad T

2012-11-05

Subsurface environment sensing and monitoring applications such as detection of water intrusion or a landslide, which could significantly change the physical properties of the host soil, can be accomplished using a novel concept, Wireless Signal Networks (WSiNs). The wireless signal networks take advantage of the variations of radio signal strength on the distributed underground sensor nodes of WSiNs to monitor and characterize the sensed area. To characterize subsurface environments for event detection and classification, this paper provides a detailed list and experimental data of soil properties on how radio propagation is affected by soil properties in subsurface communication environments. Experiments demonstrated that calibrated wireless signal strength variations can be used as indicators to sense changes in the subsurface environment. The concept of WSiNs for the subsurface event detection is evaluated with applications such as detection of water intrusion, relative density change, and relative motion using actual underground sensor nodes. To classify geo-events using the measured signal strength as a main indicator of geo-events, we propose a window-based minimum distance classifier based on Bayesian decision theory. The window-based classifier for wireless signal networks has two steps: event detection and event classification. With the event detection, the window-based classifier classifies geo-events on the event occurring regions that are called a classification window. The proposed window-based classification method is evaluated with a water leakage experiment in which the data has been measured in laboratory experiments. In these experiments, the proposed detection and classification method based on wireless signal network can detect and classify subsurface events.
Subsurface Event Detection and Classification Using Wireless Signal Networks

PubMed Central

Yoon, Suk-Un; Ghazanfari, Ehsan; Cheng, Liang; Pamukcu, Sibel; Suleiman, Muhannad T.

2012-01-01

Subsurface environment sensing and monitoring applications such as detection of water intrusion or a landslide, which could significantly change the physical properties of the host soil, can be accomplished using a novel concept, Wireless Signal Networks (WSiNs). The wireless signal networks take advantage of the variations of radio signal strength on the distributed underground sensor nodes of WSiNs to monitor and characterize the sensed area. To characterize subsurface environments for event detection and classification, this paper provides a detailed list and experimental data of soil properties on how radio propagation is affected by soil properties in subsurface communication environments. Experiments demonstrated that calibrated wireless signal strength variations can be used as indicators to sense changes in the subsurface environment. The concept of WSiNs for the subsurface event detection is evaluated with applications such as detection of water intrusion, relative density change, and relative motion using actual underground sensor nodes. To classify geo-events using the measured signal strength as a main indicator of geo-events, we propose a window-based minimum distance classifier based on Bayesian decision theory. The window-based classifier for wireless signal networks has two steps: event detection and event classification. With the event detection, the window-based classifier classifies geo-events on the event occurring regions that are called a classification window. The proposed window-based classification method is evaluated with a water leakage experiment in which the data has been measured in laboratory experiments. In these experiments, the proposed detection and classification method based on wireless signal network can detect and classify subsurface events. PMID:23202191
a Framework of Change Detection Based on Combined Morphologica Features and Multi-Index Classification

NASA Astrophysics Data System (ADS)

Li, S.; Zhang, S.; Yang, D.

2017-09-01

Remote sensing images are particularly well suited for analysis of land cover change. In this paper, we present a new framework for detection of changing land cover using satellite imagery. Morphological features and a multi-index are used to extract typical objects from the imagery, including vegetation, water, bare land, buildings, and roads. Our method, based on connected domains, is different from traditional methods; it uses image segmentation to extract morphological features, while the enhanced vegetation index (EVI), the differential water index (NDWI) are used to extract vegetation and water, and a fragmentation index is used to the correct extraction results of water. HSV transformation and threshold segmentation extract and remove the effects of shadows on extraction results. Change detection is performed on these results. One of the advantages of the proposed framework is that semantic information is extracted automatically using low-level morphological features and indexes. Another advantage is that the proposed method detects specific types of change without any training samples. A test on ZY-3 images demonstrates that our framework has a promising capability to detect change.
Mutual Comparative Filtering for Change Detection in Videos with Unstable Illumination Conditions

NASA Astrophysics Data System (ADS)

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

2016-06-01

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

NASA Technical Reports Server (NTRS)

Maruyasu, T.; Murai, S. (Principal Investigator)

1976-01-01

The author has identified the following significant results. The software program, which enables the geographically corrected LANDSAT digital data base, was developed. The data base could provide land use planners with land cover information and the environmental change pattern. Land cover was evaluated by the color representation for ratio of three primary components, water vegetation, and nonorganic matter. Software was also developed for the change detection within multidates LANDSAT MSS data.
Differences in change blindness to real-life scenes in adults with autism spectrum conditions.

PubMed

Ashwin, Chris; Wheelwright, Sally; Baron-Cohen, Simon

2017-01-01

People often fail to detect large changes to visual scenes following a brief interruption, an effect known as 'change blindness'. People with autism spectrum conditions (ASC) have superior attention to detail and better discrimination of targets, and often notice small details that are missed by others. Together these predict people with autism should show enhanced perception of changes in simple change detection paradigms, including reduced change blindness. However, change blindness studies to date have reported mixed results in ASC, which have sometimes included no differences to controls or even enhanced change blindness. Attenuated change blindness has only been reported to date in ASC in children and adolescents, with no study reporting reduced change blindness in adults with ASC. The present study used a change blindness flicker task to investigate the detection of changes in images of everyday life in adults with ASC (n = 22) and controls (n = 22) using a simple change detection task design and full range of original scenes as stimuli. Results showed the adults with ASC had reduced change blindness compared to adult controls for changes to items of marginal interest in scenes, with no group difference for changes to items of central interest. There were no group differences in overall response latencies to correctly detect changes nor in the overall number of missed detections in the experiment. However, the ASC group showed greater missed changes for marginal interest changes of location, showing some evidence of greater change blindness as well. These findings show both reduced change blindness to marginal interest changes in ASC, based on response latencies, as well as greater change blindness to changes of location of marginal interest items, based on detection rates. The findings of reduced change blindness are consistent with clinical reports that people with ASC often notice small changes to less salient items within their environment, and are in-line with theories of enhanced local processing and greater attention to detail in ASC. The findings of lower detection rates for one of the marginal interest conditions may be related to problems in shifting attention or an overly focused attention spotlight.
Detecting spatial regimes in ecosystems

USGS Publications Warehouse

Sundstrom, Shana M.; Eason, Tarsha; Nelson, R. John; Angeler, David G.; Barichievy, Chris; Garmestani, Ahjond S.; Graham, Nicholas A.J.; Granholm, Dean; Gunderson, Lance; Knutson, Melinda; Nash, Kirsty L.; Spanbauer, Trisha; Stow, Craig A.; Allen, Craig R.

2017-01-01

Research on early warning indicators has generally focused on assessing temporal transitions with limited application of these methods to detecting spatial regimes. Traditional spatial boundary detection procedures that result in ecoregion maps are typically based on ecological potential (i.e. potential vegetation), and often fail to account for ongoing changes due to stressors such as land use change and climate change and their effects on plant and animal communities. We use Fisher information, an information theory-based method, on both terrestrial and aquatic animal data (U.S. Breeding Bird Survey and marine zooplankton) to identify ecological boundaries, and compare our results to traditional early warning indicators, conventional ecoregion maps and multivariate analyses such as nMDS and cluster analysis. We successfully detected spatial regimes and transitions in both terrestrial and aquatic systems using Fisher information. Furthermore, Fisher information provided explicit spatial information about community change that is absent from other multivariate approaches. Our results suggest that defining spatial regimes based on animal communities may better reflect ecological reality than do traditional ecoregion maps, especially in our current era of rapid and unpredictable ecological change.
Vehicle tracking using fuzzy-based vehicle detection window with adaptive parameters

NASA Astrophysics Data System (ADS)

Chitsobhuk, Orachat; Kasemsiri, Watjanapong; Glomglome, Sorayut; Lapamonpinyo, Pipatphon

2018-04-01

In this paper, fuzzy-based vehicle tracking system is proposed. The proposed system consists of two main processes: vehicle detection and vehicle tracking. In the first process, the Gradient-based Adaptive Threshold Estimation (GATE) algorithm is adopted to provide the suitable threshold value for the sobel edge detection. The estimated threshold can be adapted to the changes of diverse illumination conditions throughout the day. This leads to greater vehicle detection performance compared to a fixed user's defined threshold. In the second process, this paper proposes the novel vehicle tracking algorithms namely Fuzzy-based Vehicle Analysis (FBA) in order to reduce the false estimation of the vehicle tracking caused by uneven edges of the large vehicles and vehicle changing lanes. The proposed FBA algorithm employs the average edge density and the Horizontal Moving Edge Detection (HMED) algorithm to alleviate those problems by adopting fuzzy rule-based algorithms to rectify the vehicle tracking. The experimental results demonstrate that the proposed system provides the high accuracy of vehicle detection about 98.22%. In addition, it also offers the low false detection rates about 3.92%.

Testing for the Presence of Correlation Changes in a Multivariate Time Series: A Permutation Based Approach.

PubMed

Cabrieto, Jedelyn; Tuerlinckx, Francis; Kuppens, Peter; Hunyadi, Borbála; Ceulemans, Eva

2018-01-15

Detecting abrupt correlation changes in multivariate time series is crucial in many application fields such as signal processing, functional neuroimaging, climate studies, and financial analysis. To detect such changes, several promising correlation change tests exist, but they may suffer from severe loss of power when there is actually more than one change point underlying the data. To deal with this drawback, we propose a permutation based significance test for Kernel Change Point (KCP) detection on the running correlations. Given a requested number of change points K, KCP divides the time series into K + 1 phases by minimizing the within-phase variance. The new permutation test looks at how the average within-phase variance decreases when K increases and compares this to the results for permuted data. The results of an extensive simulation study and applications to several real data sets show that, depending on the setting, the new test performs either at par or better than the state-of-the art significance tests for detecting the presence of correlation changes, implying that its use can be generally recommended.
Label-free and pH-sensitive colorimetric materials for the sensing of urea

NASA Astrophysics Data System (ADS)

Li, Lu; Long, Yue; Gao, Jin-Ming; Song, Kai; Yang, Guoqiang

2016-02-01

This communication demonstrates a facile method for naked-eye detection of urea based on the structure color change of pH-sensitive photonic crystals. The insertion of urease provides excellent selectivity over other molecules. The detection of urea in different concentration ranges could be realized by changing the molar ratio between the functional monomer and cross-linker.This communication demonstrates a facile method for naked-eye detection of urea based on the structure color change of pH-sensitive photonic crystals. The insertion of urease provides excellent selectivity over other molecules. The detection of urea in different concentration ranges could be realized by changing the molar ratio between the functional monomer and cross-linker. Electronic supplementary information (ESI) available: Materials and chemicals, characterization, experimental details, and SEM images. See DOI: 10.1039/c5nr07690k
Remote Sensing Image Change Detection Based on NSCT-HMT Model and Its Application.

PubMed

Chen, Pengyun; Zhang, Yichen; Jia, Zhenhong; Yang, Jie; Kasabov, Nikola

2017-06-06

Traditional image change detection based on a non-subsampled contourlet transform always ignores the neighborhood information's relationship to the non-subsampled contourlet coefficients, and the detection results are susceptible to noise interference. To address these disadvantages, we propose a denoising method based on the non-subsampled contourlet transform domain that uses the Hidden Markov Tree model (NSCT-HMT) for change detection of remote sensing images. First, the ENVI software is used to calibrate the original remote sensing images. After that, the mean-ratio operation is adopted to obtain the difference image that will be denoised by the NSCT-HMT model. Then, using the Fuzzy Local Information C-means (FLICM) algorithm, the difference image is divided into the change area and unchanged area. The proposed algorithm is applied to a real remote sensing data set. The application results show that the proposed algorithm can effectively suppress clutter noise, and retain more detailed information from the original images. The proposed algorithm has higher detection accuracy than the Markov Random Field-Fuzzy C-means (MRF-FCM), the non-subsampled contourlet transform-Fuzzy C-means clustering (NSCT-FCM), the pointwise approach and graph theory (PA-GT), and the Principal Component Analysis-Nonlocal Means (PCA-NLM) denosing algorithm. Finally, the five algorithms are used to detect the southern boundary of the Gurbantunggut Desert in Xinjiang Uygur Autonomous Region of China, and the results show that the proposed algorithm has the best effect on real remote sensing image change detection.
Remote Sensing Image Change Detection Based on NSCT-HMT Model and Its Application

PubMed Central

Chen, Pengyun; Zhang, Yichen; Jia, Zhenhong; Yang, Jie; Kasabov, Nikola

2017-01-01

Traditional image change detection based on a non-subsampled contourlet transform always ignores the neighborhood information’s relationship to the non-subsampled contourlet coefficients, and the detection results are susceptible to noise interference. To address these disadvantages, we propose a denoising method based on the non-subsampled contourlet transform domain that uses the Hidden Markov Tree model (NSCT-HMT) for change detection of remote sensing images. First, the ENVI software is used to calibrate the original remote sensing images. After that, the mean-ratio operation is adopted to obtain the difference image that will be denoised by the NSCT-HMT model. Then, using the Fuzzy Local Information C-means (FLICM) algorithm, the difference image is divided into the change area and unchanged area. The proposed algorithm is applied to a real remote sensing data set. The application results show that the proposed algorithm can effectively suppress clutter noise, and retain more detailed information from the original images. The proposed algorithm has higher detection accuracy than the Markov Random Field-Fuzzy C-means (MRF-FCM), the non-subsampled contourlet transform-Fuzzy C-means clustering (NSCT-FCM), the pointwise approach and graph theory (PA-GT), and the Principal Component Analysis-Nonlocal Means (PCA-NLM) denosing algorithm. Finally, the five algorithms are used to detect the southern boundary of the Gurbantunggut Desert in Xinjiang Uygur Autonomous Region of China, and the results show that the proposed algorithm has the best effect on real remote sensing image change detection. PMID:28587299
VHR satellite multitemporal data to extract cultural landscape changes in the roman site of Grumentum

NASA Astrophysics Data System (ADS)

masini, nicola; Lasaponara, Rosa

2013-04-01

The papers deals with the use of VHR satellite multitemporal data set to extract cultural landscape changes in the roman site of Grumentum Grumentum is an ancient town, 50 km south of Potenza, located near the roman road of Via Herculea which connected the Venusia, in the north est of Basilicata, with Heraclea in the Ionian coast. The first settlement date back to the 6th century BC. It was resettled by the Romans in the 3rd century BC. Its urban fabric which evidences a long history from the Republican age to late Antiquity (III BC-V AD) is composed of the typical urban pattern of cardi and decumani. Its excavated ruins include a large amphitheatre, a theatre, the thermae, the Forum and some temples. There are many techniques nowadays available to capture and record differences in two or more images. In this paper we focus and apply the two main approaches which can be distinguished into : (i) unsupervised and (ii) supervised change detection methods. Unsupervised change detection methods are generally based on the transformation of the two multispectral images in to a single band or multiband image which are further analyzed to identify changes Unsupervised change detection techniques are generally based on three basic steps (i) the preprocessing step, (ii) a pixel-by-pixel comparison is performed, (iii). Identification of changes according to the magnitude an direction (positive /negative). Unsupervised change detection are generally based on the transformation of the two multispectral images into a single band or multiband image which are further analyzed to identify changes. Than the separation between changed and unchanged classes is obtained from the magnitude of the resulting spectral change vectors by means of empirical or theoretical well founded approaches Supervised change detection methods are generally based on supervised classification methods, which require the availability of a suitable training set for the learning process of the classifiers. Unsupervised change detection techniques are generally based on three basic steps (i) the preprocessing step, (ii) supervised classification is performed on the single dates or on the map obtained as the difference of two dates, (iii). Identification of changes according to the magnitude an direction (positive /negative). Supervised change detection are generally based on supervised classification methods, which require the availability of a suitable training set for the learning process of the classifiers, therefore these algorithms require a preliminary knowledge necessary: (i) to generate representative parameters for each class of interest; and (ii) to carry out the training stage Advantages and disadvantages of the supervised and unsupervised approaches are discuss. Finally results from the the satellite multitemporal dataset was also integrated with aerial photos from historical archive in order to expand the time window of the investigation and capture landscape changes occurred from the Agrarian Reform, in the 50s, up today.
Object-based change detection: dimension of damage in residential areas of Abu Suruj, Sudan

NASA Astrophysics Data System (ADS)

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

2011-11-01

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

NASA Technical Reports Server (NTRS)

Willsky, A. S.

1984-01-01

Some of the basic ideas associated with the detection of abrupt changes in dynamic systems are presented. Multiple filter-based techniques and residual-based method and the multiple model and generalized likelihood ratio methods are considered. Issues such as the effect of unknown onset time on algorithm complexity and structure and robustness to model uncertainty are discussed.
Mixing geometric and radiometric features for change classification

NASA Astrophysics Data System (ADS)

Fournier, Alexandre; Descombes, Xavier; Zerubia, Josiane

2008-02-01

Most basic change detection algorithms use a pixel-based approach. Whereas such approach is quite well defined for monitoring important area changes (such as urban growth monitoring) in low resolution images, an object based approach seems more relevant when the change detection is specifically aimed toward targets (such as small buildings and vehicles). In this paper, we present an approach that mixes radiometric and geometric features to qualify the changed zones. The goal is to establish bounds (appearance, disappearance, substitution ...) between the detected changes and the underlying objects. We proceed by first clustering the change map (containing each pixel bitemporal radiosity) in different classes using the entropy-kmeans algorithm. Assuming that most man-made objects have a polygonal shape, a polygonal approximation algorithm is then used in order to characterize the resulting zone shapes. Hence allowing us to refine the primary rough classification, by integrating the polygon orientations in the state space. Tests are currently conducted on Quickbird data.
Single-trial lie detection using a combined fNIRS-polygraph system

PubMed Central

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
Simultaneous Detection of Static and Dynamic Signals by a Flexible Sensor Based on 3D Graphene.

PubMed

Xu, Rongqing; Wang, Di; Zhang, Hongchao; Xie, Na; Lu, Shan; Qu, Ke

2017-05-08

A flexible acoustic pressure sensor was developed based on the change in electrical resistance of three-dimensional (3D) graphene change under the acoustic waves action. The sensor was constructed by 3D graphene foam (GF) wrapped in flexible polydimethylsiloxane (PDMS). Tuning forks and human physiological tests indicated that the acoustic pressure sensor can sensitively detect the deformation and the acoustic pressure in real time. The results are of significance to the development of graphene-based applications in the field of health monitoring, in vitro diagnostics, advanced therapies, and transient pressure detection.
Detection of proteins using a colorimetric bio-barcode assay.

PubMed

Nam, Jwa-Min; Jang, Kyung-Jin; Groves, Jay T

2007-01-01

The colorimetric bio-barcode assay is a red-to-blue color change-based protein detection method with ultrahigh sensitivity. This assay is based on both the bio-barcode amplification method that allows for detecting miniscule amount of targets with attomolar sensitivity and gold nanoparticle-based colorimetric DNA detection method that allows for a simple and straightforward detection of biomolecules of interest (here we detect interleukin-2, an important biomarker (cytokine) for many immunodeficiency-related diseases and cancers). The protocol is composed of the following steps: (i) conjugation of target capture molecules and barcode DNA strands onto silica microparticles, (ii) target capture with probes, (iii) separation and release of barcode DNA strands from the separated probes, (iv) detection of released barcode DNA using DNA-modified gold nanoparticle probes and (v) red-to-blue color change analysis with a graphic software. Actual target detection and quantification steps with premade probes take approximately 3 h (whole protocol including probe preparations takes approximately 3 days).
Trajectory-based change detection for automated characterization of forest disturbance dynamics

Treesearch

Robert E. Kennedy; Warren B. Cohen; Todd A. Schroeder

2007-01-01

Satellite sensors are well suited to monitoring changes on the Earth's surface through provision of consistent and repeatable measurements at a spatial scale appropriate for many processes causing change on the land surface. Here, we describe and test a new conceptual approach to change detection of forests using a dense temporal stack of Landsat Thematic Mapper (...
Buildings Change Detection Based on Shape Matching for Multi-Resolution Remote Sensing Imagery

NASA Astrophysics Data System (ADS)

Abdessetar, M.; Zhong, Y.

2017-09-01

Buildings change detection has the ability to quantify the temporal effect, on urban area, for urban evolution study or damage assessment in disaster cases. In this context, changes analysis might involve the utilization of the available satellite images with different resolutions for quick responses. In this paper, to avoid using traditional method with image resampling outcomes and salt-pepper effect, building change detection based on shape matching is proposed for multi-resolution remote sensing images. Since the object's shape can be extracted from remote sensing imagery and the shapes of corresponding objects in multi-scale images are similar, it is practical for detecting buildings changes in multi-scale imagery using shape analysis. Therefore, the proposed methodology can deal with different pixel size for identifying new and demolished buildings in urban area using geometric properties of objects of interest. After rectifying the desired multi-dates and multi-resolutions images, by image to image registration with optimal RMS value, objects based image classification is performed to extract buildings shape from the images. Next, Centroid-Coincident Matching is conducted, on the extracted building shapes, based on the Euclidean distance measurement between shapes centroid (from shape T0 to shape T1 and vice versa), in order to define corresponding building objects. Then, New and Demolished buildings are identified based on the obtained distances those are greater than RMS value (No match in the same location).
Dynamical complexity changes during two forms of meditation

NASA Astrophysics Data System (ADS)

Li, Jin; Hu, Jing; Zhang, Yinhong; Zhang, Xiaofeng

2011-06-01

Detection of dynamical complexity changes in natural and man-made systems has deep scientific and practical meaning. We use the base-scale entropy method to analyze dynamical complexity changes for heart rate variability (HRV) series during specific traditional forms of Chinese Chi and Kundalini Yoga meditation techniques in healthy young adults. The results show that dynamical complexity decreases in meditation states for two forms of meditation. Meanwhile, we detected changes in probability distribution of m-words during meditation and explained this changes using probability distribution of sine function. The base-scale entropy method may be used on a wider range of physiologic signals.
Real-time DNA Amplification and Detection System Based on a CMOS Image Sensor.

PubMed

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.
Encapsulated Solid-Liquid Phase Change Nanoparticles as Thermal Barcodes for Highly Sensitive Detections of Multiple Lung Cancer Biomarkers

DTIC Science & Technology

2012-10-01

5e. TASK NUMBER LC90061 5f. WORK UNIT NUMBER 7. PERFORMING ORGANIZATION NAME(S) AND ADDRESS(ES) 8. PERFORMING ORGANIZATION REPORT...transduction mechanism based on solid- liquid phase change nanoparticles works for the detection of multiple proteins. A series of metal and alloy...early stage. With the support from DOD-LCRP, we have proved the new signal transduction mechanism based on solid-liquid phase change nanoparticles works
Comparison between reflectance confocal microscopy and two-photon microscopy in early detection of cutaneous radiation injury in a mouse model in-vivo.

PubMed

Jang, Won Hyuk; Kwon, Soonjae; Shim, Sehwan; Jang, Won-Suk; Myung, Jae Kyung; Yang, Sejung; Park, Sunhoo; Kim, Ki Hean

2018-05-12

Cutaneous radiation injury (CRI) is a skin injury caused by high dose exposure of ionizing radiation (IR). For proper treatment, early detection of CRI before clinical symptoms is important. Optical microscopic techniques such as reflectance confocal microscopy (RCM) and two-photon microscopy (TPM) have been tested as the early diagnosis method by detecting cellular changes. In this study, RCM and TPM were compared in the detection of cellular changes caused by CRI in an in-vivo mouse model. CRI was induced on the mouse hindlimb skin with various IR doses and the injured skin regions were imaged longitudinally by both modalities until the onset of clinical symptoms. Both RCM and TPM detected the changes of epidermal cells and sebaceous glands before clinical symptoms in different optical contrasts. RCM detected changes of cell morphology and scattering property based on light reflection. TPM detected detail changes of cellular structures based on autofluorescence of cells. Since both RCM and TPM were sensitive to the early-stage CRI by using different contrasts, the optimal method for clinical CRI diagnosis could be either individual methods or their combination. This article is protected by copyright. All rights reserved. This article is protected by copyright. All rights reserved.
Personality and attention: Levels of neuroticism and extraversion can predict attentional performance during a change detection task.

PubMed

Hahn, Sowon; Buttaccio, Daniel R; Hahn, Jungwon; Lee, Taehun

2015-01-01

The present study demonstrates that levels of extraversion and neuroticism can predict attentional performance during a change detection task. After completing a change detection task built on the flicker paradigm, participants were assessed for personality traits using the Revised Eysenck Personality Questionnaire (EPQ-R). Multiple regression analyses revealed that higher levels of extraversion predict increased change detection accuracies, while higher levels of neuroticism predict decreased change detection accuracies. In addition, neurotic individuals exhibited decreased sensitivity A' and increased fixation dwell times. Hierarchical regression analyses further revealed that eye movement measures mediate the relationship between neuroticism and change detection accuracies. Based on the current results, we propose that neuroticism is associated with decreased attentional control over the visual field, presumably due to decreased attentional disengagement. Extraversion can predict increased attentional performance, but the effect is smaller than the relationship between neuroticism and attention.
Uav-Based 3d Urban Environment Monitoring

NASA Astrophysics Data System (ADS)

Boonpook, Wuttichai; Tan, Yumin; Liu, Huaqing; Zhao, Binbin; He, Lingfeng

2018-04-01

Unmanned Aerial Vehicle (UAV) based remote sensing can be used to make three-dimensions (3D) mapping with great flexibility, besides the ability to provide high resolution images. In this paper we propose a quick-change detection method on UAV images by combining altitude from Digital Surface Model (DSM) and texture analysis from images. Cases of UAV images with and without georeferencing are both considered. Research results show that the accuracy of change detection can be enhanced with georeferencing procedure, and the accuracy and precision of change detection on UAV images which are collected both vertically and obliquely but without georeferencing also have a good performance.
Object-Based Change Detection Using High-Resolution Remotely Sensed Data and GIS

NASA Astrophysics Data System (ADS)

Sofina, N.; Ehlers, M.

2012-08-01

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

Mass detection, localization and estimation for wind turbine blades based on statistical pattern recognition

NASA Astrophysics Data System (ADS)

Colone, L.; Hovgaard, M. K.; Glavind, L.; Brincker, R.

2018-07-01

A method for mass change detection on wind turbine blades using natural frequencies is presented. The approach is based on two statistical tests. The first test decides if there is a significant mass change and the second test is a statistical group classification based on Linear Discriminant Analysis. The frequencies are identified by means of Operational Modal Analysis using natural excitation. Based on the assumption of Gaussianity of the frequencies, a multi-class statistical model is developed by combining finite element model sensitivities in 10 classes of change location on the blade, the smallest area being 1/5 of the span. The method is experimentally validated for a full scale wind turbine blade in a test setup and loaded by natural wind. Mass change from natural causes was imitated with sand bags and the algorithm was observed to perform well with an experimental detection rate of 1, localization rate of 0.88 and mass estimation rate of 0.72.
A travel time forecasting model based on change-point detection method

NASA Astrophysics Data System (ADS)

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

2017-06-01

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

NASA Astrophysics Data System (ADS)

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

2016-07-01

With the popular use of high resolution remote sensing (HRRS) satellite images, a huge research efforts have been placed on change detection (CD) problem. An effective feature selection method can significantly boost the final result. While hand-designed features have proven difficulties to design features that effectively capture high and mid-level representations, the recent developments in machine learning (Deep Learning) omit this problem by learning hierarchical representation in an unsupervised manner directly from data without human intervention. In this letter, we propose approaching the change detection problem from a feature learning perspective. A novel deep Convolutional Neural Networks (CNN) features based HR satellite images change detection method is proposed. The main guideline is to produce a change detection map directly from two images using a pretrained CNN. This method can omit the limited performance of hand-crafted features. Firstly, CNN features are extracted through different convolutional layers. Then, a concatenation step is evaluated after an normalization step, resulting in a unique higher dimensional feature map. Finally, a change map was computed using pixel-wise Euclidean distance. Our method has been validated on real bitemporal HRRS satellite images according to qualitative and quantitative analyses. The results obtained confirm the interest of the proposed method.
4963 Kanroku: Asteroid with a possible precession of rotation axis

NASA Astrophysics Data System (ADS)

Sokova, Iraida A.; Marchini, Alessandro; Franco, Lorenzo; Papini, Riccardo; Salvaggio, Fabio; Palmas, Teodora; Sokov, Eugene N.; Garlitz, Joe; Knight, Carl R.; Bretton, Marc

2018-04-01

Based on photometric observations of 4963 Kanroku as part of a campaign to measure its light-curve, changes of the light-curve profile have been detected. These changes are of a periodic nature, i.e. the profiles change with a detected period P = 16.4032 h. Based on simulations of the shape of the asteroid and using observational data, we make the assumption that such changes of the light-curve of the asteroid could be caused by the existence of a precession force acting on the axis of rotation of the asteroid. Simulations of the 4963 Kanroku light-curve, taking into account the detected precession, and the parameters for the shape of the asteroid, the modeled light-curves are in good agreement with the light-curves obtained from the observation campaign. Thus, the detected precession force may indicate a possible satellite of the asteroid 4963 Kanroku.
Amido-Schiff base derivatives as colorimetric fluoride sensor: Effect of nitro substitution on the sensitivity and color change.

PubMed

Ghosh, Soumen; Alam, Md Akhtarul; Ganguly, Aniruddha; Guchhait, Nikhil

2015-01-01

A series of Schiff bases synthesized by the condensation of benzohydrazide and -NO2 substituted benzaldehyde have been used as selective fluoride ion sensor. Test paper coated with these synthetic Schiff bases (test kits) can detect fluoride ion selectively with a drastic color change and detection can be achieved by just using the naked-eye without the help of any optical instrument. Interestingly, the position of -NO2 group in the amido Schiff bases has an effect on the sensitivity as well as on the change of color of species. Copyright © 2015 Elsevier B.V. All rights reserved.
Detection of a sudden change of the field time series based on the Lorenz system.

PubMed

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

2017-01-01

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

NASA Astrophysics Data System (ADS)

Jiang, Xiuqiang; Li, Shuang; Tao, Ting

2016-09-01

Autonomous hazard detection and avoidance (AHDA) is one of the key technologies for future safe planetary landing missions. In this paper, we address the latest progress on planetary autonomous hazard detection and avoidance technologies. First, the innovative autonomous relay hazard detection and avoidance strategy adopted in Chang'e-3 lunar soft landing mission and its flight results are reported in detail. Second, two new conceptual candidate schemes of hazard detection and avoidance are presented based on the Chang'e-3 AHDA system and the latest developing technologies for the future planetary missions, and some preliminary testing results are also given. Finally, the related supporting technologies for the two candidate schemes above are analyzed.
Detection and localization of change points in temporal networks with the aid of stochastic block models

NASA Astrophysics Data System (ADS)

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

2016-11-01

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

PubMed

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

2015-05-01

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

NASA Technical Reports Server (NTRS)

Maki, Wusi Chen (Inventor); Filanoski, Brian John (Inventor); Mishra, Nirankar (Inventor); Rastogi, Shiva (Inventor)

2010-01-01

The present invention provides for methods of DNA methylation detection. The present invention provides for methods of generating and detecting specific electronic signals that report the methylation status of targeted DNA molecules in biological samples.Two methods are described, direct and indirect detection of methylated DNA molecules in a nano transistor based device. In the direct detection, methylated target DNA molecules are captured on the sensing surface resulting in changes in the electrical properties of a nano transistor. These changes generate detectable electronic signals. In the indirect detection, antibody-DNA conjugates are used to identify methylated DNA molecules. RNA signal molecules are generated through an in vitro transcription process. These RNA molecules are captured on the sensing surface change the electrical properties of nano transistor thereby generating detectable electronic signals.
Musicians Are Better than Non-musicians in Frequency Change Detection: Behavioral and Electrophysiological Evidence

PubMed Central

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

2016-01-01

Objective: The objectives of this study were: (1) to determine if musicians have a better ability to detect frequency changes under quiet and noisy conditions; (2) to use the acoustic change complex (ACC), a type of electroencephalographic (EEG) response, to understand the neural substrates of musician vs. non-musician difference in frequency change detection abilities. Methods: Twenty-four young normal hearing listeners (12 musicians and 12 non-musicians) participated. All participants underwent psychoacoustic frequency detection tests with three types of stimuli: tones (base frequency at 160 Hz) containing frequency changes (Stim 1), tones containing frequency changes masked by low-level noise (Stim 2), and tones containing frequency changes masked by high-level noise (Stim 3). The EEG data were recorded using tones (base frequency at 160 and 1200 Hz, respectively) containing different magnitudes of frequency changes (0, 5, and 50% changes, respectively). The late-latency evoked potential evoked by the onset of the tones (onset LAEP or N1-P2 complex) and that evoked by the frequency change contained in the tone (the acoustic change complex or ACC or N1′-P2′ complex) were analyzed. Results: Musicians significantly outperformed non-musicians in all stimulus conditions. The ACC and onset LAEP showed similarities and differences. Increasing the magnitude of frequency change resulted in increased ACC amplitudes. ACC measures were found to be significantly different between musicians (larger P2′ amplitude) and non-musicians for the base frequency of 160 Hz but not 1200 Hz. Although the peak amplitude in the onset LAEP appeared to be larger and latency shorter in musicians than in non-musicians, the difference did not reach statistical significance. The amplitude of the onset LAEP is significantly correlated with that of the ACC for the base frequency of 160 Hz. Conclusion: The present study demonstrated that musicians do perform better than non-musicians in detecting frequency changes in quiet and noisy conditions. The ACC and onset LAEP may involve different but overlapping neural mechanisms. Significance: This is the first study using the ACC to examine music-training effects. The ACC measures provide an objective tool for documenting musical training effects on frequency detection. PMID:27826221
Evaluation of Landsat-7 SLC-off image products for forest change detection

USGS Publications Warehouse

Wulder, Michael A.; Ortlepp, Stephanie M.; White, Joanne C.; Maxwell, Susan

2008-01-01

Since July 2003, Landsat-7 ETM+ has been operating without the scan line corrector (SLC), which compensates for the forward motion of the satellite in the imagery acquired. Data collected in SLC-off mode have gaps in a systematic wedge-shaped pattern outside of the central 22 km swath of the imagery; however, the spatial and spectral quality of the remaining portions of the imagery are not diminished. To explore the continued use of Landsat-7 ETM+ SLC-off imagery to characterize change in forested environments, we compare the change detection results generated from a reference image pair (a 1999 Landsat-7 ETM+ image and a 2003 Landsat-5 TM image) with change detection results generated from the same 1999 Landsat-7 ETM+ image coupled with three different 2003 Landsat-7 SLC-off products: unremediated SLC-off (i.e., with gaps); histogram-based gap-filled; and segment-based gap-filled. The results are compared on both a pixel and polygon basis; on a pixel basis, the unremediated SLC-off product missed 35% of the change identified by the reference data, and the histogram- and segment-based gap-filled products missed 23% and 21% of the change, respectively. When using forest inventory polygons as a context for change (to reduce commission error), the amount of change missed was 31%, 14%, and 12% for the each of the unremediated, histogram-based gap-filled, and segment-based gap-filled products, respectively. Our results indicate that over the time period considered, and given the types and spatial distribution of change events within our study area, the gap-filled products can provide a useful data source for change detection in forested environments. The selection of which product to use is, however, very dependent on the nature of the application and the spatial configuration of change events. ?? 2008 Government of Canada.
Automatic detection of lexical change: an auditory event-related potential study.

PubMed

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

PubMed

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

2016-09-30

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

PubMed Central

Lu, Dengsheng; Hetrick, Scott; Moran, Emilio; Li, Guiying

2011-01-01

Accurately detecting urban expansion with remote sensing techniques is a challenge due to the complexity of urban landscapes. This paper explored methods for detecting urban expansion with multitemporal QuickBird images in Lucas do Rio Verde, Mato Grosso, Brazil. Different techniques, including image differencing, principal component analysis (PCA), and comparison of classified impervious surface images with the matched filtering method, were used to examine urbanization detection. An impervious surface image classified with the hybrid method was used to modify the urbanization detection results. As a comparison, the original multispectral image and segmentation-based mean-spectral images were used during the detection of urbanization. This research indicates that the comparison of classified impervious surface images with matched filtering method provides the best change detection performance, followed by the image differencing method based on segmentation-based mean spectral images. The PCA is not a good method for urban change detection in this study. Shadows and high spectral variation within the impervious surfaces represent major challenges to the detection of urban expansion when high spatial resolution images are used. PMID:21799706
Multiratio fusion change detection with adaptive thresholding

NASA Astrophysics Data System (ADS)

Hytla, Patrick C.; Balster, Eric J.; Vasquez, Juan R.; Neuroth, Robert M.

2017-04-01

A ratio-based change detection method known as multiratio fusion (MRF) is proposed and tested. The MRF framework builds on other change detection components proposed in this work: dual ratio (DR) and multiratio (MR). The DR method involves two ratios coupled with adaptive thresholds to maximize detected changes and minimize false alarms. The use of two ratios is shown to outperform the single ratio case when the means of the image pairs are not equal. MR change detection builds on the DR method by including negative imagery to produce four total ratios with adaptive thresholds. Inclusion of negative imagery is shown to improve detection sensitivity and to boost detection performance in certain target and background cases. MRF further expands this concept by fusing together the ratio outputs using a routine in which detections must be verified by two or more ratios to be classified as a true changed pixel. The proposed method is tested with synthetically generated test imagery and real datasets with results compared to other methods found in the literature. DR is shown to significantly outperform the standard single ratio method. MRF produces excellent change detection results that exhibit up to a 22% performance improvement over other methods from the literature at low false-alarm rates.
A Saliency Guided Semi-Supervised Building Change Detection Method for High Resolution Remote Sensing Images

PubMed Central

Hou, Bin; Wang, Yunhong; Liu, Qingjie

2016-01-01

Characterizations of up to date information of the Earth’s surface are an important application providing insights to urban planning, resources monitoring and environmental studies. A large number of change detection (CD) methods have been developed to solve them by utilizing remote sensing (RS) images. The advent of high resolution (HR) remote sensing images further provides challenges to traditional CD methods and opportunities to object-based CD methods. While several kinds of geospatial objects are recognized, this manuscript mainly focuses on buildings. Specifically, we propose a novel automatic approach combining pixel-based strategies with object-based ones for detecting building changes with HR remote sensing images. A multiresolution contextual morphological transformation called extended morphological attribute profiles (EMAPs) allows the extraction of geometrical features related to the structures within the scene at different scales. Pixel-based post-classification is executed on EMAPs using hierarchical fuzzy clustering. Subsequently, the hierarchical fuzzy frequency vector histograms are formed based on the image-objects acquired by simple linear iterative clustering (SLIC) segmentation. Then, saliency and morphological building index (MBI) extracted on difference images are used to generate a pseudo training set. Ultimately, object-based semi-supervised classification is implemented on this training set by applying random forest (RF). Most of the important changes are detected by the proposed method in our experiments. This study was checked for effectiveness using visual evaluation and numerical evaluation. PMID:27618903
A Saliency Guided Semi-Supervised Building Change Detection Method for High Resolution Remote Sensing Images.

PubMed

Hou, Bin; Wang, Yunhong; Liu, Qingjie

2016-08-27

Characterizations of up to date information of the Earth's surface are an important application providing insights to urban planning, resources monitoring and environmental studies. A large number of change detection (CD) methods have been developed to solve them by utilizing remote sensing (RS) images. The advent of high resolution (HR) remote sensing images further provides challenges to traditional CD methods and opportunities to object-based CD methods. While several kinds of geospatial objects are recognized, this manuscript mainly focuses on buildings. Specifically, we propose a novel automatic approach combining pixel-based strategies with object-based ones for detecting building changes with HR remote sensing images. A multiresolution contextual morphological transformation called extended morphological attribute profiles (EMAPs) allows the extraction of geometrical features related to the structures within the scene at different scales. Pixel-based post-classification is executed on EMAPs using hierarchical fuzzy clustering. Subsequently, the hierarchical fuzzy frequency vector histograms are formed based on the image-objects acquired by simple linear iterative clustering (SLIC) segmentation. Then, saliency and morphological building index (MBI) extracted on difference images are used to generate a pseudo training set. Ultimately, object-based semi-supervised classification is implemented on this training set by applying random forest (RF). Most of the important changes are detected by the proposed method in our experiments. This study was checked for effectiveness using visual evaluation and numerical evaluation.
Adaptive Detection and ISI Mitigation for Mobile Molecular Communication.

PubMed

Chang, Ge; Lin, Lin; Yan, Hao

2018-03-01

Current studies on modulation and detection schemes in molecular communication mainly focus on the scenarios with static transmitters and receivers. However, mobile molecular communication is needed in many envisioned applications, such as target tracking and drug delivery. Until now, investigations about mobile molecular communication have been limited. In this paper, a static transmitter and a mobile bacterium-based receiver performing random walk are considered. In this mobile scenario, the channel impulse response changes due to the dynamic change of the distance between the transmitter and the receiver. Detection schemes based on fixed distance fail in signal detection in such a scenario. Furthermore, the intersymbol interference (ISI) effect becomes more complex due to the dynamic character of the signal which makes the estimation and mitigation of the ISI even more difficult. In this paper, an adaptive ISI mitigation method and two adaptive detection schemes are proposed for this mobile scenario. In the proposed scheme, adaptive ISI mitigation, estimation of dynamic distance, and the corresponding impulse response reconstruction are performed in each symbol interval. Based on the dynamic channel impulse response in each interval, two adaptive detection schemes, concentration-based adaptive threshold detection and peak-time-based adaptive detection, are proposed for signal detection. Simulations demonstrate that the ISI effect is significantly reduced and the adaptive detection schemes are reliable and robust for mobile molecular communication.
Change Detection via Selective Guided Contrasting Filters

NASA Astrophysics Data System (ADS)

Vizilter, Y. V.; Rubis, A. Y.; Zheltov, S. Y.

2017-05-01

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 implemented filters provide the robustness relative to weak geometrical discrepancy of compared images. Selective guided contrasting based on morphological opening/closing and thresholded morphological correlation demonstrates the best change detection result.

Change detection in satellite images

NASA Astrophysics Data System (ADS)

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

2005-05-01

Change detection plays an important role in different military areas as strategic reconnaissance, verification of armament and disarmament control and damage assessment. It is the process of identifying differences in the state of an object or phenomenon by observing it at different times. The availability of spaceborne reconnaissance systems with high spatial resolution, multi spectral capabilities, and short revisit times offer new perspectives for change detection. Before performing any kind of change detection it is necessary to separate changes of interest from changes caused by differences in data acquisition parameters. In these cases it is necessary to perform a pre-processing to correct the data or to normalize it. Image registration and, corresponding to this task, the ortho-rectification of the image data is a further prerequisite for change detection. If feasible, a 1-to-1 geometric correspondence should be aspired for. Change detection on an iconic level with a succeeding interpretation of the changes by the observer is often proposed; nevertheless an automatic knowledge-based analysis delivering the interpretation of the changes on a semantic level should be the aim of the future. We present first results of change detection on a structural level concerning urban areas. After pre-processing, the images are segmented in areas of interest and structural analysis is applied to these regions to extract descriptions of urban infrastructure like buildings, roads and tanks of refineries. These descriptions are matched to detect changes and similarities.
Real-time 3D change detection of IEDs

NASA Astrophysics Data System (ADS)

Wathen, Mitch; Link, Norah; Iles, Peter; Jinkerson, John; Mrstik, Paul; Kusevic, Kresimir; Kovats, David

2012-06-01

Road-side bombs are a real and continuing threat to soldiers in theater. CAE USA recently developed a prototype Volume based Intelligence Surveillance Reconnaissance (VISR) sensor platform for IED detection. This vehicle-mounted, prototype sensor system uses a high data rate LiDAR (1.33 million range measurements per second) to generate a 3D mapping of roadways. The mapped data is used as a reference to generate real-time change detection on future trips on the same roadways. The prototype VISR system is briefly described. The focus of this paper is the methodology used to process the 3D LiDAR data, in real-time, to detect small changes on and near the roadway ahead of a vehicle traveling at moderate speeds with sufficient warning to stop the vehicle at a safe distance from the threat. The system relies on accurate navigation equipment to geo-reference the reference run and the change-detection run. Since it was recognized early in the project that detection of small changes could not be achieved with accurate navigation solutions alone, a scene alignment algorithm was developed to register the reference run with the change detection run prior to applying the change detection algorithm. Good success was achieved in simultaneous real time processing of scene alignment plus change detection.
Detecting event-related changes in organizational networks using optimized neural network models.

PubMed

Li, Ze; Sun, Duoyong; Zhu, Renqi; Lin, Zihan

2017-01-01

Organizational external behavior changes are caused by the internal structure and interactions. External behaviors are also known as the behavioral events of an organization. Detecting event-related changes in organizational networks could efficiently be used to monitor the dynamics of organizational behaviors. Although many different methods have been used to detect changes in organizational networks, these methods usually ignore the correlation between the internal structure and external events. Event-related change detection considers the correlation and could be used for event recognition based on social network modeling and supervised classification. Detecting event-related changes could be effectively useful in providing early warnings and faster responses to both positive and negative organizational activities. In this study, event-related change in an organizational network was defined, and artificial neural network models were used to quantitatively determine whether and when a change occurred. To achieve a higher accuracy, Back Propagation Neural Networks (BPNNs) were optimized using Genetic Algorithms (GAs) and Particle Swarm Optimization (PSO). We showed the feasibility of the proposed method by comparing its performance with that of other methods using two cases. The results suggested that the proposed method could identify organizational events based on a correlation between the organizational networks and events. The results also suggested that the proposed method not only has a higher precision but also has a better robustness than the previously used techniques.
Detecting event-related changes in organizational networks using optimized neural network models

PubMed Central

Sun, Duoyong; Zhu, Renqi; Lin, Zihan

2017-01-01

Organizational external behavior changes are caused by the internal structure and interactions. External behaviors are also known as the behavioral events of an organization. Detecting event-related changes in organizational networks could efficiently be used to monitor the dynamics of organizational behaviors. Although many different methods have been used to detect changes in organizational networks, these methods usually ignore the correlation between the internal structure and external events. Event-related change detection considers the correlation and could be used for event recognition based on social network modeling and supervised classification. Detecting event-related changes could be effectively useful in providing early warnings and faster responses to both positive and negative organizational activities. In this study, event-related change in an organizational network was defined, and artificial neural network models were used to quantitatively determine whether and when a change occurred. To achieve a higher accuracy, Back Propagation Neural Networks (BPNNs) were optimized using Genetic Algorithms (GAs) and Particle Swarm Optimization (PSO). We showed the feasibility of the proposed method by comparing its performance with that of other methods using two cases. The results suggested that the proposed method could identify organizational events based on a correlation between the organizational networks and events. The results also suggested that the proposed method not only has a higher precision but also has a better robustness than the previously used techniques. PMID:29190799
Longitudinal Detection of Optic Nerve Head Changes by Spectral Domain Optical Coherence Tomography in Early Experimental Glaucoma

PubMed Central

He, Lin; Yang, Hongli; Gardiner, Stuart K.; Williams, Galen; Hardin, Christy; Strouthidis, Nicholas G.; Fortune, Brad; Burgoyne, Claude F.

2014-01-01

Purpose. We determined if the detection of spectral-domain optical coherence tomography (SDOCT) optic nerve head (ONH) change precedes the detection of confocal scanning laser tomography (CSLT) ONH surface, SDOCT retinal nerve fiber layer (RNFL), scanning laser perimetry (SLP), and multifocal electroretinography (mfERG) change in eight experimental glaucoma (EG) eyes. Methods. Both eyes from eight monkeys were tested at least three times at baseline, and then every 2 weeks following laser-induced chronic unilateral IOP elevation. Event and trend-based definitions of onset in the control and EG eyes for 11 SDOCT neural and connective tissue, CSLT surface, SDOCT RNFL, SLP, and mfERG parameters were explored. The frequency and timing of onset for each parameter were compared using a logrank test. Results. Maximum post-laser IOP was 18 to 42 mm Hg in the EG eyes and 12 to 20 mm Hg in the control eyes. For event- and trend-based analyses, onsets were achieved earliest and most frequently within the ONH neural and connective tissues using SDOCT, and at the ONH surface using CSLT. SDOCT ONH neural and connective tissue parameter change preceded or coincided with CSLT ONH surface change in most EG eyes. The SDOCT and SLP measures of RNFL thickness, and mfERG measures of visual function demonstrated similar onset rates, but occurred later than SDOCT ONH and CSLT surface change, and in fewer eyes. Conclusions. SDOCT ONH change detection commonly precedes or coincides with CSLT ONH surface change detection, and consistently precedes RNFLT, SLP, and mfERG change detection in monkey experimental glaucoma. PMID:24255047
Research on a Denial of Service (DoS) Detection System Based on Global Interdependent Behaviors in a Sensor Network Environment

PubMed Central

Song, Jae-gu; Jung, Sungmo; Kim, Jong Hyun; Seo, Dong Il; Kim, Seoksoo

2010-01-01

This research suggests a Denial of Service (DoS) detection method based on the collection of interdependent behavior data in a sensor network environment. In order to collect the interdependent behavior data, we use a base station to analyze traffic and behaviors among nodes and introduce methods of detecting changes in the environment with precursor symptoms. The study presents a DoS Detection System based on Global Interdependent Behaviors and shows the result of detecting a sensor carrying out DoS attacks through the test-bed. PMID:22163475
Automatic background updating for video-based vehicle detection

NASA Astrophysics Data System (ADS)

Hu, Chunhai; Li, Dongmei; Liu, Jichuan

2008-03-01

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

DOE PAGES

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

2016-01-11

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

PubMed

Xie, Weizhen; Zhang, Weiwei

2017-11-01

The present study dissociated the number (i.e., quantity) and precision (i.e., quality) of visual short-term memory (STM) representations in change detection using receiver operating characteristic (ROC) and experimental manipulations. Across three experiments, participants performed both recognition and recall tests of visual STM using the change-detection task and the continuous color-wheel recall task, respectively. Experiment 1 demonstrated that the estimates of the number and precision of visual STM representations based on the ROC model of change-detection performance were robustly correlated with the corresponding estimates based on the mixture model of continuous-recall performance. Experiments 2 and 3 showed that the experimental manipulation of mnemonic precision using white-noise masking and the experimental manipulation of the number of encoded STM representations using consolidation masking produced selective effects on the corresponding measures of mnemonic precision and the number of encoded STM representations, respectively, in both change-detection and continuous-recall tasks. Altogether, using the individual-differences (Experiment 1) and experimental dissociation (Experiment 2 and 3) approaches, the present study demonstrated the some-or-none nature of visual STM representations across recall and recognition.
Multifunctional nanopipette for simultaneous ionic current and potential detection of nanoparticles

NASA Astrophysics Data System (ADS)

Panday, Namuna; He, Jin

Nanopipette has been demonstrated as a nanopore type biosensor for DNA, protein, nanoparticle and virus analysis. In the last two decades, nanopore based technologies have made remarkable progress for single entity detection and analysis. Multifunctional nanopipette for multi-parameter detection is a new trend for nanopore based technique. We have developed a technique to fabricate multifunctional nanopipette which contains both nanopore and carbon nanoelectrode (CNE) at the nanopipette tip. It can be quickly, cheaply and reproducibly fabricated from theta pipettes. We have been able to use this multifunctional nanopieptte for simultaneous detection of ionic current and local electrical potential changes during translocation of charged gold nanoparticles (GNPs) which is used as a model experiment. The CNE functions as a local potential probe. We have demonstrated that it can detect the local potential change during translocation of a single GNP as well as collective potential change due to cluster of GNPs outside the nanopore entrance. From the potential change, we can also have insight of motion of GNPs before entering the nanopore. We have also tested insulating and biological NPs with various size and charge. Observed results have shown correlations between ionic current and potential change during translocation of these NPs. Florida International University.
Land use change detection based on multi-date imagery from different satellite sensor systems

NASA Technical Reports Server (NTRS)

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

1990-01-01

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

DOT National Transportation Integrated Search

2012-03-01

construction and maintenance. Repeat surveys using terrestrial laser scanning (TLS, ground-based LiDAR) enable rapid 3D data acquisition to map, see, analyze, and understand the processes generating such problems. Previously, change detection and ana...
Detection of a sudden change of the field time series based on the Lorenz system

PubMed Central

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

2017-01-01

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

NASA Technical Reports Server (NTRS)

Eure, Kenneth W.; Hogge, Edward; Quach, Cuong C.; Vazquez, Sixto L.; Russell, Andrew; Hill, Boyd L.

2012-01-01

Structural fault detection and identification remains an area of active research. Solutions to fault detection and identification may be based on subtle changes in the time series history of vibration signals originating from various sensor locations throughout the structure. The purpose of this paper is to document the application of vibration based fault detection methods applied to several structures. Overall, this paper demonstrates the utility of vibration based methods for fault detection in a controlled laboratory setting and limitations of applying the same methods to a similar structure during flight on an experimental subscale aircraft.
Psychophysical Models for Signal Detection with Time Varying Uncertainty. Ph.D. Thesis

NASA Technical Reports Server (NTRS)

Gai, E.

1975-01-01

Psychophysical models for the behavior of the human operator in detection tasks which include change in detectability, correlation between observations and deferred decisions are developed. Classical Signal Detection Theory (SDT) is discussed and its emphasis on the sensory processes is contrasted to decision strategies. The analysis of decision strategies utilizes detection tasks with time varying signal strength. The classical theory is modified to include such tasks and several optimal decision strategies are explored. Two methods of classifying strategies are suggested. The first method is similar to the analysis of ROC curves, while the second is based on the relation between the criterion level (CL) and the detectability. Experiments to verify the analysis of tasks with changes of signal strength are designed. The results show that subjects are aware of changes in detectability and tend to use strategies that involve changes in the CL's.
Nonexplicit change detection in complex dynamic settings: what eye movements reveal.

PubMed

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.
Acoustic change detection algorithm using an FM radio

NASA Astrophysics Data System (ADS)

Goldman, Geoffrey H.; Wolfe, Owen

2012-06-01

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

NASA Astrophysics Data System (ADS)

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

2016-06-01

Persistent Scatterer Interferometry (PSI) is a technique to detect a network of extracted persistent scatterer (PS) points which feature temporal phase stability and strong radar signal throughout time-series of SAR images. The small surface deformations on such PS points are estimated. PSI particularly works well in monitoring human settlements because regular substructures of man-made objects give rise to large number of PS points. If such structures and/or substructures substantially alter or even vanish due to big change like construction, their PS points are discarded without additional explorations during standard PSI procedure. Such rejected points are called big change (BC) points. On the other hand, incoherent change detection (ICD) relies on local comparison of multi-temporal images (e.g. image difference, image ratio) to highlight scene modifications of larger size rather than detail level. However, image noise inevitably degrades ICD accuracy. We propose a change detection approach based on PSI to synergize benefits of PSI and ICD. PS points are extracted by PSI procedure. A local change index is introduced to quantify probability of a big change for each point. We propose an automatic thresholding method adopting change index to extract BC points along with a clue of the period they emerge. In the end, PS ad BC points are integrated into a change detection image. Our method is tested at a site located around north of Berlin main station where steady, demolished, and erected building substructures are successfully detected. The results are consistent with ground truth derived from time-series of aerial images provided by Google Earth. In addition, we apply our technique for traffic infrastructure, business district, and sports playground monitoring.
Neuroimaging Evidence for 2 Types of Plasticity in Association with Visual Perceptual Learning.

PubMed

Shibata, Kazuhisa; Sasaki, Yuka; Kawato, Mitsuo; Watanabe, Takeo

2016-09-01

Visual perceptual learning (VPL) is long-term performance improvement as a result of perceptual experience. It is unclear whether VPL is associated with refinement in representations of the trained feature (feature-based plasticity), improvement in processing of the trained task (task-based plasticity), or both. Here, we provide empirical evidence that VPL of motion detection is associated with both types of plasticity which occur predominantly in different brain areas. Before and after training on a motion detection task, subjects' neural responses to the trained motion stimuli were measured using functional magnetic resonance imaging. In V3A, significant response changes after training were observed specifically to the trained motion stimulus but independently of whether subjects performed the trained task. This suggests that the response changes in V3A represent feature-based plasticity in VPL of motion detection. In V1 and the intraparietal sulcus, significant response changes were found only when subjects performed the trained task on the trained motion stimulus. This suggests that the response changes in these areas reflect task-based plasticity. These results collectively suggest that VPL of motion detection is associated with the 2 types of plasticity, which occur in different areas and therefore have separate mechanisms at least to some degree. © The Author 2016. Published by Oxford University Press.
Supporting dynamic change detection: using the right tool for the task.

PubMed

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.

Target detection method by airborne and spaceborne images fusion based on past images

NASA Astrophysics Data System (ADS)

Chen, Shanjing; Kang, Qing; Wang, Zhenggang; Shen, ZhiQiang; Pu, Huan; Han, Hao; Gu, Zhongzheng

2017-11-01

To solve the problem that remote sensing target detection method has low utilization rate of past remote sensing data on target area, and can not recognize camouflage target accurately, a target detection method by airborne and spaceborne images fusion based on past images is proposed in this paper. The target area's past of space remote sensing image is taken as background. The airborne and spaceborne remote sensing data is fused and target feature is extracted by the means of airborne and spaceborne images registration, target change feature extraction, background noise suppression and artificial target feature extraction based on real-time aerial optical remote sensing image. Finally, the support vector machine is used to detect and recognize the target on feature fusion data. The experimental results have established that the proposed method combines the target area change feature of airborne and spaceborne remote sensing images with target detection algorithm, and obtains fine detection and recognition effect on camouflage and non-camouflage targets.
A Motion Detection Algorithm Using Local Phase Information

PubMed Central

Lazar, Aurel A.; Ukani, Nikul H.; Zhou, Yiyin

2016-01-01

Previous research demonstrated that global phase alone can be used to faithfully represent visual scenes. Here we provide a reconstruction algorithm by using only local phase information. We also demonstrate that local phase alone can be effectively used to detect local motion. The local phase-based motion detector is akin to models employed to detect motion in biological vision, for example, the Reichardt detector. The local phase-based motion detection algorithm introduced here consists of two building blocks. The first building block measures/evaluates the temporal change of the local phase. The temporal derivative of the local phase is shown to exhibit the structure of a second order Volterra kernel with two normalized inputs. We provide an efficient, FFT-based algorithm for implementing the change of the local phase. The second processing building block implements the detector; it compares the maximum of the Radon transform of the local phase derivative with a chosen threshold. We demonstrate examples of applying the local phase-based motion detection algorithm on several video sequences. We also show how the locally detected motion can be used for segmenting moving objects in video scenes and compare our local phase-based algorithm to segmentation achieved with a widely used optic flow algorithm. PMID:26880882
Reaction-based small-molecule fluorescent probes for dynamic detection of ROS and transient redox changes in living cells and small animals.

PubMed

Lü, Rui

2017-09-01

Dynamic detection of transient redox changes in living cells and animals has broad implications for human health and disease diagnosis, because intracellular redox homeostasis regulated by reactive oxygen species (ROS) plays important role in cell functions, normal physiological functions and some serious human diseases (e.g., cancer, Alzheimer's disease, diabetes, etc.) usually have close relationship with the intracellular redox status. Small-molecule ROS-responsive fluorescent probes can act as powerful tools for dynamic detection of ROS and redox changes in living cells and animals through fluorescence imaging techniques; and great advances have been achieved recently in the design and synthesis of small-molecule ROS-responsive fluorescent probes. This article highlights up-to-date achievements in designing and using the reaction-based small-molecule fluorescent probes (with high sensitivity and selectivity to ROS and redox cycles) in the dynamic detection of ROS and transient redox changes in living cells and animals through fluorescence imaging. Copyright © 2017. Published by Elsevier Ltd.
A targeted change-detection procedure by combining change vector analysis and post-classification approach

NASA Astrophysics Data System (ADS)

Ye, Su; Chen, Dongmei; Yu, Jie

2016-04-01

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

NASA Astrophysics Data System (ADS)

Gehrung, J.; Hebel, M.; Arens, M.; Stilla, U.

2018-05-01

Mobile laser scanning has not only the potential to create detailed representations of urban environments, but also to determine changes up to a very detailed level. An environment representation for change detection in large scale urban environments based on point clouds has drawbacks in terms of memory scalability. Volumes, however, are a promising building block for memory efficient change detection methods. The challenge of working with 3D occupancy grids is that the usual raycasting-based methods applied for their generation lead to artifacts caused by the traversal of unfavorable discretized space. These artifacts have the potential to distort the state of voxels in close proximity to planar structures. In this work we propose a raycasting approach that utilizes knowledge about planar surfaces to completely prevent this kind of artifacts. To demonstrate the capabilities of our approach, a method for the iterative volumetric approximation of point clouds that allows to speed up the raycasting by 36 percent is proposed.
Change Detection of Remote Sensing Images by Dt-Cwt and Mrf

NASA Astrophysics Data System (ADS)

Ouyang, S.; Fan, K.; Wang, H.; Wang, Z.

2017-05-01

Aiming at the significant loss of high frequency information during reducing noise and the pixel independence in change detection of multi-scale remote sensing image, an unsupervised algorithm is proposed based on the combination between Dual-tree Complex Wavelet Transform (DT-CWT) and Markov random Field (MRF) model. This method first performs multi-scale decomposition for the difference image by the DT-CWT and extracts the change characteristics in high-frequency regions by using a MRF-based segmentation algorithm. Then our method estimates the final maximum a posterior (MAP) according to the segmentation algorithm of iterative condition model (ICM) based on fuzzy c-means(FCM) after reconstructing the high-frequency and low-frequency sub-bands of each layer respectively. Finally, the method fuses the above segmentation results of each layer by using the fusion rule proposed to obtain the mask of the final change detection result. The results of experiment prove that the method proposed is of a higher precision and of predominant robustness properties.
3D change detection at street level using mobile laser scanning point clouds and terrestrial images

NASA Astrophysics Data System (ADS)

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 consistency between point clouds and stereo images. Finally, an over-segmentation based graph cut optimization is carried out, taking into account the color, depth and class information to compute the changed area in the image space. The proposed method is invariant to light changes, robust to small co-registration errors between images and point clouds, and can be applied straightforwardly to 3D polyhedral models. This method can be used for 3D street data updating, city infrastructure management and damage monitoring in complex urban scenes.
Spectral anomaly methods for aerial detection using KUT nuisance rejection

NASA Astrophysics Data System (ADS)

Detwiler, R. S.; Pfund, D. M.; Myjak, M. J.; Kulisek, J. A.; Seifert, C. E.

2015-06-01

This work discusses the application and optimization of a spectral anomaly method for the real-time detection of gamma radiation sources from an aerial helicopter platform. Aerial detection presents several key challenges over ground-based detection. For one, larger and more rapid background fluctuations are typical due to higher speeds, larger field of view, and geographically induced background changes. As well, the possible large altitude or stand-off distance variations cause significant steps in background count rate as well as spectral changes due to increased gamma-ray scatter with detection at higher altitudes. The work here details the adaptation and optimization of the PNNL-developed algorithm Nuisance-Rejecting Spectral Comparison Ratios for Anomaly Detection (NSCRAD), a spectral anomaly method previously developed for ground-based applications, for an aerial platform. The algorithm has been optimized for two multi-detector systems; a NaI(Tl)-detector-based system and a CsI detector array. The optimization here details the adaptation of the spectral windows for a particular set of target sources to aerial detection and the tailoring for the specific detectors. As well, the methodology and results for background rejection methods optimized for the aerial gamma-ray detection using Potassium, Uranium and Thorium (KUT) nuisance rejection are shown. Results indicate that use of a realistic KUT nuisance rejection may eliminate metric rises due to background magnitude and spectral steps encountered in aerial detection due to altitude changes and geographically induced steps such as at land-water interfaces.
Causal Entropies – a measure for determining changes in the temporal organization of neural systems

PubMed Central

Waddell, Jack; Dzakpasu, Rhonda; Booth, Victoria; Riley, Brett; Reasor, Jonathan; Poe, Gina; Zochowski, Michal

2009-01-01

We propose a novel measure to detect temporal ordering in the activity of individual neurons in a local network, which is thought to be a hallmark of activity-dependent synaptic modifications during learning. The measure, called Causal Entropy, is based on the time-adaptive detection of asymmetries in the relative temporal patterning between neuronal pairs. We characterize properties of the measure on both simulated data and experimental multiunit recordings of hippocampal neurons from the awake, behaving rat, and show that the metric can more readily detect those asymmetries than standard cross correlation-based techniques, especially since the temporal sensitivity of causal entropy can detect such changes rapidly and dynamically. PMID:17275095
Assessment of Data Fusion Algorithms for Earth Observation Change Detection Processes

PubMed Central

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

2016-01-01

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

NASA Astrophysics Data System (ADS)

Kumar, Manish; Raghuwanshi, Sanjeev Kumar

2018-02-01

In recent years, food safety issues caused by contamination of chemical substances or microbial species have raised a major area of concern to mankind. The conventional chromatography-based methods for detection of chemical are based on human-observation and slow for real-time monitoring. The surface plasmon resonance (SPR) sensors offers the capability of detection of very low concentrations of adulterated chemical and biological agents for real-time by monitoring. Thus, adulterant agent in food gives change in refractive index of pure food result in corresponding phase change. These changes can be detected at the output and can be related to the concentration of the chemical species present at the point.
Quantitative naturalistic methods for detecting change points in psychotherapy research: an illustration with alliance ruptures.

PubMed

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

2012-01-01

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

NASA Astrophysics Data System (ADS)

Tickle, Andrew J.; Singh, Harjap; Grindley, Josef E.

2013-06-01

Morphological Scene Change Detection (MSCD) is a process typically tasked at detecting relevant changes in a guarded environment for security applications. This can be implemented on a Field Programmable Gate Array (FPGA) by a combination of binary differences based around exclusive-OR (XOR) gates, mathematical morphology and a crucial threshold setting. This is a robust technique and can be applied many areas from leak detection to movement tracking, and further augmented to perform additional functions such as watermarking and facial detection. Fire is a severe problem, and in areas where traditional fire alarm systems are not installed or feasible, it may not be detected until it is too late. Shown here is a way of adapting the traditional Morphological Scene Change Detector (MSCD) with a temperature sensor so if both the temperature sensor and scene change detector are triggered, there is a high likelihood of fire present. Such a system would allow integration into autonomous mobile robots so that not only security patrols could be undertaken, but also fire detection.
Detection of target-probe oligonucleotide hybridization using synthetic nanopore resistive pulse sensing.

PubMed

Booth, Marsilea Adela; Vogel, Robert; Curran, James M; Harbison, SallyAnn; Travas-Sejdic, Jadranka

2013-07-15

Despite the plethora of DNA sensor platforms available, a portable, sensitive, selective and economic sensor able to rival current fluorescence-based techniques would find use in many applications. In this research, probe oligonucleotide-grafted particles are used to detect target DNA in solution through a resistive pulse nanopore detection technique. Using carbodiimide chemistry, functionalized probe DNA strands are attached to carboxylated dextran-based magnetic particles. Subsequent incubation with complementary target DNA yields a change in surface properties as the two DNA strands hybridize. Particle-by-particle analysis with resistive pulse sensing is performed to detect these changes. A variable pressure method allows identification of changes in the surface charge of particles. As proof-of-principle, we demonstrate that target hybridization is selectively detected at micromolar concentrations (nanomoles of target) using resistive pulse sensing, confirmed by fluorescence and phase analysis light scattering as complementary techniques. The advantages, feasibility and limitations of using resistive pulse sensing for sample analysis are discussed. Copyright © 2013 Elsevier B.V. All rights reserved.
Zeptomole Detection Scheme Based on Levitation Coordinate Measurements of a Single Microparticle in a Coupled Acoustic-Gravitational Field.

PubMed

Miyagawa, Akihisa; Harada, Makoto; Okada, Tetsuo

2018-02-06

We present a novel analytical principle in which an analyte (according to its concentration) induces a change in the density of a microparticle, which is measured as a vertical coordinate in a coupled acoustic-gravitational (CAG) field. The density change is caused by the binding of gold nanoparticles (AuNP's) on a polystyrene (PS) microparticle through avidin-biotin association. The density of a 10-μm PS particle increases by 2% when 500 100-nm AuNP's are bound to the PS. The CAG can detect this density change as a 5-10 μm shift of the levitation coordinate of the PS. This approach, which allows us to detect 700 AuNP's bound to a PS particle, is utilized to detect biotin in solution. Biotin is detectable at a picomolar level. The reaction kinetics plays a significant role in the entire process. The kinetic aspects are also quantitatively discussed based on the levitation behavior of the PS particles in the CAG field.
Wireless radiation sensor

DOE Office of Scientific and Technical Information (OSTI.GOV)

Lamberti, Vincent E.; Howell, Jr, Layton N.; Mee, David K.

Disclosed is a sensor for detecting radiation. The sensor includes a ferromagnetic metal and a radiation sensitive material coupled to the ferromagnetic metal. The radiation sensitive material is operable to change a tensile stress of the ferromagnetic metal upon exposure to radiation. The radiation is detected based on changes in the magnetic switching characteristics of the ferromagnetic metal caused by the changes in the tensile stress.
2.5D change detection from satellite imagery to monitor small-scale mining activities in the Democratic Republic of the Congo

NASA Astrophysics Data System (ADS)

Kranz, Olaf; Lang, Stefan; Schoepfer, Elisabeth

2017-09-01

Mining natural resources serve fundamental societal needs or commercial interests, but it may well turn into a driver of violence and regional instability. In this study, very high resolution (VHR) optical stereo satellite data are analysed to monitor processes and changes in one of the largest artisanal and small-scale mining sites in the Democratic Republic of the Congo, which is among the world's wealthiest countries in exploitable minerals To identify the subtle structural changes, the applied methodological framework employs object-based change detection (OBCD) based on optical VHR data and generated digital surface models (DSM). Results prove the DSM-based change detection approach enhances the assessment gained from sole 2D analyses by providing valuable information about changes in surface structure or volume. Land cover changes as analysed by OBCD reveal an increase in bare soil area by a rate of 47% between April 2010 and September 2010, followed by a significant decrease of 47.5% until March 2015. Beyond that, DSM differencing enabled the characterisation of small-scale features such as pits and excavations. The presented Earth observation (EO)-based monitoring of mineral exploitation aims at a better understanding of the relations between resource extraction and conflict, and thus providing relevant information for potential mitigation strategies and peace building.
Improved forest change detection with terrain illumination corrected landsat images

USDA-ARS?s Scientific Manuscript database

An illumination correction algorithm has been developed to improve the accuracy of forest change detection from Landsat reflectance data. This algorithm is based on an empirical rotation model and was tested on the Landsat imagery pair over Cherokee National Forest, Tennessee, Uinta-Wasatch-Cache N...
A new fluorescent enhanced probe based on (E)-9-(2-nitrovinyl)-anthracene for the detection of bisulfite anions and its practical application

NASA Astrophysics Data System (ADS)

Chao, Jianbin; Liu, Yuhong; Zhang, Yan; Zhang, Yongbin; Huo, Fangjun; Yin, Caixia; Wang, Yu; Qin, Liping

2015-07-01

A new fluorescent enhanced probe based on (E)-9-(2-nitrovinyl)-anthracene is developed, which shows high selectivity and sensitivity for the detection of bisulfite anions at Na2HPO4 citric acid buffer solutions (pH 5.0). When addition of HSO3-, the fluorescence intensity is significantly enhanced and the probe displays apparent fluorescence color changes from non-fluorescence to blue under a UV lamp illumination, the solution color also changes from yellow to colorless. The detection limit is determined to be as low as 6.30 μM. This offers another specific colorimetric and fluorescent probe for bisulfite anions detection, furthermore it is applied in detecting the level of bisulfite in sugar samples.
Ladar-based IED detection

NASA Astrophysics Data System (ADS)

Engström, Philip; Larsson, Hâkan; Letalick, Dietmar

2014-05-01

An improvised explosive device (IED) is a bomb constructed and deployed in a non-standard manor. Improvised means that the bomb maker took whatever he could get his hands on, making it very hard to predict and detect. Nevertheless, the matters in which the IED's are deployed and used, for example as roadside bombs, follow certain patterns. One possible approach for early warning is to record the surroundings when it is safe and use this as reference data for change detection. In this paper a LADAR-based system for IED detection is presented. The idea is to measure the area in front of the vehicle when driving and comparing this to the previously recorded reference data. By detecting new, missing or changed objects the system can make the driver aware of probable threats.

Real-time traffic sign recognition based on a general purpose GPU and deep-learning.

PubMed

Lim, Kwangyong; Hong, Yongwon; Choi, Yeongwoo; Byun, Hyeran

2017-01-01

We present a General Purpose Graphics Processing Unit (GPGPU) based real-time traffic sign detection and recognition method that is robust against illumination changes. There have been many approaches to traffic sign recognition in various research fields; however, previous approaches faced several limitations when under low illumination or wide variance of light conditions. To overcome these drawbacks and improve processing speeds, we propose a method that 1) is robust against illumination changes, 2) uses GPGPU-based real-time traffic sign detection, and 3) performs region detecting and recognition using a hierarchical model. This method produces stable results in low illumination environments. Both detection and hierarchical recognition are performed in real-time, and the proposed method achieves 0.97 F1-score on our collective dataset, which uses the Vienna convention traffic rules (Germany and South Korea).
Change detection in synthetic aperture radar images based on image fusion and fuzzy clustering.

PubMed

Gong, Maoguo; Zhou, Zhiqiang; Ma, Jingjing

2012-04-01

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

PubMed

Roncancio, Daniel; Yu, Haixiang; Xu, Xiaowen; Wu, Shuo; Liu, Ran; Debord, Joshua; Lou, Xinhui; Xiao, Yi

2014-11-18

We report a rapid and specific aptamer-based method for one-step cocaine detection with minimal reagent requirements. The feasibility of aptamer-based detection has been demonstrated with sensors that operate via target-induced conformational change mechanisms, but these have generally exhibited limited target sensitivity. We have discovered that the cocaine-binding aptamer MNS-4.1 can also bind the fluorescent molecule 2-amino-5,6,7-trimethyl-1,8-naphthyridine (ATMND) and thereby quench its fluorescence. We subsequently introduced sequence changes into MNS-4.1 to engineer a new cocaine-binding aptamer (38-GC) that exhibits higher affinity to both ligands, with reduced background signal and increased signal gain. Using this aptamer, we have developed a new sensor platform that relies on the cocaine-mediated displacement of ATMND from 38-GC as a result of competitive binding. We demonstrate that our sensor can detect cocaine within seconds at concentrations as low as 200 nM, which is 50-fold lower than existing assays based on target-induced conformational change. More importantly, our assay achieves successful cocaine detection in body fluids, with a limit of detection of 10.4, 18.4, and 36 μM in undiluted saliva, urine, and serum samples, respectively.
Testing pigeon memory in a change detection task.

PubMed

Wright, Anthony A; Katz, Jeffrey S; Magnotti, John; Elmore, L Caitlin; Babb, Stephanie; Alwin, Sarah

2010-04-01

Six pigeons were trained in a change detection task with four colors. They were shown two colored circles on a sample array, followed by a test array with the color of one circle changed. The pigeons learned to choose the changed color and transferred their performance to four unfamiliar colors, suggesting that they had learned a generalized concept of color change. They also transferred performance to test delays several times their 50-msec training delay without prior delay training. The accurate delay performance of several seconds suggests that their change detection was memory based, as opposed to a perceptual attentional capture process. These experiments are the first to show that an animal species (pigeons, in this case) can learn a change detection task identical to ones used to test human memory, thereby providing the possibility of directly comparing short-term memory processing across species.
Techniques for automatic large scale change analysis of temporal multispectral imagery

NASA Astrophysics Data System (ADS)

Mercovich, Ryan A.

Change detection in remotely sensed imagery is a multi-faceted problem with a wide variety of desired solutions. Automatic change detection and analysis to assist in the coverage of large areas at high resolution is a popular area of research in the remote sensing community. Beyond basic change detection, the analysis of change is essential to provide results that positively impact an image analyst's job when examining potentially changed areas. Present change detection algorithms are geared toward low resolution imagery, and require analyst input to provide anything more than a simple pixel level map of the magnitude of change that has occurred. One major problem with this approach is that change occurs in such large volume at small spatial scales that a simple change map is no longer useful. This research strives to create an algorithm based on a set of metrics that performs a large area search for change in high resolution multispectral image sequences and utilizes a variety of methods to identify different types of change. Rather than simply mapping the magnitude of any change in the scene, the goal of this research is to create a useful display of the different types of change in the image. The techniques presented in this dissertation are used to interpret large area images and provide useful information to an analyst about small regions that have undergone specific types of change while retaining image context to make further manual interpretation easier. This analyst cueing to reduce information overload in a large area search environment will have an impact in the areas of disaster recovery, search and rescue situations, and land use surveys among others. By utilizing a feature based approach founded on applying existing statistical methods and new and existing topological methods to high resolution temporal multispectral imagery, a novel change detection methodology is produced that can automatically provide useful information about the change occurring in large area and high resolution image sequences. The change detection and analysis algorithm developed could be adapted to many potential image change scenarios to perform automatic large scale analysis of change.
THE SCREENING AND RANKING ALGORITHM FOR CHANGE-POINTS DETECTION IN MULTIPLE SAMPLES

PubMed Central

Song, Chi; Min, Xiaoyi; Zhang, Heping

2016-01-01

The chromosome copy number variation (CNV) is the deviation of genomic regions from their normal copy number states, which may associate with many human diseases. Current genetic studies usually collect hundreds to thousands of samples to study the association between CNV and diseases. CNVs can be called by detecting the change-points in mean for sequences of array-based intensity measurements. Although multiple samples are of interest, the majority of the available CNV calling methods are single sample based. Only a few multiple sample methods have been proposed using scan statistics that are computationally intensive and designed toward either common or rare change-points detection. In this paper, we propose a novel multiple sample method by adaptively combining the scan statistic of the screening and ranking algorithm (SaRa), which is computationally efficient and is able to detect both common and rare change-points. We prove that asymptotically this method can find the true change-points with almost certainty and show in theory that multiple sample methods are superior to single sample methods when shared change-points are of interest. Additionally, we report extensive simulation studies to examine the performance of our proposed method. Finally, using our proposed method as well as two competing approaches, we attempt to detect CNVs in the data from the Primary Open-Angle Glaucoma Genes and Environment study, and conclude that our method is faster and requires less information while our ability to detect the CNVs is comparable or better. PMID:28090239
A Fast Framework for Abrupt Change Detection Based on Binary Search Trees and Kolmogorov Statistic

PubMed Central

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

2016-01-01

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

PubMed

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

2016-01-01

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

NASA Astrophysics Data System (ADS)

Ma, Yunfei; Zhang, Honglian; Liu, Fangming; Wu, Zhenhua; Lu, Shaohua; Jin, Qinghui; Zhao, Jianlong; Zhong, Xinhua; Mao, Hongju

2015-10-01

DNA methylation is the most frequently studied epigenetic modification that is strongly involved in genomic stability and cellular plasticity. Aberrant changes in DNA methylation status are ubiquitous in human cancer and the detection of these changes can be informative for cancer diagnosis. Herein, we reported a facile quantum dot-based (QD-based) fluorescence resonance energy transfer (FRET) technique for the detection of DNA methylation. The method relies on methylation-sensitive restriction enzymes for the differential digestion of genomic DNA based on its methylation status. Digested DNA is then subjected to PCR amplification for the incorporation of Alexa Fluor-647 (A647) fluorophores. DNA methylation levels can be detected qualitatively through gel analysis and quantitatively by the signal amplification from QDs to A647 during FRET. Furthermore, the methylation levels of three tumor suppressor genes, PCDHGB6, HOXA9 and RASSF1A, in 20 lung adenocarcinoma and 20 corresponding adjacent nontumorous tissue (NT) samples were measured to verify the feasibility of the QD-based FRET method and a high sensitivity for cancer detection (up to 90%) was achieved. Our QD-based FRET method is a convenient, continuous and high-throughput method, and is expected to be an alternative for detecting DNA methylation as a biomarker for certain human cancers.DNA methylation is the most frequently studied epigenetic modification that is strongly involved in genomic stability and cellular plasticity. Aberrant changes in DNA methylation status are ubiquitous in human cancer and the detection of these changes can be informative for cancer diagnosis. Herein, we reported a facile quantum dot-based (QD-based) fluorescence resonance energy transfer (FRET) technique for the detection of DNA methylation. The method relies on methylation-sensitive restriction enzymes for the differential digestion of genomic DNA based on its methylation status. Digested DNA is then subjected to PCR amplification for the incorporation of Alexa Fluor-647 (A647) fluorophores. DNA methylation levels can be detected qualitatively through gel analysis and quantitatively by the signal amplification from QDs to A647 during FRET. Furthermore, the methylation levels of three tumor suppressor genes, PCDHGB6, HOXA9 and RASSF1A, in 20 lung adenocarcinoma and 20 corresponding adjacent nontumorous tissue (NT) samples were measured to verify the feasibility of the QD-based FRET method and a high sensitivity for cancer detection (up to 90%) was achieved. Our QD-based FRET method is a convenient, continuous and high-throughput method, and is expected to be an alternative for detecting DNA methylation as a biomarker for certain human cancers. Electronic supplementary information (ESI) available: Synthesis of CdSe/CdS/ZnS core/shell/shell QDs. Sequences of primers used for amplifying the promoter regions in bisulfate-modified DNA. Comparison of detected methylation levels in different gene promoters using the QD-based FRET method versus bisulfite pyrosequencing. Methylation levels of the RASSF1A gene in one pair of NT and cancer samples as indicated by pyrosequencing. Theoretical calculation of the Förster distance R0. See DOI: 10.1039/c5nr04956c
Design-for-Hardware-Trust Techniques, Detection Strategies and Metrics for Hardware Trojans

DTIC Science & Technology

2015-12-14

down both rising and falling transitions. For Trojan detection , one fault , slow-‐to-‐rise or slow-‐to...in Jan. 2016. Through the course of this project we developed novel hardware Trojan detection techniques based on clock sweeping. The technique takes...algorithms to detect minor changes due to Trojan and compared them with those changes made by process variations. This technique was implemented on
Structural-change localization and monitoring through a perturbation-based inverse problem.

PubMed

Roux, Philippe; Guéguen, Philippe; Baillet, Laurent; Hamze, Alaa

2014-11-01

Structural-change detection and characterization, or structural-health monitoring, is generally based on modal analysis, for detection, localization, and quantification of changes in structure. Classical methods combine both variations in frequencies and mode shapes, which require accurate and spatially distributed measurements. In this study, the detection and localization of a local perturbation are assessed by analysis of frequency changes (in the fundamental mode and overtones) that are combined with a perturbation-based linear inverse method and a deconvolution process. This perturbation method is applied first to a bending beam with the change considered as a local perturbation of the Young's modulus, using a one-dimensional finite-element model for modal analysis. Localization is successful, even for extended and multiple changes. In a second step, the method is numerically tested under ambient-noise vibration from the beam support with local changes that are shifted step by step along the beam. The frequency values are revealed using the random decrement technique that is applied to the time-evolving vibrations recorded by one sensor at the free extremity of the beam. Finally, the inversion method is experimentally demonstrated at the laboratory scale with data recorded at the free end of a Plexiglas beam attached to a metallic support.
LWIR hyperspectral change detection for target acquisition and situation awareness in urban areas

NASA Astrophysics Data System (ADS)

Dekker, Rob J.; Schwering, Piet B. W.; Benoist, Koen W.; Pignatti, Stefano; Santini, Federico; Friman, Ola

2013-05-01

This paper studies change detection of LWIR (Long Wave Infrared) hyperspectral imagery. Goal is to improve target acquisition and situation awareness in urban areas with respect to conventional techniques. Hyperspectral and conventional broadband high-spatial-resolution data were collected during the DUCAS trials in Zeebrugge, Belgium, in June 2011. LWIR data were acquired using the ITRES Thermal Airborne Spectrographic Imager TASI-600 that operates in the spectral range of 8.0-11.5 μm (32 band configuration). Broadband data were acquired using two aeroplanemounted FLIR SC7000 MWIR cameras. Acquisition of the images was around noon. To limit the number of false alarms due to atmospheric changes, the time interval between the images is less than 2 hours. Local co-registration adjustment was applied to compensate for misregistration errors in the order of a few pixels. The targets in the data that will be analysed in this paper are different kinds of vehicles. Change detection algorithms that were applied and evaluated are Euclidean distance, Mahalanobis distance, Chronochrome (CC), Covariance Equalisation (CE), and Hyperbolic Anomalous Change Detection (HACD). Based on Receiver Operating Characteristics (ROC) we conclude that LWIR hyperspectral has an advantage over MWIR broadband change detection. The best hyperspectral detector is HACD because it is most robust to noise. MWIR high spatial-resolution broadband results show that it helps to apply a false alarm reduction strategy based on spatial processing.
Towards a Framework for Change Detection in Data Sets

NASA Astrophysics Data System (ADS)

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

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

PubMed

Pilling, Michael; Barrett, Doug J K

2016-07-01

We investigated how dimension-based attention influences visual short-term memory (VSTM). This was done through examining the effects of cueing a feature dimension in two perceptual comparison tasks (change detection and sameness detection). In both tasks, a memory array and a test array consisting of a number of colored shapes were presented successively, interleaved by a blank interstimulus interval (ISI). In Experiment 1 (change detection), the critical event was a feature change in one item across the memory and test arrays. In Experiment 2 (sameness detection), the critical event was the absence of a feature change in one item across the two arrays. Auditory cues indicated the feature dimension (color or shape) of the critical event with 80 % validity; the cues were presented either prior to the memory array, during the ISI, or simultaneously with the test array. In Experiment 1, the cue validity influenced sensitivity only when the cue was given at the earliest position; in Experiment 2, the cue validity influenced sensitivity at all three cue positions. We attributed the greater effectiveness of top-down guidance by cues in the sameness detection task to the more active nature of the comparison process required to detect sameness events (Hyun, Woodman, Vogel, Hollingworth, & Luck, Journal of Experimental Psychology: Human Perception and Performance, 35; 1140-1160, 2009).
Updating Landsat-derived land-cover maps using change detection and masking techniques

NASA Technical Reports Server (NTRS)

Likens, W.; Maw, K.

1982-01-01

The California Integrated Remote Sensing System's San Bernardino County Project was devised to study the utilization of a data base at a number of jurisdictional levels. The present paper discusses the implementation of change-detection and masking techniques in the updating of Landsat-derived land-cover maps. A baseline landcover classification was first created from a 1976 image, then the adjusted 1976 image was compared with a 1979 scene by the techniques of (1) multidate image classification, (2) difference image-distribution tails thresholding, (3) difference image classification, and (4) multi-dimensional chi-square analysis of a difference image. The union of the results of methods 1, 3 and 4 was used to create a mask of possible change areas between 1976 and 1979, which served to limit analysis of the update image and reduce comparison errors in unchanged areas. The techniques of spatial smoothing of change-detection products, and of combining results of difference change-detection algorithms are also shown to improve Landsat change-detection accuracies.
Nanoneedle transistor-based sensors for the selective detection of intracellular calcium ions.

PubMed

Son, Donghee; Park, Sung Young; Kim, Byeongju; Koh, Jun Tae; Kim, Tae Hyun; An, Sangmin; Jang, Doyoung; Kim, Gyu Tae; Jhe, Wonho; Hong, Seunghun

2011-05-24

We developed a nanoneedle transistor-based sensor (NTS) for the selective detection of calcium ions inside a living cell. In this work, a single-walled carbon nanotube-based field effect transistor (swCNT-FET) was first fabricated at the end of a glass nanopipette and functionalized with Fluo-4-AM probe dye. The selective binding of calcium ions onto the dye molecules altered the charge state of the dye molecules, resulting in the change of the source-drain current of the swCNT-FET as well as the fluorescence intensity from the dye. We demonstrated the electrical and fluorescence detection of the concentration change of intracellular calcium ions inside a HeLa cell using the NTS.
Multiscale Region-Level VHR Image Change Detection via Sparse Change Descriptor and Robust Discriminative Dictionary Learning

PubMed Central

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

2015-01-01

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

NASA Astrophysics Data System (ADS)

Chen, Yin; Peng, Lijuan; Cremers, Armin B.

2018-03-01

In this article, we develop two methods for unsupervised change detection in multitemporal remote sensing images based on Dempster-Shafer's theory of evidence (DST). In most unsupervised change detection methods, the probability of difference image is assumed to be characterized by mixture models, whose parameters are estimated by the expectation maximization (EM) method. However, the main drawback of the EM method is that it does not consider spatial contextual information, which may entail rather noisy detection results with numerous spurious alarms. To remedy this, we firstly develop an evidence theory based EM method (EEM) which incorporates spatial contextual information in EM by iteratively fusing the belief assignments of neighboring pixels to the central pixel. Secondly, an evidential labeling method in the sense of maximizing a posteriori probability (MAP) is proposed in order to further enhance the detection result. It first uses the parameters estimated by EEM to initialize the class labels of a difference image. Then it iteratively fuses class conditional information and spatial contextual information, and updates labels and class parameters. Finally it converges to a fixed state which gives the detection result. A simulated image set and two real remote sensing data sets are used to evaluate the two evidential change detection methods. Experimental results show that the new evidential methods are comparable to other prevalent methods in terms of total error rate.
SAR Image Change Detection Based on Fuzzy Markov Random Field Model

NASA Astrophysics Data System (ADS)

Zhao, J.; Huang, G.; Zhao, Z.

2018-04-01

Most existing SAR image change detection algorithms only consider single pixel information of different images, and not consider the spatial dependencies of image pixels. So the change detection results are susceptible to image noise, and the detection effect is not ideal. Markov Random Field (MRF) can make full use of the spatial dependence of image pixels and improve detection accuracy. When segmenting the difference image, different categories of regions have a high degree of similarity at the junction of them. It is difficult to clearly distinguish the labels of the pixels near the boundaries of the judgment area. In the traditional MRF method, each pixel is given a hard label during iteration. So MRF is a hard decision in the process, and it will cause loss of information. This paper applies the combination of fuzzy theory and MRF to the change detection of SAR images. The experimental results show that the proposed method has better detection effect than the traditional MRF method.
Change Detection in Uav Video Mosaics Combining a Feature Based Approach and Extended Image Differencing

NASA Astrophysics Data System (ADS)

Saur, Günter; Krüger, Wolfgang

2016-06-01

Change detection is an important task when using unmanned aerial vehicles (UAV) for video surveillance. We address changes of short time scale using observations in time distances of a few hours. Each observation (previous and current) is a short video sequence acquired by UAV in near-Nadir view. Relevant changes are, e.g., recently parked or moved vehicles. Examples for non-relevant changes are parallaxes caused by 3D structures of the scene, shadow and illumination changes, and compression or transmission artifacts. In this paper we present (1) a new feature based approach to change detection, (2) a combination with extended image differencing (Saur et al., 2014), and (3) the application to video sequences using temporal filtering. In the feature based approach, information about local image features, e.g., corners, is extracted in both images. The label "new object" is generated at image points, where features occur in the current image and no or weaker features are present in the previous image. The label "vanished object" corresponds to missing or weaker features in the current image and present features in the previous image. This leads to two "directed" change masks and differs from image differencing where only one "undirected" change mask is extracted which combines both label types to the single label "changed object". The combination of both algorithms is performed by merging the change masks of both approaches. A color mask showing the different contributions is used for visual inspection by a human image interpreter.

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

PubMed

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

2016-05-01

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

PubMed Central

Hong, Yongwon; Choi, Yeongwoo; Byun, Hyeran

2017-01-01

We present a General Purpose Graphics Processing Unit (GPGPU) based real-time traffic sign detection and recognition method that is robust against illumination changes. There have been many approaches to traffic sign recognition in various research fields; however, previous approaches faced several limitations when under low illumination or wide variance of light conditions. To overcome these drawbacks and improve processing speeds, we propose a method that 1) is robust against illumination changes, 2) uses GPGPU-based real-time traffic sign detection, and 3) performs region detecting and recognition using a hierarchical model. This method produces stable results in low illumination environments. Both detection and hierarchical recognition are performed in real-time, and the proposed method achieves 0.97 F1-score on our collective dataset, which uses the Vienna convention traffic rules (Germany and South Korea). PMID:28264011
Scalable Algorithms for Global Scale Remote Sensing Applications

NASA Astrophysics Data System (ADS)

Vatsavai, R. R.; Bhaduri, B. L.; Singh, N.

2015-12-01

Recent decade has witnessed major changes on the Earth, for example, deforestation, varying cropping and human settlement patterns, and crippling damages due to disasters. Accurate damage assessment caused by major natural and anthropogenic disasters is becoming critical due to increases in human and economic loss. This increase in loss of life and severe damages can be attributed to the growing population, as well as human migration to the disaster prone regions of the world. Rapid assessment of these changes and dissemination of accurate information is critical for creating an effective emergency response. Change detection using high-resolution satellite images is a primary tool in assessing damages, monitoring biomass and critical infrastructures, and identifying new settlements. Existing change detection methods suffer from registration errors and often based on pixel (location) wise comparison of spectral observations from single sensor. In this paper we present a novel probabilistic change detection framework based on patch comparison and a GPU implementation that supports near real-time rapid damage exploration capability.
Trial-Level Regressor Modulation for Functional Magnetic Resonance Imaging Designs Requiring Strict Periodicity of Stimulus Presentations: Illustrated Using a Go/No-Go Task

PubMed Central

Motes, Michael A; Rao, Neena K; Shokri-Kojori, Ehsan; Chiang, Hsueh-Sheng; Kraut, Michael A; Hart, John

2017-01-01

Computer-based assessment of many cognitive processes (eg, anticipatory and response readiness processes) requires the use of invariant stimulus display times (SDT) and intertrial intervals (ITI). Although designs with invariant SDTs and ITIs have been used in functional magnetic resonance imaging (fMRI) research, such designs are problematic for fMRI studies because of collinearity issues. This study examined regressor modulation with trial-level reaction times (RT) as a method for improving signal detection in a go/no-go task with invariant SDTs and ITIs. The effects of modulating the go regressor were evaluated with respect to the detection of BOLD signal-change for the no-go condition. BOLD signal-change to no-go stimuli was examined when the go regressor was based on a (a) canonical hemodynamic response function (HRF), (b) RT-based amplitude-modulated (AM) HRF, and (c) RT-based amplitude and duration modulated (A&DM) HRF. Reaction time–based modulation reduced the collinearity between the go and no-go regressors, with A&DM producing the greatest reductions in correlations between the regressors, and greater reductions in the correlations between regressors were associated with longer mean RTs and greater RT variability. Reaction time–based modulation increased statistical power for detecting group-level no-go BOLD signal-change across a broad set of brain regions. The findings show the efficacy of using regressor modulation to increase power in detecting BOLD signal-change in fMRI studies in which circumstances dictate the use of temporally invariant stimulus presentations. PMID:29276390
Trial-Level Regressor Modulation for Functional Magnetic Resonance Imaging Designs Requiring Strict Periodicity of Stimulus Presentations: Illustrated Using a Go/No-Go Task.

PubMed

Motes, Michael A; Rao, Neena K; Shokri-Kojori, Ehsan; Chiang, Hsueh-Sheng; Kraut, Michael A; Hart, John

2017-01-01

Computer-based assessment of many cognitive processes (eg, anticipatory and response readiness processes) requires the use of invariant stimulus display times (SDT) and intertrial intervals (ITI). Although designs with invariant SDTs and ITIs have been used in functional magnetic resonance imaging (fMRI) research, such designs are problematic for fMRI studies because of collinearity issues. This study examined regressor modulation with trial-level reaction times (RT) as a method for improving signal detection in a go / no-go task with invariant SDTs and ITIs. The effects of modulating the go regressor were evaluated with respect to the detection of BOLD signal-change for the no-go condition. BOLD signal-change to no-go stimuli was examined when the go regressor was based on a (a) canonical hemodynamic response function (HRF), (b) RT-based amplitude-modulated (AM) HRF, and (c) RT-based amplitude and duration modulated (A&DM) HRF. Reaction time-based modulation reduced the collinearity between the go and no-go regressors, with A&DM producing the greatest reductions in correlations between the regressors, and greater reductions in the correlations between regressors were associated with longer mean RTs and greater RT variability. Reaction time-based modulation increased statistical power for detecting group-level no-go BOLD signal-change across a broad set of brain regions. The findings show the efficacy of using regressor modulation to increase power in detecting BOLD signal-change in fMRI studies in which circumstances dictate the use of temporally invariant stimulus presentations.
Region-Based Building Rooftop Extraction and Change Detection

NASA Astrophysics Data System (ADS)

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.
The effect of a graphical interpretation of a statistic trend indicator (Trigg's Tracking Variable) on the detection of simulated changes.

PubMed

Kennedy, R R; Merry, A F

2011-09-01

Anaesthesia involves processing large amounts of information over time. One task of the anaesthetist is to detect substantive changes in physiological variables promptly and reliably. It has been previously demonstrated that a graphical trend display of historical data leads to more rapid detection of such changes. We examined the effect of a graphical indication of the magnitude of Trigg's Tracking Variable, a simple statistically based trend detection algorithm, on the accuracy and latency of the detection of changes in a micro-simulation. Ten anaesthetists each viewed 20 simulations with four variables displayed as the current value with a simple graphical trend display. Values for these variables were generated by a computer model, and updated every second; after a period of stability a change occurred to a new random value at least 10 units from baseline. In 50% of the simulations an indication of the rate of change was given by a five level graphical representation of the value of Trigg's Tracking Variable. Participants were asked to indicate when they thought a change was occurring. Changes were detected 10.9% faster with the trend indicator present (mean 13.1 [SD 3.1] cycles vs 14.6 [SD 3.4] cycles, 95% confidence interval 0.4 to 2.5 cycles, P = 0.013. There was no difference in accuracy of detection (median with trend detection 97% [interquartile range 95 to 100%], without trend detection 100% [98 to 100%]), P = 0.8. We conclude that simple statistical trend detection may speed detection of changes during routine anaesthesia, even when a graphical trend display is present.
Drivers' and non-drivers' performance in a change detection task with static driving scenes: is there a benefit of experience?

PubMed

Zhao, Nan; Chen, Wenfeng; Xuan, Yuming; Mehler, Bruce; Reimer, Bryan; Fu, Xiaolan

2014-01-01

The 'looked-but-failed-to-see' phenomenon is crucial to driving safety. Previous research utilising change detection tasks related to driving has reported inconsistent effects of driver experience on the ability to detect changes in static driving scenes. Reviewing these conflicting results, we suggest that drivers' increased ability to detect changes will only appear when the task requires a pattern of visual attention distribution typical of actual driving. By adding a distant fixation point on the road image, we developed a modified change blindness paradigm and measured detection performance of drivers and non-drivers. Drivers performed better than non-drivers only in scenes with a fixation point. Furthermore, experience effect interacted with the location of the change and the relevance of the change to driving. These results suggest that learning associated with driving experience reflects increased skill in the efficient distribution of visual attention across both the central focus area and peripheral objects. This article provides an explanation for the previously conflicting reports of driving experience effects in change detection tasks. We observed a measurable benefit of experience in static driving scenes, using a modified change blindness paradigm. These results have translational opportunities for picture-based training and testing tools to improve driver skill.
Pattern-histogram-based temporal change detection using personal chest radiographs

NASA Astrophysics Data System (ADS)

Ugurlu, Yucel; Obi, Takashi; Hasegawa, Akira; Yamaguchi, Masahiro; Ohyama, Nagaaki

1999-05-01

An accurate and reliable detection of temporal changes from a pair of images has considerable interest in the medical science. Traditional registration and subtraction techniques can be applied to extract temporal differences when,the object is rigid or corresponding points are obvious. However, in radiological imaging, loss of the depth information, the elasticity of object, the absence of clearly defined landmarks and three-dimensional positioning differences constraint the performance of conventional registration techniques. In this paper, we propose a new method in order to detect interval changes accurately without using an image registration technique. The method is based on construction of so-called pattern histogram and comparison procedure. The pattern histogram is a graphic representation of the frequency counts of all allowable patterns in the multi-dimensional pattern vector space. K-means algorithm is employed to partition pattern vector space successively. Any differences in the pattern histograms imply that different patterns are involved in the scenes. In our experiment, a pair of chest radiographs of pneumoconiosis is employed and the changing histogram bins are visualized on both of the images. We found that the method can be used as an alternative way of temporal change detection, particularly when the precise image registration is not available.
Molecular Thermometry

PubMed Central

McCabe, Kevin M.; Hernandez, Mark

2010-01-01

Conventional temperature measurements rely on material responses to heat, which can be detected visually. When Galileo developed an air expansion based device to detect temperature changes, Santorio, a contemporary physician, added a scale to create the first thermometer. With this instrument, patients’ temperatures could be measured, recorded and related to changing health conditions. Today, advances in materials science and bioengineering provide new ways to report temperature at the molecular level in real time. In this review the scientific foundations and history of thermometry underpin a discussion of the discoveries emerging from the field of molecular thermometry. Intracellular nanogels and heat sensing biomolecules have been shown to accurately report temperature changes at the nano-scale. Various systems will soon provide the ability to accurately measure temperature changes at the tissue, cellular, and even sub-cellular level, allowing for detection and monitoring of very small changes in local temperature. In the clinic this will lead to enhanced detection of tumors and localized infection, and accurate and precise monitoring of hyperthermia based therapies. Some nanomaterial systems have even demonstrated a theranostic capacity for heat-sensitive, local delivery of chemotherapeutics. Just as early thermometry moved into the clinic, so too will these molecular thermometers. PMID:20139796
Image Change Detection via Ensemble Learning

DOE Office of Scientific and Technical Information (OSTI.GOV)

Martin, Benjamin W; Vatsavai, Raju

2013-01-01

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

NASA Astrophysics Data System (ADS)

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.
Paper-based Platform for Urinary Creatinine Detection.

PubMed

Sittiwong, Jarinya; Unob, Fuangfa

2016-01-01

A new paper platform was developed for the colorimetric detection of creatinine. The filter paper was coated with 3-propylsulfonic acid trimethoxysilane and used as the platform. Creatinine in a cationic form was extracted onto the paper via an ion-exchange mechanism and detected through the Jaffé reaction, resulting in a yellow-orange color complex. The color change on the paper could be observed visually, and the quantitative detection of creatinine was achieved through monitoring the color intensity change. The color intensity of creatinine complexes on the paper platform as a function of the creatinine concentration provided a linear range for creatinine detection in the range of 10 - 60 mg L(-1) and a detection limit of 4.2 mg L(-1). The accuracy of the proposed paper-based method was comparable to the conventional standard Jaffé method. This paper platform could be applied for simple and rapid detection of creatinine in human urine samples with a low consumption of reagent.
Change detection of polarimetric SAR images based on the KummerU Distribution

NASA Astrophysics Data System (ADS)

Chen, Quan; Zou, Pengfei; Li, Zhen; Zhang, Ping

2014-11-01

In the society of PolSAR image segmentation, change detection and classification, the classical Wishart distribution has been used for a long time, but it especially suit to low-resolution SAR image, because in traditional sensors, only a small number of scatterers are present in each resolution cell. With the improving of SAR systems these years, the classical statistical models can therefore be reconsidered for high resolution and polarimetric information contained in the images acquired by these advanced systems. In this study, SAR image segmentation algorithm based on level-set method, added with distance regularized level-set evolution (DRLSE) is performed using Envisat/ASAR single-polarization data and Radarsat-2 polarimetric images, respectively. KummerU heterogeneous clutter model is used in the later to overcome the homogeneous hypothesis at high resolution cell. An enhanced distance regularized level-set evolution (DRLSE-E) is also applied in the later, to ensure accurate computation and stable level-set evolution. Finally, change detection based on four polarimetric Radarsat-2 time series images is carried out at Genhe area of Inner Mongolia Autonomous Region, NorthEastern of China, where a heavy flood disaster occurred during the summer of 2013, result shows the recommend segmentation method can detect the change of watershed effectively.
Wireless sensor for detecting explosive material

DOE Office of Scientific and Technical Information (OSTI.GOV)

Lamberti, Vincent E; Howell, Jr., Layton N; Mee, David K

Disclosed is a sensor for detecting explosive devices. The sensor includes a ferromagnetic metal and a molecular recognition reagent coupled to the ferromagnetic metal. The molecular recognition reagent is operable to expand upon absorption of vapor from an explosive material such that the molecular recognition reagent changes a tensile stress upon the ferromagnetic metal. The explosive device is detected based on changes in the magnetic switching characteristics of the ferromagnetic metal caused by the tensile stress.
Predicting Boat-Generated Wave Heights: A Quantitative Analysis through Video Observations of Vessel Wakes

DTIC Science & Technology

2012-05-18

by the AWAC. It is a surface- penetrating device that measures continuous changes in the water elevations over time at much higher sampling rates of...background subtraction, a technique based on detecting change from a background scene. Their study highlights the difficulty in object detection and tracking...movements (Zhang et al. 2009) Alternatively, another common object detection method , known as Optical Flow Analysis , may be utilized for vessel
Visual Analysis for Detection and Quantification of Pseudomonas cichorii Disease Severity in Tomato Plants.

PubMed

Rajendran, Dhinesh Kumar; Park, Eunsoo; Nagendran, Rajalingam; Hung, Nguyen Bao; Cho, Byoung-Kwan; Kim, Kyung-Hwan; Lee, Yong Hoon

2016-08-01

Pathogen infection in plants induces complex responses ranging from gene expression to metabolic processes in infected plants. In spite of many studies on biotic stress-related changes in host plants, little is known about the metabolic and phenotypic responses of the host plants to Pseudomonas cichorii infection based on image-based analysis. To investigate alterations in tomato plants according to disease severity, we inoculated plants with different cell densities of P. cichorii using dipping and syringe infiltration methods. High-dose inocula (≥ 10(6) cfu/ml) induced evident necrotic lesions within one day that corresponded to bacterial growth in the infected tissues. Among the chlorophyll fluorescence parameters analyzed, changes in quantum yield of PSII (ΦPSII) and non-photochemical quenching (NPQ) preceded the appearance of visible symptoms, but maximum quantum efficiency of PSII (Fv/Fm) was altered well after symptom development. Visible/near infrared and chlorophyll fluorescence hyperspectral images detected changes before symptom appearance at low-density inoculation. The results of this study indicate that the P. cichorii infection severity can be detected by chlorophyll fluorescence assay and hyperspectral images prior to the onset of visible symptoms, indicating the feasibility of early detection of diseases. However, to detect disease development by hyperspectral imaging, more detailed protocols and analyses are necessary. Taken together, change in chlorophyll fluorescence is a good parameter for early detection of P. cichorii infection in tomato plants. In addition, image-based visualization of infection severity before visual damage appearance will contribute to effective management of plant diseases.
Sensor Systems for Biological Agent Attacks: Protecting Buildings and Military Bases

DTIC Science & Technology

2004-01-01

simple aerosol detectors, to those that identify an agent based on its genetic, structural, or chemical properties , to so- called "functional...Cytometry, 122 Target Binding That Changes Detectable Properties of Smart Sensor Surfaces, 124 Colorimetric Detection, 124 Fluorescence Detection, 125 One...microscopy. In addition to particles directly derived from living organisms, other particles in air may also share properties with the bioaerosols
The signature of undetected change: an exploratory electrotomographic investigation of gradual change blindness.

PubMed

Kiat, John E; Dodd, Michael D; Belli, Robert F; Cheadle, Jacob E

2018-05-01

Neuroimaging-based investigations of change blindness, a phenomenon in which seemingly obvious changes in visual scenes fail to be detected, have significantly advanced our understanding of visual awareness. The vast majority of prior investigations, however, utilize paradigms involving visual disruptions (e.g., intervening blank screens, saccadic movements, "mudsplashes"), making it difficult to isolate neural responses toward visual changes cleanly. To address this issue in this present study, high-density EEG data (256 channel) were collected from 25 participants using a paradigm in which visual changes were progressively introduced into detailed real-world scenes without the use of visual disruption. Oscillatory activity associated with undetected changes was contrasted with activity linked to their absence using standardized low-resolution brain electromagnetic tomography (sLORETA). Although an insufficient number of detections were present to allow for analysis of actual change detection, increased beta-2 activity in the right inferior parietal lobule (rIPL), a region repeatedly associated with change blindness in disruption paradigms, followed by increased theta activity in the right superior temporal gyrus (rSTG) was noted in undetected visual change responses relative to the absence of change. We propose the rIPL beta-2 activity to be associated with orienting attention toward visual changes, with the subsequent rise in rSTG theta activity being potentially linked with updating preconscious perceptual memory representations. NEW & NOTEWORTHY This study represents the first neuroimaging-based investigation of gradual change blindness, a visual phenomenon that has significant potential to shed light on the processes underlying visual detection and conscious perception. The use of gradual change materials is reflective of real-world visual phenomena and allows for cleaner isolation of signals associated with the neural registration of change relative to the use of abrupt change transients.
A new photoelectrochemical biosensors based on DNA conformational changes and isothermal circular strand-displacement polymerization reaction.

PubMed

Zhang, Xiaoru; Xu, Yunpeng; Zhao, Yanqing; Song, Weiling

2013-01-15

We report a strategy for the transduction of DNA hybridization into a readily detectable photoelectrochemical signal by means of a conformational change analogous to electrochemical DNA (E-DNA) approach. To demonstrate the effect of distance change for photosensitizer to the surface of electrode on the change of photocurrent, photosensitizer Ru(bpy)(2)(dcbpy)(2+) tagged DNA stem-loop structures were self-assembled onto a nanogold modified ITO electrode. Hybridization induced a large conformational change in DNA structure, which in turn significantly altered the electron-transfer tunneling distance between the electrode and photosensitizer. The resulting change in photocurrent was proportional to the concentration of DNA in the range of 1.0×10(-10)-8.0×10(-9)M. In order to improve the sensitivity of the photoelectrochemical biosensor, an amplified detection method based on isothermal strand displacement polymerization reaction was employed. With multiple rounds of isothermal strand replication, which led to strand displacement and constituted consecutive signal amplification, a detection limit of 9.4×10(-14)M target DNA was achieved. Copyright © 2012 Elsevier B.V. All rights reserved.

Gold Nanoparticle-Based Facile Detection of Human Serum Albumin and Its Application as an INHIBIT Logic Gate.

PubMed

Huang, Zhenzhen; Wang, Haonan; Yang, Wensheng

2015-05-06

In this work, a facile colorimetric method is developed for quantitative detection of human serum albumin (HSA) based on the antiaggregation effect of gold nanoparticles (Au NPs) in the presence of HSA. The citrate-capped Au NPs undergo a color change from red to blue when melamine is added as a cross-linker to induce the aggregation of the NPs. Such an aggregation is efficiently suppressed upon the adsorption of HSA on the particle surface. This method provides the advantages of simplicity and cost-efficiency for quantitative detection of HSA with a detection limit of ∼1.4 nM by monitoring the colorimetric changes of the Au NPs with UV-vis spectroscopy. In addition, this approach shows good selectivity for HSA over various amino acids, peptides, and proteins and is qualified for detection of HSA in a biological sample. Such an antiaggregation effect can be further extended to fabricate an INHIBIT logic gate by using HSA and melamine as inputs and the color changes of Au NPs as outputs, which may have application potentials in point-of-care medical diagnosis.
Structural Damage Detection Using Changes in Natural Frequencies: Theory and Applications

NASA Astrophysics Data System (ADS)

He, K.; Zhu, W. D.

2011-07-01

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

DOE Office of Scientific and Technical Information (OSTI.GOV)

Armstrong, Peter R.; Laughman, C R.; Leeb, S B.

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

PubMed

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.
Analytical Calculation of Sensing Parameters on Carbon Nanotube Based Gas Sensors

PubMed Central

Akbari, Elnaz; Buntat, Zolkafle; Ahmad, Mohd Hafizi; Enzevaee, Aria; Yousof, Rubiyah; Iqbal, Syed Muhammad Zafar; Ahmadi, Mohammad Taghi.; Sidik, Muhammad Abu Bakar; Karimi, Hediyeh

2014-01-01

Carbon Nanotubes (CNTs) are generally nano-scale tubes comprising a network of carbon atoms in a cylindrical setting that compared with silicon counterparts present outstanding characteristics such as high mechanical strength, high sensing capability and large surface-to-volume ratio. These characteristics, in addition to the fact that CNTs experience changes in their electrical conductance when exposed to different gases, make them appropriate candidates for use in sensing/measuring applications such as gas detection devices. In this research, a model for a Field Effect Transistor (FET)-based structure has been developed as a platform for a gas detection sensor in which the CNT conductance change resulting from the chemical reaction between NH3 and CNT has been employed to model the sensing mechanism with proposed sensing parameters. The research implements the same FET-based structure as in the work of Peng et al. on nanotube-based NH3 gas detection. With respect to this conductance change, the I–V characteristic of the CNT is investigated. Finally, a comparative study shows satisfactory agreement between the proposed model and the experimental data from the mentioned research. PMID:24658617
Spatial early warning signals in a lake manipulation

USGS Publications Warehouse

Butitta, Vince L.; Carpenter, Stephen R.; Loken, Luke; Pace, Michael L.; Stanley, Emily H.

2017-01-01

Rapid changes in state have been documented for many of Earth's ecosystems. Despite a growing toolbox of methods for detecting declining resilience or early warning indicators (EWIs) of ecosystem transitions, these methods have rarely been evaluated in whole-ecosystem trials using reference ecosystems. In this study, we experimentally tested EWIs of cyanobacteria blooms based on changes in the spatial structure of a lake. We induced a cyanobacteria bloom by adding nutrients to an experimental lake and mapped fine-resolution spatial patterning of cyanobacteria using a mobile sensor platform. Prior to the bloom, we detected theoretically predicted spatial EWIs based on variance and spatial autocorrelation, as well as a new index based on the extreme values. Changes in EWIs were not discernible in an unenriched reference lake. Despite the fluid environment of a lake where spatial heterogeneity driven by biological processes may be overwhelmed by physical mixing, spatial EWIs detected an approaching bloom suggesting the utility of spatial metrics for signaling ecological thresholds.
PCA feature extraction for change detection in multidimensional unlabeled data.

PubMed

Kuncheva, Ludmila I; Faithfull, William J

2014-01-01

When classifiers are deployed in real-world applications, it is assumed that the distribution of the incoming data matches the distribution of the data used to train the classifier. This assumption is often incorrect, which necessitates some form of change detection or adaptive classification. While there has been a lot of work on change detection based on the classification error monitored over the course of the operation of the classifier, finding changes in multidimensional unlabeled data is still a challenge. Here, we propose to apply principal component analysis (PCA) for feature extraction prior to the change detection. Supported by a theoretical example, we argue that the components with the lowest variance should be retained as the extracted features because they are more likely to be affected by a change. We chose a recently proposed semiparametric log-likelihood change detection criterion that is sensitive to changes in both mean and variance of the multidimensional distribution. An experiment with 35 datasets and an illustration with a simple video segmentation demonstrate the advantage of using extracted features compared to raw data. Further analysis shows that feature extraction through PCA is beneficial, specifically for data with multiple balanced classes.
Development of 19F-NMR chemical shift detection of DNA B-Z equilibrium using 19F-NMR.

PubMed

Nakamura, S; Yang, H; Hirata, C; Kersaudy, F; Fujimoto, K

2017-06-28

Various DNA conformational changes are in correlation with biological events. In particular, DNA B-Z equilibrium showed a high correlation with translation and transcription. In this study, we developed a DNA probe containing 5-trifluoromethylcytidine or 5-trifluoromethylthymidine to detect DNA B-Z equilibrium using 19 F-NMR. Its probe enabled the quantitative detection of B-, Z-, and ss-DNA based on 19 F-NMR chemical shift change.
Surface plasmon resonance application for herbicide detection

NASA Astrophysics Data System (ADS)

Chegel, Vladimir I.; Shirshov, Yuri M.; Piletskaya, Elena V.; Piletsky, Sergey A.

1998-01-01

The optoelectronic biosensor, based on Surface Plasmon Resonance (SPR) for detection of photosynthesis-inhibiting herbicides in aqueous solutions is presented. The pesticide capability to replace plastoquinone from its complex with D1 protein is used for the detection. This replacement reaction results in the changes of the optical characteristics of protein layer, immobilized on the gold surface. Monitoring of these changes with SPR-technique permit to determine 0.1 - 5.0 mkg/ml herbicide in solution within one hour.
Surface plasmon resonance application for herbicide detection

NASA Astrophysics Data System (ADS)

Chegel, Vladimir I.; Shirshov, Yuri M.; Piletskaya, Elena V.; Piletsky, Sergey A.

1997-12-01

The optoelectronic biosensor, based on Surface Plasmon Resonance (SPR) for detection of photosynthesis-inhibiting herbicides in aqueous solutions is presented. The pesticide capability to replace plastoquinone from its complex with D1 protein is used for the detection. This replacement reaction results in the changes of the optical characteristics of protein layer, immobilized on the gold surface. Monitoring of these changes with SPR-technique permit to determine 0.1 - 5.0 mkg/ml herbicide in solution within one hour.
Electrical response of culture media during bacterial growth on a paper-based device

NASA Astrophysics Data System (ADS)

Srimongkon, Tithimanan; Buerkle, Marius; Nakamura, Akira; Enomae, Toshiharu; Ushijima, Hirobumi; Fukuda, Nobuko

2017-05-01

In this work, we evaluated the feasibility of a paper-based bacterial detection system. The paper served as a substrate for the measurement electrodes and the culture medium. Using a printing technique, we patterned gold electrodes onto the paper substrate and applied Luria broth (LB) agar gel as a culture medium on top of the electrodes. As the first step towards the development of a bacterial detection system, we determined changes in the surface potential during bacterial growth and monitored these changes over 24 h. This allowed us to correlate changes in the surface potential with the different growth phases of the bacteria.
rSPACE: Spatially based power analysis for conservation and ecology

Treesearch

Martha M. Ellis; Jacob S. Ivan; Jody M. Tucker; Michael K. Schwartz

2015-01-01

1.) Power analysis is an important step in designing effective monitoring programs to detect trends in plant or animal populations. Although project goals often focus on detecting changes in population abundance, logistical constraints may require data collection on population indices, such as detection/non-detection data for occupancy estimation. 2.) We describe the...
Feature learning and change feature classification based on deep learning for ternary change detection in SAR images

NASA Astrophysics Data System (ADS)

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.
DOE Office of Scientific and Technical Information (OSTI.GOV)

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

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

PubMed Central

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

2017-01-01

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

NASA Astrophysics Data System (ADS)

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

2011-12-01

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

NASA Technical Reports Server (NTRS)

Scholtz, P.; Smyth, P.

1992-01-01

This article describes an investigation of a statistical hypothesis testing method for detecting changes in the characteristics of an observed time series. The work is motivated by the need for practical automated methods for on-line monitoring of Deep Space Network (DSN) equipment to detect failures and changes in behavior. In particular, on-line monitoring of the motor current in a DSN 34-m beam waveguide (BWG) antenna is used as an example. The algorithm is based on a measure of the information theoretic distance between two autoregressive models: one estimated with data from a dynamic reference window and one estimated with data from a sliding reference window. The Hinkley cumulative sum stopping rule is utilized to detect a change in the mean of this distance measure, corresponding to the detection of a change in the underlying process. The basic theory behind this two-model test is presented, and the problem of practical implementation is addressed, examining windowing methods, model estimation, and detection parameter assignment. Results from the five fault-transition simulations are presented to show the possible limitations of the detection method, and suggestions for future implementation are given.
Vibration Based Crack Detection in a Rotating Disk. Part 2; Experimental Results

NASA Technical Reports Server (NTRS)

Gyekenyesi, Andrew L.; Sawicki, Jerzy T.; Martin, Richard E.; Haase, Wayne C.; Baaklini, George

2005-01-01

This paper describes the experimental results concerning the detection of a crack in a rotating disk. The goal was to utilize blade tip clearance and shaft vibration measurements to monitor changes in the system's center of mass and/or blade deformation behaviors. The concept of the approach is based on the fact that the development of a disk crack results in a distorted strain field within the component. As a result, a minute deformation in the disk's geometry as well as a change in the system's center of mass occurs. Here, a notch was used to simulate an actual crack. The vibration based experimental results failed to identify the existence of a notch when utilizing the approach described above, even with a rather large, circumferential notch (l.2 in.) located approximately mid-span on the disk (disk radius = 4.63 in. with notch at r = 2.12 in.). This was somewhat expected, since the finite element based results in Part 1 of this study predicted changes in blade tip clearance as well as center of mass shifts due to a notch to be less than 0.001 in. Therefore, the small changes incurred by the notch could not be differentiated from the mechanical and electrical noise of the rotor system. Although the crack detection technique of interest failed to identify the existence ofthe notch, the vibration data produced and captured here will be utilized in upcoming studies that will focus on different data mining techniques concerning damage detection in a disk.
Terahertz wave electro-optic measurements with optical spectral filtering

DOE Office of Scientific and Technical Information (OSTI.GOV)

Ilyakov, I. E., E-mail: igor-ilyakov@mail.ru; Shishkin, B. V.; Kitaeva, G. Kh.

We propose electro-optic detection techniques based on variations of the laser pulse spectrum induced during pulse co-propagation with terahertz wave radiation in a nonlinear crystal. Quantitative comparison with two other detection methods is made. Substantial improvement of the sensitivity compared to the standard electro-optic detection technique (at high frequencies) and to the previously shown technique based on laser pulse energy changes is demonstrated in experiment.
Tracing the conformational changes in BSA using FRET with environmentally-sensitive squaraine probes

NASA Astrophysics Data System (ADS)

Govor, Iryna V.; Tatarets, Anatoliy L.; Obukhova, Olena M.; Terpetschnig, Ewald A.; Gellerman, Gary; Patsenker, Leonid D.

2016-06-01

A new potential method of detecting the conformational changes in hydrophobic proteins such as bovine serum albumin (BSA) is introduced. The method is based on the change in the Förster resonance energy transfer (FRET) efficiency between protein-sensitive fluorescent probes. As compared to conventional FRET based methods, in this new approach the donor and acceptor dyes are not covalently linked to protein molecules. Performance of the new method is demonstrated using the protein-sensitive squaraine probes Square-634 (donor) and Square-685 (acceptor) to detect the urea-induced conformational changes of BSA. The FRET efficiency between these probes can be considered a more sensitive parameter to trace protein unfolding as compared to the changes in fluorescence intensity of each of these probes. Addition of urea followed by BSA unfolding causes a noticeable decrease in the emission intensities of these probes (factor of 5.6 for Square-634 and 3.0 for Square-685), and the FRET efficiency changes by a factor of up to 17. Compared to the conventional method the new approach therefore demonstrates to be a more sensitive way to detect the conformational changes in BSA.

Experience with dynamic reinforcement rates decreases resistance to extinction.

PubMed

Craig, Andrew R; Shahan, Timothy A

2016-03-01

The ability of organisms to detect reinforcer-rate changes in choice preparations is positively related to two factors: the magnitude of the change in rate and the frequency with which rates change. Gallistel (2012) suggested similar rate-detection processes are responsible for decreases in responding during operant extinction. Although effects of magnitude of change in reinforcer rate on resistance to extinction are well known (e.g., the partial-reinforcement-extinction effect), effects of frequency of changes in rate prior to extinction are unknown. Thus, the present experiments examined whether frequency of changes in baseline reinforcer rates impacts resistance to extinction. Pigeons pecked keys for variable-interval food under conditions where reinforcer rates were stable and where they changed within and between sessions. Overall reinforcer rates between conditions were controlled. In Experiment 1, resistance to extinction was lower following exposure to dynamic reinforcement schedules than to static schedules. Experiment 2 showed that resistance to presession feeding, a disruptor that should not involve change-detection processes, was unaffected by baseline-schedule dynamics. These findings are consistent with the suggestion that change detection contributes to extinction. We discuss implications of change-detection processes for extinction of simple and discriminated operant behavior and relate these processes to the behavioral-momentum based approach to understanding extinction. © 2016 Society for the Experimental Analysis of Behavior.
Exponentially Weighted Moving Average Change Detection Around the Country (and the World)

NASA Astrophysics Data System (ADS)

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

2014-12-01

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

NASA Astrophysics Data System (ADS)

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

NASA Astrophysics Data System (ADS)

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

2010-12-01

Long-term change detection often implies the challenge of incorporating multitemporal data from different sensors. Most of the conventional change detection algorithms are designed for bi-temporal datasets from the same sensors detecting only the existence of changes. The labeling of change areas remains a difficult task. To overcome such drawbacks, much attention has been given lately to algorithms arising from machine learning, such as Support Vector Machines (SVMs). While SVMs have been applied successfully for land cover classifications, the exploitation of this approach for change detection is still in its infancy. Few studies have already proven the applicability of SVMs for bi- and multitemporal change detection using data from one sensor only. In this paper we demonstrate the application of SVM for multitemporal and -sensor change detection. Our study site covers lignite open pit mining areas in the German state North Rhine-Westphalia. The dataset consists of bi-temporal Landsat data and multi-temporal ERS SAR data covering two time slots (2001 and 2009). The SVM is conducted using the IDL program imageSVM. Change is deduced from one time slot to the next resulting in two change maps. In contrast to change detection, which is based on post-classification comparison, change detection is seen here as a specific classification problem. Thus, changes are directly classified from a layer-stack of the two years. To reduce the number of change classes, we created a change mask using the magnitude of Change Vector Analysis (CVA). Training data were selected for different change classes (e.g. forest to mining or mining to agriculture) as well as for the no-change classes (e.g. agriculture). Subsequently, they were divided in two independent sets for training the SVMs and accuracy assessment, respectively. Our study shows the applicability of SVMs to classify changes via SVMs. The proposed method yielded a change map of reclaimed and active mines. The use of ERS SAR data, however, did not add to the accuracy compared to Landsat data only. A great advantage compared to other change detection approaches are the labeled change maps, which are a direct output of the methodology. Our approach also overcomes the drawback of post-classification comparison, namely the propagation of classification inaccuracies.
Multi-Temporal Classification and Change Detection Using Uav Images

NASA Astrophysics Data System (ADS)

Makuti, S.; Nex, F.; Yang, M. Y.

2018-05-01

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.
Development of an algorithm for an EEG-based driver fatigue countermeasure.

PubMed

Lal, Saroj K L; Craig, Ashley; Boord, Peter; Kirkup, Les; Nguyen, Hung

2003-01-01

Fatigue affects a driver's ability to proceed safely. Driver-related fatigue and/or sleepiness are a significant cause of traffic accidents, which makes this an area of great socioeconomic concern. Monitoring physiological signals while driving provides the possibility of detecting and warning of fatigue. The aim of this paper is to describe an EEG-based fatigue countermeasure algorithm and to report its reliability. Changes in all major EEG bands during fatigue were used to develop the algorithm for detecting different levels of fatigue. The software was shown to be capable of detecting fatigue accurately in 10 subjects tested. The percentage of time the subjects were detected to be in a fatigue state was significantly different than the alert phase (P<.01). This is the first countermeasure software described that has shown to detect fatigue based on EEG changes in all frequency bands. Field research is required to evaluate the fatigue software in order to produce a robust and reliable fatigue countermeasure system. The development of the fatigue countermeasure algorithm forms the basis of a future fatigue countermeasure device. Implementation of electronic devices for fatigue detection is crucial for reducing fatigue-related road accidents and their associated costs.
Using Covariance Matrix for Change Detection of Polarimetric SAR Data

NASA Astrophysics Data System (ADS)

Esmaeilzade, M.; Jahani, F.; Amini, J.

2017-09-01

Nowadays change detection is an important role in civil and military fields. The Synthetic Aperture Radar (SAR) images due to its independent of atmospheric conditions and cloud cover, have attracted much attention in the change detection applications. When the SAR data are used, one of the appropriate ways to display the backscattered signal is using covariance matrix that follows the Wishart distribution. Based on this distribution a statistical test for equality of two complex variance-covariance matrices can be used. In this study, two full polarization data in band L from UAVSAR are used for change detection in agricultural fields and urban areas in the region of United States which the first image belong to 2014 and the second one is from 2017. To investigate the effect of polarization on the rate of change, full polarization data and dual polarization data were used and the results were compared. According to the results, full polarization shows more changes than dual polarization.
How Knowledge Organizations Work: The Case of Detectives

ERIC Educational Resources Information Center

Gottschalk, Petter; Holgersson, Stefan; Karlsen, Jan Terje

2009-01-01

Purpose: The purpose of this paper is to conceptualize detectives in police investigations as knowledge workers. Design/methodology/approach: The paper is based on a literature review covering knowledge organizations, police organizations, police investigations, and detectives as knowledge workers. Findings: The paper finds that the changing role…
Automatic updating and 3D modeling of airport information from high resolution images using GIS and LIDAR data

NASA Astrophysics Data System (ADS)

Lv, Zheng; Sui, Haigang; Zhang, Xilin; Huang, Xianfeng

2007-11-01

As one of the most important geo-spatial objects and military establishment, airport is always a key target in fields of transportation and military affairs. Therefore, automatic recognition and extraction of airport from remote sensing images is very important and urgent for updating of civil aviation and military application. In this paper, a new multi-source data fusion approach on automatic airport information extraction, updating and 3D modeling is addressed. Corresponding key technologies including feature extraction of airport information based on a modified Ostu algorithm, automatic change detection based on new parallel lines-based buffer detection algorithm, 3D modeling based on gradual elimination of non-building points algorithm, 3D change detecting between old airport model and LIDAR data, typical CAD models imported and so on are discussed in detail. At last, based on these technologies, we develop a prototype system and the results show our method can achieve good effects.
Comparative study of performance of neutral axis tracking based damage detection

NASA Astrophysics Data System (ADS)

Soman, R.; Malinowski, P.; Ostachowicz, W.

2015-07-01

This paper presents a comparative study of a novel SHM technique for damage isolation. The performance of the Neutral Axis (NA) tracking based damage detection strategy is compared to other popularly used vibration based damage detection methods viz. ECOMAC, Mode Shape Curvature Method and Strain Flexibility Index Method. The sensitivity of the novel method is compared under changing ambient temperature conditions and in the presence of measurement noise. Finite Element Analysis (FEA) of the DTU 10 MW Wind Turbine was conducted to compare the local damage identification capability of each method and the results are presented. Under the conditions examined, the proposed method was found to be robust to ambient condition changes and measurement noise. The damage identification in some is either at par with the methods mentioned in the literature or better under the investigated damage scenarios.
Wireless sensor

DOE Office of Scientific and Technical Information (OSTI.GOV)

Lamberti, Vincent E.; Howell, JR, Layton N.; Mee, David K.

Disclosed is a sensor for detecting a target material. The sensor includes a ferromagnetic metal and a molecular recognition reagent coupled to the ferromagnetic metal. The molecular recognition reagent is operable to expand upon exposure to vapor or liquid from the target material such that the molecular recognition reagent changes a tensile stress upon the ferromagnetic metal. The target material is detected based on changes in the magnetic switching characteristics of the ferromagnetic metal caused by the changes in the tensile stress.
A change detection method for remote sensing image based on LBP and SURF feature

NASA Astrophysics Data System (ADS)

Hu, Lei; Yang, Hao; Li, Jin; Zhang, Yun

2018-04-01

Finding the change in multi-temporal remote sensing image is important in many the image application. Because of the infection of climate and illumination, the texture of the ground object is more stable relative to the gray in high-resolution remote sensing image. And the texture features of Local Binary Patterns (LBP) and Speeded Up Robust Features (SURF) are outstanding in extracting speed and illumination invariance. A method of change detection for matched remote sensing image pair is present, which compares the similarity by LBP and SURF to detect the change and unchanged of the block after blocking the image. And region growing is adopted to process the block edge zone. The experiment results show that the method can endure some illumination change and slight texture change of the ground object.
Detecting cell death with optical coherence tomography and envelope statistics

NASA Astrophysics Data System (ADS)

Farhat, Golnaz; Yang, Victor X. D.; Czarnota, Gregory J.; Kolios, Michael C.

2011-02-01

Currently no standard clinical or preclinical noninvasive method exists to monitor cell death based on morphological changes at the cellular level. In our past work we have demonstrated that quantitative high frequency ultrasound imaging can detect cell death in vitro and in vivo. In this study we apply quantitative methods previously used with high frequency ultrasound to optical coherence tomography (OCT) to detect cell death. The ultimate goal of this work is to use these methods for optically-based clinical and preclinical cancer treatment monitoring. Optical coherence tomography data were acquired from acute myeloid leukemia cells undergoing three modes of cell death. Significant increases in integrated backscatter were observed for cells undergoing apoptosis and mitotic arrest, while necrotic cells induced a decrease. These changes appear to be linked to structural changes observed in histology obtained from the cell samples. Signal envelope statistics were analyzed from fittings of the generalized gamma distribution to histograms of envelope intensities. The parameters from this distribution demonstrated sensitivities to morphological changes in the cell samples. These results indicate that OCT integrated backscatter and first order envelope statistics can be used to detect and potentially differentiate between modes of cell death in vitro.
Object-based change detection method using refined Markov random field

NASA Astrophysics Data System (ADS)

Peng, Daifeng; Zhang, Yongjun

2017-01-01

In order to fully consider the local spatial constraints between neighboring objects in object-based change detection (OBCD), an OBCD approach is presented by introducing a refined Markov random field (MRF). First, two periods of images are stacked and segmented to produce image objects. Second, object spectral and textual histogram features are extracted and G-statistic is implemented to measure the distance among different histogram distributions. Meanwhile, object heterogeneity is calculated by combining spectral and textual histogram distance using adaptive weight. Third, an expectation-maximization algorithm is applied for determining the change category of each object and the initial change map is then generated. Finally, a refined change map is produced by employing the proposed refined object-based MRF method. Three experiments were conducted and compared with some state-of-the-art unsupervised OBCD methods to evaluate the effectiveness of the proposed method. Experimental results demonstrate that the proposed method obtains the highest accuracy among the methods used in this paper, which confirms its validness and effectiveness in OBCD.
Urban Change Detection of Pingtan City based on Bi-temporal Remote Sensing Images

NASA Astrophysics Data System (ADS)

Degang, JIANG; Jinyan, XU; Yikang, GAO

2017-02-01

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

NASA Technical Reports Server (NTRS)

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

1983-01-01

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

NASA Astrophysics Data System (ADS)

Nirala, Narsingh R.; Saxena, Preeti S.; Srivastava, Anchal

2018-02-01

We develop a simple colorimetric method for determination of free cholesterol in aqueous solution based on functionalized gold nanoparticles with cholesterol oxidase. Functionalized gold nanoparticles interact with free cholesterol to produce H2O2 in proportion to the level of cholesterol visually is being detected. The quenching in optical properties and agglomeration of functionalized gold nanoparticles play a key role in cholesterol sensing due to the electron accepting property of H2O2. While the lower ranges of cholesterol (lower detection limit i.e. 0.2 mg/dL) can be effectively detected using fluorescence study, the absorption study attests evident visual color change which becomes effective for detection of higher ranges of cholesterol (lower detection limit i.e. 19 mg/dL). The shades of red gradually change to blue/purple as the level of cholesterol detected (as evident at 100 mg/dL) using unaided eye without the use of expensive instruments. The potential of the proposed method to be applied in the field is shown by the proposed cholesterol measuring color wheel.
Transducer-based evaluation of tremor

PubMed Central

Haubenberger, Dietrich; Abbruzzese, Giovanni; Bain, Peter G; Bajaj, Nin; Benito-León, Julián; Bhatia, Kailash P; Deuschl, Günther; Forjaz, Maria João; Hallett, Mark; Louis, Elan D; Lyons, Kelly E; Mestre, Tiago A; Raethjen, Jan; Stamelou, Maria; Tan, Eng-King; Testa, Claudia M; Elble, Rodger J

2016-01-01

The Movement Disorder Society (MDS) established a task force on tremor that reviewed the use of transducer-based measures in the quantification and characterization of tremor. Studies of accelerometry, electromyography, activity monitoring, gyroscopy, digitizing tablet-based measures, vocal acoustic analysis, and several other transducer-based methods were identified by searching PubMed.gov. The availability, use, acceptability, reliability, validity, and responsiveness were reviewed for each measure using the following criteria: 1) used in the assessment of tremor, 2) used in published studies by people other than the developers, and 3) adequate clinimetric testing. Accelerometry, gyroscopy, electromyography, and digitizing tablet-based measures fulfilled all three criteria. Compared to rating scales, transducers are far more sensitive to changes in tremor amplitude and frequency, but they do not appear to be more capable of detecting a change that exceeds random variability in tremor amplitude (minimum detectable change). The use of transducer-based measures requires careful attention to their limitations and validity in a particular clinical or research setting. PMID:27273470
A megahertz-frequency tunable piecewise-linear electromechanical resonator realized via nonlinear feedback

NASA Astrophysics Data System (ADS)

Bajaj, Nikhil; Chiu, George T.-C.; Rhoads, Jeffrey F.

2018-07-01

Vibration-based sensing modalities traditionally have relied upon monitoring small shifts in natural frequency in order to detect structural changes (such as those in mass or stiffness). In contrast, bifurcation-based sensing schemes rely on the detection of a qualitative change in the behavior of a system as a parameter is varied. This can produce easy-to-detect changes in response amplitude with high sensitivity to structural change, but requires resonant devices with specific dynamic behavior which is not always easily reproduced. Desirable behavior for such devices can be produced reliably via nonlinear feedback circuitry, but has in past efforts been largely limited to sub-MHz operation, partially due to the time delay limitations present in certain nonlinear feedback circuits, such as multipliers. This work demonstrates the design and implementation of a piecewise-linear resonator realized via diode- and integrated circuit-based feedback electronics and a quartz crystal resonator. The proposed system is fabricated and characterized, and the creation and selective placement of the bifurcation points of the overall electromechanical system is demonstrated by tuning the circuit gains. The demonstrated circuit operates at 16 MHz. Preliminary modeling and analysis is presented that qualitatively agrees with the experimentally-observed behavior.
Vibration-based monitoring to detect mass changes in satellites

NASA Astrophysics Data System (ADS)

Maji, Arup; Vernon, Breck

2012-04-01

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

Zinc finger point mutations within the WT1 gene in Wilms tumor patients.

PubMed Central

Little, M H; Prosser, J; Condie, A; Smith, P J; Van Heyningen, V; Hastie, N D

1992-01-01

A proposed Wilms tumor gene, WT1, which encodes a zinc finger protein, has previously been isolated from human chromosome 11p13. Chemical mismatch cleavage analysis was used to identify point mutations in the zinc finger region of this gene in a series of 32 Wilms tumors. Two exonic single base changes were detected. In zinc finger 3 of a bilateral Wilms tumor patient, a constitutional de novo C----T base change was found changing an arginine to a stop codon. One tumor from this patient showed allele loss leading to 11p hemizygosity of the abnormal allele. In zinc finger 2 of a sporadic Wilms tumor patient, a C----T base change resulted in an arginine to cysteine amino acid change. To our knowledge, a WT1 gene missense mutation has not been detected previously in a Wilms tumor. By comparison with a recent NMR and x-ray crystallographic analysis of an analogous zinc finger gene, early growth response gene 1 (EGR1), this amino acid change in WT1 occurs at a residue predicted to be critical for DNA binding capacity and site specificity. The detection of one nonsense point mutation and one missense WT1 gene point mutation adds to the accumulating evidence implicating this gene in a proportion of Wilms tumor patients. Images PMID:1317572
Nationwide Hybrid Change Detection of Buildings

NASA Astrophysics Data System (ADS)

Hron, V.; Halounova, L.

2016-06-01

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

NASA Astrophysics Data System (ADS)

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

2013-05-01

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

NASA Astrophysics Data System (ADS)

Morton, Kenneth, Jr.; Ratto, Christopher; Malof, Jordan; Gunter, Michael; Collins, Leslie; Torrione, Peter

2012-06-01

Roadside explosive threats continue to pose a significant risk to soldiers and civilians in conflict areas around the world. These objects are easy to manufacture and procure, but due to their ad hoc nature, they are difficult to reliably detect using standard sensing technologies. Although large roadside explosive hazards may be difficult to conceal in rural environments, urban settings provide a much more complicated background where seemingly innocuous objects (e.g., piles of trash, roadside debris) may be used to obscure threats. Since direct detection of all innocuous objects would flag too many objects to be of use, techniques must be employed to reduce the number of alarms generated and highlight only a limited subset of possibly threatening regions for the user. In this work, change detection techniques are used to reduce false alarm rates and increase detection capabilities for possible threat identification in urban environments. The proposed model leverages data from multiple video streams collected over the same regions by first applying video aligning and then using various distance metrics to detect changes based on image keypoints in the video streams. Data collected at an urban warfare simulation range at an Eastern US test site was used to evaluate the proposed approach, and significant reductions in false alarm rates compared to simpler techniques are illustrated.
Plasmonic SERS nanochips and nanoprobes for medical diagnostics and bio-energy applications

NASA Astrophysics Data System (ADS)

Ngo, Hoan T.; Wang, Hsin-Neng; Crawford, Bridget M.; Fales, Andrew M.; Vo-Dinh, Tuan

2017-02-01

The development of rapid, easy-to-use, cost-effective, high accuracy, and high sensitive DNA detection methods for molecular diagnostics has been receiving increasing interest. Over the last five years, our laboratory has developed several chip-based DNA detection techniques including the molecular sentinel-on-chip (MSC), the multiplex MSC, and the inverse molecular sentinel-on-chip (iMS-on-Chip). In these techniques, plasmonic surface-enhanced Raman scattering (SERS) Nanowave chips were functionalized with DNA probes for single-step DNA detection. Sensing mechanisms were based on hybridization of target sequences and DNA probes, resulting in a distance change between SERS reporters and the Nanowave chip's gold surface. This distance change resulted in change in SERS intensity, thus indicating the presence and capture of the target sequences. Our techniques were single-step DNA detection techniques. Target sequences were detected by simple delivery of sample solutions onto DNA probe-functionalized Nanowave chips and SERS signals were measured after 1h - 2h incubation. Target sequence labeling or washing to remove unreacted components was not required, making the techniques simple, easy-to-use, and cost effective. The usefulness of the techniques for medical diagnostics was illustrated by the detection of genetic biomarkers for respiratory viral infection and of dengue virus 4 DNA.
SQUID-detected FMR: Resonance in single crystalline and polycrystalline yttrium iron garnet

NASA Astrophysics Data System (ADS)

O'Reilly, J. M.; Stamenov, P.

2018-04-01

Here two new techniques for the detection of broadband (100 MHz-20 GHz) ferromagnetic resonance (FMR)/ferrimagnetic resonance in single and poly-crystalline materials, which rely on SQUID-based gradiometry detection of small changes in the magnetisation, are developed. In the first method, small changes in the along-the-applied-field projection of the coupled magnetic moment (Δmz) are detected as the material is driven into resonance. Absolute measurement of the longitudinal component of the magnetisation and the resonance induced lowering of this moment makes estimation of the precession cone angle accessible, which is typically difficult to extract using conventional cavity or stripline based detection methods. The second method invokes the change in Δmz with the resonance-induced thermal heating (d/mz d T ). Magnetisation dynamics in bulk Y3Fe5O12 are observed over a broad range of experimental temperatures (4 K-400 K) and fields (10-500 mT). The inhomogeneous microwave excitation allows for the observation of higher magnetostatic modes and the convenient tracking of very broad resonances. The two SQUID-detection techniques when combined with conventional broadband vector network analyser-FMR, low-frequency magnetic susceptibility, and DC magnetometry, all easily realised, essentially concurrently, using the same module, greatly expand the amount of static and dynamic information accessible.
Development of a database and processing method for detecting hematotoxicity adverse drug events.

PubMed

Shimai, Yoshie; Takeda, Toshihiro; Manabe, Shirou; Teramoto, Kei; Mihara, Naoki; Matsumura, Yasushi

2015-01-01

Adverse events are detected by monitoring the patient's status, including blood test results. However, it is difficult to identify all adverse events based on recognition by individual doctors. We developed a system that can be used to detect hematotoxicity adverse events according to blood test results recorded in an electronic medical record system. The blood test results were graded based on Common Terminology Criteria for Adverse Events (CTCAE) and changes in the blood test results (Up, Down, Flat) were assessed according to the variation in the grade. The changes in the blood test and injection data were stored in a database. By comparing the date of injection and start and end dates of the change in the blood test results, adverse events related to a designated drug were detected. Using this method, we searched for the occurrence of serious adverse events (CTCAE Grades 3 or 4) concerning WBC, ALT and creatinine related to paclitaxel at Osaka University Hospital. The rate of occurrence of a decreased WBC count, increased ALT level and increased creatinine level was 36.0%, 0.6% and 0.4%, respectively. This method is useful for detecting and estimating the rate of occurrence of hematotoxicity adverse drug events.
Two-stage Keypoint Detection Scheme for Region Duplication Forgery Detection in Digital Images.

PubMed

Emam, Mahmoud; Han, Qi; Zhang, Hongli

2018-01-01

In digital image forensics, copy-move or region duplication forgery detection became a vital research topic recently. Most of the existing keypoint-based forgery detection methods fail to detect the forgery in the smooth regions, rather than its sensitivity to geometric changes. To solve these problems and detect points which cover all the regions, we proposed two steps for keypoint detection. First, we employed the scale-invariant feature operator to detect the spatially distributed keypoints from the textured regions. Second, the keypoints from the missing regions are detected using Harris corner detector with nonmaximal suppression to evenly distribute the detected keypoints. To improve the matching performance, local feature points are described using Multi-support Region Order-based Gradient Histogram descriptor. Based on precision-recall rates and commonly tested dataset, comprehensive performance evaluation is performed. The results demonstrated that the proposed scheme has better detection and robustness against some geometric transformation attacks compared with state-of-the-art methods. © 2017 American Academy of Forensic Sciences.
An Evaluation of Psychophysical Models of Auditory Change Perception

PubMed Central

Micheyl, Christophe; Kaernbach, Christian; Demany, Laurent

2009-01-01

In many psychophysical experiments, the participant's task is to detect small changes along a given stimulus dimension, or to identify the direction (e.g., upward vs. downward) of such changes. The results of these experiments are traditionally analyzed using a constant-variance Gaussian (CVG) model or a high-threshold (HT) model. Here, the authors demonstrate that for changes along three basic sound dimensions (frequency, intensity, and amplitude-modulation rate), such models cannot account for the observed relationship between detection thresholds and direction-identification thresholds. It is shown that two alternative models can account for this relationship. One of them is based on the idea of sensory “quanta”; the other assumes that small changes are detected on the basis of Poisson processes with low means. The predictions of these two models are then compared against receiver operating characteristics (ROCs) for the detection of changes in sound intensity. It is concluded that human listeners' perception of small and unidimensional acoustic changes is better described by a discrete-state Poisson model than by the more commonly used CVG model or by the less favored HT and quantum models. PMID:18954215
Probability of detection of clinical seizures using heart rate changes.

PubMed

Osorio, Ivan; Manly, B F J

2015-08-01

Heart rate-based seizure detection is a viable complement or alternative to ECoG/EEG. This study investigates the role of various biological factors on the probability of clinical seizure detection using heart rate. Regression models were applied to 266 clinical seizures recorded from 72 subjects to investigate if factors such as age, gender, years with epilepsy, etiology, seizure site origin, seizure class, and data collection centers, among others, shape the probability of EKG-based seizure detection. Clinical seizure detection probability based on heart rate changes, is significantly (p<0.001) shaped by patients' age and gender, seizure class, and years with epilepsy. The probability of detecting clinical seizures (>0.8 in the majority of subjects) using heart rate is highest for complex partial seizures, increases with a patient's years with epilepsy, is lower for females than for males and is unrelated to the side of hemisphere origin. Clinical seizure detection probability using heart rate is multi-factorially dependent and sufficiently high (>0.8) in most cases to be clinically useful. Knowledge of the role that these factors play in shaping said probability will enhance its applicability and usefulness. Heart rate is a reliable and practical signal for extra-cerebral detection of clinical seizures originating from or spreading to central autonomic network structures. Copyright © 2015 British Epilepsy Association. Published by Elsevier Ltd. All rights reserved.
Automated Landslides Detection for Mountain Cities Using Multi-Temporal Remote Sensing Imagery.

PubMed

Chen, Zhong; Zhang, Yifei; Ouyang, Chao; Zhang, Feng; Ma, Jie

2018-03-09

Landslides that take place in mountain cities tend to cause huge casualties and economic losses, and a precise survey of landslide areas is a critical task for disaster emergency. However, because of the complicated appearance of the nature, it is difficult to find a spatial regularity that only relates to landslides, thus landslides detection based on only spatial information or artificial features usually performs poorly. In this paper, an automated landslides detection approach that is aiming at mountain cities has been proposed based on pre- and post-event remote sensing images, it mainly utilizes the knowledge of landslide-related surface covering changes, and makes full use of the temporal and spatial information. A change detection method using Deep Convolution Neural Network (DCNN) was introduced to extract the areas where drastic alterations have taken place; then, focusing on the changed areas, the Spatial Temporal Context Learning (STCL) was conducted to identify the landslides areas; finally, we use slope degree which is derived from digital elevation model (DEM) to make the result more reliable, and the change of DEM is used for making the detected areas more complete. The approach was applied to detecting the landslides in Shenzhen, Zhouqu County and Beichuan County in China, and a quantitative accuracy assessment has been taken. The assessment indicates that this approach can guarantee less commission error of landslide areal extent which is below 17.6% and achieves a quality percentage above 61.1%, and for landslide areas, the detection percentage is also competitive, the experimental results proves the feasibility and accuracy of the proposed approach for the detection landslides in mountain cities.
Automated Landslides Detection for Mountain Cities Using Multi-Temporal Remote Sensing Imagery

PubMed Central

Chen, Zhong; Zhang, Yifei; Ouyang, Chao; Zhang, Feng; Ma, Jie

2018-01-01

Landslides that take place in mountain cities tend to cause huge casualties and economic losses, and a precise survey of landslide areas is a critical task for disaster emergency. However, because of the complicated appearance of the nature, it is difficult to find a spatial regularity that only relates to landslides, thus landslides detection based on only spatial information or artificial features usually performs poorly. In this paper, an automated landslides detection approach that is aiming at mountain cities has been proposed based on pre- and post-event remote sensing images, it mainly utilizes the knowledge of landslide-related surface covering changes, and makes full use of the temporal and spatial information. A change detection method using Deep Convolution Neural Network (DCNN) was introduced to extract the areas where drastic alterations have taken place; then, focusing on the changed areas, the Spatial Temporal Context Learning (STCL) was conducted to identify the landslides areas; finally, we use slope degree which is derived from digital elevation model (DEM) to make the result more reliable, and the change of DEM is used for making the detected areas more complete. The approach was applied to detecting the landslides in Shenzhen, Zhouqu County and Beichuan County in China, and a quantitative accuracy assessment has been taken. The assessment indicates that this approach can guarantee less commission error of landslide areal extent which is below 17.6% and achieves a quality percentage above 61.1%, and for landslide areas, the detection percentage is also competitive, the experimental results proves the feasibility and accuracy of the proposed approach for the detection landslides in mountain cities. PMID:29522424
Paper based colorimetric biosensing platform utilizing cross-linked siloxane as probe.

PubMed

Zhou, Miao; Yang, Minghui; Zhou, Feimeng

2014-05-15

Paper based colorimetric biosensing platform utilizing cross-linked siloxane 3-aminopropyltriethoxysilane (APTMS) as probe was developed for the detection of a broad range of targets including H2O2, glucose and protein biomarker. APTMS was extensively used for the modification of filter papers to develop paper based analytical devices. We discovered when APTMS was cross-linked with glutaraldehyde (GA), the resulting complex (APTMS-GA) displays brick-red color, and a visual color change was observed when the complex reacted with H2O2. By integrating the APTMS-GA complex with filter paper, the modified paper enables quantitative detection of H2O2 through the monitoring of the color intensity change of the paper via software Image J. Then, with the immobilization of glucose oxidase (GOx) onto the modified paper, glucose can be detected through the detection of enzymatically generated H2O2. For protein biomarker prostate specific antigen (PSA) assay, we immobilized capture, not captured anti-PSA antibody (Ab1) onto the paper surface and using GOx modified gold nanorod (GNR) as detection anti-PSA antibody (Ab2) label. The detection of PSA was also achieved via the liberated H2O2 when the GOx label reacted with glucose. The results demonstrated the possibility of this paper based sensor for the detection of different analytes with wide linear range. The low cost and simplicity of this paper based sensor could be developed for "point-of-care" analysis and find wide application in different areas. © 2013 Published by Elsevier B.V.
Evaluation of an Automatic Registration-Based Algorithm for Direct Measurement of Volume Change in Tumors

DOE Office of Scientific and Technical Information (OSTI.GOV)

Sarkar, Saradwata; Johnson, Timothy D.; Ma, Bing

2012-07-01

Purpose: Assuming that early tumor volume change is a biomarker for response to therapy, accurate quantification of early volume changes could aid in adapting an individual patient's therapy and lead to shorter clinical trials. We investigated an image registration-based approach for tumor volume change quantification that may more reliably detect smaller changes that occur in shorter intervals than can be detected by existing algorithms. Methods and Materials: Variance and bias of the registration-based approach were evaluated using retrospective, in vivo, very-short-interval diffusion magnetic resonance imaging scans where true zero tumor volume change is unequivocally known and synthetic data, respectively. Themore » interval scans were nonlinearly registered using two similarity measures: mutual information (MI) and normalized cross-correlation (NCC). Results: The 95% confidence interval of the percentage volume change error was (-8.93% to 10.49%) for MI-based and (-7.69%, 8.83%) for NCC-based registrations. Linear mixed-effects models demonstrated that error in measuring volume change increased with increase in tumor volume and decreased with the increase in the tumor's normalized mutual information, even when NCC was the similarity measure being optimized during registration. The 95% confidence interval of the relative volume change error for the synthetic examinations with known changes over {+-}80% of reference tumor volume was (-3.02% to 3.86%). Statistically significant bias was not demonstrated. Conclusion: A low-noise, low-bias tumor volume change measurement algorithm using nonlinear registration is described. Errors in change measurement were a function of tumor volume and the normalized mutual information content of the tumor.« less
Does MRI scan acceleration affect power to track brain change?

PubMed

Ching, Christopher R K; Hua, Xue; Hibar, Derrek P; Ward, Chadwick P; Gunter, Jeffrey L; Bernstein, Matt A; Jack, Clifford R; Weiner, Michael W; Thompson, Paul M

2015-01-01

The Alzheimer's Disease Neuroimaging Initiative recently implemented accelerated T1-weighted structural imaging to reduce scan times. Faster scans may reduce study costs and patient attrition by accommodating people who cannot tolerate long scan sessions. However, little is known about how scan acceleration affects the power to detect longitudinal brain change. Using tensor-based morphometry, no significant difference was detected in numerical summaries of atrophy rates from accelerated and nonaccelerated scans in subgroups of patients with Alzheimer's disease, early or late mild cognitive impairment, or healthy controls over a 6- and 12-month scan interval. Whole-brain voxelwise mapping analyses revealed some apparent regional differences in 6-month atrophy rates when comparing all subjects irrespective of diagnosis (n = 345). No such whole-brain difference was detected for the 12-month scan interval (n = 156). Effect sizes for structural brain changes were not detectably different in accelerated versus nonaccelerated data. Scan acceleration may influence brain measures but has minimal effects on tensor-based morphometry-derived atrophy measures, at least over the 6- and 12-month intervals examined here. Copyright © 2015 Elsevier Inc. All rights reserved.
SuBSENSE: a universal change detection method with local adaptive sensitivity.

PubMed

St-Charles, Pierre-Luc; Bilodeau, Guillaume-Alexandre; Bergevin, Robert

2015-01-01

Foreground/background segmentation via change detection in video sequences is often used as a stepping stone in high-level analytics and applications. Despite the wide variety of methods that have been proposed for this problem, none has been able to fully address the complex nature of dynamic scenes in real surveillance tasks. In this paper, we present a universal pixel-level segmentation method that relies on spatiotemporal binary features as well as color information to detect changes. This allows camouflaged foreground objects to be detected more easily while most illumination variations are ignored. Besides, instead of using manually set, frame-wide constants to dictate model sensitivity and adaptation speed, we use pixel-level feedback loops to dynamically adjust our method's internal parameters without user intervention. These adjustments are based on the continuous monitoring of model fidelity and local segmentation noise levels. This new approach enables us to outperform all 32 previously tested state-of-the-art methods on the 2012 and 2014 versions of the ChangeDetection.net dataset in terms of overall F-Measure. The use of local binary image descriptors for pixel-level modeling also facilitates high-speed parallel implementations: our own version, which used no low-level or architecture-specific instruction, reached real-time processing speed on a midlevel desktop CPU. A complete C++ implementation based on OpenCV is available online.
Evaluating fuzzy operators of an object-based image analysis for detecting landslides and their changes

NASA Astrophysics Data System (ADS)

Feizizadeh, Bakhtiar; Blaschke, Thomas; Tiede, Dirk; Moghaddam, Mohammad Hossein Rezaei

2017-09-01

This article presents a method of object-based image analysis (OBIA) for landslide delineation and landslide-related change detection from multi-temporal satellite images. It uses both spatial and spectral information on landslides, through spectral analysis, shape analysis, textural measurements using a gray-level co-occurrence matrix (GLCM), and fuzzy logic membership functionality. Following an initial segmentation step, particular combinations of various information layers were investigated to generate objects. This was achieved by applying multi-resolution segmentation to IRS-1D, SPOT-5, and ALOS satellite imagery in sequential steps of feature selection and object classification, and using slope and flow direction derivatives from a digital elevation model together with topographically-oriented gray level co-occurrence matrices. Fuzzy membership values were calculated for 11 different membership functions using 20 landslide objects from a landslide training data. Six fuzzy operators were used for the final classification and the accuracies of the resulting landslide maps were compared. A Fuzzy Synthetic Evaluation (FSE) approach was adapted for validation of the results and for an accuracy assessment using the landslide inventory database. The FSE approach revealed that the AND operator performed best with an accuracy of 93.87% for 2005 and 94.74% for 2011, closely followed by the MEAN Arithmetic operator, while the OR and AND (*) operators yielded relatively low accuracies. An object-based change detection was then applied to monitor landslide-related changes that occurred in northern Iran between 2005 and 2011. Knowledge rules to detect possible landslide-related changes were developed by evaluating all possible landslide-related objects for both time steps.
Aqueous contaminant detection via UiO-66 thin film optical fiber sensor platform with fast Fourier transform based spectrum analysis

NASA Astrophysics Data System (ADS)

Nazari, Marziyeh; Rubio-Martinez, Marta; Babarao, Ravichandar; Ayad Younis, Adel; Collins, Stephen F.; Hill, Matthew R.; Duke, Mikel C.

2018-01-01

Routine water quality monitoring is required in drinking and waste water management. A particular interest is to measure concentrations of a range of diverse contaminants on-site or remotely in real time. Here we present metal organic framework (MOF) integrated optical fiber sensor that allows for rapid optical measurement based on fast Fourier transform (FFT) spectrum analysis. The end-face of these glass optical fibers was modified with UiO-66(Zr) MOF thin film by in situ hydrothermal synthesis for the detection of the model contaminants, Rhodamine-B and 4-Aminopyridine, in water. The sensing mechanism is based on the change in the optical path length of the thin film induced by the adsorption of chemical molecules by UiO-66. Using FFT analysis, various modes of interaction (physical and chemical) became apparent, showing both irreversible changes upon contact with the contaminant, as well as reversible changes according to actual concentration. This was indicated by the second harmonic elevation to a certain level translating to high sensitivity detection.
Rapid Disaster Analysis based on Remote Sensing: A Case Study about the Tohoku Tsunami Disaster 2011

NASA Astrophysics Data System (ADS)

Yang, C. H.; Soergel, U.; Lanaras, Ch.; Baltsavias, E.; Cho, K.; Remondino, F.; Wakabayashi, H.

2014-09-01

In this study, we present first results of RAPIDMAP, a project funded by European Union in a framework aiming to foster the cooperation of European countries with Japan in R&D. The main objective of RAPIDMAP is to construct a Decision Support System (DSS) based on remote sensing data and WebGIS technologies, where users can easily access real-time information assisting with disaster analysis. In this paper, we present a case study of the Tohoku Tsunami Disaster 2011. We address two approaches namely change detection based on SAR data and co-registration of optical and SAR satellite images. With respect to SAR data, our efforts are subdivided into three parts: (1) initial coarse change detection for entire area, (2) flood area detection, and (3) linearfeature change detection. The investigations are based on pre- and post-event TerraSAR-X images. In (1), two pre- and post-event TerraSAR-X images are accurately co-registered and radiometrically calibrated. Data are fused in a false-color image that provides a quick and rough overview of potential changes, which is useful for initial decision making and identifying areas worthwhile to be analysed further in more depth. However, a bunch of inevitable false alarms appear within the scene caused by speckle, temporal decorrelation, co-registration inaccuracy and so on. In (2), the post-event TerraSAR-X data are used to extract the flood area by using thresholding and morphological approaches. The validated result indicates that using SAR data combining with suitable morphological approaches is a quick and effective way to detect flood area. Except for usage of SAR data, the false-color image composed of optical images are also used to detect flood area for further exploration in this part. In (3), Curvelet filtering is applied in the difference image of pre- and post-event TerraSAR-X images not only to suppress false alarms of irregular-features, but also to enhance the change signals of linear-features (e.g. buildings) in settlements. Afterwards, thresholding is exploited to extract the linear-feature changes. In rapid mapping of disasters various sensors are often employed, including optical and SAR, since they provide complementary information. Such data needs to be analyzed in an integrated fashion and the results from each dataset should be integrated in a GIS with a common coordinate reference system. Thus, if no orthoimages can be generated, the images should be co-registered employing matching of common features. We present results of co-registration between optical (FORMOSAT-2) and TerraSAR-X images based on different matching methods, and also techniques for detecting and eliminating matching errors.
Change detection and classification of land cover in multispectral satellite imagery using clustering of sparse approximations (CoSA) over learned feature dictionaries

DOE Office of Scientific and Technical Information (OSTI.GOV)

Moody, Daniela I.; Brumby, Steven P.; Rowland, Joel C.

Neuromimetic machine vision and pattern recognition algorithms are of great interest for landscape characterization and change detection in satellite imagery in support of global climate change science and modeling. We present results from an ongoing effort to extend machine vision methods to the environmental sciences, using adaptive sparse signal processing combined with machine learning. A Hebbian learning rule is used to build multispectral, multiresolution dictionaries from regional satellite normalized band difference index data. Land cover labels are automatically generated via our CoSA algorithm: Clustering of Sparse Approximations, using a clustering distance metric that combines spectral and spatial textural characteristics tomore » help separate geologic, vegetative, and hydrologie features. We demonstrate our method on example Worldview-2 satellite images of an Arctic region, and use CoSA labels to detect seasonal surface changes. In conclusion, our results suggest that neuroscience-based models are a promising approach to practical pattern recognition and change detection problems in remote sensing.« less

Change detection and classification of land cover in multispectral satellite imagery using clustering of sparse approximations (CoSA) over learned feature dictionaries

DOE PAGES

Moody, Daniela I.; Brumby, Steven P.; Rowland, Joel C.; ...

2014-10-01

Neuromimetic machine vision and pattern recognition algorithms are of great interest for landscape characterization and change detection in satellite imagery in support of global climate change science and modeling. We present results from an ongoing effort to extend machine vision methods to the environmental sciences, using adaptive sparse signal processing combined with machine learning. A Hebbian learning rule is used to build multispectral, multiresolution dictionaries from regional satellite normalized band difference index data. Land cover labels are automatically generated via our CoSA algorithm: Clustering of Sparse Approximations, using a clustering distance metric that combines spectral and spatial textural characteristics tomore » help separate geologic, vegetative, and hydrologie features. We demonstrate our method on example Worldview-2 satellite images of an Arctic region, and use CoSA labels to detect seasonal surface changes. In conclusion, our results suggest that neuroscience-based models are a promising approach to practical pattern recognition and change detection problems in remote sensing.« less
Advanced selective non-invasive ketone body detection sensors based on new ionophores

NASA Astrophysics Data System (ADS)

Sathyapalan, A.; Sarswat, P. K.; Zhu, Y.; Free, M. L.

2014-12-01

New molecules and methods were examined that can be used to detect trace level ketone bodies. Diseases such as type 1 diabetes, childhood hypo-glycaemia-growth hormone deficiency, toxic inhalation, and body metabolism changes are linked with ketone bodies concentration. Here we introduce, selective ketone body detection sensors based on small, environmentally friendly organic molecules with Lewis acid additives. Density functional theory (DFT) simulation of the sensor molecules (Bromo-acetonaphthone tungstate (BANT) and acetonaphthophenyl ether propiono hydroxyl tungstate (APPHT)), indicated a fully relaxed geometry without symmetry attributes and specific coordination which enhances ketone bodies sensitivity. A portable sensing unit was made in which detection media containing ketone bodies at low concentration and new molecules show color change in visible light as well as unique irradiance during UV illumination. RGB analysis, electrochemical tests, SEM characterization, FTIR, absorbance and emission spectroscopy were also performed in order to validate the ketone sensitivity of these new molecules.
Thin film sensor materials for detection of Nitro-Aromatic explosives

NASA Astrophysics Data System (ADS)

Ramdasi, Dipali; Mudhalwadkar, Rohini

2018-03-01

Many countries have experienced terrorist activities and innocent people have suffered. Timely detection of explosives can avoid this situation. This paper targets the detection of Nitrobenzene and Nitrotoluene, which are nitroaromatic compounds possessing explosive properties. As direct sensors for detecting these compounds are not available, Polyaniline based thin film sensors doped with palladium are developed using the spin coating technique. The response of the developed sensors is observed for varying concentrations of explosives. It is observed that zinc oxide based sensor is more sensitive to Nitrotoluene exhibiting a relative change in resistance of 0.78. The tungsten oxide sensor is more sensitive to Nitrobenzene with a relative change in resistance of 0.48. The sensor performance is assessed by measuring the response and recovery time. The cross sensitivity of the sensors is evaluated for ethanol, acetone and methanol which was observed as very low.
A dual-responsive colorimetric and fluorescent chemosensor based on diketopyrrolopyrrole derivative for naked-eye detection of Fe3 + and its practical application

NASA Astrophysics Data System (ADS)

Zhang, Shanshan; Sun, Tao; Xiao, Dejun; Yuan, Fang; Li, Tianduo; Wang, Enhua; Liu, Haixia; Niu, Qingfen

2018-01-01

A novel dual-responsive colorimetric and fluorescent chemosensor L based on diketopyrrolopyrrole derivative for Fe3 + detection was designed and synthesized. In presence of Fe3 +, sensor L displayed strong colorimetric response as amaranth to rose pink and significant fluorescence enhancement and chromogenic change, which served as a naked-eye indicator by an obvious color change from purple to red. The binding constant for L-Fe3 + complex was found as 2.4 × 104 with the lower detection limit of 14.3 nM. The sensing mechanism was investigated in detail by fluorescence measurements, IR and 1H NMR spectra. Sensor L for Fe3 + detection also exhibited high anti-interference performance, good reversibility, wide pH response range and instantaneous response time. Furthermore, the sensor L has been used to quantify Fe3 + ions in practical water samples with good recovery.
Experimental and environmental factors affect spurious detection of ecological thresholds

USGS Publications Warehouse

Daily, Jonathan P.; Hitt, Nathaniel P.; Smith, David; Snyder, Craig D.

2012-01-01

Threshold detection methods are increasingly popular for assessing nonlinear responses to environmental change, but their statistical performance remains poorly understood. We simulated linear change in stream benthic macroinvertebrate communities and evaluated the performance of commonly used threshold detection methods based on model fitting (piecewise quantile regression [PQR]), data partitioning (nonparametric change point analysis [NCPA]), and a hybrid approach (significant zero crossings [SiZer]). We demonstrated that false detection of ecological thresholds (type I errors) and inferences on threshold locations are influenced by sample size, rate of linear change, and frequency of observations across the environmental gradient (i.e., sample-environment distribution, SED). However, the relative importance of these factors varied among statistical methods and between inference types. False detection rates were influenced primarily by user-selected parameters for PQR (τ) and SiZer (bandwidth) and secondarily by sample size (for PQR) and SED (for SiZer). In contrast, the location of reported thresholds was influenced primarily by SED. Bootstrapped confidence intervals for NCPA threshold locations revealed strong correspondence to SED. We conclude that the choice of statistical methods for threshold detection should be matched to experimental and environmental constraints to minimize false detection rates and avoid spurious inferences regarding threshold location.
An Approach to V&V of Embedded Adaptive Systems

NASA Technical Reports Server (NTRS)

Liu, Yan; Yerramalla, Sampath; Fuller, Edgar; Cukic, Bojan; Gururajan, Srikaruth

2004-01-01

Rigorous Verification and Validation (V&V) techniques are essential for high assurance systems. Lately, the performance of some of these systems is enhanced by embedded adaptive components in order to cope with environmental changes. Although the ability of adapting is appealing, it actually poses a problem in terms of V&V. Since uncertainties induced by environmental changes have a significant impact on system behavior, the applicability of conventional V&V techniques is limited. In safety-critical applications such as flight control system, the mechanisms of change must be observed, diagnosed, accommodated and well understood prior to deployment. In this paper, we propose a non-conventional V&V approach suitable for online adaptive systems. We apply our approach to an intelligent flight control system that employs a particular type of Neural Networks (NN) as the adaptive learning paradigm. Presented methodology consists of a novelty detection technique and online stability monitoring tools. The novelty detection technique is based on Support Vector Data Description that detects novel (abnormal) data patterns. The Online Stability Monitoring tools based on Lyapunov's Stability Theory detect unstable learning behavior in neural networks. Cases studies based on a high fidelity simulator of NASA's Intelligent Flight Control System demonstrate a successful application of the presented V&V methodology. ,
VisitSense: Sensing Place Visit Patterns from Ambient Radio on Smartphones for Targeted Mobile Ads in Shopping Malls.

PubMed

Kim, Byoungjip; Kang, Seungwoo; Ha, Jin-Young; Song, Junehwa

2015-07-16

In this paper, we introduce a novel smartphone framework called VisitSense that automatically detects and predicts a smartphone user's place visits from ambient radio to enable behavioral targeting for mobile ads in large shopping malls. VisitSense enables mobile app developers to adopt visit-pattern-aware mobile advertising for shopping mall visitors in their apps. It also benefits mobile users by allowing them to receive highly relevant mobile ads that are aware of their place visit patterns in shopping malls. To achieve the goal, VisitSense employs accurate visit detection and prediction methods. For accurate visit detection, we develop a change-based detection method to take into consideration the stability change of ambient radio and the mobility change of users. It performs well in large shopping malls where ambient radio is quite noisy and causes existing algorithms to easily fail. In addition, we proposed a causality-based visit prediction model to capture the causality in the sequential visit patterns for effective prediction. We have developed a VisitSense prototype system, and a visit-pattern-aware mobile advertising application that is based on it. Furthermore, we deploy the system in the COEX Mall, one of the largest shopping malls in Korea, and conduct diverse experiments to show the effectiveness of VisitSense.
Temporal monitoring of vessels activity using day/night band in Suomi NPP on South China Sea

NASA Astrophysics Data System (ADS)

Yamaguchi, Takashi; Asanuma, Ichio; Park, Jong Geol; Mackin, Kenneth J.; Mittleman, John

2017-05-01

In this research, we focus on vessel detection using the satellite imagery of day/night band (DNB) on Suomi NPP in order to monitor the change of vessel activity on the region of South China Sea. In this paper, we consider the relation between the temporal change of vessel activities and the events on maritime environment based on the vessel traffic density estimation using DNB. DNB is a moderate resolution (350-700m) satellite imagery but can detect the fishing light of fishery boats in night time for every day. The advantage of DNB is the continuous monitoring on wide area compared to another vessel detection and locating system. However, DNB gave strong influence of cloud and lunar refection. Therefore, we additionally used Brightness Temperature at 3.7μm(BT3.7) for cloud information. In our previous research, we construct an empirical vessel detection model that based on the DNB contrast and the estimation of cloud condition using BT3.7. Moreover, we proposed a vessel traffic density estimation method based on empirical model. In this paper, we construct the time temporal density estimation map on South China Sea and East China Sea in order to extract the knowledge from vessel activities change.
Detectability of CO2 Flux Signals by a Space-Based Lidar Mission

NASA Technical Reports Server (NTRS)

Hammerling, Dorit M.; Kawa, S. Randolph; Schaefer, Kevin; Doney, Scott; Michalak, Anna M.

2015-01-01

Satellite observations of carbon dioxide (CO2) offer novel and distinctive opportunities for improving our quantitative understanding of the carbon cycle. Prospective observations include those from space-based lidar such as the Active Sensing of CO2 Emissions over Nights, Days, and Seasons (ASCENDS) mission. Here we explore the ability of such a mission to detect regional changes in CO2 fluxes. We investigate these using three prototypical case studies, namely the thawing of permafrost in the Northern High Latitudes, the shifting of fossil fuel emissions from Europe to China, and changes in the source-sink characteristics of the Southern Ocean. These three scenarios were used to design signal detection studies to investigate the ability to detect the unfolding of these scenarios compared to a baseline scenario. Results indicate that the ASCENDS mission could detect the types of signals investigated in this study, with the caveat that the study is based on some simplifying assumptions. The permafrost thawing flux perturbation is readily detectable at a high level of significance. The fossil fuel emission detectability is directly related to the strength of the signal and the level of measurement noise. For a nominal (lower) fossil fuel emission signal, only the idealized noise-free instrument test case produces a clearly detectable signal, while experiments with more realistic noise levels capture the signal only in the higher (exaggerated) signal case. For the Southern Ocean scenario, differences due to the natural variability in the ENSO climatic mode are primarily detectable as a zonal increase.
Detecting P and S-wave of Mt. Rinjani seismic based on a locally stationary autoregressive (LSAR) model

NASA Astrophysics Data System (ADS)

Nurhaida, Subanar, Abdurakhman, Abadi, Agus Maman

2017-08-01

Seismic data is usually modelled using autoregressive processes. The aim of this paper is to find the arrival times of the seismic waves of Mt. Rinjani in Indonesia. Kitagawa algorithm's is used to detect the seismic P and S-wave. Householder transformation used in the algorithm made it effectively finding the number of change points and parameters of the autoregressive models. The results show that the use of Box-Cox transformation on the variable selection level makes the algorithm works well in detecting the change points. Furthermore, when the basic span of the subinterval is set 200 seconds and the maximum AR order is 20, there are 8 change points which occur at 1601, 2001, 7401, 7601,7801, 8001, 8201 and 9601. Finally, The P and S-wave arrival times are detected at time 1671 and 2045 respectively using a precise detection algorithm.
Cloud-based computation for accelerating vegetation mapping and change detection at regional to national scales

Treesearch

Matthew J. Gregory; Zhiqiang Yang; David M. Bell; Warren B. Cohen; Sean Healey; Janet L. Ohmann; Heather M. Roberts

2015-01-01

Mapping vegetation and landscape change at fine spatial scales is needed to inform natural resource and conservation planning, but such maps are expensive and time-consuming to produce. For Landsat-based methodologies, mapping efforts are hampered by the daunting task of manipulating multivariate data for millions to billions of pixels. The advent of cloud-based...
Feature extraction for change analysis in SAR time series

NASA Astrophysics Data System (ADS)

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

2015-10-01

In remote sensing, the change detection topic represents a broad field of research. If time series data is available, change detection can be used for monitoring applications. These applications require regular image acquisitions at identical time of day along a defined period. Focusing on remote sensing sensors, radar is especially well-capable for applications requiring regularity, since it is independent from most weather and atmospheric influences. Furthermore, regarding the image acquisitions, the time of day plays no role due to the independence from daylight. Since 2007, the German SAR (Synthetic Aperture Radar) satellite TerraSAR-X (TSX) permits the acquisition of high resolution radar images capable for the analysis of dense built-up areas. In a former study, we presented the change analysis of the Stuttgart (Germany) airport. The aim of this study is the categorization of detected changes in the time series. This categorization is motivated by the fact that it is a poor statement only to describe where and when a specific area has changed. At least as important is the statement about what has caused the change. The focus is set on the analysis of so-called high activity areas (HAA) representing areas changing at least four times along the investigated period. As first step for categorizing these HAAs, the matching HAA changes (blobs) have to be identified. Afterwards, operating in this object-based blob level, several features are extracted which comprise shape-based, radiometric, statistic, morphological values and one context feature basing on a segmentation of the HAAs. This segmentation builds on the morphological differential attribute profiles (DAPs). Seven context classes are established: Urban, infrastructure, rural stable, rural unstable, natural, water and unclassified. A specific HA blob is assigned to one of these classes analyzing the CovAmCoh time series signature of the surrounding segments. In combination, also surrounding GIS information is included to verify the CovAmCoh based context assignment. In this paper, the focus is set on the features extracted for a later change categorization procedure.
Flow Injection Analysis with Electrochemical Detection for Rapid Identification of Platinum-Based Cytostatics and Platinum Chlorides in Water

PubMed Central

Kominkova, Marketa; Heger, Zbynek; Zitka, Ondrej; Kynicky, Jindrich; Pohanka, Miroslav; Beklova, Miroslava; Adam, Vojtech; Kizek, Rene

2014-01-01

Platinum-based cytostatics, such as cisplatin, carboplatin or oxaliplatin are widely used agents in the treatment of various types of tumors. Large amounts of these drugs are excreted through the urine of patients into wastewaters in unmetabolised forms. This phenomenon leads to increased amounts of platinum ions in the water environment. The impacts of these pollutants on the water ecosystem are not sufficiently investigated as well as their content in water sources. In order to facilitate the detection of various types of platinum, we have developed a new, rapid, screening flow injection analysis method with electrochemical detection (FIA-ED). Our method, based on monitoring of the changes in electrochemical behavior of analytes, maintained by various pH buffers (Britton-Robinson and phosphate buffer) and potential changes (1,000, 1,100 and 1,200 mV) offers rapid and cheap selective determination of platinum-based cytostatics and platinum chlorides, which can also be present as contaminants in water environments. PMID:24499878
Object-based land cover classification and change analysis in the Baltimore metropolitan area using multitemporal high resolution remote sensing data

Treesearch

Weiqi Zhou; Austin Troy; Morgan Grove

2008-01-01

Accurate and timely information about land cover pattern and change in urban areas is crucial for urban land management decision-making, ecosystem monitoring and urban planning. This paper presents the methods and results of an object-based classification and post-classification change detection of multitemporal high-spatial resolution Emerge aerial imagery in the...
Research on Dust Concentration Measurement Technique Based on the Theory of Ultrasonic Attenuation

NASA Astrophysics Data System (ADS)

Zhang, Yan; Lou, Wenzhong; Liao, Maohao

2018-03-01

In this paper, a method of characteristics dust concentration is proposed, which based on ultrasonic changes of MEMS piezoelectric ultrasonic transducer. The principle is that the intensity of the ultrasonic will produce attenuation with the propagation medium and propagation distance, the attenuation coefficient is affect by dust concentration. By detecting the changes of ultra acoustic in the dust, the concentration of the dust is calculate by the attenuation-concentration model, and the EACH theory model is based on this principle. The experimental results show that the MEMS piezoelectric ultrasonic transducer can be use for dust concentration of 100-900 g/m3 detection, the deviation between theory and experiments is smaller than 10.4%.
Label-free detection of specific DNA sequence-telomere using unmodified gold nanoparticles as colorimetric probes

NASA Astrophysics Data System (ADS)

Qi, Yingying; Li, Li; Li, Baoxin

2009-09-01

A simple and sensitive label-free colorimetric detection of telomere DNA has been developed. It was based on the color change of gold nanoparticles (AuNPs) due to DNA hybridization. UV-vis spectra and transmission electron microscopy (TEM) were used to investigate the change of AuNPs. Under the optimized conditions, the linear range for determination of telomere DNA was 5.7 × 10 -13 to 4.5 × 10 -6 mol/L. The detection limit (3 σ) of this method has decreased to pico-molar level.
Detection of Structural Abnormalities Using Neural Nets

NASA Technical Reports Server (NTRS)

Zak, M.; Maccalla, A.; Daggumati, V.; Gulati, S.; Toomarian, N.

1996-01-01

This paper describes a feed-forward neural net approach for detection of abnormal system behavior based upon sensor data analyses. A new dynamical invariant representing structural parameters of the system is introduced in such a way that any structural abnormalities in the system behavior are detected from the corresponding changes to the invariant.
Highly sensitive on-site detection of glucose in human urine with naked eye based on enzymatic-like reaction mediated etching of gold nanorods.

PubMed

Zhang, Zhiyang; Chen, Zhaopeng; Cheng, Fangbin; Zhang, Yaowen; Chen, Lingxin

2017-03-15

Based on enzymatic-like reaction mediated etching of gold nanorods (GNRs), an ultrasensitive visual method was developed for on-site detection of urine glucose. With the catalysis of MoO 4 2 - , GNRs were efficiently etched by H 2 O 2 which was generated by glucose-glucose oxidase enzymatic reaction. The etching of GNRs lead to a blue-shift of logitudinal localized surface plasmon resonance of GNRs, accompanied by an obvious color change from blue to red. The peak-shift and the color change can be used for detection of glucose by the spectrophotometer and the naked eyes. Under optimal condition, an excellent sensitivity toward glucose is obtained with a detection limit of 0.1μM and a visual detection limit of 3μM in buffer solution. Benefiting from the high sensitivity, the successful colorimetric detection of glucose in original urine samples was achieved, which indicates the practical applicability to the on-site determination of urine glucose. Copyright © 2016 Elsevier B.V. All rights reserved.
Iodine-Mediated Etching of Gold Nanorods for Plasmonic ELISA Based on Colorimetric Detection of Alkaline Phosphatase.

PubMed

Zhang, Zhiyang; Chen, Zhaopeng; Wang, Shasha; Cheng, Fangbin; Chen, Lingxin

2015-12-23

Here, we propose a plasmonic enzyme-linked immunosorbent assay (ELISA) based on highly sensitive colorimetric detection of alkaline phosphatase (ALP), which is achieved by iodine-mediated etching of gold nanorods (AuNRs). Once the sandwich-type immunocomplex is formed, the ALP bound on the polystyrene microwells will hydrolyze ascorbic acid 2-phosphate into ascorbic acid. Subsequently, iodate is reduced to iodine, a moderate oxidant, which etches AuNRs from rod to sphere in shape. The shape change of AuNRs leads to a blue-shift of longitudinal localized surface plasmon resonance. As a result, the solution of AuNRs changes from blue to red. Benefiting from the highly sensitive detection of ALP, the proposed plasmonic ELISA has achieved an ultralow detection limit (100 pg/mL) for human immunoglobulin G (IgG). Importantly, the visual detection limit (3.0 ng/mL) allows the rapid differential diagnosis with the naked eye. The further detection of human IgG in fetal bovine serum indicates its applicability to the determination of low abundance protein in complex biological samples.
[Application of optical flow dynamic texture in land use/cover change detection].

PubMed

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

2014-11-01

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

Sensitive-cell-based fish chromatophore biosensor

NASA Astrophysics Data System (ADS)

Plant, Thomas K.; Chaplen, Frank W.; Jovanovic, Goran; Kolodziej, Wojtek; Trempy, Janine E.; Willard, Corwin; Liburdy, James A.; Pence, Deborah V.; Paul, Brian K.

2004-07-01

A sensitive biosensor (cytosensor) has been developed based on color changes in the toxin-sensitive colored living cells of fish. These chromatophores are highly sensitive to the presence of many known and unknown toxins produced by microbial pathogens and undergo visible color changes in a dose-dependent manner. The chromatophores are immobilized and maintained in a viable state while potential pathogens multiply and fish cell-microbe interactions are monitored. Low power LED lighting is used to illuminate the chromatophores which are magnified using standard optical lenses and imaged onto a CCD array. Reaction to toxins is detected by observing changes is the total area of color in the cells. These fish chromatophores are quite sensitive to cholera toxin, Staphococcus alpha toxin, and Bordatella pertussis toxin. Numerous other toxic chemical and biological agents besides bacterial toxins also cause readily detectable color effects in chromatophores. The ability of the chromatophore cell-based biosensor to distinguish between different bacterial pathogens was examined. Toxin producing strains of Salmonella enteritis, Vibrio parahaemolyticus, and Bacillus cereus induced movement of pigmented organelles in the chromatophore cells and this movement was measured by changes in the optical density over time. Each bacterial pathogen elicited this measurable response in a distinctive and signature fashion. These results suggest a chromatophore cell-based biosensor assay may be applicable for the detection and identification of virulence activities associated with certain air-, food-, and water-borne bacterial pathogens.
Direct fluorescence anisotropy assay for cocaine using tetramethylrhodamine-labeled aptamer.

PubMed

Liu, Yingxiong; Zhao, Qiang

2017-06-01

Development of simple, sensitive, and rapid method for cocaine detection is important in medicine and drug abuse monitoring. Taking advantage of fluorescence anisotropy and aptamer, this study reports a direct fluorescence anisotropy (FA) assay for cocaine by employing an aptamer probe with tetramethylrhodamine (TMR) labeled on a specific position. The binding of cocaine and the aptamer causes a structure change of the TMR-labeled aptamer, leading to changes of the interaction between labeled TMR and adjacent G bases in aptamer sequence, so FA of TMR varies with increasing of cocaine. After screening different labeling positions of the aptamer, including thymine (T) bases and terminals of the aptamer, we obtained a favorable aptamer probe with TMR labeled on the 25th base T in the sequence, which exhibited sensitive and significant FA-decreasing responses upon cocaine. Under optimized assay conditions, this TMR-labeled aptamer allowed for direct FA detection of cocaine as low as 5 μM. The maximum FA change reached about 0.086. This FA method also enabled the detection of cocaine spiked in diluted serum and urine samples, showing potential for applications. Graphical Abstract The binding of cocaine to the TMR-labeled aptamer causes conformation change and alteration of the intramolecular interaction between TMR and bases of aptamer, leading to variance of fluorescence anisotropy (FA) of TMR, so direct FA analyis of cocaine is achieved.
Incremental Principal Component Analysis Based Outlier Detection Methods for Spatiotemporal Data Streams

NASA Astrophysics Data System (ADS)

Bhushan, A.; Sharker, M. H.; Karimi, H. A.

2015-07-01

In this paper, we address outliers in spatiotemporal data streams obtained from sensors placed across geographically distributed locations. Outliers may appear in such sensor data due to various reasons such as instrumental error and environmental change. Real-time detection of these outliers is essential to prevent propagation of errors in subsequent analyses and results. Incremental Principal Component Analysis (IPCA) is one possible approach for detecting outliers in such type of spatiotemporal data streams. IPCA has been widely used in many real-time applications such as credit card fraud detection, pattern recognition, and image analysis. However, the suitability of applying IPCA for outlier detection in spatiotemporal data streams is unknown and needs to be investigated. To fill this research gap, this paper contributes by presenting two new IPCA-based outlier detection methods and performing a comparative analysis with the existing IPCA-based outlier detection methods to assess their suitability for spatiotemporal sensor data streams.
Automated detection of slum area change in Hyderabad, India using multitemporal satellite imagery

NASA Astrophysics Data System (ADS)

Kit, Oleksandr; Lüdeke, Matthias

2013-09-01

This paper presents an approach to automated identification of slum area change patterns in Hyderabad, India, using multi-year and multi-sensor very high resolution satellite imagery. It relies upon a lacunarity-based slum detection algorithm, combined with Canny- and LSD-based imagery pre-processing routines. This method outputs plausible and spatially explicit slum locations for the whole urban agglomeration of Hyderabad in years 2003 and 2010. The results indicate a considerable growth of area occupied by slums between these years and allow identification of trends in slum development in this urban agglomeration.
FRF-based structural damage detection of controlled buildings with podium structures: Experimental investigation

NASA Astrophysics Data System (ADS)

Xu, Y. L.; Huang, Q.; Zhan, S.; Su, Z. Q.; Liu, H. J.

2014-06-01

How to use control devices to enhance system identification and damage detection in relation to a structure that requires both vibration control and structural health monitoring is an interesting yet practical topic. In this study, the possibility of using the added stiffness provided by control devices and frequency response functions (FRFs) to detect damage in a building complex was explored experimentally. Scale models of a 12-storey main building and a 3-storey podium structure were built to represent a building complex. Given that the connection between the main building and the podium structure is most susceptible to damage, damage to the building complex was experimentally simulated by changing the connection stiffness. To simulate the added stiffness provided by a semi-active friction damper, a steel circular ring was designed and used to add the related stiffness to the building complex. By varying the connection stiffness using an eccentric wheel excitation system and by adding or not adding the circular ring, eight cases were investigated and eight sets of FRFs were measured. The experimental results were used to detect damage (changes in connection stiffness) using a recently proposed FRF-based damage detection method. The experimental results showed that the FRF-based damage detection method could satisfactorily locate and quantify damage.
A joint estimation detection of Glaucoma progression in 3D spectral domain optical coherence tomography optic nerve head images

NASA Astrophysics Data System (ADS)

Belghith, Akram; Bowd, Christopher; Weinreb, Robert N.; Zangwill, Linda M.

2014-03-01

Glaucoma is an ocular disease characterized by distinctive changes in the optic nerve head (ONH) and visual field. Glaucoma can strike without symptoms and causes blindness if it remains without treatment. Therefore, early disease detection is important so that treatment can be initiated and blindness prevented. In this context, important advances in technology for non-invasive imaging of the eye have been made providing quantitative tools to measure structural changes in ONH topography, an essential element for glaucoma detection and monitoring. 3D spectral domain optical coherence tomography (SD-OCT), an optical imaging technique, has been commonly used to discriminate glaucomatous from healthy subjects. In this paper, we present a new framework for detection of glaucoma progression using 3D SD-OCT images. In contrast to previous works that the retinal nerve fiber layer (RNFL) thickness measurement provided by commercially available spectral-domain optical coherence tomograph, we consider the whole 3D volume for change detection. To integrate a priori knowledge and in particular the spatial voxel dependency in the change detection map, we propose the use of the Markov Random Field to handle a such dependency. To accommodate the presence of false positive detection, the estimated change detection map is then used to classify a 3D SDOCT image into the "non-progressing" and "progressing" glaucoma classes, based on a fuzzy logic classifier. We compared the diagnostic performance of the proposed framework to existing methods of progression detection.
A joint estimation detection of Glaucoma progression in 3D spectral domain optical coherence tomography optic nerve head images.

PubMed

Belghith, Akram; Bowd, Christopher; Weinreb, Robert N; Zangwill, Linda M

2014-03-18

Glaucoma is an ocular disease characterized by distinctive changes in the optic nerve head (ONH) and visual field. Glaucoma can strike without symptoms and causes blindness if it remains without treatment. Therefore, early disease detection is important so that treatment can be initiated and blindness prevented. In this context, important advances in technology for non-invasive imaging of the eye have been made providing quantitative tools to measure structural changes in ONH topography, an essential element for glaucoma detection and monitoring. 3D spectral domain optical coherence tomography (SD-OCT), an optical imaging technique, has been commonly used to discriminate glaucomatous from healthy subjects. In this paper, we present a new framework for detection of glaucoma progression using 3D SD-OCT images. In contrast to previous works that the retinal nerve fiber layer (RNFL) thickness measurement provided by commercially available spectral-domain optical coherence tomograph, we consider the whole 3D volume for change detection. To integrate a priori knowledge and in particular the spatial voxel dependency in the change detection map, we propose the use of the Markov Random Field to handle a such dependency. To accommodate the presence of false positive detection, the estimated change detection map is then used to classify a 3D SDOCT image into the "non-progressing" and "progressing" glaucoma classes, based on a fuzzy logic classifier. We compared the diagnostic performance of the proposed framework to existing methods of progression detection.
Automated Land Cover Change Detection and Mapping from Hidden Parameter Estimates of Normalized Difference Vegetation Index (NDVI) Time-Series

NASA Astrophysics Data System (ADS)

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

2017-12-01

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

NASA Astrophysics Data System (ADS)

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.
Identifying changing aviation threat environments within an adaptive Homeland Security Advisory System.

PubMed

Lee, Adrian J; Jacobson, Sheldon H

2012-02-01

A critical component of aviation security consists of screening passengers and baggage to protect airports and aircraft from terrorist threats. Advancements in screening device technology have increased the ability to detect these threats; however, specifying the operational configurations of these devices in response to changes in the threat environment can become difficult. This article proposes to use Fisher information as a statistical measure for detecting changes in the threat environment. The perceived risk of passengers, according to prescreening information and behavior analysis, is analyzed as the passengers sequentially enter the security checkpoint. The alarm responses from the devices used to detect threats are also analyzed to monitor significant changes in the frequency of threat items uncovered. The key results are that this information-based measure can be used within the Homeland Security Advisory System to indicate changes in threat conditions in real time, and provide the flexibility of security screening detection devices to responsively and automatically adapt operational configurations to these changing threat conditions. © 2012 Society for Risk Analysis. All rights reserved.
The detection of climate change due to the enhanced greenhouse effect

NASA Technical Reports Server (NTRS)

Schiffer, Robert A.; Unninayar, Sushel

1991-01-01

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

PubMed Central

2013-01-01

Background Pathogenic mutations range from single nucleotide changes to deletions or duplications that encompass a single exon to several genes. The use of gene-centric high-density array comparative genomic hybridization (aCGH) has revolutionized the detection of intragenic copy number variations. We implemented an exon-centric design of high-resolution aCGH to detect single- and multi-exon deletions and duplications in a large set of genes using the OGT 60 K and 180 K arrays. Here we describe the molecular characterization and breakpoint mapping of deletions at the smaller end of the detectable range in several genes using aCGH. Results The method initially implemented to detect single to multiple exon deletions, was able to detect deletions much smaller than anticipated. The selected deletions we describe vary in size, ranging from over 2 kb to as small as 12 base pairs. The smallest of these deletions are only detectable after careful manual review during data analysis. Suspected deletions smaller than the detection size for which the method was optimized, were rigorously followed up and confirmed with PCR-based investigations to uncover the true detection size limit of intragenic deletions with this technology. False-positive deletion calls often demonstrated single nucleotide changes or an insertion causing lower hybridization of probes demonstrating the sensitivity of aCGH. Conclusions With optimizing aCGH design and careful review process, aCGH can uncover intragenic deletions as small as dozen bases. These data provide insight that will help optimize probe coverage in array design and illustrate the true assay sensitivity. Mapping of the breakpoints confirms smaller deletions and contributes to the understanding of the mechanism behind these events. Our knowledge of the mutation spectra of several genes can be expected to change as previously unrecognized intragenic deletions are uncovered. PMID:24304607
Automated health alerts from Kinect-based in-home gait measurements.

PubMed

Stone, Erik E; Skubic, Marjorie; Back, Jessica

2014-01-01

A method for automatically generating alerts to clinicians in response to changes in in-home gait parameters is investigated. Kinect-based gait measurement systems were installed in apartments in a senior living facility. The systems continuously monitored the walking speed, stride time, and stride length of apartment residents. A framework for modeling uncertainty in the residents' gait parameter estimates, which is critical for robust change detection, is developed; along with an algorithm for detecting changes that may be clinically relevant. Three retrospective case studies, of individuals who had their gait monitored for periods ranging from 12 to 29 months, are presented to illustrate use of the alert method. Evidence suggests that clinicians could be alerted to health changes at an early stage, while they are still small and interventions may be most successful. Additional potential uses are also discussed.
Generalized Optical Theorem Detection in Random and Complex Media

NASA Astrophysics Data System (ADS)

Tu, Jing

The problem of detecting changes of a medium or environment based on active, transmit-plus-receive wave sensor data is at the heart of many important applications including radar, surveillance, remote sensing, nondestructive testing, and cancer detection. This is a challenging problem because both the change or target and the surrounding background medium are in general unknown and can be quite complex. This Ph.D. dissertation presents a new wave physics-based approach for the detection of targets or changes in rather arbitrary backgrounds. The proposed methodology is rooted on a fundamental result of wave theory called the optical theorem, which gives real physical energy meaning to the statistics used for detection. This dissertation is composed of two main parts. The first part significantly expands the theory and understanding of the optical theorem for arbitrary probing fields and arbitrary media including nonreciprocal media, active media, as well as time-varying and nonlinear scatterers. The proposed formalism addresses both scalar and full vector electromagnetic fields. The second contribution of this dissertation is the application of the optical theorem to change detection with particular emphasis on random, complex, and active media, including single frequency probing fields and broadband probing fields. The first part of this work focuses on the generalization of the existing theoretical repertoire and interpretation of the scalar and electromagnetic optical theorem. Several fundamental generalizations of the optical theorem are developed. A new theory is developed for the optical theorem for scalar fields in nonhomogeneous media which can be bounded or unbounded. The bounded media context is essential for applications such as intrusion detection and surveillance in enclosed environments such as indoor facilities, caves, tunnels, as well as for nondestructive testing and communication systems based on wave-guiding structures. The developed scalar optical theorem theory applies to arbitrary lossless backgrounds and quite general probing fields including near fields which play a key role in super-resolution imaging. The derived formulation holds for arbitrary passive scatterers, which can be dissipative, as well as for the more general class of active scatterers which are composed of a (passive) scatterer component and an active, radiating (antenna) component. Furthermore, the generalization of the optical theorem to active scatterers is relevant to many applications such as surveillance of active targets including certain cloaks, invisible scatterers, and wireless communications. The latter developments have important military applications. The derived theoretical framework includes the familiar real power optical theorem describing power extinction due to both dissipation and scattering as well as a reactive optical theorem related to the reactive power changes. Meanwhile, the developed approach naturally leads to three optical theorem indicators or statistics, which can be used to detect changes or targets in unknown complex media. In addition, the optical theorem theory is generalized in the time domain so that it applies to arbitrary full vector fields, and arbitrary media including anisotropic media, nonreciprocal media, active media, as well as time-varying and nonlinear scatterers. The second component of this Ph.D. research program focuses on the application of the optical theorem to change detection. Three different forms of indicators or statistics are developed for change detection in unknown background media: a real power optical theorem detector, a reactive power optical theorem detector, and a total apparent power optical theorem detector. No prior knowledge is required of the background or the change or target. The performance of the three proposed optical theorem detectors is compared with the classical energy detector approach for change detection. The latter uses a mathematical or functional energy while the optical theorem detectors are based on real physical energy. For reference, the optical theorem detectors are also compared with the matched filter approach which (unlike the optical theorem detectors) assumes perfect target and medium information. The practical implementation of the optical theorem detectors is based for certain random and complex media on the exploitation of time reversal focusing ideas developed in the past 20 years in electromagnetics and acoustics. In the final part of the dissertation, we also discuss the implementation of the optical theorem sensors for one-dimensional propagation systems such as transmission lines. We also present a new generalized likelihood ratio test for detection that exploits a prior data constraint based on the optical theorem. Finally, we also address the practical implementation of the optical theorem sensors for optical imaging systems, by means of holography. The later is the first holographic implementation the optical theorem for arbitrary scenes and targets.
Image denoising based on noise detection

NASA Astrophysics Data System (ADS)

Jiang, Yuanxiang; Yuan, Rui; Sun, Yuqiu; Tian, Jinwen

2018-03-01

Because of the noise points in the images, any operation of denoising would change the original information of non-noise pixel. A noise detection algorithm based on fractional calculus was proposed to denoise in this paper. Convolution of the image was made to gain direction gradient masks firstly. Then, the mean gray was calculated to obtain the gradient detection maps. Logical product was made to acquire noise position image next. Comparisons in the visual effect and evaluation parameters after processing, the results of experiment showed that the denoising algorithms based on noise were better than that of traditional methods in both subjective and objective aspects.
Change Detection in High-Resolution Remote Sensing Images Using Levene-Test and Fuzzy Evaluation

NASA Astrophysics Data System (ADS)

Wang, G. H.; Wang, H. B.; Fan, W. F.; Liu, Y.; Liu, H. J.

2018-04-01

High-resolution remote sensing images possess complex spatial structure and rich texture information, according to these, this paper presents a new method of change detection based on Levene-Test and Fuzzy Evaluation. It first got map-spots by segmenting two overlapping images which had been pretreated, extracted features such as spectrum and texture. Then, changed information of all map-spots which had been treated by the Levene-Test were counted to obtain the candidate changed regions, hue information (H component) was extracted through the IHS Transform and conducted change vector analysis combined with the texture information. Eventually, the threshold was confirmed by an iteration method, the subject degrees of candidate changed regions were calculated, and final change regions were determined. In this paper experimental results on multi-temporal ZY-3 high-resolution images of some area in Jiangsu Province show that: Through extracting map-spots of larger difference as the candidate changed regions, Levene-Test decreases the computing load, improves the precision of change detection, and shows better fault-tolerant capacity for those unchanged regions which are of relatively large differences. The combination of Hue-texture features and fuzzy evaluation method can effectively decrease omissions and deficiencies, improve the precision of change detection.
An Unsupervised kNN Method to Systematically Detect Changes in Protein Localization in High-Throughput Microscopy Images.

PubMed

Lu, Alex Xijie; Moses, Alan M

2016-01-01

Despite the importance of characterizing genes that exhibit subcellular localization changes between conditions in proteome-wide imaging experiments, many recent studies still rely upon manual evaluation to assess the results of high-throughput imaging experiments. We describe and demonstrate an unsupervised k-nearest neighbours method for the detection of localization changes. Compared to previous classification-based supervised change detection methods, our method is much simpler and faster, and operates directly on the feature space to overcome limitations in needing to manually curate training sets that may not generalize well between screens. In addition, the output of our method is flexible in its utility, generating both a quantitatively ranked list of localization changes that permit user-defined cut-offs, and a vector for each gene describing feature-wise direction and magnitude of localization changes. We demonstrate that our method is effective at the detection of localization changes using the Δrpd3 perturbation in Saccharomyces cerevisiae, where we capture 71.4% of previously known changes within the top 10% of ranked genes, and find at least four new localization changes within the top 1% of ranked genes. The results of our analysis indicate that simple unsupervised methods may be able to identify localization changes in images without laborious manual image labelling steps.
Are morphological changes necessary to mediate the therapeutic effects of electroconvulsive therapy?

PubMed

Nickl-Jockschat, Thomas; Palomero Gallagher, Nicola; Kumar, Vinod; Hoffstaedter, Felix; Brügmann, Elisabeth; Habel, Ute; Eickhoff, Simon B; Grözinger, Michael

2016-04-01

The neurotrophic hypothesis has become the favorite model to explain the antidepressant properties of electroconvulsive therapy (ECT). It is based on the assumption that a restoration of previously defective neural networks drives therapeutic effects. Recent data in rather young patients suggest that neurotrophic effects of ECT might be detectable by diffusion tensor imaging. We here aimed to investigate whether the therapeutic response to ECT necessarily goes along with mesoscopic effects in gray matter (GM) or white matter (WM) in our patients in advanced age. Patients (n = 21, 15 males and 7 females) suffering from major depressive disorder were treated with ECT. Before the start of treatment and after the completion of the index series, they underwent magnetic resonance imaging, including a diffusion-weighed sequence. We used voxel-based morphometry to assess GM changes and tract-based spatial statistics and an SPM-based whole-brain analysis to detect WM changes in the course of treatment. Patients significantly improved clinically during the course of ECT. This was, however, not accompanied by GM or WM changes. This result challenges the notion that mesoscopic brain structure changes are an obligatory prerequisite for the antidepressant effects of ECT.
Automated baseline change detection -- Phases 1 and 2. Final report

DOE Office of Scientific and Technical Information (OSTI.GOV)

Byler, E.

1997-10-31

The primary objective of this project is to apply robotic and optical sensor technology to the operational inspection of mixed toxic and radioactive waste stored in barrels, using Automated Baseline Change Detection (ABCD), based on image subtraction. Absolute change detection is based on detecting any visible physical changes, regardless of cause, between a current inspection image of a barrel and an archived baseline image of the same barrel. Thus, in addition to rust, the ABCD system can also detect corrosion, leaks, dents, and bulges. The ABCD approach and method rely on precise camera positioning and repositioning relative to the barrelmore » 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.« less
Fiber-Optic Based Compact Gas Leak Detection System

NASA Technical Reports Server (NTRS)

deGroot, Wim A.

1995-01-01

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

Object-based land-use/land-cover change detection using Landsat imagery: a case study of Ardabil, Namin, and Nir counties in northwest Iran.

PubMed

Aslami, Farnoosh; Ghorbani, Ardavan

2018-06-03

In this study, land-use/land-cover (LULC) change in the Ardabil, Namin, and Nir counties, in the Ardabil province in the northwest of Iran, was detected using an object-based method. Landsat images including Thematic Mapper (TM), Landsat Enhanced Thematic Mapper Plus (ETM + ), and Operational Land Imager (OLI) were used. Preprocessing methods, including geometric and radiometric correction, and topographic normalization were performed. Image processing was conducted according to object-based image analysis using the nearest neighbor algorithm. An accuracy assessment was conducted using overall accuracy and Kappa statistics. Results show that maps obtained from images for 1987, 2002, and 2013 had an overall accuracy of 91.76, 91.06, and 93.00%, and a Kappa coefficient of 0.90, 0.83, and 0.91, respectively. Change detection between 1987 and 2013 shows that most of the rangelands (97,156.6 ha) have been converted to dry farming; moreover, residential and other urban land uses have also increased. The largest change in land use has occurred for irrigated farming, rangelands, and dry farming, of which approximately 3539.8, 3086.9, and 2271.9 ha, respectively, have given way to urban land use for each of the studied years.
Sample size and power calculations for detecting changes in malaria transmission using antibody seroconversion rate.

PubMed

Sepúlveda, Nuno; Paulino, Carlos Daniel; Drakeley, Chris

2015-12-30

Several studies have highlighted the use of serological data in detecting a reduction in malaria transmission intensity. These studies have typically used serology as an adjunct measure and no formal examination of sample size calculations for this approach has been conducted. A sample size calculator is proposed for cross-sectional surveys using data simulation from a reverse catalytic model assuming a reduction in seroconversion rate (SCR) at a given change point before sampling. This calculator is based on logistic approximations for the underlying power curves to detect a reduction in SCR in relation to the hypothesis of a stable SCR for the same data. Sample sizes are illustrated for a hypothetical cross-sectional survey from an African population assuming a known or unknown change point. Overall, data simulation demonstrates that power is strongly affected by assuming a known or unknown change point. Small sample sizes are sufficient to detect strong reductions in SCR, but invariantly lead to poor precision of estimates for current SCR. In this situation, sample size is better determined by controlling the precision of SCR estimates. Conversely larger sample sizes are required for detecting more subtle reductions in malaria transmission but those invariantly increase precision whilst reducing putative estimation bias. The proposed sample size calculator, although based on data simulation, shows promise of being easily applicable to a range of populations and survey types. Since the change point is a major source of uncertainty, obtaining or assuming prior information about this parameter might reduce both the sample size and the chance of generating biased SCR estimates.
A new metric for detecting change in slowly evolving brain tumors: validation in meningioma patients.

PubMed

Pohl, Kilian M; Konukoglu, Ender; Novellas, Sebastian; Ayache, Nicholas; Fedorov, Andriy; Talos, Ion-Florin; Golby, Alexandra; Wells, William M; Kikinis, Ron; Black, Peter M

2011-03-01

Change detection is a critical component in the diagnosis and monitoring of many slowly evolving pathologies. This article describes a semiautomatic monitoring approach using longitudinal medical images. We test the method on brain scans of patients with meningioma, which experts have found difficult to monitor because the tumor evolution is very slow and may be obscured by artifacts related to image acquisition. We describe a semiautomatic procedure targeted toward identifying difficult-to-detect changes in brain tumor imaging. The tool combines input from a medical expert with state-of-the-art technology. The software is easy to calibrate and, in less than 5 minutes, returns the total volume of tumor change in mm. We test the method on postgadolinium, T1-weighted magnetic resonance images of 10 patients with meningioma and compare our results with experts' findings. We also perform benchmark testing with synthetic data. Our experiments indicated that experts' visual inspections are not sensitive enough to detect subtle growth. Measurements based on experts' manual segmentations were highly accurate but also labor intensive. The accuracy of our approach was comparable to the experts' results. However, our approach required far less user input and generated more consistent measurements. The sensitivity of experts' visual inspection is often too low to detect subtle growth of meningiomas from longitudinal scans. Measurements based on experts' segmentation are highly accurate but generally too labor intensive for standard clinical settings. We described an alternative metric that provides accurate and robust measurements of subtle tumor changes while requiring a minimal amount of user input.
Hydatid detection using the near-infrared transmission angular spectra of porous silicon microcavity biosensors

NASA Astrophysics Data System (ADS)

Li, Peng; Jia, Zhenhong; Lü, Guodong

2017-03-01

Hydatid, which is a parasitic disease, occurs today in many regions worldwide. Because it can present a serious threat to people’s health, finding a fast, convenient, and economical means of detection is important. This paper proposes a label- and spectrophotometer-free apparatus that uses optical biological detection based on porous silicon microcavities. In this approach, the refractive index change induced by the biological reactions of a sample in a porous silicon microcavity is detected by measuring the change in the incidence angle corresponding to the maximum transmitted intensity of a near-infrared probe laser. This paper reports that the proposed method can achieve the label-free detection of 43 kDa molecular weight hydatid disease antigens with high sensitivity.
Object-based analysis of multispectral airborne laser scanner data for land cover classification and map updating

NASA Astrophysics Data System (ADS)

Matikainen, Leena; Karila, Kirsi; Hyyppä, Juha; Litkey, Paula; Puttonen, Eetu; Ahokas, Eero

2017-06-01

During the last 20 years, airborne laser scanning (ALS), often combined with passive multispectral information from aerial images, has shown its high feasibility for automated mapping processes. The main benefits have been achieved in the mapping of elevated objects such as buildings and trees. Recently, the first multispectral airborne laser scanners have been launched, and active multispectral information is for the first time available for 3D ALS point clouds from a single sensor. This article discusses the potential of this new technology in map updating, especially in automated object-based land cover classification and change detection in a suburban area. For our study, Optech Titan multispectral ALS data over a suburban area in Finland were acquired. Results from an object-based random forests analysis suggest that the multispectral ALS data are very useful for land cover classification, considering both elevated classes and ground-level classes. The overall accuracy of the land cover classification results with six classes was 96% compared with validation points. The classes under study included building, tree, asphalt, gravel, rocky area and low vegetation. Compared to classification of single-channel data, the main improvements were achieved for ground-level classes. According to feature importance analyses, multispectral intensity features based on several channels were more useful than those based on one channel. Automatic change detection for buildings and roads was also demonstrated by utilising the new multispectral ALS data in combination with old map vectors. In change detection of buildings, an old digital surface model (DSM) based on single-channel ALS data was also used. Overall, our analyses suggest that the new data have high potential for further increasing the automation level in mapping. Unlike passive aerial imaging commonly used in mapping, the multispectral ALS technology is independent of external illumination conditions, and there are no shadows on intensity images produced from the data. These are significant advantages in developing automated classification and change detection procedures.
Evaluating the performance of the quick CSF method in detecting contrast sensitivity function changes

PubMed Central

Hou, Fang; Lesmes, Luis Andres; Kim, Woojae; Gu, Hairong; Pitt, Mark A.; Myung, Jay I.; Lu, Zhong-Lin

2016-01-01

The contrast sensitivity function (CSF) has shown promise as a functional vision endpoint for monitoring the changes in functional vision that accompany eye disease or its treatment. However, detecting CSF changes with precision and efficiency at both the individual and group levels is very challenging. By exploiting the Bayesian foundation of the quick CSF method (Lesmes, Lu, Baek, & Albright, 2010), we developed and evaluated metrics for detecting CSF changes at both the individual and group levels. A 10-letter identification task was used to assess the systematic changes in the CSF measured in three luminance conditions in 112 naïve normal observers. The data from the large sample allowed us to estimate the test–retest reliability of the quick CSF procedure and evaluate its performance in detecting CSF changes at both the individual and group levels. The test–retest reliability reached 0.974 with 50 trials. In 50 trials, the quick CSF method can detect a medium 0.30 log unit area under log CSF change with 94.0% accuracy at the individual observer level. At the group level, a power analysis based on the empirical distribution of CSF changes from the large sample showed that a very small area under log CSF change (0.025 log unit) could be detected by the quick CSF method with 112 observers and 50 trials. These results make it plausible to apply the method to monitor the progression of visual diseases or treatment effects on individual patients and greatly reduce the time, sample size, and costs in clinical trials at the group level. PMID:27120074
Building Development Monitoring in Multitemporal Remotely Sensed Image Pairs with Stochastic Birth-Death Dynamics.

PubMed

Benedek, C; Descombes, X; Zerubia, J

2012-01-01

In this paper, we introduce a new probabilistic method which integrates building extraction with change detection in remotely sensed image pairs. A global optimization process attempts to find the optimal configuration of buildings, considering the observed data, prior knowledge, and interactions between the neighboring building parts. We present methodological contributions in three key issues: 1) We implement a novel object-change modeling approach based on Multitemporal Marked Point Processes, which simultaneously exploits low-level change information between the time layers and object-level building description to recognize and separate changed and unaltered buildings. 2) To answer the challenges of data heterogeneity in aerial and satellite image repositories, we construct a flexible hierarchical framework which can create various building appearance models from different elementary feature-based modules. 3) To simultaneously ensure the convergence, optimality, and computation complexity constraints raised by the increased data quantity, we adopt the quick Multiple Birth and Death optimization technique for change detection purposes, and propose a novel nonuniform stochastic object birth process which generates relevant objects with higher probability based on low-level image features.
Detection of greenhouse-gas-induced climatic change. Progress report, July 1, 1994--July 31, 1995

DOE Office of Scientific and Technical Information (OSTI.GOV)

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

1995-07-21

The objective of this research is to assembly and analyze instrumental climate data and to develop and apply climate models as a basis for detecting greenhouse-gas-induced climatic change, and validation of General Circulation Models. In addition to changes due to variations in anthropogenic forcing, including greenhouse gas and aerosol concentration changes, the global climate system exhibits a high degree of internally-generated and externally-forced natural variability. To detect the anthropogenic effect, its signal must be isolated from the ``noise`` of this natural climatic variability. A high quality, spatially extensive data base is required to define the noise and its spatial characteristics.more » To facilitate this, available land and marine data bases will be updated and expanded. The data will be analyzed to determine the potential effects on climate of greenhouse gas and aerosol concentration changes and other factors. Analyses will be guided by a variety of models, from simple energy balance climate models to coupled atmosphere ocean General Circulation Models. These analyses are oriented towards obtaining early evidence of anthropogenic climatic change that would lead either to confirmation, rejection or modification of model projections, and towards the statistical validation of General Circulation Model control runs and perturbation experiments.« less
Highly sensitive MicroRNA 146a detection using a gold nanoparticle-based CTG repeat probing system and isothermal amplification.

PubMed

Le, Binh Huy; Seo, Young Jun

2018-01-25

We have developed a gold nanoparticle (AuNP)-based CTG repeat probing system displaying high quenching capability and combined it with isothermal amplification for the detection of miRNA 146a. This method of using a AuNP-based CTG repeat probing system with isothermal amplification allowed the highly sensitive (14 aM) and selective detection of miRNA 146a. A AuNP-based CTG repeat probing system having a hairpin structure and a dT F fluorophore exhibited highly efficient quenching because the CTG repeat-based stable hairpin structure imposed a close distance between the AuNP and the dT F residue. A small amount of miRNA 146a induced multiple copies of the CAG repeat sequence during rolling circle amplification; the AuNP-based CTG repeat probing system then bound to the complementary multiple-copy CAG repeat sequence, thereby inducing a structural change from a hairpin to a linear structure with amplified fluorescence. This AuNP-based CTG probing system combined with isothermal amplification could also discriminate target miRNA 146a from one- and two-base-mismatched miRNAs (ORN 1 and ORN 2, respectively). This simple AuNP-based CTG probing system, combined with isothermal amplification to induce a highly sensitive change in fluorescence, allows the detection of miRNA 146a with high sensitivity (14 aM) and selectivity. Copyright © 2017 Elsevier B.V. All rights reserved.
Oil Spill Detection and Tracking Using Lipschitz Regularity and Multiscale Techniques in Synthetic Aperture Radar Imagery

NASA Astrophysics Data System (ADS)

Ajadi, O. A.; Meyer, F. J.

2014-12-01

Automatic oil spill detection and tracking from Synthetic Aperture Radar (SAR) images is a difficult task, due in large part to the inhomogeneous properties of the sea surface, the high level of speckle inherent in SAR data, the complexity and the highly non-Gaussian nature of amplitude information, and the low temporal sampling that is often achieved with SAR systems. This research presents a promising new oil spill detection and tracking method that is based on time series of SAR images. Through the combination of a number of advanced image processing techniques, the develop approach is able to mitigate some of these previously mentioned limitations of SAR-based oil-spill detection and enables fully automatic spill detection and tracking across a wide range of spatial scales. The method combines an initial automatic texture analysis with a consecutive change detection approach based on multi-scale image decomposition. The first step of the approach, a texture transformation of the original SAR images, is performed in order to normalize the ocean background and enhance the contrast between oil-covered and oil-free ocean surfaces. The Lipschitz regularity (LR), a local texture parameter, is used here due to its proven ability to normalize the reflectivity properties of ocean water and maximize the visibly of oil in water. To calculate LR, the images are decomposed using two-dimensional continuous wavelet transform (2D-CWT), and transformed into Holder space to measure LR. After texture transformation, the now normalized images are inserted into our multi-temporal change detection algorithm. The multi-temporal change detection approach is a two-step procedure including (1) data enhancement and filtering and (2) multi-scale automatic change detection. The performance of the developed approach is demonstrated by an application to oil spill areas in the Gulf of Mexico. In this example, areas affected by oil spills were identified from a series of ALOS PALSAR images acquired in 2010. The comparison showed exceptional performance of our method. This method can be applied to emergency management and decision support systems with a need for real-time data, and it shows great potential for rapid data analysis in other areas, including volcano detection, flood boundaries, forest health, and wildfires.
Transducer-based evaluation of tremor.

PubMed

Haubenberger, Dietrich; Abbruzzese, Giovanni; Bain, Peter G; Bajaj, Nin; Benito-León, Julián; Bhatia, Kailash P; Deuschl, Günther; Forjaz, Maria João; Hallett, Mark; Louis, Elan D; Lyons, Kelly E; Mestre, Tiago A; Raethjen, Jan; Stamelou, Maria; Tan, Eng-King; Testa, Claudia M; Elble, Rodger J

2016-09-01

The International Parkinson and Movement Disorder Society established a task force on tremor that reviewed the use of transducer-based measures in the quantification and characterization of tremor. Studies of accelerometry, electromyography, activity monitoring, gyroscopy, digitizing tablet-based measures, vocal acoustic analysis, and several other transducer-based methods were identified by searching PubMed.gov. The availability, use, acceptability, reliability, validity, and responsiveness were reviewed for each measure using the following criteria: (1) used in the assessment of tremor; (2) used in published studies by people other than the developers; and (3) adequate clinimetric testing. Accelerometry, gyroscopy, electromyography, and digitizing tablet-based measures fulfilled all three criteria. Compared to rating scales, transducers are far more sensitive to changes in tremor amplitude and frequency, but they do not appear to be more capable of detecting a change that exceeds random variability in tremor amplitude (minimum detectable change). The use of transducer-based measures requires careful attention to their limitations and validity in a particular clinical or research setting. © 2016 International Parkinson and Movement Disorder Society. © 2016 International Parkinson and Movement Disorder Society.
A comprehensive change detection method for updating the National Land Cover Database to circa 2011

USGS Publications Warehouse

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

2013-01-01

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

PubMed

Tan, Jie; Lai, Zongqiang; Zhong, Liping; Zhang, Zhenghua; Zheng, Rong; Su, Jing; Huang, Yong; Huang, Panpan; Song, Hui; Yang, Nuo; Zhou, Sufang; Zhao, Yongxiang

2018-04-01

A convenient, low-cost, and highly sensitive fluorescent aptasensor for detection of leukemia has been developed based on graphene oxide-aptamer complex (GO-apt). Graphene oxide (GO) can absorb carboxyfluorescein-labeled Sgc8 aptamer (FAM-apt) by π-π stacking and quench the fluorescence through fluorescence resonance energy transfer (FRET). In the absence of Sgc8 target cell CCRF-CEM, the fluorescence is almost all quenched. Conversely, when the CCRF-CEM cells are added, the quenched fluorescence can be recovered rapidly and significantly. Therefore, based on the change of fluorescence signals, we can detect the number of CCRF-CEM cells in a wide range from 1 × 10 2 to 1 × 10 7 cells/mL with a limit of detection (LOD) of 10 cells/mL. Therefore, this strategy of graphene oxide-based fluorescent aptasensor may be promising for the detection of cancer.
Gear-box fault detection using time-frequency based methods

DOE Office of Scientific and Technical Information (OSTI.GOV)

Odgaard, Peter Fogh; Stoustrup, Jakob

2015-01-01

Gear-box fault monitoring and detection is important for optimization of power generation and availability of wind turbines. The current industrial approach is to use condition monitoring systems, which runs in parallel with the wind turbine control system, using expensive additional sensors. An alternative would be to use the existing measurements which are normally available for the wind turbine control system. The usage of these sensors instead would cut down the cost of the wind turbine by not using additional sensors. One of these available measurements is the generator speed, in which changes in the gear-box resonance frequency can be detected.more » Two different time-frequency based approaches are presented in this paper. One is a filter based approach and the other is based on a Karhunen-Loeve basis. Both of them detects the gear-box fault with an acceptable detection delay.« less
Video change detection for fixed wing UAVs

NASA Astrophysics Data System (ADS)

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

2017-10-01

In this paper we proceed the work of Bartelsen et al.1 We present the draft of a process chain for an image based change detection which is designed for videos acquired by fixed wing unmanned aerial vehicles (UAVs). From our point of view, automatic video change detection for aerial images can be useful to recognize functional activities which are typically caused by the deployment of improvised explosive devices (IEDs), e.g. excavations, skid marks, footprints, left-behind tooling equipment, and marker stones. Furthermore, in case of natural disasters, like flooding, imminent danger can be recognized quickly. Due to the necessary flight range, we concentrate on fixed wing UAVs. Automatic change detection can be reduced to a comparatively simple photogrammetric problem when the perspective change between the "before" and "after" image sets is kept as small as possible. Therefore, the aerial image acquisition demands a mission planning with a clear purpose including flight path and sensor configuration. While the latter can be enabled simply by a fixed and meaningful adjustment of the camera, ensuring a small perspective change for "before" and "after" videos acquired by fixed wing UAVs is a challenging problem. Concerning this matter, we have performed tests with an advanced commercial off the shelf (COTS) system which comprises a differential GPS and autopilot system estimating the repetition accuracy of its trajectory. Although several similar approaches have been presented,23 as far as we are able to judge, the limits for this important issue are not estimated so far. Furthermore, we design a process chain to enable the practical utilization of video change detection. It consists of a front-end of a database to handle large amounts of video data, an image processing and change detection implementation, and the visualization of the results. We apply our process chain on the real video data acquired by the advanced COTS fixed wing UAV and synthetic data. For the image processing and change detection, we use the approach of Muller.4 Although it was developed for unmanned ground vehicles (UGVs), it enables a near real time video change detection for aerial videos. Concluding, we discuss the demands on sensor systems in the matter of change detection.
Testing visual short-term memory of pigeons (Columba livia) and a rhesus monkey (Macaca mulatta) with a location change detection task.

PubMed

Leising, Kenneth J; Elmore, L Caitlin; Rivera, Jacquelyne J; Magnotti, John F; Katz, Jeffrey S; Wright, Anthony A

2013-09-01

Change detection is commonly used to assess capacity (number of objects) of human visual short-term memory (VSTM). Comparisons with the performance of non-human animals completing similar tasks have shown similarities and differences in object-based VSTM, which is only one aspect ("what") of memory. Another important aspect of memory, which has received less attention, is spatial short-term memory for "where" an object is in space. In this article, we show for the first time that a monkey and pigeons can be accurately trained to identify location changes, much as humans do, in change detection tasks similar to those used to test object capacity of VSTM. The subject's task was to identify (touch/peck) an item that changed location across a brief delay. Both the monkey and pigeons showed transfer to delays longer than the training delay, to greater and smaller distance changes than in training, and to novel colors. These results are the first to demonstrate location-change detection in any non-human species and encourage comparative investigations into the nature of spatial and visual short-term memory.
Change Analysis in Structural Laser Scanning Point Clouds: The Baseline Method

PubMed Central

Shen, Yueqian; Lindenbergh, Roderik; Wang, Jinhu

2016-01-01

A method is introduced for detecting changes from point clouds that avoids registration. For many applications, changes are detected between two scans of the same scene obtained at different times. Traditionally, these scans are aligned to a common coordinate system having the disadvantage that this registration step introduces additional errors. In addition, registration requires stable targets or features. To avoid these issues, we propose a change detection method based on so-called baselines. Baselines connect feature points within one scan. To analyze changes, baselines connecting corresponding points in two scans are compared. As feature points either targets or virtual points corresponding to some reconstructable feature in the scene are used. The new method is implemented on two scans sampling a masonry laboratory building before and after seismic testing, that resulted in damages in the order of several centimeters. The centres of the bricks of the laboratory building are automatically extracted to serve as virtual points. Baselines connecting virtual points and/or target points are extracted and compared with respect to a suitable structural coordinate system. Changes detected from the baseline analysis are compared to a traditional cloud to cloud change analysis demonstrating the potential of the new method for structural analysis. PMID:28029121
Change Analysis in Structural Laser Scanning Point Clouds: The Baseline Method.

PubMed

Shen, Yueqian; Lindenbergh, Roderik; Wang, Jinhu

2016-12-24

A method is introduced for detecting changes from point clouds that avoids registration. For many applications, changes are detected between two scans of the same scene obtained at different times. Traditionally, these scans are aligned to a common coordinate system having the disadvantage that this registration step introduces additional errors. In addition, registration requires stable targets or features. To avoid these issues, we propose a change detection method based on so-called baselines. Baselines connect feature points within one scan. To analyze changes, baselines connecting corresponding points in two scans are compared. As feature points either targets or virtual points corresponding to some reconstructable feature in the scene are used. The new method is implemented on two scans sampling a masonry laboratory building before and after seismic testing, that resulted in damages in the order of several centimeters. The centres of the bricks of the laboratory building are automatically extracted to serve as virtual points. Baselines connecting virtual points and/or target points are extracted and compared with respect to a suitable structural coordinate system. Changes detected from the baseline analysis are compared to a traditional cloud to cloud change analysis demonstrating the potential of the new method for structural analysis.
Saliency-Guided Change Detection of Remotely Sensed Images Using Random Forest

NASA Astrophysics Data System (ADS)

Feng, W.; Sui, H.; Chen, X.

2018-04-01

Studies based on object-based image analysis (OBIA) representing the paradigm shift in change detection (CD) have achieved remarkable progress in the last decade. Their aim has been developing more intelligent interpretation analysis methods in the future. The prediction effect and performance stability of random forest (RF), as a new kind of machine learning algorithm, are better than many single predictors and integrated forecasting method. In this paper, we present a novel CD approach for high-resolution remote sensing images, which incorporates visual saliency and RF. First, highly homogeneous and compact image super-pixels are generated using super-pixel segmentation, and the optimal segmentation result is obtained through image superimposition and principal component analysis (PCA). Second, saliency detection is used to guide the search of interest regions in the initial difference image obtained via the improved robust change vector analysis (RCVA) algorithm. The salient regions within the difference image that correspond to the binarized saliency map are extracted, and the regions are subject to the fuzzy c-means (FCM) clustering to obtain the pixel-level pre-classification result, which can be used as a prerequisite for superpixel-based analysis. Third, on the basis of the optimal segmentation and pixel-level pre-classification results, different super-pixel change possibilities are calculated. Furthermore, the changed and unchanged super-pixels that serve as the training samples are automatically selected. The spectral features and Gabor features of each super-pixel are extracted. Finally, superpixel-based CD is implemented by applying RF based on these samples. Experimental results on Ziyuan 3 (ZY3) multi-spectral images show that the proposed method outperforms the compared methods in the accuracy of CD, and also confirm the feasibility and effectiveness of the proposed approach.
DNA aptamer-based colorimetric detection platform for Salmonella Enteritidis.

PubMed

Bayraç, Ceren; Eyidoğan, Füsun; Avni Öktem, Hüseyin

2017-12-15

Food safety is a major issue to protect public health and a key challenge is to find detection methods for identification of hazards in food. Food borne infections affects millions of people each year and among pathogens, Salmonella Enteritidis is most widely found bacteria causing food borne diseases. Therefore, simple, rapid, and specific detection methods are needed for food safety. In this study, we demonstrated the selection of DNA aptamers with high affinity and specificity against S. Enteritidis via Cell Systematic Evolution of Ligands by Exponential Enrichment (Cell-SELEX) and development of sandwich type aptamer-based colorimetric platforms for its detection. Two highly specific aptamers, crn-1 and crn-2, were developed through 12 rounds of selection with K d of 0.971µM and 0.309µM, respectively. Both aptamers were used to construct sandwich type capillary detection platforms. With the detection limit of 10 3 CFU/mL, crn-1 and crn-2 based platforms detected target bacteria specifically based on color change. This platform is also suitable for detection of S. Enteritidis in complex food matrix. Thus, this is the first to demonstrate use of Salmonella aptamers for development of the colorimetric aptamer-based detection platform in its identification and detection with naked eye in point-of-care. Copyright © 2017 Elsevier B.V. All rights reserved.

Temperature-Sensitive Coating Sensor Based on Hematite

NASA Technical Reports Server (NTRS)

Bencic, Timothy J.

2011-01-01

A temperature-sensitive coating, based on hematite (iron III oxide), has been developed to measure surface temperature using spectral techniques. The hematite powder is added to a binder that allows the mixture to be painted on the surface of a test specimen. The coating dynamically changes its relative spectral makeup or color with changes in temperature. The color changes from a reddish-brown appearance at room temperature (25 C) to a black-gray appearance at temperatures around 600 C. The color change is reversible and repeatable with temperature cycling from low to high and back to low temperatures. Detection of the spectral changes can be recorded by different sensors, including spectrometers, photodiodes, and cameras. Using a-priori information obtained through calibration experiments in known thermal environments, the color change can then be calibrated to yield accurate quantitative temperature information. Temperature information can be obtained at a point, or over an entire surface, depending on the type of equipment used for data acquisition. Because this innovation uses spectrophotometry principles of operation, rather than the current methods, which use photoluminescence principles, white light can be used for illumination rather than high-intensity short wavelength excitation. The generation of high-intensity white (or potentially filtered long wavelength light) is much easier, and is used more prevalently for photography and video technologies. In outdoor tests, the Sun can be used for short durations as an illumination source as long as the amplitude remains relatively constant. The reflected light is also much higher in intensity than the emitted light from the inefficient current methods. Having a much brighter surface allows a wider array of detection schemes and devices. Because color change is the principle of operation, the development of high-quality, lower-cost digital cameras can be used for detection, as opposed to the high-cost imagers needed for intensity measurements with the current methods. Alternative methods of detection are possible to increase the measurement sensitivity. For example, a monochrome camera can be used with an appropriate filter and a radiometric measurement of normalized intensity change that is proportional to the change coating temperature. Using different spectral regions yields different sensitivities and calibration curves for converting intensity change to temperature units. Alternatively, using a color camera, a ratio of the standard red, green, and blue outputs can be used as a self-referenced change. The blue region (less than 500 nm) does not change nearly as much as the red region (greater than 575 nm), so a ratio of color intensities will yield a calibrated temperature image. The new temperature sensor coating is easy to apply, is inexpensive, can contour complex shape surfaces, and can be a global surface measurement system based on spectrophotometry. The color change, or relative intensity change, at different colors makes the optical detection under white light illumination, and associated interpretation, much easier to measure and interpret than in the detection systems of the current methods.
Thorough statistical comparison of machine learning regression models and their ensembles for sub-pixel imperviousness and imperviousness change mapping

NASA Astrophysics Data System (ADS)

Drzewiecki, Wojciech

2017-12-01

We evaluated the performance of nine machine learning regression algorithms and their ensembles for sub-pixel estimation of impervious areas coverages from Landsat imagery. The accuracy of imperviousness mapping in individual time points was assessed based on RMSE, MAE and R2. These measures were also used for the assessment of imperviousness change intensity estimations. The applicability for detection of relevant changes in impervious areas coverages at sub-pixel level was evaluated using overall accuracy, F-measure and ROC Area Under Curve. The results proved that Cubist algorithm may be advised for Landsat-based mapping of imperviousness for single dates. Stochastic gradient boosting of regression trees (GBM) may be also considered for this purpose. However, Random Forest algorithm is endorsed for both imperviousness change detection and mapping of its intensity. In all applications the heterogeneous model ensembles performed at least as well as the best individual models or better. They may be recommended for improving the quality of sub-pixel imperviousness and imperviousness change mapping. The study revealed also limitations of the investigated methodology for detection of subtle changes of imperviousness inside the pixel. None of the tested approaches was able to reliably classify changed and non-changed pixels if the relevant change threshold was set as one or three percent. Also for fi ve percent change threshold most of algorithms did not ensure that the accuracy of change map is higher than the accuracy of random classifi er. For the threshold of relevant change set as ten percent all approaches performed satisfactory.
Earlier Detection of Tumor Treatment Response Using Magnetic Resonance Diffusion Imaging with Oscillating Gradients

PubMed Central

Colvin, Daniel C.; Loveless, Mary E.; Does, Mark D.; Yue, Zou; Yankeelov, Thomas E.; Gore, John C.

2011-01-01

An improved method for detecting early changes in tumors in response to treatment, based on a modification of diffusion-weighted magnetic resonance imaging, has been demonstrated in an animal model. Early detection of therapeutic response in tumors is important both clinically and in pre-clinical assessments of novel treatments. Non-invasive imaging methods that can detect and assess tumor response early in the course of treatment, and before frank changes in tumor morphology are evident, are of considerable interest as potential biomarkers of treatment efficacy. Diffusion-weighted magnetic resonance imaging is sensitive to changes in water diffusion rates in tissues that result from structural variations in the local cellular environment, but conventional methods mainly reflect changes in tissue cellularity and do not convey information specific to micro-structural variations at sub-cellular scales. We implemented a modified imaging technique using oscillating gradients of the magnetic field for evaluating water diffusion rates over very short spatial scales that are more specific for detecting changes in intracellular structure that may precede changes in cellularity. Results from a study of orthotopic 9L gliomas in rat brains indicate that this method can detect changes as early as 24 hours following treatment with 1,3-bis(2-chloroethyl)-1-nitrosourea (BCNU), when conventional approaches do not find significant effects. These studies suggest that diffusion imaging using oscillating gradients may be used to obtain an earlier indication of treatment efficacy than previous magnetic resonance imaging methods. PMID:21190804
Development of Ultrasensitive Plasmonic Nanosensors

NASA Astrophysics Data System (ADS)

Joshi, Gayatribahen K.

Nanostructures (NSs) based localized surface plasmon resonance (LSPR) sensors have brought a transformation in development of sensing devices due to their ability to detect extremely small changes in surrounding refractive index (R.I.). NS-based LSPR sensing approaches have been employed to enhance the sensitivity for a variety of applications, such as diagnosis of disease, food and environmental analysis, and chemical and biological threat detection. Generally in LSPR spectroscopy, absorption and scattering of light is greatly enhanced at a frequency that excites the NS's LSPR and results in well-defined LSPR extinction peak (lambdaLSPR). This lambdaLSPR is highly dependent on the size, shape, and surrounding R.I. of NSs. Compositional and confirmational change within the surrounding R.I. near the NS could be detected by monitoring the shifts in lambdaLSPR. This thesis specifically focuses on the rational development of the plasmonic nanosensors for various sensing applications by utilizing the LSPR properties of Au NS with prismatic shape. First the chemical synthetic approach that can produce Au nanoprisms, which displayed lambdaLSPR in 650-850 nm range corresponding to 20-50 nm edge lengths has been developed. The chemically synthesized Au nanoprisms were attached to silanized glass substrate and employed as a solid-state sensing platform for the development of label-free plasmonic nanosensors. The size, shape, and surface of nanoprisms were characterized through transmission electron microscopy (TEM), scanning electron microscopy (SEM), atomic force microscopy (AFM), and UV-visible spectroscopy. Further, the influence of the structure, size and surface ligand chemistry onto the lambda LSPR of nanoprisms were investigated in detail. Both bulk and local R.I. sensitivity, and the electromagnetic-field (EM-field) decay length were derived for various edge lengths of nanoprisms through measuring the lambda LSPR shifts by UV-visible spectroscopy. Finally, nanoprisms-based LSPR nanosensors ("plasmonic nanosensors") have been developed for different sensing applications. Specifically, these plasmonic nanosensors displayed capacity to detect streptavidine, glucose, microRNA (cancer biomarker), as well as molecular and stimuli-responsive polymers conformational changes. In this study we found that the plasmonic nanosensors are exceptionally sensitive compared to other NSs and the sensitivity is highly edge length dependent. An ultrasensitive plasmonic nanosensor has been developed for the detection of microRNAs in crude plasma collected from pancreatic cancer patients. It shows that the LSPR-based nanosensor has the ability to detect and quantify the microRNA concentrations in clinical samples without any purification. The results presented here show potential for patients to commence treatment in early stage cancer diagnosis. The effect of various physiological medias and edge length of nanoprisms on the sensitivity of this nanosensor has been discussed. Second, molecular sensors have been developed by functionalization of azobenzene molecule contain alkanethiols onto the nanoprisms surface. Molecular conformational changes basis on a very less dielectric thickness changes have been detected through lambdaLSPR shift of nanoprisms and confirmed through surface enhanced Raman spectroscopy (SERS). In this study, the influence of resonance energy transfer between the molecule and nanoprisms onto the lambda LSPR shift and Raman intensity has been investigated by changing the distance between them. Finally, utilization of stimuli-responsive polymers structural change in the development of stimuli-responsive such as pH and temperature-responsive plasmonic nanosensors has been demonstrated. It was found that the stimuli-responsive nanosensors were able to detect very small R.I. change due to the polymers structural change. The enzymatic reaction between glucose and glucose oxidase has been used to detect glucose in bovine plasma using pH-responsive nanosensor. Results of this work displays potential of replacing finger prick methodology in glucose self-monitoring for diabetes patients with use of plasma/urine samples. Overall, the research work demonstrated here provides a significant progress in the development of LSPR-based plasmonic nanosensors and addresses the resolution of many scientific complications, fundamental, chemical, and biological.
Identifying reprioritization response shift in a stroke caregiver population: a comparison of missing data methods.

PubMed

Sajobi, Tolulope T; Lix, Lisa M; Singh, Gurbakhshash; Lowerison, Mark; Engbers, Jordan; Mayo, Nancy E

2015-03-01

Response shift (RS) is an important phenomenon that influences the assessment of longitudinal changes in health-related quality of life (HRQOL) studies. Given that RS effects are often small, missing data due to attrition or item non-response can contribute to failure to detect RS effects. Since missing data are often encountered in longitudinal HRQOL data, effective strategies to deal with missing data are important to consider. This study aims to compare different imputation methods on the detection of reprioritization RS in the HRQOL of caregivers of stroke survivors. Data were from a Canadian multi-center longitudinal study of caregivers of stroke survivors over a one-year period. The Stroke Impact Scale physical function score at baseline, with a cutoff of 75, was used to measure patient stroke severity for the reprioritization RS analysis. Mean imputation, likelihood-based expectation-maximization imputation, and multiple imputation methods were compared in test procedures based on changes in relative importance weights to detect RS in SF-36 domains over a 6-month period. Monte Carlo simulation methods were used to compare the statistical powers of relative importance test procedures for detecting RS in incomplete longitudinal data under different missing data mechanisms and imputation methods. Of the 409 caregivers, 15.9 and 31.3 % of them had missing data at baseline and 6 months, respectively. There were no statistically significant changes in relative importance weights on any of the domains when complete-case analysis was adopted. But statistical significant changes were detected on physical functioning and/or vitality domains when mean imputation or EM imputation was adopted. There were also statistically significant changes in relative importance weights for physical functioning, mental health, and vitality domains when multiple imputation method was adopted. Our simulations revealed that relative importance test procedures were least powerful under complete-case analysis method and most powerful when a mean imputation or multiple imputation method was adopted for missing data, regardless of the missing data mechanism and proportion of missing data. Test procedures based on relative importance measures are sensitive to the type and amount of missing data and imputation method. Relative importance test procedures based on mean imputation and multiple imputation are recommended for detecting RS in incomplete data.
Early Detection of Physical Activity for People With Type 1 Diabetes Mellitus.

PubMed

Dasanayake, Isuru S; Bevier, Wendy C; Castorino, Kristin; Pinsker, Jordan E; Seborg, Dale E; Doyle, Francis J; Dassau, Eyal

2015-06-30

Early detection of exercise in individuals with type 1 diabetes mellitus (T1DM) may allow changes in therapy to prevent hypoglycemia. Currently there is limited experience with automated methods that detect the onset and end of exercise in this population. We sought to develop a novel method to quickly and reliably detect the onset and end of exercise in these individuals before significant changes in blood glucose (BG) occur. Sixteen adults with T1DM were studied as outpatients using a diary, accelerometer, heart rate monitor, and continuous glucose monitor for 2 days. These data were used to develop a principal component analysis based exercise detection method. Subjects also performed 60 and 30 minute exercise sessions at 30% and 50% predicted heart rate reserve (HRR), respectively. The detection method was applied to the exercise sessions to determine how quickly the detection of start and end of exercise occurred relative to change in BG. Mild 30% HRR and moderate 50% HRR exercise onset was identified in 6 ± 3 and 5 ± 2 (mean ± SD) minutes, while completion was detected in 3 ± 8 and 6 ± 5 minutes, respectively. BG change from start of exercise to detection time was 1 ± 6 and -1 ± 3 mg/dL, and, from the end of exercise to detection time was 6 ± 4 and -17 ± 13 mg/dL, respectively, for the 2 exercise sessions. False positive and negative ratios were 4 ± 2% and 21 ± 22%. The novel method for exercise detection identified the onset and end of exercise in approximately 5 minutes, with an average BG change of only -6 mg/dL. © 2015 Diabetes Technology Society.
A comparison of moving object detection methods for real-time moving object detection

NASA Astrophysics Data System (ADS)

Roshan, Aditya; Zhang, Yun

2014-06-01

Moving object detection has a wide variety of applications from traffic monitoring, site monitoring, automatic theft identification, face detection to military surveillance. Many methods have been developed across the globe for moving object detection, but it is very difficult to find one which can work globally in all situations and with different types of videos. The purpose of this paper is to evaluate existing moving object detection methods which can be implemented in software on a desktop or laptop, for real time object detection. There are several moving object detection methods noted in the literature, but few of them are suitable for real time moving object detection. Most of the methods which provide for real time movement are further limited by the number of objects and the scene complexity. This paper evaluates the four most commonly used moving object detection methods as background subtraction technique, Gaussian mixture model, wavelet based and optical flow based methods. The work is based on evaluation of these four moving object detection methods using two (2) different sets of cameras and two (2) different scenes. The moving object detection methods have been implemented using MatLab and results are compared based on completeness of detected objects, noise, light change sensitivity, processing time etc. After comparison, it is observed that optical flow based method took least processing time and successfully detected boundary of moving objects which also implies that it can be implemented for real-time moving object detection.
Moving human full body and body parts detection, tracking, and applications on human activity estimation, walking pattern and face recognition

NASA Astrophysics Data System (ADS)

Chen, Hai-Wen; McGurr, Mike

2016-05-01

We have developed a new way for detection and tracking of human full-body and body-parts with color (intensity) patch morphological segmentation and adaptive thresholding for security surveillance cameras. An adaptive threshold scheme has been developed for dealing with body size changes, illumination condition changes, and cross camera parameter changes. Tests with the PETS 2009 and 2014 datasets show that we can obtain high probability of detection and low probability of false alarm for full-body. Test results indicate that our human full-body detection method can considerably outperform the current state-of-the-art methods in both detection performance and computational complexity. Furthermore, in this paper, we have developed several methods using color features for detection and tracking of human body-parts (arms, legs, torso, and head, etc.). For example, we have developed a human skin color sub-patch segmentation algorithm by first conducting a RGB to YIQ transformation and then applying a Subtractive I/Q image Fusion with morphological operations. With this method, we can reliably detect and track human skin color related body-parts such as face, neck, arms, and legs. Reliable body-parts (e.g. head) detection allows us to continuously track the individual person even in the case that multiple closely spaced persons are merged. Accordingly, we have developed a new algorithm to split a merged detection blob back to individual detections based on the detected head positions. Detected body-parts also allow us to extract important local constellation features of the body-parts positions and angles related to the full-body. These features are useful for human walking gait pattern recognition and human pose (e.g. standing or falling down) estimation for potential abnormal behavior and accidental event detection, as evidenced with our experimental tests. Furthermore, based on the reliable head (face) tacking, we have applied a super-resolution algorithm to enhance the face resolution for improved human face recognition performance.
Gold nanoparticles-based protease assay

PubMed Central

Guarise, Cristian; Pasquato, Lucia; De Filippis, Vincenzo; Scrimin, Paolo

2006-01-01

We describe here a simple assay that allows the visual detection of a protease. The method takes advantage of the high molar absorptivity of the plasmon band of gold colloids and is based on the color change of their solution when treated with dithiols. We used C- and N-terminal cysteinyl derivatives of a peptide substrate exploiting its selective recognition and cleavage by a specific protease. Contrary to the native ones, cleaved peptides are unable to induce nanoparticles aggregation; hence, the color of the solution does not change. The detection of two proteases is reported: thrombin (involved in blood coagulation and thrombosis) and lethal factor (an enzyme component of the toxin produced by Bacillus anthracis). The sensitivity of this nanoparticle-based assay is in the low nanomolar range. PMID:16537471
Gold nanoparticles-based protease assay.

PubMed

Guarise, Cristian; Pasquato, Lucia; De Filippis, Vincenzo; Scrimin, Paolo

2006-03-14

We describe here a simple assay that allows the visual detection of a protease. The method takes advantage of the high molar absorptivity of the plasmon band of gold colloids and is based on the color change of their solution when treated with dithiols. We used C- and N-terminal cysteinyl derivatives of a peptide substrate exploiting its selective recognition and cleavage by a specific protease. Contrary to the native ones, cleaved peptides are unable to induce nanoparticles aggregation; hence, the color of the solution does not change. The detection of two proteases is reported: thrombin (involved in blood coagulation and thrombosis) and lethal factor (an enzyme component of the toxin produced by Bacillus anthracis). The sensitivity of this nanoparticle-based assay is in the low nanomolar range.
New Optical Methods for Liveness Detection on Fingers

PubMed Central

Dolezel, Michal; Vana, Jan; Brezinova, Eva; Yim, Jaegeol; Shim, Kyubark

2013-01-01

This paper is devoted to new optical methods, which are supposed to be used for liveness detection on fingers. First we describe the basics about fake finger use in fingerprint recognition process and the possibilities of liveness detection. Then we continue with introducing three new liveness detection methods, which we developed and tested in the scope of our research activities—the first one is based on measurement of the pulse, the second one on variations of optical characteristics caused by pressure change, and the last one is based on reaction of skin to illumination with different wavelengths. The last part deals with the influence of skin diseases on fingerprint recognition, especially on liveness detection. PMID:24151584
A new rhodamine-based colorimetric chemosensor for naked-eye detection of Cu2 + in aqueous solution

NASA Astrophysics Data System (ADS)

Hu, Yang; Zhang, Jing; Lv, Yuan-Zheng; Huang, Xiao-Huan; Hu, Sheng-li

2016-03-01

A new colorimetric probe 1 based on rhodamine B lactam was developed for naked-eye detection of Cu2 +. The optical feature of 1 for Cu2 + was investigated by UV-vis absorption spectroscopy. Upon the addition of Cu2 +, the 1 displayed a distinct color change from colorless to pink, which can be directly detected by the naked eye. The stoichiometry of 1 to Cu2 + complex was found to be 1:1 and the naked-eye detection limit was determined as low as 2 μM. The results suggest that the probe 1 may provide a convenient method for visual detection of Cu2 + with high sensitivity.
Removing Parallax-Induced False Changes in Change Detection

DTIC Science & Technology

2014-03-27

viii Figure Page 11 Three hypothetical ROC curves. The probability of detection (PD) is plotted against the probability of false alarm ( PFA ) based on...red and green) approach the value of PD = 1 and PFA = 0, the detector performance is said to improve. . . . . . . . . . . . . . . . 32 12 Possible... sorption are commonly among those with low SNRs as the gases and vapor in the atmosphere between the (airborne) sensor and the ground plane tend to
Integrated MOSFET-Embedded-Cantilever-Based Biosensor Characteristic for Detection of Anthrax Simulant

DOE Office of Scientific and Technical Information (OSTI.GOV)

Mostafa, Salwa; Lee, Ida; Islam, Syed K

2011-01-01

In this work, MOSFET-embedded cantilevers are configured as microbial sensors for detection of anthrax simulants, Bacillus thuringiensis. Anthrax simulants attached to the chemically treated gold-coated cantilever cause changes in the MOSFET drain current due to the bending of the cantilever which indicates the detection of anthrax simulant. Electrical properties of the anthrax simulant are also responsible for the change in the drain current. The test results suggest a detection range of 10 L of stimulant test solution (a suspension population of 1.3 107 colony-forming units/mL diluted in 40% ethanol and 60% deionized water) with a linear response of 31 A/more » L.« less
Vision-Based Autonomous Sensor-Tasking in Uncertain Adversarial Environments

DTIC Science & Technology

2015-01-02

motion segmentation and change detection in crowd behavior. In particular we investigated Finite Time Lyapunov Exponents, Perron Frobenius Operator and...deformation tensor [11]. On the other hand, eigenfunctions of, the Perron Frobenius operator can be used to detect Almost Invariant Sets (AIS) which are... Perron Frobenius operator. Finally, Figure 1.12d shows the ergodic partitions (EP) obtained based on the eigenfunctions of the Koopman operator
A Hopfield neural network for image change detection.

PubMed

Pajares, Gonzalo

2006-09-01

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

NASA Astrophysics Data System (ADS)

Chen, Justin G.; Büyüköztürk, Oral

2017-11-01

This paper introduces a feature for detecting damage or changes in structures, the continuous symmetry measure, which can quantify the amount of a particular rotational, mirror, or translational symmetry in a mode shape of a structure. Many structures in the built environment have geometries that are either symmetric or almost symmetric, however damage typically occurs in a local manner causing asymmetric changes in the structure's geometry or material properties, and alters its mode shapes. The continuous symmetry measure can quantify these changes in symmetry as a novel indicator of damage for data-based structural health monitoring approaches. This paper describes the concept as a basis for detecting changes in mode shapes and detecting structural damage. Application of the method is demonstrated in various structures with different symmetrical properties: a pipe cross-section with a finite element model and experimental study, the NASA 8-bay truss model, and the simulated IASC-ASCE structural health monitoring benchmark structure. The applicability and limitations of the feature in applying it to structures of varying geometries is discussed.
Evaluation of two outlier-detection-based methods for detecting tissue-selective genes from microarray data.

PubMed

Kadota, Koji; Konishi, Tomokazu; Shimizu, Kentaro

2007-05-01

Large-scale expression profiling using DNA microarrays enables identification of tissue-selective genes for which expression is considerably higher and/or lower in some tissues than in others. Among numerous possible methods, only two outlier-detection-based methods (an AIC-based method and Sprent's non-parametric method) can treat equally various types of selective patterns, but they produce substantially different results. We investigated the performance of these two methods for different parameter settings and for a reduced number of samples. We focused on their ability to detect selective expression patterns robustly. We applied them to public microarray data collected from 36 normal human tissue samples and analyzed the effects of both changing the parameter settings and reducing the number of samples. The AIC-based method was more robust in both cases. The findings confirm that the use of the AIC-based method in the recently proposed ROKU method for detecting tissue-selective expression patterns is correct and that Sprent's method is not suitable for ROKU.
VisitSense: Sensing Place Visit Patterns from Ambient Radio on Smartphones for Targeted Mobile Ads in Shopping Malls

PubMed Central

Kim, Byoungjip; Kang, Seungwoo; Ha, Jin-Young; Song, Junehwa

2015-01-01

In this paper, we introduce a novel smartphone framework called VisitSense that automatically detects and predicts a smartphone user’s place visits from ambient radio to enable behavioral targeting for mobile ads in large shopping malls. VisitSense enables mobile app developers to adopt visit-pattern-aware mobile advertising for shopping mall visitors in their apps. It also benefits mobile users by allowing them to receive highly relevant mobile ads that are aware of their place visit patterns in shopping malls. To achieve the goal, VisitSense employs accurate visit detection and prediction methods. For accurate visit detection, we develop a change-based detection method to take into consideration the stability change of ambient radio and the mobility change of users. It performs well in large shopping malls where ambient radio is quite noisy and causes existing algorithms to easily fail. In addition, we proposed a causality-based visit prediction model to capture the causality in the sequential visit patterns for effective prediction. We have developed a VisitSense prototype system, and a visit-pattern-aware mobile advertising application that is based on it. Furthermore, we deploy the system in the COEX Mall, one of the largest shopping malls in Korea, and conduct diverse experiments to show the effectiveness of VisitSense. PMID:26193275
A magnetostatic-coupling based remote query sensor for environmental monitoring

NASA Technical Reports Server (NTRS)

Grimes, C. A.; Stoyanov, P. G.; Liu, Y.; Tong, C.; Ong, K. G.; Loiselle, K.; Shaw, M.; Doherty, S. A.; Seitz, W. R.

1999-01-01

A new type of in situ, remotely monitored magnetism-based sensor is presented that is comprised of an array of magnetically soft, magnetostatically-coupled ferromagnetic thin-film elements or particles combined with a chemically responsive material that swells or shrinks in response to the analyte of interest. As the chemically responsive material changes size the distance between the ferromagnetic elements changes, altering the inter-element magnetostatic coupling. This in turn changes the coercive force of the sensor, the amplitude of the voltage spikes detected in nearby pick-up coils upon magnetization reversal and the number of higher-order harmonics generated by the flux reversal. Since the sensor is monitored through changes in magnetic flux, no physical connections such as wires or cables are needed to obtain sensor information, nor is line of sight alignment required as with laser telemetry; the sensors can be detected from within sealed, opaque or thin metallic enclosures.

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

NASA Astrophysics Data System (ADS)

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

2018-04-01

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

NASA Astrophysics Data System (ADS)

Kucharczyk, Daniel; Wyłomańska, Agnieszka; Sikora, Grzegorz

2018-01-01

Fractional Brownian motion is one of the main stochastic processes used for describing the long-range dependence phenomenon for self-similar processes. It appears that for many real time series, characteristics of the data change significantly over time. Such behaviour one can observe in many applications, including physical and biological experiments. In this paper, we present a new technique for the critical change point detection for cases where the data under consideration are driven by fractional Brownian motion with a time-changed diffusion coefficient. The proposed methodology is based on the likelihood ratio approach and represents an extension of a similar methodology used for Brownian motion, the process with independent increments. Here, we also propose a statistical test for testing the significance of the estimated critical point. In addition to that, an extensive simulation study is provided to test the performance of the proposed method.
Simultaneous Ionic Current and Potential Detection of Nanoparticles by a Multifunctional Nanopipette.

PubMed

Panday, Namuna; Qian, Gongming; Wang, Xuewen; Chang, Shuai; Pandey, Popular; He, Jin

2016-12-27

Nanopore sensing-based technologies have made significant progress for single molecule and single nanoparticle detection and analysis. In recent years, multimode sensing by multifunctional nanopores shows the potential to greatly improve the sensitivity and selectivity of traditional resistive-pulse sensing methods. In this paper, we showed that two label-free electric sensing modes could work cooperatively to detect the motion of 40 nm diameter spherical gold nanoparticles (GNPs) in solution by a multifunctional nanopipette. The multifunctional nanopipettes containing both nanopore and nanoelectrode (pyrolytic carbon) at the tip were fabricated quickly and cheaply. We demonstrated that the ionic current and local electrical potential changes could be detected simultaneously during the translocation of individual GNPs. We also showed that the nanopore/CNE tip geometry enabled the CNE not only to detect the translocation of single GNP but also to collectively detect several GNPs outside the nanopore entrance. The dynamic accumulation of GNPs near the nanopore entrance resulted in no detectable current changes, but was detected by the potential changes at the CNE. We revealed the motions of GNPs both outside and inside the nanopore, individually and collectively, with the combination of ionic current and potential measurements.
An Investigation of Automatic Change Detection for Topographic Map Updating

NASA Astrophysics Data System (ADS)

Duncan, P.; Smit, J.

2012-08-01

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.
Confocal laser feedback tomography for skin cancer detection

PubMed Central

Mowla, Alireza; Du, Benjamin Wensheng; Taimre, Thomas; Bertling, Karl; Wilson, Stephen; Soyer, H. Peter; Rakić, Aleksandar D.

2017-01-01

Tomographic imaging of soft tissue such as skin has a potential role in cancer detection. The penetration of infrared wavelengths makes a confocal approach based on laser feedback interferometry feasible. We present a compact system using a semiconductor laser as both transmitter and receiver. Numerical and physical models based on the known optical properties of keratinocyte cancers were developed. We validated the technique on three phantoms containing macro-structural changes in optical properties. Experimental results were in agreement with numerical simulations and structural changes were evident which would permit discrimination of healthy tissue and tumour. Furthermore, cancer type discrimination was also able to be visualized using this imaging technique. PMID:28966845
Confocal laser feedback tomography for skin cancer detection.

PubMed

Mowla, Alireza; Du, Benjamin Wensheng; Taimre, Thomas; Bertling, Karl; Wilson, Stephen; Soyer, H Peter; Rakić, Aleksandar D

2017-09-01

Tomographic imaging of soft tissue such as skin has a potential role in cancer detection. The penetration of infrared wavelengths makes a confocal approach based on laser feedback interferometry feasible. We present a compact system using a semiconductor laser as both transmitter and receiver. Numerical and physical models based on the known optical properties of keratinocyte cancers were developed. We validated the technique on three phantoms containing macro-structural changes in optical properties. Experimental results were in agreement with numerical simulations and structural changes were evident which would permit discrimination of healthy tissue and tumour. Furthermore, cancer type discrimination was also able to be visualized using this imaging technique.
Conformational Change of Bacteriorhodopsin Quantitatively Monitored by Microcantilever Sensors

PubMed Central

Braun, Thomas; Backmann, Natalija; Vögtli, Manuel; Bietsch, Alexander; Engel, Andreas; Lang, Hans-Peter; Gerber, Christoph; Hegner, Martin

2006-01-01

Bacteriorhodopsin proteoliposomes were used as a model system to explore the applicability of micromechanical cantilever arrays to detect conformational changes in membrane protein patches. The three main results of our study concern: 1), reliable functionalization of micromechanical cantilever arrays with proteoliposomes using ink jet spotting; 2), successful detection of the prosthetic retinal removal (bleaching) from the bacteriorhodopsin protein by measuring the induced nanomechanical surface stress change; and 3), the quantitative response thereof, which depends linearly on the amount of removed retinal. Our results show this technique to be a potential tool to measure membrane protein-based receptor-ligand interactions and conformational changes. PMID:16443650
A detection of the evolutionary time scale of the DA white dwarf G117 - B15A with the Whole Earth Telescope

NASA Technical Reports Server (NTRS)

Kepler, S. O.; Fontaine, G.; Bergeron, P.; Winget, D. E.; Nather, R. E.; Bradley, P. A.; Claver, C. F.; Grauer, A. D.; Vauclair, G.; Marar, T. M. K.

1991-01-01

The time rate of change for the main pulsation period of the 13,000 K DA white dwarf G117 - B15A has been detected using the Whole Earth Telescope (WET). The observed rate of period change, P(dot) = (12.0 + or - 3.5) x 10 to the -15th s/s, is somewhat larger than the published theoretical calculations of the rate of period change due to cooling, based on carbon core white dwarf models. Other effects that could contribute to the observed rate of period change are discussed.
Land-based lidar mapping: a new surveying technique to shed light on rapid topographic change

USGS Publications Warehouse

Collins, Brian D.; Kayen, Robert

2006-01-01

The rate of natural change in such dynamic environments as rivers and coastlines can sometimes overwhelm the monitoring capacity of conventional surveying methods. In response to this limitation, U.S. Geological Survey (USGS) scientists are pioneering new applications of light detection and ranging (lidar), a laser-based scanning technology that promises to greatly increase our ability to track rapid topographic changes and manage their impact on affected communities.
The Network for the Detection of Atmospheric Composition Change (NDACC): history, status and perspectives

NASA Astrophysics Data System (ADS)

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.
An Online 3D Database System for Endangered Architectural and Archaeological Heritage in the South-Eastern Mediterranean

NASA Astrophysics Data System (ADS)

Abate, D.; Avgousti, A.; Faka, M.; Hermon, S.; Bakirtzis, N.; Christofi, P.

2017-10-01

This study compares performance of aerial image based point clouds (IPCs) and light detection and ranging (LiDAR) based point clouds in detection of thinnings and clear cuts in forests. IPCs are an appealing method to update forest resource data, because of their accuracy in forest height estimation and cost-efficiency of aerial image acquisition. We predicted forest changes over a period of three years by creating difference layers that displayed the difference in height or volume between the initial and subsequent time points. Both IPCs and LiDAR data were used in this process. The IPCs were constructed with the Semi-Global Matching (SGM) algorithm. Difference layers were constructed by calculating differences in fitted height or volume models or in canopy height models (CHMs) from both time points. The LiDAR-derived digital terrain model (DTM) was used to scale heights to above ground level. The study area was classified in logistic regression into the categories ClearCut, Thinning or NoChange with the values from the difference layers. We compared the predicted changes with the true changes verified in the field, and obtained at best a classification accuracy for clear cuts 93.1 % with IPCs and 91.7 % with LiDAR data. However, a classification accuracy for thinnings was only 8.0 % with IPCs. With LiDAR data 41.4 % of thinnings were detected. In conclusion, the LiDAR data proved to be more accurate method to predict the minor changes in forests than IPCs, but both methods are useful in detection of major changes.
The Network for the Detection of Atmospheric Composition Change (NDACC): History, Status and Perspectives

NASA Technical Reports Server (NTRS)

Simon, Paul C.; De Maziere, Martine; Bernhard, Germar; Blumenstock, Thomas; McGee, Thomas J.; Petropavlovskikh, Irina; Steinbrecht, Wolfgang; Wild, Jeannette D.; Lambert, Jean-Christopher; Seckmeyer, Gunther;

2018-01-01

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.

Color-Change Detection Activity in the Primate Superior Colliculus.

PubMed

Herman, James P; Krauzlis, Richard J

2017-01-01

The primate superior colliculus (SC) is a midbrain structure that participates in the control of spatial attention. Previous studies examining the role of the SC in attention have mostly used luminance-based visual features (e.g., motion, contrast) as the stimuli and saccadic eye movements as the behavioral response, both of which are known to modulate the activity of SC neurons. To explore the limits of the SC's involvement in the control of spatial attention, we recorded SC neuronal activity during a task using color, a visual feature dimension not traditionally associated with the SC, and required monkeys to detect threshold-level changes in the saturation of a cued stimulus by releasing a joystick during maintained fixation. Using this color-based spatial attention task, we found substantial cue-related modulation in all categories of visually responsive neurons in the intermediate layers of the SC. Notably, near-threshold changes in color saturation, both increases and decreases, evoked phasic bursts of activity with magnitudes as large as those evoked by stimulus onset. This change-detection activity had two distinctive features: activity for hits was larger than for misses, and the timing of change-detection activity accounted for 67% of joystick release latency, even though it preceded the release by at least 200 ms. We conclude that during attention tasks, SC activity denotes the behavioral relevance of the stimulus regardless of feature dimension and that phasic event-related SC activity is suitable to guide the selection of manual responses as well as saccadic eye movements.
Application of a SERS-based lateral flow immunoassay strip for the rapid and sensitive detection of staphylococcal enterotoxin B

NASA Astrophysics Data System (ADS)

Hwang, Joonki; Lee, Sangyeop; Choo, Jaebum

2016-06-01

A novel surface-enhanced Raman scattering (SERS)-based lateral flow immunoassay (LFA) biosensor was developed to resolve problems associated with conventional LFA strips (e.g., limits in quantitative analysis and low sensitivity). In our SERS-based biosensor, Raman reporter-labeled hollow gold nanospheres (HGNs) were used as SERS detection probes instead of gold nanoparticles. With the proposed SERS-based LFA strip, the presence of a target antigen can be identified through a colour change in the test zone. Furthermore, highly sensitive quantitative evaluation is possible by measuring SERS signals from the test zone. To verify the feasibility of the SERS-based LFA strip platform, an immunoassay of staphylococcal enterotoxin B (SEB) was performed as a model reaction. The limit of detection (LOD) for SEB, as determined with the SERS-based LFA strip, was estimated to be 0.001 ng mL-1. This value is approximately three orders of magnitude more sensitive than that achieved with the corresponding ELISA-based method. The proposed SERS-based LFA strip sensor shows significant potential for the rapid and sensitive detection of target markers in a simplified manner.A novel surface-enhanced Raman scattering (SERS)-based lateral flow immunoassay (LFA) biosensor was developed to resolve problems associated with conventional LFA strips (e.g., limits in quantitative analysis and low sensitivity). In our SERS-based biosensor, Raman reporter-labeled hollow gold nanospheres (HGNs) were used as SERS detection probes instead of gold nanoparticles. With the proposed SERS-based LFA strip, the presence of a target antigen can be identified through a colour change in the test zone. Furthermore, highly sensitive quantitative evaluation is possible by measuring SERS signals from the test zone. To verify the feasibility of the SERS-based LFA strip platform, an immunoassay of staphylococcal enterotoxin B (SEB) was performed as a model reaction. The limit of detection (LOD) for SEB, as determined with the SERS-based LFA strip, was estimated to be 0.001 ng mL-1. This value is approximately three orders of magnitude more sensitive than that achieved with the corresponding ELISA-based method. The proposed SERS-based LFA strip sensor shows significant potential for the rapid and sensitive detection of target markers in a simplified manner. Electronic supplementary information (ESI) available. See DOI: 10.1039/c5nr07243c
Monitoring of Building Construction by 4D Change Detection Using Multi-temporal SAR Images

NASA Astrophysics Data System (ADS)

Yang, C. H.; Pang, Y.; Soergel, U.

2017-05-01

Monitoring urban changes is important for city management, urban planning, updating of cadastral map, etc. In contrast to conventional field surveys, which are usually expensive and slow, remote sensing techniques are fast and cost-effective alternatives. Spaceborne synthetic aperture radar (SAR) sensors provide radar images captured rapidly over vast areas at fine spatiotemporal resolution. In addition, the active microwave sensors are capable of day-and-night vision and independent of weather conditions. These advantages make multi-temporal SAR images suitable for scene monitoring. Persistent scatterer interferometry (PSI) detects and analyses PS points, which are characterized by strong, stable, and coherent radar signals throughout a SAR image sequence and can be regarded as substructures of buildings in built-up cities. Attributes of PS points, for example, deformation velocities, are derived and used for further analysis. Based on PSI, a 4D change detection technique has been developed to detect disappearance and emergence of PS points (3D) at specific times (1D). In this paper, we apply this 4D technique to the centre of Berlin, Germany, to investigate its feasibility and application for construction monitoring. The aims of the three case studies are to monitor construction progress, business districts, and single buildings, respectively. The disappearing and emerging substructures of the buildings are successfully recognized along with their occurrence times. The changed substructures are then clustered into single construction segments based on DBSCAN clustering and α-shape outlining for object-based analysis. Compared with the ground truth, these spatiotemporal results have proven able to provide more detailed information for construction monitoring.
PSO-based methods for medical image registration and change assessment of pigmented skin

NASA Astrophysics Data System (ADS)

Kacenjar, Steve; Zook, Matthew; Balint, Michael

2011-03-01

There are various scientific and technological areas in which it is imperative to rapidly detect and quantify changes in imagery over time. In fields such as earth remote sensing, aerospace systems, and medical imaging, searching for timedependent, regional changes across deformable topographies is complicated by varying camera acquisition geometries, lighting environments, background clutter conditions, and occlusion. Under these constantly-fluctuating conditions, the use of standard, rigid-body registration approaches often fail to provide sufficient fidelity to overlay image scenes together. This is problematic because incorrect assessments of the underlying changes of high-level topography can result in systematic errors in the quantification and classification of interested areas. For example, in the current naked-eye detection strategies of melanoma, a dermatologist often uses static morphological attributes to identify suspicious skin lesions for biopsy. This approach does not incorporate temporal changes which suggest malignant degeneration. By performing the co-registration of time-separated skin imagery, a dermatologist may more effectively detect and identify early morphological changes in pigmented lesions; enabling the physician to detect cancers at an earlier stage resulting in decreased morbidity and mortality. This paper describes an image processing system which will be used to detect changes in the characteristics of skin lesions over time. The proposed system consists of three main functional elements: 1.) coarse alignment of timesequenced imagery, 2.) refined alignment of local skin topographies, and 3.) assessment of local changes in lesion size. During the coarse alignment process, various approaches can be used to obtain a rough alignment, including: 1.) a manual landmark/intensity-based registration method1, and 2.) several flavors of autonomous optical matched filter methods2. These procedures result in the rough alignment of a patient's back topography. Since the skin is a deformable membrane, this process only provides an initial condition for subsequent refinements in aligning the localized topography of the skin. To achieve a refined enhancement, a Particle Swarm Optimizer (PSO) is used to optimally determine the local camera models associated with a generalized geometric transform. Here the optimization process is driven using the minimization of entropy between the multiple time-separated images. Once the camera models are corrected for local skin deformations, the images are compared using both pixel-based and regional-based methods. Limits on the detectability of change are established by the fidelity to which the algorithm corrects for local skin deformation and background alterations. These limits provide essential information in establishing early-warning thresholds for Melanoma detection. Key to this work is the development of a PSO alignment algorithm to perform the refined alignment in local skin topography between the time sequenced imagery (TSI). Test and validation of this alignment process is achieved using a forward model producing known geometric artifacts in the images and afterwards using a PSO algorithm to demonstrate the ability to identify and correct for these artifacts. Specifically, the forward model introduces local translational, rotational, and magnification changes within the image. These geometric modifiers are expected during TSI acquisition because of logistical issues to precisely align the patient to the image recording geometry and is therefore of paramount importance to any viable image registration system. This paper shows that the PSO alignment algorithm is effective in autonomously determining and mitigating these geometric modifiers. The degree of efficacy is measured by several statistically and morphologically based pre-image filtering operations applied to the TSI imagery before applying the PSO alignment algorithm. These trade studies show that global image threshold binarization provides rapid and superior convergence characteristics relative to that of morphologically based methods.
Change Detection by Rhesus Monkeys (Macaca mulatta) and Pigeons (Columba livia)

PubMed Central

Elmore, L. Caitlin; Magnotti, John F.; Katz, Jeffrey S.; Wright, Anthony A.

2012-01-01

Two monkeys learned a color change-detection task where two colored circles (selected from a 4-color set) were presented on a 4×4 invisible matrix. Following a delay, the correct response was to touch the changed colored circle. The monkeys' learning, color transfer, and delay transfer were compared to a similar experiment with pigeons. Monkeys, like pigeons, showed full transfer to four novel colors, and to delays as long as 6.4 s, suggesting they remembered the colors as opposed to perceptual based attentional capture process that may work at very short delays. The monkeys and pigeons were further tested to compare transfer to other dimensions. Monkeys transferred to shape and location changes, unlike the pigeons, but neither species transferred to size changes. Thus, monkeys were less restricted in their domain to detect change than pigeons, but both species learned the basic task and appear suitable for comparative studies of visual short-term memory. PMID:22428982
A robust multi-kernel change detection framework for detecting leaf beetle defoliation using Landsat 7 ETM+ data

NASA Astrophysics Data System (ADS)

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

2016-12-01

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

NASA Astrophysics Data System (ADS)

Luo, Aiwen; An, Fengwei; Zhang, Xiangyu; Chen, Lei; Huang, Zunkai; Jürgen Mattausch, Hans

2018-04-01

Feature extraction techniques are a cornerstone of object detection in computer-vision-based applications. The detection performance of vison-based detection systems is often degraded by, e.g., changes in the illumination intensity of the light source, foreground-background contrast variations or automatic gain control from the camera. In order to avoid such degradation effects, we present a block-based L1-norm-circuit architecture which is configurable for different image-cell sizes, cell-based feature descriptors and image resolutions according to customization parameters from the circuit input. The incorporated flexibility in both the image resolution and the cell size for multi-scale image pyramids leads to lower computational complexity and power consumption. Additionally, an object-detection prototype for performance evaluation in 65 nm CMOS implements the proposed L1-norm circuit together with a histogram of oriented gradients (HOG) descriptor and a support vector machine (SVM) classifier. The proposed parallel architecture with high hardware efficiency enables real-time processing, high detection robustness, small chip-core area as well as low power consumption for multi-scale object detection.
The Development of Change Blindness: Children's Attentional Priorities whilst Viewing Naturalistic Scenes

ERIC Educational Resources Information Center

Fletcher-Watson, S.; Collis, J. M.; Findlay, J. M.; Leekam, S. R.

2009-01-01

Change blindness describes the surprising difficulty of detecting large changes in visual scenes when changes occur during a visual disruption. In order to study the developmental course of this phenomenon, a modified version of the flicker paradigm, based on Rensink, O'Regan & Clark (1997), was given to three groups of children aged 6-12 years…

A novel fluorescein-based "turn-on" probe for the detection of hydrazine and its application in living cells

NASA Astrophysics Data System (ADS)

Xu, Wen-Zhi; Liu, Wei-Yan; Zhou, Ting-Ting; Yang, Yu-Tao; Li, Wei

2018-03-01

We constructed a novel probe for hydrazine detection based on ICT and PET mechanism. Phthalimide and acetyl ester groups were used as the recognition units. Addition of hydrazine produced a turn-on fluorescence at 525 nm along with the fluorescent color change from dark to yellow. The probe could selectively detect hydrazine over other related interfering species. The detection limit of the probe for hydrazine was calculated to be 0.057 μM which was lower than the EPA standard (0.320 μM). Furthermore, the probe could also be applied for the imaging of hydrazine in living cells.
Moving target detection method based on improved Gaussian mixture model

NASA Astrophysics Data System (ADS)

Ma, J. Y.; Jie, F. R.; Hu, Y. J.

2017-07-01

Gaussian Mixture Model is often employed to build background model in background difference methods for moving target detection. This paper puts forward an adaptive moving target detection algorithm based on improved Gaussian Mixture Model. According to the graylevel convergence for each pixel, adaptively choose the number of Gaussian distribution to learn and update background model. Morphological reconstruction method is adopted to eliminate the shadow.. Experiment proved that the proposed method not only has good robustness and detection effect, but also has good adaptability. Even for the special cases when the grayscale changes greatly and so on, the proposed method can also make outstanding performance.
Localized surface plasmon resonance-based abscisic acid biosensor using aptamer-functionalized gold nanoparticles

PubMed Central

Wang, Shun; Li, Wei; Chang, Keke; Liu, Juan; Guo, Qingqian; Sun, Haifeng; Jiang, Min; Zhang, Hao; Chen, Jing

2017-01-01

Abscisic acid (ABA) plays an important role in abiotic stress response and physiological signal transduction resisting to the adverse environment. Therefore, it is very essential for the quantitative detection of abscisic acid (ABA) due to its indispensable role in plant physiological activities. Herein, a new detection method based on localized surface plasmon resonance (LSPR) using aptamer-functionalized gold nanoparticles (AuNPs) is developed without using expensive instrument and antibody. In the presence of ABA, ABA specifically bind with their aptamers to form the ABA-aptamer complexes with G-quadruplex-like structure and lose the ability to stabilize AuNPs against NaCl-induced aggregation. Meanwhile, the changes of the LSPR spectra of AuNP solution occur and therefore the detection of ABA achieved. Under optimized conditions, this method showed a good linear range covering from 5×10−7 M to 5×10−5 M with a detection limit of 0.33 μM. In practice, the usage of this novel method has been demonstrated by its application to detect ABA from fresh leaves of rice with the relative error of 6.59%-7.93% compared with ELISA bioassay. The experimental results confirmed that this LSPR-based biosensor is simple, selective and sensitive for the detection of ABA. The proposed LSPR method could offer a new analytical platform for the detection of other plant hormones by changing the corresponding aptamer. PMID:28953934
Spatial Probability Dynamically Modulates Visual Target Detection in Chickens

PubMed Central

Sridharan, Devarajan; Ramamurthy, Deepa L.; Knudsen, Eric I.

2013-01-01

The natural world contains a rich and ever-changing landscape of sensory information. To survive, an organism must be able to flexibly and rapidly locate the most relevant sources of information at any time. Humans and non-human primates exploit regularities in the spatial distribution of relevant stimuli (targets) to improve detection at locations of high target probability. Is the ability to flexibly modify behavior based on visual experience unique to primates? Chickens (Gallus domesticus) were trained on a multiple alternative Go/NoGo task to detect a small, briefly-flashed dot (target) in each of the quadrants of the visual field. When targets were presented with equal probability (25%) in each quadrant, chickens exhibited a distinct advantage for detecting targets at lower, relative to upper, hemifield locations. Increasing the probability of presentation in the upper hemifield locations (to 80%) dramatically improved detection performance at these locations to be on par with lower hemifield performance. Finally, detection performance in the upper hemifield changed on a rapid timescale, improving with successive target detections, and declining with successive detections at the diagonally opposite location in the lower hemifield. These data indicate the action of a process that in chickens, as in primates, flexibly and dynamically modulates detection performance based on the spatial probabilities of sensory stimuli as well as on recent performance history. PMID:23734188
Detailed sensory memory, sloppy working memory.

PubMed

Sligte, Ilja G; Vandenbroucke, Annelinde R E; Scholte, H Steven; Lamme, Victor A F

2010-01-01

Visual short-term memory (VSTM) enables us to actively maintain information in mind for a brief period of time after stimulus disappearance. According to recent studies, VSTM consists of three stages - iconic memory, fragile VSTM, and visual working memory - with increasingly stricter capacity limits and progressively longer lifetimes. Still, the resolution (or amount of visual detail) of each VSTM stage has remained unexplored and we test this in the present study. We presented people with a change detection task that measures the capacity of all three forms of VSTM, and we added an identification display after each change trial that required people to identify the "pre-change" object. Accurate change detection plus pre-change identification requires subjects to have a high-resolution representation of the "pre-change" object, whereas change detection or identification only can be based on the hunch that something has changed, without exactly knowing what was presented before. We observed that people maintained 6.1 objects in iconic memory, 4.6 objects in fragile VSTM, and 2.1 objects in visual working memory. Moreover, when people detected the change, they could also identify the pre-change object on 88% of the iconic memory trials, on 71% of the fragile VSTM trials and merely on 53% of the visual working memory trials. This suggests that people maintain many high-resolution representations in iconic memory and fragile VSTM, but only one high-resolution object representation in visual working memory.
An ultrasensitive universal detector based on neutralizer displacement

NASA Astrophysics Data System (ADS)

Das, Jagotamoy; Cederquist, Kristin B.; Zaragoza, Alexandre A.; Lee, Paul E.; Sargent, Edward H.; Kelley, Shana O.

2012-08-01

Diagnostic technologies that can provide the simultaneous detection of nucleic acids for gene expression, proteins for host response and small molecules for profiling the human metabolome will have a significant advantage in providing comprehensive patient monitoring. Molecular sensors that report changes in the electrostatics of a sensor's surface on analyte binding have shown unprecedented sensitivity in the detection of charged biomolecules, but do not lend themselves to the detection of small molecules, which do not carry significant charge. Here, we introduce the neutralizer displacement assay that allows charge-based sensing to be applied to any class of molecule irrespective of the analyte charge. The neutralizer displacement assay starts with an aptamer probe bound to a neutralizer. When analyte binding occurs the neutralizer is displaced, which results in a dramatic change in the surface charge for all types of analytes. We have tested the sensitivity, speed and specificity of this system in the detection of a panel of molecules: (deoxy)ribonucleic acid, ribonucleic acid, cocaine, adenosine triphosphate and thrombin.
A dual-responsive colorimetric and fluorescent chemosensor based on diketopyrrolopyrrole derivative for naked-eye detection of Fe3+ and its practical application.

PubMed

Zhang, Shanshan; Sun, Tao; Xiao, Dejun; Yuan, Fang; Li, Tianduo; Wang, Enhua; Liu, Haixia; Niu, Qingfen

2018-01-15

A novel dual-responsive colorimetric and fluorescent chemosensor L based on diketopyrrolopyrrole derivative for Fe 3+ detection was designed and synthesized. In presence of Fe 3+ , sensor L displayed strong colorimetric response as amaranth to rose pink and significant fluorescence enhancement and chromogenic change, which served as a naked-eye indicator by an obvious color change from purple to red. The binding constant for L-Fe 3+ complex was found as 2.4×10 4 with the lower detection limit of 14.3nM. The sensing mechanism was investigated in detail by fluorescence measurements, IR and 1 H NMR spectra. Sensor L for Fe 3+ detection also exhibited high anti-interference performance, good reversibility, wide pH response range and instantaneous response time. Furthermore, the sensor L has been used to quantify Fe 3+ ions in practical water samples with good recovery. Copyright © 2017 Elsevier B.V. All rights reserved.
Damage detection of engine bladed-disks using multivariate statistical analysis

NASA Astrophysics Data System (ADS)

Fang, X.; Tang, J.

2006-03-01

The timely detection of damage in aero-engine bladed-disks is an extremely important and challenging research topic. Bladed-disks have high modal density and, particularly, their vibration responses are subject to significant uncertainties due to manufacturing tolerance (blade-to-blade difference or mistuning), operating condition change and sensor noise. In this study, we present a new methodology for the on-line damage detection of engine bladed-disks using their vibratory responses during spin-up or spin-down operations which can be measured by blade-tip-timing sensing technique. We apply a principle component analysis (PCA)-based approach for data compression, feature extraction, and denoising. The non-model based damage detection is achieved by analyzing the change between response features of the healthy structure and of the damaged one. We facilitate such comparison by incorporating the Hotelling's statistic T2 analysis, which yields damage declaration with a given confidence level. The effectiveness of the method is demonstrated by case studies.
Accelerometer-based method for correcting signal baseline changes caused by motion artifacts in medical near-infrared spectroscopy

NASA Astrophysics Data System (ADS)

Virtanen, Jaakko; Noponen, Tommi; Kotilahti, Kalle; Virtanen, Juha; Ilmoniemi, Risto J.

2011-08-01

In medical near-infrared spectroscopy (NIRS), movements of the subject often cause large step changes in the baselines of the measured light attenuation signals. This prevents comparison of hemoglobin concentration levels before and after movement. We present an accelerometer-based motion artifact removal (ABAMAR) algorithm for correcting such baseline motion artifacts (BMAs). ABAMAR can be easily adapted to various long-term monitoring applications of NIRS. We applied ABAMAR to NIRS data collected in 23 all-night sleep measurements and containing BMAs from involuntary movements during sleep. For reference, three NIRS researchers independently identified BMAs from the data. To determine whether the use of an accelerometer improves BMA detection accuracy, we compared ABAMAR to motion detection based on peaks in the moving standard deviation (SD) of NIRS data. The number of BMAs identified by ABAMAR was similar to the number detected by the humans, and 79% of the artifacts identified by ABAMAR were confirmed by at least two humans. While the moving SD of NIRS data could also be used for motion detection, on average 2 out of the 10 largest SD peaks in NIRS data each night occurred without the presence of movement. Thus, using an accelerometer improves BMA detection accuracy in NIRS.
Scientific Uncertainties in Climate Change Detection and Attribution Studies

NASA Astrophysics Data System (ADS)

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.
An Optoelectronic Nose for Detection of Toxic Gases

PubMed Central

Lim, Sung H.; Feng, Liang; Kemling, Jonathan W.; Musto, Christopher J.; Suslick, Kenneth S.

2009-01-01

We have developed a simple colorimetric sensor array (CSA) for the detection of a wide range of volatile analytes and applied it to the detection of toxic gases. The sensor consists of a disposable array of cross-responsive nanoporous pigments whose colors are changed by diverse chemical interactions with analytes. Although no single chemically responsive pigment is specific for any one analyte, the pattern of color change for the array is a unique molecular fingerprint. Clear differentiation among 19 different toxic industrial chemicals (TICs) within two minutes of exposure at IDLH (immediately dangerous to life or health) concentration has been demonstrated. Quantification of each analyte is easily accomplished based on the color change of the array, and excellent detection limits have been demonstrated, generally below the PELs (permissible exposure limits). Identification of the TICs was readily achieved using a standard chemometric approach, i.e., hierarchical clustering analysis (HCA), with no misclassifications over 140 trials. PMID:20160982
Signal processing for non-destructive testing of railway tracks

NASA Astrophysics Data System (ADS)

Heckel, Thomas; Casperson, Ralf; Rühe, Sven; Mook, Gerhard

2018-04-01

Increased speed, heavier loads, altered material and modern drive systems result in an increasing number of rail flaws. The appearance of these flaws also changes continually due to the rapid change in damage mechanisms of modern rolling stock. Hence, interpretation has become difficult when evaluating non-destructive rail testing results. Due to the changed interplay between detection methods and flaws, the recorded signals may result in unclassified types of rail flaws. Methods for automatic rail inspection (according to defect detection and classification) undergo continual development. Signal processing is a key technology to master the challenge of classification and maintain resolution and detection quality, independent of operation speed. The basic ideas of signal processing, based on the Glassy-Rail-Diagram for classification purposes, are presented herein. Examples for the detection of damages caused by rolling contact fatigue also are given, and synergetic effects of combined evaluation of diverse inspection methods are shown.
An optoelectronic nose for the detection of toxic gases.

PubMed

Lim, Sung H; Feng, Liang; Kemling, Jonathan W; Musto, Christopher J; Suslick, Kenneth S

2009-10-01

We have developed a simple colorimetric sensor array that detects a wide range of volatile analytes and then applied it to the detection of toxic gases. The sensor consists of a disposable array of cross-responsive nanoporous pigments with colours that are changed by diverse chemical interactions with analytes. Although no single chemically responsive pigment is specific for any one analyte, the pattern of colour change for the array is a unique molecular fingerprint. Clear differentiation among 19 different toxic industrial chemicals (TICs) within two minutes of exposure at concentrations immediately dangerous to life or health were demonstrated. Based on the colour change of the array, quantification of each analyte was accomplished easily, and excellent detection limits were achieved, generally below the permissible exposure limits. Different TICs were identified readily using a standard chemometric approach (hierarchical clustering analysis), with no misclassifications over 140 trials.
Charged Particle Detection: Potential of Love Wave Acoustic Devices

NASA Astrophysics Data System (ADS)

Pedrick, Michael; Tittmann, Bernhard

2006-03-01

An investigation of the dependence of film density on group and phase velocities in a Love Wave Device shows potential for acoustic-based charged particle detection (CPD). Exposure of an ion sensitive photoresist to charged particles causes localized changes in density through either scission or cross-linking. A theoretical model was developed to study ion fluence effects on Love Wave sensitivity based on: ion energy, effective density changes, layer thickness and mode selection. The model is based on a Poly(Methyl Methacralate) (PMMA) film deposited on a Quartz substrate. The effect of Helium ion fluence on the properties of PMMA has previously been studied. These guidelines were used as an initial basis for the prediction of helium ion detection in a PMMA layer. Procedures for experimental characterization of ion effects on the material properties of PMMA are reviewed. Techniques for experimental validation of the predicted velocity shifts are discussed. A Love Wave Device for CPD could potentially provide a cost-effective alternative to semiconductor or photo-based counterparts. The potential for monitoring ion implantation effects on material properties is also discussed.
Quantitative detection of bovine and porcine gelatin difference using surface plasmon resonance based biosensor

NASA Astrophysics Data System (ADS)

Wardani, Devy P.; Arifin, Muhammad; Suharyadi, Edi; Abraha, Kamsul

2015-05-01

Gelatin is a biopolymer derived from collagen that is widely used in food and pharmaceutical products. Due to some religion restrictions and health issues regarding the gelatin consumption which is extracted from certain species, it is necessary to establish a robust, reliable, sensitive and simple quantitative method to detect gelatin from different parent collagen species. To the best of our knowledge, there has not been a gelatin differentiation method based on optical sensor that could detect gelatin from different species quantitatively. Surface plasmon resonance (SPR) based biosensor is known to be a sensitive, simple and label free optical method for detecting biomaterials that is able to do quantitative detection. Therefore, we have utilized SPR-based biosensor to detect the differentiation between bovine and porcine gelatin in various concentration, from 0% to 10% (w/w). Here, we report the ability of SPR-based biosensor to detect difference between both gelatins, its sensitivity toward the gelatin concentration change, its reliability and limit of detection (LOD) and limit of quantification (LOQ) of the sensor. The sensor's LOD and LOQ towards bovine gelatin concentration are 0.38% and 1.26% (w/w), while towards porcine gelatin concentration are 0.66% and 2.20% (w/w), respectively. The results show that SPR-based biosensor is a promising tool for detecting gelatin from different raw materials quantitatively.
RNA-templated single-base mutation detection based on T4 DNA ligase and reverse molecular beacon.

PubMed

Tang, Hongxing; Yang, Xiaohai; Wang, Kemin; Tan, Weihong; Li, Huimin; He, Lifang; Liu, Bin

2008-06-15

A novel RNA-templated single-base mutation detection method based on T4 DNA ligase and reverse molecular beacon (rMB) has been developed and successfully applied to identification of single-base mutation in codon 273 of the p53 gene. The discrimination was carried out using allele-specific primers, which flanked the variable position in the target RNA and was ligated using T4 DNA ligase only when the primers perfectly matched the RNA template. The allele-specific primers also carried complementary stem structures with end-labels (fluorophore TAMRA, quencher DABCYL), which formed a molecular beacon after RNase H digestion. One-base mismatch can be discriminated by analyzing the change of fluorescence intensity before and after RNase H digestion. This method has several advantages for practical applications, such as direct discrimination of single-base mismatch of the RNA extracted from cell; no requirement of PCR amplification; performance of homogeneous detection; and easily design of detection probes.
Convolutional Neural Network-Based Shadow Detection in Images Using Visible Light Camera Sensor.

PubMed

Kim, Dong Seop; Arsalan, Muhammad; Park, Kang Ryoung

2018-03-23

Recent developments in intelligence surveillance camera systems have enabled more research on the detection, tracking, and recognition of humans. Such systems typically use visible light cameras and images, in which shadows make it difficult to detect and recognize the exact human area. Near-infrared (NIR) light cameras and thermal cameras are used to mitigate this problem. However, such instruments require a separate NIR illuminator, or are prohibitively expensive. Existing research on shadow detection in images captured by visible light cameras have utilized object and shadow color features for detection. Unfortunately, various environmental factors such as illumination change and brightness of background cause detection to be a difficult task. To overcome this problem, we propose a convolutional neural network-based shadow detection method. Experimental results with a database built from various outdoor surveillance camera environments, and from the context-aware vision using image-based active recognition (CAVIAR) open database, show that our method outperforms previous works.
Convolutional Neural Network-Based Shadow Detection in Images Using Visible Light Camera Sensor

PubMed Central

Kim, Dong Seop; Arsalan, Muhammad; Park, Kang Ryoung

2018-01-01

Recent developments in intelligence surveillance camera systems have enabled more research on the detection, tracking, and recognition of humans. Such systems typically use visible light cameras and images, in which shadows make it difficult to detect and recognize the exact human area. Near-infrared (NIR) light cameras and thermal cameras are used to mitigate this problem. However, such instruments require a separate NIR illuminator, or are prohibitively expensive. Existing research on shadow detection in images captured by visible light cameras have utilized object and shadow color features for detection. Unfortunately, various environmental factors such as illumination change and brightness of background cause detection to be a difficult task. To overcome this problem, we propose a convolutional neural network-based shadow detection method. Experimental results with a database built from various outdoor surveillance camera environments, and from the context-aware vision using image-based active recognition (CAVIAR) open database, show that our method outperforms previous works. PMID:29570690
Monitoring endemic livestock diseases using laboratory diagnostic data: A simulation study to evaluate the performance of univariate process monitoring control algorithms.

PubMed

Lopes Antunes, Ana Carolina; Dórea, Fernanda; Halasa, Tariq; Toft, Nils

2016-05-01

Surveillance systems are critical for accurate, timely monitoring and effective disease control. In this study, we investigated the performance of univariate process monitoring control algorithms in detecting changes in seroprevalence for endemic diseases. We also assessed the effect of sample size (number of sentinel herds tested in the surveillance system) on the performance of the algorithms. Three univariate process monitoring control algorithms were compared: Shewart p Chart(1) (PSHEW), Cumulative Sum(2) (CUSUM) and Exponentially Weighted Moving Average(3) (EWMA). Increases in seroprevalence were simulated from 0.10 to 0.15 and 0.20 over 4, 8, 24, 52 and 104 weeks. Each epidemic scenario was run with 2000 iterations. The cumulative sensitivity(4) (CumSe) and timeliness were used to evaluate the algorithms' performance with a 1% false alarm rate. Using these performance evaluation criteria, it was possible to assess the accuracy and timeliness of the surveillance system working in real-time. The results showed that EWMA and PSHEW had higher CumSe (when compared with the CUSUM) from week 1 until the end of the period for all simulated scenarios. Changes in seroprevalence from 0.10 to 0.20 were more easily detected (higher CumSe) than changes from 0.10 to 0.15 for all three algorithms. Similar results were found with EWMA and PSHEW, based on the median time to detection. Changes in the seroprevalence were detected later with CUSUM, compared to EWMA and PSHEW for the different scenarios. Increasing the sample size 10 fold halved the time to detection (CumSe=1), whereas increasing the sample size 100 fold reduced the time to detection by a factor of 6. This study investigated the performance of three univariate process monitoring control algorithms in monitoring endemic diseases. It was shown that automated systems based on these detection methods identified changes in seroprevalence at different times. Increasing the number of tested herds would lead to faster detection. However, the practical implications of increasing the sample size (such as the costs associated with the disease) should also be taken into account. Copyright © 2016 Elsevier B.V. All rights reserved.
Investigation of hydrogen sulfide gas using Pd/Pt material based fiber Bragg grating sensor

NASA Astrophysics Data System (ADS)

Bedi, Amna; Rao, Dusari Nageswara; Kumar, Santosh

2018-02-01

In this work, Pd/Pt material based fiber Bragg grating (FBG) sensors has been proposed for detection of hydrogen sulfide gas. Here, characteristics of FBG parameters were numerically calculated and simulated. The variation in reflectivity based on refractive index has been shown. The reflectivity of FBG can be varied when refractive index is changed. The proposed sensor works on very low concentration i.e., 0% to 1%, which has the capability to detect in the early stage.

A platform for proactive, risk-based slope asset management, phase II.

DOT National Transportation Integrated Search

2015-03-01

The lidar visualization technique developed by this project enables highway managers to understand changes in slope characteristics : along highways. This change detection and analysis can be the basis of informed decisions for slope inspection and r...
A platform for proactive, risk-based slope asset management, phase II.

DOT National Transportation Integrated Search

2015-08-01

The lidar visualization technique developed by this project enables highway managers to understand changes : in slope characteristics along highways. This change detection and analysis can be the basis of informed : decisions for slope inspection and...
Iris features-based heart disease diagnosis by computer vision

NASA Astrophysics Data System (ADS)

Nguchu, Benedictor A.; Li, Li

2017-07-01

The study takes advantage of several new breakthroughs in computer vision technology to develop a new mid-irisbiomedical platform that processes iris image for early detection of heart-disease. Guaranteeing early detection of heart disease provides a possibility of having non-surgical treatment as suggested by biomedical researchers and associated institutions. However, our observation discovered that, a clinical practicable solution which could be both sensible and specific for early detection is still lacking. Due to this, the rate of majority vulnerable to death is highly increasing. The delayed diagnostic procedures, inefficiency, and complications of available methods are the other reasons for this catastrophe. Therefore, this research proposes the novel IFB (Iris Features Based) method for diagnosis of premature, and early stage heart disease. The method incorporates computer vision and iridology to obtain a robust, non-contact, nonradioactive, and cost-effective diagnostic tool. The method analyzes abnormal inherent weakness in tissues, change in color and patterns, of a specific region of iris that responds to impulses of heart organ as per Bernard Jensen-iris Chart. The changes in iris infer the presence of degenerative abnormalities in heart organ. These changes are precisely detected and analyzed by IFB method that includes, tensor-based-gradient(TBG), multi orientations gabor filters(GF), textural oriented features(TOF), and speed-up robust features(SURF). Kernel and Multi class oriented support vector machines classifiers are used for classifying normal and pathological iris features. Experimental results demonstrated that the proposed method, not only has better diagnostic performance, but also provides an insight for early detection of other diseases.
Detection of greenhouse-gas-induced climatic change. Progress report, 1 December 1991--30 June 1994

DOE Office of Scientific and Technical Information (OSTI.GOV)

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

1994-07-01

In addition to changes due to variations in greenhouse gas concentrations, the global climate system exhibits a high degree of internally-generated and externally-forced natural variability. To detect the enhanced greenhouse effect, its signal must be isolated from the ``noise`` of this natural climatic variability. A high quality, spatially extensive data base is required to define the noise and its spatial characteristics. To facilitate this, available land and marine data bases will be updated and expanded. The data will be analyzed to determine the potential effects on climate of greenhouse gas concentration changes and other factors. Analyses will be guided bymore » a variety of models, from simple energy balance climate models to ocean General Circulation Models. Appendices A--G contain the following seven papers: (A) Recent global warmth moderated by the effects of the Mount Pinatubo eruption; (B) Recent warming in global temperature series; (C) Correlation methods in fingerprint detection studies; (D) Balancing the carbon budget. Implications for projections of future carbon dioxide concentration changes; (E) A simple model for estimating methane concentration and lifetime variations; (F) Implications for climate and sea level of revised IPCC emissions scenarios; and (G) Sulfate aerosol and climatic change.« less
A comparison of methods for monitoring photon beam energy constancy.

PubMed

Gao, Song; Balter, Peter A; Rose, Mark; Simon, William E

2016-11-08

In extension of a previous study, we compared several photon beam energy metrics to determine which was the most sensitive to energy change; in addition to those, we accounted for both the sensitivity of each metric and the uncertainty in determining that metric for both traditional flattening filter (FF) beams (4, 6, 8, and 10 MV) and for flattening filter-free (FFF) beams (6 and 10 MV) on a Varian TrueBeam. We examined changes in these energy metrics when photon energies were changed to ± 5% and ± 10% from their nominal energies: 1) an attenuation-based metric (the percent depth dose at 10 cm depth, PDD(10)) and, 2) profile-based metrics, including flatness (Flat) and off-axis ratios (OARs) measured on the orthogonal axes or on the diagonals (diagonal normalized flatness, FDN). Profile-based metrics were measured near dmax and also near 10 cm depth in water (using a 3D scanner) and with ioniza-tion chamber array (ICA). PDD(10) was measured only in water. Changes in PDD, OAR, and FDN were nearly linear to the changes in the bend magnet current (BMI) over the range from -10% to +10% for both FF and FFF beams: a ± 10% change in energy resulted in a ± 1.5% change in PDD(10) for both FF and FFF beams, and changes in OAR and FDN were > 3.0% for FF beams and > 2.2% for FFF beams. The uncertainty in determining PDD(10) was estimated to be 0.15% and that for OAR and FDN about 0.07%. This resulted in minimally detectable changes in energy of 2.5% for PDD(10) and 0.5% for OAR and FDN. We found that the OAR- or FDN- based metrics were the best for detecting energy changes for both FF and FFF beams. The ability of the OAR-based metrics determined with a water scanner to detect energy changes was equivalent to that using an ionization chamber array. We recommend that OAR be measured either on the orthogonal axes or the diagonals, using an ionization chamber array near the depth of maximum dose, as a sensitive and efficient way to confirm stability of photon beam energy. © 2016 The Authors.
Detection of motion and posture change using an IR-UWB radar.

PubMed

Van Nguyen; Javaid, Abdul Q; Weitnauer, Mary A

2016-08-01

Impulse radio ultra-wide band (IR-UWB) radar has recently emerged as a promising candidate for non-contact monitoring of respiration and heart rate. Different studies have reported various radar based algorithms for estimation of these physiological parameters. The radar can be placed under a subject's mattress as he lays stationary on his back or it can be attached to the ceiling directly above the subject's bed. However, advertent or inadvertent movement on part of the subject and different postures can affect the radar returned signal and also the accuracy of the estimated parameters from it. The detection and analysis of these postural changes can not only lead to improvement in estimation algorithms but also towards prevention of bed sores and ulcers in patients who require periodic posture changes. In this paper, we present an algorithm that detects and quantifies different types of motion events using an under-the-mattress IR-UWB radar. The algorithm also indicates a change in posture after a macro-movement event. Based on the findings of this paper, we anticipate that IR-UWB radar can be used for extracting posture related information in non-clinical enviroments for patients who are bed-ridden.
Time Series UAV Image-Based Point Clouds for Landslide Progression Evaluation Applications

PubMed Central

Moussa, Adel; El-Sheimy, Naser; Habib, Ayman

2017-01-01

Landslides are major and constantly changing threats to urban landscapes and infrastructure. It is essential to detect and capture landslide changes regularly. Traditional methods for monitoring landslides are time-consuming, costly, dangerous, and the quality and quantity of the data is sometimes unable to meet the necessary requirements of geotechnical projects. This motivates the development of more automatic and efficient remote sensing approaches for landslide progression evaluation. Automatic change detection involving low-altitude unmanned aerial vehicle image-based point clouds, although proven, is relatively unexplored, and little research has been done in terms of accounting for volumetric changes. In this study, a methodology for automatically deriving change displacement rates, in a horizontal direction based on comparisons between extracted landslide scarps from multiple time periods, has been developed. Compared with the iterative closest projected point (ICPP) registration method, the developed method takes full advantage of automated geometric measuring, leading to fast processing. The proposed approach easily processes a large number of images from different epochs and enables the creation of registered image-based point clouds without the use of extensive ground control point information or further processing such as interpretation and image correlation. The produced results are promising for use in the field of landslide research. PMID:29057847
Time Series UAV Image-Based Point Clouds for Landslide Progression Evaluation Applications.

PubMed

Al-Rawabdeh, Abdulla; Moussa, Adel; Foroutan, Marzieh; El-Sheimy, Naser; Habib, Ayman

2017-10-18

Landslides are major and constantly changing threats to urban landscapes and infrastructure. It is essential to detect and capture landslide changes regularly. Traditional methods for monitoring landslides are time-consuming, costly, dangerous, and the quality and quantity of the data is sometimes unable to meet the necessary requirements of geotechnical projects. This motivates the development of more automatic and efficient remote sensing approaches for landslide progression evaluation. Automatic change detection involving low-altitude unmanned aerial vehicle image-based point clouds, although proven, is relatively unexplored, and little research has been done in terms of accounting for volumetric changes. In this study, a methodology for automatically deriving change displacement rates, in a horizontal direction based on comparisons between extracted landslide scarps from multiple time periods, has been developed. Compared with the iterative closest projected point (ICPP) registration method, the developed method takes full advantage of automated geometric measuring, leading to fast processing. The proposed approach easily processes a large number of images from different epochs and enables the creation of registered image-based point clouds without the use of extensive ground control point information or further processing such as interpretation and image correlation. The produced results are promising for use in the field of landslide research.
3D change detection - Approaches and applications

NASA Astrophysics Data System (ADS)

Qin, Rongjun; Tian, Jiaojiao; Reinartz, Peter

2016-12-01

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

DOEpatents

Roberts, Peter M.; Fehler, Michael C.; Johnson, Paul A.; Phillips, W. Scott

1994-01-01

Small changes in material properties of a work piece are detected by measuring small changes in elastic wave velocity and attenuation within a work piece. Active, repeatable source generate coda wave responses from a work piece, where the coda wave responses are temporally displaced. By analyzing progressive relative phase and amplitude changes between the coda wave responses as a function of elapsed time, accurate determinations of velocity and attenuation changes are made. Thus, a small change in velocity occurring within a sample region during the time periods between excitation origin times (herein called "doublets") will produce a relative delay that changes with elapsed time over some portion of the scattered waves. This trend of changing delay is easier to detect than an isolated delay based on a single arrival and provides a direct measure of elastic wave velocity changes arising from changed material properties of the work piece.
Laser-induced photo emission detection: data acquisition based on light intensity counting

NASA Astrophysics Data System (ADS)

Yulianto, N.; Yudasari, N.; Putri, K. Y.

2017-04-01

Laser Induced Breakdown Detection (LIBD) is one of the quantification techniques for colloids. There are two ways of detection in LIBD: optical detection and acoustic detection. LIBD is based on the detection of plasma emission due to the interaction between particle and laser beam. In this research, the changing of light intensity during plasma formations was detected by a photodiode sensor. A photo emission data acquisition system was built to collect and transform them into digital counts. The real-time system used data acquisition device National Instrument DAQ 6009 and LABVIEW software. The system has been tested on distilled water and tap water samples. The result showed 99.8% accuracy by using counting technique in comparison to the acoustic detection with sample rate of 10 Hz, thus the acquisition system can be applied as an alternative method to the existing LIBD acquisition system.
Detection, monitoring, and evaluation of spatio-temporal change in mosquito populations

USDA-ARS?s Scientific Manuscript database

USDA-ARS scientists seek to implement a sampling and global information technology based system that can be used for mosquito detection and trap deployment, to estimate mosquito species composition and distribution in space and time, and for targeting and evaluation of mosquito controls. Knowledge ...
Characterizing body temperature and activity changes at the onset of estrus in replacement gilts

USDA-ARS?s Scientific Manuscript database

Accurate estrus detection can improve sow conception rates and increase swine production efficiency. Unfortunately, current estrus detection practices based on individual animal behavior may be inefficient due to large sow populations at commercial farms and the associated labor required. Therefore,...
Indigenous Knowledge and Long-term Ecological Change: Detection, Interpretation, and Responses to Changing Ecological Conditions in Pacific Island Communities

PubMed Central

Aswani, Shankar

2010-01-01

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

NASA Astrophysics Data System (ADS)

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 increase the sampling frequency, while the developed multiscale-driven approach reliably identified changes embedded in largely stationary background scenes. With this technique, I was able to identify the extent of burn scars with high accuracy. I further applied the application of the change detection technology to oil spill mapping. The analysis highlights that the approach described in Chapter 3 can be applied to this drastically different change detection problem with only little modification. While the core of the change detection technique remained unchanged, I made modifications to the pre-processing step to enable change detection from scenes of continuously varying background. I introduced the Lipschitz regularity (LR) transformation as a technique to normalize the typically dynamic ocean surface, facilitating high performance oil spill detection independent of environmental conditions during image acquisition. For instance, I showed that LR processing reduces the sensitivity of change detection performance to variations in surface winds, which is a known limitation in oil spill detection from SAR. Finally, I applied the change detection technique to aufeis flood mapping along the Sagavanirktok River. Due to the complex nature of aufeis flooded areas, I substituted the resolution-preserving speckle filter used in Chapter 3 with curvelet filters. In addition to validating the performance of the change detection results, I also provide evidence of the wealth of information that can be extracted about aufeis flooding events once a time series of change detection information was extracted from SAR imagery. A summary of the developed change detection techniques is conducted and suggested future work is presented in Chapter 6.
Iohexol clearance is superior to creatinine-based renal function estimating equations in detecting short-term renal function decline in chronic heart failure.

PubMed

Cvan Trobec, Katja; Kerec Kos, Mojca; von Haehling, Stephan; Anker, Stefan D; Macdougall, Iain C; Ponikowski, Piotr; Lainscak, Mitja

2015-12-01

To compare the performance of iohexol plasma clearance and creatinine-based renal function estimating equations in monitoring longitudinal renal function changes in chronic heart failure (CHF) patients, and to assess the effects of body composition on the equation performance. Iohexol plasma clearance was measured in 43 CHF patients at baseline and after at least 6 months. Simultaneously, renal function was estimated with five creatinine-based equations (four- and six-variable Modification of Diet in Renal Disease, Cockcroft-Gault, Cockcroft-Gault adjusted for lean body mass, Chronic Kidney Disease Epidemiology Collaboration equation) and body composition was assessed using bioimpedance and dual-energy x-ray absorptiometry. Over a median follow-up of 7.5 months (range 6-17 months), iohexol clearance significantly declined (52.8 vs 44.4 mL/[min ×1.73 m2], P=0.001). This decline was significantly higher in patients receiving mineralocorticoid receptor antagonists at baseline (mean decline -22% of baseline value vs -3%, P=0.037). Mean serum creatinine concentration did not change significantly during follow-up and no creatinine-based renal function estimating equation was able to detect the significant longitudinal decline of renal function determined by iohexol clearance. After accounting for body composition, the accuracy of the equations improved, but not their ability to detect renal function decline. Renal function measured with iohexol plasma clearance showed relevant decline in CHF patients, particularly in those treated with mineralocorticoid receptor antagonists. None of the equations for renal function estimation was able to detect these changes. ClinicalTrials.gov registration number: NCT01829880.
Factors influencing variation in physician adenoma detection rates: a theory-based approach for performance improvement.

PubMed

Atkins, Louise; Hunkeler, Enid M; Jensen, Christopher D; Michie, Susan; Lee, Jeffrey K; Doubeni, Chyke A; Zauber, Ann G; Levin, Theodore R; Quinn, Virginia P; Corley, Douglas A

2016-03-01

Interventions to improve physician adenoma detection rates for colonoscopy have generally not been successful, and there are little data on the factors contributing to variation that may be appropriate targets for intervention. We sought to identify factors that may influence variation in detection rates by using theory-based tools for understanding behavior. We separately studied gastroenterologists and endoscopy nurses at 3 Kaiser Permanente Northern California medical centers to identify potentially modifiable factors relevant to physician adenoma detection rate variability by using structured group interviews (focus groups) and theory-based tools for understanding behavior and eliciting behavior change: the Capability, Opportunity, and Motivation behavior model; the Theoretical Domains Framework; and the Behavior Change Wheel. Nine factors potentially associated with adenoma detection rate variability were identified, including 6 related to capability (uncertainty about which types of polyps to remove, style of endoscopy team leadership, compromised ability to focus during an examination due to distractions, examination technique during withdrawal, difficulty detecting certain types of adenomas, and examiner fatigue and pain), 2 related to opportunity (perceived pressure due to the number of examinations expected per shift and social pressure to finish examinations before scheduled breaks or the end of a shift), and 1 related to motivation (valuing a meticulous examination as the top priority). Examples of potential intervention strategies are provided. By using theory-based tools, this study identified several novel and potentially modifiable factors relating to capability, opportunity, and motivation that may contribute to adenoma detection rate variability and be appropriate targets for future intervention trials. Copyright © 2016 American Society for Gastrointestinal Endoscopy. Published by Elsevier Inc. All rights reserved.
An Adaptive Failure Detector Based on Quality of Service in Peer-to-Peer Networks

PubMed Central

Dong, Jian; Ren, Xiao; Zuo, Decheng; Liu, Hongwei

2014-01-01

The failure detector is one of the fundamental components that maintain high availability of Peer-to-Peer (P2P) networks. Under different network conditions, the adaptive failure detector based on quality of service (QoS) can achieve the detection time and accuracy required by upper applications with lower detection overhead. In P2P systems, complexity of network and high churn lead to high message loss rate. To reduce the impact on detection accuracy, baseline detection strategy based on retransmission mechanism has been employed widely in many P2P applications; however, Chen's classic adaptive model cannot describe this kind of detection strategy. In order to provide an efficient service of failure detection in P2P systems, this paper establishes a novel QoS evaluation model for the baseline detection strategy. The relationship between the detection period and the QoS is discussed and on this basis, an adaptive failure detector (B-AFD) is proposed, which can meet the quantitative QoS metrics under changing network environment. Meanwhile, it is observed from the experimental analysis that B-AFD achieves better detection accuracy and time with lower detection overhead compared to the traditional baseline strategy and the adaptive detectors based on Chen's model. Moreover, B-AFD has better adaptability to P2P network. PMID:25198005
Nanoparticle-Based and Bioengineered Probes and Sensors to Detect Physiological and Pathological Biomarkers in Neural Cells

PubMed Central

Maysinger, Dusica; Ji, Jeff; Hutter, Eliza; Cooper, Elis

2015-01-01

Nanotechnology, a rapidly evolving field, provides simple and practical tools to investigate the nervous system in health and disease. Among these tools are nanoparticle-based probes and sensors that detect biochemical and physiological properties of neurons and glia, and generate signals proportionate to physical, chemical, and/or electrical changes in these cells. In this context, quantum dots (QDs), carbon-based structures (C-dots, grapheme, and nanodiamonds) and gold nanoparticles are the most commonly used nanostructures. They can detect and measure enzymatic activities of proteases (metalloproteinases, caspases), ions, metabolites, and other biomolecules under physiological or pathological conditions in neural cells. Here, we provide some examples of nanoparticle-based and genetically engineered probes and sensors that are used to reveal changes in protease activities and calcium ion concentrations. Although significant progress in developing these tools has been made for probing neural cells, several challenges remain. We review many common hurdles in sensor development, while highlighting certain advances. In the end, we propose some future directions and ideas for developing practical tools for neural cell investigations, based on the maxim “Measure what is measurable, and make measurable what is not so” (Galileo Galilei). PMID:26733793
Sensitivity of acoustic nonlinearity parameter to the microstructural changes in cement-based materials

NASA Astrophysics Data System (ADS)

Kim, Gun; Kim, Jin-Yeon; Kurtis, Kimberly E.; Jacobs, Laurence J.

2015-03-01

This research experimentally investigates the sensitivity of the acoustic nonlinearity parameter to microcracks in cement-based materials. Based on the second harmonic generation (SHG) technique, an experimental setup using non-contact, air-coupled detection is used to receive the consistent Rayleigh surface waves. To induce variations in the extent of microscale cracking in two types of specimens (concrete and mortar), shrinkage reducing admixture (SRA), is used in one set, while a companion specimen is prepared without SRA. A 50 kHz wedge transducer and a 100 kHz air-coupled transducer are implemented for the generation and detection of nonlinear Rayleigh waves. It is shown that the air-coupled detection method provides more repeatable fundamental and second harmonic amplitudes of the propagating Rayleigh waves. The obtained amplitudes are then used to calculate the relative nonlinearity parameter βre, the ratio of the second harmonic amplitude to the square of the fundamental amplitude. The experimental results clearly demonstrate that the nonlinearity parameter (βre) is highly sensitive to the microstructural changes in cement-based materials than the Rayleigh phase velocity and attenuation and that SRA has great potential to avoid shrinkage cracking in cement-based materials.

Unsupervised change detection in a particular vegetation land cover type using spectral angle mapper

NASA Astrophysics Data System (ADS)

Renza, Diego; Martinez, Estibaliz; Molina, Iñigo; Ballesteros L., Dora M.

2017-04-01

This paper presents a new unsupervised change detection methodology for multispectral images applied to specific land covers. The proposed method involves comparing each image against a reference spectrum, where the reference spectrum is obtained from the spectral signature of the type of coverage you want to detect. In this case the method has been tested using multispectral images (SPOT5) of the community of Madrid (Spain), and multispectral images (Quickbird) of an area over Indonesia that was impacted by the December 26, 2004 tsunami; here, the tests have focused on the detection of changes in vegetation. The image comparison is obtained by applying Spectral Angle Mapper between the reference spectrum and each multitemporal image. Then, a threshold to produce a single image of change is applied, which corresponds to the vegetation zones. The results for each multitemporal image are combined through an exclusive or (XOR) operation that selects vegetation zones that have changed over time. Finally, the derived results were compared against a supervised method based on classification with the Support Vector Machine. Furthermore, the NDVI-differencing and the Spectral Angle Mapper techniques were selected as unsupervised methods for comparison purposes. The main novelty of the method consists in the detection of changes in a specific land cover type (vegetation), therefore, for comparison purposes, the best scenario is to compare it with methods that aim to detect changes in a specific land cover type (vegetation). This is the main reason to select NDVI-based method and the post-classification method (SVM implemented in a standard software tool). To evaluate the improvements using a reference spectrum vector, the results are compared with the basic-SAM method. In SPOT5 image, the overall accuracy was 99.36% and the κ index was 90.11%; in Quickbird image, the overall accuracy was 97.5% and the κ index was 82.16%. Finally, the precision results of the method are comparable to those of a supervised method, supported by low detection of false positives and false negatives, along with a high overall accuracy and a high kappa index. On the other hand, the execution times were comparable to those of unsupervised methods of low computational load.
A new rhodamine-based colorimetric chemosensor for naked-eye detection of Cu(2+) in aqueous solution.

PubMed

Hu, Yang; Zhang, Jing; Lv, Yuan-Zheng; Huang, Xiao-Huan; Hu, Sheng-Li

2016-03-15

A new colorimetric probe 1 based on rhodamine B lactam was developed for naked-eye detection of Cu(2+). The optical feature of 1 for Cu(2+) was investigated by UV-vis absorption spectroscopy. Upon the addition of Cu(2+), the 1 displayed a distinct color change from colorless to pink, which can be directly detected by the naked eye. The stoichiometry of 1 to Cu(2+) complex was found to be 1:1 and the naked-eye detection limit was determined as low as 2 μM. The results suggest that the probe 1 may provide a convenient method for visual detection of Cu(2+) with high sensitivity. Copyright © 2015 Elsevier B.V. All rights reserved.
A wire-based dual-analyte sensor for glucose and lactate: in vitro and in vivo evaluation.

PubMed

Ward, W Kenneth; House, Jody L; Birck, Jonathan; Anderson, Ellen M; Jansen, Lawrence B

2004-06-01

Continuous measurement of lactate is potentially useful for detecting physical exhaustion and for monitoring critical care conditions characterized by hypoperfusion, such as heart failure. In some conditions, it may be desirable to monitor more than one metabolic parameter concurrently. For this reason, we designed and fabricated twisted wire-based microelectrodes that can measure both lactate and glucose. These dual-analyte sensors were characterized in vitro by measuring their response to the analyte of interest and to assess whether they were susceptible to interference from the other analyte. When measured in stirred aqueous buffer, lactate sensors detected a very small amount of crosstalk from glucose in vitro, although this signal was less than 3% of the response to lactate. Glucose sensors did not detect crosstalk from lactate. Sensors were implanted subcutaneously in rats and tested during infusions of lactate and glucose. Each sensing electrode responded rapidly to changes in its analyte concentration, and there was no evidence of in vivo crosstalk. This study constitutes proof of the concept that oxidase-based, amperometric wire microsensors can detect changes in glucose and lactate during subcutaneous implantation in rats.
Rapid systematic assessment of the detection and attribution of regional anthropogenic climate change

NASA Astrophysics Data System (ADS)

Stone, Dáithí A.; Hansen, Gerrit

2016-09-01

Despite being a well-established research field, the detection and attribution of observed climate change to anthropogenic forcing is not yet provided as a climate service. One reason for this is the lack of a methodology for performing tailored detection and attribution assessments on a rapid time scale. Here we develop such an approach, based on the translation of quantitative analysis into the "confidence" language employed in recent Assessment Reports of the Intergovernmental Panel on Climate Change. While its systematic nature necessarily ignores some nuances examined in detailed expert assessments, the approach nevertheless goes beyond most detection and attribution studies in considering contributors to building confidence such as errors in observational data products arising from sparse monitoring networks. When compared against recent expert assessments, the results of this approach closely match those of the existing assessments. Where there are small discrepancies, these variously reflect ambiguities in the details of what is being assessed, reveal nuances or limitations of the expert assessments, or indicate limitations of the accuracy of the sort of systematic approach employed here. Deployment of the method on 116 regional assessments of recent temperature and precipitation changes indicates that existing rules of thumb concerning the detectability of climate change ignore the full range of sources of uncertainty, most particularly the importance of adequate observational monitoring.
High-Performance Sensors Based on Resistance Fluctuations of Single-Layer-Graphene Transistors.

PubMed

Amin, Kazi Rafsanjani; Bid, Aveek

2015-09-09

One of the most interesting predicted applications of graphene-monolayer-based devices is as high-quality sensors. In this article, we show, through systematic experiments, a chemical vapor sensor based on the measurement of low-frequency resistance fluctuations of single-layer-graphene field-effect-transistor devices. The sensor has extremely high sensitivity, very high specificity, high fidelity, and fast response times. The performance of the device using this scheme of measurement (which uses resistance fluctuations as the detection parameter) is more than 2 orders of magnitude better than a detection scheme in which changes in the average value of the resistance is monitored. We propose a number-density-fluctuation-based model to explain the superior characteristics of a noise-measurement-based detection scheme presented in this article.
Dissociable loss of the representations in visual short-term memory.

PubMed

Li, Jie

2016-01-01

The present study investigated in what manner the information in visual short-term memory (VSTM) is lost. Participants memorized four items, one of which was given higher priority later by a retro-cue. Then participants were required to detect a possible change, which could be either a large or small change, occurred to one of the items. The results showed that the detection performance for the small change of the uncued items was poorer than the cued item, yet large change that occurred to all four memory items could be detected perfectly, indicating that the uncued representations lost some detailed information yet still had some basic features retained in VSTM. The present study suggests that after being encoded into VSTM, the information is not lost in an object-based manner; rather, features of an item are still dissociable, so that they can be lost separately.
Arterial endothelial function measurement method and apparatus

DOEpatents

Maltz, Jonathan S; Budinger, Thomas F

2014-03-04

A "relaxoscope" (100) detects the degree of arterial endothelial function. Impairment of arterial endothelial function is an early event in atherosclerosis and correlates with the major risk factors for cardiovascular disease. An artery (115), such as the brachial artery (BA) is measured for diameter before and after several minutes of either vasoconstriction or vasorelaxation. The change in arterial diameter is a measure of flow-mediated vasomodification (FMVM). The relaxoscope induces an artificial pulse (128) at a superficial radial artery (115) via a linear actuator (120). An ultrasonic Doppler stethoscope (130) detects this pulse 10-20 cm proximal to the point of pulse induction (125). The delay between pulse application and detection provides the pulse transit time (PTT). By measuring PTT before (160) and after arterial diameter change (170), FMVM may be measured based on the changes in PTT caused by changes in vessel caliber, smooth muscle tone and wall thickness.
Paper-based chemiresistor for detection of ultralow concentrations of protein.

PubMed

Pozuelo, Marta; Blondeau, Pascal; Novell, Marta; Andrade, Francisco J; Xavier Rius, F; Riu, Jordi

2013-11-15

A new paper-based chemiresistor composed of a network of single-wall carbon nanotubes (SWCNTs) and anti-human immunoglobulin G (anti-HIgG) is reported herein. SWCNTs act as outstanding transducers because they provide high sensitivity in terms of resistance changes due to immunoreaction. As a result, the resistance-based biosensor reaches concentration detection as low as picomolar. The resulting paper-based biosensor is sensitive, selective and employs low-cost substrate and simple manufacturing stages. Since chemiresistors require low-power equipment and are able to detect low concentrations with inexpensive materials, the present approach may pave the way for the development of resistive biosensors at very low-cost with high performances. Copyright © 2013 Elsevier B.V. All rights reserved.
Wetware, Hardware, or Software Incapacitation: Observational Methods to Determine When Autonomy Should Assume Control

NASA Technical Reports Server (NTRS)

Trujillo, Anna C.; Gregory, Irene M.

2014-01-01

Control-theoretic modeling of human operator's dynamic behavior in manual control tasks has a long, rich history. There has been significant work on techniques used to identify the pilot model of a given structure. This research attempts to go beyond pilot identification based on experimental data to develop a predictor of pilot behavior. Two methods for pre-dicting pilot stick input during changing aircraft dynamics and deducing changes in pilot behavior are presented This approach may also have the capability to detect a change in a subject due to workload, engagement, etc., or the effects of changes in vehicle dynamics on the pilot. With this ability to detect changes in piloting behavior, the possibility now exists to mediate human adverse behaviors, hardware failures, and software anomalies with autono-my that may ameliorate these undesirable effects. However, appropriate timing of when au-tonomy should assume control is dependent on criticality of actions to safety, sensitivity of methods to accurately detect these adverse changes, and effects of changes in levels of auto-mation of the system as a whole.
When can ocean acidification impacts be detected from decadal alkalinity measurements?

NASA Astrophysics Data System (ADS)

Carter, B. R.; Frölicher, T. L.; Dunne, J. P.; Rodgers, K. B.; Slater, R. D.; Sarmiento, J. L.

2016-04-01

We use a large initial condition suite of simulations (30 runs) with an Earth system model to assess the detectability of biogeochemical impacts of ocean acidification (OA) on the marine alkalinity distribution from decadally repeated hydrographic measurements such as those produced by the Global Ship-Based Hydrographic Investigations Program (GO-SHIP). Detection of these impacts is complicated by alkalinity changes from variability and long-term trends in freshwater and organic matter cycling and ocean circulation. In our ensemble simulation, variability in freshwater cycling generates large changes in alkalinity that obscure the changes of interest and prevent the attribution of observed alkalinity redistribution to OA. These complications from freshwater cycling can be mostly avoided through salinity normalization of alkalinity. With the salinity-normalized alkalinity, modeled OA impacts are broadly detectable in the surface of the subtropical gyres by 2030. Discrepancies between this finding and the finding of an earlier analysis suggest that these estimates are strongly sensitive to the patterns of calcium carbonate export simulated by the model. OA impacts are detectable later in the subpolar and equatorial regions due to slower responses of alkalinity to OA in these regions and greater seasonal equatorial alkalinity variability. OA impacts are detectable later at depth despite lower variability due to smaller rates of change and consistent measurement uncertainty.
Ground-Based Photomonitoring of Ecoregional Ecological Changes in Northwestern Yunnan, China

Treesearch

James P. Lassoie; Kiran E. Goldman; Robert K. Moseley

2006-01-01

Barring abrupt natural or anthropogenic disasters, ecological changes in terrestrial landscapes proceed at a pace not readily detected by humans. The use of historical repeat photography can provide valuable information about such changes, but, these studies are opportunistic in that they must rely on old photographs. Hence, their ecological interpretative power is...
Label-free DNA biosensor based on resistance change of platinum nanoparticles assemblies.

PubMed

Skotadis, Evangelos; Voutyras, Konstantinos; Chatzipetrou, Marianneza; Tsekenis, Georgios; Patsiouras, Lampros; Madianos, Leonidas; Chatzandroulis, Stavros; Zergioti, Ioanna; Tsoukalas, Dimitris

2016-07-15

A novel nanoparticle based biosensor for the fast and simple detection of DNA hybridization events is presented. The sensor utilizes hybridized DNA's charge transport properties, combining them with metallic nanoparticle networks that act as nano-gapped electrodes. The DNA hybridization events can be detected by a significant reduction in the sensor's resistance due to the conductive bridging offered by hybridized DNA. By modifying the nanoparticle surface coverage, which can be controlled experimentally being a function of deposition time, and the structural properties of the electrodes, an optimized biosensor for the in situ detection of DNA hybridization events is ultimately fabricated. The fabricated biosensor exhibits a wide response range, covering four orders of magnitude, a limit of detection of 1nM and can detect a single base pair mismatch between probe and complementary DNA. Copyright © 2016 Elsevier B.V. All rights reserved.
A Data Stream Model For Runoff Simulation In A Changing Environment

NASA Astrophysics Data System (ADS)

Yang, Q.; Shao, J.; Zhang, H.; Wang, G.

2017-12-01

Runoff simulation is of great significance for water engineering design, water disaster control, water resources planning and management in a catchment or region. A large number of methods including concept-based process-driven models and statistic-based data-driven models, have been proposed and widely used in worldwide during past decades. Most existing models assume that the relationship among runoff and its impacting factors is stationary. However, in the changing environment (e.g., climate change, human disturbance), their relationship usually evolves over time. In this study, we propose a data stream model for runoff simulation in a changing environment. Specifically, the proposed model works in three steps: learning a rule set, expansion of a rule, and simulation. The first step is to initialize a rule set. When a new observation arrives, the model will check which rule covers it and then use the rule for simulation. Meanwhile, Page-Hinckley (PH) change detection test is used to monitor the online simulation error of each rule. If a change is detected, the corresponding rule is removed from the rule set. In the second step, for each rule, if it covers more than a given number of instance, the rule is expected to expand. In the third step, a simulation model of each leaf node is learnt with a perceptron without activation function, and is updated with adding a newly incoming observation. Taking Fuxi River catchment as a case study, we applied the model to simulate the monthly runoff in the catchment. Results show that abrupt change is detected in the year of 1997 by using the Page-Hinckley change detection test method, which is consistent with the historic record of flooding. In addition, the model achieves good simulation results with the RMSE of 13.326, and outperforms many established methods. The findings demonstrated that the proposed data stream model provides a promising way to simulate runoff in a changing environment.
Instrument characterization for the detection of long-term changes in stratospheric ozone - An analysis of the SBUV/2 radiometer

NASA Technical Reports Server (NTRS)

Frederick, J. E.; Heath, D. F.; Cebula, R. P.

1986-01-01

The scientific objective of unambiguously detecting subtle global trends in upper stratospheric ozone requires that one maintains a thorough understanding of the satellite-based remote sensors intended for this task. The instrument now in use for long term ozone monitoring is the SBUV/2 being flown on NOAA operational satellites. A critical activity in the data interpretation involves separating small changes in measurement sensitivity from true atmospheric variability. By defining the specific issues that must be addressed and presenting results derived early in the mission of the first SBUV/2 flight model, this work serves as a guide to the instrument investigations that are essential in the attempt to detect long-term changes in the ozone layer.
Rapid surface defect detection based on singular value decomposition using steel strips as an example

NASA Astrophysics Data System (ADS)

Sun, Qianlai; Wang, Yin; Sun, Zhiyi

2018-05-01

For most surface defect detection methods based on image processing, image segmentation is a prerequisite for determining and locating the defect. In our previous work, a method based on singular value decomposition (SVD) was used to determine and approximately locate surface defects on steel strips without image segmentation. For the SVD-based method, the image to be inspected was projected onto its first left and right singular vectors respectively. If there were defects in the image, there would be sharp changes in the projections. Then the defects may be determined and located according sharp changes in the projections of each image to be inspected. This method was simple and practical but the SVD should be performed for each image to be inspected. Owing to the high time complexity of SVD itself, it did not have a significant advantage in terms of time consumption over image segmentation-based methods. Here, we present an improved SVD-based method. In the improved method, a defect-free image is considered as the reference image which is acquired under the same environment as the image to be inspected. The singular vectors of each image to be inspected are replaced by the singular vectors of the reference image, and SVD is performed only once for the reference image off-line before detecting of the defects, thus greatly reducing the time required. The improved method is more conducive to real-time defect detection. Experimental results confirm its validity.
An approach to calculating metal particle detection in lubrication oil based on a micro inductive sensor

NASA Astrophysics Data System (ADS)

Wu, Yu; Zhang, Hongpeng

2017-12-01

A new microfluidic chip is presented to enhance the sensitivity of a micro inductive sensor, and an approach to coil inductance change calculation is introduced for metal particle detection in lubrication oil. Electromagnetic knowledge is used to establish a mathematical model of an inductive sensor for metal particle detection, and the analytic expression of coil inductance change is obtained by a magnetic vector potential. Experimental verification is carried out. The results show that copper particles 50-52 µm in diameter have been detected; the relative errors between the theoretical and experimental values are 7.68% and 10.02% at particle diameters of 108-110 µm and 50-52 µm, respectively. The approach presented here can provide a theoretical basis for an inductive sensor in metal particle detection in oil and other areas of application.
Eye gazing direction inspection based on image processing technique

NASA Astrophysics Data System (ADS)

Hao, Qun; Song, Yong

2005-02-01

According to the research result in neural biology, human eyes can obtain high resolution only at the center of view of field. In the research of Virtual Reality helmet, we design to detect the gazing direction of human eyes in real time and feed it back to the control system to improve the resolution of the graph at the center of field of view. In the case of current display instruments, this method can both give attention to the view field of virtual scene and resolution, and improve the immersion of virtual system greatly. Therefore, detecting the gazing direction of human eyes rapidly and exactly is the basis of realizing the design scheme of this novel VR helmet. In this paper, the conventional method of gazing direction detection that based on Purklinje spot is introduced firstly. In order to overcome the disadvantage of the method based on Purklinje spot, this paper proposed a method based on image processing to realize the detection and determination of the gazing direction. The locations of pupils and shapes of eye sockets change with the gazing directions. With the aid of these changes, analyzing the images of eyes captured by the cameras, gazing direction of human eyes can be determined finally. In this paper, experiments have been done to validate the efficiency of this method by analyzing the images. The algorithm can carry out the detection of gazing direction base on normal eye image directly, and it eliminates the need of special hardware. Experiment results show that the method is easy to implement and have high precision.
Investigating Low-Cost Optical Spectroscopy for Sensing Pressure Ulcers

NASA Astrophysics Data System (ADS)

Mirchandani, Smruti Suresh

Diffuse Reflectance Spectroscopy has been used widely to characterize tissue properties for diagnostic and therapeutic applications. This thesis focuses on the use of spectroscopy for early pressure ulcer detection. The most common early diagnosis technique for pressure ulcers is a blanch test. A major issue with a blanch test is that it is purely visual and cannot be visibly observed on dark skinned individuals. Studies have already proven that spectroscopy can be used to detect blanch response in skin across light and dark skinned individuals. The portable reflectance spectroscopy setup showed that pressure changes to the skin can be detected spectroscopically. Some work on an iPhone based spectrometer was also done to have a low-cost spectroscopy alternative to the usual DRS equipment. This study failed to develop an iPhone based spectrometer but various factors that can be changed to better this research have been mentioned in this thesis.
Development of a conductivity-based photothermal absorbance detection microchip using polyelectrolytic gel electrodes.

PubMed

Chun, Honggu; Dennis, Patty J; Ferguson Welch, Erin R; Alarie, Jean Pierre; Jorgenson, James W; Ramsey, J Michael

2017-11-10

The development and application of polyelectrolytic gel electrodes (PGEs) for a microfluidic photothermal absorbance detection system is described. The PGEs are used to measure changes in conductivity based on heat generation by analytes absorbing light and changing the solution viscosity. The PGEs are suitable for direct contact conductivity measurements since they do not degrade with exposure to high electric fields. Both a 2-electrode system with DC voltages and a 3-electrode system with AC voltages were investigated. Experimental factors including excitation voltage, excitation frequency, laser modulation frequency, laser power, and path length were tested. The limits of detection for the 3-electrode and 2-electrode systems are 500nM and 0.55nM for DABSYL-tagged glucosamine, respectively. In addition, an electrokinetic separation of a potassium, DABSYL-tagged glucosamine, Rhodamine 6G, and Rhodamine B mixture was demonstrated. Copyright © 2017 Elsevier B.V. All rights reserved.
Use of change detection in assessing development plans - A Philippine example. [aircraft/Landsat remote sensing information system for regional planning

NASA Technical Reports Server (NTRS)

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.

Adaptive Automation for Human Supervision of Multiple Uninhabited Vehicles: Effects on Change Detection, Situation Awareness, and Mental Workload

DTIC Science & Technology

2009-01-01

transient was present. BASELINE EXPERIMENT Methods Participants Sixteen young adults (9 women , 7 men ) aged 18–26 years (mean = 20.5) partic- ipated...Sixteen young adults (8 women , 8 men ) aged 18–28 years (mean = 21.9) partici- pated. The experiment lasted approximately 2 hours and participants were...based on the operator’s change detection performance. Mis- sion scenarios involved supervision of multiple UVs and required multitasking . Effects of
Ozone Air Quality over North America: Part II-An Analysis of Trend Detection and Attribution Techniques.

PubMed

Porter, P Steven; Rao, S Trivikrama; Zurbenko, Igor G; Dunker, Alan M; Wolff, George T

2001-02-01

Assessment of regulatory programs aimed at improving ambient O 3 air quality is of considerable interest to the scientific community and to policymakers. Trend detection, the identification of statistically significant long-term changes, and attribution, linking change to specific clima-tological and anthropogenic forcings, are instrumental to this assessment. Detection and attribution are difficult because changes in pollutant concentrations of interest to policymakers may be much smaller than natural variations due to weather and climate. In addition, there are considerable differences in reported trends seemingly based on similar statistical methods and databases. Differences arise from the variety of techniques used to reduce nontrend variation in time series, including mitigating the effects of meteorology and the variety of metrics used to track changes. In this paper, we review the trend assessment techniques being used in the air pollution field and discuss their strengths and limitations in discerning and attributing changes in O 3 to emission control policies.
Satellite change detection of forest damage near the Chernobyl accident

DOE Office of Scientific and Technical Information (OSTI.GOV)

McClellan, G.E.; Anno, G.H.

1992-01-01

A substantial amount of forest within a few kilometers of the Chernobyl nuclear reactor station was badly contaminated with radionuclides by the April 26, 1986, explosion and ensuing fire at reactor No. 4. Radiation doses to conifers in some areas were sufficient to cause discoloration of needles within a few weeks. Other areas, receiving smaller doses, showed foliage changes beginning 6 months to a year later. Multispectral imagery available from Landsat sensors is especially suited for monitoring such changes in vegetation. A series of Landsat Thematic Mapper images was developed that span the 2 yr following the accident. Quantitative dosemore » estimation for the exposed conifers requires an objective change detection algorithm and knowledge of the dose-time response of conifers to ionizing radiation. Pacific-Sierra Research Corporation's Hyperscout{trademark} algorithm is based on an advanced, sensitive technique for change detection particularly suited for multispectral images. The Hyperscout algorithm has been used to assess radiation damage to the forested areas around the Chernobyl nuclear power plant.« less
DOE Office of Scientific and Technical Information (OSTI.GOV)

Ryan Hruska

Currently, small Unmanned Aerial Vehicles (UAVs) are primarily used for capturing and down-linking real-time video. To date, their role as a low-cost airborne platform for capturing high-resolution, georeferenced still imagery has not been fully utilized. On-going work within the Unmanned Vehicle Systems Program at the Idaho National Laboratory (INL) is attempting to exploit this small UAV-acquired, still imagery potential. Initially, a UAV-based still imagery work flow model was developed that includes initial UAV mission planning, sensor selection, UAV/sensor integration, and imagery collection, processing, and analysis. Components to support each stage of the work flow are also being developed. Critical tomore » use of acquired still imagery is the ability to detect changes between images of the same area over time. To enhance the analysts’ change detection ability, a UAV-specific, GIS-based change detection system called SADI or System for Analyzing Differences in Imagery is under development. This paper will discuss the associated challenges and approaches to collecting still imagery with small UAVs. Additionally, specific components of the developed work flow system will be described and graphically illustrated using varied examples of small UAV-acquired still imagery.« less
CMOS image sensor-based immunodetection by refractive-index change.

PubMed

Devadhasan, Jasmine P; Kim, Sanghyo

2012-01-01

A complementary metal oxide semiconductor (CMOS) image sensor is an intriguing technology for the development of a novel biosensor. Indeed, the CMOS image sensor mechanism concerning the detection of the antigen-antibody (Ag-Ab) interaction at the nanoscale has been ambiguous so far. To understand the mechanism, more extensive research has been necessary to achieve point-of-care diagnostic devices. This research has demonstrated a CMOS image sensor-based analysis of cardiovascular disease markers, such as C-reactive protein (CRP) and troponin I, Ag-Ab interactions on indium nanoparticle (InNP) substrates by simple photon count variation. The developed sensor is feasible to detect proteins even at a fg/mL concentration under ordinary room light. Possible mechanisms, such as dielectric constant and refractive-index changes, have been studied and proposed. A dramatic change in the refractive index after protein adsorption on an InNP substrate was observed to be a predominant factor involved in CMOS image sensor-based immunoassay.
A Portable Smart-Phone Readout Device for the Detection of Mercury Contamination Based on an Aptamer-Assay Nanosensor.

PubMed

Xiao, Wei; Xiao, Meng; Fu, Qiangqiang; Yu, Shiting; Shen, Haicong; Bian, Hongfen; Tang, Yong

2016-11-08

The detection of environmental mercury (Hg) contamination requires complex and expensive instruments and professional technicians. We present a simple, sensitive, and portable Hg 2+ detection system based on a smartphone and colorimetric aptamer nanosensor. A smartphone equipped with a light meter app was used to detect, record, and process signals from a smartphone-based microwell reader (MR S-phone), which is composed of a simple light source and a miniaturized assay platform. The colorimetric readout of the aptamer nanosensor is based on a specific interaction between the selected aptamer and Hg 2+ , which leads to a color change in the reaction solution due to an aggregation of gold nanoparticles (AuNPs). The MR S-phone-based AuNPs-aptamer colorimetric sensor system could reliably detect Hg 2+ in both tap water and Pearl River water samples and produced a linear colorimetric readout of Hg 2+ concentration in the range of 1 ng/mL-32 ng/mL with a correlation of 0.991, and a limit of detection (LOD) of 0.28 ng/mL for Hg 2+ . The detection could be quickly completed in only 20 min. Our novel mercury detection assay is simple, rapid, and sensitive, and it provides new strategies for the on-site detection of mercury contamination in any environment.
A Portable Smart-Phone Readout Device for the Detection of Mercury Contamination Based on an Aptamer-Assay Nanosensor

PubMed Central

Xiao, Wei; Xiao, Meng; Fu, Qiangqiang; Yu, Shiting; Shen, Haicong; Bian, Hongfen; Tang, Yong

2016-01-01

The detection of environmental mercury (Hg) contamination requires complex and expensive instruments and professional technicians. We present a simple, sensitive, and portable Hg2+ detection system based on a smartphone and colorimetric aptamer nanosensor. A smartphone equipped with a light meter app was used to detect, record, and process signals from a smartphone-based microwell reader (MR S-phone), which is composed of a simple light source and a miniaturized assay platform. The colorimetric readout of the aptamer nanosensor is based on a specific interaction between the selected aptamer and Hg2+, which leads to a color change in the reaction solution due to an aggregation of gold nanoparticles (AuNPs). The MR S-phone-based AuNPs-aptamer colorimetric sensor system could reliably detect Hg2+ in both tap water and Pearl River water samples and produced a linear colorimetric readout of Hg2+ concentration in the range of 1 ng/mL–32 ng/mL with a correlation of 0.991, and a limit of detection (LOD) of 0.28 ng/mL for Hg2+. The detection could be quickly completed in only 20 min. Our novel mercury detection assay is simple, rapid, and sensitive, and it provides new strategies for the on-site detection of mercury contamination in any environment. PMID:27834794
Evaluation of Decision Trees for Cloud Detection from AVHRR Data

NASA Technical Reports Server (NTRS)

Shiffman, Smadar; Nemani, Ramakrishna

2005-01-01

Automated cloud detection and tracking is an important step in assessing changes in radiation budgets associated with global climate change via remote sensing. Data products based on satellite imagery are available to the scientific community for studying trends in the Earth's atmosphere. The data products include pixel-based cloud masks that assign cloud-cover classifications to pixels. Many cloud-mask algorithms have the form of decision trees. The decision trees employ sequential tests that scientists designed based on empirical astrophysics studies and simulations. Limitations of existing cloud masks restrict our ability to accurately track changes in cloud patterns over time. In a previous study we compared automatically learned decision trees to cloud masks included in Advanced Very High Resolution Radiometer (AVHRR) data products from the year 2000. In this paper we report the replication of the study for five-year data, and for a gold standard based on surface observations performed by scientists at weather stations in the British Islands. For our sample data, the accuracy of automatically learned decision trees was greater than the accuracy of the cloud masks p < 0.001.
Change Detection Processing Chain Dedicated to Sentinel Data Time Series. Application to Forest and Water Bodies Monitoring

NASA Astrophysics Data System (ADS)

Perez Saavedra, L.-M.; Mercier, G.; Yesou, H.; Liege, F.; Pasero, G.

2016-08-01

The Copernicus program of ESA and European commission (6 Sentinels Missions, among them Sentinel-1 with Synthetic Aperture Radar sensor and Sentinel-2 with 13-band 10 to 60 meter resolution optical sensors), offers a new opportunity to Earth Observation with high temporal acquisition capability ( 12 days repetitiveness and 5 days in some geographic areas of the world) with high spatial resolution.Due to these high temporal and spatial resolutions, it opens new challenges in several fields such as image processing, new algorithms for Time Series and big data analysis. In addition, these missions will be able to analyze several topics of earth temporal evolution such as crop vegetation, water bodies, Land use and Land Cover (LULC), sea and ice information, etc. This is particularly useful for end users and policy makers to detect early signs of damages, vegetation illness, flooding areas, etc.From the state of the art, one can find algorithms and methods that use a bi-date comparison for change detection [1-3] or time series analysis. Actually, these methods are essentially used for target detection or for abrupt change detection that requires 2 observations only.A Hölder means-based change detection technique has been proposed in [2,3] for high resolution radar images. This so-called MIMOSA technique has been mainly dedicated to man-made change detection in urban areas and CARABAS - II project by using a couple of SAR images. An extension to multitemporal change detection technique has been investigated but its application to land use and cover changes still has to be validated.The Hölder Hp is a Time Series pixel by pixel feature extraction and is defined by:H𝑝[X]=[1/n∑ⁿᵢ₌1 Xᴾᵢ]1/p p∈R Hp[X] : N images * S Bandes * t datesn is the number of images in the time series. N > 2Hp (X) is continuous and monotonic increasing in p for - ∞ < p < ∞
On resilience studies of system detection and recovery techniques against stealthy insider attacks

NASA Astrophysics Data System (ADS)

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.
Face Detection Technique as Interactive Audio/Video Controller for a Mother-Tongue-Based Instructional Material

NASA Astrophysics Data System (ADS)

Guidang, Excel Philip B.; Llanda, Christopher John R.; Palaoag, Thelma D.

2018-03-01

Face Detection Technique as a strategy in controlling a multimedia instructional material was implemented in this study. Specifically, it achieved the following objectives: 1) developed a face detection application that controls an embedded mother-tongue-based instructional material for face-recognition configuration using Python; 2) determined the perceptions of the students using the Mutt Susan’s student app review rubric. The study concludes that face detection technique is effective in controlling an electronic instructional material. It can be used to change the method of interaction of the student with an instructional material. 90% of the students perceived the application to be a great app and 10% rated the application to be good.
Compact Surface Plasmon Resonance Biosensor for Fieldwork Environmental Detection

NASA Astrophysics Data System (ADS)

Boyd, Margrethe; Drake, Madison; Stipe, Kristian; Serban, Monica; Turner, Ivana; Thomas, Aaron; Macaluso, David

2017-04-01

The ability to accurately and reliably detect biomolecular targets is important in innumerable applications, including the identification of food-borne parasites, viral pathogens in human tissue, and environmental pollutants. While detection methods do exist, they are typically slow, expensive, and restricted to laboratory use. The method of surface plasmon resonance based biosensing offers a unique opportunity to characterize molecular targets while avoiding these constraints. By incorporating a plasmon-supporting gold film within a prism/laser optical system, it is possible to reliably detect and quantify the presence of specific biomolecules of interest in real time. This detection is accomplished by observing shifts in plasmon formation energies corresponding to optical absorption due to changes in index of refraction near the gold-prism interface caused by the binding of target molecules. A compact, inexpensive, battery-powered surface plasmon resonance biosensor based on this method is being developed at the University of Montana to detect waterborne pollutants in field-based environmental research.
Genetic Particle Swarm Optimization-Based Feature Selection for Very-High-Resolution Remotely Sensed Imagery Object Change Detection.

PubMed

Chen, Qiang; Chen, Yunhao; Jiang, Weiguo

2016-07-30

In the field of multiple features Object-Based Change Detection (OBCD) for very-high-resolution remotely sensed images, image objects have abundant features and feature selection affects the precision and efficiency of OBCD. Through object-based image analysis, this paper proposes a Genetic Particle Swarm Optimization (GPSO)-based feature selection algorithm to solve the optimization problem of feature selection in multiple features OBCD. We select the Ratio of Mean to Variance (RMV) as the fitness function of GPSO, and apply the proposed algorithm to the object-based hybrid multivariate alternative detection model. Two experiment cases on Worldview-2/3 images confirm that GPSO can significantly improve the speed of convergence, and effectively avoid the problem of premature convergence, relative to other feature selection algorithms. According to the accuracy evaluation of OBCD, GPSO is superior at overall accuracy (84.17% and 83.59%) and Kappa coefficient (0.6771 and 0.6314) than other algorithms. Moreover, the sensitivity analysis results show that the proposed algorithm is not easily influenced by the initial parameters, but the number of features to be selected and the size of the particle swarm would affect the algorithm. The comparison experiment results reveal that RMV is more suitable than other functions as the fitness function of GPSO-based feature selection algorithm.
Land cover change detection using a GIS-guided, feature-based classification of Landsat thematic mapper data. [Geographic Information System

NASA Technical Reports Server (NTRS)

Enslin, William R.; Ton, Jezching; Jain, Anil

1987-01-01

Landsat TM data were combined with land cover and planimetric data layers contained in the State of Michigan's geographic information system (GIS) to identify changes in forestlands, specifically new oil/gas wells. A GIS-guided feature-based classification method was developed. The regions extracted by the best image band/operator combination were studied using a set of rules based on the characteristics of the GIS oil/gas pads.
A symmetric metamaterial element-based RF biosensor for rapid and label-free detection

NASA Astrophysics Data System (ADS)

Lee, Hee-Jo; Lee, Jung-Hyun; Jung, Hyo-Il

2011-10-01

A symmetric metamaterial element-based RF biosensing scheme is experimentally demonstrated by detecting biomolecular binding between a prostate-specific antigen (PSA) and its antibody. The metamaterial element in a high-impedance microstrip line shows an intrinsic S21 resonance having a Q-factor of 55. The frequency shift with PSA concentration, i.e., 100 ng/ml, 10 ng/ml, and 1 ng/ml, is observed and the changes are Δf ≈ 20 MHz, 10 MHz, and 5 MHz, respectively. The proposed biosensor offers advantages of label-free detection, a simple and direct scheme, and cost-efficient fabrication.
Robust failure detection filters. M.S. Thesis

NASA Technical Reports Server (NTRS)

Sanmartin, A. M.

1985-01-01

The robustness of detection filters applied to the detection of actuator failures on a free-free beam is analyzed. This analysis is based on computer simulation tests of the detection filters in the presence of different types of model mismatch, and on frequency response functions of the transfers corresponding to the model mismatch. The robustness of detection filters based on a model of the beam containing a large number of structural modes varied dramatically with the placement of some of the filter poles. The dynamics of these filters were very hard to analyze. The design of detection filters with a number of modes equal to the number of sensors was trivial. They can be configured to detect any number of actuator failure events. The dynamics of these filters were very easy to analyze and their robustness properties were much improved. A change of the output transformation allowed the filter to perform satisfactorily with realistic levels of model mismatch.
Evaluation of Two Outlier-Detection-Based Methods for Detecting Tissue-Selective Genes from Microarray Data

PubMed Central

Kadota, Koji; Konishi, Tomokazu; Shimizu, Kentaro

2007-01-01

Large-scale expression profiling using DNA microarrays enables identification of tissue-selective genes for which expression is considerably higher and/or lower in some tissues than in others. Among numerous possible methods, only two outlier-detection-based methods (an AIC-based method and Sprent’s non-parametric method) can treat equally various types of selective patterns, but they produce substantially different results. We investigated the performance of these two methods for different parameter settings and for a reduced number of samples. We focused on their ability to detect selective expression patterns robustly. We applied them to public microarray data collected from 36 normal human tissue samples and analyzed the effects of both changing the parameter settings and reducing the number of samples. The AIC-based method was more robust in both cases. The findings confirm that the use of the AIC-based method in the recently proposed ROKU method for detecting tissue-selective expression patterns is correct and that Sprent’s method is not suitable for ROKU. PMID:19936074
Implementing and testing a fiber-optic polarization-based intrusion detection system

NASA Astrophysics Data System (ADS)

Hajj, Rasha El; MacDonald, Gregory; Verma, Pramode; Huck, Robert

2015-09-01

We describe a layer-1-based intrusion detection system for fiber-optic-based networks. Layer-1-based intrusion detection represents a significant elevation in security as it prohibits an adversary from obtaining information in the first place (no cryptanalysis is possible). We describe the experimental setup of the intrusion detection system, which is based on monitoring the behavior of certain attributes of light both in unperturbed and perturbed optical fiber links. The system was tested with optical fiber links of various lengths and types, under different environmental conditions, and under changes in fiber geometry similar to what is experienced during tapping activity. Comparison of the results for perturbed and unperturbed links has shown that the state of polarization is more sensitive to intrusion activity than the degree of polarization or power of the received light. The testing was conducted in a simulated telecommunication network environment that included both underground and aerial links. The links were monitored for intrusion activity. Attempts to tap the link were easily detected with no apparent degradation in the visual quality of the real-time surveillance video.
A colorimetric detection of acrylamide in potato chips based on nucleophile-initiated thiol-ene Michael addition.

PubMed

Hu, Qinqin; Fu, Yingchun; Xu, Xiahong; Qiao, Zhaohui; Wang, Ronghui; Zhang, Ying; Li, Yanbin

2016-02-07

Acrylamide (AA), a neurotoxin and a potential carcinogen, has been found in various thermally processed foods such as potato chips, biscuits, and coffee. Simple, cost-effective, and sensitive methods for the rapid detection of AA are needed to ensure food safety. Herein, a novel colorimetric method was proposed for the visual detection of AA based on a nucleophile-initiated thiol-ene Michael addition reaction. Gold nanoparticles (AuNPs) were aggregated by glutathione (GSH) because of a ligand-replacement, accompanied by a color change from red to purple. In the presence of AA, after the thiol-ene Michael addition reaction between GSH and AA with the catalysis of a nucleophile, the sulfhydryl group of GSH was consumed by AA, which hindered the subsequent ligand-replacement and the aggregation of AuNPs. Therefore, the concentration of AA could be determined by the visible color change caused by dispersion/aggregation of AuNPs. This new method showed high sensitivity with a linear range from 0.1 μmol L(-1) to 80 μmol L(-1) and a detection limit of 28.6 nmol L(-1), and especially revealed better selectivity than the fluorescence sensing method reported previously. Moreover, this new method was used to detect AA in potato chips with a satisfactory result in comparison with the standard methods based on chromatography, which indicated that the colorimetric method can be expanded for the rapid detection of AA in thermally processed foods.
Action change detection in video using a bilateral spatial-temporal constraint

NASA Astrophysics Data System (ADS)

Tian, Jing; Chen, Li

2016-08-01

Action change detection in video aims to detect action discontinuity in video. The silhouettes-based features are desirable for action change detection. This paper studies the problem of silhouette-quality assessment. For that, a non-reference approach without the need for ground truth is proposed in this paper to evaluate the quality of silhouettes, by exploiting both the boundary contrast of the silhouettes in the spatial domain and the consistency of the silhouettes in the temporal domain. This is in contrast to that either only spatial information or only temporal information of silhouettes is exploited in conventional approaches. Experiments are conducted using artificially generated degraded silhouettes to show that the proposed approach outperforms conventional approaches to achieve more accurate quality assessment. Furthermore, experiments are performed to show that the proposed approach is able to improve the accuracy performance of conventional action change approaches in two human action video data-sets. The average runtime of the proposed approach for Weizmann action video data-set is 0.08 second for one frame using Matlab programming language. It is computationally efficient and potential to real-time implementations.

Portable ceria nanoparticle-based assay for rapid detection of food antioxidants (NanoCerac)

PubMed Central

Sharpe, Erica; Frasco, Thalia; Andreescu, Daniel; Andreescu, Silvana

2012-01-01

With increased awareness of nutrition and the advocacy for healthier food choices, there exists a great demand for a simple, easy-to-use test that can reliably measure the antioxidant capacity of dietary products. We report development and characterization of a portable nanoparticle based-assay, similar to a small sensor patch, for rapid and sensitive detection of food antioxidants. The assay is based on the use of immobilized ceria nanoparticles, which change color after interaction with antioxidants by means of redox and surface chemistry reactions. Monitoring corresponding optical changes enables sensitive detection of antioxidants in which the nanoceria provides an optical ‘signature’ of antioxidant power, while the antioxidants act as reducing agents. The sensor has been tested for the detection of common antioxidant compounds including ascorbic acid, gallic acid, vanilic acid, quercetin, caffeic acid, and epigallocatechin gallate and its function has been successfully applied for the assessment of antioxidant activity in real samples (teas and medicinal mushrooms). The colorimetric response was concentration dependent, with detection limits ranging from 20–400 μM depending on the antioxidant involved. Steady-state color intensity was achieved within seconds upon addition of antioxidants. The results are presented in terms of Gallic Acid Equivalents (GAE). The sensor performed favorably when compared with commonly used antioxidant detection methods. This assay is particularly appealing for remote sensing applications, where specialized equipment is not available, and also for high throughput analysis of a large number of samples. Potential applications for antioxidant detection in remote locations are envisioned. PMID:23139929
Floor Identification with Commercial Smartphones in Wifi-Based Indoor Localization System

NASA Astrophysics Data System (ADS)

Ai, H. J.; Liu, M. Y.; Shi, Y. M.; Zhao, J. Q.

2016-06-01

In this paper, we utilize novel sensors built-in commercial smart devices to propose a schema which can identify floors with high accuracy and efficiency. This schema can be divided into two modules: floor identifying and floor change detection. Floor identifying module starts at initial phase of positioning, and responsible for determining which floor the positioning start. We have estimated two methods to identify initial floor based on K-Nearest Neighbors (KNN) and BP Neural Network, respectively. In order to improve performance of KNN algorithm, we proposed a novel method based on weighting signal strength, which can identify floors robust and quickly. Floor change detection module turns on after entering into continues positioning procedure. In this module, sensors (such as accelerometer and barometer) of smart devices are used to determine whether the user is going up and down stairs or taking an elevator. This method has fused different kinds of sensor data and can adapt various motion pattern of users. We conduct our experiment with mobile client on Android Phone (Nexus 5) at a four-floors building with an open area between the second and third floor. The results demonstrate that our scheme can achieve an accuracy of 99% to identify floor and 97% to detecting floor changes as a whole.
Early breast cancer screening using iron/iron oxide-based nanoplatforms with sub-femtomolar limits of detection.

PubMed

Udukala, Dinusha N; Wang, Hongwang; Wendel, Sebastian O; Malalasekera, Aruni P; Samarakoon, Thilani N; Yapa, Asanka S; Abayaweera, Gayani; Basel, Matthew T; Maynez, Pamela; Ortega, Raquel; Toledo, Yubisela; Bossmann, Leonie; Robinson, Colette; Janik, Katharine E; Koper, Olga B; Li, Ping; Motamedi, Massoud; Higgins, Daniel A; Gadbury, Gary; Zhu, Gaohong; Troyer, Deryl L; Bossmann, Stefan H

2016-01-01

Proteases, including matrix metalloproteinases (MMPs), tissue serine proteases, and cathepsins (CTS) exhibit numerous functions in tumor biology. Solid tumors are characterized by changes in protease expression levels by tumor and surrounding tissue. Therefore, monitoring protease levels in tissue samples and liquid biopsies is a vital strategy for early cancer detection. Water-dispersable Fe/Fe3O4-core/shell based nanoplatforms for protease detection are capable of detecting protease activity down to sub-femtomolar limits of detection. They feature one dye (tetrakis(carboxyphenyl)porphyrin (TCPP)) that is tethered to the central nanoparticle by means of a protease-cleavable consensus sequence and a second dye (Cy 5.5) that is directly linked. Based on the protease activities of urokinase plasminogen activator (uPA), MMPs 1, 2, 3, 7, 9, and 13, as well as CTS B and L, human breast cancer can be detected at stage I by means of a simple serum test. By monitoring CTS B and L stage 0 detection may be achieved. This initial study, comprised of 46 breast cancer patients and 20 apparently healthy human subjects, demonstrates the feasibility of protease-activity-based liquid biopsies for early cancer diagnosis.
Early breast cancer screening using iron/iron oxide-based nanoplatforms with sub-femtomolar limits of detection

PubMed Central

Samarakoon, Thilani N; Yapa, Asanka S; Abayaweera, Gayani; Basel, Matthew T; Maynez, Pamela; Ortega, Raquel; Toledo, Yubisela; Bossmann, Leonie; Robinson, Colette; Janik, Katharine E; Koper, Olga B; Li, Ping; Motamedi, Massoud; Higgins, Daniel A; Gadbury, Gary

2016-01-01

Summary Proteases, including matrix metalloproteinases (MMPs), tissue serine proteases, and cathepsins (CTS) exhibit numerous functions in tumor biology. Solid tumors are characterized by changes in protease expression levels by tumor and surrounding tissue. Therefore, monitoring protease levels in tissue samples and liquid biopsies is a vital strategy for early cancer detection. Water-dispersable Fe/Fe3O4-core/shell based nanoplatforms for protease detection are capable of detecting protease activity down to sub-femtomolar limits of detection. They feature one dye (tetrakis(carboxyphenyl)porphyrin (TCPP)) that is tethered to the central nanoparticle by means of a protease-cleavable consensus sequence and a second dye (Cy 5.5) that is directly linked. Based on the protease activities of urokinase plasminogen activator (uPA), MMPs 1, 2, 3, 7, 9, and 13, as well as CTS B and L, human breast cancer can be detected at stage I by means of a simple serum test. By monitoring CTS B and L stage 0 detection may be achieved. This initial study, comprised of 46 breast cancer patients and 20 apparently healthy human subjects, demonstrates the feasibility of protease-activity-based liquid biopsies for early cancer diagnosis. PMID:27335730
Earthquake Damage Assessment Using Objective Image Segmentation: A Case Study of 2010 Haiti Earthquake

NASA Technical Reports Server (NTRS)

Oommen, Thomas; Rebbapragada, Umaa; Cerminaro, Daniel

2012-01-01

In this study, we perform a case study on imagery from the Haiti earthquake that evaluates a novel object-based approach for characterizing earthquake induced surface effects of liquefaction against a traditional pixel based change technique. Our technique, which combines object-oriented change detection with discriminant/categorical functions, shows the power of distinguishing earthquake-induced surface effects from changes in buildings using the object properties concavity, convexity, orthogonality and rectangularity. Our results suggest that object-based analysis holds promise in automatically extracting earthquake-induced damages from high-resolution aerial/satellite imagery.
Look Again: An Investigation of False Positive Detections in Combat Models

DTIC Science & Technology

2008-06-01

those states (Macmillan & Creelman , 1991). Denoted by d’, sensitivity is scaled between zero and one, with an infallible observer having a d’ equal to...Macmillan & Creelman , 1991), and is also scaled between zero and one. Varying either the observer’s sensitivity or bias, or both, changes his...Graphics Based Target Detection Model, Master of Science, Naval Postgraduate School, September. Macmillan, N. A., & Creelman , C. D., 1991, Detection Theory
Development of a method for detecting trace metals in aqueous solutions based on the coordination chemistry of hexahydrotriazines.

PubMed

Wojtecki, Rudy J; Yuen, Alexander Y; Zimmerman, Thomas G; Jones, Gavin O; Horn, Hans W; Boday, Dylan J; Hedrick, James L; García, Jeannette M

2015-08-07

The detection of trace amounts (<10 ppb) of heavy metals in aqueous solutions is described using 1,3,5-hexahydro-1,3,5-triazines (HTs) as chemical indicators and a low cost fluorimeter-based detection system. This method takes advantage of the inherent properties of HTs to coordinate strongly with metal ions in solution, a fundamental property that was studied using a combination of analytical tools (UV-Vis titrations, (1)H-NMR titrations and computational modeling). Based on these fundamental studies that show significant changes in the HT UV signature when a metal ion is present, HT compounds were used to prepare indicator strips that resulted in significant fluorescence changes when a metal was present. A portable and economical approach was adopted to test the concept of utilizing HTs to detect heavy metals using a fluorimeter system that consisted of a low-pressure mercury lamp, a photo-detector, a monolithic photodiode and an amplifier, which produces a voltage proportional to the magnitude of the visible fluorescence emission. Readings of the prepared HT test strips were evaluated by exposure to two different heavy metals at the safe threshold concentration described by the U.S. Environmental Protection Agency (EPA) for Cr(3+) and Ag(2+) (100 μg L(-1) and 6.25, respectively). This method of detection could be used to the presence of either metal at these threshold concentrations.
Organic conjugated small molecule materials based optical probe for rapid, colorimetric and UV-vis spectral detection of phosphorylated protein in placental tissue

NASA Astrophysics Data System (ADS)

Wang, Yanfang; Yang, Na; Liu, Yi

2018-04-01

A novel organic small molecule with D-Pi-A structure was prepared, which was found to be a promising colorimetric and ratiometric UV-vis spetral probe for detection of phosphorylated proteins with the help of tetravalent zirconium ion. Such optical probe based on chromophore WYF-1 shows a rapid response (within 10 s) and high selectivity and sensitivity for phosphorylated proteins, giving distinct colorimetric and ratiometric UV-vis changes at 720 and 560 nm. The detection limit for phosphorylated proteins was estimated to be 100 nM. In addition, detection of phosphorylated proteins in placental tissue samples with this probe was successfully applied, which indicates that this probe holds great potential for phosphorylated proteins detection.
Documentation and Detection of Colour Changes of Bas Relieves Using Close Range Photogrammetry

NASA Astrophysics Data System (ADS)

Malinverni, E. S.; Pierdicca, R.; Sturari, M.; Colosi, F.; Orazi, R.

2017-05-01

The digitization of complex buildings, findings or bas relieves can strongly facilitate the work of archaeologists, mainly for in depth analysis tasks. Notwithstanding, whether new visualization techniques ease the study phase, a classical naked-eye approach for determining changes or surface alteration could bring towards several drawbacks. The research work described in these pages is aimed at providing experts with a workflow for the evaluation of alterations (e.g. color decay or surface alterations), allowing a more rapid and objective monitoring of monuments. More in deep, a pipeline of work has been tested in order to evaluate the color variation between surfaces acquired at different époques. The introduction of reliable tools of change detection in the archaeological domain is needful; in fact, the most widespread practice, among archaeologists and practitioners, is to perform a traditional monitoring of surfaces that is made of three main steps: production of a hand-made map based on a subjective analysis, selection of a sub-set of regions of interest, removal of small portion of surface for in depth analysis conducted in laboratory. To overcome this risky and time consuming process, digital automatic change detection procedure represents a turning point. To do so, automatic classification has been carried out according to two approaches: a pixel-based and an object-based method. Pixel-based classification aims to identify the classes by means of the spectral information provided by each pixel belonging to the original bands. The object-based approach operates on sets of pixels (objects/regions) grouped together by means of an image segmentation technique. The methodology was tested by studying the bas-relieves of a temple located in Peru, named Huaca de la Luna. Despite the data sources were collected with unplanned surveys, the workflow proved to be a valuable solution useful to understand which are the main changes over time.
Change detection of bitemporal multispectral images based on FCM and D-S theory

NASA Astrophysics Data System (ADS)

Shi, Aiye; Gao, Guirong; Shen, Shaohong

2016-12-01

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

USGS Publications Warehouse

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

2014-01-01

This study evaluated the effectiveness of different band combinations and classifiers (unsupervised, supervised, object-oriented nearest neighbor, and object-oriented decision rule) for quantifying mangrove forest change using multitemporal Landsat data. A discriminant analysis using spectra of different vegetation types determined that bands 2 (0.52 to 0.6 μm), 5 (1.55 to 1.75 μm), and 7 (2.08 to 2.35 μm) were the most effective bands for differentiating mangrove forests from surrounding land cover types. A ranking of thirty-six change maps, produced by comparing the classification accuracy of twelve change detection approaches, was used. The object-based Nearest Neighbor classifier produced the highest mean overall accuracy (84 percent) regardless of band combinations. The automated decision rule-based approach (mean overall accuracy of 88 percent) as well as a composite of bands 2, 5, and 7 used with the unsupervised classifier and the same composite or all band difference with the object-oriented Nearest Neighbor classifier were the most effective approaches.
Highly selective and sensitive determination of Cu2+ in drink and water samples based on a 1,8-diaminonaphthalene derived fluorescent sensor

NASA Astrophysics Data System (ADS)

Sun, Tao; Li, Yang; Niu, Qingfen; Li, Tianduo; Liu, Yan

2018-04-01

A new simple and efficient fluorescent sensor L based on 1,8-diaminonaphthalene Schiff-base for highly sensitive and selective determination of Cu2+ in drink and water has been developed. This Cu2+-selective detection over other tested metal ions displayed an obvious color change from blue to colorless easily detected by naked eye. The detection limit is determined to be as low as 13.2 nM and the response time is very fast within 30 s. The 1:1 binding mechanism was well confirmed by fluorescence measurements, IR analysis and DFT calculations. Importantly, this sensor L was employed for quick detection of Cu2+ in drink and environmental water samples with satisfactory results, providing a simple, rapid, reliable and feasible Cu2+-sensing method.
Brain changes detected by functional magnetic resonance imaging and spectroscopy in patients with Crohn's disease.

PubMed

Lv, Kun; Fan, Yi-Hong; Xu, Li; Xu, Mao-Sheng

2017-05-28

Crohn's disease (CD) is a chronic, non-specific granulomatous inflammatory disorder that commonly affects the small intestine and is a phenotype of inflammatory bowel disease (IBD). CD is prone to relapse, and its incidence displays a persistent increase in developing countries. However, the pathogenesis of CD is poorly understood, with some studies emphasizing the link between CD and the intestinal microbiota. Specifically, studies point to the brain-gut-enteric microbiota axis as a key player in the occurrence and development of CD. Furthermore, investigations have shown white-matter lesions and neurologic deficits in patients with IBD. Based on these findings, brain activity changes in CD patients have been detected by blood oxygenation level dependent functional magnetic resonance imaging (BOLD-fMRI). BOLD-fMRI functions by detecting a local increase in relative blood oxygenation that results from neurotransmitter activity and thus reflects local neuronal firing rates. Therefore, biochemical concentrations of neurotransmitters or metabolites may change in corresponding brain regions of CD patients. To further study this phenomenon, brain changes of CD patients can be detected non-invasively, effectively and accurately by BOLD-fMRI combined with magnetic resonance spectroscopy (MRS). This approach can further shed light on the mechanisms of the occurrence and development of neurological CD. Overall, this paper reviews the current status and prospects on fMRI and MRS for evaluation of patients with CD based on the brain-gut-enteric microbiota axis.
Detecting changes in student teachers' conceptions of teaching science to adolescent English language learners

NASA Astrophysics Data System (ADS)

Pomeroy, Jonathon Richard

2000-10-01

This research study investigated the changes that occurred in six student teachers' conceptions of teaching science to adolescent English language learners over the duration of their participation in a one-year, graduate level, science teacher education program. Cases were created for each of the student teachers based on their concept maps, writing samples, interviews, lesson plans, informal interviews with cooperating teachers, and observation notes collected on biweekly visitations. The cases were divided into three dyads each consisting of two student teachers with similar preprogram and student teaching experiences. Cross case analysis revealed the existence of seven themes related to teaching science to adolescent English language learners. Further analysis suggested that student teachers that worked with experienced cooperating teachers and who had achieved a sense of autonomy over their student teaching demonstrated broad and sophisticated growth across all seven themes. Student teachers who had not achieved a sense of autonomy, demonstrated growth in two to three themes. Student teachers who demonstrated broad and sophisticated growth were able to clearly articulate their conceptions of teaching science to English language learners where as those who demonstrated limited growth were not. This research establishes the use of concept maps as a tool for detecting changes in student teachers' conceptions of teaching science to adolescent English language learners as well as the sensitivity of concept maps to detect the types of changes historically detected by writing samples and interviews. Recommendations based on the implications from are included.
Class imbalance in unsupervised change detection - A diagnostic analysis from urban remote sensing

NASA Astrophysics Data System (ADS)

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

2017-08-01

Automatic monitoring of changes on the Earth's surface is an intrinsic capability and simultaneously a persistent methodological challenge in remote sensing, especially regarding imagery with very-high spatial resolution (VHR) and complex urban environments. In order to enable a high level of automatization, the change detection problem is solved in an unsupervised way to alleviate efforts associated with collection of properly encoded prior knowledge. In this context, this paper systematically investigates the nature and effects of class distribution and class imbalance in an unsupervised binary change detection application based on VHR imagery over urban areas. For this purpose, a diagnostic framework for sensitivity analysis of a large range of possible degrees of class imbalance is presented, which is of particular importance with respect to unsupervised approaches where the content of images and thus the occurrence and the distribution of classes are generally unknown a priori. Furthermore, this framework can serve as a general technique to evaluate model transferability in any two-class classification problem. The applied change detection approach is based on object-based difference features calculated from VHR imagery and subsequent unsupervised two-class clustering using k-means, genetic k-means and self-organizing map (SOM) clustering. The results from two test sites with different structural characteristics of the built environment demonstrated that classification performance is generally worse in imbalanced class distribution settings while best results were reached in balanced or close to balanced situations. Regarding suitable accuracy measures for evaluating model performance in imbalanced settings, this study revealed that the Kappa statistics show significant response to class distribution while the true skill statistic was widely insensitive to imbalanced classes. In general, the genetic k-means clustering algorithm achieved the most robust results with respect to class imbalance while the SOM clustering exhibited a distinct optimization towards a balanced distribution of classes.
Surge in North Atlantic hurricanes due to detectors, not climate change

NASA Astrophysics Data System (ADS)

Schultz, Colin

2011-07-01

A spate of research has indicated there may be a link between climate change and the prevalence of North Atlantic tropical cyclones. In a new paper, researchers note upon closer inspection that the prominent upswing in tropical cyclone detections beginning in the midtwentieth century is attributable predominantly to the detection of “shorties” tropical cyclones with durations of less than 2 days. That the apparent surge in cyclone activity could be attributable to changes in the quality and quantity of detections has gained ground as a potential alternative explanation. Using a database of hurricane observations stretching back to 1878, Villarini et al. try to tease out any detectable climate signal from the records. The authors note that between 1878 and 1943 there were 0.58 shorty detections per year, and between 1944 and 2008 there were 2.58 shorty detections per year. This increase in shorties, which the authors propose may be related to the end of World War II and the dawn of air-based reconnaissance and weather tracking, was not mirrored by an increase in tropical cyclone activity for storms longer than 2 days. (Journal of Geophysical Research-Atmospheres, doi:10.1029/2010JD015493, 2011
A preliminary evaluation of land use mapping and change detection capabilities using an ERTS image covering a portion of the CARETS region

NASA Technical Reports Server (NTRS)

Fitzpatrick, K. A.; Lins, H. F., Jr.

1972-01-01

The author has identified the following significant results. A preliminary study on the capabilities of ERTS data in land use mapping and change detection was carried out in the area around Frederick County, Maryland, which lies in the northwest corner of the Central Atlantic Regional Ecological Test Site. The investigation has revealed that Level 1 (of the Anderson classification system) land use mapping can be performed and that, in some cases, land undergoing change can be identified. Results to date suggest that more work should be done in areas where land use changes are known to exist, in order to establish some form of base for recognizing the spectral signature indicative of change areas.
A colorimetric aptasensor for sulfadimethoxine detection based on peroxidase-like activity of graphene/nickel@palladium hybrids.

PubMed

Wang, Aicheng; Zhao, Huimin; Chen, Xiaochi; Tan, Bing; Zhang, Yaobin; Quan, Xie

2017-05-15

A sensitive, rapid and label-free colorimetric aptasensor for sulfadimethoxine (SDM) detection was developed based on the tunable peroxidase-like activity of graphene/nickel@palladium nanoparticle (Gr/Ni@Pd) hybrids. The addition of the SDM aptamer could inhibit the peroxidase-like catalytic activity of the hybrids. However, the target SDM and aptamer could be triggered tightly and recover the catalytic activity of the Gr/Ni@Pd hybrids. Due to the peroxidase-like catalytic activity, Gr/Ni@Pd could catalyze the decomposition of H 2 O 2 with releasing hydroxyl radicals which further oxidized reagent 3, 3', 5, 5'-Tetramethylbenzidine (TMB) to oxTMB accompanied with a colorless-to-blue color change. The original color change could be applied to obtain quantitative detection of SDM, due to the relationship between the concentration of the target and the color difference. As a result, this approach performed a linear response for SDM from 1 to 500 ng/mL with a limit detection of 0.7 ng/mL (S/N = 3) under the optimized conditions and realized the detection of SDM in spiked lake water samples. Therefore, this colorimetric aptasensor was an alternative assay for SDM detection in real water. Moreover, with its design principle, this work might be applied to detecting other small molecule by employing appropriate aptamer. Copyright © 2017 Elsevier Inc. All rights reserved.
Detection of atrial fibrillation with seismocardiography.

PubMed

Pankaala, Mikko; Koivisto, Tero; Lahdenoja, Olli; Kiviniemi, Tuomas; Saraste, Antti; Vasankari, Tuija; Airaksinen, Juhani

2016-08-01

In this paper we study the feasibility of seismocardiography (SCG) for the detection of Atrial Fibrillation (AF). In this preclinical study, data acquired from one patient having paroxysmal AF (no other heart diseases) is used to introduce specific changes in SCG signal due to AF. Observed changes and phenomena create a foundation for the development of SCG-based AF detection algorithms. SCG data was recorded from the sternum of an AF patient in dorso-ventral direction while at rest in a supine position using a three-axis high precision MEMS accelerometer simultaneously with a one-lead ECG. In contrast to ECG, the magnitude of beats registered with SCG varies considerably from beat to beat during AF. We show that the magnitude of the beats is not random but is in relation to beat intervals. It is shown that extra indicators for detecting AF become available when SCG data is combined with electrocardiographical (ECG) data; there is a certain behavior in the electromechanical delay characteristic of the AF. It is discussed how all this information can be taken advantage of in the detection of AF. Today electrocardiography (ECG) is the primary method for diagnosing arrhythmias, but there is a growing need for simpler and more convenient method for detecting asymptomatic AF. Given the very small dimensions of modern MEMS accelerometers (2mm×2mm), a reliable MEMS based measurement may provide totally new venues for arrhythmia detection.
Comparison of multiple methods for detecting changes in urban areas in TerraSAR-X data

NASA Astrophysics Data System (ADS)

Hammer, Horst; Dubois, Clémence; Boldt, Markus; Kuny, Silvia; Cadario, Erich; Thiele, Antje

2016-10-01

The current generation of SAR satellites such as TerraSAR-X, TanDEM-X and COSMO-SkyMed provide resolutions below one meter, permitting the detailed analysis of urban areas while covering large zones. Furthermore, as they are deployable independently of daylight and weather, such remote sensing SAR data are particularly popular for purposes such as rapid damage assessment at building level after a natural disaster. The purpose of our study is the investigation of techniques for the detection of changes based on one pre-event and one post-event SAR amplitude image. We provide a comparison of several methods for detecting changes in urban areas. Especially, changes at building locations are looked for. We analyzed two areas affected differently in detail. First, a suburban area of Paris, France, was considered due to changes caused by an urbanization project. Here, we have two TanDEM-X acquisitions available, before (November 4, 2012) and after (May 10, 2013) the changes. Second, we investigated changes that happened in Kathmandu, Nepal, after the April 25, 2015 earthquake. For this analysis, we have two TerraSAR-X acquisitions, one before (October 13, 2013) and one immediately after (April 27, 2015) the earthquake. Both areas differ by the building types, the image resolution and the available reference, which makes it an interesting challenge. In this paper, we compare six different methods for change detection. The investigated methods contain both standard criteria such as Log Ratio, Kullback-Leibler and the Difference of Entropies detector, and methods developed by the authors such as a Log Ratio combined with an Alternating Sequential Filter. All change detection results are presented and discussed by considering the available ground truth.

Highly sensitive colorimetric detection of glucose in a serum based on DNA-embeded Au@Ag core-shell nanoparticles

NASA Astrophysics Data System (ADS)

Kang, Fei; Hou, Xiangshu; Xu, Kun

2015-10-01

Glucose is a key energy substance in diverse biology and closely related to the life activities of the organism. To develop a simple and sensitive method for glucose detection is extremely urgent but still remains a key challenge. Herein, we report a colorimetric glucose sensor in a homogeneous system based on DNA-embedded core-shell Au@Ag nanoparticles. In this assay, a glucose substrate was first catalytically oxidized by glucose oxidase to produce H2O2 which would further oxidize and gradually etch the outer silver shell of Au@Ag nanoparticles. Afterwards, the solution color changed from yellow to red and the surface plasmon resonance (SPR) band of Au@Ag nanoparticles declined and red-shifted from 430 to 516 nm. Compared with previous silver-based glucose colorimetric detection strategies, the distinctive SPR band change is superior to the color variation, which is critical to the high sensitivity of this assay. Benefiting from the outstanding optical property, robust stability and well-dispersion of the core-shell Au@AgNPs hybrid, this colorimetric assay obtained a detection limit of glucose as low as 10 nM, which is at least a 10-fold improvement over other AgNPs-based procedures. Moreover, this optical biosensor was successfully employed to the determination of glucose in fetal bovine serum.
Colorimetric detection of Cr (VI) based on the leaching of gold nanoparticles using a paper-based sensor.

PubMed

Guo, Jian-Feng; Huo, Dan-Qun; Yang, Mei; Hou, Chang-Jun; Li, Jun-Jie; Fa, Huan-Bao; Luo, Hui-Bo; Yang, Ping

2016-12-01

Herein, we have developed a simple, sensitive and paper-based colorimetric sensor for the selective detection of Chromium (Ⅵ) ions (Cr (VI)). Silanization-titanium dioxide modified filter paper (STCP) was used to trap bovine serum albumin capped gold nanoparticles (BSA-Au NPs), leading to the fabrication of BSA-Au NPs decorated membrane (BSA-Au NPs/STCP). The BSA-Au NPs/STCP operated on the principle that BSA-Au NPs anchored on the STCP were gradually etched by Cr (VI) as the leaching process of gold in the presence of hydrobromic acid (HBr) and hence induced a visible color change. Under optimum conditions, the paper-based colorimetric sensor showed clear color change after reaction with Cr (VI) as well as with favorable selectivity to a variety of possible interfering counterparts. The amount-dependent colorimetric response was linearly correlated with the Cr (VI) concentrations ranging from 0.5µM to 50.0µM with a detection limit down to 280nM. Moreover, the developed cost-effective colorimetric sensor has been successfully applied to real environmental samples which demonstrated the potential for field applications. Copyright © 2016 Elsevier B.V. All rights reserved.
Iohexol clearance is superior to creatinine-based renal function estimating equations in detecting short-term renal function decline in chronic heart failure

PubMed Central

Cvan Trobec, Katja; Kerec Kos, Mojca; von Haehling, Stephan; Anker, Stefan D.; Macdougall, Iain C.; Ponikowski, Piotr; Lainscak, Mitja

2015-01-01

Aim To compare the performance of iohexol plasma clearance and creatinine-based renal function estimating equations in monitoring longitudinal renal function changes in chronic heart failure (CHF) patients, and to assess the effects of body composition on the equation performance. Methods Iohexol plasma clearance was measured in 43 CHF patients at baseline and after at least 6 months. Simultaneously, renal function was estimated with five creatinine-based equations (four- and six-variable Modification of Diet in Renal Disease, Cockcroft-Gault, Cockcroft-Gault adjusted for lean body mass, Chronic Kidney Disease Epidemiology Collaboration equation) and body composition was assessed using bioimpedance and dual-energy x-ray absorptiometry. Results Over a median follow-up of 7.5 months (range 6-17 months), iohexol clearance significantly declined (52.8 vs 44.4 mL/[min ×1.73 m2], P = 0.001). This decline was significantly higher in patients receiving mineralocorticoid receptor antagonists at baseline (mean decline -22% of baseline value vs -3%, P = 0.037). Mean serum creatinine concentration did not change significantly during follow-up and no creatinine-based renal function estimating equation was able to detect the significant longitudinal decline of renal function determined by iohexol clearance. After accounting for body composition, the accuracy of the equations improved, but not their ability to detect renal function decline. Conclusions Renal function measured with iohexol plasma clearance showed relevant decline in CHF patients, particularly in those treated with mineralocorticoid receptor antagonists. None of the equations for renal function estimation was able to detect these changes. ClinicalTrials.gov registration number NCT01829880 PMID:26718759
Anomalous change detection in imagery

DOEpatents

Theiler, James P [Los Alamos, NM; Perkins, Simon J [Santa Fe, NM

2011-05-31

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

NASA Astrophysics Data System (ADS)

Luo, Ji; Yao, Jun; Wang, Wei-min; Zhuang, Xu-ye; Ma, Wen-ying; Lin, Qiao

2013-08-01

Melamine is an insalubrious chemical, and has been frequently added into milk products illegally, to make the products more protein-rich. However, it can cause some various diseases, such as kidney stones and bladder cancer. In this paper, a novel optical fiber sensor with high sensitivity based on absorption of the evanescent field for melamine detection is successfully proposed and developed. Different concentrations of melamine changing from 0 to 10mg/mL have been detected using the micro/nano-sensing fiber decorated with silver nanoparticles cluster layer. As the concentration increases, the sensing fiber's output intensity gradually deceases and the absorption of the analyte becomes large. The concentration changing of 1mg/ml can cause the absorbance varying 0.664 and the limit of the melamine detectable concentration is 1ug/mL. Besides, the coupling properties between silver nanoparticles have also been analyzed by the FDTD method. Overall, this evanescent field enhanced optical fiber sensor has potential to be used in oligo-analyte detection and will promote the development of biomolecular and chemical sensing applications.
Potentiometric Aptasensing of Vibrio alginolyticus Based on DNA Nanostructure-Modified Magnetic Beads.

PubMed

Zhao, Guangtao; Ding, Jiawang; Yu, Han; Yin, Tanji; Qin, Wei

2016-12-02

A potentiometric aptasensing assay that couples the DNA nanostructure-modified magnetic beads with a solid-contact polycation-sensitive membrane electrode for the detection of Vibrio alginolyticus is herein described. The DNA nanostructure-modified magnetic beads are used for amplification of the potential response and elimination of the interfering effect from a complex sample matrix. The solid-contact polycation-sensitive membrane electrode using protamine as an indicator is employed to chronopotentiometrically detect the change in the charge or DNA concentration on the magnetic beads, which is induced by the interaction between Vibrio alginolyticus and the aptamer on the DNA nanostructures. The present potentiometric aptasensing method shows a linear range of 10-100 CFU mL -1 with a detection limit of 10 CFU mL -1 , and a good specificity for the detection of Vibrio alginolyticus . This proposed strategy can be used for the detection of other microorganisms by changing the aptamers in the DNA nanostructures.
Damage detection methodology under variable load conditions based on strain field pattern recognition using FBGs, nonlinear principal component analysis, and clustering techniques

NASA Astrophysics Data System (ADS)

Sierra-Pérez, Julián; Torres-Arredondo, M.-A.; Alvarez-Montoya, Joham

2018-01-01

Structural health monitoring consists of using sensors integrated within structures together with algorithms to perform load monitoring, damage detection, damage location, damage size and severity, and prognosis. One possibility is to use strain sensors to infer structural integrity by comparing patterns in the strain field between the pristine and damaged conditions. In previous works, the authors have demonstrated that it is possible to detect small defects based on strain field pattern recognition by using robust machine learning techniques. They have focused on methodologies based on principal component analysis (PCA) and on the development of several unfolding and standardization techniques, which allow dealing with multiple load conditions. However, before a real implementation of this approach in engineering structures, changes in the strain field due to conditions different from damage occurrence need to be isolated. Since load conditions may vary in most engineering structures and promote significant changes in the strain field, it is necessary to implement novel techniques for uncoupling such changes from those produced by damage occurrence. A damage detection methodology based on optimal baseline selection (OBS) by means of clustering techniques is presented. The methodology includes the use of hierarchical nonlinear PCA as a nonlinear modeling technique in conjunction with Q and nonlinear-T 2 damage indices. The methodology is experimentally validated using strain measurements obtained by 32 fiber Bragg grating sensors bonded to an aluminum beam under dynamic bending loads and simultaneously submitted to variations in its pitch angle. The results demonstrated the capability of the methodology for clustering data according to 13 different load conditions (pitch angles), performing the OBS and detecting six different damages induced in a cumulative way. The proposed methodology showed a true positive rate of 100% and a false positive rate of 1.28% for a 99% of confidence.
The INTELSAT VI SSTDMA network diagnostic system

NASA Astrophysics Data System (ADS)

Tamboli, Satish P.; Zhu, Xiaobo; Wilkins, Kim N.; Gupta, Ramesh K.

The system-level design of an expert-system-based, near-real-time diagnostic system for INTELSAT VI satellite-switched time-division multiple access (SSTDMA) network is described. The challenges of INTELSAT VI diagnostics are discussed, along with alternative approaches for network diagnostics and the rationale for choosing a method based on burst unique-word detection. The focal point of the diagnostic system is the diagnostic processor, which resides in the central control and monitoring facility known as the INTELSAT Operations Center TDMA Facility (IOCTF). As real-time information such as burst unique-word detection data, reference terminal status data, and satellite telemetry alarm data are received at the IOCTF, the diagnostic processor continuously monitors the data streams. When a burst status change is detected, a 'snapshot' of the real-time data is forwarded to the expert system. Receipt of the change causes a set of rules to be invoked which associate the traffic pattern with a set of probable causes. A user-friendly interface allows a graphical view of the burst time plan and provides the ability to browse through the knowledge bases.
Non-specific monitoring to resolve intermittent pollutant problems associated with wastewater treatment and potable supply.

PubMed

Stuetz, R M

2004-01-01

An online monitoring system based on an array of non-specific sensors was used for the detection of chemical pollutants in wastewater and water. By superimposing sensor profiles for defined sampling window, the identification of data points outside these normal sensor response patterns was used to represent potential pollution episodes or other abnormalities within the process stream. Principle component analysis supported the detection of outliers or rapid changes in the sensor responses as an indicator of chemical pollutants. A model based on the comparison of sensor relative responses to a moving average for a defined sample window was tested for detecting and identifying sudden changes in the online data over a 6-month period. These results show the technical advantages of using a non-specific based monitoring system that can respond to a range of chemical species, due to broad selectivity of the sensor compositions. The findings demonstrate how this non-invasive technique could be further developed to provide early warning systems for application at the inlet of wastewater treatment plants.
Colorimetric determination of Timolol concentration based on localized surface plasmon resonance of silver nanoparticles

NASA Astrophysics Data System (ADS)

Amirjani, Amirmostafa; Bagheri, Mozhgan; Heydari, Mojgan; Hesaraki, Saeed

2016-09-01

In this work, a rapid and simple colorimetric method based on the surface plasmon resonance of silver nanoparticles (AgNPs) was developed for the detection of the drug Timolol. The method used is based on the interaction of Timolol with the surface of the as-synthesized AgNPs, which promotes aggregation of the nanoparticles. This aggregation exploits the surface plasmon resonance through the electric dipole-dipole interaction and coupling among the agglomerated particles, hence bringing forth distinctive changes in the spectra as well as the color of colloidal silver. UV-vis spectrophotometery was used to monitor the changes of the localized surface plasmon resonance of AgNPs at wavelengths of 400 and 550 nm. The developed colorimetric sensor has a wide dynamic range of 1.0 × 10-7 M-1.0 × 10-3 M for detection of Timolol with a low detection limit of 1.2 × 10-6 M. The proposed method was successfully applied for the determination of Timolol concentration in ophthalmic eye-drop solution with a response time lower than 40 s.
Optical fiber F-P magnetic field sensor based on magnetostrictive effect of magnetic fluid

NASA Astrophysics Data System (ADS)

Shi, Fuquan; Luo, Yan; Che, Jiajia; Ren, Zhijun; peng, Baojin

2018-07-01

magnetic field sensor of air-gap Fabry-Perot fiber interferometersis proposed based on magnetostrictive effect. The sensor is consisted of single-model fiber (SMF), air-gap, no-core fiber (NCF) and magnetic fluid. Those are sealed in the capillary, SMF and NCF are connect with air chamber and magnetic fluid column. With the presence of an external magnetic field, air chamber cavity length changes because of the magneto-volume variation of magnetic fluids. This situation causes a change in the optical path difference. Detection of the drift of interference spectrum leads to the detection of the change in magnetic field. When the magnetic field is parallel to the direction in which the capillary is placed, the sensitivity is 0.2347 nm/mT; when the magnetic fluid is perpendicular to the direction in which the capillary is placed, the sensitivity is 0.325 nm/http://mT.%20In.
Evolution of the Ultrasonic Inspection Requirements of Heavy Rotor Forgings Over the Past Decades

NASA Astrophysics Data System (ADS)

Vrana, J.; Zimmer, A.; Bailey, K.; Angal, R.; Zombo, P.; Büchner, U.; Buschmann, A.; Shannon, R. E.; Lohmann, H.-P.; Heinrich, W.

2010-02-01

Heavy rotor forgings for land-based power generation turbines and generators are inspected ultrasonically. Several decades ago the first inspections were conducted using manual, straight beam, contact transducers with simple, non-descript reporting requirements. The development of ultrasonic inspection capabilities, the change in design engineer requirements, improvements of fracture mechanics calculations, experience with turbine operation, experience with the inspection technology, and probability of detection drove the changes that have resulted in the current day inspection requirements: sizing technologies were implemented, detection limits were lowered, angle and pitch/catch (dual crystal) scans were introduced, and most recently automated equipment for the inspection was required. Due to all these changes, model based sizing techniques, like DGS, and modern ultrasonic techniques, like phased array, are being introduced globally. This paper describes the evolution of the ultrasonic inspection requirements over the last decades and presents an outlook for tomorrow.
Study on thin wideband applicator for detecting blood characteristics in human body

NASA Astrophysics Data System (ADS)

Bamba, Kazuki; Kuki, Takao; Nikawa, Yoshio

2016-11-01

Preventive care as well as early detection method and monitoring technique for diseases are highly attracted attention to increase quality of life. Noninvasive measurement method for blood characteristics in body is expected by patients with kidney dysfunction. Complex permittivity of blood is changed a few present at 6GHz. This change is caused by the change of water and albumin contents in blood. In this study, to detect blood characteristics in human body, experiments with phantom model has been performed using thin wideband applicator for examining microwave transmission up to 6GHz. The thin wideband applicator has advantages for detecting living body information in detail. The thin wideband applicator is designed based on Antipodal Vivaldi Antenna and is not required any balun and is very easy handling. Using developed Antipodal Vivaldi Antenna, transmission coefficient can be obtained as a function of thickness of phantom model with high sensitivity. Using this method, highly sensitive sensor for obtaining characteristics of blood in body can be developed.
Change detection on UGV patrols with respect to a reference tour using VIS imagery

NASA Astrophysics Data System (ADS)

Müller, Thomas

2015-05-01

Autonomous driving robots (UGVs, Unmanned Ground Vehicles) equipped with visual-optical (VIS) cameras offer a high potential to automatically detect suspicious occurrences and dangerous or threatening situations on patrol. In order to explore this potential, the scene of interest is recorded first on a reference tour representing the 'everything okay' situation. On further patrols changes are detected with respect to the reference in a two step processing scheme. In the first step, an image retrieval is done to find the reference images that are closest to the current camera image on patrol. This is done efficiently based on precalculated image-to-image registrations of the reference by optimizing image overlap in a local reference search (after a global search when that is needed). In the second step, a robust spatio-temporal change detection is performed that widely compensates 3-D parallax according to variations of the camera position. Various results document the performance of the presented approach.
The unsuitability of implantable Doppler probes for the early detection of renal vascular complications – a porcine model for prevention of renal transplant loss

PubMed Central

Jespersen, Bente; Møldrup, Ulla; Keller, Anna K.

2017-01-01

Background Vascular occlusion is a rare, but serious complication after kidney transplantation often resulting in graft loss. We therefore aimed to develop an experimental porcine model for stepwise reduction of the renal venous blood flow and to compare an implantable Doppler probe and microdialysis for fast detection of vascular occlusion. Methods In 20 pigs, implantable Doppler probes were placed on the renal artery and vein and a microdialysis catheter was placed in the renal cortex. An arterial flowprobe served as gold standard. Following two-hour baseline measurements, the pigs were randomised to stepwise venous occlusion, complete venous occlusion, complete arterial occlusion or controls. Results All parameters were stable through baseline measurements. Glutamate and lactate measured by microdialysis increased significantly (p = 0.02 and p = 0.03 respectively) 30 minutes after a 2/3 (66%) reduction in renal blood flow. The implantable Doppler probe was not able to detect flow changes until there was total venous occlusion. Microdialysis detected changes in local metabolism after both arterial and venous occlusion; the implantable Doppler probe could only detect vascular occlusions on the vessel it was placed. Conclusions We developed a new model for stepwise renal venous blood flow occlusion. Furthermore, the first comparison of the implantable Doppler probe and microdialysis for detection of renal vascular occlusions was made. The implantable Doppler probe could only detect flow changes after a complete occlusion, whereas microdialysis detected changes earlier, and could detect both arterial and venous occlusion. Based on these results, the implantable Doppler probe for early detection of vascular occlusions cannot be recommended. PMID:28542429
Evolution of a genetic polymorphism with climate change in a Mediterranean landscape

PubMed Central

Thompson, John; Charpentier, Anne; Bouguet, Guillaume; Charmasson, Faustine; Roset, Stephanie; Buatois, Bruno; Vernet, Philippe; Gouyon, Pierre-Henri

2013-01-01

Many species show changes in distribution and phenotypic trait variation in response to climatic warming. Evidence of genetically based trait responses to climate change is, however, less common. Here, we detected evolutionary variation in the landscape-scale distribution of a genetically based chemical polymorphism in Mediterranean wild thyme (Thymus vulgaris) in association with modified extreme winter freezing events. By comparing current data on morph distribution with that observed in the early 1970s, we detected a significant increase in the proportion of morphs that are sensitive to winter freezing. This increase in frequency was observed in 17 of the 24 populations in which, since the 1970s, annual extreme winter freezing temperatures have risen above the thresholds that cause mortality of freezing-sensitive morphs. Our results provide an original example of rapid ongoing evolutionary change associated with relaxed selection (less extreme freezing events) on a local landscape scale. In species whose distribution and genetic variability are shaped by strong selection gradients, there may be little time lag associated with their ecological and evolutionary response to long-term environmental change. PMID:23382198
Feature-based registration of historical aerial images by Area Minimization

NASA Astrophysics Data System (ADS)

Nagarajan, Sudhagar; Schenk, Toni

2016-06-01

The registration of historical images plays a significant role in assessing changes in land topography over time. By comparing historical aerial images with recent data, geometric changes that have taken place over the years can be quantified. However, the lack of ground control information and precise camera parameters has limited scientists' ability to reliably incorporate historical images into change detection studies. Other limitations include the methods of determining identical points between recent and historical images, which has proven to be a cumbersome task due to continuous land cover changes. Our research demonstrates a method of registering historical images using Time Invariant Line (TIL) features. TIL features are different representations of the same line features in multi-temporal data without explicit point-to-point or straight line-to-straight line correspondence. We successfully determined the exterior orientation of historical images by minimizing the area formed between corresponding TIL features in recent and historical images. We then tested the feasibility of the approach with synthetic and real data and analyzed the results. Based on our analysis, this method shows promise for long-term 3D change detection studies.
Design of stepwise screening for prediabetes and type 2 diabetes based on costs and cases detected.

PubMed

de Graaf, Gimon; Postmus, Douwe; Bakker, Stephan J L; Buskens, Erik

2015-09-01

To provide insight into the trade-off between cost per case detected (CPCD) and the detection rate in questionnaire-based stepwise screening for impaired fasting glucose and undiagnosed type 2 diabetes. We considered a stepwise screening in which individuals whose risk score exceeds a predetermined cutoff value are invited for further blood glucose testing. Using individual patient data to determine questionnaire sensitivity and specificity and external sources to determine screening costs and patient response rates, we rolled back a decision tree to estimate the CPCD and the detection rate for all possible cutoffs on the questionnaire. We found a U-shaped relation between CPCD and detection rate, with high costs per case detected at very low and very high detection rates. Changes in patient response rates had a large impact on both the detection rate and the CPCD, whereas screening costs and questionnaire accuracy mainly impacted the CPCD. Our applied method makes it possible to identify a range of efficient cutoffs where higher detection rates can be achieved at an additional cost per detected patient. This enables decision makers to choose an optimal cutoff based on their willingness to pay for additional detected patients. Copyright © 2015 Elsevier Inc. All rights reserved.
Dual-Emitting Fluorescent Metal-Organic Framework Nanocomposites as a Broad-Range pH Sensor for Fluorescence Imaging.

PubMed

Chen, Haiyong; Wang, Jing; Shan, Duoliang; Chen, Jing; Zhang, Shouting; Lu, Xiaoquan

2018-05-15

pH plays an important role in understanding physiological/pathologic processes, and abnormal pH is a symbol of many common diseases such as cancer, stroke, and Alzheimer's disease. In this work, an effective dual-emission fluorescent metal-organic framework nanocomposite probe (denoted as RB-PCN) has been constructed for sensitive and broad-range detection of pH. RB-PCN was prepared by encapsulating the DBI-PEG-NH 2 -functionalized Fe 3 O 4 into Zr-MOFs and then further reacting it with rhodamine B isothiocyanates (RBITC). In RB-PCN, RBITC is capable of sensing changes in pH in acidic solutions. Zr-MOFs not only enrich the target analyte but also exhibit a fluorescence response to pH changes in alkaline solutions. Based on the above structural and compositional features, RB-PCN could detect a wide range of pH changes. Importantly, such a nanoprobe could "see" the intracellular pH changes by fluorescence confocal imaging as well as "measure" the wider range of pH in actual samples by fluorescence spectroscopy. To the best of our knowledge, this is the first time a MOF-based dual-emitting fluorescent nanoprobe has been used for a wide range of pH detection.
Optimal Parameter Exploration for Online Change-Point Detection in Activity Monitoring Using Genetic Algorithms

PubMed Central

Khan, Naveed; McClean, Sally; Zhang, Shuai; Nugent, Chris

2016-01-01

In recent years, smart phones with inbuilt sensors have become popular devices to facilitate activity recognition. The sensors capture a large amount of data, containing meaningful events, in a short period of time. The change points in this data are used to specify transitions to distinct events and can be used in various scenarios such as identifying change in a patient’s vital signs in the medical domain or requesting activity labels for generating real-world labeled activity datasets. Our work focuses on change-point detection to identify a transition from one activity to another. Within this paper, we extend our previous work on multivariate exponentially weighted moving average (MEWMA) algorithm by using a genetic algorithm (GA) to identify the optimal set of parameters for online change-point detection. The proposed technique finds the maximum accuracy and F_measure by optimizing the different parameters of the MEWMA, which subsequently identifies the exact location of the change point from an existing activity to a new one. Optimal parameter selection facilitates an algorithm to detect accurate change points and minimize false alarms. Results have been evaluated based on two real datasets of accelerometer data collected from a set of different activities from two users, with a high degree of accuracy from 99.4% to 99.8% and F_measure of up to 66.7%. PMID:27792177

Assessment of landscape change associated with tropical cyclone phenomena in Baja California Sur, Mexico, using satellite remote sensing

NASA Astrophysics Data System (ADS)

Martinez-Gutierrez, Genaro

Baja California Sur (Mexico), as well as mainland Mexico, is affected by tropical cyclone storms, which originate in the eastern north Pacific. Historical records show that Baja has been damaged by intense summer storms. An arid to semiarid climate characterizes the study area, where precipitation mainly occurs during the summer and winter seasons. Natural and anthropogenic changes have impacted the landscape of southern Baja. The present research documents the effects of tropical storms over the southern region of Baja California for a period of approximately twenty-six years. The goal of the research is to demonstrate how remote sensing can be used to detect the important effects of tropical storms including: (a) evaluation of change detection algorithms, and (b) delineating changes to the landscape including coastal modification, fluvial erosion and deposition, vegetation change, river avulsion using change detection algorithms. Digital image processing methods with temporal Landsat satellite remotely sensed data from the North America Landscape Characterization archive (NALC), Thematic Mapper (TM), and Enhanced Thematic Mapper (ETM) images were used to document the landscape change. Two image processing methods were tested including Image differencing (ID), and Principal Component Analysis (PCA). Landscape changes identified with the NALC archive and TM images showed that the major changes included a rapid change of land use in the towns of San Jose del Cabo and Cabo San Lucas between 1973 and 1986. The features detected using the algorithms included flood deposits within the channels of active streams, erosion banks, and new channels caused by channel avulsion. Despite the 19 year period covered by the NALC data and approximately 10 year intervals between acquisition dates, there were changed features that could be identified in the images. The TM images showed that flooding from Hurricane Isis (1998) produced new large deposits within the stream channels. This research has shown that remote sensing based change detection can delineate the effects of flooding on the landscape at scales down to the nominal resolution of the sensor. These findings indicate that many other applications for change detection are both viable and important. These include disaster response, flood hazard planning, geomorphic studies, water supply management in deserts.
Rapid systematic assessment of the detection and attribution of regional anthropogenic climate change

DOE PAGES

Stone, Daithi A.; Hansen, Gerrit

2015-11-21

Despite being a well-established research field, the detection and attribution of observed climate change to anthropogenic forcing is not yet provided as a climate service. One reason for this is the lack of a methodology for performing tailored detection and attribution assessments on a rapid time scale. Here we develop such an approach, based on the translation of quantitative analysis into the “confidence” language employed in recent Assessment Reports of the Intergovernmental Panel on Climate Change. While its systematic nature necessarily ignores some nuances examined in detailed expert assessments, the approach nevertheless goes beyond most detection and attribution studies inmore » considering contributors to building confidence such as errors in observational data products arising from sparse monitoring networks. When compared against recent expert assessments, the results of this approach closely match those of the existing assessments. Where there are small discrepancies, these variously reflect ambiguities in the details of what is being assessed, reveal nuances or limitations of the expert assessments, or indicate limitations of the accuracy of the sort of systematic approach employed here. Deployment of the method on 116 regional assessments of recent temperature and precipitation changes indicates that existing rules of thumb concerning the detectability of climate change ignore the full range of sources of uncertainty, most particularly the importance of adequate observational monitoring.« less
Global warming alters sound transmission: differential impact on the prey detection ability of echolocating bats

PubMed Central

Luo, Jinhong; Koselj, Klemen; Zsebők, Sándor; Siemers, Björn M.; Goerlitz, Holger R.

2014-01-01

Climate change impacts the biogeography and phenology of plants and animals, yet the underlying mechanisms are little known. Here, we present a functional link between rising temperature and the prey detection ability of echolocating bats. The maximum distance for echo-based prey detection is physically determined by sound attenuation. Attenuation is more pronounced for high-frequency sound, such as echolocation, and is a nonlinear function of both call frequency and ambient temperature. Hence, the prey detection ability, and thus possibly the foraging efficiency, of echolocating bats and susceptible to rising temperatures through climate change. Using present-day climate data and projected temperature rises, we modelled this effect for the entire range of bat call frequencies and climate zones around the globe. We show that depending on call frequency, the prey detection volume of bats will either decrease or increase: species calling above a crossover frequency will lose and species emitting lower frequencies will gain prey detection volume, with crossover frequency and magnitude depending on the local climatic conditions. Within local species assemblages, this may cause a change in community composition. Global warming can thus directly affect the prey detection ability of individual bats and indirectly their interspecific interactions with competitors and prey. PMID:24335559
Global warming alters sound transmission: differential impact on the prey detection ability of echolocating bats.

PubMed

Luo, Jinhong; Koselj, Klemen; Zsebok, Sándor; Siemers, Björn M; Goerlitz, Holger R

2014-02-06

Climate change impacts the biogeography and phenology of plants and animals, yet the underlying mechanisms are little known. Here, we present a functional link between rising temperature and the prey detection ability of echolocating bats. The maximum distance for echo-based prey detection is physically determined by sound attenuation. Attenuation is more pronounced for high-frequency sound, such as echolocation, and is a nonlinear function of both call frequency and ambient temperature. Hence, the prey detection ability, and thus possibly the foraging efficiency, of echolocating bats and susceptible to rising temperatures through climate change. Using present-day climate data and projected temperature rises, we modelled this effect for the entire range of bat call frequencies and climate zones around the globe. We show that depending on call frequency, the prey detection volume of bats will either decrease or increase: species calling above a crossover frequency will lose and species emitting lower frequencies will gain prey detection volume, with crossover frequency and magnitude depending on the local climatic conditions. Within local species assemblages, this may cause a change in community composition. Global warming can thus directly affect the prey detection ability of individual bats and indirectly their interspecific interactions with competitors and prey.
Rapid Landslide Mapping by Means of Post-Event Polarimetric SAR Imagery

NASA Astrophysics Data System (ADS)

Plank, Simon; Martinis, Sandro; Twele, Andre

2016-08-01

Rapid mapping of landslides, quickly providing information about the extent of the affected area and type and grade of damage, is crucial to enable fast crisis response. Reviewing the literature shows that most synthetic aperture radar (SAR) data-based landslide mapping procedures use change detection techniques. However, the required very high resolution (VHR) pre-event SAR imagery, acquired shortly before the landslide event, is commonly not available. Due to limitations in onboard disk space and downlink transmission rates modern VHR SAR missions do not systematically cover the entire world. We present a fast and robust procedure for mapping of landslides, based on change detection between freely available and systematically acquired pre-event optical and post-event polarimetric SAR data.
Tapered optical fiber sensor based on localized surface plasmon resonance.

PubMed

Lin, Hsing-Ying; Huang, Chen-Han; Cheng, Gia-Ling; Chen, Nan-Kuang; Chui, Hsiang-Chen

2012-09-10

A tapered fiber localized surface plasmon resonance (LSPR) sensor is demonstrated for refractive index sensing and label-free biochemical detection. The sensing strategy relies on the interrogation of the transmission intensity change due to the evanescent field absorption of immobilized gold nanoparticles on the tapered fiber surface. The refractive index resolution based on the interrogation of transmission intensity change is calculated to be 3.2×10⁻⁵ RIU. The feasibility of DNP-functionalized tapered fiber LSPR sensor in monitoring anti-DNP antibody with different concentrations spiked in buffer is examined. Results suggest that the compact sensor can perform qualitative and quantitative biochemical detection in real-time and thus has potential to be used in biomolecular sensing applications.
Spatial and temporal variation in distribution of mangroves in Moreton Bay, subtropical Australia: a comparison of pattern metrics and change detection analyses based on aerial photographs

NASA Astrophysics Data System (ADS)

Manson, F. J.; Loneragan, N. R.; Phinn, S. R.

2003-07-01

An assessment of the changes in the distribution and extent of mangroves within Moreton Bay, southeast Queensland, Australia, was carried out. Two assessment methods were evaluated: spatial and temporal pattern metrics analysis, and change detection analysis. Currently, about 15,000 ha of mangroves are present in Moreton Bay. These mangroves are important ecosystems, but are subject to disturbance from a number of sources. Over the past 25 years, there has been a loss of more than 3800 ha, as a result of natural losses and mangrove clearing (e.g. for urban and industrial development, agriculture and aquaculture). However, areas of new mangroves have become established over the same time period, offsetting these losses to create a net loss of about 200 ha. These new mangroves have mainly appeared in the southern bay region and the bay islands, particularly on the landward edge of existing mangroves. In addition, spatial patterns and species composition of mangrove patches have changed. The pattern metrics analysis provided an overview of mangrove distribution and change in the form of single metric values, while the change detection analysis gave a more detailed and spatially explicit description of change. An analysis of the effects of spatial scales on the pattern metrics indicated that they were relatively insensitive to scale at spatial resolutions less than 50 m, but that most metrics became sensitive at coarser resolutions, a finding which has implications for mapping of mangroves based on remotely sensed data.
Complementary molecular and elemental detection of speciated thioarsenicals using ESI-MS in combination with a xenon-based collision-cell ICP-MS with application to fortified NIST freeze-dried urine.

PubMed

Ellis, Jenny L; Conklin, Sean D; Gallawa, Christina M; Kubachka, Kevin M; Young, Andrea R; Creed, Patricia A; Caruso, Joseph A; Creed, John T

2008-04-01

The simultaneous detection of arsenic and sulfur in thioarsenicals was achieved using xenon-based collision-cell inductively coupled plasma (ICP) mass spectrometry (MS) in combination with high-performance liquid chromatography. In an attempt to minimize the (16)O(16)O(+) interference at m/z 32, both sample introduction and collision-cell experimental parameters were optimized. Low flow rates (0.25 mL/min) and a high methanol concentration (8%) in the mobile phase produced a fourfold decrease in the m/z 32 background. A plasma sampling depth change from 3 to 7 mm produced a twofold decrease in background at m/z 32, with a corresponding fourfold increase in the signal associated with a high ionization surrogate for sulfur. The quadrupole bias and the octopole bias were used as a kinetic energy discriminator between background and analyte ions, but a variety of tuning conditions produced similar (less than twofold change) detection limits for sulfur ((32)S). A 34-fold improvement in the (32)S detection limit was achieved using xenon instead of helium as a collision gas. The optimized xenon-based collision cell ICP mass spectrometer was then used with electrospray ionization MS to provide elemental and molecular-based information for the analysis of a fortified sample of NIST freeze-dried urine. The 3sigma detection limits, based on peak height for dimethylthioarsinic acid (DMTA) and trimethylarsine sulfide (TMAS), were 15 and 12 ng/g, respectively. Finally, the peak area reproducibilities (percentage relative standard deviation) of a 5-ppm fortified sample of NIST freeze dried urine for DMTA and TMAS were 7.4 and 5.4%, respectively.
Implementation of a channelized Hotelling observer model to assess image quality of x-ray angiography systems.

PubMed

Favazza, Christopher P; Fetterly, Kenneth A; Hangiandreou, Nicholas J; Leng, Shuai; Schueler, Beth A

2015-01-01

Evaluation of flat-panel angiography equipment through conventional image quality metrics is limited by the scope of standard spatial-domain image quality metric(s), such as contrast-to-noise ratio and spatial resolution, or by restricted access to appropriate data to calculate Fourier domain measurements, such as modulation transfer function, noise power spectrum, and detective quantum efficiency. Observer models have been shown capable of overcoming these limitations and are able to comprehensively evaluate medical-imaging systems. We present a spatial domain-based channelized Hotelling observer model to calculate the detectability index (DI) of our different sized disks and compare the performance of different imaging conditions and angiography systems. When appropriate, changes in DIs were compared to expectations based on the classical Rose model of signal detection to assess linearity of the model with quantum signal-to-noise ratio (SNR) theory. For these experiments, the estimated uncertainty of the DIs was less than 3%, allowing for precise comparison of imaging systems or conditions. For most experimental variables, DI changes were linear with expectations based on quantum SNR theory. DIs calculated for the smallest objects demonstrated nonlinearity with quantum SNR theory due to system blur. Two angiography systems with different detector element sizes were shown to perform similarly across the majority of the detection tasks.
Value of automatic patient motion detection and correction in myocardial perfusion imaging using a CZT-based SPECT camera.

PubMed

van Dijk, Joris D; van Dalen, Jorn A; Mouden, Mohamed; Ottervanger, Jan Paul; Knollema, Siert; Slump, Cornelis H; Jager, Pieter L

2018-04-01

Correction of motion has become feasible on cadmium-zinc-telluride (CZT)-based SPECT cameras during myocardial perfusion imaging (MPI). Our aim was to quantify the motion and to determine the value of automatic correction using commercially available software. We retrospectively included 83 consecutive patients who underwent stress-rest MPI CZT-SPECT and invasive fractional flow reserve (FFR) measurement. Eight-minute stress acquisitions were reformatted into 1.0- and 20-second bins to detect respiratory motion (RM) and patient motion (PM), respectively. RM and PM were quantified and scans were automatically corrected. Total perfusion deficit (TPD) and SPECT interpretation-normal, equivocal, or abnormal-were compared between the noncorrected and corrected scans. Scans with a changed SPECT interpretation were compared with FFR, the reference standard. Average RM was 2.5 ± 0.4 mm and maximal PM was 4.5 ± 1.3 mm. RM correction influenced the diagnostic outcomes in two patients based on TPD changes ≥7% and in nine patients based on changed visual interpretation. In only four of these patients, the changed SPECT interpretation corresponded with FFR measurements. Correction for PM did not influence the diagnostic outcomes. Respiratory motion and patient motion were small. Motion correction did not appear to improve the diagnostic outcome and, hence, the added value seems limited in MPI using CZT-based SPECT cameras.
Water Level Monitoring on Tibetan Lakes Based on Icesat and Envisat Data Series

NASA Astrophysics Data System (ADS)

Li, H. W.; Qiao, G.; Wu, Y. J.; Cao, Y. J.; Mi, H.

2017-09-01

Satellite altimetry technique is an effective method to monitor the water level of lakes in a wide range, especially in sparsely populated areas, such as the Tibet Plateau (TP). To provide high quality data for time-series change detection of lake water level, an automatic and efficient algorithm for lake water footprint (LWF) detection in a wide range is used. Based on ICESat GLA14 Release634 data and ENVISat GDR 1Hz data, water level of 167 lakes were obtained from ICESat data series, and water level of 120 lakes were obtained from ENVISat data series. Among them, 67 lakes contained two data series. Mean standard deviation of all lakes is 0.088 meters (ICESat), 0.339 meters (ENVISat). Combination of multi-source altimetry data is helpful for us to get longer and more dense periods cover water level, study the lake level changes, manage water resources and understand the impacts of climate change better. In addition, the standard deviation of LWF elevation used to calculate the water level were analyzed by month. Based on lake data set for the TP from the 1960s, 2005, and 2014 in Scientific Data, it is found that the water level changes in the TP have a strong spatial correlation with the area changes.
Detection of Terrestrial Planets Using Transit Photometry

NASA Technical Reports Server (NTRS)

Koch, David; Witteborn, Fred; Jenkins, Jon; Dunham, Edward; Boruci, William; DeVincenzi, Donald (Technical Monitor)

2001-01-01

Transit photometry detection of planets offers many advantages: an ability to detect terrestrial size planets, direct determination of the planet's size, applicability to all main-sequence stars, and a differential brightness change of the periodic signature being independent of stellar distance or planetary orbital semi-major axis. Ground and space based photometry have already been successful in detecting transits of the giant planet HD209458b. However, photometry 100 times better is required to detect terrestrial planets. We present results of laboratory measurements of an end-to-end photometric system incorporating all of the important confounding noise features of both the sky and a space based photometer including spacecraft jitter. In addition to demonstrating an instrumental noise of less than 10 ppm (an Earth transit of a solar-like star is 80 ppm), the brightnesses of individual stars were dimmed to simulate Earth-size transit signals. These 'transits' were reliably detected as part of the tests.
Ten Years of Land Cover Change on the California Coast Detected using Landsat Satellite Image Analysis

NASA Technical Reports Server (NTRS)

Potter, Christopher S.

2013-01-01

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

NASA Astrophysics Data System (ADS)

Barriga, Eduardo S.; T'so, Dan; Pattichis, Marios; Kwon, Young; Kardon, Randy; Abramoff, Michael; Soliz, Peter

2006-02-01

In the early stages of some retinal diseases, such as glaucoma, loss of retinal activity may be difficult to detect with today's clinical instruments. Many of today's instruments focus on detecting changes in anatomical structures, such as the nerve fiber layer. Our device, which is based on a modified fundus camera, seeks to detect changes in optical signals that reflect functional changes in the retina. The functional imager uses a patterned stimulus at wavelength of 535nm. An intrinsic functional signal is collected at a near infrared wavelength. Measured changes in reflectance in response to the visual stimulus are on the order of 0.1% to 1% of the total reflected intensity level, which makes the functional signal difficult to detect by standard methods because it is masked by other physiological signals and by imaging system noise. In this paper, we analyze the video sequences from a set of 60 experiments with different patterned stimuli from cats. Using a set of statistical techniques known as Independent Component Analysis (ICA), we estimate the signals present in the videos. Through controlled simulation experiments, we quantify the limits of signal strength in order to detect the physiological signal of interest. The results of the analysis show that, in principle, signal levels of 0.1% (-30dB) can be detected. The study found that in 86% of the animal experiments the patterned stimuli effects on the retina can be detected and extracted. The analysis of the different responses extracted from the videos can give an insight of the functional processes present during the stimulation of the retina.
Population variability complicates the accurate detection of climate change responses.

PubMed

McCain, Christy; Szewczyk, Tim; Bracy Knight, Kevin

2016-06-01

The rush to assess species' responses to anthropogenic climate change (CC) has underestimated the importance of interannual population variability (PV). Researchers assume sampling rigor alone will lead to an accurate detection of response regardless of the underlying population fluctuations of the species under consideration. Using population simulations across a realistic, empirically based gradient in PV, we show that moderate to high PV can lead to opposite and biased conclusions about CC responses. Between pre- and post-CC sampling bouts of modeled populations as in resurvey studies, there is: (i) A 50% probability of erroneously detecting the opposite trend in population abundance change and nearly zero probability of detecting no change. (ii) Across multiple years of sampling, it is nearly impossible to accurately detect any directional shift in population sizes with even moderate PV. (iii) There is up to 50% probability of detecting a population extirpation when the species is present, but in very low natural abundances. (iv) Under scenarios of moderate to high PV across a species' range or at the range edges, there is a bias toward erroneous detection of range shifts or contractions. Essentially, the frequency and magnitude of population peaks and troughs greatly impact the accuracy of our CC response measurements. Species with moderate to high PV (many small vertebrates, invertebrates, and annual plants) may be inaccurate 'canaries in the coal mine' for CC without pertinent demographic analyses and additional repeat sampling. Variation in PV may explain some idiosyncrasies in CC responses detected so far and urgently needs more careful consideration in design and analysis of CC responses. © 2016 John Wiley & Sons Ltd.
A magnetic/fluorometric bimodal sensor based on a carbon dots-MnO2 platform for glutathione detection

NASA Astrophysics Data System (ADS)

Xu, Yang; Chen, Xi; Chai, Ran; Xing, Chengfen; Li, Huanrong; Yin, Xue-Bo

2016-07-01

A novel magnetic/fluorometric bimodal sensor was built from carbon dots (CDs) and MnO2. The resulting sensor was sensitive to glutathione (GSH), leading to apparent enhancement of magnetic resonance (MR) and fluorescence signals along with visual changes. The bimodal detection strategy is based on the decomposition of the CDs-MnO2 through a redox reaction between GSH and MnO2. This process causes the transformation from non-MR-active MnO2 to MR-active Mn2+, and is accompanied by fluorescence restoration of CDs. Compared with a range of other CDs, the polyethylenimine (PEI) passivated CDs (denoted as pCDs) were suitable for detection due to their positive surface potential. Cross-validation between MR and fluorescence provided detailed information regarding the MnO2 reduction process, and revealed the three distinct stages of the redox process. Thus, the design of a CD-based sensor for the magnetic/fluorometric bimodal detection of GSH was emphasized for the first time. This platform showed a detection limit of 0.6 μM with a linear range of 1-200 μM in the fluorescence mode, while the MR mode exhibited a linear range of 5-200 μM and a GSH detection limit of 2.8 μM with a visible change being observed rapidly at 1 μM in the MR images. Furthermore, the introduction of the MR mode allowed the biothiols to be easily identified. The integration of CD fluorescence with an MR response was demonstrated to be promising for providing detailed information and discriminating power, and therefore extend the application of CDs in sensing and imaging.A novel magnetic/fluorometric bimodal sensor was built from carbon dots (CDs) and MnO2. The resulting sensor was sensitive to glutathione (GSH), leading to apparent enhancement of magnetic resonance (MR) and fluorescence signals along with visual changes. The bimodal detection strategy is based on the decomposition of the CDs-MnO2 through a redox reaction between GSH and MnO2. This process causes the transformation from non-MR-active MnO2 to MR-active Mn2+, and is accompanied by fluorescence restoration of CDs. Compared with a range of other CDs, the polyethylenimine (PEI) passivated CDs (denoted as pCDs) were suitable for detection due to their positive surface potential. Cross-validation between MR and fluorescence provided detailed information regarding the MnO2 reduction process, and revealed the three distinct stages of the redox process. Thus, the design of a CD-based sensor for the magnetic/fluorometric bimodal detection of GSH was emphasized for the first time. This platform showed a detection limit of 0.6 μM with a linear range of 1-200 μM in the fluorescence mode, while the MR mode exhibited a linear range of 5-200 μM and a GSH detection limit of 2.8 μM with a visible change being observed rapidly at 1 μM in the MR images. Furthermore, the introduction of the MR mode allowed the biothiols to be easily identified. The integration of CD fluorescence with an MR response was demonstrated to be promising for providing detailed information and discriminating power, and therefore extend the application of CDs in sensing and imaging. Electronic supplementary information (ESI) available. See DOI: 10.1039/c6nr03129c
Temporal change in forest fragmentation at multiple scales

Treesearch

J.D. Wickham; K.H. Riitters; T.G. Wade; J.W. Coulston

2007-01-01

Previous studies of temporal changes in fragmentation have focused almost exclusively on patch and edge statistics, which might not detect changes in the spatial scale at which forest occurs in or dominates the landscape. We used temporal land-cover data for the Chesapeake Bay region and the state of New Jersey to compare patch-based and areaâdensity scaling measures...
A statistical power analysis of woody carbon flux from forest inventory data

Treesearch

James A. Westfall; Christopher W. Woodall; Mark A. Hatfield

2013-01-01

At a national scale, the carbon (C) balance of numerous forest ecosystem C pools can be monitored using a stock change approach based on national forest inventory data. Given the potential influence of disturbance events and/or climate change processes, the statistical detection of changes in forest C stocks is paramount to maintaining the net sequestration status of...
Ratio-based estimators for a change point in persistence.

PubMed

Halunga, Andreea G; Osborn, Denise R

2012-11-01

We study estimation of the date of change in persistence, from [Formula: see text] to [Formula: see text] or vice versa. Contrary to statements in the original papers, our analytical results establish that the ratio-based break point estimators of Kim [Kim, J.Y., 2000. Detection of change in persistence of a linear time series. Journal of Econometrics 95, 97-116], Kim et al. [Kim, J.Y., Belaire-Franch, J., Badillo Amador, R., 2002. Corringendum to "Detection of change in persistence of a linear time series". Journal of Econometrics 109, 389-392] and Busetti and Taylor [Busetti, F., Taylor, A.M.R., 2004. Tests of stationarity against a change in persistence. Journal of Econometrics 123, 33-66] are inconsistent when a mean (or other deterministic component) is estimated for the process. In such cases, the estimators converge to random variables with upper bound given by the true break date when persistence changes from [Formula: see text] to [Formula: see text]. A Monte Carlo study confirms the large sample downward bias and also finds substantial biases in moderate sized samples, partly due to properties at the end points of the search interval.
Online Adaboost-Based Parameterized Methods for Dynamic Distributed Network Intrusion Detection.

PubMed

Hu, Weiming; Gao, Jun; Wang, Yanguo; Wu, Ou; Maybank, Stephen

2014-01-01

Current network intrusion detection systems lack adaptability to the frequently changing network environments. Furthermore, intrusion detection in the new distributed architectures is now a major requirement. In this paper, we propose two online Adaboost-based intrusion detection algorithms. In the first algorithm, a traditional online Adaboost process is used where decision stumps are used as weak classifiers. In the second algorithm, an improved online Adaboost process is proposed, and online Gaussian mixture models (GMMs) are used as weak classifiers. We further propose a distributed intrusion detection framework, in which a local parameterized detection model is constructed in each node using the online Adaboost algorithm. A global detection model is constructed in each node by combining the local parametric models using a small number of samples in the node. This combination is achieved using an algorithm based on particle swarm optimization (PSO) and support vector machines. The global model in each node is used to detect intrusions. Experimental results show that the improved online Adaboost process with GMMs obtains a higher detection rate and a lower false alarm rate than the traditional online Adaboost process that uses decision stumps. Both the algorithms outperform existing intrusion detection algorithms. It is also shown that our PSO, and SVM-based algorithm effectively combines the local detection models into the global model in each node; the global model in a node can handle the intrusion types that are found in other nodes, without sharing the samples of these intrusion types.

Hippocampal atrophy in people with memory deficits: results from the population-based IPREA study.

PubMed

Ferrarini, Luca; van Lew, Baldur; Reiber, Johan H C; Gandin, Claudia; Galluzzo, Lucia; Scafato, Emanuele; Frisoni, Giovanni B; Milles, Julien; Pievani, Michela

2014-07-01

Clinical studies have shown that hippocampal atrophy is present before dementia in people with memory deficits and can predict dementia development. The question remains whether this association holds in the general population. This is of interest for the possible use of hippocampal atrophy to screen population for preventive interventions. The aim of this study was to assess hippocampal volume and shape abnormalities in elderly adults with memory deficits in a cross-sectional population-based study. We included individuals participating in the Italian Project on the Epidemiology of Alzheimer Disease (IPREA) study: 75 cognitively normal individuals (HC), 31 individuals with memory deficits (MEM), and 31 individuals with memory deficits not otherwise specified (MEMnos). Hippocampal volumes and shape were extracted through manual tracing and the growing and adaptive meshes (GAMEs) shape-modeling algorithm. We investigated between-group differences in hippocampal volume and shape, and correlations with memory deficits. In MEM participants, hippocampal volumes were significantly smaller than in HC and were mildly associated with worse memory scores. Memory-associated shape changes mapped to the anterior hippocampus. Shape-based analysis detected no significant difference between MEM and HC, while MEMnos showed shape changes in the posterior hippocampus compared with HC and MEM groups. These findings support the discriminant validity of hippocampal volumetry as a biomarker of memory impairment in the general population. The detection of shape changes in MEMnos but not in MEM participants suggests that shape-based biomarkers might lack sensitivity to detect Alzheimer's-like pathology in the general population.
Liquid crystal-based biosensor with backscattering interferometry: A quantitative approach.

PubMed

Khan, Mashooq; Park, Soo-Young

2017-01-15

We developed a new technology that uses backscattering interferometry (BSI) to quantitatively measure nematic liquid crystal (NLC)-based biosensors, those usually relied on texture reading for on/off signals. The LC-based BSI comprised an octadecyltrichlorosilane (OTS)-coated square capillary filled with 4-cyano-4'-pentylbiphenyl (5CB, a nematic LC at room temperature). The LC/water interface in the capillary was functionalized by a coating of poly(acrylicacid-b-4-cyanobiphenyl-4'-oxyundecylacrylate) (PAA-b-LCP) and immobilized with the enzymes glucose oxidase (GOx) and horseradish peroxidase (HRP) through covalent linkage to the PAA chains (5CB PAA-GOx:HRP ) for glucose detection. Laser irradiation of the LC near the LC/water interface resulted in backscattered fringes with high contrast. The change in the spatial position of the fringes (because of the change in the orientation of the LC caused by the GOx:HRP enzymatic reaction of glucose) altered the output voltage of the photodetector when its active area was aligned with the edge of one of the fringes. The change in the intensity at the photodetector allowed the detection limit of the instrument to be as low as 0.008mM with a linear range of 0.02-9mM in a short response time (~60s). This LC-based BSI technique allows for quantitative, sensitive, selective, reproducible, easily obtainable, and interference-free detection in a large linear dynamic range and for practical applications with human serum. Copyright Â© 2016 Elsevier B.V. All rights reserved.
Sensitivity of cell-based biosensors to environmental variables.

PubMed

Gilchrist, Kristin H; Giovangrandi, Laurent; Whittington, R Hollis; Kovacs, Gregory T A

2005-01-15

Electrically active living cells cultured on extracellular electrode arrays are utilized to detect biologically active agents. Because cells are highly sensitive to environmental conditions, environmental fluctuations can elicit cellular responses that contribute to the noise in a cell-based biosensor system. Therefore, the characterization and control of environmental factors such as temperature, pH, and osmolarity is critical in such a system. The cell-based biosensor platform described here utilizes the measurement of action potentials from cardiac cells cultured on electrode arrays. A recirculating fluid flow system is presented for use in dose-response experiments that regulates temperature within +/-0.2 degrees C, pH to within +/-0.05 units, and allows no significant change in osmolarity. Using this system, the relationship between the sensor output parameters and environmental variation was quantified. Under typical experimental conditions, beat rate varied approximately 10% per degree change in temperature or per 0.1 unit change in pH. Similar relationships were measured for action potential amplitude, duration, and conduction velocity. For the specific flow system used in this work, the measured environmental sensitivity resulted in an overall beat rate variation of +/-4.7% and an overall amplitude variation of +/-3.3%. The magnitude of the noise due to environmental sensitivity has a large impact on the detection capability of the cell-based system. The significant responses to temperature, pH, and osmolarity have important implications for the use of living cells in detection systems and should be considered in the design and evaluation of such systems.
Luminescent Li-based metal-organic framework tailored for the selective detection of explosive nitroaromatic compounds: direct observation of interaction sites.

PubMed

Kim, Tae Kyung; Lee, Jae Hwa; Moon, Dohyun; Moon, Hoi Ri

2013-01-18

A luminescent lithium metal-organic framework (MOF) is constructed from the solvothermal reaction of Li(+) and a well-designed organic ligand, bis(4-carboxyphenyl)-N-methylamine (H(2)CPMA). A Li-based MOF can detect an explosive aromatic compound containing nitro groups as an explosophore, by showing a dramatic color change with concurrent luminescence quenching in the solid state. The detection sites are proven directly through single-crystal-to-single-crystal transformations, which show strong interactions between the aromatic rings of the electron-rich CPMA(2-) molecules and the electron-deficient nitrobenzene.
A New Intrusion Detection Method Based on Antibody Concentration

NASA Astrophysics Data System (ADS)

Zeng, Jie; Li, Tao; Li, Guiyang; Li, Haibo

Antibody is one kind of protein that fights against the harmful antigen in human immune system. In modern medical examination, the health status of a human body can be diagnosed by detecting the intrusion intensity of a specific antigen and the concentration indicator of corresponding antibody from human body’s serum. In this paper, inspired by the principle of antigen-antibody reactions, we present a New Intrusion Detection Method Based on Antibody Concentration (NIDMBAC) to reduce false alarm rate without affecting detection rate. In our proposed method, the basic definitions of self, nonself, antigen and detector in the intrusion detection domain are given. Then, according to the antigen intrusion intensity, the change of antibody number is recorded from the process of clone proliferation for detectors based on the antigen classified recognition. Finally, building upon the above works, a probabilistic calculation method for the intrusion alarm production, which is based on the correlation between the antigen intrusion intensity and the antibody concen-tration, is proposed. Our theoretical analysis and experimental results show that our proposed method has a better performance than traditional methods.
Lake Chapala change detection using time series

NASA Astrophysics Data System (ADS)

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

2008-10-01

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

NASA Astrophysics Data System (ADS)

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

2015-04-01

Natural changes and human modifications in hydrological systems coevolve and interact in a coupled and interlinked way. If, on one hand, climatic changes are stochastic, non-steady, and affect the hydrological systems, on the other hand, human-induced changes due to over-exploitation of soils and water resources modifies the natural landscape, water fluxes and its partitioning. Indeed, the traditional assumption of static systems in hydrological analysis, which has been adopted for long time, fails whenever transient climatic conditions and/or land use changes occur. Time series analysis is a way to explore environmental changes together with societal changes; unfortunately, the not distinguishability between causes restrict the scope of this method. In order to overcome this limitation, it is possible to couple time series analysis with an opportune hydrological model, such as a conceptual hydrological model, which offers a schematization of complex dynamics acting within a basin. Assuming that model parameters represent morphological basin characteristics and that calibration is a way to detect hydrological signature at a specific moment, it is possible to argue that calibrating the model over different time windows could be a method for detecting potential hydrological changes. In order to test the capabilities of a conceptual model in detecting hydrological changes, this work presents different "in silico" experiments. A synthetic-basin is forced with an ensemble of possible future scenarios generated with a stochastic weather generator able to simulate steady and non-steady climatic conditions. The experiments refer to Mediterranean climate, which is characterized by marked seasonality, and consider the outcomes of the IPCC 5th report for describing climate evolution in the next century. In particular, in order to generate future climate change scenarios, a stochastic downscaling in space and time is carried out using realizations of an ensemble of General Circulation Models (GCMs) for the future scenarios 2046-2065 and 2081-2100. Land use changes (i.e., changes in the fraction of impervious area due to increasing urbanization) are explicitly simulated, while the reference hydrological responses are assessed by the spatially distributed, process-based hydrological model tRIBS, the TIN-based Real-time Integrated Basin Simulator. Several scenarios have been created, describing hypothetical centuries with steady conditions, climate change conditions, land use change conditions and finally complex conditions involving both transient climatic modifications and gradual land use changes. A conceptual lumped model, the EHSM (EcoHydrological Streamflow Model) is calibrated for the above mentioned scenarios with regard to different time-windows. The calibrated parameters show high sensitivity to anthropic variations in land use and/or climatic variability. Land use changes are clearly visible from parameters evolution especially when steady climatic conditions are considered. When the increase in urbanization is coupled with rainfall reduction the ability to detect human interventions through the analysis of conceptual model parameters is weakened.
Damage Detection and Verification System (DDVS) for In-Situ Health Monitoring

NASA Technical Reports Server (NTRS)

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.
Motion-based video monitoring for early detection of livestock diseases: The case of African swine fever

PubMed Central

Martínez-Avilés, Marta; Ivorra, Benjamin; Martínez-López, Beatriz; Ramos, Ángel Manuel; Sánchez-Vizcaíno, José Manuel

2017-01-01

Early detection of infectious diseases can substantially reduce the health and economic impacts on livestock production. Here we describe a system for monitoring animal activity based on video and data processing techniques, in order to detect slowdown and weakening due to infection with African swine fever (ASF), one of the most significant threats to the pig industry. The system classifies and quantifies motion-based animal behaviour and daily activity in video sequences, allowing automated and non-intrusive surveillance in real-time. The aim of this system is to evaluate significant changes in animals’ motion after being experimentally infected with ASF virus. Indeed, pig mobility declined progressively and fell significantly below pre-infection levels starting at four days after infection at a confidence level of 95%. Furthermore, daily motion decreased in infected animals by approximately 10% before the detection of the disease by clinical signs. These results show the promise of video processing techniques for real-time early detection of livestock infectious diseases. PMID:28877181
Aptamer-based SERRS Sensor for Thrombin Detection

PubMed Central

Cho, Hansang; Baker, Brian R.; Wachsmann-Hogiu, Sebastian; Pagba, Cynthia V.; Laurence, Ted A.; Lane, Stephen M.; Lee, Luke P.; Tok, Jeffrey B.-H.

2012-01-01

We describe an aptamer-based Surface Enhanced Resonance Raman Scattering (SERRS) sensor with high sensitivity, specificity, and stability for the detection of a coagulation protein, human α-thrombin. The sensor achieves high sensitivity and a limit of detection of 100 pM by monitoring the SERRS signal change upon the single step of thrombin binding to immobilized thrombin binding aptamer. The selectivity of the sensor is demonstrated by the specific discrimination of thrombin from other protein analytes. The specific recognition and binding of thrombin by the thrombin binding aptamer is essential to the mechanism of the aptamer-based sensor, as shown through measurements using negative control oligonucleotides. In addition, the sensor can detect 1 nM thrombin in the presence of complex biofluids, such as 10% fetal calf serum, demonstrating that the immobilized, 5'-capped, 3'-capped aptamer is sufficiently robust for clinical diagnostic applications. Furthermore, the proposed sensor may be implemented for multiplexed detection using different aptamer-Raman probe complexes. PMID:19367849
Single Zno Nanowire-Based Biofet Sensors for Ultrasensitive, Label-Free and Real-Time Detection of Uric Acid

NASA Astrophysics Data System (ADS)

Lin, Pei; Liu, Xi; Yan, Xiaoqin; Kang, Zhuo; Lei, Yang; Zhao, Yanguang

2012-08-01

Qualitative and quantitative detection of biological and chemical species is crucial in many areas, ranging from clinical diagnosis to homeland security. Due to the advantages of ultrahigh sensitivity, label-free, fast readout and easy fabrication over the traditional detection systems, semiconductor nanowire based electronic devices have emerged as a potential platform. In this paper, we fabricated a single ZnO nanowire-based bioFET sensor for the detection of low and high concentration uric acid solution at the same time. The addition of uric acid with the concentrations from 1 pM to 0.5 mM resulted in the electrical conductance changes of up to 227 nS, and the response time turns out to be in the order of millisecond. The ZnO NW biosensor could easily detect as low as 1 pM of the uric acid with 14.7 nS of conductance increase, which implied that the sensitivity of the biosensor can be below the 1pM concentration.
Automatic spatiotemporal matching of detected pleural thickenings

NASA Astrophysics Data System (ADS)

Chaisaowong, Kraisorn; Keller, Simon Kai; Kraus, Thomas

2014-01-01

Pleural thickenings can be found in asbestos exposed patient's lung. Non-invasive diagnosis including CT imaging can detect aggressive malignant pleural mesothelioma in its early stage. In order to create a quantitative documentation of automatic detected pleural thickenings over time, the differences in volume and thickness of the detected thickenings have to be calculated. Physicians usually estimate the change of each thickening via visual comparison which provides neither quantitative nor qualitative measures. In this work, automatic spatiotemporal matching techniques of the detected pleural thickenings at two points of time based on the semi-automatic registration have been developed, implemented, and tested so that the same thickening can be compared fully automatically. As result, the application of the mapping technique using the principal components analysis turns out to be advantageous than the feature-based mapping using centroid and mean Hounsfield Units of each thickening, since the resulting sensitivity was improved to 98.46% from 42.19%, while the accuracy of feature-based mapping is only slightly higher (84.38% to 76.19%).
Organic conjugated small molecule materials based optical probe for rapid, colorimetric and UV-vis spectral detection of phosphorylated protein in placental tissue.

PubMed

Wang, Yanfang; Yang, Na; Liu, Yi

2018-04-05

A novel organic small molecule with D-Pi-A structure was prepared, which was found to be a promising colorimetric and ratiometric UV-vis spetral probe for detection of phosphorylated proteins with the help of tetravalent zirconium ion. Such optical probe based on chromophore WYF-1 shows a rapid response (within 10s) and high selectivity and sensitivity for phosphorylated proteins, giving distinct colorimetric and ratiometric UV-vis changes at 720 and 560nm. The detection limit for phosphorylated proteins was estimated to be 100nM. In addition, detection of phosphorylated proteins in placental tissue samples with this probe was successfully applied, which indicates that this probe holds great potential for phosphorylated proteins detection. Copyright © 2018 Elsevier B.V. All rights reserved.
On event-based optical flow detection

PubMed Central

Brosch, Tobias; Tschechne, Stephan; Neumann, Heiko

2015-01-01

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

NASA Astrophysics Data System (ADS)

Sánchez, Clara I.; Hornero, Roberto; Mayo, Agustín; García, María

2009-02-01

Diabetic Retinopathy is one of the leading causes of blindness and vision defects in developed countries. An early detection and diagnosis is crucial to avoid visual complication. Microaneurysms are the first ocular signs of the presence of this ocular disease. Their detection is of paramount importance for the development of a computer-aided diagnosis technique which permits a prompt diagnosis of the disease. However, the detection of microaneurysms in retinal images is a difficult task due to the wide variability that these images usually present in screening programs. We propose a statistical approach based on mixture model-based clustering and logistic regression which is robust to the changes in the appearance of retinal fundus images. The method is evaluated on the public database proposed by the Retinal Online Challenge in order to obtain an objective performance measure and to allow a comparative study with other proposed algorithms.
Alginate cryogel based glucose biosensor

NASA Astrophysics Data System (ADS)

Fatoni, Amin; Windy Dwiasi, Dian; Hermawan, Dadan

2016-02-01

Cryogel is macroporous structure provides a large surface area for biomolecule immobilization. In this work, an alginate cryogel based biosensor was developed to detect glucose. The cryogel was prepared using alginate cross-linked by calcium chloride under sub-zero temperature. This porous structure was growth in a 100 μL micropipette tip with a glucose oxidase enzyme entrapped inside the cryogel. The glucose detection was based on the colour change of redox indicator, potassium permanganate, by the hydrogen peroxide resulted from the conversion of glucose. The result showed a porous structure of alginate cryogel with pores diameter of 20-50 μm. The developed glucose biosensor was showed a linear response in the glucose detection from 1.0 to 5.0 mM with a regression of y = 0.01x+0.02 and R2 of 0.994. Furthermore, the glucose biosensor was showed a high operational stability up to 10 times of uninterrupted glucose detections.
Highly selectively monitoring heavy and transition metal ions by a fluorescent sensor based on dipeptide.

PubMed

Neupane, Lok Nath; Thirupathi, Ponnaboina; Jang, Sujung; Jang, Min Jung; Kim, Jung Hwa; Lee, Keun-Hyeung

2011-09-15

Fluorescent sensor (DMH) based on dipeptide was efficiently synthesized in solid phase synthesis. The dipeptide sensor shows sensitive response to Ag(I), Hg(II), and Cu(II) among 14 metal ions in 100% aqueous solution. The fluorescent sensor differentiates three heavy metal ions by response type; turn on response to Ag(I), ratiometric response to Hg(II), and turn off detection of Cu(II). The detection limits of the sensor for Ag(I) and Cu(II) were much lower than the EPA's drinking water maximum contaminant levels (MCL). Specially, DMH penetrated live cells and detected intracellular Ag(+) by turn on response. We described the fluorescent change, binding affinity, detection limit for the metal ions. The study of a heavy metal-responsive sensor based on dipeptide demonstrates its potential utility in the environment field. Copyright © 2011 Elsevier B.V. All rights reserved.
Computational tools for copy number variation (CNV) detection using next-generation sequencing data: features and perspectives.

PubMed

Zhao, Min; Wang, Qingguo; Wang, Quan; Jia, Peilin; Zhao, Zhongming

2013-01-01

Copy number variation (CNV) is a prevalent form of critical genetic variation that leads to an abnormal number of copies of large genomic regions in a cell. Microarray-based comparative genome hybridization (arrayCGH) or genotyping arrays have been standard technologies to detect large regions subject to copy number changes in genomes until most recently high-resolution sequence data can be analyzed by next-generation sequencing (NGS). During the last several years, NGS-based analysis has been widely applied to identify CNVs in both healthy and diseased individuals. Correspondingly, the strong demand for NGS-based CNV analyses has fuelled development of numerous computational methods and tools for CNV detection. In this article, we review the recent advances in computational methods pertaining to CNV detection using whole genome and whole exome sequencing data. Additionally, we discuss their strengths and weaknesses and suggest directions for future development.
Computational tools for copy number variation (CNV) detection using next-generation sequencing data: features and perspectives

PubMed Central

2013-01-01

Copy number variation (CNV) is a prevalent form of critical genetic variation that leads to an abnormal number of copies of large genomic regions in a cell. Microarray-based comparative genome hybridization (arrayCGH) or genotyping arrays have been standard technologies to detect large regions subject to copy number changes in genomes until most recently high-resolution sequence data can be analyzed by next-generation sequencing (NGS). During the last several years, NGS-based analysis has been widely applied to identify CNVs in both healthy and diseased individuals. Correspondingly, the strong demand for NGS-based CNV analyses has fuelled development of numerous computational methods and tools for CNV detection. In this article, we review the recent advances in computational methods pertaining to CNV detection using whole genome and whole exome sequencing data. Additionally, we discuss their strengths and weaknesses and suggest directions for future development. PMID:24564169
Graphene-interfaced electrical biosensor for label-free and sensitive detection of foodborne pathogenic E. coli O157:H7.

PubMed

Pandey, Ashish; Gurbuz, Yasar; Ozguz, Volkan; Niazi, Javed H; Qureshi, Anjum

2017-05-15

E. coli O157:H7 is an enterohemorrhagic bacteria responsible for serious foodborne outbreaks that causes diarrhoea, fever and vomiting in humans. Recent foodborne E. coli outbreaks has left a serious concern to public health. Therefore, there is an increasing demand for a simple, rapid and sensitive method for pathogen detection in contaminated foods. In this study, we developed a label-free electrical biosensor interfaced with graphene for sensitive detection of pathogenic bacteria. This biosensor was fabricated by interfacing graphene with interdigitated microelectrodes of capacitors that were biofunctionalized with E. coli O157:H7 specific antibodies for sensitive pathogenic bacteria detection. Here, graphene nanostructures on the sensor surface provided superior chemical properties such as high carrier mobility and biocompatibility with antibodies and bacteria. The sensors transduced the signal based on changes in dielectric properties (capacitance) through (i) polarization of captured cell-surface charges, (ii) cells' internal bioactivity, (iii) cell-wall's electronegativity or dipole moment and their relaxation and (iv) charge carrier mobility of graphene that modulated the electrical properties once the pathogenic E. coli O157:H7 captured on the sensor surface. Sensitive capacitance changes thus observed with graphene based capacitors were specific to E. coli O157:H7 strain with a sensitivity as low as 10-100 cells/ml. The proposed graphene based electrical biosensor provided advantages of speed, sensitivity, specificity and in-situ bacterial detection with no chemical mediators, represents a versatile approach for detection of a wide variety of other pathogens. Copyright © 2016 Elsevier B.V. All rights reserved.

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