Population variability complicates the accurate detection of climate change responses.
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 dual-process account of auditory change detection.
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.
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.
Accurate Detection of Dysmorphic Nuclei Using Dynamic Programming and Supervised Classification.
Verschuuren, Marlies; De Vylder, Jonas; Catrysse, Hannes; Robijns, Joke; Philips, Wilfried; De Vos, Winnok H
2017-01-01
A vast array of pathologies is typified by the presence of nuclei with an abnormal morphology. Dysmorphic nuclear phenotypes feature dramatic size changes or foldings, but also entail much subtler deviations such as nuclear protrusions called blebs. Due to their unpredictable size, shape and intensity, dysmorphic nuclei are often not accurately detected in standard image analysis routines. To enable accurate detection of dysmorphic nuclei in confocal and widefield fluorescence microscopy images, we have developed an automated segmentation algorithm, called Blebbed Nuclei Detector (BleND), which relies on two-pass thresholding for initial nuclear contour detection, and an optimal path finding algorithm, based on dynamic programming, for refining these contours. Using a robust error metric, we show that our method matches manual segmentation in terms of precision and outperforms state-of-the-art nuclear segmentation methods. Its high performance allowed for building and integrating a robust classifier that recognizes dysmorphic nuclei with an accuracy above 95%. The combined segmentation-classification routine is bound to facilitate nucleus-based diagnostics and enable real-time recognition of dysmorphic nuclei in intelligent microscopy workflows.
Accurate Detection of Dysmorphic Nuclei Using Dynamic Programming and Supervised Classification
Verschuuren, Marlies; De Vylder, Jonas; Catrysse, Hannes; Robijns, Joke; Philips, Wilfried
2017-01-01
A vast array of pathologies is typified by the presence of nuclei with an abnormal morphology. Dysmorphic nuclear phenotypes feature dramatic size changes or foldings, but also entail much subtler deviations such as nuclear protrusions called blebs. Due to their unpredictable size, shape and intensity, dysmorphic nuclei are often not accurately detected in standard image analysis routines. To enable accurate detection of dysmorphic nuclei in confocal and widefield fluorescence microscopy images, we have developed an automated segmentation algorithm, called Blebbed Nuclei Detector (BleND), which relies on two-pass thresholding for initial nuclear contour detection, and an optimal path finding algorithm, based on dynamic programming, for refining these contours. Using a robust error metric, we show that our method matches manual segmentation in terms of precision and outperforms state-of-the-art nuclear segmentation methods. Its high performance allowed for building and integrating a robust classifier that recognizes dysmorphic nuclei with an accuracy above 95%. The combined segmentation-classification routine is bound to facilitate nucleus-based diagnostics and enable real-time recognition of dysmorphic nuclei in intelligent microscopy workflows. PMID:28125723
Accurate registration of temporal CT images for pulmonary nodules detection
NASA Astrophysics Data System (ADS)
Yan, Jichao; Jiang, Luan; Li, Qiang
2017-02-01
Interpretation of temporal CT images could help the radiologists to detect some subtle interval changes in the sequential examinations. The purpose of this study was to develop a fully automated scheme for accurate registration of temporal CT images for pulmonary nodule detection. Our method consisted of three major registration steps. Firstly, affine transformation was applied in the segmented lung region to obtain global coarse registration images. Secondly, B-splines based free-form deformation (FFD) was used to refine the coarse registration images. Thirdly, Demons algorithm was performed to align the feature points extracted from the registered images in the second step and the reference images. Our database consisted of 91 temporal CT cases obtained from Beijing 301 Hospital and Shanghai Changzheng Hospital. The preliminary results showed that approximately 96.7% cases could obtain accurate registration based on subjective observation. The subtraction images of the reference images and the rigid and non-rigid registered images could effectively remove the normal structures (i.e. blood vessels) and retain the abnormalities (i.e. pulmonary nodules). This would be useful for the screening of lung cancer in our future study.
Urban change detection procedures using Landsat digital data
NASA Technical Reports Server (NTRS)
Jensen, J. R.; Toll, D. L.
1982-01-01
Landsat multispectral scanner data was applied to an urban change detection problem in Denver, CO. A dichotomous key yielding ten stages of residential development at the urban fringe was developed. This heuristic model allowed one to identify certain stages of development which are difficult to detect when performing digital change detection using Landsat data. The stages of development were evaluated in terms of their spectral and derived textural characteristics. Landsat band 5 (0.6-0.7 micron) and texture data produced change detection maps which were approximately 81 percent accurate. Results indicated that the stage of development and the spectral/textural features affect the change in the spectral values used for change detection. These preliminary findings will hopefully prove valuable for improved change detection at the urban fringe.
High Frequency QRS ECG Accurately Detects Cardiomyopathy
NASA Technical Reports Server (NTRS)
Schlegel, Todd T.; Arenare, Brian; Poulin, Gregory; Moser, Daniel R.; Delgado, Reynolds
2005-01-01
High frequency (HF, 150-250 Hz) analysis over the entire QRS interval of the ECG is more sensitive than conventional ECG for detecting myocardial ischemia. However, the accuracy of HF QRS ECG for detecting cardiomyopathy is unknown. We obtained simultaneous resting conventional and HF QRS 12-lead ECGs in 66 patients with cardiomyopathy (EF = 23.2 plus or minus 6.l%, mean plus or minus SD) and in 66 age- and gender-matched healthy controls using PC-based ECG software recently developed at NASA. The single most accurate ECG parameter for detecting cardiomyopathy was an HF QRS morphological score that takes into consideration the total number and severity of reduced amplitude zones (RAZs) present plus the clustering of RAZs together in contiguous leads. This RAZ score had an area under the receiver operator curve (ROC) of 0.91, and was 88% sensitive, 82% specific and 85% accurate for identifying cardiomyopathy at optimum score cut-off of 140 points. Although conventional ECG parameters such as the QRS and QTc intervals were also significantly longer in patients than controls (P less than 0.001, BBBs excluded), these conventional parameters were less accurate (area under the ROC = 0.77 and 0.77, respectively) than HF QRS morphological parameters for identifying underlying cardiomyopathy. The total amplitude of the HF QRS complexes, as measured by summed root mean square voltages (RMSVs), also differed between patients and controls (33.8 plus or minus 11.5 vs. 41.5 plus or minus 13.6 mV, respectively, P less than 0.003), but this parameter was even less accurate in distinguishing the two groups (area under ROC = 0.67) than the HF QRS morphologic and conventional ECG parameters. Diagnostic accuracy was optimal (86%) when the RAZ score from the HF QRS ECG and the QTc interval from the conventional ECG were used simultaneously with cut-offs of greater than or equal to 40 points and greater than or equal to 445 ms, respectively. In conclusion 12-lead HF QRS ECG employing
Woodman, Geoffrey F.; Vogel, Edward K.; Luck, Steven J.
2012-01-01
Many recent studies of visual working memory have used change-detection tasks in which subjects view sequential displays and are asked to report whether they are identical or if one object has changed. A key question is whether the memory system used to perform this task is sufficiently flexible to detect changes in object identity independent of spatial transformations, but previous research has yielded contradictory results. To address this issue, the present study compared standard change-detection tasks with tasks in which the objects varied in size or position between successive arrays. Performance was nearly identical across the standard and transformed tasks unless the task implicitly encouraged spatial encoding. These results resolve the discrepancies in prior studies and demonstrate that the visual working memory system can detect changes in object identity across spatial transformations. PMID:22287933
Accurately estimating PSF with straight lines detected by Hough transform
NASA Astrophysics Data System (ADS)
Wang, Ruichen; Xu, Liangpeng; Fan, Chunxiao; Li, Yong
2018-04-01
This paper presents an approach to estimating point spread function (PSF) from low resolution (LR) images. Existing techniques usually rely on accurate detection of ending points of the profile normal to edges. In practice however, it is often a great challenge to accurately localize profiles of edges from a LR image, which hence leads to a poor PSF estimation of the lens taking the LR image. For precisely estimating the PSF, this paper proposes firstly estimating a 1-D PSF kernel with straight lines, and then robustly obtaining the 2-D PSF from the 1-D kernel by least squares techniques and random sample consensus. Canny operator is applied to the LR image for obtaining edges and then Hough transform is utilized to extract straight lines of all orientations. Estimating 1-D PSF kernel with straight lines effectively alleviates the influence of the inaccurate edge detection on PSF estimation. The proposed method is investigated on both natural and synthetic images for estimating PSF. Experimental results show that the proposed method outperforms the state-ofthe- art and does not rely on accurate edge detection.
Testing pigeon memory in a change detection task.
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.
Embodied memory allows accurate and stable perception of hidden objects despite orientation change.
Pan, Jing Samantha; Bingham, Ned; Bingham, Geoffrey P
2017-07-01
Rotating a scene in a frontoparallel plane (rolling) yields a change in orientation of constituent images. When using only information provided by static images to perceive a scene after orientation change, identification performance typically decreases (Rock & Heimer, 1957). However, rolling generates optic flow information that relates the discrete, static images (before and after the change) and forms an embodied memory that aids recognition. The embodied memory hypothesis predicts that upon detecting a continuous spatial transformation of image structure, or in other words, seeing the continuous rolling process and objects undergoing rolling observers should accurately perceive objects during and after motion. Thus, in this case, orientation change should not affect performance. We tested this hypothesis in three experiments and found that (a) using combined optic flow and image structure, participants identified locations of previously perceived but currently occluded targets with great accuracy and stability (Experiment 1); (b) using combined optic flow and image structure information, participants identified hidden targets equally well with or without 30° orientation changes (Experiment 2); and (c) when the rolling was unseen, identification of hidden targets after orientation change became worse (Experiment 3). Furthermore, when rolling was unseen, although target identification was better when participants were told about the orientation change than when they were not told, performance was still worse than when there was no orientation change. Therefore, combined optic flow and image structure information, not mere knowledge about the rolling, enables accurate and stable perception despite orientation change. (PsycINFO Database Record (c) 2017 APA, all rights reserved).
Rapid glucosinolate detection and identification using accurate mass MS-MS
USDA-ARS?s Scientific Manuscript database
Currently, there is a demand for accurate evaluation of brassica plat species for their glucosinolate content. An optimized method has been developed for detecting and identifying glucosinolates in plant extracts using MS-MS fragmentation with ion trap collision induced dissociation (CID) and higher...
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.
Angelone, Bonnie L; Levin, Daniel T; Simons, Daniel J
2003-01-01
Observers typically detect changes to central objects more readily than changes to marginal objects, but they sometimes miss changes to central, attended objects as well. However, even if observers do not report such changes, they may be able to recognize the changed object. In three experiments we explored change detection and recognition memory for several types of changes to central objects in motion pictures. Observers who failed to detect a change still performed at above chance levels on a recognition task in almost all conditions. In addition, observers who detected the change were no more accurate in their recognition than those who did not detect the change. Despite large differences in the detectability of changes across conditions, those observers who missed the change did not vary in their ability to recognize the changing object.
Detecting and Reacting to Change: The Effect of Exposure to Narrow Categorizations
ERIC Educational Resources Information Center
Chakravarti, Amitav; Fang, Christina; Shapira, Zur
2011-01-01
The ability to detect a change, to accurately assess the magnitude of the change, and to react to that change in a commensurate fashion are of critical importance in many decision domains. Thus, it is important to understand the factors that systematically affect people's reactions to change. In this article we document a novel effect: Decision…
Accurate mobile malware detection and classification in the cloud.
Wang, Xiaolei; Yang, Yuexiang; Zeng, Yingzhi
2015-01-01
As the dominator of the Smartphone operating system market, consequently android has attracted the attention of s malware authors and researcher alike. The number of types of android malware is increasing rapidly regardless of the considerable number of proposed malware analysis systems. In this paper, by taking advantages of low false-positive rate of misuse detection and the ability of anomaly detection to detect zero-day malware, we propose a novel hybrid detection system based on a new open-source framework CuckooDroid, which enables the use of Cuckoo Sandbox's features to analyze Android malware through dynamic and static analysis. Our proposed system mainly consists of two parts: anomaly detection engine performing abnormal apps detection through dynamic analysis; signature detection engine performing known malware detection and classification with the combination of static and dynamic analysis. We evaluate our system using 5560 malware samples and 6000 benign samples. Experiments show that our anomaly detection engine with dynamic analysis is capable of detecting zero-day malware with a low false negative rate (1.16 %) and acceptable false positive rate (1.30 %); it is worth noting that our signature detection engine with hybrid analysis can accurately classify malware samples with an average positive rate 98.94 %. Considering the intensive computing resources required by the static and dynamic analysis, our proposed detection system should be deployed off-device, such as in the Cloud. The app store markets and the ordinary users can access our detection system for malware detection through cloud service.
Catalyzing Novel Approaches to Rapid, Accurate, and Affordable Early Cancer Detection.
Dhar, Asif; Meagher, Beth; Ryscavage, Andrew
Inspired by the Cancer Moonshot, a dedicated team of professionals worked with leaders across the cancer ecosystem to look for an opportunity to radically reduce cancer mortality globally by focusing on early cancer detection. After an initial survey of cancer innovation, progress, and pitfalls, the team believed that if new rapid, affordable, and accurate early detection solutions were appropriately brought to market, it would be possible to intervene earlier when cancer is most treatable.An extensive process began, informed by dozens of experts in the cancer ecosystem. The Cancer XPRIZE team designed a prize competition where "the winning team will develop a means to rapidly, accurately, and affordably screen for early cancers where intervention can reduce human suffering."The following outlines the Cancer XPRIZE's experience using a powerful approach-the radical prize design-to catch more cancers in time to make a difference saving lives, dollars, and suffering.
Detecting changes during pregnancy with Raman spectroscopy
NASA Astrophysics Data System (ADS)
Vargis, Elizabeth; Robertson, Kesha; Al-Hendy, Ayman; Reese, Jeff; Mahadevan-Jansen, Anita
2010-02-01
Preterm labor is the second leading cause of neonatal mortality and leads to a myriad of complications like delayed development and cerebral palsy. Currently, there is no way to accurately predict preterm labor, making its prevention and treatment virtually impossible. While there are some at-risk patients, over half of all preterm births do not fall into any high-risk category. This study seeks to predict and prevent preterm labor by using Raman spectroscopy to detect changes in the cervix during pregnancy. Since Raman spectroscopy has been used to detect cancers in vivo in organs like the cervix and skin, it follows that spectra will change over the course of pregnancy. Previous studies have shown that fluorescence decreased during pregnancy and increased during post-partum exams to pre-pregnancy levels. We believe significant changes will occur in the Raman spectra obtained during the course of pregnancy. In this study, Raman spectra from the cervix of pregnant mice and women will be acquired. Specific changes that occur due to cervical softening or changes in hormonal levels will be observed to understand the likelihood that a female mouse or a woman will enter labor.
Building dynamic population graph for accurate correspondence detection.
Du, Shaoyi; Guo, Yanrong; Sanroma, Gerard; Ni, Dong; Wu, Guorong; Shen, Dinggang
2015-12-01
In medical imaging studies, there is an increasing trend for discovering the intrinsic anatomical difference across individual subjects in a dataset, such as hand images for skeletal bone age estimation. Pair-wise matching is often used to detect correspondences between each individual subject and a pre-selected model image with manually-placed landmarks. However, the large anatomical variability across individual subjects can easily compromise such pair-wise matching step. In this paper, we present a new framework to simultaneously detect correspondences among a population of individual subjects, by propagating all manually-placed landmarks from a small set of model images through a dynamically constructed image graph. Specifically, we first establish graph links between models and individual subjects according to pair-wise shape similarity (called as forward step). Next, we detect correspondences for the individual subjects with direct links to any of model images, which is achieved by a new multi-model correspondence detection approach based on our recently-published sparse point matching method. To correct those inaccurate correspondences, we further apply an error detection mechanism to automatically detect wrong correspondences and then update the image graph accordingly (called as backward step). After that, all subject images with detected correspondences are included into the set of model images, and the above two steps of graph expansion and error correction are repeated until accurate correspondences for all subject images are established. Evaluations on real hand X-ray images demonstrate that our proposed method using a dynamic graph construction approach can achieve much higher accuracy and robustness, when compared with the state-of-the-art pair-wise correspondence detection methods as well as a similar method but using static population graph. Copyright © 2015 Elsevier B.V. All rights reserved.
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.
Comparison of methods for accurate end-point detection of potentiometric titrations
NASA Astrophysics Data System (ADS)
Villela, R. L. A.; Borges, P. P.; Vyskočil, L.
2015-01-01
Detection of the end point in potentiometric titrations has wide application on experiments that demand very low measurement uncertainties mainly for certifying reference materials. Simulations of experimental coulometric titration data and consequential error analysis of the end-point values were conducted using a programming code. These simulations revealed that the Levenberg-Marquardt method is in general more accurate than the traditional second derivative technique used currently as end-point detection for potentiometric titrations. Performance of the methods will be compared and presented in this paper.
Accurate band-to-band registration of AOTF imaging spectrometer using motion detection technology
NASA Astrophysics Data System (ADS)
Zhou, Pengwei; Zhao, Huijie; Jin, Shangzhong; Li, Ningchuan
2016-05-01
This paper concerns the problem of platform vibration induced band-to-band misregistration with acousto-optic imaging spectrometer in spaceborne application. Registrating images of different bands formed at different time or different position is difficult, especially for hyperspectral images form acousto-optic tunable filter (AOTF) imaging spectrometer. In this study, a motion detection method is presented using the polychromatic undiffracted beam of AOTF. The factors affecting motion detect accuracy are analyzed theoretically, and calculations show that optical distortion is an easily overlooked factor to achieve accurate band-to-band registration. Hence, a reflective dual-path optical system has been proposed for the first time, with reduction of distortion and chromatic aberration, indicating the potential of higher registration accuracy. Consequently, a spectra restoration experiment using additional motion detect channel is presented for the first time, which shows the accurate spectral image registration capability of this technique.
Robust and Accurate Anomaly Detection in ECG Artifacts Using Time Series Motif Discovery
Sivaraks, Haemwaan
2015-01-01
Electrocardiogram (ECG) anomaly detection is an important technique for detecting dissimilar heartbeats which helps identify abnormal ECGs before the diagnosis process. Currently available ECG anomaly detection methods, ranging from academic research to commercial ECG machines, still suffer from a high false alarm rate because these methods are not able to differentiate ECG artifacts from real ECG signal, especially, in ECG artifacts that are similar to ECG signals in terms of shape and/or frequency. The problem leads to high vigilance for physicians and misinterpretation risk for nonspecialists. Therefore, this work proposes a novel anomaly detection technique that is highly robust and accurate in the presence of ECG artifacts which can effectively reduce the false alarm rate. Expert knowledge from cardiologists and motif discovery technique is utilized in our design. In addition, every step of the algorithm conforms to the interpretation of cardiologists. Our method can be utilized to both single-lead ECGs and multilead ECGs. Our experiment results on real ECG datasets are interpreted and evaluated by cardiologists. Our proposed algorithm can mostly achieve 100% of accuracy on detection (AoD), sensitivity, specificity, and positive predictive value with 0% false alarm rate. The results demonstrate that our proposed method is highly accurate and robust to artifacts, compared with competitive anomaly detection methods. PMID:25688284
A Comprehensive Strategy for Accurate Mutation Detection of the Highly Homologous PMS2.
Li, Jianli; Dai, Hongzheng; Feng, Yanming; Tang, Jia; Chen, Stella; Tian, Xia; Gorman, Elizabeth; Schmitt, Eric S; Hansen, Terah A A; Wang, Jing; Plon, Sharon E; Zhang, Victor Wei; Wong, Lee-Jun C
2015-09-01
Germline mutations in the DNA mismatch repair gene PMS2 underlie the cancer susceptibility syndrome, Lynch syndrome. However, accurate molecular testing of PMS2 is complicated by a large number of highly homologous sequences. To establish a comprehensive approach for mutation detection of PMS2, we have designed a strategy combining targeted capture next-generation sequencing (NGS), multiplex ligation-dependent probe amplification, and long-range PCR followed by NGS to simultaneously detect point mutations and copy number changes of PMS2. Exonic deletions (E2 to E9, E5 to E9, E8, E10, E14, and E1 to E15), duplications (E11 to E12), and a nonsense mutation, p.S22*, were identified. Traditional multiplex ligation-dependent probe amplification and Sanger sequencing approaches cannot differentiate the origin of the exonic deletions in the 3' region when PMS2 and PMS2CL share identical sequences as a result of gene conversion. Our approach allows unambiguous identification of mutations in the active gene with a straightforward long-range-PCR/NGS method. Breakpoint analysis of multiple samples revealed that recurrent exon 14 deletions are mediated by homologous Alu sequences. Our comprehensive approach provides a reliable tool for accurate molecular analysis of genes containing multiple copies of highly homologous sequences and should improve PMS2 molecular analysis for patients with Lynch syndrome. Copyright © 2015 American Society for Investigative Pathology and the Association for Molecular Pathology. Published by Elsevier Inc. All rights reserved.
Accurate LC Peak Boundary Detection for 16 O/ 18 O Labeled LC-MS Data
Cui, Jian; Petritis, Konstantinos; Tegeler, Tony; Petritis, Brianne; Ma, Xuepo; Jin, Yufang; Gao, Shou-Jiang (SJ); Zhang, Jianqiu (Michelle)
2013-01-01
In liquid chromatography-mass spectrometry (LC-MS), parts of LC peaks are often corrupted by their co-eluting peptides, which results in increased quantification variance. In this paper, we propose to apply accurate LC peak boundary detection to remove the corrupted part of LC peaks. Accurate LC peak boundary detection is achieved by checking the consistency of intensity patterns within peptide elution time ranges. In addition, we remove peptides with erroneous mass assignment through model fitness check, which compares observed intensity patterns to theoretically constructed ones. The proposed algorithm can significantly improve the accuracy and precision of peptide ratio measurements. PMID:24115998
Lopes, Ana Catarina; Sagasti, Ariane; Lasheras, Andoni; Muto, Virginia; Gutiérrez, Jon; Kouzoudis, Dimitris; Barandiarán, José Manuel
2018-01-01
The main parameters of magnetoelastic resonators in the detection of chemical (i.e., salts, gases, etc.) or biological (i.e., bacteria, phages, etc.) agents are the sensitivity S (or external agent change magnitude per Hz change in the resonance frequency) and the quality factor Q of the resonance. We present an extensive study on the experimental determination of the Q factor in such magnetoelastic resonant platforms, using three different strategies: (a) analyzing the real and imaginary components of the susceptibility at resonance; (b) numerical fitting of the modulus of the susceptibility; (c) using an exact mathematical expression for the real part of the susceptibility. Q values obtained by the three methods are analyzed and discussed, aiming to establish the most adequate one to accurately determine the quality factor of the magnetoelastic resonance. PMID:29547578
Lopes, Ana Catarina; Sagasti, Ariane; Lasheras, Andoni; Muto, Virginia; Gutiérrez, Jon; Kouzoudis, Dimitris; Barandiarán, José Manuel
2018-03-16
The main parameters of magnetoelastic resonators in the detection of chemical (i.e., salts, gases, etc.) or biological (i.e., bacteria, phages, etc.) agents are the sensitivity S (or external agent change magnitude per Hz change in the resonance frequency) and the quality factor Q of the resonance. We present an extensive study on the experimental determination of the Q factor in such magnetoelastic resonant platforms, using three different strategies: (a) analyzing the real and imaginary components of the susceptibility at resonance; (b) numerical fitting of the modulus of the susceptibility; (c) using an exact mathematical expression for the real part of the susceptibility. Q values obtained by the three methods are analyzed and discussed, aiming to establish the most adequate one to accurately determine the quality factor of the magnetoelastic resonance.
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.
A Multi-Index Integrated Change detection method for updating the National Land Cover Database
Jin, Suming; Yang, Limin; Xian, George Z.; Danielson, Patrick; Homer, Collin G.
2010-01-01
Land cover change is typically captured by comparing two or more dates of imagery and associating spectral change with true thematic change. A new change detection method, Multi-Index Integrated Change (MIIC), has been developed to capture a full range of land cover disturbance patterns for updating the National Land Cover Database (NLCD). Specific indices typically specialize in identifying only certain types of disturbances; for example, the Normalized Burn Ratio (NBR) has been widely used for monitoring fire disturbance. Recognizing the potential complementary nature of multiple indices, we integrated four indices into one model to more accurately detect true change between two NLCD time periods. The four indices are NBR, Normalized Difference Vegetation Index (NDVI), Change Vector (CV), and a newly developed index called the Relative Change Vector (RCV). The model is designed to provide both change location and change direction (e.g. biomass increase or biomass decrease). The integrated change model has been tested on five image pairs from different regions exhibiting a variety of disturbance types. Compared with a simple change vector method, MIIC can better capture the desired change without introducing additional commission errors. The model is particularly accurate at detecting forest disturbances, such as forest harvest, forest fire, and forest regeneration. Agreement between the initial change map areas derived from MIIC and the retained final land cover type change areas will be showcased from the pilot test sites.
NASA Astrophysics Data System (ADS)
Kumamoto, Yasuaki; Minamikawa, Takeo; Kawamura, Akinori; Matsumura, Junichi; Tsuda, Yuichiro; Ukon, Juichiro; Harada, Yoshinori; Tanaka, Hideo; Takamatsu, Tetsuro
2017-02-01
Nerve-sparing surgery is essential to avoid functional deficits of the limbs and organs. Raman scattering, a label-free, minimally invasive, and accurate modality, is one of the best candidate technologies to detect nerves for nerve-sparing surgery. However, Raman scattering imaging is too time-consuming to be employed in surgery. Here we present a rapid and accurate nerve visualization method using a multipoint Raman imaging technique that has enabled simultaneous spectra measurement from different locations (n=32) of a sample. Five sec is sufficient for measuring n=32 spectra with good S/N from a given tissue. Principal component regression discriminant analysis discriminated spectra obtained from peripheral nerves (n=863 from n=161 myelinated nerves) and connective tissue (n=828 from n=121 tendons) with sensitivity and specificity of 88.3% and 94.8%, respectively. To compensate the spatial information of a multipoint-Raman-derived tissue discrimination image that is too sparse to visualize nerve arrangement, we used morphological information obtained from a bright-field image. When merged with the sparse tissue discrimination image, a morphological image of a sample shows what portion of Raman measurement points in arbitrary structure is determined as nerve. Setting a nerve detection criterion on the portion of "nerve" points in the structure as 40% or more, myelinated nerves (n=161) and tendons (n=121) were discriminated with sensitivity and specificity of 97.5%. The presented technique utilizing a sparse multipoint Raman image and a bright-field image has enabled rapid, safe, and accurate detection of peripheral nerves.
a Landsat Time-Series Stacks Model for Detection of Cropland Change
NASA Astrophysics Data System (ADS)
Chen, J.; Chen, J.; Zhang, J.
2017-09-01
Global, timely, accurate and cost-effective cropland monitoring with a fine spatial resolution will dramatically improve our understanding of the effects of agriculture on greenhouse gases emissions, food safety, and human health. Time-series remote sensing imagery have been shown particularly potential to describe land cover dynamics. The traditional change detection techniques are often not capable of detecting land cover changes within time series that are severely influenced by seasonal difference, which are more likely to generate pseuso changes. Here,we introduced and tested LTSM ( Landsat time-series stacks model), an improved Continuous Change Detection and Classification (CCDC) proposed previously approach to extract spectral trajectories of land surface change using a dense Landsat time-series stacks (LTS). The method is expected to eliminate pseudo changes caused by phenology driven by seasonal patterns. The main idea of the method is that using all available Landsat 8 images within a year, LTSM consisting of two term harmonic function are estimated iteratively for each pixel in each spectral band .LTSM can defines change area by differencing the predicted and observed Landsat images. The LTSM approach was compared with change vector analysis (CVA) method. The results indicated that the LTSM method correctly detected the "true change" without overestimating the "false" one, while CVA pointed out "true change" pixels with a large number of "false changes". The detection of change areas achieved an overall accuracy of 92.37 %, with a kappa coefficient of 0.676.
Kawai, Nobuyuki; He, Hongshen
2016-01-01
Humans and non-human primates are extremely sensitive to snakes as exemplified by their ability to detect pictures of snakes more quickly than those of other animals. These findings are consistent with the Snake Detection Theory, which hypothesizes that as predators, snakes were a major source of evolutionary selection that favored expansion of the visual system of primates for rapid snake detection. Many snakes use camouflage to conceal themselves from both prey and their own predators, making it very challenging to detect them. If snakes have acted as a selective pressure on primate visual systems, they should be more easily detected than other animals under difficult visual conditions. Here we tested whether humans discerned images of snakes more accurately than those of non-threatening animals (e.g., birds, cats, or fish) under conditions of less perceptual information by presenting a series of degraded images with the Random Image Structure Evolution technique (interpolation of random noise). We find that participants recognize mosaic images of snakes, which were regarded as functionally equivalent to camouflage, more accurately than those of other animals under dissolved conditions. The present study supports the Snake Detection Theory by showing that humans have a visual system that accurately recognizes snakes under less discernible visual conditions.
He, Hongshen
2016-01-01
Humans and non-human primates are extremely sensitive to snakes as exemplified by their ability to detect pictures of snakes more quickly than those of other animals. These findings are consistent with the Snake Detection Theory, which hypothesizes that as predators, snakes were a major source of evolutionary selection that favored expansion of the visual system of primates for rapid snake detection. Many snakes use camouflage to conceal themselves from both prey and their own predators, making it very challenging to detect them. If snakes have acted as a selective pressure on primate visual systems, they should be more easily detected than other animals under difficult visual conditions. Here we tested whether humans discerned images of snakes more accurately than those of non-threatening animals (e.g., birds, cats, or fish) under conditions of less perceptual information by presenting a series of degraded images with the Random Image Structure Evolution technique (interpolation of random noise). We find that participants recognize mosaic images of snakes, which were regarded as functionally equivalent to camouflage, more accurately than those of other animals under dissolved conditions. The present study supports the Snake Detection Theory by showing that humans have a visual system that accurately recognizes snakes under less discernible visual conditions. PMID:27783686
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.
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.
Beanland, Vanessa; Filtness, Ashleigh J; Jeans, Rhiannon
2017-03-01
The ability to detect changes is crucial for safe driving. Previous research has demonstrated that drivers often experience change blindness, which refers to failed or delayed change detection. The current study explored how susceptibility to change blindness varies as a function of the driving environment, type of object changed, and safety relevance of the change. Twenty-six fully-licenced drivers completed a driving-related change detection task. Changes occurred to seven target objects (road signs, cars, motorcycles, traffic lights, pedestrians, animals, or roadside trees) across two environments (urban or rural). The contextual safety relevance of the change was systematically manipulated within each object category, ranging from high safety relevance (i.e., requiring a response by the driver) to low safety relevance (i.e., requiring no response). When viewing rural scenes, compared with urban scenes, participants were significantly faster and more accurate at detecting changes, and were less susceptible to "looked-but-failed-to-see" errors. Interestingly, safety relevance of the change differentially affected performance in urban and rural environments. In urban scenes, participants were more efficient at detecting changes with higher safety relevance, whereas in rural scenes the effect of safety relevance has marginal to no effect on change detection. Finally, even after accounting for safety relevance, change blindness varied significantly between target types. Overall the results suggest that drivers are less susceptible to change blindness for objects that are likely to change or move (e.g., traffic lights vs. road signs), and for moving objects that pose greater danger (e.g., wild animals vs. pedestrians). Copyright © 2017 Elsevier Ltd. All rights reserved.
Detecting regional patterns of changing CO2 flux in Alaska
Parazoo, Nicholas C.; Wofsy, Steven C.; Koven, Charles D.; Sweeney, Colm; Lawrence, David M.; Lindaas, Jakob; Chang, Rachel Y.-W.; Miller, Charles E.
2016-01-01
With rapid changes in climate and the seasonal amplitude of carbon dioxide (CO2) in the Arctic, it is critical that we detect and quantify the underlying processes controlling the changing amplitude of CO2 to better predict carbon cycle feedbacks in the Arctic climate system. We use satellite and airborne observations of atmospheric CO2 with climatically forced CO2 flux simulations to assess the detectability of Alaskan carbon cycle signals as future warming evolves. We find that current satellite remote sensing technologies can detect changing uptake accurately during the growing season but lack sufficient cold season coverage and near-surface sensitivity to constrain annual carbon balance changes at regional scale. Airborne strategies that target regular vertical profile measurements within continental interiors are more sensitive to regional flux deeper into the cold season but currently lack sufficient spatial coverage throughout the entire cold season. Thus, the current CO2 observing network is unlikely to detect potentially large CO2 sources associated with deep permafrost thaw and cold season respiration expected over the next 50 y. Although continuity of current observations is vital, strategies and technologies focused on cold season measurements (active remote sensing, aircraft, and tall towers) and systematic sampling of vertical profiles across continental interiors over the full annual cycle are required to detect the onset of carbon release from thawing permafrost. PMID:27354511
Detecting regional patterns of changing CO 2 flux in Alaska
Parazoo, Nicholas C.; Commane, Roisin; Wofsy, Steven C.; ...
2016-06-27
With rapid changes in climate and the seasonal amplitude of carbon dioxide (CO 2) in the Arctic, it is critical that we detect and quantify the underlying processes controlling the changing amplitude of CO 2 to better predict carbon cycle feedbacks in the Arctic climate system. We use satellite and airborne observations of atmospheric CO 2 with climatically forced CO 2 flux simulations to assess the detectability of Alaskan carbon cycle signals as future warming evolves. We find that current satellite remote sensing technologies can detect changing uptake accurately during the growing season but lack sufficient cold season coverage andmore » near-surface sensitivity to constrain annual carbon balance changes at regional scale. Airborne strategies that target regular vertical profile measurements within continental interiors are more sensitive to regional flux deeper into the cold season but currently lack sufficient spatial coverage throughout the entire cold season. Thus, the current CO 2 observing network is unlikely to detect potentially large CO 2 sources associated with deep permafrost thaw and cold season respiration expected over the next 50 y. In conclusion, although continuity of current observations is vital, strategies and technologies focused on cold season measurements (active remote sensing, aircraft, and tall towers) and systematic sampling of vertical profiles across continental interiors over the full annual cycle are required to detect the onset of carbon release from thawing permafrost.« less
Accurate detection of blood vessels improves the detection of exudates in color fundus images.
Youssef, Doaa; Solouma, Nahed H
2012-12-01
Exudates are one of the earliest and most prevalent symptoms of diseases leading to blindness such as diabetic retinopathy and macular degeneration. Certain areas of the retina with such conditions are to be photocoagulated by laser to stop the disease progress and prevent blindness. Outlining these areas is dependent on outlining the lesions and the anatomic structures of the retina. In this paper, we provide a new method for the detection of blood vessels that improves the detection of exudates in fundus photographs. The method starts with an edge detection algorithm which results in a over segmented image. Then the new feature-based algorithm can be used to accurately detect the blood vessels. This algorithm considers the characteristics of a retinal blood vessel such as its width range, intensities and orientations for the purpose of selective segmentation. Because of its bulb shape and its color similarity with exudates, the optic disc can be detected using the common Hough transform technique. The extracted blood vessel tree and optic disc could be subtracted from the over segmented image to get an initial estimate of exudates. The final estimation of exudates can then be obtained by morphological reconstruction based on the appearance of exudates. This method is shown to be promising since it increases the sensitivity and specificity of exudates detection to 80% and 100% respectively. Copyright © 2012 Elsevier Ireland Ltd. All rights reserved.
Change Detection: Training and Transfer
Gaspar, John G.; Neider, Mark B.; Simons, Daniel J.; McCarley, Jason S.; Kramer, Arthur F.
2013-01-01
Observers often fail to notice even dramatic changes to their environment, a phenomenon known as change blindness. If training could enhance change detection performance in general, then it might help to remedy some real-world consequences of change blindness (e.g. failing to detect hazards while driving). We examined whether adaptive training on a simple change detection task could improve the ability to detect changes in untrained tasks for young and older adults. Consistent with an effective training procedure, both young and older adults were better able to detect changes to trained objects following training. However, neither group showed differential improvement on untrained change detection tasks when compared to active control groups. Change detection training led to improvements on the trained task but did not generalize to other change detection tasks. PMID:23840775
Detecting and reacting to change: the effect of exposure to narrow categorizations.
Chakravarti, Amitav; Fang, Christina; Shapira, Zur
2011-11-01
The ability to detect a change, to accurately assess the magnitude of the change, and to react to that change in a commensurate fashion are of critical importance in many decision domains. Thus, it is important to understand the factors that systematically affect people's reactions to change. In this article we document a novel effect: decision makers' reactions to a change (e.g., a visual change, a technology change) were systematically affected by the type of categorizations they encountered in an unrelated prior task (e.g., the response categories associated with a survey question). We found that prior exposure to narrow, as opposed to broad, categorizations improved decision makers' ability to detect change and led to stronger reactions to a given change. These differential reactions occurred because the prior categorizations, even though unrelated, altered the extent to which the subsequently presented change was perceived as either a relatively large change or a relatively small one.
NASA Astrophysics Data System (ADS)
Zhuo, Zhao; Cai, Shi-Min; Tang, Ming; Lai, Ying-Cheng
2018-04-01
One of the most challenging problems in network science is to accurately detect communities at distinct hierarchical scales. Most existing methods are based on structural analysis and manipulation, which are NP-hard. We articulate an alternative, dynamical evolution-based approach to the problem. The basic principle is to computationally implement a nonlinear dynamical process on all nodes in the network with a general coupling scheme, creating a networked dynamical system. Under a proper system setting and with an adjustable control parameter, the community structure of the network would "come out" or emerge naturally from the dynamical evolution of the system. As the control parameter is systematically varied, the community hierarchies at different scales can be revealed. As a concrete example of this general principle, we exploit clustered synchronization as a dynamical mechanism through which the hierarchical community structure can be uncovered. In particular, for quite arbitrary choices of the nonlinear nodal dynamics and coupling scheme, decreasing the coupling parameter from the global synchronization regime, in which the dynamical states of all nodes are perfectly synchronized, can lead to a weaker type of synchronization organized as clusters. We demonstrate the existence of optimal choices of the coupling parameter for which the synchronization clusters encode accurate information about the hierarchical community structure of the network. We test and validate our method using a standard class of benchmark modular networks with two distinct hierarchies of communities and a number of empirical networks arising from the real world. Our method is computationally extremely efficient, eliminating completely the NP-hard difficulty associated with previous methods. The basic principle of exploiting dynamical evolution to uncover hidden community organizations at different scales represents a "game-change" type of approach to addressing the problem of community
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.
Chatlapalli, S; Nazeran, H; Melarkod, V; Krishnam, R; Estrada, E; Pamula, Y; Cabrera, S
2004-01-01
The electrocardiogram (ECG) signal is used extensively as a low cost diagnostic tool to provide information concerning the heart's state of health. Accurate determination of the QRS complex, in particular, reliable detection of the R wave peak, is essential in computer based ECG analysis. ECG data from Physionet's Sleep-Apnea database were used to develop, test, and validate a robust heart rate variability (HRV) signal derivation algorithm. The HRV signal was derived from pre-processed ECG signals by developing an enhanced Hilbert transform (EHT) algorithm with built-in missing beat detection capability for reliable QRS detection. The performance of the EHT algorithm was then compared against that of a popular Hilbert transform-based (HT) QRS detection algorithm. Autoregressive (AR) modeling of the HRV power spectrum for both EHT- and HT-derived HRV signals was achieved and different parameters from their power spectra as well as approximate entropy were derived for comparison. Poincare plots were then used as a visualization tool to highlight the detection of the missing beats in the EHT method After validation of the EHT algorithm on ECG data from the Physionet, the algorithm was further tested and validated on a dataset obtained from children undergoing polysomnography for detection of sleep disordered breathing (SDB). Sensitive measures of accurate HRV signals were then derived to be used in detecting and diagnosing sleep disordered breathing in children. All signal processing algorithms were implemented in MATLAB. We present a description of the EHT algorithm and analyze pilot data for eight children undergoing nocturnal polysomnography. The pilot data demonstrated that the EHT method provides an accurate way of deriving the HRV signal and plays an important role in extraction of reliable measures to distinguish between periods of normal and sleep disordered breathing (SDB) in children.
Hirschmann, J; Schoffelen, J M; Schnitzler, A; van Gerven, M A J
2017-10-01
To investigate the possibility of tremor detection based on deep brain activity. We re-analyzed recordings of local field potentials (LFPs) from the subthalamic nucleus in 10 PD patients (12 body sides) with spontaneously fluctuating rest tremor. Power in several frequency bands was estimated and used as input to Hidden Markov Models (HMMs) which classified short data segments as either tremor-free rest or rest tremor. HMMs were compared to direct threshold application to individual power features. Applying a threshold directly to band-limited power was insufficient for tremor detection (mean area under the curve [AUC] of receiver operating characteristic: 0.64, STD: 0.19). Multi-feature HMMs, in contrast, allowed for accurate detection (mean AUC: 0.82, STD: 0.15), using four power features obtained from a single contact pair. Within-patient training yielded better accuracy than across-patient training (0.84vs. 0.78, p=0.03), yet tremor could often be detected accurately with either approach. High frequency oscillations (>200Hz) were the best performing individual feature. LFP-based markers of tremor are robust enough to allow for accurate tremor detection in short data segments, provided that appropriate statistical models are used. LFP-based markers of tremor could be useful control signals for closed-loop deep brain stimulation. Copyright © 2017 International Federation of Clinical Neurophysiology. Published by Elsevier B.V. All rights reserved.
Detecting Cancer Quickly and Accurately
NASA Astrophysics Data System (ADS)
Gourley, Paul; McDonald, Anthony; Hendricks, Judy; Copeland, Guild; Hunter, John; Akhil, Ohmar; Capps, Heather; Curry, Marc; Skirboll, Steve
2000-03-01
We present a new technique for high throughput screening of tumor cells in a sensitive nanodevice that has the potential to quickly identify a cell population that has begun the rapid protein synthesis and mitosis characteristic of cancer cell proliferation. Currently, pathologists rely on microscopic examination of cell morphology using century-old staining methods that are labor-intensive, time-consuming and frequently in error. New micro-analytical methods for automated, real time screening without chemical modification are critically needed to advance pathology and improve diagnoses. We have teamed scientists with physicians to create a microlaser biochip (based upon our R&D award winning bio-laser concept)1 which evaluates tumor cells by quantifying their growth kinetics. The key new discovery was demonstrating that the lasing spectra are sensitive to the biomolecular mass in the cell, which changes the speed of light in the laser microcavity. Initial results with normal and cancerous human brain cells show that only a few hundred cells -- the equivalent of a billionth of a liter -- are required to detect abnormal growth. The ability to detect cancer in such a minute tissue sample is crucial for resecting a tumor margin or grading highly localized tumor malignancy. 1. P. L. Gourley, NanoLasers, Scientific American, March 1998, pp. 56-61. This work supported under DOE contract DE-AC04-94AL85000 and the Office of Basic Energy Sciences.
SpotCaliper: fast wavelet-based spot detection with accurate size estimation.
Püspöki, Zsuzsanna; Sage, Daniel; Ward, John Paul; Unser, Michael
2016-04-15
SpotCaliper is a novel wavelet-based image-analysis software providing a fast automatic detection scheme for circular patterns (spots), combined with the precise estimation of their size. It is implemented as an ImageJ plugin with a friendly user interface. The user is allowed to edit the results by modifying the measurements (in a semi-automated way), extract data for further analysis. The fine tuning of the detections includes the possibility of adjusting or removing the original detections, as well as adding further spots. The main advantage of the software is its ability to capture the size of spots in a fast and accurate way. http://bigwww.epfl.ch/algorithms/spotcaliper/ zsuzsanna.puspoki@epfl.ch Supplementary data are available at Bioinformatics online. © The Author 2015. Published by Oxford University Press. All rights reserved. For Permissions, please e-mail: journals.permissions@oup.com.
Building change detection via a combination of CNNs using only RGB aerial imageries
NASA Astrophysics Data System (ADS)
Nemoto, Keisuke; Hamaguchi, Ryuhei; Sato, Masakazu; Fujita, Aito; Imaizumi, Tomoyuki; Hikosaka, Shuhei
2017-10-01
Building change information extracted from remote sensing imageries is important for various applications such as urban management and marketing planning. The goal of this work is to develop a methodology for automatically capturing building changes from remote sensing imageries. Recent studies have addressed this goal by exploiting 3-D information as a proxy for building height. In contrast, because in practice it is expensive or impossible to prepare 3-D information, we do not rely on 3-D data but focus on using only RGB aerial imageries. Instead, we employ deep convolutional neural networks (CNNs) to extract effective features, and improve change detection accuracy in RGB remote sensing imageries. We consider two aspects of building change detection, building detection and subsequent change detection. Our proposed methodology was tested on several areas, which has some differences such as dominant building characteristics and varying brightness values. On all over the tested areas, the proposed method provides good results for changed objects, with recall values over 75 % with a strict overlap requirement of over 50% in intersection-over-union (IoU). When the IoU threshold was relaxed to over 10%, resulting recall values were over 81%. We conclude that use of CNNs enables accurate detection of building changes without employing 3-D information.
Fast and accurate spectral estimation for online detection of partial broken bar in induction motors
NASA Astrophysics Data System (ADS)
Samanta, Anik Kumar; Naha, Arunava; Routray, Aurobinda; Deb, Alok Kanti
2018-01-01
In this paper, an online and real-time system is presented for detecting partial broken rotor bar (BRB) of inverter-fed squirrel cage induction motors under light load condition. This system with minor modifications can detect any fault that affects the stator current. A fast and accurate spectral estimator based on the theory of Rayleigh quotient is proposed for detecting the spectral signature of BRB. The proposed spectral estimator can precisely determine the relative amplitude of fault sidebands and has low complexity compared to available high-resolution subspace-based spectral estimators. Detection of low-amplitude fault components has been improved by removing the high-amplitude fundamental frequency using an extended-Kalman based signal conditioner. Slip is estimated from the stator current spectrum for accurate localization of the fault component. Complexity and cost of sensors are minimal as only a single-phase stator current is required. The hardware implementation has been carried out on an Intel i7 based embedded target ported through the Simulink Real-Time. Evaluation of threshold and detectability of faults with different conditions of load and fault severity are carried out with empirical cumulative distribution function.
Accurate LC peak boundary detection for ¹⁶O/¹⁸O labeled LC-MS data.
Cui, Jian; Petritis, Konstantinos; Tegeler, Tony; Petritis, Brianne; Ma, Xuepo; Jin, Yufang; Gao, Shou-Jiang S J; Zhang, Jianqiu Michelle
2013-01-01
In liquid chromatography-mass spectrometry (LC-MS), parts of LC peaks are often corrupted by their co-eluting peptides, which results in increased quantification variance. In this paper, we propose to apply accurate LC peak boundary detection to remove the corrupted part of LC peaks. Accurate LC peak boundary detection is achieved by checking the consistency of intensity patterns within peptide elution time ranges. In addition, we remove peptides with erroneous mass assignment through model fitness check, which compares observed intensity patterns to theoretically constructed ones. The proposed algorithm can significantly improve the accuracy and precision of peptide ratio measurements.
Spibey, C A; Jackson, P; Herick, K
2001-03-01
In recent years the use of fluorescent dyes in biological applications has dramatically increased. The continual improvement in the capabilities of these fluorescent dyes demands increasingly sensitive detection systems that provide accurate quantitation over a wide linear dynamic range. In the field of proteomics, the detection, quantitation and identification of very low abundance proteins are of extreme importance in understanding cellular processes. Therefore, the instrumentation used to acquire an image of such samples, for spot picking and identification by mass spectrometry, must be sensitive enough to be able, not only, to maximise the sensitivity and dynamic range of the staining dyes but, as importantly, adapt to the ever changing portfolio of fluorescent dyes as they become available. Just as the available fluorescent probes are improving and evolving so are the users application requirements. Therefore, the instrumentation chosen must be flexible to address and adapt to those changing needs. As a result, a highly competitive market for the supply and production of such dyes and the instrumentation for their detection and quantitation have emerged. The instrumentation currently available is based on either laser/photomultiplier tube (PMT) scanning or lamp/charge-coupled device (CCD) based mechanisms. This review briefly discusses the advantages and disadvantages of both System types for fluorescence imaging, gives a technical overview of CCD technology and describes in detail a unique xenon/are lamp CCD based instrument, from PerkinElmer Life Sciences. The Wallac-1442 ARTHUR is unique in its ability to scan both large areas at high resolution and give accurate selectable excitation over the whole of the UV/visible range. It operates by filtering both the excitation and emission wavelengths, providing optimal and accurate measurement and quantitation of virtually any available dye and allows excellent spectral resolution between different fluorophores
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
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.
Lung ultrasound accurately detects pneumothorax in a preterm newborn lamb model.
Blank, Douglas A; Hooper, Stuart B; Binder-Heschl, Corinna; Kluckow, Martin; Gill, Andrew W; LaRosa, Domenic A; Inocencio, Ishmael M; Moxham, Alison; Rodgers, Karyn; Zahra, Valerie A; Davis, Peter G; Polglase, Graeme R
2016-06-01
Pneumothorax is a common emergency affecting extremely preterm. In adult studies, lung ultrasound has performed better than chest x-ray in the diagnosis of pneumothorax. The purpose of this study was to determine the efficacy of lung ultrasound (LUS) examination to detect pneumothorax using a preterm animal model. This was a prospective, observational study using newborn Border-Leicester lambs at gestational age = 126 days (equivalent to gestational age = 26 weeks in humans) receiving mechanical ventilation from birth to 2 h of life. At the conclusion of the experiment, LUS was performed, the lambs were then euthanised and a post-mortem exam was immediately performed. We used previously published ultrasound techniques to identify pneumothorax. Test characteristics of LUS to detect pneumothorax were calculated, using the post-mortem exam as the 'gold standard' test. Nine lambs (18 lungs) were examined. Four lambs had a unilateral pneumothorax, all of which were identified by LUS with no false positives. This was the first study to use post-mortem findings to test the efficacy of LUS to detect pneumothorax in a newborn animal model. Lung ultrasound accurately detected pneumothorax, verified by post-mortem exam, in premature, newborn lambs. © 2016 Paediatrics and Child Health Division (The Royal Australasian College of Physicians).
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.
Accurate Fall Detection in a Top View Privacy Preserving Configuration.
Ricciuti, Manola; Spinsante, Susanna; Gambi, Ennio
2018-05-29
Fall detection is one of the most investigated themes in the research on assistive solutions for aged people. In particular, a false-alarm-free discrimination between falls and non-falls is indispensable, especially to assist elderly people living alone. Current technological solutions designed to monitor several types of activities in indoor environments can guarantee absolute privacy to the people that decide to rely on them. Devices integrating RGB and depth cameras, such as the Microsoft Kinect, can ensure privacy and anonymity, since the depth information is considered to extract only meaningful information from video streams. In this paper, we propose an accurate fall detection method investigating the depth frames of the human body using a single device in a top-view configuration, with the subjects located under the device inside a room. Features extracted from depth frames train a classifier based on a binary support vector machine learning algorithm. The dataset includes 32 falls and 8 activities considered for comparison, for a total of 800 sequences performed by 20 adults. The system showed an accuracy of 98.6% and only one false positive.
Can phenological models predict tree phenology accurately under climate change conditions?
NASA Astrophysics Data System (ADS)
Chuine, Isabelle; Bonhomme, Marc; Legave, Jean Michel; García de Cortázar-Atauri, Inaki; Charrier, Guillaume; Lacointe, André; Améglio, Thierry
2014-05-01
The onset of the growing season of trees has been globally earlier by 2.3 days/decade during the last 50 years because of global warming and this trend is predicted to continue according to climate forecast. The effect of temperature on plant phenology is however not linear because temperature has a dual effect on bud development. On one hand, low temperatures are necessary to break bud dormancy, and on the other hand higher temperatures are necessary to promote bud cells growth afterwards. Increasing phenological changes in temperate woody species have strong impacts on forest trees distribution and productivity, as well as crops cultivation areas. Accurate predictions of trees phenology are therefore a prerequisite to understand and foresee the impacts of climate change on forests and agrosystems. Different process-based models have been developed in the last two decades to predict the date of budburst or flowering of woody species. They are two main families: (1) one-phase models which consider only the ecodormancy phase and make the assumption that endodormancy is always broken before adequate climatic conditions for cell growth occur; and (2) two-phase models which consider both the endodormancy and ecodormancy phases and predict a date of dormancy break which varies from year to year. So far, one-phase models have been able to predict accurately tree bud break and flowering under historical climate. However, because they do not consider what happens prior to ecodormancy, and especially the possible negative effect of winter temperature warming on dormancy break, it seems unlikely that they can provide accurate predictions in future climate conditions. It is indeed well known that a lack of low temperature results in abnormal pattern of bud break and development in temperate fruit trees. An accurate modelling of the dormancy break date has thus become a major issue in phenology modelling. Two-phases phenological models predict that global warming should delay
Methodological Guidelines for Accurate Detection of Viruses in Wild Plant Species
Renner, Kurra; Cole, Ellen; Seabloom, Eric W.; Borer, Elizabeth T.; Malmstrom, Carolyn M.
2016-01-01
Ecological understanding of disease risk, emergence, and dynamics and of the efficacy of control strategies relies heavily on efficient tools for microorganism identification and characterization. Misdetection, such as the misclassification of infected hosts as healthy, can strongly bias estimates of disease prevalence and lead to inaccurate conclusions. In natural plant ecosystems, interest in assessing microbial dynamics is increasing exponentially, but guidelines for detection of microorganisms in wild plants remain limited, particularly so for plant viruses. To address this gap, we explored issues and solutions associated with virus detection by serological and molecular methods in noncrop plant species as applied to the globally important Barley yellow dwarf virus PAV (Luteoviridae), which infects wild native plants as well as crops. With enzyme-linked immunosorbent assays (ELISA), we demonstrate how virus detection in a perennial wild plant species may be much greater in stems than in leaves, although leaves are most commonly sampled, and may also vary among tillers within an individual, thereby highlighting the importance of designing effective sampling strategies. With reverse transcription-PCR (RT-PCR), we demonstrate how inhibitors in tissues of perennial wild hosts can suppress virus detection but can be overcome with methods and products that improve isolation and amplification of nucleic acids. These examples demonstrate the paramount importance of testing and validating survey designs and virus detection methods for noncrop plant communities to ensure accurate ecological surveys and reliable assumptions about virus dynamics in wild hosts. PMID:26773088
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.
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
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
Change detection and change blindness in pigeons (Columba livia).
Herbranson, Walter T; Trinh, Yvan T; Xi, Patricia M; Arand, Mark P; Barker, Michael S K; Pratt, Theodore H
2014-05-01
Change blindness is a phenomenon in which even obvious details in a visual scene change without being noticed. Although change blindness has been studied extensively in humans, we do not yet know if it is a phenomenon that also occurs in other animals. Thus, investigation of change blindness in a nonhuman species may prove to be valuable by beginning to provide some insight into its ultimate causes. Pigeons learned a change detection task in which pecks to the location of a change in a sequence of stimulus displays were reinforced. They were worse at detecting changes if the stimulus displays were separated by a brief interstimulus interval, during which the display was blank, and this primary result matches the general pattern seen in previous studies of change blindness in humans. A second experiment attempted to identify specific stimulus characteristics that most reliably produced a failure to detect changes. Change detection was more difficult when interstimulus intervals were longer and when the change was iterated fewer times. ©2014 APA, all rights reserved.
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.
Multisensor Fusion for Change Detection
NASA Astrophysics Data System (ADS)
Schenk, T.; Csatho, B.
2005-12-01
Combining sensors that record different properties of a 3-D scene leads to complementary and redundant information. If fused properly, a more robust and complete scene description becomes available. Moreover, fusion facilitates automatic procedures for object reconstruction and modeling. For example, aerial imaging sensors, hyperspectral scanning systems, and airborne laser scanning systems generate complementary data. We describe how data from these sensors can be fused for such diverse applications as mapping surface erosion and landslides, reconstructing urban scenes, monitoring urban land use and urban sprawl, and deriving velocities and surface changes of glaciers and ice sheets. An absolute prerequisite for successful fusion is a rigorous co-registration of the sensors involved. We establish a common 3-D reference frame by using sensor invariant features. Such features are caused by the same object space phenomena and are extracted in multiple steps from the individual sensors. After extracting, segmenting and grouping the features into more abstract entities, we discuss ways on how to automatically establish correspondences. This is followed by a brief description of rigorous mathematical models suitable to deal with linear and area features. In contrast to traditional, point-based registration methods, lineal and areal features lend themselves to a more robust and more accurate registration. More important, the chances to automate the registration process increases significantly. The result of the co-registration of the sensors is a unique transformation between the individual sensors and the object space. This makes spatial reasoning of extracted information more versatile; reasoning can be performed in sensor space or in 3-D space where domain knowledge about features and objects constrains reasoning processes, reduces the search space, and helps to make the problem well-posed. We demonstrate the feasibility of the proposed multisensor fusion approach
The Development of Change Detection
ERIC Educational Resources Information Center
Shore, David I.; Burack, Jacob A.; Miller, Danny; Joseph, Shari; Enns, James T.
2006-01-01
Changes to a scene often go unnoticed if the objects of the change are unattended, making change detection an index of where attention is focused during scene perception. We measured change detection in school-age children and young adults by repeatedly alternating two versions of an image. To provide an age-fair assessment we used a bimanual…
Methodological Guidelines for Accurate Detection of Viruses in Wild Plant Species.
Lacroix, Christelle; Renner, Kurra; Cole, Ellen; Seabloom, Eric W; Borer, Elizabeth T; Malmstrom, Carolyn M
2016-01-15
Ecological understanding of disease risk, emergence, and dynamics and of the efficacy of control strategies relies heavily on efficient tools for microorganism identification and characterization. Misdetection, such as the misclassification of infected hosts as healthy, can strongly bias estimates of disease prevalence and lead to inaccurate conclusions. In natural plant ecosystems, interest in assessing microbial dynamics is increasing exponentially, but guidelines for detection of microorganisms in wild plants remain limited, particularly so for plant viruses. To address this gap, we explored issues and solutions associated with virus detection by serological and molecular methods in noncrop plant species as applied to the globally important Barley yellow dwarf virus PAV (Luteoviridae), which infects wild native plants as well as crops. With enzyme-linked immunosorbent assays (ELISA), we demonstrate how virus detection in a perennial wild plant species may be much greater in stems than in leaves, although leaves are most commonly sampled, and may also vary among tillers within an individual, thereby highlighting the importance of designing effective sampling strategies. With reverse transcription-PCR (RT-PCR), we demonstrate how inhibitors in tissues of perennial wild hosts can suppress virus detection but can be overcome with methods and products that improve isolation and amplification of nucleic acids. These examples demonstrate the paramount importance of testing and validating survey designs and virus detection methods for noncrop plant communities to ensure accurate ecological surveys and reliable assumptions about virus dynamics in wild hosts. Copyright © 2016, American Society for Microbiology. All Rights Reserved.
Tissue resonance interaction accurately detects colon lesions: A double-blind pilot study.
Dore, Maria P; Tufano, Marcello O; Pes, Giovanni M; Cuccu, Marianna; Farina, Valentina; Manca, Alessandra; Graham, David Y
2015-07-07
To investigated the performance of the tissue resonance interaction method (TRIM) for the non-invasive detection of colon lesions. We performed a prospective single-center blinded pilot study of consecutive adults undergoing colonoscopy at the University Hospital in Sassari, Italy. Before patients underwent colonoscopy, they were examined by the TRIMprobe which detects differences in electromagnetic properties between pathological and normal tissues. All patients had completed the polyethylene glycol-containing bowel prep for the colonoscopy procedure before being screened. During the procedure the subjects remained fully dressed. A hand-held probe was moved over the abdomen and variations in electromagnetic signals were recorded for 3 spectral lines (462-465 MHz, 930 MHz, and 1395 MHz). A single investigator, blind to any clinical information, performed the test using the TRIMprob system. Abnormal signals were identified and recorded as malignant or benign (adenoma or hyperplastic polyps). Findings were compared with those from colonoscopy with histologic confirmation. Statistical analysis was performed by χ(2) test. A total of 305 consecutive patients fulfilling the inclusion criteria were enrolled over a period of 12 months. The most frequent indication for colonoscopy was abdominal pain (33%). The TRIMprob was well accepted by all patients; none spontaneously complained about the procedure, and no adverse effects were observed. TRIM proved inaccurate for polyp detection in patients with inflammatory bowel disease (IBD) and they were excluded leaving 281 subjects (mean age 59 ± 13 years; 107 males). The TRIM detected and accurately characterized all 12 adenocarcinomas and 135/137 polyps (98.5%) including 64 adenomatous (100%) found. The method identified cancers and polyps with 98.7% sensitivity, 96.2% specificity, and 97.5% diagnostic accuracy, compared to colonoscopy and histology analyses. The positive predictive value was 96.7% and the negative predictive
Tissue resonance interaction accurately detects colon lesions: A double-blind pilot study
Dore, Maria P; Tufano, Marcello O; Pes, Giovanni M; Cuccu, Marianna; Farina, Valentina; Manca, Alessandra; Graham, David Y
2015-01-01
AIM: To investigated the performance of the tissue resonance interaction method (TRIM) for the non-invasive detection of colon lesions. METHODS: We performed a prospective single-center blinded pilot study of consecutive adults undergoing colonoscopy at the University Hospital in Sassari, Italy. Before patients underwent colonoscopy, they were examined by the TRIMprobe which detects differences in electromagnetic properties between pathological and normal tissues. All patients had completed the polyethylene glycol-containing bowel prep for the colonoscopy procedure before being screened. During the procedure the subjects remained fully dressed. A hand-held probe was moved over the abdomen and variations in electromagnetic signals were recorded for 3 spectral lines (462-465 MHz, 930 MHz, and 1395 MHz). A single investigator, blind to any clinical information, performed the test using the TRIMprob system. Abnormal signals were identified and recorded as malignant or benign (adenoma or hyperplastic polyps). Findings were compared with those from colonoscopy with histologic confirmation. Statistical analysis was performed by χ2 test. RESULTS: A total of 305 consecutive patients fulfilling the inclusion criteria were enrolled over a period of 12 months. The most frequent indication for colonoscopy was abdominal pain (33%). The TRIMprob was well accepted by all patients; none spontaneously complained about the procedure, and no adverse effects were observed. TRIM proved inaccurate for polyp detection in patients with inflammatory bowel disease (IBD) and they were excluded leaving 281 subjects (mean age 59 ± 13 years; 107 males). The TRIM detected and accurately characterized all 12 adenocarcinomas and 135/137 polyps (98.5%) including 64 adenomatous (100%) found. The method identified cancers and polyps with 98.7% sensitivity, 96.2% specificity, and 97.5% diagnostic accuracy, compared to colonoscopy and histology analyses. The positive predictive value was 96.7% and the
Ouyang, Lei; Yao, Ling; Zhou, Taohong; Zhu, Lihua
2018-10-16
Malachite Green (MG) is a banned pesticide for aquaculture products. As a required inspection item, its fast and accurate determination before the products' accessing market is very important. Surface enhanced Raman scattering (SERS) is a promising tool for MG sensing, but it requires the overcoming of several problems such as fairly poor sensitivity and reproducibility, especially laser induced chemical conversion and photo-bleaching during SERS observation. By using a graphene wrapped Ag array based flexible membrane sensor, a modified SERS strategy was proposed for the sensitive and accurate detection of MG. The graphene layer functioned as an inert protector for impeding chemical transferring of the bioproduct Leucomalachite Green (LMG) to MG during the SERS detection, and as a heat transmitter for preventing laser induced photo-bleaching, which enables the separate detection of MG and LMG in fish extracts. The combination of the Ag array and the graphene cover also produced plentiful densely and uniformly distributed hot spots, leading to analytical enhancement factor up to 3.9 × 10 8 and excellent reproducibility (relative standard deviation low to 5.8% for 70 runs). The proposed method was easily used for MG detection with limit of detection (LOD) as low as 2.7 × 10 -11 mol L -1 . The flexibility of the sensor enable it have a merit for in-field fast detection of MG residues on the scale of a living fish through a surface extraction and paste transferring manner. The developed strategy was successfully applied in the analysis of real samples, showing good prospects for both the fast inspection and quantitative detection of MG. Copyright © 2018 Elsevier B.V. All rights reserved.
Dynamic Network Change Detection
2008-12-01
Change Detection 5a. CONTRACT NUMBER 5b. GRANT NUMBER 5c. PROGRAM ELEMENT NUMBER 6. AUTHOR(S) 5d. PROJECT NUMBER 5e. TASK NUMBER 5f. WORK UNIT...Fisher and Mackenzie, 1922). These methods are used in quality engineering to detect small changes in a process (Montgomery, 1991; Ryan , 2000). Larger...Social Network Modeling and Analysis: Workshop Summary and Papers, Ronald Breiger, Kathleen Carley, and Philippa Pattison, (Eds
Luo, Jun; Li, Junhua; Yang, Hang; Yu, Junping; Wei, Hongping
2017-10-01
Accurate and rapid identification of methicillin-resistant Staphylococcus aureus (MRSA) is needed to screen MRSA carriers and improve treatment. The current widely used duplex PCR methods are not able to differentiate MRSA from coexisting methicillin-susceptible S. aureus (MSSA) or other methicillin-resistant staphylococci. In this study, we aimed to develop a direct method for accurate and rapid detection of MRSA in clinical samples from open environments, such as nasal swabs. The new molecular assay is based on detecting the cooccurrence of nuc and mecA markers in a single bacterial cell by utilizing droplet digital PCR (ddPCR) with the chimeric lysin ClyH for cell lysis. The method consists of (i) dispersion of an intact single bacterium into nanoliter droplets, (ii) temperature-controlled release of genomic DNA (gDNA) by ClyH at 37°C, and (iii) amplification and detection of the markers ( nuc and mecA ) using standard TaqMan chemistries with ddPCR. Results were analyzed based on MRSA index ratios used for indicating the presence of the duplex-positive markers in droplets. The method was able to achieve an absolute limit of detection (LOD) of 2,900 CFU/ml for MRSA in nasal swabs spiked with excess amounts of Escherichia coli , MSSA, and other mecA -positive bacteria within 4 h. Initial testing of 104 nasal swabs showed that the method had 100% agreement with the standard culture method, while the normal duplex qPCR method had only about 87.5% agreement. The single-bacterium duplex ddPCR assay is rapid and powerful for more accurate detection of MRSA directly from clinical specimens. Copyright © 2017 American Society for Microbiology.
Luo, Jun; Li, Junhua; Yang, Hang; Yu, Junping
2017-01-01
ABSTRACT Accurate and rapid identification of methicillin-resistant Staphylococcus aureus (MRSA) is needed to screen MRSA carriers and improve treatment. The current widely used duplex PCR methods are not able to differentiate MRSA from coexisting methicillin-susceptible S. aureus (MSSA) or other methicillin-resistant staphylococci. In this study, we aimed to develop a direct method for accurate and rapid detection of MRSA in clinical samples from open environments, such as nasal swabs. The new molecular assay is based on detecting the cooccurrence of nuc and mecA markers in a single bacterial cell by utilizing droplet digital PCR (ddPCR) with the chimeric lysin ClyH for cell lysis. The method consists of (i) dispersion of an intact single bacterium into nanoliter droplets, (ii) temperature-controlled release of genomic DNA (gDNA) by ClyH at 37°C, and (iii) amplification and detection of the markers (nuc and mecA) using standard TaqMan chemistries with ddPCR. Results were analyzed based on MRSA index ratios used for indicating the presence of the duplex-positive markers in droplets. The method was able to achieve an absolute limit of detection (LOD) of 2,900 CFU/ml for MRSA in nasal swabs spiked with excess amounts of Escherichia coli, MSSA, and other mecA-positive bacteria within 4 h. Initial testing of 104 nasal swabs showed that the method had 100% agreement with the standard culture method, while the normal duplex qPCR method had only about 87.5% agreement. The single-bacterium duplex ddPCR assay is rapid and powerful for more accurate detection of MRSA directly from clinical specimens. PMID:28724560
Point pattern match-based change detection in a constellation of previously detected objects
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.
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.
Classification of change detection and change blindness from near-infrared spectroscopy signals
NASA Astrophysics Data System (ADS)
Tanaka, Hirokazu; Katura, Takusige
2011-08-01
Using a machine-learning classification algorithm applied to near-infrared spectroscopy (NIRS) signals, we classify a success (change detection) or a failure (change blindness) in detecting visual changes for a change-detection task. Five subjects perform a change-detection task, and their brain activities are continuously monitored. A support-vector-machine algorithm is applied to classify the change-detection and change-blindness trials, and correct classification probability of 70-90% is obtained for four subjects. Two types of temporal shapes in classification probabilities are found: one exhibiting a maximum value after the task is completed (postdictive type), and another exhibiting a maximum value during the task (predictive type). As for the postdictive type, the classification probability begins to increase immediately after the task completion and reaches its maximum in about the time scale of neuronal hemodynamic response, reflecting a subjective report of change detection. As for the predictive type, the classification probability shows an increase at the task initiation and is maximal while subjects are performing the task, predicting the task performance in detecting a change. We conclude that decoding change detection and change blindness from NIRS signal is possible and argue some future applications toward brain-machine interfaces.
lidar change detection using building models
NASA Astrophysics Data System (ADS)
Kim, Angela M.; Runyon, Scott C.; Jalobeanu, Andre; Esterline, Chelsea H.; Kruse, Fred A.
2014-06-01
Terrestrial LiDAR scans of building models collected with a FARO Focus3D and a RIEGL VZ-400 were used to investigate point-to-point and model-to-model LiDAR change detection. LiDAR data were scaled, decimated, and georegistered to mimic real world airborne collects. Two physical building models were used to explore various aspects of the change detection process. The first model was a 1:250-scale representation of the Naval Postgraduate School campus in Monterey, CA, constructed from Lego blocks and scanned in a laboratory setting using both the FARO and RIEGL. The second model at 1:8-scale consisted of large cardboard boxes placed outdoors and scanned from rooftops of adjacent buildings using the RIEGL. A point-to-point change detection scheme was applied directly to the point-cloud datasets. In the model-to-model change detection scheme, changes were detected by comparing Digital Surface Models (DSMs). The use of physical models allowed analysis of effects of changes in scanner and scanning geometry, and performance of the change detection methods on different types of changes, including building collapse or subsistence, construction, and shifts in location. Results indicate that at low false-alarm rates, the point-to-point method slightly outperforms the model-to-model method. The point-to-point method is less sensitive to misregistration errors in the data. Best results are obtained when the baseline and change datasets are collected using the same LiDAR system and collection geometry.
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
Optical Coherence Tomography Accurately Measures Corneal Power Change From Laser Refractive Surgery
McNabb, Ryan P.; Farsiu, Sina; Stinnett, Sandra S.; Izatt, Joseph A.; Kuo, Anthony N.
2014-01-01
Purpose To determine the ability of motion corrected optical coherence tomography (OCT) to measure the corneal refractive power change due to laser in situ keratomileusis (LASIK). Design Evaluation of a diagnostic test or technology in a cohort. Subjects 70 eyes from 37 subjects undergoing LASIK were measured preoperatively. 39 eyes from 22 subjects were measured postoperatively and completed the study. Methods Consecutive patients undergoing LASIK at the Duke Eye Center who consented to participate were imaged with Placido-ring topography, Scheimpflug photography and OCT on the day of their surgery. Patients were then reimaged with the same imaging systems at the post-operative month 3 visit. Change in pre- to post-operative corneal refractive power as measured by each of the imaging modalities was compared to the pre- to post-operative change in manifest refraction using t-test with generalized estimating equations. Main Outcome Measures Corneal refractive power change due to LASIK as measured by Placido-ring topography, Scheimpflug Photography, and OCT compared to the manifest refraction change vertexed to the corneal plane. The change in manifest refraction should correspond to the change in the corneal refractive power from LASIK and was considered the reference measurement. Results In 22 returning post-LASIK individuals (39 eyes), we found no significant difference between the clinically measured pre to post LASIK change in manifest refraction and both Scheimpflug photography (p = 0.714) and OCT (p = 0.216). In contrast, keratometry values from Placido-ring topography were found to be significantly different from the measured refractive change (p < 0.001). Additionally, of the three imaging modalities, OCT recorded the smallest mean absolute difference from the reference measurement with the least amount of variability. Conclusion Motion corrected OCT more accurately measures the change in corneal refractive power due to laser refractive surgery than
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.
Nayor, Jennifer; Borges, Lawrence F; Goryachev, Sergey; Gainer, Vivian S; Saltzman, John R
2018-07-01
ADR is a widely used colonoscopy quality indicator. Calculation of ADR is labor-intensive and cumbersome using current electronic medical databases. Natural language processing (NLP) is a method used to extract meaning from unstructured or free text data. (1) To develop and validate an accurate automated process for calculation of adenoma detection rate (ADR) and serrated polyp detection rate (SDR) on data stored in widely used electronic health record systems, specifically Epic electronic health record system, Provation ® endoscopy reporting system, and Sunquest PowerPath pathology reporting system. Screening colonoscopies performed between June 2010 and August 2015 were identified using the Provation ® reporting tool. An NLP pipeline was developed to identify adenomas and sessile serrated polyps (SSPs) on pathology reports corresponding to these colonoscopy reports. The pipeline was validated using a manual search. Precision, recall, and effectiveness of the natural language processing pipeline were calculated. ADR and SDR were then calculated. We identified 8032 screening colonoscopies that were linked to 3821 pathology reports (47.6%). The NLP pipeline had an accuracy of 100% for adenomas and 100% for SSPs. Mean total ADR was 29.3% (range 14.7-53.3%); mean male ADR was 35.7% (range 19.7-62.9%); and mean female ADR was 24.9% (range 9.1-51.0%). Mean total SDR was 4.0% (0-9.6%). We developed and validated an NLP pipeline that accurately and automatically calculates ADRs and SDRs using data stored in Epic, Provation ® and Sunquest PowerPath. This NLP pipeline can be used to evaluate colonoscopy quality parameters at both individual and practice levels.
Developing Best Practices for Detecting Change at Marine Renewable Energy Sites
NASA Astrophysics Data System (ADS)
Linder, H. L.; Horne, J. K.
2016-02-01
In compliance with the National Environmental Policy Act (NEPA), an evaluation of environmental effects is mandatory for obtaining permits for any Marine Renewable Energy (MRE) project in the US. Evaluation includes an assessment of baseline conditions and on-going monitoring during operation to determine if biological conditions change relative to the baseline. Currently, there are no best practices for the analysis of MRE monitoring data. We have developed an approach to evaluate and recommend analytic models used to characterize and detect change in biological monitoring data. The approach includes six steps: review current MRE monitoring practices, identify candidate models to analyze data, fit models to a baseline dataset, develop simulated scenarios of change, evaluate model fit to simulated data, and produce recommendations on the choice of analytic model for monitoring data. An empirical data set from a proposed tidal turbine site at Admiralty Inlet, Puget Sound, Washington was used to conduct the model evaluation. Candidate models that were evaluated included: linear regression, time series, and nonparametric models. Model fit diagnostics Root-Mean-Square-Error and Mean-Absolute-Scaled-Error were used to measure accuracy of predicted values from each model. A power analysis was used to evaluate the ability of each model to measure and detect change from baseline conditions. As many of these models have yet to be applied in MRE monitoring studies, results of this evaluation will generate comprehensive guidelines on choice of model to detect change in environmental monitoring data from MRE sites. The creation of standardized guidelines for model selection enables accurate comparison of change between life stages of a MRE project, within life stages to meet real time regulatory requirements, and comparison of environmental changes among MRE sites.
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
Aircraft MSS data registration and vegetation classification of wetland change detection
Christensen, E.J.; Jensen, J.R.; Ramsey, Elijah W.; Mackey, H.E.
1988-01-01
Portions of the Savannah River floodplain swamp were evaluated for vegetation change using high resolution (5a??6 m) aircraft multispectral scanner (MSS) data. Image distortion from aircraft movement prevented precise image-to-image registration in some areas. However, when small scenes were used (200-250 ha), a first-order linear transformation provided registration accuracies of less than or equal to one pixel. A larger area was registered using a piecewise linear method. Five major wetland classes were identified and evaluated for change. Phenological differences and the variable distribution of vegetation limited wetland type discrimination. Using unsupervised methods and ground-collected vegetation data, overall classification accuracies ranged from 84 per cent to 87 per cent for each scene. Results suggest that high-resolution aircraft MSS data can be precisely registered, if small areas are used, and that wetland vegetation change can be accurately detected and monitored.
Automated detection of changes in sequential color ocular fundus images
NASA Astrophysics Data System (ADS)
Sakuma, Satoshi; Nakanishi, Tadashi; Takahashi, Yasuko; Fujino, Yuichi; Tsubouchi, Tetsuro; Nakanishi, Norimasa
1998-06-01
A recent trend is the automatic screening of color ocular fundus images. The examination of such images is used in the early detection of several adult diseases such as hypertension and diabetes. Since this type of examination is easier than CT, costs less, and has no harmful side effects, it will become a routine medical examination. Normal ocular fundus images are found in more than 90% of all people. To deal with the increasing number of such images, this paper proposes a new approach to process them automatically and accurately. Our approach, based on individual comparison, identifies changes in sequential images: a previously diagnosed normal reference image is compared to a non- diagnosed image.
Change detection and classification in brain MR images using change vector analysis.
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.
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.
Attribute and topology based change detection in a constellation of previously detected objects
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.
Detecting forest canopy change due to insect activity using Landsat MSS
NASA Technical Reports Server (NTRS)
Nelson, R. F.
1983-01-01
Multitemporal Landsat multispectral scanner data were analyzed to test various computer-aided analysis techniques for detecting significant forest canopy alteration. Three data transformations - differencing, ratioing, and a vegetative index difference - were tested to determine which best delineated gypsy moth defoliation. Response surface analyses were conducted to determine optimal threshold levels for the individual transformed bands and band combinations. Results indicate that, of the three transformations investigated, a vegetative index difference (VID) transformation most accurately delineates forest canopy change. Band 5 (0.6 to 0.7 micron ratioed data did nearly as well. However, other single bands and band combinations did not improve upon the band 5 ratio and VID results.
Indigenous people's detection of rapid ecological change.
Aswani, Shankar; Lauer, Matthew
2014-06-01
When sudden catastrophic events occur, it becomes critical for coastal communities to detect and respond to environmental transformations because failure to do so may undermine overall ecosystem resilience and threaten people's livelihoods. We therefore asked how capable of detecting rapid ecological change following massive environmental disruptions local, indigenous people are. We assessed the direction and periodicity of experimental learning of people in the Western Solomon Islands after a tsunami in 2007. We compared the results of marine science surveys with local ecological knowledge of the benthos across 3 affected villages and 3 periods before and after the tsunami. We sought to determine how people recognize biophysical changes in the environment before and after catastrophic events such as earthquakes and tsunamis and whether people have the ability to detect ecological changes over short time scales or need longer time scales to recognize changes. Indigenous people were able to detect changes in the benthos over time. Detection levels differed between marine science surveys and local ecological knowledge sources over time, but overall patterns of statistically significant detection of change were evident for various habitats. Our findings have implications for marine conservation, coastal management policies, and disaster-relief efforts because when people are able to detect ecological changes, this, in turn, affects how they exploit and manage their marine resources. © 2014 Society for Conservation Biology.
Change Point Detection in Correlation Networks
NASA Astrophysics Data System (ADS)
Barnett, Ian; Onnela, Jukka-Pekka
2016-01-01
Many systems of interacting elements can be conceptualized as networks, where network nodes represent the elements and network ties represent interactions between the elements. In systems where the underlying network evolves, it is useful to determine the points in time where the network structure changes significantly as these may correspond to functional change points. We propose a method for detecting change points in correlation networks that, unlike previous change point detection methods designed for time series data, requires minimal distributional assumptions. We investigate the difficulty of change point detection near the boundaries of the time series in correlation networks and study the power of our method and competing methods through simulation. We also show the generalizable nature of the method by applying it to stock price data as well as fMRI data.
Zou, Han; Jiang, Hao; Luo, Yiwen; Zhu, Jianjie; Lu, Xiaoxuan; Xie, Lihua
2016-01-01
The location and contextual status (indoor or outdoor) is fundamental and critical information for upper-layer applications, such as activity recognition and location-based services (LBS) for individuals. In addition, optimizations of building management systems (BMS), such as the pre-cooling or heating process of the air-conditioning system according to the human traffic entering or exiting a building, can utilize the information, as well. The emerging mobile devices, which are equipped with various sensors, become a feasible and flexible platform to perform indoor-outdoor (IO) detection. However, power-hungry sensors, such as GPS and WiFi, should be used with caution due to the constrained battery storage on mobile device. We propose BlueDetect: an accurate, fast response and energy-efficient scheme for IO detection and seamless LBS running on the mobile device based on the emerging low-power iBeacon technology. By leveraging the on-broad Bluetooth module and our proposed algorithms, BlueDetect provides a precise IO detection service that can turn on/off on-board power-hungry sensors smartly and automatically, optimize their performances and reduce the power consumption of mobile devices simultaneously. Moreover, seamless positioning and navigation services can be realized by it, especially in a semi-outdoor environment, which cannot be achieved by GPS or an indoor positioning system (IPS) easily. We prototype BlueDetect on Android mobile devices and evaluate its performance comprehensively. The experimental results have validated the superiority of BlueDetect in terms of IO detection accuracy, localization accuracy and energy consumption. PMID:26907295
Priming effects under correct change detection and change blindness.
Caudek, Corrado; Domini, Fulvio
2013-03-01
In three experiments, we investigated the priming effects induced by an image change on a successive animate/inanimate decision task. We studied both perceptual (Experiments 1 and 2) and conceptual (Experiment 3) priming effects, under correct change detection and change blindness (CB). Under correct change detection, we found larger positive priming effects on congruent trials for probes representing animate entities than for probes representing artifactual objects. Under CB, we found performance impairment relative to a "no-change" baseline condition. This inhibition effect induced by CB was modulated by the semantic congruency between the changed item and the probe in the case of probe images, but not for probe words. We discuss our results in the context of the literature on the negative priming effect. Copyright © 2012 Elsevier Inc. All rights reserved.
NASA Technical Reports Server (NTRS)
Sandford, Stephen P.
2010-01-01
The Climate Absolute Radiance and Refractivity Observatory (CLARREO) is one of four Tier 1 missions recommended by the recent NRC Decadal Survey report on Earth Science and Applications from Space (NRC, 2007). The CLARREO mission addresses the need to provide accurate, broadly acknowledged climate records that are used to enable validated long-term climate projections that become the foundation for informed decisions on mitigation and adaptation policies that address the effects of climate change on society. The CLARREO mission accomplishes this critical objective through rigorous SI traceable decadal change observations that are sensitive to many of the key uncertainties in climate radiative forcings, responses, and feedbacks that in turn drive uncertainty in current climate model projections. These same uncertainties also lead to uncertainty in attribution of climate change to anthropogenic forcing. For the first time CLARREO will make highly accurate, global, SI-traceable decadal change observations sensitive to the most critical, but least understood, climate forcings, responses, and feedbacks. The CLARREO breakthrough is to achieve the required levels of accuracy and traceability to SI standards for a set of observations sensitive to a wide range of key decadal change variables. The required accuracy levels are determined so that climate trend signals can be detected against a background of naturally occurring variability. Climate system natural variability therefore determines what level of accuracy is overkill, and what level is critical to obtain. In this sense, the CLARREO mission requirements are considered optimal from a science value perspective. The accuracy for decadal change traceability to SI standards includes uncertainties associated with instrument calibration, satellite orbit sampling, and analysis methods. Unlike most space missions, the CLARREO requirements are driven not by the instantaneous accuracy of the measurements, but by accuracy in
DOE Office of Scientific and Technical Information (OSTI.GOV)
Botvinick, E.H.; Frais, M.A.; Shosa, D.W.
1982-08-01
The ability of scintigraphic phase image analysis to characterize patterns of abnormal ventricular activation was investigated. The pattern of phase distribution and sequential phase changes over both right and left ventricular regions of interest were evaluated in 16 patients with normal electrical activation and wall motion and compared with those in 8 patients with an artificial pacemaker and 4 patients with sinus rhythm with the Wolff-Parkinson-White syndrome and delta waves. Normally, the site of earliest phase angle was seen at the base of the interventricular septum, with sequential change affecting the body of the septum and the cardiac apex andmore » then spreading laterally to involve the body of both ventricles. The site of earliest phase angle was located at the apex of the right ventricle in seven patients with a right ventricular endocardial pacemaker and on the lateral left ventricular wall in one patient with a left ventricular epicardial pacemaker. In each case the site corresponded exactly to the position of the pacing electrode as seen on posteroanterior and left lateral chest X-ray films, and sequential phase changes spread from the initial focus to affect both ventricles. In each of the patients with the Wolff-Parkinson-White syndrome, the site of earliest ventricular phase angle was located, and it corresponded exactly to the site of the bypass tract as determined by endocardial mapping. In this way, four bypass pathways, two posterior left paraseptal, one left lateral and one right lateral, were correctly localized scintigraphically. On the basis of the sequence of mechanical contraction, phase image analysis provides an accurate noninvasive method of detecting abnormal foci of ventricular activation.« less
Probabilistic BPRRC: Robust Change Detection against Illumination Changes and Background Movements
NASA Astrophysics Data System (ADS)
Yokoi, Kentaro
This paper presents Probabilistic Bi-polar Radial Reach Correlation (PrBPRRC), a change detection method that is robust against illumination changes and background movements. Most of the traditional change detection methods are robust against either illumination changes or background movements; BPRRC is one of the illumination-robust change detection methods. We introduce a probabilistic background texture model into BPRRC and add the robustness against background movements including foreground invasions such as moving cars, walking people, swaying trees, and falling snow. We show the superiority of PrBPRRC in the environment with illumination changes and background movements by using three public datasets and one private dataset: ATON Highway data, Karlsruhe traffic sequence data, PETS 2007 data, and Walking-in-a-room data.
Pigeons (Columba livia) show change blindness in a color-change detection task.
Herbranson, Walter T; Jeffers, Jacob S
2017-07-01
Change blindness is a phenomenon whereby changes to a stimulus are more likely go unnoticed under certain circumstances. Pigeons learned a change detection task, in which they observed sequential stimulus displays consisting of individual colors back-projected onto three response keys. The color of one response key changed during each sequence and pecks to the key that displayed the change were reinforced. Pigeons showed a change blindness effect, in that change detection accuracy was worse when there was an inter-stimulus interval interrupting the transition between consecutive stimulus displays. Birds successfully transferred to stimulus displays involving novel colors, indicating that pigeons learned a general change detection rule. Furthermore, analysis of responses to specific color combinations showed that pigeons could detect changes involving both spectral and non-spectral colors and that accuracy was better for changes involving greater differences in wavelength. These results build upon previous investigations of change blindness in both humans and pigeons and suggest that change blindness may be a general consequence of selective visual attention relevant to multiple species and stimulus dimensions.
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
Poppenga, Sandra K.; Gesch, Dean B.; Worstell, Bruce B.
2013-01-01
The 1:24,000-scale high-resolution National Hydrography Dataset (NHD) mapped hydrography flow lines require regular updating because land surface conditions that affect surface channel drainage change over time. Historically, NHD flow lines were created by digitizing surface water information from aerial photography and paper maps. Using these same methods to update nationwide NHD flow lines is costly and inefficient; furthermore, these methods result in hydrography that lacks the horizontal and vertical accuracy needed for fully integrated datasets useful for mapping and scientific investigations. Effective methods for improving mapped hydrography employ change detection analysis of surface channels derived from light detection and ranging (LiDAR) digital elevation models (DEMs) and NHD flow lines. In this article, we describe the usefulness of surface channels derived from LiDAR DEMs for hydrography change detection to derive spatially accurate and time-relevant mapped hydrography. The methods employ analyses of horizontal and vertical differences between LiDAR-derived surface channels and NHD flow lines to define candidate locations of hydrography change. These methods alleviate the need to analyze and update the nationwide NHD for time relevant hydrography, and provide an avenue for updating the dataset where change has occurred.
Accurate Sybil Attack Detection Based on Fine-Grained Physical Channel Information.
Wang, Chundong; Zhu, Likun; Gong, Liangyi; Zhao, Zhentang; Yang, Lei; Liu, Zheli; Cheng, Xiaochun
2018-03-15
With the development of the Internet-of-Things (IoT), wireless network security has more and more attention paid to it. The Sybil attack is one of the famous wireless attacks that can forge wireless devices to steal information from clients. These forged devices may constantly attack target access points to crush the wireless network. In this paper, we propose a novel Sybil attack detection based on Channel State Information (CSI). This detection algorithm can tell whether the static devices are Sybil attackers by combining a self-adaptive multiple signal classification algorithm with the Received Signal Strength Indicator (RSSI). Moreover, we develop a novel tracing scheme to cluster the channel characteristics of mobile devices and detect dynamic attackers that change their channel characteristics in an error area. Finally, we experiment on mobile and commercial WiFi devices. Our algorithm can effectively distinguish the Sybil devices. The experimental results show that our Sybil attack detection system achieves high accuracy for both static and dynamic scenarios. Therefore, combining the phase and similarity of channel features, the multi-dimensional analysis of CSI can effectively detect Sybil nodes and improve the security of wireless networks.
Accurate Sybil Attack Detection Based on Fine-Grained Physical Channel Information
Wang, Chundong; Zhao, Zhentang; Yang, Lei; Liu, Zheli; Cheng, Xiaochun
2018-01-01
With the development of the Internet-of-Things (IoT), wireless network security has more and more attention paid to it. The Sybil attack is one of the famous wireless attacks that can forge wireless devices to steal information from clients. These forged devices may constantly attack target access points to crush the wireless network. In this paper, we propose a novel Sybil attack detection based on Channel State Information (CSI). This detection algorithm can tell whether the static devices are Sybil attackers by combining a self-adaptive multiple signal classification algorithm with the Received Signal Strength Indicator (RSSI). Moreover, we develop a novel tracing scheme to cluster the channel characteristics of mobile devices and detect dynamic attackers that change their channel characteristics in an error area. Finally, we experiment on mobile and commercial WiFi devices. Our algorithm can effectively distinguish the Sybil devices. The experimental results show that our Sybil attack detection system achieves high accuracy for both static and dynamic scenarios. Therefore, combining the phase and similarity of channel features, the multi-dimensional analysis of CSI can effectively detect Sybil nodes and improve the security of wireless networks. PMID:29543773
3D registration of surfaces for change detection in medical images
NASA Astrophysics Data System (ADS)
Fisher, Elizabeth; van der Stelt, Paul F.; Dunn, Stanley M.
1997-04-01
Spatial registration of data sets is essential for quantifying changes that take place over time in cases where the position of a patient with respect to the sensor has been altered. Changes within the region of interest can be problematic for automatic methods of registration. This research addresses the problem of automatic 3D registration of surfaces derived from serial, single-modality images for the purpose of quantifying changes over time. The registration algorithm utilizes motion-invariant, curvature- based geometric properties to derive an approximation to an initial rigid transformation to align two image sets. Following the initial registration, changed portions of the surface are detected and excluded before refining the transformation parameters. The performance of the algorithm was tested using simulation experiments. To quantitatively assess the registration, random noise at various levels, known rigid motion transformations, and analytically-defined volume changes were applied to the initial surface data acquired from models of teeth. These simulation experiments demonstrated that the calculated transformation parameters were accurate to within 1.2 percent of the total applied rotation and 2.9 percent of the total applied translation, even at the highest applied noise levels and simulated wear values.
Vehicle Localization by LIDAR Point Correlation Improved by Change Detection
NASA Astrophysics Data System (ADS)
Schlichting, A.; Brenner, C.
2016-06-01
LiDAR sensors are proven sensors for accurate vehicle localization. Instead of detecting and matching features in the LiDAR data, we want to use the entire information provided by the scanners. As dynamic objects, like cars, pedestrians or even construction sites could lead to wrong localization results, we use a change detection algorithm to detect these objects in the reference data. If an object occurs in a certain number of measurements at the same position, we mark it and every containing point as static. In the next step, we merge the data of the single measurement epochs to one reference dataset, whereby we only use static points. Further, we also use a classification algorithm to detect trees. For the online localization of the vehicle, we use simulated data of a vertical aligned automotive LiDAR sensor. As we only want to use static objects in this case as well, we use a random forest classifier to detect dynamic scan points online. Since the automotive data is derived from the LiDAR Mobile Mapping System, we are able to use the labelled objects from the reference data generation step to create the training data and further to detect dynamic objects online. The localization then can be done by a point to image correlation method using only static objects. We achieved a localization standard deviation of about 5 cm (position) and 0.06° (heading), and were able to successfully localize the vehicle in about 93 % of the cases along a trajectory of 13 km in Hannover, Germany.
Dao, Duy; Salehizadeh, S M A; Noh, Yeonsik; Chong, Jo Woon; Cho, Chae Ho; McManus, Dave; Darling, Chad E; Mendelson, Yitzhak; Chon, Ki H
2017-09-01
Motion and noise artifacts (MNAs) impose limits on the usability of the photoplethysmogram (PPG), particularly in the context of ambulatory monitoring. MNAs can distort PPG, causing erroneous estimation of physiological parameters such as heart rate (HR) and arterial oxygen saturation (SpO2). In this study, we present a novel approach, "TifMA," based on using the time-frequency spectrum of PPG to first detect the MNA-corrupted data and next discard the nonusable part of the corrupted data. The term "nonusable" refers to segments of PPG data from which the HR signal cannot be recovered accurately. Two sequential classification procedures were included in the TifMA algorithm. The first classifier distinguishes between MNA-corrupted and MNA-free PPG data. Once a segment of data is deemed MNA-corrupted, the next classifier determines whether the HR can be recovered from the corrupted segment or not. A support vector machine (SVM) classifier was used to build a decision boundary for the first classification task using data segments from a training dataset. Features from time-frequency spectra of PPG were extracted to build the detection model. Five datasets were considered for evaluating TifMA performance: (1) and (2) were laboratory-controlled PPG recordings from forehead and finger pulse oximeter sensors with subjects making random movements, (3) and (4) were actual patient PPG recordings from UMass Memorial Medical Center with random free movements and (5) was a laboratory-controlled PPG recording dataset measured at the forehead while the subjects ran on a treadmill. The first dataset was used to analyze the noise sensitivity of the algorithm. Datasets 2-4 were used to evaluate the MNA detection phase of the algorithm. The results from the first phase of the algorithm (MNA detection) were compared to results from three existing MNA detection algorithms: the Hjorth, kurtosis-Shannon entropy, and time-domain variability-SVM approaches. This last is an approach
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
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
Montagne, Louise; Derhourhi, Mehdi; Piton, Amélie; Toussaint, Bénédicte; Durand, Emmanuelle; Vaillant, Emmanuel; Thuillier, Dorothée; Gaget, Stefan; De Graeve, Franck; Rabearivelo, Iandry; Lansiaux, Amélie; Lenne, Bruno; Sukno, Sylvie; Desailloud, Rachel; Cnop, Miriam; Nicolescu, Ramona; Cohen, Lior; Zagury, Jean-François; Amouyal, Mélanie; Weill, Jacques; Muller, Jean; Sand, Olivier; Delobel, Bruno; Froguel, Philippe; Bonnefond, Amélie
2018-05-16
The molecular diagnosis of extreme forms of obesity, in which accurate detection of both copy number variations (CNVs) and point mutations, is crucial for an optimal care of the patients and genetic counseling for their families. Whole-exome sequencing (WES) has benefited considerably this molecular diagnosis, but its poor ability to detect CNVs remains a major limitation. We aimed to develop a method (CoDE-seq) enabling the accurate detection of both CNVs and point mutations in one step. CoDE-seq is based on an augmented WES method, using probes distributed uniformly throughout the genome. CoDE-seq was validated in 40 patients for whom chromosomal DNA microarray was available. CNVs and mutations were assessed in 82 children/young adults with suspected Mendelian obesity and/or intellectual disability and in their parents when available (n total = 145). CoDE-seq not only detected all of the 97 CNVs identified by chromosomal DNA microarrays but also found 84 additional CNVs, due to a better resolution. When compared to CoDE-seq and chromosomal DNA microarrays, WES failed to detect 37% and 14% of CNVs, respectively. In the 82 patients, a likely molecular diagnosis was achieved in >30% of the patients. Half of the genetic diagnoses were explained by CNVs while the other half by mutations. CoDE-seq has proven cost-efficient and highly effective as it avoids the sequential genetic screening approaches currently used in clinical practice for the accurate detection of CNVs and point mutations. Copyright © 2018 The Authors. Published by Elsevier GmbH.. All rights reserved.
Detection theory for accurate and non-invasive skin cancer diagnosis using dynamic thermal imaging
Godoy, Sebastián E.; Hayat, Majeed M.; Ramirez, David A.; Myers, Stephen A.; Padilla, R. Steven; Krishna, Sanjay
2017-01-01
Skin cancer is the most common cancer in the United States with over 3.5M annual cases. Presently, visual inspection by a dermatologist has good sensitivity (> 90%) but poor specificity (< 10%), especially for melanoma, which leads to a high number of unnecessary biopsies. Here we use dynamic thermal imaging (DTI) to demonstrate a rapid, accurate and non-invasive imaging system for detection of skin cancer. In DTI, the lesion is cooled down and the thermal recovery is recorded using infrared imaging. The thermal recovery curves of the suspected lesions are then utilized in the context of continuous-time detection theory in order to define an optimal statistical decision rule such that the sensitivity of the algorithm is guaranteed to be at a maximum for every prescribed false-alarm probability. The proposed methodology was tested in a pilot study including 140 human subjects demonstrating a sensitivity in excess of 99% for a prescribed specificity in excess of 99% for detection of skin cancer. To the best of our knowledge, this is the highest reported accuracy for any non-invasive skin cancer diagnosis method. PMID:28736673
Krishnan, Neeraja M.; Gaur, Prakhar; Chaudhary, Rakshit; Rao, Arjun A.; Panda, Binay
2012-01-01
Copy Number Alterations (CNAs) such as deletions and duplications; compose a larger percentage of genetic variations than single nucleotide polymorphisms or other structural variations in cancer genomes that undergo major chromosomal re-arrangements. It is, therefore, imperative to identify cancer-specific somatic copy number alterations (SCNAs), with respect to matched normal tissue, in order to understand their association with the disease. We have devised an accurate, sensitive, and easy-to-use tool, COPS, COpy number using Paired Samples, for detecting SCNAs. We rigorously tested the performance of COPS using short sequence simulated reads at various sizes and coverage of SCNAs, read depths, read lengths and also with real tumor:normal paired samples. We found COPS to perform better in comparison to other known SCNA detection tools for all evaluated parameters, namely, sensitivity (detection of true positives), specificity (detection of false positives) and size accuracy. COPS performed well for sequencing reads of all lengths when used with most upstream read alignment tools. Additionally, by incorporating a downstream boundary segmentation detection tool, the accuracy of SCNA boundaries was further improved. Here, we report an accurate, sensitive and easy to use tool in detecting cancer-specific SCNAs using short-read sequence data. In addition to cancer, COPS can be used for any disease as long as sequence reads from both disease and normal samples from the same individual are available. An added boundary segmentation detection module makes COPS detected SCNA boundaries more specific for the samples studied. COPS is available at ftp://115.119.160.213 with username “cops” and password “cops”. PMID:23110103
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.
Clustering approaches to feature change detection
NASA Astrophysics Data System (ADS)
G-Michael, Tesfaye; Gunzburger, Max; Peterson, Janet
2018-05-01
The automated detection of changes occurring between multi-temporal images is of significant importance in a wide range of medical, environmental, safety, as well as many other settings. The usage of k-means clustering is explored as a means for detecting objects added to a scene. The silhouette score for the clustering is used to define the optimal number of clusters that should be used. For simple images having a limited number of colors, new objects can be detected by examining the change between the optimal number of clusters for the original and modified images. For more complex images, new objects may need to be identified by examining the relative areas covered by corresponding clusters in the original and modified images. Which method is preferable depends on the composition and range of colors present in the images. In addition to describing the clustering and change detection methodology of our proposed approach, we provide some simple illustrations of its application.
The role of iconic memory in change-detection tasks.
Becker, M W; Pashler, H; Anstis, S M
2000-01-01
In three experiments, subjects attempted to detect the change of a single item in a visually presented array of items. Subjects' ability to detect a change was greatly reduced if a blank interstimulus interval (ISI) was inserted between the original array and an array in which one item had changed ('change blindness'). However, change detection improved when the location of the change was cued during the blank ISI. This suggests that people represent more information of a scene than change blindness might suggest. We test two possible hypotheses why, in the absence of a cue, this representation fails to produce good change detection. The first claims that the intervening events employed to create change blindness result in multiple neural transients which co-occur with the to-be-detected change. Poor detection rates occur because a serial search of all the transient locations is required to detect the change, during which time the representation of the original scene fades. The second claims that the occurrence of the second frame overwrites the representation of the first frame, unless that information is insulated against overwriting by attention. The results support the second hypothesis. We conclude that people may have a fairly rich visual representation of a scene while the scene is present, but fail to detect changes because they lack the ability to simultaneously represent two complete visual representations.
Capnography as a tool to detect metabolic changes in patients cared for in the emergency setting
Cereceda-Sánchez, Francisco José; Molina-Mula, Jesús
2017-01-01
ABSTRACT Objective: to evaluate the usefulness of capnography for the detection of metabolic changes in spontaneous breathing patients, in the emergency and intensive care settings. Methods: in-depth and structured bibliographical search in the databases EBSCOhost, Virtual Health Library, PubMed, Cochrane Library, among others, identifying studies that assessed the relationship between capnography values and the variables involved in blood acid-base balance. Results: 19 studies were found, two were reviews and 17 were observational studies. In nine studies, capnography values were correlated with carbon dioxide (CO2), eight with bicarbonate (HCO3), three with lactate, and four with blood pH. Conclusions: most studies have found a good correlation between capnography values and blood biomarkers, suggesting the usefulness of this parameter to detect patients at risk of severe metabolic change, in a fast, economical and accurate way. PMID:28513767
Wee, Eugene J.H.; Wang, Yuling; Tsao, Simon Chang-Hao; Trau, Matt
2016-01-01
Sensitive and accurate identification of specific DNA mutations can influence clinical decisions. However accurate diagnosis from limiting samples such as circulating tumour DNA (ctDNA) is challenging. Current approaches based on fluorescence such as quantitative PCR (qPCR) and more recently, droplet digital PCR (ddPCR) have limitations in multiplex detection, sensitivity and the need for expensive specialized equipment. Herein we describe an assay capitalizing on the multiplexing and sensitivity benefits of surface-enhanced Raman spectroscopy (SERS) with the simplicity of standard PCR to address the limitations of current approaches. This proof-of-concept method could reproducibly detect as few as 0.1% (10 copies, CV < 9%) of target sequences thus demonstrating the high sensitivity of the method. The method was then applied to specifically detect three important melanoma mutations in multiplex. Finally, the PCR/SERS assay was used to genotype cell lines and ctDNA from serum samples where results subsequently validated with ddPCR. With ddPCR-like sensitivity and accuracy yet at the convenience of standard PCR, we believe this multiplex PCR/SERS method could find wide applications in both diagnostics and research. PMID:27446486
Wee, Eugene J H; Wang, Yuling; Tsao, Simon Chang-Hao; Trau, Matt
2016-01-01
Sensitive and accurate identification of specific DNA mutations can influence clinical decisions. However accurate diagnosis from limiting samples such as circulating tumour DNA (ctDNA) is challenging. Current approaches based on fluorescence such as quantitative PCR (qPCR) and more recently, droplet digital PCR (ddPCR) have limitations in multiplex detection, sensitivity and the need for expensive specialized equipment. Herein we describe an assay capitalizing on the multiplexing and sensitivity benefits of surface-enhanced Raman spectroscopy (SERS) with the simplicity of standard PCR to address the limitations of current approaches. This proof-of-concept method could reproducibly detect as few as 0.1% (10 copies, CV < 9%) of target sequences thus demonstrating the high sensitivity of the method. The method was then applied to specifically detect three important melanoma mutations in multiplex. Finally, the PCR/SERS assay was used to genotype cell lines and ctDNA from serum samples where results subsequently validated with ddPCR. With ddPCR-like sensitivity and accuracy yet at the convenience of standard PCR, we believe this multiplex PCR/SERS method could find wide applications in both diagnostics and research.
Detecting and Attributing Health Burdens to Climate Change.
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.
Kim, Jae-Hwan; Park, Saet-Byul; Roh, Hyo-Jeong; Park, Sunghoon; Shin, Min-Ki; Moon, Gui Im; Hong, Jin-Hwan; Kim, Hae-Yeong
2015-06-01
With the increasing number of genetically modified (GM) events, unauthorized GMO releases into the food market have increased dramatically, and many countries have developed detection tools for them. This study described the qualitative and quantitative detection methods of unauthorized the GM wheat MON71800 with a reference plasmid (pGEM-M71800). The wheat acetyl-CoA carboxylase (acc) gene was used as the endogenous gene. The plasmid pGEM-M71800, which contains both the acc gene and the event-specific target MON71800, was constructed as a positive control for the qualitative and quantitative analyses. The limit of detection in the qualitative PCR assay was approximately 10 copies. In the quantitative PCR assay, the standard deviation and relative standard deviation repeatability values ranged from 0.06 to 0.25 and from 0.23% to 1.12%, respectively. This study supplies a powerful and very simple but accurate detection strategy for unauthorized GM wheat MON71800 that utilizes a single calibrator plasmid. Copyright © 2014 Elsevier Ltd. All rights reserved.
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.
Detecting impossible changes in infancy: a three-system account
Wang, Su-hua; Baillargeon, Renée
2012-01-01
Can infants detect that an object has magically disappeared, broken apart or changed color while briefly hidden? Recent research suggests that infants detect some but not other ‘impossible’ changes; and that various contextual manipulations can induce infants to detect changes they would not otherwise detect. We present an account that includes three systems: a physical-reasoning, an object-tracking, and an object-representation system. What impossible changes infants detect depends on what object information is included in the physical-reasoning system; this information becomes subject to a principle of persistence, which states that objects can undergo no spontaneous or uncaused change. What contextual manipulations induce infants to detect impossible changes depends on complex interplays between the physical-reasoning system and the object-tracking and object-representation systems. PMID:18078778
Joint Dictionary Learning for Multispectral Change Detection.
Lu, Xiaoqiang; Yuan, Yuan; Zheng, Xiangtao
2017-04-01
Change detection is one of the most important applications of remote sensing technology. It is a challenging task due to the obvious variations in the radiometric value of spectral signature and the limited capability of utilizing spectral information. In this paper, an improved sparse coding method for change detection is proposed. The intuition of the proposed method is that unchanged pixels in different images can be well reconstructed by the joint dictionary, which corresponds to knowledge of unchanged pixels, while changed pixels cannot. First, a query image pair is projected onto the joint dictionary to constitute the knowledge of unchanged pixels. Then reconstruction error is obtained to discriminate between the changed and unchanged pixels in the different images. To select the proper thresholds for determining changed regions, an automatic threshold selection strategy is presented by minimizing the reconstruction errors of the changed pixels. Adequate experiments on multispectral data have been tested, and the experimental results compared with the state-of-the-art methods prove the superiority of the proposed method. Contributions of the proposed method can be summarized as follows: 1) joint dictionary learning is proposed to explore the intrinsic information of different images for change detection. In this case, change detection can be transformed as a sparse representation problem. To the authors' knowledge, few publications utilize joint learning dictionary in change detection; 2) an automatic threshold selection strategy is presented, which minimizes the reconstruction errors of the changed pixels without the prior assumption of the spectral signature. As a result, the threshold value provided by the proposed method can adapt to different data due to the characteristic of joint dictionary learning; and 3) the proposed method makes no prior assumption of the modeling and the handling of the spectral signature, which can be adapted to different data.
Saliency predicts change detection in pictures of natural scenes.
Wright, Michael J
2005-01-01
It has been proposed that the visual system encodes the salience of objects in the visual field in an explicit two-dimensional map that guides visual selective attention. Experiments were conducted to determine whether salience measurements applied to regions of pictures of outdoor scenes could predict the detection of changes in those regions. To obtain a quantitative measure of change detection, observers located changes in pairs of colour pictures presented across an interstimulus interval (ISI). Salience measurements were then obtained from different observers for image change regions using three independent methods, and all were positively correlated with change detection. Factor analysis extracted a single saliency factor that accounted for 62% of the variance contained in the four measures. Finally, estimates of the magnitude of the image change in each picture pair were obtained, using nine separate visual filters representing low-level vision features (luminance, colour, spatial frequency, orientation, edge density). None of the feature outputs was significantly associated with change detection or saliency. On the other hand it was shown that high-level (structural) properties of the changed region were related to saliency and to change detection: objects were more salient than shadows and more detectable when changed.
Evaluation of change detection techniques for monitoring coastal zone environments
NASA Technical Reports Server (NTRS)
Weismiller, R. A. (Principal Investigator); Kristof, S. J.; Scholz, D. K.; Anuta, P. E.; Momin, S. M.
1977-01-01
The author has identified the following significant results. Four change detection techniques were designed and implemented for evaluation: (1) post classification comparison change detection, (2) delta data change detection, (3) spectral/temporal change classification, and (4) layered spectral/temporal change classification. The post classification comparison technique reliably identified areas of change and was used as the standard for qualitatively evaluating the other three techniques. The layered spectral/temporal change classification and the delta data change detection results generally agreed with the post classification comparison technique results; however, many small areas of change were not identified. Major discrepancies existed between the post classification comparison and spectral/temporal change detection results.
NASA Technical Reports Server (NTRS)
Benkelman, Cody A.
1997-01-01
The project team has outlined several technical objectives which will allow the companies to improve on their current capabilities. These include modifications to the imaging system, enabling it to operate more cost effectively and with greater ease of use, automation of the post-processing software to mosaic and orthorectify the image scenes collected, and the addition of radiometric calibration to greatly aid in the ability to perform accurate change detection. Business objectives include fine tuning of the market plan plus specification of future product requirements, expansion of sales activities (including identification of necessary additional resources required to meet stated revenue objectives), development of a product distribution plan, and implementation of a world wide sales effort.
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.
The fate of object memory traces under change detection and change blindness.
Busch, Niko A
2013-07-03
Observers often fail to detect substantial changes in a visual scene. This so-called change blindness is often taken as evidence that visual representations are sparse and volatile. This notion rests on the assumption that the failure to detect a change implies that representations of the changing objects are lost all together. However, recent evidence suggests that under change blindness, object memory representations may be formed and stored, but not retrieved. This study investigated the fate of object memory representations when changes go unnoticed. Participants were presented with scenes consisting of real world objects, one of which changed on each trial, while recording event-related potentials (ERPs). Participants were first asked to localize where the change had occurred. In an additional recognition task, participants then discriminated old objects, either from the pre-change or the post-change scene, from entirely new objects. Neural traces of object memories were studied by comparing ERPs for old and novel objects. Participants performed poorly in the detection task and often failed to recognize objects from the scene, especially pre-change objects. However, a robust old/novel effect was observed in the ERP, even when participants were change blind and did not recognize the old object. This implicit memory trace was found both for pre-change and post-change objects. These findings suggest that object memories are stored even under change blindness. Thus, visual representations may not be as sparse and volatile as previously thought. Rather, change blindness may point to a failure to retrieve and use these representations for change detection. Copyright © 2013 Elsevier B.V. All rights reserved.
Chen, Xuexia; Giri, Chandra; Vogelmann, James
2012-01-01
Land cover is the biophysical material on the surface of the earth. Land-cover types include grass, shrubs, trees, barren, water, and man-made features. Land cover changes continuously. The rate of change can be either dramatic and abrupt, such as the changes caused by logging, hurricanes and fire, or subtle and gradual, such as regeneration of forests and damage caused by insects (Verbesselt et al., 2001). Previous studies have shown that land cover has changed dramatically during the past sevearal centuries and that these changes have severely affected our ecosystems (Foody, 2010; Lambin et al., 2001). Lambin and Strahlers (1994b) summarized five types of cause for land-cover changes: (1) long-term natural changes in climate conditions, (2) geomorphological and ecological processes, (3) human-induced alterations of vegetation cover and landscapes, (4) interannual climate variability, and (5) human-induced greenhouse effect. Tools and techniques are needed to detect, describe, and predict these changes to facilitate sustainable management of natural resources.
Cabrieto, Jedelyn; Tuerlinckx, Francis; Kuppens, Peter; Grassmann, Mariel; Ceulemans, Eva
2017-06-01
Change point detection in multivariate time series is a complex task since next to the mean, the correlation structure of the monitored variables may also alter when change occurs. DeCon was recently developed to detect such changes in mean and\\or correlation by combining a moving windows approach and robust PCA. However, in the literature, several other methods have been proposed that employ other non-parametric tools: E-divisive, Multirank, and KCP. Since these methods use different statistical approaches, two issues need to be tackled. First, applied researchers may find it hard to appraise the differences between the methods. Second, a direct comparison of the relative performance of all these methods for capturing change points signaling correlation changes is still lacking. Therefore, we present the basic principles behind DeCon, E-divisive, Multirank, and KCP and the corresponding algorithms, to make them more accessible to readers. We further compared their performance through extensive simulations using the settings of Bulteel et al. (Biological Psychology, 98 (1), 29-42, 2014) implying changes in mean and in correlation structure and those of Matteson and James (Journal of the American Statistical Association, 109 (505), 334-345, 2014) implying different numbers of (noise) variables. KCP emerged as the best method in almost all settings. However, in case of more than two noise variables, only DeCon performed adequately in detecting correlation changes.
Eye Movements and Display Change Detection during Reading
ERIC Educational Resources Information Center
Slattery, Timothy J.; Angele, Bernhard; Rayner, Keith
2011-01-01
In the boundary change paradigm (Rayner, 1975), when a reader's eyes cross an invisible boundary location, a preview word is replaced by a target word. Readers are generally unaware of such changes due to saccadic suppression. However, some readers detect changes on a few trials and a small percentage of them detect many changes. Two experiments…
Neural correlates of change detection and change blindness in a working memory task.
Pessoa, Luiz; Ungerleider, Leslie G
2004-05-01
Detecting changes in an ever-changing environment is highly advantageous, and this ability may be critical for survival. In the present study, we investigated the neural substrates of change detection in the context of a visual working memory task. Subjects maintained a sample visual stimulus in short-term memory for 6 s, and were asked to indicate whether a subsequent, test stimulus matched or did not match the original sample. To study change detection largely uncontaminated by attentional state, we compared correct change and correct no-change trials at test. Our results revealed that correctly detecting a change was associated with activation of a network comprising parietal and frontal brain regions, as well as activation of the pulvinar, cerebellum, and inferior temporal gyrus. Moreover, incorrectly reporting a change when none occurred led to a very similar pattern of activations. Finally, few regions were differentially activated by trials in which a change occurred but subjects failed to detect it (change blindness). Thus, brain activation was correlated with a subject's report of a change, instead of correlated with the physical change per se. We propose that frontal and parietal regions, possibly assisted by the cerebellum and the pulvinar, might be involved in controlling the deployment of attention to the location of a change, thereby allowing further processing of the visual stimulus. Visual processing areas, such as the inferior temporal gyrus, may be the recipients of top-down feedback from fronto-parietal regions that control the reactive deployment of attention, and thus exhibit increased activation when a change is reported (irrespective of whether it occurred or not). Whereas reporting that a change occurred, be it correctly or incorrectly, was associated with strong activation in fronto-parietal sites, change blindness appears to involve very limited territories.
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.
Sensor for detecting changes in magnetic fields
Praeg, Walter F.
1981-01-01
A sensor for detecting changes in the magnetic field of the equilibrium-field coil of a Tokamak plasma device comprises a pair of bifilar wires disposed circumferentially, one inside and one outside the equilibrium-field coil. Each is shorted at one end. The difference between the voltages detected at the other ends of the bifilar wires provides a measure of changing flux in the equilibrium-field coil. This difference can be used to detect faults in the coil in time to take action to protect the coil.
Adhikari, Shyam Prasad; Yang, Changju; Slot, Krzysztof; Kim, Hyongsuk
2018-01-10
This paper presents a vision sensor-based solution to the challenging problem of detecting and following trails in highly unstructured natural environments like forests, rural areas and mountains, using a combination of a deep neural network and dynamic programming. The deep neural network (DNN) concept has recently emerged as a very effective tool for processing vision sensor signals. A patch-based DNN is trained with supervised data to classify fixed-size image patches into "trail" and "non-trail" categories, and reshaped to a fully convolutional architecture to produce trail segmentation map for arbitrary-sized input images. As trail and non-trail patches do not exhibit clearly defined shapes or forms, the patch-based classifier is prone to misclassification, and produces sub-optimal trail segmentation maps. Dynamic programming is introduced to find an optimal trail on the sub-optimal DNN output map. Experimental results showing accurate trail detection for real-world trail datasets captured with a head mounted vision system are presented.
Yamagata, Koichi; Yamanishi, Ayako; Kokubu, Chikara; Takeda, Junji; Sese, Jun
2016-01-01
An important challenge in cancer genomics is precise detection of structural variations (SVs) by high-throughput short-read sequencing, which is hampered by the high false discovery rates of existing analysis tools. Here, we propose an accurate SV detection method named COSMOS, which compares the statistics of the mapped read pairs in tumor samples with isogenic normal control samples in a distinct asymmetric manner. COSMOS also prioritizes the candidate SVs using strand-specific read-depth information. Performance tests on modeled tumor genomes revealed that COSMOS outperformed existing methods in terms of F-measure. We also applied COSMOS to an experimental mouse cell-based model, in which SVs were induced by genome engineering and gamma-ray irradiation, followed by polymerase chain reaction-based confirmation. The precision of COSMOS was 84.5%, while the next best existing method was 70.4%. Moreover, the sensitivity of COSMOS was the highest, indicating that COSMOS has great potential for cancer genome analysis. PMID:26833260
Hollingworth, Andrew; Henderson, John M
2004-07-01
In a change detection paradigm, the global orientation of a natural scene was incrementally changed in 1 degree intervals. In Experiments 1 and 2, participants demonstrated sustained change blindness to incremental rotation, often coming to consider a significantly different scene viewpoint as an unchanged continuation of the original view. Experiment 3 showed that participants who failed to detect the incremental rotation nevertheless reliably detected a single-step rotation back to the initial view. Together, these results demonstrate an important dissociation between explicit change detection and visual memory. Following a change, visual memory is updated to reflect the changed state of the environment, even if the change was not detected.
Davidson, Josy; dos Santos, Amelia Miyashiro N; Garcia, Kessey Maria B; Yi, Liu C; João, Priscila C; Miyoshi, Milton H; Goulart, Ana Lucia
2012-09-01
To analyse the accuracy and reproducibility of photogrammetry in detecting thoracic abnormalities in infants born prematurely. Cross-sectional study. The Premature Clinic at the Federal University of São Paolo. Fifty-eight infants born prematurely in their first year of life. Measurement of the manubrium/acromion/trapezius angle (degrees) and the deepest thoracic retraction (cm). Digitised photographs were analysed by two blinded physiotherapists using a computer program (SAPO; http://SAPO.incubadora.fapesp.br) to detect shoulder elevation and thoracic retraction. Physical examinations performed independently by two physiotherapists were used to assess the accuracy of the new tool. Thoracic alterations were detected in 39 (67%) and in 40 (69%) infants by Physiotherapists 1 and 2, respectively (kappa coefficient=0.80). Using a receiver operating characteristic curve, measurement of the manubrium/acromion/trapezius angle and the deepest thoracic retraction indicated accuracy of 0.79 and 0.91, respectively. For measurement of the manubrium/acromion/trapezius angle, the Bland and Altman limits of agreement were -6.22 to 7.22° [mean difference (d)=0.5] for repeated measures by one physiotherapist, and -5.29 to 5.79° (d=0.75) between two physiotherapists. For thoracic retraction, the intra-rater limits of agreement were -0.14 to 0.18cm (d=0.02) and the inter-rater limits of agreement were -0.20 to -0.17cm (d=0.02). SAPO provided an accurate and reliable tool for the detection of thoracic abnormalities in preterm infants. Copyright © 2011 Chartered Society of Physiotherapy. Published by Elsevier Ltd. All rights reserved.
Change-point detection of induced and natural seismicity
NASA Astrophysics Data System (ADS)
Fiedler, B.; Holschneider, M.; Zoeller, G.; Hainzl, S.
2016-12-01
Earthquake rates are influenced by tectonic stress buildup, earthquake-induced stress changes, and transient aseismic sources. While the first two sources can be well modeled due to the fact that the source is known, transient aseismic processes are more difficult to detect. However, the detection of the associated changes of the earthquake activity is of great interest, because it might help to identify natural aseismic deformation patterns (such as slow slip events) and the occurrence of induced seismicity related to human activities. We develop a Bayesian approach to detect change-points in seismicity data which are modeled by Poisson processes. By means of a Likelihood-Ratio-Test, we proof the significance of the change of the intensity. The model is also extended to spatiotemporal data to detect the area of the transient changes. The method is firstly tested for synthetic data and then applied to observational data from central US and the Bardarbunga volcano in Iceland.
Sensor for detecting changes in magnetic fields
Praeg, W.F.
1980-02-26
A sensor is described for detecting changes in the magnetic field of the equilibrium-field coil of a Tokamak plasma device that comprises a pair of bifilar wires disposed circumferentially, one inside and one outside the equilibrium-field coil. Each is shorted at one end. The difference between the voltages detected at the other ends of the bifilar wires provides a measure of changing flux in the equilibrium-field coil. This difference can be used to detect faults in the coil in time to take action to protect the coil.
NASA Astrophysics Data System (ADS)
Loureiro, A. D.; Gomes, L. M.; Ventura, L.
2018-02-01
The international standard ISO 12312-1 proposes transmittance tests that quantify how dark sunglasses lenses are and whether or not they are suitable for driving. To perform these tests a spectrometer is required. In this study, we present and analyze theoretically an accurate alternative method for performing these measurements using simple components. Using three LEDs and a four-channel sensor we generated weighting functions similar to the standard ones for luminous and traffic lights transmittances. From 89 sunglasses lens spectroscopy data, we calculated luminous transmittance and signal detection quotients using our obtained weighting functions and the standard ones. Mean-difference Tukey plots were used to compare the results. All tested sunglasses lenses were classified in the right category and correctly as suitable or not for driving. The greatest absolute errors for luminous transmittance and red, yellow, green and blue signal detection quotients were 0.15%, 0.17, 0.06, 0.04 and 0.18, respectively. This method will be used in a device capable to perform transmittance tests (visible, traffic lights and ultraviolet (UV)) according to the standard. It is important to measure rightly luminous transmittance and relative visual attenuation quotients to report correctly whether or not sunglasses are suitable for driving. Moreover, standard UV requirements depend on luminous transmittance.
Fiedler, Klaus; Kareev, Yaakov; Avrahami, Judith; Beier, Susanne; Kutzner, Florian; Hütter, Mandy
2016-01-01
Detecting changes, in performance, sales, markets, risks, social relations, or public opinions, constitutes an important adaptive function. In a sequential paradigm devised to investigate detection of change, every trial provides a sample of binary outcomes (e.g., correct vs. incorrect student responses). Participants have to decide whether the proportion of a focal feature (e.g., correct responses) in the population from which the sample is drawn has decreased, remained constant, or increased. Strong and persistent anomalies in change detection arise when changes in proportional quantities vary orthogonally to changes in absolute sample size. Proportional increases are readily detected and nonchanges are erroneously perceived as increases when absolute sample size increases. Conversely, decreasing sample size facilitates the correct detection of proportional decreases and the erroneous perception of nonchanges as decreases. These anomalies are however confined to experienced samples of elementary raw events from which proportions have to be inferred inductively. They disappear when sample proportions are described as percentages in a normalized probability format. To explain these challenging findings, it is essential to understand the inductive-learning constraints imposed on decisions from experience.
Acoustic change detection algorithm using an FM radio
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.
Accurate Monitoring and Fault Detection in Wind Measuring Devices through Wireless Sensor Networks
Khan, Komal Saifullah; Tariq, Muhammad
2014-01-01
Many wind energy projects report poor performance as low as 60% of the predicted performance. The reason for this is poor resource assessment and the use of new untested technologies and systems in remote locations. Predictions about the potential of an area for wind energy projects (through simulated models) may vary from the actual potential of the area. Hence, introducing accurate site assessment techniques will lead to accurate predictions of energy production from a particular area. We solve this problem by installing a Wireless Sensor Network (WSN) to periodically analyze the data from anemometers installed in that area. After comparative analysis of the acquired data, the anemometers transmit their readings through a WSN to the sink node for analysis. The sink node uses an iterative algorithm which sequentially detects any faulty anemometer and passes the details of the fault to the central system or main station. We apply the proposed technique in simulation as well as in practical implementation and study its accuracy by comparing the simulation results with experimental results to analyze the variation in the results obtained from both simulation model and implemented model. Simulation results show that the algorithm indicates faulty anemometers with high accuracy and low false alarm rate when as many as 25% of the anemometers become faulty. Experimental analysis shows that anemometers incorporating this solution are better assessed and performance level of implemented projects is increased above 86% of the simulated models. PMID:25421739
Multislice Computed Tomography Accurately Detects Stenosis in Coronary Artery Bypass Conduits
Duran, Cihan; Sagbas, Ertan; Caynak, Baris; Sanisoglu, Ilhan; Akpinar, Belhhan; Gulbaran, Murat
2007-01-01
The aim of this study was to evaluate the accuracy of multislice computed tomography in detecting graft stenosis or occlusion after coronary artery bypass grafting, using coronary angiography as the standard. From January 2005 through May 2006, 25 patients (19 men and 6 women; mean age, 54 ± 11.3 years) underwent diagnostic investigation of their bypass grafts by multislice computed tomography within 1 month of coronary angiography. The mean time elapsed after coronary artery bypass grafting was 6.2 years. In these 25 patients, we examined 65 bypass conduits (24 arterial and 41 venous) and 171 graft segments (the shaft, proximal anastomosis, and distal anastomosis). Compared with coronary angiography, the segment-based sensitivity, specificity, and positive and negative predictive values of multislice computed tomography in the evaluation of stenosis were 89%, 100%, 100%, and 99%, respectively. The patency rate for multislice compu-ted tomography was 85% (55/65: 3 arterial and 7 venous grafts were occluded), with 100% sensitivity and specificity. From these data, we conclude that multislice computed tomography can accurately evaluate the patency and stenosis of bypass grafts during outpatient follow-up. PMID:17948078
A new maximum-likelihood change estimator for two-pass SAR coherent change detection
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
Nateghi, Ramin; Danyali, Habibollah; Helfroush, Mohammad Sadegh
2017-08-14
Based on the Nottingham criteria, the number of mitosis cells in histopathological slides is an important factor in diagnosis and grading of breast cancer. For manual grading of mitosis cells, histopathology slides of the tissue are examined by pathologists at 40× magnification for each patient. This task is very difficult and time-consuming even for experts. In this paper, a fully automated method is presented for accurate detection of mitosis cells in histopathology slide images. First a method based on maximum-likelihood is employed for segmentation and extraction of mitosis cell. Then a novel Maximized Inter-class Weighted Mean (MIWM) method is proposed that aims at reducing the number of extracted non-mitosis candidates that results in reducing the false positive mitosis detection rate. Finally, segmented candidates are classified into mitosis and non-mitosis classes by using a support vector machine (SVM) classifier. Experimental results demonstrate a significant improvement in accuracy of mitosis cells detection in different grades of breast cancer histopathological images.
Planetary-scale surface water detection from space
NASA Astrophysics Data System (ADS)
Donchyts, G.; Baart, F.; Winsemius, H.; Gorelick, N.
2017-12-01
Accurate, efficient and high-resolution methods of surface water detection are needed for a better water management. Datasets on surface water extent and dynamics are crucial for a better understanding of natural and human-made processes, and as an input data for hydrological and hydraulic models. In spite of considerable progress in the harmonization of freely available satellite data, producing accurate and efficient higher-level surface water data products remains very challenging. This presentation will provide an overview of existing methods for surface water extent and change detection from multitemporal and multi-sensor satellite imagery. An algorithm to detect surface water changes from multi-temporal satellite imagery will be demonstrated as well as its open-source implementation (http://aqua-monitor.deltares.nl). This algorithm was used to estimate global surface water changes at high spatial resolution. These changes include climate change, land reclamation, reservoir construction/decommissioning, erosion/accretion, and many other. This presentation will demonstrate how open satellite data and open platforms such as Google Earth Engine have helped with this research.
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.
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
Time series change detection: Algorithms for land cover change
NASA Astrophysics Data System (ADS)
Boriah, Shyam
can be used for decision making and policy planning purposes. In particular, previous change detection studies have primarily relied on examining differences between two or more satellite images acquired on different dates. Thus, a technological solution that detects global land cover change using high temporal resolution time series data will represent a paradigm-shift in the field of land cover change studies. To realize these ambitious goals, a number of computational challenges in spatio-temporal data mining need to be addressed. Specifically, analysis and discovery approaches need to be cognizant of climate and ecosystem data characteristics such as seasonality, non-stationarity/inter-region variability, multi-scale nature, spatio-temporal autocorrelation, high-dimensionality and massive data size. This dissertation, a step in that direction, translates earth science challenges to computer science problems, and provides computational solutions to address these problems. In particular, three key technical capabilities are developed: (1) Algorithms for time series change detection that are effective and can scale up to handle the large size of earth science data; (2) Change detection algorithms that can handle large numbers of missing and noisy values present in satellite data sets; and (3) Spatio-temporal analysis techniques to identify the scale and scope of disturbance events.
Hood, D C; Harizman, N; Kanadani, F N; Grippo, T M; Baharestani, S; Greenstein, V C; Liebmann, J M; Ritch, R
2007-07-01
To assess the accuracy of optical coherence tomography (OCT) in detecting damage to a hemifield, patients with hemifield defects confirmed on both static automated perimetry (SAP) and multifocal visual evoked potentials (mfVEP) were studied. Eyes of 40 patients with concomitant SAP and mfVEP glaucomatous loss and 25 controls underwent OCT retinal nerve fibre layer (RNFL), mfVEP and 24-2 SAP tests. For the mfVEP and 24-2 SAP, a hemifield was defined as abnormal based upon cluster criteria. On OCT, a hemifield was considered abnormal if one of the five clock hour sectors (3 and 9 o'clock excluded) was at <1% (red) or two were at <5% (yellow). Seventy seven (43%) of the hemifields were abnormal on both mfVEP and SAP tests. The OCT was abnormal for 73 (95%) of these. Only 1 (1%) of the 100 hemifields of the controls was abnormal on OCT. Sensitivity/specificity (one eye per person) was 95/98%. The OCT RNFL test accurately detects abnormal hemifields confirmed on both subjective and objective functional tests. Identifying abnormal hemifields with a criterion of 1 red (1%) or 2 yellow (5%) clock hours may prove useful in clinical practice.
Detecting changes in dynamic and complex acoustic environments
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
Yang, Cheng-Ta
2011-12-01
Change detection requires perceptual comparison and decision processes on different features of multiattribute objects. How relative salience between two feature-changes influences the processes has not been addressed. This study used the systems factorial technology to investigate the processes when detecting changes in a Gabor patch with visual inputs from orientation and spatial frequency channels. Two feature-changes were equally salient in Experiment 1, but a frequency-change was more salient than an orientation-change in Experiment 2. Results showed that all four observers adopted parallel self-terminating processing with limited- to unlimited-capacity processing in Experiment 1. In Experiment 2, one observer used parallel self-terminating processing with unlimited-capacity processing, and the others adopted serial self-terminating processing with limited- to unlimited-capacity processing to detect changes. Postexperimental interview revealed that subjective utility of feature information underlay the adoption of a decision strategy. These results highlight that observers alter decision strategies in change detection depending on the relative saliency in change signals, with relative saliency being determined by both physical salience and subjective weight of feature information. When relative salience exists, individual differences in the process characteristics emerge.
ERIC Educational Resources Information Center
Yang, Cheng-Ta
2011-01-01
Change detection requires perceptual comparison and decision processes on different features of multiattribute objects. How relative salience between two feature-changes influences the processes has not been addressed. This study used the systems factorial technology to investigate the processes when detecting changes in a Gabor patch with visual…
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.
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.
Detection of ocean color changes from high altitudes
NASA Technical Reports Server (NTRS)
Hovis, W. A.; Forman, M. L.; Blaine, L. R.
1973-01-01
The detection of ocean color changes, thought to be due to chlorophyll concentrations and gelbstoffe variations, is attempted from high altitude (11.3km) and low altitude (0.3km). The atmospheric back scattering is shown to reduce contrast, but not sufficiently to obscure color change detection at high altitudes.
Zheng, Yun; Ji, Bo; Song, Renhua; Wang, Shengpeng; Li, Ting; Zhang, Xiaotuo; Chen, Kun; Li, Tianqing; Li, Jinyan
2016-01-01
Various types of mutation and editing (M/E) events in microRNAs (miRNAs) can change the stabilities of pre-miRNAs and/or complementarities between miRNAs and their targets. Small RNA (sRNA) high-throughput sequencing (HTS) profiles can contain many mutated and edited miRNAs. Systematic detection of miRNA mutation and editing sites from the huge volume of sRNA HTS profiles is computationally difficult, as high sensitivity and low false positive rate (FPR) are both required. We propose a novel method (named MiRME) for an accurate and fast detection of miRNA M/E sites using a progressive sequence alignment approach which refines sensitivity and improves FPR step-by-step. From 70 sRNA HTS profiles with over 1.3 billion reads, MiRME has detected thousands of statistically significant M/E sites, including 3′-editing sites, 57 A-to-I editing sites (of which 32 are novel), as well as some putative non-canonical editing sites. We demonstrated that a few non-canonical editing sites were not resulted from mutations in genome by integrating the analysis of genome HTS profiles of two human cell lines, suggesting the existence of new editing types to further diversify the functions of miRNAs. Compared with six existing studies or methods, MiRME has shown much superior performance for the identification and visualization of the M/E sites of miRNAs from the ever-increasing sRNA HTS profiles. PMID:27229138
Yamagata, Koichi; Yamanishi, Ayako; Kokubu, Chikara; Takeda, Junji; Sese, Jun
2016-05-05
An important challenge in cancer genomics is precise detection of structural variations (SVs) by high-throughput short-read sequencing, which is hampered by the high false discovery rates of existing analysis tools. Here, we propose an accurate SV detection method named COSMOS, which compares the statistics of the mapped read pairs in tumor samples with isogenic normal control samples in a distinct asymmetric manner. COSMOS also prioritizes the candidate SVs using strand-specific read-depth information. Performance tests on modeled tumor genomes revealed that COSMOS outperformed existing methods in terms of F-measure. We also applied COSMOS to an experimental mouse cell-based model, in which SVs were induced by genome engineering and gamma-ray irradiation, followed by polymerase chain reaction-based confirmation. The precision of COSMOS was 84.5%, while the next best existing method was 70.4%. Moreover, the sensitivity of COSMOS was the highest, indicating that COSMOS has great potential for cancer genome analysis. © The Author(s) 2016. Published by Oxford University Press on behalf of Nucleic Acids Research.
A Hopfield neural network for image change detection.
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.
Kjölhede, Henrik; Bratt, Ola; Gudjonsson, Sigurdur; Sundqvist, Pernilla; Liedberg, Fredrik
2015-04-01
The reference standard for lymph-node staging in prostate cancer is currently an extended pelvic lymph-node dissection (ePLND), which detects most, but not all, regional lymph-node metastases. As an alternative to ePLND, sentinel-node dissection with preoperative isotope injection and imaging has been reported. The objective was to determine whether intraoperative sentinel-node detection with a simplified protocol can accurately determine lymph-node stage in prostate cancer patients. Patients with biopsy-verified high-risk prostate cancer with tumour stage T2-3 were included in the study. All patients underwent both ePLND and sentinel-node detection. (99m)Tc-marked nanocolloid was injected peritumourally by the operating urologist after induction of anaesthesia just before surgery. Sentinel nodes were detected both in vivo and ex vivo intraoperatively using a gamma probe. Sentinel nodes and metastases and their locations were recorded. Sensitivity and specificity were calculated. At least one sentinel node was detected in 72 (87%) of the 83 patients. In 13 (18%) of these 72 patients sentinel nodes were detected outside the ePLND template. In six of these 13 patients, the Sentinel nodes from outside the template contained metastases, which proved to be the only metastases in two. For 12 patients the only metastatic deposit found was a micrometastasis (≤2 mm) in a sentinel node. In the 72 patients with detectable sentinel nodes, pathological analysis of the sentinel node correctly categorized 71 and ePLND 70 patients. This protocol yielded results comparable to the commonly used technique of sentinel-node detection, but with more cases of non-detection.
NASA Astrophysics Data System (ADS)
Wei, Minsong; Xing, Fei; You, Zheng
2017-01-01
The advancing growth of micro- and nano-satellites requires miniaturized sun sensors which could be conveniently applied in the attitude determination subsystem. In this work, a profile detecting technology based high accurate wireless digital sun sensor was proposed, which could transform a two-dimensional image into two-linear profile output so that it can realize a high update rate under a very low power consumption. A multiple spots recovery approach with an asymmetric mask pattern design principle was introduced to fit the multiplexing image detector method for accuracy improvement of the sun sensor within a large Field of View (FOV). A FOV determination principle based on the concept of FOV region was also proposed to facilitate both sub-FOV analysis and the whole FOV determination. A RF MCU, together with solar cells, was utilized to achieve the wireless and self-powered functionality. The prototype of the sun sensor is approximately 10 times lower in size and weight compared with the conventional digital sun sensor (DSS). Test results indicated that the accuracy of the prototype was 0.01° within a cone FOV of 100°. Such an autonomous DSS could be equipped flexibly on a micro- or nano-satellite, especially for highly accurate remote sensing applications.
Insar Unwrapping Error Correction Based on Quasi-Accurate Detection of Gross Errors (quad)
NASA Astrophysics Data System (ADS)
Kang, Y.; Zhao, C. Y.; Zhang, Q.; Yang, C. S.
2018-04-01
Unwrapping error is a common error in the InSAR processing, which will seriously degrade the accuracy of the monitoring results. Based on a gross error correction method, Quasi-accurate detection (QUAD), the method for unwrapping errors automatic correction is established in this paper. This method identifies and corrects the unwrapping errors by establishing a functional model between the true errors and interferograms. The basic principle and processing steps are presented. Then this method is compared with the L1-norm method with simulated data. Results show that both methods can effectively suppress the unwrapping error when the ratio of the unwrapping errors is low, and the two methods can complement each other when the ratio of the unwrapping errors is relatively high. At last the real SAR data is tested for the phase unwrapping error correction. Results show that this new method can correct the phase unwrapping errors successfully in the practical application.
Optimal use of land surface temperature data to detect changes in tropical forest cover
NASA Astrophysics Data System (ADS)
van Leeuwen, Thijs T.; Frank, Andrew J.; Jin, Yufang; Smyth, Padhraic; Goulden, Michael L.; van der Werf, Guido R.; Randerson, James T.
2011-06-01
Rapid and accurate assessment of global forest cover change is needed to focus conservation efforts and to better understand how deforestation is contributing to the buildup of atmospheric CO2. Here we examined different ways to use land surface temperature (LST) to detect changes in tropical forest cover. In our analysis we used monthly 0.05° × 0.05° Terra Moderate Resolution Imaging Spectroradiometer (MODIS) observations of LST and Program for the Estimation of Deforestation in the Brazilian Amazon (PRODES) estimates of forest cover change. We also compared MODIS LST observations with an independent estimate of forest cover loss derived from MODIS and Landsat observations. Our study domain of approximately 10° × 10° included the Brazilian state of Mato Grosso. For optimal use of LST data to detect changes in tropical forest cover in our study area, we found that using data sampled during the end of the dry season (˜1-2 months after minimum monthly precipitation) had the greatest predictive skill. During this part of the year, precipitation was low, surface humidity was at a minimum, and the difference between day and night LST was the largest. We used this information to develop a simple temporal sampling algorithm appropriate for use in pantropical deforestation classifiers. Combined with the normalized difference vegetation index, a logistic regression model using day-night LST did moderately well at predicting forest cover change. Annual changes in day-night LST decreased during 2006-2009 relative to 2001-2005 in many regions within the Amazon, providing independent confirmation of lower deforestation levels during the latter part of this decade as reported by PRODES.
Hood, D C; Harizman, N; Kanadani, F N; Grippo, T M; Baharestani, S; Greenstein, V C; Liebmann, J M; Ritch, R
2007-01-01
Aim To assess the accuracy of optical coherence tomography (OCT) in detecting damage to a hemifield, patients with hemifield defects confirmed on both static automated perimetry (SAP) and multifocal visual evoked potentials (mfVEP) were studied. Methods Eyes of 40 patients with concomitant SAP and mfVEP glaucomatous loss and 25 controls underwent OCT retinal nerve fibre layer (RNFL), mfVEP and 24‐2 SAP tests. For the mfVEP and 24‐2 SAP, a hemifield was defined as abnormal based upon cluster criteria. On OCT, a hemifield was considered abnormal if one of the five clock hour sectors (3 and 9 o'clock excluded) was at <1% (red) or two were at <5% (yellow). Results Seventy seven (43%) of the hemifields were abnormal on both mfVEP and SAP tests. The OCT was abnormal for 73 (95%) of these. Only 1 (1%) of the 100 hemifields of the controls was abnormal on OCT. Sensitivity/specificity (one eye per person) was 95/98%. Conclusions The OCT RNFL test accurately detects abnormal hemifields confirmed on both subjective and objective functional tests. Identifying abnormal hemifields with a criterion of 1 red (1%) or 2 yellow (5%) clock hours may prove useful in clinical practice. PMID:17301118
Change Detection in Naturalistic Pictures among Children with Autism
ERIC Educational Resources Information Center
Burack, Jacob A.; Joseph, Shari; Russo, Natalie; Shore, David I.; Porporino, Mafalda; Enns, James T.
2009-01-01
Persons with autism often show strong reactions to changes in the environment, suggesting that they may detect changes more efficiently than typically developing (TD) persons. However, Fletcher-Watson et al. (Br J Psychol 97:537-554, 2006) reported no differences between adults with autism and TD adults with a change-detection task. In this study,…
Detection and Attribution of Anthropogenic Climate Change Impacts
NASA Technical Reports Server (NTRS)
Rosenzweig, Cynthia; Neofotis, Peter
2013-01-01
Human-influenced climate change is an observed phenomenon affecting physical and biological systems across the globe. The majority of observed impacts are related to temperature changes and are located in the northern high- and midlatitudes. However, new evidence is emerging that demonstrates that impacts are related to precipitation changes as well as temperature, and that climate change is impacting systems and sectors beyond the Northern Hemisphere. In this paper, we highlight some of this new evidence-focusing on regions and sectors that the Intergovernmental Panel on Climate Change Fourth Assessment Report (IPCC AR4) noted as under-represented-in the context of observed climate change impacts, direct and indirect drivers of change (including carbon dioxide itself), and methods of detection. We also present methods and studies attributing observed impacts to anthropogenic forcing. We argue that the expansion of methods of detection (in terms of a broader array of climate variables and data sources, inclusion of the major modes of climate variability, and incorporation of other drivers of change) is key to discerning the climate sensitivities of sectors and systems in regions where the impacts of climate change currently remain elusive. Attributing such changes to human forcing of the climate system, where possible, is important for development of effective mitigation and adaptation. Current challenges in documenting adaptation and the role of indigenous knowledge in detection and attribution are described.
Towards a Framework for Change Detection in Data Sets
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.
Experiments in Coherent Change Detection for Synthetic Aperture Sonar
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
Investigation of Coherent and Incoherent Change Detection Algorithms
2017-12-01
Office of Management and Budget, Paperwork Reduction Project (0704-0188) Washington DC 20503. 1. AGENCY USE ONLY (Leave blank) 2. REPORT DATE December...Data Management System (SDMS) in order to compare the various change detection techniques. These change detection methods include the following: a...SAR) is presented in this thesis. This investigation utilizes data gathered from the Air Force Research Laboratory (AFRL) Sensor Data Management
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.
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.
On the pilot's behavior of detecting a system parameter change
NASA Technical Reports Server (NTRS)
Morizumi, N.; Kimura, H.
1986-01-01
The reaction of a human pilot, engaged in compensatory control, to a sudden change in the controlled element's characteristics is described. Taking the case where the change manifests itself as a variance change of the monitored signal, it is shown that the detection time, defined to be the time elapsed until the pilot detects the change, is related to the monitored signal and its derivative. Then, the detection behavior is modeled by an optimal controller, an optimal estimator, and a variance-ratio test mechanism that is performed for the monitored signal and its derivative. Results of a digital simulation show that the pilot's detection behavior can be well represented by the model proposed here.
Geomorphological change detection of fluvial processes of lower Siret channel using LIDAR data
NASA Astrophysics Data System (ADS)
Niculita, Mihai; Obreja, Florin; Boca, Bogdan
2015-04-01
:121-134. Lague D., Brodu N., Leroux J., 2013. Accurate 3D comparison of complex topography with terrestrial laser scanner: application to the Rangitikei canyon (N-Z), ISPRS journal of Photogrammmetry and Remote Sensing, 80:10-26. James L.A., Hodgson M.E., Ghoshal S., Latiolais M.M., 2012. Geomorphic change detection using historic maps and DEM differencing: the temporal dimension of geospatial analysis. Geomorphology, 137:181-198. Nedelcu G., Borcan M., Branescu E., Petre C., Teleanu B., Preda A., Murafa R., 2011. Exceptional floods from the years 2008 and 2010 in Siret river basin, Proceedings of the Annual Scientific Conference of National Romanian Institute of Hydrology and Water Administration, 1-3 November 2011. (in Romanian) Olariu P., Obreja F., Obreja I., 2009. Some aspects regarding the sediment transit from Trotus catchment and lower sector of Siret river during the exceptional floods from 1991 and 2005, Annals of Stefan cel Mare University of Suceava, XVIII:93-104.(in Romanian) Serbu M., Obreja F., Olariu P., 2009. The 2008 floods from upper Siret catchment. Causes, effects, evaluation, Hidrotechnics, 54(12):1-38. (in Romanian) Wheaton J.M., Brasington J., Darby S., Sear D., 2009. Accounting for uncertainty in DEMs from repeat topographic surveys: improved sediment budgets. Earth Surface Processes and Landforms, 35(2):136-156.
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.
Optimal use of land surface temperature data to detect changes in tropical forest cover
NASA Astrophysics Data System (ADS)
Van Leeuwen, T. T.; Frank, A. J.; Jin, Y.; Smyth, P.; Goulden, M.; van der Werf, G.; Randerson, J. T.
2011-12-01
Rapid and accurate assessment of global forest cover change is needed to focus conservation efforts and to better understand how deforestation is contributing to the build up of atmospheric CO2. Here we examined different ways to use remotely sensed land surface temperature (LST) to detect changes in tropical forest cover. In our analysis we used monthly 0.05×0.05 degree Terra MODerate Resolution Imaging Spectroradiometer (MODIS) observations of LST and PRODES (Program for the Estimation of Deforestation in the Brazilian Amazon) estimates of forest cover change. We also compared MODIS LST observations with an independent estimate of forest cover loss derived from MODIS and Landsat observations. Our study domain of approximately 10×10 degree included most of the Brazilian state of Mato Grosso. For optimal use of LST data to detect changes in tropical forest cover in our study area, we found that using data sampled during the end of the dry season (~1-2 months after minimum monthly precipitation) had the greatest predictive skill. During this part of the year, precipitation was low, surface humidity was at a minimum, and the difference between day and night LST was the largest. We used this information to develop a simple temporal sampling algorithm appropriate for use in pan-tropical deforestation classifiers. Combined with the normalized difference vegetation index (NDVI), a logistic regression model using day-night LST did moderately well at predicting forest cover change. Annual changes in day-night LST difference decreased during 2006-2009 relative to 2001-2005 in many regions within the Amazon, providing independent confirmation of lower deforestation levels during the latter part of this decade as reported by PRODES. The use of day-night LST differences may be particularly valuable for use with satellites that do not have spectral bands that allow for the estimation of NDVI or other vegetation indices.
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
Supporting dynamic change detection: using the right tool for the task.
Vallières, Benoît R; Hodgetts, Helen M; Vachon, François; Tremblay, Sébastien
2016-01-01
Detecting task-relevant changes in a visual scene is necessary for successfully monitoring and managing dynamic command and control situations. Change blindness-the failure to notice visual changes-is an important source of human error. Change History EXplicit (CHEX) is a tool developed to aid change detection and maintain situation awareness; and in the current study we test the generality of its ability to facilitate the detection of changes when this subtask is embedded within a broader dynamic decision-making task. A multitasking air-warfare simulation required participants to perform radar-based subtasks, for which change detection was a necessary aspect of the higher-order goal of protecting one's own ship. In this task, however, CHEX rendered the operator even more vulnerable to attentional failures in change detection and increased perceived workload. Such support was only effective when participants performed a change detection task without concurrent subtasks. Results are interpreted in terms of the NSEEV model of attention behavior (Steelman, McCarley, & Wickens, Hum. Factors 53:142-153, 2011; J. Exp. Psychol. Appl. 19:403-419, 2013), and suggest that decision aids for use in multitasking contexts must be designed to fit within the available workload capacity of the user so that they may truly augment cognition.
Nonexplicit change detection in complex dynamic settings: what eye movements reveal.
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.
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.
Attentional Modulation of Change Detection ERP Components by Peripheral Retro-Cueing
Pazo-Álvarez, Paula; Roca-Fernández, Adriana; Gutiérrez-Domínguez, Francisco-Javier; Amenedo, Elena
2017-01-01
Change detection is essential for visual perception and performance in our environment. However, observers often miss changes that should be easily noticed. A failure in any of the processes involved in conscious detection (encoding the pre-change display, maintenance of that information within working memory, and comparison of the pre and post change displays) can lead to change blindness. Given that unnoticed visual changes in a scene can be easily detected once attention is drawn to them, it has been suggested that attention plays an important role on visual awareness. In the present study, we used behavioral and electrophysiological (ERPs) measures to study whether the manipulation of retrospective spatial attention affects performance and modulates brain activity related to the awareness of a change. To that end, exogenous peripheral cues were presented during the delay period (retro-cues) between the first and the second array using a one-shot change detection task. Awareness of a change was associated with a posterior negative amplitude shift around 228–292 ms (“Visual Awareness Negativity”), which was independent of retrospective spatial attention, as it was elicited to both validly and invalidly cued change trials. Change detection was also associated with a larger positive deflection around 420–580 ms (“Late Positivity”), but only when the peripheral retro-cues correctly predicted the change. Present results confirm that the early and late ERP components related to change detection can be functionally dissociated through manipulations of exogenous retro-cueing using a change blindness paradigm. PMID:28270759
Brain correlates of automatic visual change detection.
Cléry, H; Andersson, F; Fonlupt, P; Gomot, M
2013-07-15
A number of studies support the presence of visual automatic detection of change, but little is known about the brain generators involved in such processing and about the modulation of brain activity according to the salience of the stimulus. The study presented here was designed to locate the brain activity elicited by unattended visual deviant and novel stimuli using fMRI. Seventeen adult participants were presented with a passive visual oddball sequence while performing a concurrent visual task. Variations in BOLD signal were observed in the modality-specific sensory cortex, but also in non-specific areas involved in preattentional processing of changing events. A degree-of-deviance effect was observed, since novel stimuli elicited more activity in the sensory occipital regions and at the medial frontal site than small changes. These findings could be compared to those obtained in the auditory modality and might suggest a "general" change detection process operating in several sensory modalities. Copyright © 2013 Elsevier Inc. All rights reserved.
Nishiyama, Megumi; Kawaguchi, Jun
2014-11-01
To clarify the relationship between visual long-term memory (VLTM) and online visual processing, we investigated whether and how VLTM involuntarily affects the performance of a one-shot change detection task using images consisting of six meaningless geometric objects. In the study phase, participants observed pre-change (Experiment 1), post-change (Experiment 2), or both pre- and post-change (Experiment 3) images appearing in the subsequent change detection phase. In the change detection phase, one object always changed between pre- and post-change images and participants reported which object was changed. Results showed that VLTM of pre-change images enhanced the performance of change detection, while that of post-change images decreased accuracy. Prior exposure to both pre- and post-change images did not influence performance. These results indicate that pre-change information plays an important role in change detection, and that information in VLTM related to the current task does not always have a positive effect on performance. Copyright © 2014 Elsevier Inc. All rights reserved.
Matthews, Stephen G; Miller, Amy L; Clapp, James; Plötz, Thomas; Kyriazakis, Ilias
2016-11-01
Early detection of health and welfare compromises in commercial piggeries is essential for timely intervention to enhance treatment success, reduce impact on welfare, and promote sustainable pig production. Behavioural changes that precede or accompany subclinical and clinical signs may have diagnostic value. Often referred to as sickness behaviour, this encompasses changes in feeding, drinking, and elimination behaviours, social behaviours, and locomotion and posture. Such subtle changes in behaviour are not easy to quantify and require lengthy observation input by staff, which is impractical on a commercial scale. Automated early-warning systems may provide an alternative by objectively measuring behaviour with sensors to automatically monitor and detect behavioural changes. This paper aims to: (1) review the quantifiable changes in behaviours with potential diagnostic value; (2) subsequently identify available sensors for measuring behaviours; and (3) describe the progress towards automating monitoring and detection, which may allow such behavioural changes to be captured, measured, and interpreted and thus lead to automation in commercial, housed piggeries. Multiple sensor modalities are available for automatic measurement and monitoring of behaviour, which require humans to actively identify behavioural changes. This has been demonstrated for the detection of small deviations in diurnal drinking, deviations in feeding behaviour, monitoring coughs and vocalisation, and monitoring thermal comfort, but not social behaviour. However, current progress is in the early stages of developing fully automated detection systems that do not require humans to identify behavioural changes; e.g., through automated alerts sent to mobile phones. Challenges for achieving automation are multifaceted and trade-offs are considered between health, welfare, and costs, between analysis of individuals and groups, and between generic and compromise-specific behaviours. Copyright © 2016
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.
A Dual-Process Account of Auditory Change Detection
ERIC Educational Resources Information Center
McAnally, Ken I.; Martin, Russell L.; Eramudugolla, Ranmalee; Stuart, Geoffrey W.; Irvine, Dexter R. F.; Mattingley, Jason B.
2010-01-01
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…
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
Detection of cardiac activity changes from human speech
NASA Astrophysics Data System (ADS)
Tovarek, Jaromir; Partila, Pavol; Voznak, Miroslav; Mikulec, Martin; Mehic, Miralem
2015-05-01
Impact of changes in blood pressure and pulse from human speech is disclosed in this article. The symptoms of increased physical activity are pulse, systolic and diastolic pressure. There are many methods of measuring and indicating these parameters. The measurements must be carried out using devices which are not used in everyday life. In most cases, the measurement of blood pressure and pulse following health problems or other adverse feelings. Nowadays, research teams are trying to design and implement modern methods in ordinary human activities. The main objective of the proposal is to reduce the delay between detecting the adverse pressure and to the mentioned warning signs and feelings. Common and frequent activity of man is speaking, while it is known that the function of the vocal tract can be affected by the change in heart activity. Therefore, it can be a useful parameter for detecting physiological changes. A method for detecting human physiological changes by speech processing and artificial neural network classification is described in this article. The pulse and blood pressure changes was induced by physical exercises in this experiment. The set of measured subjects was formed by ten healthy volunteers of both sexes. None of the subjects was a professional athlete. The process of the experiment was divided into phases before, during and after physical training. Pulse, systolic, diastolic pressure was measured and voice activity was recorded after each of them. The results of this experiment describe a method for detecting increased cardiac activity from human speech using artificial neural network.
PCA feature extraction for change detection in multidimensional unlabeled data.
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.
Detection of changes in leaf water content using near- and middle-infrared reflectances
NASA Technical Reports Server (NTRS)
Hunt, E. Raymond, Jr.; Rock, Barrett N.
1989-01-01
A method to detect plant water stress by remote sensing is proposed using indices of near-IR and mid-IR wavelengths. The ability of the Leaf Water Content Index (LWCI) to determine leaf relative water content (RWC) is tested on species with different leaf morphologies. The way in which the Misture Stress Index (MSI) varies with RWC is studied. On test with several species, it is found that LWCI is equal to RWC, although the reflectances at 1.6 microns for two different RWC must be known to accurately predict unknown RWC. A linear correlation is found between MSI and RWC with each species having a different regression equation. Also, MSI is correlated with log sub 10 Equivalent Water Thickness (EWT) with data for all species falling on the same regression line. It is found that the minimum significant change of RWC that could be detected by appying the linear regression equation of MSI to EWT is 52 percent. Because the natural RWC variation from water stress is about 20 percent for most species, it is concluded that the near-IR and mid-IR reflectances cannot be used to remotely sense water stress.
David, Simon; Visvikis, Dimitris; Quellec, Gwénolé; Le Rest, Catherine Cheze; Fernandez, Philippe; Allard, Michèle; Roux, Christian; Hatt, Mathieu
2012-09-01
In clinical oncology, positron emission tomography (PET) imaging can be used to assess therapeutic response by quantifying the evolution of semi-quantitative values such as standardized uptake value, early during treatment or after treatment. Current guidelines do not include metabolically active tumor volume (MATV) measurements and derived parameters such as total lesion glycolysis (TLG) to characterize the response to the treatment. To achieve automatic MATV variation estimation during treatment, we propose an approach based on the change detection principle using the recent paradoxical theory, which models imprecision, uncertainty, and conflict between sources. It was applied here simultaneously to pre- and post-treatment PET scans. The proposed method was applied to both simulated and clinical datasets, and its performance was compared to adaptive thresholding applied separately on pre- and post-treatment PET scans. On simulated datasets, the adaptive threshold was associated with significantly higher classification errors than the developed approach. On clinical datasets, the proposed method led to results more consistent with the known partial responder status of these patients. The method requires accurate rigid registration of both scans which can be obtained only in specific body regions and does not explicitly model uptake heterogeneity. In further investigations, the change detection of intra-MATV tracer uptake heterogeneity will be developed by incorporating textural features into the proposed approach.
Accurate high-throughput structure mapping and prediction with transition metal ion FRET
Yu, Xiaozhen; Wu, Xiongwu; Bermejo, Guillermo A.; Brooks, Bernard R.; Taraska, Justin W.
2013-01-01
Mapping the landscape of a protein’s conformational space is essential to understanding its functions and regulation. The limitations of many structural methods have made this process challenging for most proteins. Here, we report that transition metal ion FRET (tmFRET) can be used in a rapid, highly parallel screen, to determine distances from multiple locations within a protein at extremely low concentrations. The distances generated through this screen for the protein Maltose Binding Protein (MBP) match distances from the crystal structure to within a few angstroms. Furthermore, energy transfer accurately detects structural changes during ligand binding. Finally, fluorescence-derived distances can be used to guide molecular simulations to find low energy states. Our results open the door to rapid, accurate mapping and prediction of protein structures at low concentrations, in large complex systems, and in living cells. PMID:23273426
Object memory and change detection: dissociation as a function of visual and conceptual similarity.
Yeh, Yei-Yu; Yang, Cheng-Ta
2008-01-01
People often fail to detect a change between two visual scenes, a phenomenon referred to as change blindness. This study investigates how a post-change object's similarity to the pre-change object influences memory of the pre-change object and affects change detection. The results of Experiment 1 showed that similarity lowered detection sensitivity but did not affect the speed of identifying the pre-change object, suggesting that similarity between the pre- and post-change objects does not degrade the pre-change representation. Identification speed for the pre-change object was faster than naming the new object regardless of detection accuracy. Similarity also decreased detection sensitivity in Experiment 2 but improved the recognition of the pre-change object under both correct detection and detection failure. The similarity effect on recognition was greatly reduced when 20% of each pre-change stimulus was masked by random dots in Experiment 3. Together the results suggest that the level of pre-change representation under detection failure is equivalent to the level under correct detection and that the pre-change representation is almost complete. Similarity lowers detection sensitivity but improves explicit access in recognition. Dissociation arises between recognition and change detection as the two judgments rely on the match-to-mismatch signal and mismatch-to-match signal, respectively.
Distinct frontal and amygdala correlates of change detection for facial identity and expression
Achaibou, Amal; Loth, Eva
2016-01-01
Recruitment of ‘top-down’ frontal attentional mechanisms is held to support detection of changes in task-relevant stimuli. Fluctuations in intrinsic frontal activity have been shown to impact task performance more generally. Meanwhile, the amygdala has been implicated in ‘bottom-up’ attentional capture by threat. Here, 22 adult human participants took part in a functional magnetic resonance change detection study aimed at investigating the correlates of successful (vs failed) detection of changes in facial identity vs expression. For identity changes, we expected prefrontal recruitment to differentiate ‘hit’ from ‘miss’ trials, in line with previous reports. Meanwhile, we postulated that a different mechanism would support detection of emotionally salient changes. Specifically, elevated amygdala activation was predicted to be associated with successful detection of threat-related changes in expression, over-riding the influence of fluctuations in top-down attention. Our findings revealed that fusiform activity tracked change detection across conditions. Ventrolateral prefrontal cortical activity was uniquely linked to detection of changes in identity not expression, and amygdala activity to detection of changes from neutral to fearful expressions. These results are consistent with distinct mechanisms supporting detection of changes in face identity vs expression, the former potentially reflecting top-down attention, the latter bottom-up attentional capture by stimulus emotional salience. PMID:26245835
Detecting Brain State Changes via Fiber-Centered Functional Connectivity Analysis
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
Electrophysiological Correlates of Automatic Visual Change Detection in School-Age Children
ERIC Educational Resources Information Center
Clery, Helen; Roux, Sylvie; Besle, Julien; Giard, Marie-Helene; Bruneau, Nicole; Gomot, Marie
2012-01-01
Automatic stimulus-change detection is usually investigated in the auditory modality by studying Mismatch Negativity (MMN). Although the change-detection process occurs in all sensory modalities, little is known about visual deviance detection, particularly regarding the development of this brain function throughout childhood. The aim of the…
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.
Using adversary text to detect adversary phase changes.
DOE Office of Scientific and Technical Information (OSTI.GOV)
Speed, Ann Elizabeth; Doser, Adele Beatrice; Warrender, Christina E.
2009-05-01
The purpose of this work was to help develop a research roadmap and small proof ofconcept for addressing key problems and gaps from the perspective of using text analysis methods as a primary tool for detecting when a group is undergoing a phase change. Self- rganizing map (SOM) techniques were used to analyze text data obtained from the tworld-wide web. Statistical studies indicate that it may be possible to predict phase changes, as well as detect whether or not an example of writing can be attributed to a group of interest.
Mbagwu, Michael; French, Dustin D; Gill, Manjot; Mitchell, Christopher; Jackson, Kathryn; Kho, Abel; Bryar, Paul J
2016-05-04
Visual acuity is the primary measure used in ophthalmology to determine how well a patient can see. Visual acuity for a single eye may be recorded in multiple ways for a single patient visit (eg, Snellen vs. Jäger units vs. font print size), and be recorded for either distance or near vision. Capturing the best documented visual acuity (BDVA) of each eye in an individual patient visit is an important step for making electronic ophthalmology clinical notes useful in research. Currently, there is limited methodology for capturing BDVA in an efficient and accurate manner from electronic health record (EHR) notes. We developed an algorithm to detect BDVA for right and left eyes from defined fields within electronic ophthalmology clinical notes. We designed an algorithm to detect the BDVA from defined fields within 295,218 ophthalmology clinical notes with visual acuity data present. About 5668 unique responses were identified and an algorithm was developed to map all of the unique responses to a structured list of Snellen visual acuities. Visual acuity was captured from a total of 295,218 ophthalmology clinical notes during the study dates. The algorithm identified all visual acuities in the defined visual acuity section for each eye and returned a single BDVA for each eye. A clinician chart review of 100 random patient notes showed a 99% accuracy detecting BDVA from these records and 1% observed error. Our algorithm successfully captures best documented Snellen distance visual acuity from ophthalmology clinical notes and transforms a variety of inputs into a structured Snellen equivalent list. Our work, to the best of our knowledge, represents the first attempt at capturing visual acuity accurately from large numbers of electronic ophthalmology notes. Use of this algorithm can benefit research groups interested in assessing visual acuity for patient centered outcome. All codes used for this study are currently available, and will be made available online at https://phekb.org.
French, Dustin D; Gill, Manjot; Mitchell, Christopher; Jackson, Kathryn; Kho, Abel; Bryar, Paul J
2016-01-01
Background Visual acuity is the primary measure used in ophthalmology to determine how well a patient can see. Visual acuity for a single eye may be recorded in multiple ways for a single patient visit (eg, Snellen vs. Jäger units vs. font print size), and be recorded for either distance or near vision. Capturing the best documented visual acuity (BDVA) of each eye in an individual patient visit is an important step for making electronic ophthalmology clinical notes useful in research. Objective Currently, there is limited methodology for capturing BDVA in an efficient and accurate manner from electronic health record (EHR) notes. We developed an algorithm to detect BDVA for right and left eyes from defined fields within electronic ophthalmology clinical notes. Methods We designed an algorithm to detect the BDVA from defined fields within 295,218 ophthalmology clinical notes with visual acuity data present. About 5668 unique responses were identified and an algorithm was developed to map all of the unique responses to a structured list of Snellen visual acuities. Results Visual acuity was captured from a total of 295,218 ophthalmology clinical notes during the study dates. The algorithm identified all visual acuities in the defined visual acuity section for each eye and returned a single BDVA for each eye. A clinician chart review of 100 random patient notes showed a 99% accuracy detecting BDVA from these records and 1% observed error. Conclusions Our algorithm successfully captures best documented Snellen distance visual acuity from ophthalmology clinical notes and transforms a variety of inputs into a structured Snellen equivalent list. Our work, to the best of our knowledge, represents the first attempt at capturing visual acuity accurately from large numbers of electronic ophthalmology notes. Use of this algorithm can benefit research groups interested in assessing visual acuity for patient centered outcome. All codes used for this study are currently
McGann, Patrick T.; Tyburski, Erika A.; de Oliveira, Vysolela; Santos, Brigida; Ware, Russell E.; Lam, Wilbur A.
2016-01-01
Severe anemia is an important cause of morbidity and mortality among children in resource-poor settings, but laboratory diagnostics are often limited in these locations. To address this need, we developed a simple, inexpensive, and color-based point-of-care (POC) assay to detect severe anemia. The purpose of this study was to evaluate the accuracy of this novel POC assay to detect moderate and severe anemia in a limited-resource setting. The study was a cross-sectional study conducted on children with sickle cell anemia in Luanda, Angola. The hemoglobin concentrations obtained by the POC assay were compared to reference values measured by a calibrated automated hematology analyzer. A total of 86 samples were analyzed (mean hemoglobin concentration 6.6 g/dL). There was a strong correlation between the hemoglobin concentrations obtained by the POC assay and reference values obtained from an automated hematology analyzer (r=0.88, P<0.0001). The POC assay demonstrated excellent reproducibility (r=0.93, P<0.0001) and the reagents appeared to be durable in a tropical setting (r=0.93, P<0.0001). For the detection of severe anemia that may require blood transfusion (hemoglobin <5 g/dL), the POC assay had sensitivity of 88.9% and specificity of 98.7%. These data demonstrate that an inexpensive (<$0.25 USD) POC assay accurately estimates low hemoglobin concentrations and has the potential to become a transformational diagnostic tool for severe anemia in limited-resource settings. PMID:26317494
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.
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.
Change detection of medical images using dictionary learning techniques and PCA
NASA Astrophysics Data System (ADS)
Nika, Varvara; Babyn, Paul; Zhu, Hongmei
2014-03-01
Automatic change detection methods for identifying the changes of serial MR images taken at different times are of great interest to radiologists. The majority of existing change detection methods in medical imaging, and those of brain images in particular, include many preprocessing steps and rely mostly on statistical analysis of MRI scans. Although most methods utilize registration software, tissue classification remains a difficult and overwhelming task. Recently, dictionary learning techniques are used in many areas of image processing, such as image surveillance, face recognition, remote sensing, and medical imaging. In this paper we present the Eigen-Block Change Detection algorithm (EigenBlockCD). It performs local registration and identifies the changes between consecutive MR images of the brain. Blocks of pixels from baseline scan are used to train local dictionaries that are then used to detect changes in the follow-up scan. We use PCA to reduce the dimensionality of the local dictionaries and the redundancy of data. Choosing the appropriate distance measure significantly affects the performance of our algorithm. We examine the differences between L1 and L2 norms as two possible similarity measures in the EigenBlockCD. We show the advantages of L2 norm over L1 norm theoretically and numerically. We also demonstrate the performance of the EigenBlockCD algorithm for detecting changes of MR images and compare our results with those provided in recent literature. Experimental results with both simulated and real MRI scans show that the EigenBlockCD outperforms the previous methods. It detects clinical changes while ignoring the changes due to patient's position and other acquisition artifacts.
A pdf-Free Change Detection Test Based on Density Difference Estimation.
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.
Accurate clinical detection of exon copy number variants in a targeted NGS panel using DECoN.
Fowler, Anna; Mahamdallie, Shazia; Ruark, Elise; Seal, Sheila; Ramsay, Emma; Clarke, Matthew; Uddin, Imran; Wylie, Harriet; Strydom, Ann; Lunter, Gerton; Rahman, Nazneen
2016-11-25
Background: Targeted next generation sequencing (NGS) panels are increasingly being used in clinical genomics to increase capacity, throughput and affordability of gene testing. Identifying whole exon deletions or duplications (termed exon copy number variants, 'exon CNVs') in exon-targeted NGS panels has proved challenging, particularly for single exon CNVs. Methods: We developed a tool for the Detection of Exon Copy Number variants (DECoN), which is optimised for analysis of exon-targeted NGS panels in the clinical setting. We evaluated DECoN performance using 96 samples with independently validated exon CNV data. We performed simulations to evaluate DECoN detection performance of single exon CNVs and to evaluate performance using different coverage levels and sample numbers. Finally, we implemented DECoN in a clinical laboratory that tests BRCA1 and BRCA2 with the TruSight Cancer Panel (TSCP). We used DECoN to analyse 1,919 samples, validating exon CNV detections by multiplex ligation-dependent probe amplification (MLPA). Results: In the evaluation set, DECoN achieved 100% sensitivity and 99% specificity for BRCA exon CNVs, including identification of 8 single exon CNVs. DECoN also identified 14/15 exon CNVs in 8 other genes. Simulations of all possible BRCA single exon CNVs gave a mean sensitivity of 98% for deletions and 95% for duplications. DECoN performance remained excellent with different levels of coverage and sample numbers; sensitivity and specificity was >98% with the typical NGS run parameters. In the clinical pipeline, DECoN automatically analyses pools of 48 samples at a time, taking 24 minutes per pool, on average. DECoN detected 24 BRCA exon CNVs, of which 23 were confirmed by MLPA, giving a false discovery rate of 4%. Specificity was 99.7%. Conclusions: DECoN is a fast, accurate, exon CNV detection tool readily implementable in research and clinical NGS pipelines. It has high sensitivity and specificity and acceptable false discovery rate
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.
Ability of the Masimo pulse CO-Oximeter to detect changes in hemoglobin.
Colquhoun, Douglas A; Forkin, Katherine T; Durieux, Marcel E; Thiele, Robert H
2012-04-01
The decision to administer blood products is complex and multifactorial. Accurate assessment of the concentration of hemoglobin [Hgb] is a key component of this evaluation. Recently a noninvasive method of continuously measuring hemoglobin (SpHb) has become available with multi-wavelength Pulse CO-Oximetry. The accuracy of this device is well documented, but the trending ability of this monitor has not been previously described. Twenty patients undergoing major thoracic and lumbar spine surgery were recruited. All patients received radial arterial lines. On the contralateral index finger, a R1 25 sensor (Rev E) was applied and connected to a Radical-7 Pulse CO-Oximeter (both Masimo Corp, Irvine, CA). Blood samples were drawn intermittently at the anesthesia provider's discretion and were analyzed by the operating room satellite laboratory CO-Oximeter. The value of Hgb and SpHb at that time point was compared. Trend analysis was performed by the four quadrant plot technique, testing directionality of change, and Critchley's polar plot method testing both directionality and magnitude of the change in values. Eighty-eight samples recorded at times of sufficient signal quality were available for analysis. Four quadrant plot analysis revealed 94% of data within the quadrants associated with the correct direction change, and 90% of data points lay within the analysis bounds proposed by Critchley. Pulse CO-Oximetry offers an acceptable trend monitor in patients undergoing major spine surgery. Future work should explore the ability of this device to detect large changes in hemoglobin, as well as its applicability in additional surgical and non-surgical patient populations.
Temporal Forest Change Detection and Forest Health Assessment using Remote Sensing
NASA Astrophysics Data System (ADS)
Ya'acob, Norsuzila; Mohd Azize, Aziean Binti; Anis Mahmon, Nur; Laily Yusof, Azita; Farhana Azmi, Nor; Mustafa, Norfazira
2014-03-01
This paper presents the detection of Angsi and Berembun Reserve Forest change for years 1996 and 2013. Forest is an important part of our ecosystem. The main function is to absorb carbon oxide and produce oxygen in their cycle of photosynthesis to maintain a balance and healthy atmosphere. However, forest changes as time changes. Some changes are necessary as to give way for economic growth. Nevertheless, it is important to monitor forest change so that deforestation and development can be planned and the balance of ecosystem is still preserved. It is important because there are number of unfavorable effects of deforestation that include environmental and economic such as erosion of soil, loss of biodiversity and climate change. The forest change detection can be studied with reference of several satellite images using remote sensing application. Forest change detection is best done with remote sensing due to large and remote study area. The objective of this project is to detect forest change over time and to compare forest health indicated by Normalized Difference Vegetation Index (NDVI) using remote sensing and image processing. The forest under study shows depletion of forest area by 12% and 100% increment of deforestation activities. The NDVI value which is associated with the forest health also shows 13% of reduction.
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
A network of superconducting gravimeters detects submicrogal coseismic gravity changes.
Imanishi, Yuichi; Sato, Tadahiro; Higashi, Toshihiro; Sun, Wenke; Okubo, Shuhei
2004-10-15
With high-resolution continuous gravity recordings from a regional network of superconducting gravimeters, we have detected permanent changes in gravity acceleration associated with a recent large earthquake. Detected changes in gravity acceleration are smaller than 10(-8) meters seconds(-2) (1 micro-Galileo, about 10(-9) times the surface gravity acceleration) and agree with theoretical values calculated from a dislocation model. Superconducting gravimetry can contribute to the studies of secular gravity changes associated with tectonic processes.
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.
Change Detection Analysis of Water Pollution in Coimbatore Region using Different Color Models
NASA Astrophysics Data System (ADS)
Jiji, G. Wiselin; Devi, R. Naveena
2017-12-01
The data acquired through remote sensing satellites furnish facts about the land and water at varying resolutions and has been widely used for several change detection studies. Apart from the existence of many change detection methodologies and techniques, emergence of new ones continues to subsist. Existing change detection techniques exploit images that are either in gray scale or RGB color model. In this paper we introduced color models for performing change detection for water pollution. Here the polluted lakes are classified and post-classification change detection techniques are applied to RGB images and results obtained are analysed for changes to exist or not. Furthermore RGB images obtained after classification when converted to any of the two color models YCbCr and YIQ is found to produce the same results as that of the RGB model images. Thus it can be concluded that other color models like YCbCr, YIQ can be used as substitution to RGB color model for analysing change detection with regard to water pollution.
High-accurate optical fiber liquid level sensor
NASA Astrophysics Data System (ADS)
Sun, Dexing; Chen, Shouliu; Pan, Chao; Jin, Henghuan
1991-08-01
A highly accurate optical fiber liquid level sensor is presented. The single-chip microcomputer is used to process and control the signal. This kind of sensor is characterized by self-security and is explosion-proof, so it can be applied in any liquid level detecting areas, especially in the oil and chemical industries. The theories and experiments about how to improve the measurement accuracy are described. The relative error for detecting the measurement range 10 m is up to 0.01%.
Detecting temporal changes in acoustic scenes: The variable benefit of selective attention.
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.
Desingu, P A; Singh, S D; Dhama, K; Kumar, O R Vinodh; Singh, R; Singh, R K
2015-02-01
A rapid and accurate method of detection and differentiation of virulent and avirulent Newcastle disease virus (NDV) pathotypes was developed. The NDV detection was carried out for different domestic avian field isolates and pigeon paramyxo virus-1 (25 field isolates and 9 vaccine strains) by using APMV-I "fusion" (F) gene Class II specific external primer A and B (535bp), internal primer C and D (238bp) based reverses transcriptase PCR (RT-PCR). The internal degenerative reverse primer D is specific for F gene cleavage position of virulent strain of NDV. The nested RT-PCR products of avirulent strains showed two bands (535bp and 424bp) while virulent strains showed four bands (535bp, 424bp, 349bp and 238bp) on agar gel electrophoresis. This is the first report regarding development and use of degenerate primer based nested RT-PCR for accurate detection and differentiation of NDV pathotypes by demonstrating multiple PCR band patterns. Being a rapid, simple, and economical test, the developed method could serve as a valuable alternate diagnostic tool for characterizing NDV isolates and carrying out molecular epidemiological surveillance studies for this important pathogen of poultry. Copyright © 2014 Elsevier B.V. All rights reserved.
Automatic detection of lexical change: an auditory event-related potential study.
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.
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.
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.
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.
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.
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.
An accurate method of extracting fat droplets in liver images for quantitative evaluation
NASA Astrophysics Data System (ADS)
Ishikawa, Masahiro; Kobayashi, Naoki; Komagata, Hideki; Shinoda, Kazuma; Yamaguchi, Masahiro; Abe, Tokiya; Hashiguchi, Akinori; Sakamoto, Michiie
2015-03-01
The steatosis in liver pathological tissue images is a promising indicator of nonalcoholic fatty liver disease (NAFLD) and the possible risk of hepatocellular carcinoma (HCC). The resulting values are also important for ensuring the automatic and accurate classification of HCC images, because the existence of many fat droplets is likely to create errors in quantifying the morphological features used in the process. In this study we propose a method that can automatically detect, and exclude regions with many fat droplets by using the feature values of colors, shapes and the arrangement of cell nuclei. We implement the method and confirm that it can accurately detect fat droplets and quantify the fat droplet ratio of actual images. This investigation also clarifies the effective characteristics that contribute to accurate detection.
Detection of Greenhouse-Gas-Induced Climatic Change
DOE Office of Scientific and Technical Information (OSTI.GOV)
Jones, P.D.; Wigley, T.M.L.
1998-05-26
The objective of this report is to assemble and analyze instrumental climate data and to develop and apply climate models as a basis for (1) detecting greenhouse-gas-induced climatic change, and (2) validation of General Circulation Models.
NASA Astrophysics Data System (ADS)
Hester, David Barry
The objective of this research was to develop methods for urban land cover analysis using QuickBird high spatial resolution satellite imagery. Such imagery has emerged as a rich commercially available remote sensing data source and has enjoyed high-profile broadcast news media and Internet applications, but methods of quantitative analysis have not been thoroughly explored. The research described here consists of three studies focused on the use of pan-sharpened 61-cm spatial resolution QuickBird imagery, the spatial resolution of which is the highest of any commercial satellite. In the first study, a per-pixel land cover classification method is developed for use with this imagery. This method utilizes a per-pixel classification approach to generate an accurate six-category high spatial resolution land cover map of a developing suburban area. The primary objective of the second study was to develop an accurate land cover change detection method for use with QuickBird land cover products. This work presents an efficient fuzzy framework for transforming map uncertainty into accurate and meaningful high spatial resolution land cover change analysis. The third study described here is an urban planning application of the high spatial resolution QuickBird-based land cover product developed in the first study. This work both meaningfully connects this exciting new data source to urban watershed management and makes an important empirical contribution to the study of suburban watersheds. Its analysis of residential roads and driveways as well as retail parking lots sheds valuable light on the impact of transportation-related land use on the suburban landscape. Broadly, these studies provide new methods for using state-of-the-art remote sensing data to inform land cover analysis and urban planning. These methods are widely adaptable and produce land cover products that are both meaningful and accurate. As additional high spatial resolution satellites are launched and the
Optimal pcr primers for rapid and accurate detection of Aspergillus flavus isolates.
Al-Shuhaib, Mohammed Baqur S; Albakri, Ali H; Alwan, Sabah H; Almandil, Noor B; AbdulAzeez, Sayed; Borgio, J Francis
2018-03-01
Aspergillus flavus is among the most devastating opportunistic pathogens of several food crops including rice, due to its high production of carcinogenic aflatoxins. The presence of these organisms in economically important rice strip farming is a serious food safety concern. Several polymerase chain reaction (PCR) primers have been designed to detect this species; however, a comparative assessment of their accuracy has not been conducted. This study aims to identify the optimal diagnostic PCR primers for the identification of A. flavus, among widely available primers. We isolated 122 A. flavus native isolates from randomly collected rice strips (N = 300). We identified 109 isolates to the genus level using universal fungal PCR primer pairs. Nine pairs of primers were examined for their PCR diagnostic specificity on the 109 isolates. FLA PCR was found to be the optimal PCR primer pair for specific identification of the native isolates, over aflP(1), aflM, aflA, aflD, aflP(3), aflP(2), and aflR. The PEP primer pair was found to be the most unsuitable for A. flavus identification. In conclusion, the present study indicates the powerful specificity of the FLA PCR primer over other commonly available diagnostic primers for accurate, rapid, and large-scale identification of A. flavus native isolates. This study provides the first simple, practical comparative guide to PCR-based screening of A. flavus infection in rice strips. Copyright © 2018 Elsevier Ltd. All rights reserved.
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.
Resampling approach for anomalous change detection
NASA Astrophysics Data System (ADS)
Theiler, James; Perkins, Simon
2007-04-01
We investigate the problem of identifying pixels in pairs of co-registered images that correspond to real changes on the ground. Changes that are due to environmental differences (illumination, atmospheric distortion, etc.) or sensor differences (focus, contrast, etc.) will be widespread throughout the image, and the aim is to avoid these changes in favor of changes that occur in only one or a few pixels. Formal outlier detection schemes (such as the one-class support vector machine) can identify rare occurrences, but will be confounded by pixels that are "equally rare" in both images: they may be anomalous, but they are not changes. We describe a resampling scheme we have developed that formally addresses both of these issues, and reduces the problem to a binary classification, a problem for which a large variety of machine learning tools have been developed. In principle, the effects of misregistration will manifest themselves as pervasive changes, and our method will be robust against them - but in practice, misregistration remains a serious issue.
Extended image differencing for change detection in UAV video mosaics
NASA Astrophysics Data System (ADS)
Saur, Günter; Krüger, Wolfgang; Schumann, Arne
2014-03-01
Change detection is one of the most important tasks when using unmanned aerial vehicles (UAV) for video reconnaissance and surveillance. We address changes of short time scale, i.e. the observations are taken in time distances from several minutes up to a few hours. Each observation is a short video sequence acquired by the UAV in near-nadir view and the relevant changes are, e.g., recently parked or moved vehicles. In this paper we extend our previous approach of image differencing for single video frames to video mosaics. A precise image-to-image registration combined with a robust matching approach is needed to stitch the video frames to a mosaic. Additionally, this matching algorithm is applied to mosaic pairs in order to align them to a common geometry. The resulting registered video mosaic pairs are the input of the change detection procedure based on extended image differencing. A change mask is generated by an adaptive threshold applied to a linear combination of difference images of intensity and gradient magnitude. The change detection algorithm has to distinguish between relevant and non-relevant changes. Examples for non-relevant changes are stereo disparity at 3D structures of the scene, changed size of shadows, and compression or transmission artifacts. The special effects of video mosaicking such as geometric distortions and artifacts at moving objects have to be considered, too. In our experiments we analyze the influence of these effects on the change detection results by considering several scenes. The results show that for video mosaics this task is more difficult than for single video frames. Therefore, we extended the image registration by estimating an elastic transformation using a thin plate spline approach. The results for mosaics are comparable to that of single video frames and are useful for interactive image exploitation due to a larger scene coverage.
Accurate metacognition for visual sensory memory representations.
Vandenbroucke, Annelinde R E; Sligte, Ilja G; Barrett, Adam B; Seth, Anil K; Fahrenfort, Johannes J; Lamme, Victor A F
2014-04-01
The capacity to attend to multiple objects in the visual field is limited. However, introspectively, people feel that they see the whole visual world at once. Some scholars suggest that this introspective feeling is based on short-lived sensory memory representations, whereas others argue that the feeling of seeing more than can be attended to is illusory. Here, we investigated this phenomenon by combining objective memory performance with subjective confidence ratings during a change-detection task. This allowed us to compute a measure of metacognition--the degree of knowledge that subjects have about the correctness of their decisions--for different stages of memory. We show that subjects store more objects in sensory memory than they can attend to but, at the same time, have similar metacognition for sensory memory and working memory representations. This suggests that these subjective impressions are not an illusion but accurate reflections of the richness of visual perception.
Accessing long-term memory representations during visual change detection.
Beck, Melissa R; van Lamsweerde, Amanda E
2011-04-01
In visual change detection tasks, providing a cue to the change location concurrent with the test image (post-cue) can improve performance, suggesting that, without a cue, not all encoded representations are automatically accessed. Our studies examined the possibility that post-cues can encourage the retrieval of representations stored in long-term memory (LTM). Participants detected changes in images composed of familiar objects. Performance was better when the cue directed attention to the post-change object. Supporting the role of LTM in the cue effect, the effect was similar regardless of whether the cue was presented during the inter-stimulus interval, concurrent with the onset of the test image, or after the onset of the test image. Furthermore, the post-cue effect and LTM performance were similarly influenced by encoding time. These findings demonstrate that monitoring the visual world for changes does not automatically engage LTM retrieval.
Climate Change Detection and Attribution of Infrared Spectrum Measurements
NASA Technical Reports Server (NTRS)
Phojanamongkolkij, Nipa; Parker, Peter A.; Mlynczak, Martin G.
2012-01-01
Climate change occurs when the Earth's energy budget changes due to natural or possibly anthropogenic forcings. These forcings cause the climate system to adjust resulting in a new climate state that is warmer or cooler than the original. The key question is how to detect and attribute climate change. The inference of infrared spectral signatures of climate change has been discussed in the literature for nearly 30 years. Pioneering work in the 1980s noted that distinct spectral signatures would be evident in changes in the infrared radiance emitted by the Earth and its atmosphere, and that these could be observed from orbiting satellites. Since then, a number of other studies have advanced the concepts of spectral signatures of climate change. Today the concept of using spectral signatures to identify and attribute atmospheric composition change is firmly accepted and is the foundation of the Climate Absolute Radiance and Refractivity Observatory (CLARREO) satellite mission being developed at NASA. In this work, we will present an overview of the current climate change detection concept using climate model calculations as surrogates for climate change. Any future research work improving the methodology to achieve this concept will be valuable to our society.
Landsat change detection can aid in water quality monitoring
NASA Technical Reports Server (NTRS)
Macdonald, H. C.; Steele, K. F.; Waite, W. P.; Shinn, M. R.
1977-01-01
Comparison between Landsat-1 and -2 imagery of Arkansas provided evidence of significant land use changes during the 1972-75 time period. Analysis of Arkansas historical water quality information has shown conclusively that whereas point source pollution generally can be detected by use of water quality data collected by state and federal agencies, sampling methodologies for nonpoint source contamination attributable to surface runoff are totally inadequate. The expensive undertaking of monitoring all nonpoint sources for numerous watersheds can be lessened by implementing Landsat change detection analyses.
Simulation of TanDEM-X interferograms for urban change detection
NASA Astrophysics Data System (ADS)
Welte, Amelie; Hammer, Horst; Thiele, Antje; Hinz, Stefan
2017-10-01
Damage detection after natural disasters is one of the remote sensing tasks in which Synthetic Aperture Radar (SAR) sensors play an important role. Since SAR is an active sensor, it can record images at all times of day and in all weather conditions, making it ideally suited for this task. While with the newer generation of SAR satellites such as TerraSAR-X or COSMOSkyMed amplitude change detection has become possible even for urban areas, interferometric phase change detection has not been published widely. This is mainly because of the long revisit times of common SAR sensors leading to temporal decorrelation. This situation has changed dramatically with the advent of the TanDEM-X constellation, which can create single-pass interferograms from space at very high resolutions, avoiding temporal decorrelation almost completely. In this paper the basic structures that are present for any building in InSAR phases, i.e. layover, shadow, and roof areas, are examined. Approaches for their extraction from TanDEM-X interferograms are developed using simulated SAR interferograms. The extracted features of the building signature will in the future be used for urban change detection in real TanDEM-X High Resolution Spotlight interferograms.
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.
NASA Astrophysics Data System (ADS)
Diffenbaugh, N. S.; Horton, D. E.; Singh, D.; Swain, D. L.; Touma, D. E.; Mankin, J. S.
2015-12-01
Because of the high cost of extreme events and the growing evidence that global warming is likely to alter the statistical distribution of climate variables, detection and attribution of changes in the probability of extreme climate events has become a pressing topic for the scientific community, elected officials, and the public. While most of the emphasis has thus far focused on analyzing the climate variable of interest (most often temperature or precipitation, but also flooding and drought), there is an emerging emphasis on applying detection and attribution analysis techniques to the underlying physical causes of individual extreme events. This approach is promising in part because the underlying physical causes (such as atmospheric circulation patterns) can in some cases be more accurately represented in climate models than the more proximal climate variable (such as precipitation). In addition, and more scientifically critical, is the fact that the most extreme events result from a rare combination of interacting causes, often referred to as "ingredients". Rare events will therefore always have a strong influence of "natural" variability. Analyzing the underlying physical mechanisms can therefore help to test whether there have been changes in the probability of the constituent conditions of an individual event, or whether the co-occurrence of causal conditions cannot be distinguished from random chance. This presentation will review approaches to applying detection/attribution analysis to the underlying physical causes of extreme events (including both "thermodynamic" and "dynamic" causes), and provide a number of case studies, including the role of frequency of atmospheric circulation patterns in the probability of hot, cold, wet and dry events.
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).
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.
Goddard, Erin; Clifford, Colin W G
2013-04-22
Attending selectively to changes in our visual environment may help filter less important, unchanging information within a scene. Here, we demonstrate that color changes can go unnoticed even when they occur throughout an otherwise static image. The novelty of this demonstration is that it does not rely upon masking by a visual disruption or stimulus motion, nor does it require the change to be very gradual and restricted to a small section of the image. Using a two-interval, forced-choice change-detection task and an odd-one-out localization task, we showed that subjects were slowest to respond and least accurate (implying that change was hardest to detect) when the color changes were isoluminant, smoothly varying, and asynchronous with one another. This profound change blindness offers new constraints for theories of visual change detection, implying that, in the absence of transient signals, changes in color are typically monitored at a coarse spatial scale.
A Doubly Stochastic Change Point Detection Algorithm for Noisy Biological Signals.
Gold, Nathan; Frasch, Martin G; Herry, Christophe L; Richardson, Bryan S; Wang, Xiaogang
2017-01-01
Experimentally and clinically collected time series data are often contaminated with significant confounding noise, creating short, noisy time series. This noise, due to natural variability and measurement error, poses a challenge to conventional change point detection methods. We propose a novel and robust statistical method for change point detection for noisy biological time sequences. Our method is a significant improvement over traditional change point detection methods, which only examine a potential anomaly at a single time point. In contrast, our method considers all suspected anomaly points and considers the joint probability distribution of the number of change points and the elapsed time between two consecutive anomalies. We validate our method with three simulated time series, a widely accepted benchmark data set, two geological time series, a data set of ECG recordings, and a physiological data set of heart rate variability measurements of fetal sheep model of human labor, comparing it to three existing methods. Our method demonstrates significantly improved performance over the existing point-wise detection methods.
Perspective Effects during Reading: Evidence from Text Change-Detection
ERIC Educational Resources Information Center
Bohan, Jason; Filik, Ruth
2018-01-01
We report two text change-detection studies in which we investigate the influence of reading perspective on text memory. In Experiment 1 participants read from the perspective of one of two characters in a series of short stories, and word changes were either semantically close or distant. Participants correctly reported more changes to…
3D change detection in staggered voxels model for robotic sensing and navigation
NASA Astrophysics Data System (ADS)
Liu, Ruixu; Hampshire, Brandon; Asari, Vijayan K.
2016-05-01
3D scene change detection is a challenging problem in robotic sensing and navigation. There are several unpredictable aspects in performing scene change detection. A change detection method which can support various applications in varying environmental conditions is proposed. Point cloud models are acquired from a RGB-D sensor, which provides the required color and depth information. Change detection is performed on robot view point cloud model. A bilateral filter smooths the surface and fills the holes as well as keeps the edge details on depth image. Registration of the point cloud model is implemented by using Random Sample Consensus (RANSAC) algorithm. It uses surface normal as the previous stage for the ground and wall estimate. After preprocessing the data, we create a point voxel model which defines voxel as surface or free space. Then we create a color model which defines each voxel that has a color by the mean of all points' color value in this voxel. The preliminary change detection is detected by XOR subtract on the point voxel model. Next, the eight neighbors for this center voxel are defined. If they are neither all `changed' voxels nor all `no changed' voxels, a histogram of location and hue channel color is estimated. The experimental evaluations performed to evaluate the capability of our algorithm show promising results for novel change detection that indicate all the changing objects with very limited false alarm rate.
Accurate FRET Measurements within Single Diffusing Biomolecules Using Alternating-Laser Excitation
Lee, Nam Ki; Kapanidis, Achillefs N.; Wang, You; Michalet, Xavier; Mukhopadhyay, Jayanta; Ebright, Richard H.; Weiss, Shimon
2005-01-01
Fluorescence resonance energy transfer (FRET) between a donor (D) and an acceptor (A) at the single-molecule level currently provides qualitative information about distance, and quantitative information about kinetics of distance changes. Here, we used the sorting ability of confocal microscopy equipped with alternating-laser excitation (ALEX) to measure accurate FRET efficiencies and distances from single molecules, using corrections that account for cross-talk terms that contaminate the FRET-induced signal, and for differences in the detection efficiency and quantum yield of the probes. ALEX yields accurate FRET independent of instrumental factors, such as excitation intensity or detector alignment. Using DNA fragments, we showed that ALEX-based distances agree well with predictions from a cylindrical model of DNA; ALEX-based distances fit better to theory than distances obtained at the ensemble level. Distance measurements within transcription complexes agreed well with ensemble-FRET measurements, and with structural models based on ensemble-FRET and x-ray crystallography. ALEX can benefit structural analysis of biomolecules, especially when such molecules are inaccessible to conventional structural methods due to heterogeneity or transient nature. PMID:15653725
Frąc, Magdalena; Gryta, Agata; Oszust, Karolina; Kotowicz, Natalia
2016-01-01
The need for finding fungicides against Fusarium is a key step in the chemical plant protection and using appropriate chemical agents. Existing, conventional methods of evaluation of Fusarium isolates resistance to fungicides are costly, time-consuming and potentially environmentally harmful due to usage of high amounts of potentially toxic chemicals. Therefore, the development of fast, accurate and effective detection methods for Fusarium resistance to fungicides is urgently required. MT2 microplates (Biolog(TM)) method is traditionally used for bacteria identification and the evaluation of their ability to utilize different carbon substrates. However, to the best of our knowledge, there is no reports concerning the use of this technical tool to determine fungicides resistance of the Fusarium isolates. For this reason, the objectives of this study are to develop a fast method for Fusarium resistance to fungicides detection and to validate the effectiveness approach between both traditional hole-plate and MT2 microplates assays. In presented study MT2 microplate-based assay was evaluated for potential use as an alternative resistance detection method. This was carried out using three commercially available fungicides, containing following active substances: triazoles (tebuconazole), benzimidazoles (carbendazim) and strobilurins (azoxystrobin), in six concentrations (0, 0.0005, 0.005, 0.05, 0.1, 0.2%), for nine selected Fusarium isolates. In this study, the particular concentrations of each fungicides was loaded into MT2 microplate wells. The wells were inoculated with the Fusarium mycelium suspended in PM4-IF inoculating fluid. Before inoculation the suspension was standardized for each isolates into 75% of transmittance. Traditional hole-plate method was used as a control assay. The fungicides concentrations in control method were the following: 0, 0.0005, 0.005, 0.05, 0.5, 1, 2, 5, 10, 25, and 50%. Strong relationships between MT2 microplate and traditional hole
Frąc, Magdalena; Gryta, Agata; Oszust, Karolina; Kotowicz, Natalia
2016-01-01
The need for finding fungicides against Fusarium is a key step in the chemical plant protection and using appropriate chemical agents. Existing, conventional methods of evaluation of Fusarium isolates resistance to fungicides are costly, time-consuming and potentially environmentally harmful due to usage of high amounts of potentially toxic chemicals. Therefore, the development of fast, accurate and effective detection methods for Fusarium resistance to fungicides is urgently required. MT2 microplates (BiologTM) method is traditionally used for bacteria identification and the evaluation of their ability to utilize different carbon substrates. However, to the best of our knowledge, there is no reports concerning the use of this technical tool to determine fungicides resistance of the Fusarium isolates. For this reason, the objectives of this study are to develop a fast method for Fusarium resistance to fungicides detection and to validate the effectiveness approach between both traditional hole-plate and MT2 microplates assays. In presented study MT2 microplate-based assay was evaluated for potential use as an alternative resistance detection method. This was carried out using three commercially available fungicides, containing following active substances: triazoles (tebuconazole), benzimidazoles (carbendazim) and strobilurins (azoxystrobin), in six concentrations (0, 0.0005, 0.005, 0.05, 0.1, 0.2%), for nine selected Fusarium isolates. In this study, the particular concentrations of each fungicides was loaded into MT2 microplate wells. The wells were inoculated with the Fusarium mycelium suspended in PM4-IF inoculating fluid. Before inoculation the suspension was standardized for each isolates into 75% of transmittance. Traditional hole-plate method was used as a control assay. The fungicides concentrations in control method were the following: 0, 0.0005, 0.005, 0.05, 0.5, 1, 2, 5, 10, 25, and 50%. Strong relationships between MT2 microplate and traditional hole
The Feasibility Evaluation of Land Use Change Detection Using GAOFEN-3 Data
NASA Astrophysics Data System (ADS)
Huang, G.; Sun, Y.; Zhao, Z.
2018-04-01
GaoFen-3 (GF-3) satellite, is the first C band and multi-polarimetric Synthetic Aperture Radar (SAR) satellite in China. In order to explore the feasibility of GF-3 satellite in remote sensing interpretation and land-use remote sensing change detection, taking Guangzhou, China as a study area, the full polarimetric image of GF-3 satellite with 8 m resolution of two temporal as the data source. Firstly, the image is pre-processed by orthorectification, image registration and mosaic, and the land-use remote sensing digital orthophoto map (DOM) in 2017 is made according to the each county. Then the classification analysis and judgment of ground objects on the image are carried out by means of ArcGIS combining with the auxiliary data and using artificial visual interpretation, to determine the area of changes and the category of change objects. According to the unified change information extraction principle to extract change areas. Finally, the change detection results are compared with 3 m resolution TerraSAR-X data and 2 m resolution multi-spectral image, and the accuracy is evaluated. Experimental results show that the accuracy of the GF-3 data is over 75 % in detecting the change of ground objects, and the detection capability of new filling soil is better than that of TerraSAR-X data, verify the detection and monitoring capability of GF-3 data to the change information extraction, also, it shows that GF-3 can provide effective data support for the remote sensing detection of land resources.
Vater, Christian; Kredel, Ralf; Hossner, Ernst-Joachim
2017-05-01
In the current study, dual-task performance is examined with multiple-object tracking as a primary task and target-change detection as a secondary task. The to-be-detected target changes in conditions of either change type (form vs. motion; Experiment 1) or change salience (stop vs. slowdown; Experiment 2), with changes occurring at either near (5°-10°) or far (15°-20°) eccentricities (Experiments 1 and 2). The aim of the study was to test whether changes can be detected solely with peripheral vision. By controlling for saccades and computing gaze distances, we could show that participants used peripheral vision to monitor the targets and, additionally, to perceive changes at both near and far eccentricities. Noticeably, gaze behavior was not affected by the actual target change. Detection rates as well as response times generally varied as a function of change condition and eccentricity, with faster detections for motion changes and near changes. However, in contrast to the effects found for motion changes, sharp declines in detection rates and increased response times were observed for form changes as a function of the eccentricities. This result can be ascribed to properties of the visual system, namely to the limited spatial acuity in the periphery and the comparably receptive motion sensitivity of peripheral vision. These findings show that peripheral vision is functional for simultaneous target monitoring and target-change detection as saccadic information suppression can be avoided and covert attention can be optimally distributed to all targets. (PsycINFO Database Record (c) 2017 APA, all rights reserved).
DOE Office of Scientific and Technical Information (OSTI.GOV)
Takao, Seishin, E-mail: takao@mech-me.eng.hokudai.ac.jp; Tadano, Shigeru; Taguchi, Hiroshi
2011-11-01
Purpose: To establish a method for the accurate acquisition and analysis of the variations in tumor volume, location, and three-dimensional (3D) shape of tumors during radiotherapy in the era of image-guided radiotherapy. Methods and Materials: Finite element models of lymph nodes were developed based on computed tomography (CT) images taken before the start of treatment and every week during the treatment period. A surface geometry map with a volumetric scale was adopted and used for the analysis. Six metastatic cervical lymph nodes, 3.5 to 55.1 cm{sup 3} before treatment, in 6 patients with head and neck carcinomas were analyzed inmore » this study. Three fiducial markers implanted in mouthpieces were used for the fusion of CT images. Changes in the location of the lymph nodes were measured on the basis of these fiducial markers. Results: The surface geometry maps showed convex regions in red and concave regions in blue to ensure that the characteristics of the 3D tumor geometries are simply understood visually. After the irradiation of 66 to 70 Gy in 2 Gy daily doses, the patterns of the colors had not changed significantly, and the maps before and during treatment were strongly correlated (average correlation coefficient was 0.808), suggesting that the tumors shrank uniformly, maintaining the original characteristics of the shapes in all 6 patients. The movement of the gravitational center of the lymph nodes during the treatment period was everywhere less than {+-}5 mm except in 1 patient, in whom the change reached nearly 10 mm. Conclusions: The surface geometry map was useful for an accurate evaluation of the changes in volume and 3D shapes of metastatic lymph nodes. The fusion of the initial and follow-up CT images based on fiducial markers enabled an analysis of changes in the location of the targets. Metastatic cervical lymph nodes in patients were suggested to decrease in size without significant changes in the 3D shape during radiotherapy. The
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.
[Application of optical flow dynamic texture in land use/cover change detection].
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
Formenti, Federico; Chen, Rongsheng; McPeak, Hanne; Matejovic, Martin; Farmery, Andrew D.; Hahn, Clive E.W.
2014-01-01
Two challenges in the management of Acute Respiratory Distress Syndrome are the difficulty in diagnosing cyclical atelectasis, and in individualising mechanical ventilation therapy in real-time. Commercial optical oxygen sensors can detect PaO2 oscillations associated with cyclical atelectasis, but are not accurate at saturation levels below 90%, and contain a toxic fluorophore. We present a computer-controlled test rig, together with an in-house constructed ultra-rapid sensor to test the limitations of these sensors when exposed to rapidly changing PO2 in blood in vitro. We tested the sensors’ responses to simulated respiratory rates between 10 and 60 breaths per minute. Our sensor was able to detect the whole amplitude of the imposed PO2 oscillations, even at the highest respiratory rate. We also examined our sensor's resistance to clot formation by continuous in vivo deployment in non-heparinised flowing animal blood for 24 h, after which no adsorption of organic material on the sensor's surface was detectable by scanning electron microscopy. PMID:24184746
Sznitman, Sharon R; Zlotnick, Cheryl; Harel-Fisch, Yossi
2016-07-01
The multiple risk model postulates that accumulating risk factors increase adolescent drunkenness and smoking. The normalisation theory adds to this by arguing that the relation between accumulative risk and drunkenness and smoking is dependent on the distribution of these behaviours in the larger population. More concretely, normalisation theory predicts that: (i) when population level use increases, low risk adolescents will be more likely to use alcohol and cigarettes; and (ii) adolescents facing multiple risk factors will be equally likely to use alcohol and cigarettes, regardless of trends in population level use. The current study empirically tests these assumptions on five waves of nationally representative samples of Israeli Jewish youth. Five cross-sectional waves of data from the Israeli Health Behaviour in School-aged Children survey for Jewish 10th graders were used. Logistic regression models measured the impact of changes in population level use across waves on drunkenness and smoking, and their association with differing levels of risk factors. Between zero and two risk factors, the risk of drunkenness and smoking increases for each additional risk factor. When reaching two risk factors, added risk does not significantly increase the likelihood of smoking and drunkenness. Changes in population level drunkenness and smoking did not systematically relate to changes in the individual level relationship between risk factors and smoking and drunkenness. The pattern of results in this study provides strong evidence for the multiple risk factor model and inconsistent evidence for the normalisation theory. [Sznitman SR, Zlotnick C, Harel-Fisch Y. Normalisation theory: Does it accurately describe temporal changes in adolescent drunkenness and smoking? Drug Alcohol Rev 2016;35:424-432]. © 2015 Australasian Professional Society on Alcohol and other Drugs.
Towards Accurate Node-Based Detection of P2P Botnets
2014-01-01
Botnets are a serious security threat to the current Internet infrastructure. In this paper, we propose a novel direction for P2P botnet detection called node-based detection. This approach focuses on the network characteristics of individual nodes. Based on our model, we examine node's flows and extract the useful features over a given time period. We have tested our approach on real-life data sets and achieved detection rates of 99-100% and low false positives rates of 0–2%. Comparison with other similar approaches on the same data sets shows that our approach outperforms the existing approaches. PMID:25089287
Accurate formulas for interaction force and energy in frequency modulation force spectroscopy
NASA Astrophysics Data System (ADS)
Sader, John E.; Jarvis, Suzanne P.
2004-03-01
Frequency modulation atomic force microscopy utilizes the change in resonant frequency of a cantilever to detect variations in the interaction force between cantilever tip and sample. While a simple relation exists enabling the frequency shift to be determined for a given force law, the required complementary inverse relation does not exist for arbitrary oscillation amplitudes of the cantilever. In this letter we address this problem and present simple yet accurate formulas that enable the interaction force and energy to be determined directly from the measured frequency shift. These formulas are valid for any oscillation amplitude and interaction force, and are therefore of widespread applicability in frequency modulation dynamic force spectroscopy.
Automated particle correspondence and accurate tilt-axis detection in tilted-image pairs
Shatsky, Maxim; Arbelaez, Pablo; Han, Bong-Gyoon; ...
2014-07-01
Tilted electron microscope images are routinely collected for an ab initio structure reconstruction as a part of the Random Conical Tilt (RCT) or Orthogonal Tilt Reconstruction (OTR) methods, as well as for various applications using the "free-hand" procedure. These procedures all require identification of particle pairs in two corresponding images as well as accurate estimation of the tilt-axis used to rotate the electron microscope (EM) grid. Here we present a computational approach, PCT (particle correspondence from tilted pairs), based on tilt-invariant context and projection matching that addresses both problems. The method benefits from treating the two problems as a singlemore » optimization task. It automatically finds corresponding particle pairs and accurately computes tilt-axis direction even in the cases when EM grid is not perfectly planar.« less
NASA Astrophysics Data System (ADS)
Park, Seyoun; Robinson, Adam; Quon, Harry; Kiess, Ana P.; Shen, Colette; Wong, John; Plishker, William; Shekhar, Raj; Lee, Junghoon
2016-03-01
In this paper, we propose a CT-CBCT registration method to accurately predict the tumor volume change based on daily cone-beam CTs (CBCTs) during radiotherapy. CBCT is commonly used to reduce patient setup error during radiotherapy, but its poor image quality impedes accurate monitoring of anatomical changes. Although physician's contours drawn on the planning CT can be automatically propagated to daily CBCTs by deformable image registration (DIR), artifacts in CBCT often cause undesirable errors. To improve the accuracy of the registration-based segmentation, we developed a DIR method that iteratively corrects CBCT intensities by local histogram matching. Three popular DIR algorithms (B-spline, demons, and optical flow) with the intensity correction were implemented on a graphics processing unit for efficient computation. We evaluated their performances on six head and neck (HN) cancer cases. For each case, four trained scientists manually contoured the nodal gross tumor volume (GTV) on the planning CT and every other fraction CBCTs to which the propagated GTV contours by DIR were compared. The performance was also compared with commercial image registration software based on conventional mutual information (MI), VelocityAI (Varian Medical Systems Inc.). The volume differences (mean±std in cc) between the average of the manual segmentations and automatic segmentations are 3.70+/-2.30 (B-spline), 1.25+/-1.78 (demons), 0.93+/-1.14 (optical flow), and 4.39+/-3.86 (VelocityAI). The proposed method significantly reduced the estimation error by 9% (B-spline), 38% (demons), and 51% (optical flow) over the results using VelocityAI. Although demonstrated only on HN nodal GTVs, the results imply that the proposed method can produce improved segmentation of other critical structures over conventional methods.
NASA Astrophysics Data System (ADS)
Maurer, Joshua; Rupper, Summer
2015-10-01
Declassified historical imagery from the Hexagon spy satellite database has near-global coverage, yet remains a largely untapped resource for geomorphic change studies. Unavailable satellite ephemeris data make DEM (digital elevation model) extraction difficult in terms of time and accuracy. A new fully-automated pipeline for DEM extraction and image orthorectification is presented which yields accurate results and greatly increases efficiency over traditional photogrammetric methods, making the Hexagon image database much more appealing and accessible. A 1980 Hexagon DEM is extracted and geomorphic change computed for the Thistle Creek Landslide region in the Wasatch Range of North America to demonstrate an application of the new method. Surface elevation changes resulting from the landslide show an average elevation decrease of 14.4 ± 4.3 m in the source area, an increase of 17.6 ± 4.7 m in the deposition area, and a decrease of 30.2 ± 5.1 m resulting from a new roadcut. Two additional applications of the method include volume estimates of material excavated during the Mount St. Helens volcanic eruption and the volume of net ice loss over a 34-year period for glaciers in the Bhutanese Himalayas. These results show the value of Hexagon imagery in detecting and quantifying historical geomorphic change, especially in regions where other data sources are limited.
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.
Gowda, Dhananjaya; Airaksinen, Manu; Alku, Paavo
2017-09-01
Recently, a quasi-closed phase (QCP) analysis of speech signals for accurate glottal inverse filtering was proposed. However, the QCP analysis which belongs to the family of temporally weighted linear prediction (WLP) methods uses the conventional forward type of sample prediction. This may not be the best choice especially in computing WLP models with a hard-limiting weighting function. A sample selective minimization of the prediction error in WLP reduces the effective number of samples available within a given window frame. To counter this problem, a modified quasi-closed phase forward-backward (QCP-FB) analysis is proposed, wherein each sample is predicted based on its past as well as future samples thereby utilizing the available number of samples more effectively. Formant detection and estimation experiments on synthetic vowels generated using a physical modeling approach as well as natural speech utterances show that the proposed QCP-FB method yields statistically significant improvements over the conventional linear prediction and QCP methods.
NASA Astrophysics Data System (ADS)
Hu, Y.; Jia, G.
2009-12-01
Change vector analysis (CVA) is an effective approach for detecting and characterizing land-cover change by comparing pairs of multi-spectral and multi-temporal datasets over certain area derived from various satellite platforms. NDVI is considered as an effective detector for biophysical changes due to its sensitivity to red and near infrared signals, while land surface temperature (LST) is considered as a valuable indicator for changes of ground thermal conditions. Here we try to apply CVA over satellite derived LST datasets to detect changes of land surface thermal properties parallel to climate change and anthropogenic influence in a city cluster since 2001. In this study, monthly land surface temperature datasets from 2001-2008 derived from MODIS collection 5 were used to examine change pattern of thermal environment over the Bohai coastal region by using spectral change vector analysis. The results from principle component analysis (PCA) for LST show that the PC 1-3 contain over 80% information on monthly variations and these PCA components represent the main processes of land thermal environment change over the study area. Time series of CVA magnitude combined with land cover information show that greatest change occurred in urban and heavily populated area, featured with expansion of urban heat island, while moderate change appeared in grassland area in the north. However few changes were observed over large plain area and forest area. Strong signals also are related to economy level and especially the events of surface cover change, such as emergence of railway and port. Two main processes were also noticed about the changes of thermal environment. First, weak signal was detected in mostly natural area influenced by interannual climate change in temperate broadleaf forest area. Second, land surface temperature changes were controlled by human activities as 1) moderate change of LST happened in grassland influenced by grazing and 2) urban heat island was
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.
Imamura, K; Takayama, S; Saito, A; Inoue, E; Nakayama, Y; Ogata, Y; Shirakawa, S; Nagano, T; Gomi, K; Morozumi, T; Akiishi, K; Watanabe, K; Yoshie, H
2015-10-01
An important goal for the improved diagnosis and management of infectious and inflammatory diseases, such as periodontitis, is the development of rapid and accurate technologies for the decentralized detection of bacterial pathogens. The aim of this prospective multicenter study was to evaluate the clinical use of a novel immunochromatographic device with monoclonal antibodies for the rapid point-of-care detection and semi-quantification of Porphyromonas gingivalis in subgingival plaque. Sixty-three patients with chronic periodontitis and 28 periodontally healthy volunteers were subjected to clinical and microbiological examinations. Subgingival plaque samples were analyzed for the presence of P. gingivalis using a novel immunochromatography based device DK13-PG-001, designed to detect the 40k-outer membrane protein of P. gingivalis, and compared with a PCR-Invader method. In the periodontitis group, a significant strong positive correlation in detection results was found between the test device score and the PCR-Invader method (Spearman rank correlation, r=0.737, p<0.0001). The sensitivity, specificity, and positive and negative predictive values of the test device were 96.2%, 91.8%, 90.4% and 96.7%, respectively. The detection threshold of the test device was determined to be approximately 10(4) (per two paper points). There were significant differences in the bacterial counts by the PCR-Invader method among groups with different ranges of device scores. With a cut-off value of ≥0.25 in device score, none of periodontally healthy volunteers were tested positive for the subgingival presence of P. gingivalis, whereas 76% (n=48) of periodontitis subjects were tested positive. There was a significant positive correlation between device scores for P. gingivalis and periodontal parameters including probing pocket depth and clinical attachment level (r=0.317 and 0.281, respectively, p<0.01). The results suggested that the DK13-PG-001 device kit can be effectively used
A Generalized Machine Fault Detection Method Using Unified Change Detection
2014-10-02
SOCIETY 2014 11 of the extension shaft. It can be induced by a lack of tightening torque of the end-nut and consequently causes a load...Test Facility (HTTF). The objective of the study was to provide HUMS systems with the capability to detect the loss of tightening torque of the end...from pinion SSA (at Ring-Front sensor & cruise power) change signal with cross-over at 75th shaft order Ten end-nut tightening torques were used in
Detection of short-term changes in vegetation cover by use of LANDSAT imagery. [Arizona
NASA Technical Reports Server (NTRS)
Turner, R. M. (Principal Investigator); Wiseman, F. M.
1975-01-01
The author has identified the following significant results. By using a constant band 6 to band 5 radiance ratio of 1.25, the changing pattern of areas of relatively dense vegetation cover was detected for the semiarid region in the vicinity of Tucson, Arizona. Electronically produced binary thematic masks were used to map areas with dense vegetation. The foliar cover threshold represented by the ratio was not accurately determined but field measurements show that the threshold lies in the range of 10 to 25 percent foliage cover. Montana evergreen forests with constant dense cover were correctly shown to exceed the threshold on all dates. The summer active grassland exceeded the threshold in the summer unless rainfall was insufficient. Desert areas exceeded the threshold during the spring of 1973 following heavy rains; the same areas during the rainless spring of 1974 did not exceed threshold. Irrigated fields, parks, golf courses, and riparian communities were among the habitats most frequently surpassing the threshold.
A comparison of change detection methods using multispectral scanner data
Seevers, Paul M.; Jones, Brenda K.; Qiu, Zhicheng; Liu, Yutong
1994-01-01
Change detection methods were investigated as a cooperative activity between the U.S. Geological Survey and the National Bureau of Surveying and Mapping, People's Republic of China. Subtraction of band 2, band 3, normalized difference vegetation index, and tasseled cap bands 1 and 2 data from two multispectral scanner images were tested using two sites in the United States and one in the People's Republic of China. A new statistical method also was tested. Band 2 subtraction gives the best results for detecting change from vegetative cover to urban development. The statistical method identifies areas that have changed and uses a fast classification algorithm to classify the original data of the changed areas by land cover type present for each image date.
Detecting Landscape Change: The View from Above
ERIC Educational Resources Information Center
Porter, Jess
2008-01-01
This article will demonstrate an approach for discovering and assessing local landscape change through the use of remotely sensed images. A brief introduction to remotely sensed imagery is followed by a discussion of relevant ways to introduce this technology into the college science classroom. The Map Detective activity demonstrates the…
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.
Detecting ecological change on coral reefs
NASA Astrophysics Data System (ADS)
Dustan, P.
2011-12-01
Remote sensing offers the potential to observe the response of coral reef ecosystems to environmental perturbations on a geographical scale not previously accessible. However, coral reef environments are optically, spatially, and temporally complex habitats which all present significant challenges for extracting meaningful information. Virtually every member of the reef community possesses some degree of photosynthetic capability. The community thus generates a matrix of fine scale features with bio-optical signatures that blend as the scale of observation increases. Furthermore, to have any validity, the remotely sensed signal must be "calibrated" to the bio-optics of the reef, a difficult and resource intensive process due to a convergence of photosynthetic light harvesting by green, red, and brown algal pigment systems. To make matters more complex, reefs are overlain by a seawater skin with its own set of hydrological optical challenges. Rather than concentrating on classification, my research has attempted to track change by following the variation in geo-referenced pixel brightness over time with a technique termed temporal texture. Environmental periodicities impart a phenology to the variation in brightness and departures from the norm are easily detected as statistical outliers. This opens the door to using current orbiting technology to efficiently examine large areas of sea for change. If hot spots are detected, higher resolution sensors and field studies can be focused as resources permit. While this technique does not identify the type of change, it is sensitive, simple to compute, easy to automate and grounded in ecological niche theory
Ochiai, Nobuo; Mitsui, Kazuhisa; Sasamoto, Kikuo; Yoshimura, Yuta; David, Frank; Sandra, Pat
2014-09-05
A method is developed for identification of sulfur compounds in tobacco smoke extract. The method is based on large volume injection (LVI) of 10μL of tobacco smoke extract followed by selectable one-dimensional ((1)D) or two-dimensional ((2)D) gas chromatography (GC) coupled to a hybrid quadrupole time-of-flight mass spectrometer (Q-TOF-MS) using electron ionization (EI) and positive chemical ionization (PCI), with parallel sulfur chemiluminescence detection (SCD). In order to identify each individual sulfur compound, sequential heart-cuts of 28 sulfur fractions from (1)D GC to (2)D GC were performed with the three MS detection modes (SCD/EI-TOF-MS, SCD/PCI-TOF-MS, and SCD/PCI-Q-TOF-MS). Thirty sulfur compounds were positively identified by MS library search, linear retention indices (LRI), molecular mass determination using PCI accurate mass spectra, formula calculation using EI and PCI accurate mass spectra, and structure elucidation using collision activated dissociation (CAD) of the protonated molecule. Additionally, 11 molecular formulas were obtained for unknown sulfur compounds. The determined values of the identified and unknown sulfur compounds were in the range of 10-740ngmg total particulate matter (TPM) (RSD: 1.2-12%, n=3). Copyright © 2014 The Authors. Published by Elsevier B.V. All rights reserved.
Detecting Abrupt Changes in a Piecewise Locally Stationary Time Series
Last, Michael; Shumway, Robert
2007-01-01
Non-stationary time series arise in many settings, such as seismology, speech-processing, and finance. In many of these settings we are interested in points where a model of local stationarity is violated. We consider the problem of how to detect these change-points, which we identify by finding sharp changes in the time-varying power spectrum. Several different methods are considered, and we find that the symmetrized Kullback-Leibler information discrimination performs best in simulation studies. We derive asymptotic normality of our test statistic, and consistency of estimated change-point locations. We then demonstrate the technique on the problem of detecting arrival phases in earthquakes. PMID:19190715
Ao, Dongyang; Li, Yuanhao; Hu, Cheng; Tian, Weiming
2017-12-22
The dihedral corner reflectors are the basic geometric structure of many targets and are the main contributions of radar cross section (RCS) in the synthetic aperture radar (SAR) images. In stealth technologies, the elaborate design of the dihedral corners with different opening angles is a useful approach to reduce the high RCS generated by multiple reflections. As bistatic synthetic aperture sensors have flexible geometric configurations and are sensitive to the dihedral corners with different opening angles, they specially fit for the stealth target detections. In this paper, the scattering characteristic of dihedral corner reflectors is accurately analyzed in bistatic synthetic aperture images. The variation of RCS with the changing opening angle is formulated and the method to design a proper bistatic radar for maximizing the detection capability is provided. Both the results of the theoretical analysis and the experiments show the bistatic SAR could detect the dihedral corners, under a certain bistatic angle which is related to the geometry of target structures.
Accurate Analysis of Target Characteristic in Bistatic SAR Images: A Dihedral Corner Reflectors Case
Ao, Dongyang; Hu, Cheng; Tian, Weiming
2017-01-01
The dihedral corner reflectors are the basic geometric structure of many targets and are the main contributions of radar cross section (RCS) in the synthetic aperture radar (SAR) images. In stealth technologies, the elaborate design of the dihedral corners with different opening angles is a useful approach to reduce the high RCS generated by multiple reflections. As bistatic synthetic aperture sensors have flexible geometric configurations and are sensitive to the dihedral corners with different opening angles, they specially fit for the stealth target detections. In this paper, the scattering characteristic of dihedral corner reflectors is accurately analyzed in bistatic synthetic aperture images. The variation of RCS with the changing opening angle is formulated and the method to design a proper bistatic radar for maximizing the detection capability is provided. Both the results of the theoretical analysis and the experiments show the bistatic SAR could detect the dihedral corners, under a certain bistatic angle which is related to the geometry of target structures. PMID:29271917
Rate change detection of frequency modulated signals: developmental trends.
Cohen-Mimran, Ravit; Sapir, Shimon
2011-08-26
The aim of this study was to examine developmental trends in rate change detection of auditory rhythmic signals (repetitive sinusoidally frequency modulated tones). Two groups of children (9-10 years old and 11-12 years old) and one group of young adults performed a rate change detection (RCD) task using three types of stimuli. The rate of stimulus modulation was either constant (CR), raised by 1 Hz in the middle of the stimulus (RR1) or raised by 2 Hz in the middle of the stimulus (RR2). Performance on the RCD task significantly improved with age. Also, the different stimuli showed different developmental trajectories. When the RR2 stimulus was used, results showed adult-like performance by the age of 10 years but when the RR1 stimulus was used performance continued to improve beyond 12 years of age. Rate change detection of repetitive sinusoidally frequency modulated tones show protracted development beyond the age of 12 years. Given evidence for abnormal processing of auditory rhythmic signals in neurodevelopmental conditions, such as dyslexia, the present methodology might help delineate the nature of these conditions.
Detection of small orientation changes and the precision of visual working memory.
Salmela, Viljami R; Saarinen, Jussi
2013-01-14
We investigated the precision of orientation representations with two tasks, change detection and recall. Previously change detection has been measured only with relatively large orientation changes compared to psychophysical thresholds. In the first experiment, we measured the observers' ability (d') to detect small changes in orientation (5-30°) with 1-4 Gabor items. With one item even a 10° change was well detected (average d'=2.5). As the amount of change increased to 30°, the d' increased to 5.2. When the number of items was increased, the d's gradually decreased. In the second experiment, we used a recall task and the observers adjusted the orientation of a probe Gabor to match the orientation of a Gabor held in the memory. The standard deviation (s.d.) of errors was calculated from the Gaussian distribution fitted to the data. As the number of items increased from 1 to 6, the s.d. increased from 8.6° to 19.6°. Even with six items, the observers did not make any random adjustments. The results show a square root relation between the d'/s.d. and the number of items. The d' in change detection is directly proportional to the square root of (1/n) and the orientation change. The increase of the s.d. in recall task is inversely proportional to square root of (1/n). The results suggest that limited resources and precision of representations, without additional assumptions, determine the memory performance. Copyright © 2012 Elsevier Ltd. All rights reserved.
Change point detection of the Persian Gulf sea surface temperature
NASA Astrophysics Data System (ADS)
Shirvani, A.
2017-01-01
In this study, the Student's t parametric and Mann-Whitney nonparametric change point models (CPMs) were applied to detect change point in the annual Persian Gulf sea surface temperature anomalies (PGSSTA) time series for the period 1951-2013. The PGSSTA time series, which were serially correlated, were transformed to produce an uncorrelated pre-whitened time series. The pre-whitened PGSSTA time series were utilized as the input file of change point models. Both the applied parametric and nonparametric CPMs estimated the change point in the PGSSTA in 1992. The PGSSTA follow the normal distribution up to 1992 and thereafter, but with a different mean value after year 1992. The estimated slope of linear trend in PGSSTA time series for the period 1951-1992 was negative; however, that was positive after the detected change point. Unlike the PGSSTA, the applied CPMs suggested no change point in the Niño3.4SSTA time series.
Automatic detection of unattended changes in room acoustics.
Frey, Johannes Daniel; Wendt, Mike; Jacobsen, Thomas
2015-01-01
Previous research has shown that the human auditory system continuously monitors its acoustic environment, detecting a variety of irregularities (e.g., deviance from prior stimulation regularity in pitch, loudness, duration, and (perceived) sound source location). Detection of irregularities can be inferred from a component of the event-related brain potential (ERP), referred to as the mismatch negativity (MMN), even in conditions in which participants are instructed to ignore the auditory stimulation. The current study extends previous findings by demonstrating that auditory irregularities brought about by a change in room acoustics elicit a MMN in a passive oddball protocol (acoustic stimuli with differing room acoustics, that were otherwise identical, were employed as standard and deviant stimuli), in which participants watched a fiction movie (silent with subtitles). While the majority of participants reported no awareness for any changes in the auditory stimulation, only one out of 14 participants reported to have become aware of changing room acoustics or sound source location. Together, these findings suggest automatic monitoring of room acoustics. Copyright © 2014 Elsevier Ireland Ltd. All rights reserved.
Detecting Change in Landscape Greenness over Large Areas: An Example for New Mexico, USA
Monitoring and quantifying changes in vegetation cover over large areas using remote sensing can potentially detect large-scale, slow changes (e.g., climate change), as well as more local and rapid changes (e.g., fire, land development). A useful indicator for detecting change i...
Climate change and the detection of trends in annual runoff
McCabe, G.J.; Wolock, D.M.
1997-01-01
This study examines the statistical likelihood of detecting a trend in annual runoff given an assumed change in mean annual runoff, the underlying year-to-year variability in runoff, and serial correlation of annual runoff. Means, standard deviations, and lag-1 serial correlations of annual runoff were computed for 585 stream gages in the conterminous United States, and these statistics were used to compute the probability of detecting a prescribed trend in annual runoff. Assuming a linear 20% change in mean annual runoff over a 100 yr period and a significance level of 95%, the average probability of detecting a significant trend was 28% among the 585 stream gages. The largest probability of detecting a trend was in the northwestern U.S., the Great Lakes region, the northeastern U.S., the Appalachian Mountains, and parts of the northern Rocky Mountains. The smallest probability of trend detection was in the central and southwestern U.S., and in Florida. Low probabilities of trend detection were associated with low ratios of mean annual runoff to the standard deviation of annual runoff and with high lag-1 serial correlation in the data.
MIDAS robust trend estimator for accurate GPS station velocities without step detection
NASA Astrophysics Data System (ADS)
Blewitt, Geoffrey; Kreemer, Corné; Hammond, William C.; Gazeaux, Julien
2016-03-01
Automatic estimation of velocities from GPS coordinate time series is becoming required to cope with the exponentially increasing flood of available data, but problems detectable to the human eye are often overlooked. This motivates us to find an automatic and accurate estimator of trend that is resistant to common problems such as step discontinuities, outliers, seasonality, skewness, and heteroscedasticity. Developed here, Median Interannual Difference Adjusted for Skewness (MIDAS) is a variant of the Theil-Sen median trend estimator, for which the ordinary version is the median of slopes vij = (xj-xi)/(tj-ti) computed between all data pairs i > j. For normally distributed data, Theil-Sen and least squares trend estimates are statistically identical, but unlike least squares, Theil-Sen is resistant to undetected data problems. To mitigate both seasonality and step discontinuities, MIDAS selects data pairs separated by 1 year. This condition is relaxed for time series with gaps so that all data are used. Slopes from data pairs spanning a step function produce one-sided outliers that can bias the median. To reduce bias, MIDAS removes outliers and recomputes the median. MIDAS also computes a robust and realistic estimate of trend uncertainty. Statistical tests using GPS data in the rigid North American plate interior show ±0.23 mm/yr root-mean-square (RMS) accuracy in horizontal velocity. In blind tests using synthetic data, MIDAS velocities have an RMS accuracy of ±0.33 mm/yr horizontal, ±1.1 mm/yr up, with a 5th percentile range smaller than all 20 automatic estimators tested. Considering its general nature, MIDAS has the potential for broader application in the geosciences.
Standardizing a simpler, more sensitive and accurate tail bleeding assay in mice
Liu, Yang; Jennings, Nicole L; Dart, Anthony M; Du, Xiao-Jun
2012-01-01
AIM: To optimize the experimental protocols for a simple, sensitive and accurate bleeding assay. METHODS: Bleeding assay was performed in mice by tail tip amputation, immersing the tail in saline at 37 °C, continuously monitoring bleeding patterns and measuring bleeding volume from changes in the body weight. Sensitivity and extent of variation of bleeding time and bleeding volume were compared in mice treated with the P2Y receptor inhibitor prasugrel at various doses or in mice deficient of FcRγ, a signaling protein of the glycoprotein VI receptor. RESULTS: We described details of the bleeding assay with the aim of standardizing this commonly used assay. The bleeding assay detailed here was simple to operate and permitted continuous monitoring of bleeding pattern and detection of re-bleeding. We also reported a simple and accurate way of quantifying bleeding volume from changes in the body weight, which correlated well with chemical assay of hemoglobin levels (r2 = 0.990, P < 0.0001). We determined by tail bleeding assay the dose-effect relation of the anti-platelet drug prasugrel from 0.015 to 5 mg/kg. Our results showed that the correlation of bleeding time and volume was unsatisfactory and that compared with the bleeding time, bleeding volume was more sensitive in detecting a partial inhibition of platelet’s haemostatic activity (P < 0.01). Similarly, in mice with genetic disruption of FcRγ as a signaling molecule of P-selectin glycoprotein ligand-1 leading to platelet dysfunction, both increased bleeding volume and repeated bleeding pattern defined the phenotype of the knockout mice better than that of a prolonged bleeding time. CONCLUSION: Determination of bleeding pattern and bleeding volume, in addition to bleeding time, improved the sensitivity and accuracy of this assay, particularly when platelet function is partially inhibited. PMID:24520531
Accurate feature detection and estimation using nonlinear and multiresolution analysis
NASA Astrophysics Data System (ADS)
Rudin, Leonid; Osher, Stanley
1994-11-01
A program for feature detection and estimation using nonlinear and multiscale analysis was completed. The state-of-the-art edge detection was combined with multiscale restoration (as suggested by the first author) and robust results in the presence of noise were obtained. Successful applications to numerous images of interest to DOD were made. Also, a new market in the criminal justice field was developed, based in part, on this work.
The Nature of Change Detection and Online Representations of Scenes
ERIC Educational Resources Information Center
Ryan,J ennifer D.; Cohen, Neal J.
2004-01-01
This article provides evidence for implicit change detection and for the contribution of multiple memory sources to online representations. Multiple eye-movement measures distinguished original from changed scenes, even when college students had no conscious awareness for the change. Patients with amnesia showed a systematic deficit on 1 class of…
Accurate Ultrasonic Measurement of Surface Profile Using Phase Shift of Echo and Inverse Filtering
NASA Astrophysics Data System (ADS)
Arihara, Chihiro; Hasegawa, Hideyuki; Kanai, Hiroshi
2006-05-01
Atherosclerosis is the main cause of circulatory diseases such as myocardial infarction and cerebral infarction, and it is very important to diagnose atherosclerosis in its early stage. In the early stage of atherosclerosis, the luminal surface of an arterial wall becomes rough because of the injury of the endothelium [R. Ross: New Engl. J. Med. 340 (2004) 115]. Conventional ultrasonic diagnostic equipments cannot detect such roughness on the order of micrometer because of their low resolution of approximately 0.1 mm. In this study, for the accurate detection of surface roughness, an ultrasonic beam was scanned in the direction that is parallel to the surface of an object. When there is a gap on the surface, the phase of the echo from the surface changes because the distance between the probe and the surface changes during the scanning. Therefore, surface roughness can be assessed by estimating the phase shift of echoes obtained during the beam scanning. Furthermore, lateral resolution, which is deteriorated by a finite diameter of the ultrasound beam, was improved by an inverse filter. By using the proposed method, the surface profile of a phantom, which had surface roughness on the micrometer order, was detected, and the estimated surface profiles became more precise by applying the inverse filter.
Assessment of Data Fusion Algorithms for Earth Observation Change Detection Processes.
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.
Assessment of Data Fusion Algorithms for Earth Observation Change Detection Processes
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
Detection of kinetic change points in piece-wise linear single molecule motion
NASA Astrophysics Data System (ADS)
Hill, Flynn R.; van Oijen, Antoine M.; Duderstadt, Karl E.
2018-03-01
Single-molecule approaches present a powerful way to obtain detailed kinetic information at the molecular level. However, the identification of small rate changes is often hindered by the considerable noise present in such single-molecule kinetic data. We present a general method to detect such kinetic change points in trajectories of motion of processive single molecules having Gaussian noise, with a minimum number of parameters and without the need of an assumed kinetic model beyond piece-wise linearity of motion. Kinetic change points are detected using a likelihood ratio test in which the probability of no change is compared to the probability of a change occurring, given the experimental noise. A predetermined confidence interval minimizes the occurrence of false detections. Applying the method recursively to all sub-regions of a single molecule trajectory ensures that all kinetic change points are located. The algorithm presented allows rigorous and quantitative determination of kinetic change points in noisy single molecule observations without the need for filtering or binning, which reduce temporal resolution and obscure dynamics. The statistical framework for the approach and implementation details are discussed. The detection power of the algorithm is assessed using simulations with both single kinetic changes and multiple kinetic changes that typically arise in observations of single-molecule DNA-replication reactions. Implementations of the algorithm are provided in ImageJ plugin format written in Java and in the Julia language for numeric computing, with accompanying Jupyter Notebooks to allow reproduction of the analysis presented here.
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.
Detection of Deforestation and Land Conversion in Rondonia, Brazil Using Change Detection Techniques
NASA Technical Reports Server (NTRS)
Guild, Liane S.; Cohen, Warren B,; Kauffman, J. Boone; Peterson, David L. (Technical Monitor)
2001-01-01
Fires associated with tropical deforestation, land conversion, and land use greatly contribute to emissions as well as the depletion of carbon and nutrient pools. The objective of this research was to compare change detection techniques for identifying deforestation and cattle pasture formation during a period of early colonization and agricultural expansion in the vicinity of Jamari, Rond6nia. Multi-date Landsat Thematic Mapper (TM) data between 1984 and 1992 was examined in a 94 370-ha area of active deforestation to map land cover change. The Tasseled Cap (TC) transformation was used to enhance the contrast between forest, cleared areas, and regrowth. TC images were stacked into a composite multi-date TC and used in a principal components (PC) transformation to identify change components. In addition, consecutive TC image pairs were differenced and stacked into a composite multi-date differenced image. A maximum likelihood classification of each image composite was compared for identification of land cover change. The multi-date TC composite classification had the best accuracy of 78.1% (kappa). By 1984, only 5% of the study area had been cleared, but by 1992, 11% of the area had been deforested, primarily for pasture and 7% lost due to hydroelectric dam flooding. Finally, discrimination of pasture versus cultivation was improved due to the ability to detect land under sustained clearing opened to land exhibiting regrowth with infrequent clearing.
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.
NASA Astrophysics Data System (ADS)
Silverman, N. L.; Maneta, M. P.
2016-06-01
Detecting long-term change in seasonal precipitation using ground observations is dependent on the representativity of the point measurement to the surrounding landscape. In mountainous regions, representativity can be poor and lead to large uncertainties in precipitation estimates at high elevations or in areas where observations are sparse. If the uncertainty in the estimate is large compared to the long-term shifts in precipitation, then the change will likely go undetected. In this analysis, we examine the minimum detectable change across mountainous terrain in western Montana, USA. We ask the question: What is the minimum amount of change that is necessary to be detected using our best estimates of precipitation in complex terrain? We evaluate the spatial uncertainty in the precipitation estimates by conditioning historic regional climate model simulations to ground observations using Bayesian inference. By using this uncertainty as a null hypothesis, we test for detectability across the study region. To provide context for the detectability calculations, we look at a range of future scenarios from the Coupled Model Intercomparison Project 5 (CMIP5) multimodel ensemble downscaled to 4 km resolution using the MACAv2-METDATA data set. When using the ensemble averages we find that approximately 65% of the significant increases in winter precipitation go undetected at midelevations. At high elevation, approximately 75% of significant increases in winter precipitation are undetectable. Areas where change can be detected are largely controlled by topographic features. Elevation and aspect are key characteristics that determine whether or not changes in winter precipitation can be detected. Furthermore, we find that undetected increases in winter precipitation at high elevation will likely remain as snow under climate change scenarios. Therefore, there is potential for these areas to offset snowpack loss at lower elevations and confound the effects of climate change
Accurate step-hold tracking of smoothly varying periodic and aperiodic probability.
Ricci, Matthew; Gallistel, Randy
2017-07-01
Subjects observing many samples from a Bernoulli distribution are able to perceive an estimate of the generating parameter. A question of fundamental importance is how the current percept-what we think the probability now is-depends on the sequence of observed samples. Answers to this question are strongly constrained by the manner in which the current percept changes in response to changes in the hidden parameter. Subjects do not update their percept trial-by-trial when the hidden probability undergoes unpredictable and unsignaled step changes; instead, they update it only intermittently in a step-hold pattern. It could be that the step-hold pattern is not essential to the perception of probability and is only an artifact of step changes in the hidden parameter. However, we now report that the step-hold pattern obtains even when the parameter varies slowly and smoothly. It obtains even when the smooth variation is periodic (sinusoidal) and perceived as such. We elaborate on a previously published theory that accounts for: (i) the quantitative properties of the step-hold update pattern; (ii) subjects' quick and accurate reporting of changes; (iii) subjects' second thoughts about previously reported changes; (iv) subjects' detection of higher-order structure in patterns of change. We also call attention to the challenges these results pose for trial-by-trial updating theories.
Accurate, multi-kb reads resolve complex populations and detect rare microorganisms.
Sharon, Itai; Kertesz, Michael; Hug, Laura A; Pushkarev, Dmitry; Blauwkamp, Timothy A; Castelle, Cindy J; Amirebrahimi, Mojgan; Thomas, Brian C; Burstein, David; Tringe, Susannah G; Williams, Kenneth H; Banfield, Jillian F
2015-04-01
Accurate evaluation of microbial communities is essential for understanding global biogeochemical processes and can guide bioremediation and medical treatments. Metagenomics is most commonly used to analyze microbial diversity and metabolic potential, but assemblies of the short reads generated by current sequencing platforms may fail to recover heterogeneous strain populations and rare organisms. Here we used short (150-bp) and long (multi-kb) synthetic reads to evaluate strain heterogeneity and study microorganisms at low abundance in complex microbial communities from terrestrial sediments. The long-read data revealed multiple (probably dozens of) closely related species and strains from previously undescribed Deltaproteobacteria and Aminicenantes (candidate phylum OP8). Notably, these are the most abundant organisms in the communities, yet short-read assemblies achieved only partial genome coverage, mostly in the form of short scaffolds (N50 = ∼ 2200 bp). Genome architecture and metabolic potential for these lineages were reconstructed using a new synteny-based method. Analysis of long-read data also revealed thousands of species whose abundances were <0.1% in all samples. Most of the organisms in this "long tail" of rare organisms belong to phyla that are also represented by abundant organisms. Genes encoding glycosyl hydrolases are significantly more abundant than expected in rare genomes, suggesting that rare species may augment the capability for carbon turnover and confer resilience to changing environmental conditions. Overall, the study showed that a diversity of closely related strains and rare organisms account for a major portion of the communities. These are probably common features of many microbial communities and can be effectively studied using a combination of long and short reads. © 2015 Sharon et al.; Published by Cold Spring Harbor Laboratory Press.
Space-based detection of wetlands' surface water level changes from L-band SAR interferometry
Wdowinski, S.; Kim, S.-W.; Amelung, F.; Dixon, T.H.; Miralles-Wilhelm, F.; Sonenshein, R.
2008-01-01
Interferometric processing of JERS-1 L-band Synthetic Aperture Radar (SAR) data acquired over south Florida during 1993-1996 reveals detectable surface changes in the Everglades wetlands. Although our study is limited to south Florida it has implication for other large-scale wetlands, because south Florida wetlands have diverse vegetation types and both managed and natural flow environments. Our analysis reveals that interferometric coherence level is sensitive to wetland vegetation type and to the interferogram time span. Interferograms with time spans less than six months maintain phase observations for all wetland types, allowing characterization of water level changes in different wetland environments. The most noticeable changes occur between the managed and the natural flow wetlands. In the managed wetlands, fringes are organized, follow patterns related to some of the managed water control structures and have high fringe-rate. In the natural flow areas, fringes are irregular and have a low fringe-rate. The high fringe rate in managed areas reflects dynamic water topography caused by high flow rate due to gate operation. Although this organized fringe pattern is not characteristic of most large-scale wetlands, the high level of water level change enables accurate estimation of the wetland InSAR technique, which lies in the range of 5-10??cm. The irregular and low rate fringe pattern in the natural flow area reflects uninterrupted flow that diffuses water efficiently and evenly. Most of the interferograms in the natural flow area show an elongated fringe located along the transitional zone between salt- and fresh-water wetlands, reflecting water level changes due to ocean tides. ?? 2007 Elsevier Inc. All rights reserved.
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
LAND-COVER CHANGE DETECTION USING MULTI-TEMPORAL MODIS NDVI DATA
Monitoring the locations and distributions of land-cover changes is important for establishing linkages between policy decisions, regulatory actions and subsequent land-use activities. Past studies incorporating two-date change detection using Landsat data have tended to be perfo...
Land-Cover Change Detection Using Multi-Temporal MODIS NDVI Imagery
Monitoring the locations and distributions of land-cover change is important for establishing linkages between policy decisions, regulatory actions and subsequent land-use activities. Past studies incorporating two-date change detection using Landsat data have tended to be perfor...
Detecting event-related changes in organizational networks using optimized neural network models.
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
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
Mu, Di; Yan, Liang; Tang, Hui; Liao, Yong
2015-10-01
To develop a sensitive and accurate assay system for the quantification of covalently closed circular HBV DNA (cccDNA) for future clinical monitoring of cccDNA fluctuation during antiviral therapy in the liver of infected patients. A droplet digital PCR (ddPCR)-based assay system detected template DNA input at the single copy level (or ~10(-5) pg of plasmid HBV DNA) by using serially diluted plasmid HBV DNA samples. Compared with the conventional quantitative PCR assay in the detection of cccDNA, which required at least 50 ng of template DNA input, a parallel experiment applying a ddPCR system demonstrates that the lowest detection limit of cccDNA from HepG2.215 cellular DNA samples is around 1 ng, which is equivalent to 0.54 ± 0.94 copies of cccDNA. In addition, we demonstrated that the addition of cccDNA-safe exonuclease and utilization of cccDNA-specific primers in the ddPCR assay system significantly improved the detection accuracy of HBV cccDNA from HepG2.215 cellular DNA samples. The ddPCR-based cccDNA detection system is a sensitive and accurate assay for the quantification of cccDNA in HBV-transfected HepG2.215 cellular DNA samples and may represent an important method for future application in monitoring cccDNA fluctuation during antiviral therapy.
NASA Astrophysics Data System (ADS)
Cong, Runmin; Han, Ping; Li, Chongyi; He, Jiaji; Zhang, Zaiji
2016-09-01
Targets of interest are different in various applications in which manmade targets, such as aircraft, ships, and buildings, are given more attention. Manmade target extraction methods using synthetic aperture radar (SAR) images are designed in response to various demands, which include civil uses, business purposes, and military industries. This plays an increasingly vital role in monitoring, military reconnaissance, and precision strikes. Achieving accurate and complete results through traditional methods is becoming more challenging because of the scattered complexity of polarization in polarimetric synthetic aperture radar (PolSAR) image. A multistage decision-based method is proposed composed of power decision, dominant scattering mechanism decision, and reflection symmetry decision. In addition, the theories of polarimetric contrast enhancement, generalized Y decomposition, and maximum eigenvalue ratio are applied to assist the decision. Fully PolSAR data are adopted to evaluate and verify the approach. Experimental results show that the method can achieve an effective result with a lower false alarm rate and clear contours. Finally, on this basis, a universal framework of change detection for manmade targets is presented as an application of our method. Two sets of measured data are also used to evaluate and verify the effectiveness of the change-detection algorithm.
Using the morphology of photoplethysmogram peaks to detect changes in posture.
Linder, Stephen P; Wendelken, Suzanne M; Wei, Edward; McGrath, Susan P
2006-06-01
The morphology of the pulsatile component of the photoplethysmogram (PPG) has been shown to vary with physiology, but changes in the morphology caused by the baroreflex response to orthostatic stress have not been investigated. Using two FDA approved Nonin pulse oximeters placed on the finger and ear, we monitored 11 subjects, for three trials each, as they stood from a supine position. Each cardiac cycle was automatically extracted from the PPG waveform and characterized using statistics corresponding to normalized peak width, instantaneous heart rate, and amplitude of the pulsatile component of the ear PPG. A nonparametric Wilcoxon rank sum test was then used to detect in real-time changes in these features with p < 0.01. In all 33 trials, the standing event was detected as an abrupt change in at least two of these features, with only one false alarm. In 26 trials, an abrupt change was detected in all three features, with no false alarms. An increase in the normalize peak width was detected before an increase in heart rate, and in 21 trials a peak in the feature was detected before or as standing commenced. During standing, the pulse rate always increases, and then amplitude of the ear PPG constricts by a factor of two or more. We hypothesis that the baroreflex first reduces the percentage of time blood flow is stagnant during the cardiac cycle, then increases the hear rate, and finally vasoconstricts the peripheral tissue in order to reestablishing a nominal blood pressure. These three features therefore can be used as a detector of the baroreflex response to changes in posture or other forms of blood volume sequestration.
TREFEX: Trend Estimation and Change Detection in the Response of MOX Gas Sensors
Pashami, Sepideh; Lilienthal, Achim J.; Schaffernicht, Erik; Trincavelli, Marco
2013-01-01
Many applications of metal oxide gas sensors can benefit from reliable algorithms to detect significant changes in the sensor response. Significant changes indicate a change in the emission modality of a distant gas source and occur due to a sudden change of concentration or exposure to a different compound. As a consequence of turbulent gas transport and the relatively slow response and recovery times of metal oxide sensors, their response in open sampling configuration exhibits strong fluctuations that interfere with the changes of interest. In this paper we introduce TREFEX, a novel change point detection algorithm, especially designed for metal oxide gas sensors in an open sampling system. TREFEX models the response of MOX sensors as a piecewise exponential signal and considers the junctions between consecutive exponentials as change points. We formulate non-linear trend filtering and change point detection as a parameter-free convex optimization problem for single sensors and sensor arrays. We evaluate the performance of the TREFEX algorithm experimentally for different metal oxide sensors and several gas emission profiles. A comparison with the previously proposed GLR method shows a clearly superior performance of the TREFEX algorithm both in detection performance and in estimating the change time. PMID:23736853
Peri-ictal ECG changes in childhood epilepsy: implications for detection systems.
Jansen, Katrien; Varon, Carolina; Van Huffel, Sabine; Lagae, Lieven
2013-10-01
Early detection of seizures could reduce associated morbidity and mortality and improve the quality of life of patients with epilepsy. In this study, the aim was to investigate whether ictal tachycardia is present in focal and generalized epileptic seizures in children. We sought to predict in which type of seizures tachycardia can be identified before actual seizure onset. Electrocardiogram segments in 80 seizures were analyzed in time and frequency domains before and after the onset of epileptic seizures on EEG. These ECG parameters were analyzed to find the most informative ones that can be used for seizure detection. The algorithm of Leutmezer et al. was used to find the temporal relationship between the change in heart rate and seizure onset. In the time domain, the mean RR shows a significant difference before compared to after onset of the seizure in focal seizures. This can be observed in temporal lobe seizures as well as frontal lobe seizures. Calculation of mean RR interval has a high specificity for detection of ictal heart rate changes. Preictal heart rate changes are observed in 70% of the partial seizures. Ictal heart rate changes are present only in partial seizures in this childhood epilepsy study. The changes can be observed in temporal lobe seizures as well as in frontal lobe seizures. Heart rate changes precede seizure onset in 70% of the focal seizures, making seizure detection and closed-loop systems a possible therapeutic alternative in the population of children with refractory epilepsy. © 2013.
NASA Astrophysics Data System (ADS)
Hwang, Jangsun; Seo, Youngmin; Jo, Yeonho; Son, Jaewoo; Choi, Jonghoon
2016-10-01
C-reactive protein (CRP) is a pentameric protein that is present in the bloodstream during inflammatory events, e.g., liver failure, leukemia, and/or bacterial infection. The level of CRP indicates the progress and prognosis of certain diseases; it is therefore necessary to measure CRP levels in the blood accurately. The normal concentration of CRP is reported to be 1-3 mg/L. Inflammatory events increase the level of CRP by up to 500 times; accordingly, CRP is a biomarker of acute inflammatory disease. In this study, we demonstrated the preparation of DNA aptamer-conjugated peripheral blood mononuclear cells (Apt-PBMCs) that specifically capture human CRP. Live PBMCs functionalized with aptamers could detect different levels of human CRP by producing immune complexes with reporter antibody. The binding behavior of Apt-PBMCs toward highly concentrated CRP sites was also investigated. The immune responses of Apt-PBMCs were evaluated by measuring TNF-alpha secretion after stimulating the PBMCs with lipopolysaccharides. In summary, engineered Apt-PBMCs have potential applications as live cell based biosensors and for in vitro tracing of CRP secretion sites.
An Accurate Framework for Arbitrary View Pedestrian Detection in Images
NASA Astrophysics Data System (ADS)
Fan, Y.; Wen, G.; Qiu, S.
2018-01-01
We consider the problem of detect pedestrian under from images collected under various viewpoints. This paper utilizes a novel framework called locality-constrained affine subspace coding (LASC). Firstly, the positive training samples are clustered into similar entities which represent similar viewpoint. Then Principal Component Analysis (PCA) is used to obtain the shared feature of each viewpoint. Finally, the samples that can be reconstructed by linear approximation using their top- k nearest shared feature with a small error are regarded as a correct detection. No negative samples are required for our method. Histograms of orientated gradient (HOG) features are used as the feature descriptors, and the sliding window scheme is adopted to detect humans in images. The proposed method exploits the sparse property of intrinsic information and the correlations among the multiple-views samples. Experimental results on the INRIA and SDL human datasets show that the proposed method achieves a higher performance than the state-of-the-art methods in form of effect and efficiency.
A New CCI ECV Release (v2.0) to Accurately Measure the Sea Level Change (1993-2015)
NASA Astrophysics Data System (ADS)
Legeais, J.; Cazenave, A. A.; Ablain, M.; Gilles, G.; Johannessen, J. A.; Scharffenberg, M. G.; Timms, G.; Andersen, O. B.; Cipollini, P.; Roca, M.; Rudenko, S.; Fernandes, J.; Balmaseda, M.; Quartly, G.; Fenoglio Marc, L.; Meyssignac, B.; Benveniste, J.; Ambrozio, A.; Restano, M.
2016-12-01
Accurate monitoring of the sea level is required to better understand its variability and changes. Sea level is one of the Essential Climate Variables (ECV) selected in the frame of the ESA Climate Change Initiative (CCI) program. It aims at providing a long-term homogeneous and accurate sea level record. The needs and feedback of the climate research community have been collected and a first version of the sea level ECV product has been generated with the best algorithms and altimeter standards. This record (1993-2014) has been validated by the climate research community. Within phase II (2014-2016), the 15 partner consortium has prepared the production of a new reprocessed homogeneous and accurate altimeter sea level record which will be distributed in Autumn 2016. New level 2 altimeter standards developed and tested within the project as well as external contributions have been identified, processed and evaluated by comparison with a reference for different altimeter missions (TOPEX/Poseidon, Jason-1 & 2, ERS-1 & 2, Envisat and GFO). The main evolutions are associated with the wet troposphere correction (based on the GPD+ algorithm including inter calibration with respect to external sensors) but also to the orbit solutions (POE-E and GFZ15), the ERA-Interim based atmospheric corrections and the FES2014 ocean tide model. A new pole tide solution is used and anomalies are referenced to the MSS DTU15. The presentation will focus on the main achievements of the ESA CCI Sea Level project and on the description of the new SL_cci ECV release covering 1993-2015. The major steps required to produce the reprocessed 23 year climate time series will be described. The impacts of the selected level 2 altimeter standards on the SL_cci ECV have been assessed on different spatial scales (global, regional, mesoscale) and temporal scales (long-term, inter-annual, periodic). A significant improvement is expected compared to the current v1.1, with the main impacts observed on the
MIDAS robust trend estimator for accurate GPS station velocities without step detection
Kreemer, Corné; Hammond, William C.; Gazeaux, Julien
2016-01-01
Abstract Automatic estimation of velocities from GPS coordinate time series is becoming required to cope with the exponentially increasing flood of available data, but problems detectable to the human eye are often overlooked. This motivates us to find an automatic and accurate estimator of trend that is resistant to common problems such as step discontinuities, outliers, seasonality, skewness, and heteroscedasticity. Developed here, Median Interannual Difference Adjusted for Skewness (MIDAS) is a variant of the Theil‐Sen median trend estimator, for which the ordinary version is the median of slopes vij = (xj–xi)/(tj–ti) computed between all data pairs i > j. For normally distributed data, Theil‐Sen and least squares trend estimates are statistically identical, but unlike least squares, Theil‐Sen is resistant to undetected data problems. To mitigate both seasonality and step discontinuities, MIDAS selects data pairs separated by 1 year. This condition is relaxed for time series with gaps so that all data are used. Slopes from data pairs spanning a step function produce one‐sided outliers that can bias the median. To reduce bias, MIDAS removes outliers and recomputes the median. MIDAS also computes a robust and realistic estimate of trend uncertainty. Statistical tests using GPS data in the rigid North American plate interior show ±0.23 mm/yr root‐mean‐square (RMS) accuracy in horizontal velocity. In blind tests using synthetic data, MIDAS velocities have an RMS accuracy of ±0.33 mm/yr horizontal, ±1.1 mm/yr up, with a 5th percentile range smaller than all 20 automatic estimators tested. Considering its general nature, MIDAS has the potential for broader application in the geosciences. PMID:27668140
MIDAS robust trend estimator for accurate GPS station velocities without step detection.
Blewitt, Geoffrey; Kreemer, Corné; Hammond, William C; Gazeaux, Julien
2016-03-01
Automatic estimation of velocities from GPS coordinate time series is becoming required to cope with the exponentially increasing flood of available data, but problems detectable to the human eye are often overlooked. This motivates us to find an automatic and accurate estimator of trend that is resistant to common problems such as step discontinuities, outliers, seasonality, skewness, and heteroscedasticity. Developed here, Median Interannual Difference Adjusted for Skewness (MIDAS) is a variant of the Theil-Sen median trend estimator, for which the ordinary version is the median of slopes v ij = ( x j -x i )/( t j -t i ) computed between all data pairs i > j . For normally distributed data, Theil-Sen and least squares trend estimates are statistically identical, but unlike least squares, Theil-Sen is resistant to undetected data problems. To mitigate both seasonality and step discontinuities, MIDAS selects data pairs separated by 1 year. This condition is relaxed for time series with gaps so that all data are used. Slopes from data pairs spanning a step function produce one-sided outliers that can bias the median. To reduce bias, MIDAS removes outliers and recomputes the median. MIDAS also computes a robust and realistic estimate of trend uncertainty. Statistical tests using GPS data in the rigid North American plate interior show ±0.23 mm/yr root-mean-square (RMS) accuracy in horizontal velocity. In blind tests using synthetic data, MIDAS velocities have an RMS accuracy of ±0.33 mm/yr horizontal, ±1.1 mm/yr up, with a 5th percentile range smaller than all 20 automatic estimators tested. Considering its general nature, MIDAS has the potential for broader application in the geosciences.
A new method of real-time detection of changes in periodic data stream
NASA Astrophysics Data System (ADS)
Lyu, Chen; Lu, Guoliang; Cheng, Bin; Zheng, Xiangwei
2017-07-01
The change point detection in periodic time series is much desirable in many practical usages. We present a novel algorithm for this task, which includes two phases: 1) anomaly measure- on the basis of a typical regression model, we propose a new computation method to measure anomalies in time series which does not require any reference data from other measurement(s); 2) change detection- we introduce a new martingale test for detection which can be operated in an unsupervised and nonparametric way. We have conducted extensive experiments to systematically test our algorithm. The results make us believe that our algorithm can be directly applicable in many real-world change-point-detection applications.
Chen, Wenfeng; Liu, Chang Hong; Nakabayashi, Kazuyo
2012-01-01
Recent research has shown that the presence of a task-irrelevant attractive face can induce a transient diversion of attention from a perceptual task that requires covert deployment of attention to one of the two locations. However, it is not known whether this spontaneous appraisal for facial beauty also modulates attention in change detection among multiple locations, where a slower, and more controlled search process is simultaneously affected by the magnitude of a change and the facial distinctiveness. Using the flicker paradigm, this study examines how spontaneous appraisal for facial beauty affects the detection of identity change among multiple faces. Participants viewed a display consisting of two alternating frames of four faces separated by a blank frame. In half of the trials, one of the faces (target face) changed to a different person. The task of the participant was to indicate whether a change of face identity had occurred. The results showed that (1) observers were less efficient at detecting identity change among multiple attractive faces relative to unattractive faces when the target and distractor faces were not highly distinctive from one another; and (2) it is difficult to detect a change if the new face is similar to the old. The findings suggest that attractive faces may interfere with the attention-switch process in change detection. The results also show that attention in change detection was strongly modulated by physical similarity between the alternating faces. Although facial beauty is a powerful stimulus that has well-demonstrated priority, its influence on change detection is easily superseded by low-level image similarity. The visual system appears to take a different approach to facial beauty when a task requires resource-demanding feature comparisons.
Detecting Children's Lies: Are Parents Accurate Judges of Their Own Children's Lies?
ERIC Educational Resources Information Center
Talwar, Victoria; Renaud, Sarah-Jane; Conway, Lauryn
2015-01-01
The current study investigated whether parents are accurate judges of their own children's lie-telling behavior. Participants included 250 mother-child dyads. Children were between three and 11 years of age. A temptation resistance paradigm was used to elicit a minor transgressive behavior from the children involving peeking at a forbidden toy and…
An accurate algorithm to match imperfectly matched images for lung tumor detection without markers
Rozario, Timothy; Bereg, Sergey; Yan, Yulong; Chiu, Tsuicheng; Liu, Honghuan; Kearney, Vasant; Jiang, Lan
2015-01-01
implanted and used as ground truth for tumor positions. Although other organs and bony structures introduced strong signals superimposed on tumors at some angles, this method accurately located tumors on every projection over 12 gantry angles. The maximum error was less than 2.2 mm, while the total average error was less than 0.9 mm. This algorithm was capable of detecting tumors without markers, despite strong background signals. PACS numbers: 87.57.cj, 87.57.cp87.57.nj, 87.57.np, 87.57.Q‐, 87.59.bf, 87.63.lm
Reid-Bayliss, Kate S; Loeb, Lawrence A
2017-08-29
Transcriptional mutagenesis (TM) due to misincorporation during RNA transcription can result in mutant RNAs, or epimutations, that generate proteins with altered properties. TM has long been hypothesized to play a role in aging, cancer, and viral and bacterial evolution. However, inadequate methodologies have limited progress in elucidating a causal association. We present a high-throughput, highly accurate RNA sequencing method to measure epimutations with single-molecule sensitivity. Accurate RNA consensus sequencing (ARC-seq) uniquely combines RNA barcoding and generation of multiple cDNA copies per RNA molecule to eliminate errors introduced during cDNA synthesis, PCR, and sequencing. The stringency of ARC-seq can be scaled to accommodate the quality of input RNAs. We apply ARC-seq to directly assess transcriptome-wide epimutations resulting from RNA polymerase mutants and oxidative stress.
Yavaş, Gökhan; Koyutürk, Mehmet; Gould, Meetha P; McMahon, Sarah; LaFramboise, Thomas
2014-03-05
With the advent of paired-end high throughput sequencing, it is now possible to identify various types of structural variation on a genome-wide scale. Although many methods have been proposed for structural variation detection, most do not provide precise boundaries for identified variants. In this paper, we propose a new method, Distribution Based detection of Duplication Boundaries (DB2), for accurate detection of tandem duplication breakpoints, an important class of structural variation, with high precision and recall. Our computational experiments on simulated data show that DB2 outperforms state-of-the-art methods in terms of finding breakpoints of tandem duplications, with a higher positive predictive value (precision) in calling the duplications' presence. In particular, DB2's prediction of tandem duplications is correct 99% of the time even for very noisy data, while narrowing down the space of possible breakpoints within a margin of 15 to 20 bps on the average. Most of the existing methods provide boundaries in ranges that extend to hundreds of bases with lower precision values. Our method is also highly robust to varying properties of the sequencing library and to the sizes of the tandem duplications, as shown by its stable precision, recall and mean boundary mismatch performance. We demonstrate our method's efficacy using both simulated paired-end reads, and those generated from a melanoma sample and two ovarian cancer samples. Newly discovered tandem duplications are validated using PCR and Sanger sequencing. Our method, DB2, uses discordantly aligned reads, taking into account the distribution of fragment length to predict tandem duplications along with their breakpoints on a donor genome. The proposed method fine tunes the breakpoint calls by applying a novel probabilistic framework that incorporates the empirical fragment length distribution to score each feasible breakpoint. DB2 is implemented in Java programming language and is freely available
Color-Change Detection Activity in the Primate Superior Colliculus.
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.
THE SCREENING AND RANKING ALGORITHM FOR CHANGE-POINTS DETECTION IN MULTIPLE SAMPLES
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
Gray, Bradley E; McMahon, Robert P; Green, Michael F; Seidman, Larry J; Mesholam-Gately, Raquelle I; Kern, Robert S; Nuechterlein, Keith H; Keefe, Richard S; Gold, James M
2014-10-01
Clinicians often need to evaluate the treatment response of an individual person and to know that observed change is true improvement or worsening beyond usual week-to-week changes. This paper gives clinicians tools to evaluate individual changes on the MATRICS Consensus Cognitive Battery (MCCB). We compare three different approaches: a descriptive analysis of MCCB test-retest performance with no intervention, a reliable change index (RCI) approach controlling for average practice effects, and a regression approach. Data were gathered as part of the MATRICS PASS study (Nuechterlein et al., 2008). A total of 159 people with schizophrenia completed the MCCB at baseline and 4weeks later. Data were analyzed using an RCI and a regression formula establishing confidence intervals. The RCI and regression approaches agree within one point when baseline values are close to the sample mean. However, the regression approach offers more accurate limits for expected change at the tails of the distribution of baseline scores. Although both approaches have their merits, the regression approach provides the most accurate measure of significant change across the full range of scores. As the RCI does not account for regression to the mean and has confidence limits that remain constant across baseline scores, the RCI approach effectively gives narrower confidence limits around an inaccurately predicted average change value. Further, despite the high test-retest reliability of the MCCB, a change in an individual's score must be relatively large to be confident that it is beyond normal month-to-month variation. Copyright © 2014 Elsevier B.V. All rights reserved.
Land cover change detection of Hatiya Island, Bangladesh, using remote sensing techniques
NASA Astrophysics Data System (ADS)
Kumar, Lalit; Ghosh, Manoj Kumer
2012-01-01
Land cover change is a significant issue for environmental managers for sustainable management. Remote sensing techniques have been shown to have a high probability of recognizing land cover patterns and change detection due to periodic coverage, data integrity, and provision of data in a broad range of the electromagnetic spectrum. We evaluate the applicability of remote sensing techniques for land cover pattern recognition, as well as land cover change detection of the Hatiya Island, Bangladesh, and quantify land cover changes from 1977 to 1999. A supervised classification approach was used to classify Landsat Enhanced Thematic Mapper (ETM), Thematic Mapper (TM), and Multispectral Scanner (MSS) images into eight major land cover categories. We detected major land cover changes over the 22-year study period. During this period, marshy land, mud, mud with small grass, and bare soil had decreased by 85%, 46%, 44%, and 24%, respectively, while agricultural land, medium forest, forest, and settlement had positive changes of 26%, 45%, 363%, and 59%, respectively. The primary drivers of such landscape change were erosion and accretion processes, human pressure, and the reforestation and land reclamation programs of the Bangladesh Government.
Hidalgo, H.G.; Das, T.; Dettinger, M.D.; Cayan, D.R.; Pierce, D.W.; Barnett, T.P.; Bala, G.; Mirin, A.; Wood, A.W.; Bonfils, Celine; Santer, B.D.; Nozawa, T.
2009-01-01
This article applies formal detection and attribution techniques to investigate the nature of observed shifts in the timing of streamflow in the western United States. Previous studies have shown that the snow hydrology of the western United States has changed in the second half of the twentieth century. Such changes manifest themselves in the form of more rain and less snow, in reductions in the snow water contents, and in earlier snowmelt and associated advances in streamflow "center" timing (the day in the "water-year" on average when half the water-year flow at a point has passed). However, with one exception over a more limited domain, no other study has attempted to formally attribute these changes to anthropogenic increases of greenhouse gases in the atmosphere. Using the observations together with a set of global climate model simulations and a hydrologic model (applied to three major hydrological regions of the western United States_the California region, the upper Colorado River basin, and the Columbia River basin), it is found that the observed trends toward earlier "center" timing of snowmelt-driven streamflows in the western United States since 1950 are detectably different from natural variability (significant at the p < 0.05 level). Furthermore, the nonnatural parts of these changes can be attributed confidently to climate changes induced by anthropogenic greenhouse gases, aerosols, ozone, and land use. The signal from the Columbia dominates the analysis, and it is the only basin that showed a detectable signal when the analysis was performed on individual basins. It should be noted that although climate change is an important signal, other climatic processes have also contributed to the hydrologic variability of large basins in the western United States. ?? 2009 American Meteorological Society.
Zelinsky, G J
2001-02-01
Search, memory, and strategy constraints on change detection were analyzed in terms of oculomotor variables. Observers viewed a repeating sequence of three displays (Scene 1-->Mask-->Scene 2-->Mask...) and indicated the presence-absence of a changing object between Scenes 1 and 2. Scenes depicted real-world objects arranged on a surface. Manipulations included set size (one, three, or nine items) and the orientation of the changing objects (similar or different). Eye movements increased with the number of potentially changing objects in the scene, with this set size effect suggesting a relationship between change detection and search. A preferential fixation analysis determined that memory constraints are better described by the operation comparing the pre- and postchange objects than as a capacity limitation, and a scanpath analysis revealed a change detection strategy relying on the peripheral encoding and comparison of display items. These findings support a signal-in-noise interpretation of change detection in which the signal varies with the similarity of the changing objects and the noise is determined by the distractor objects and scene background.
Chen, X.; Vierling, Lee; Deering, D.
2005-01-01
Satellite data offer unrivaled utility in monitoring and quantifying large scale land cover change over time. Radiometric consistency among collocated multi-temporal imagery is difficult to maintain, however, due to variations in sensor characteristics, atmospheric conditions, solar angle, and sensor view angle that can obscure surface change detection. To detect accurate landscape change using multi-temporal images, we developed a variation of the pseudoinvariant feature (PIF) normalization scheme: the temporally invariant cluster (TIC) method. Image data were acquired on June 9, 1990 (Landsat 4), June 20, 2000 (Landsat 7), and August 26, 2001 (Landsat 7) to analyze boreal forests near the Siberian city of Krasnoyarsk using the normalized difference vegetation index (NDVI), enhanced vegetation index (EVI), and reduced simple ratio (RSR). The temporally invariant cluster (TIC) centers were identified via a point density map of collocated pixel VIs from the base image and the target image, and a normalization regression line was created to intersect all TIC centers. Target image VI values were then recalculated using the regression function so that these two images could be compared using the resulting common radiometric scale. We found that EVI was very indicative of vegetation structure because of its sensitivity to shadowing effects and could thus be used to separate conifer forests from deciduous forests and grass/crop lands. Conversely, because NDVI reduced the radiometric influence of shadow, it did not allow for distinctions among these vegetation types. After normalization, correlations of NDVI and EVI with forest leaf area index (LAI) field measurements combined for 2000 and 2001 were significantly improved; the r 2 values in these regressions rose from 0.49 to 0.69 and from 0.46 to 0.61, respectively. An EVI "cancellation effect" where EVI was positively related to understory greenness but negatively related to forest canopy coverage was evident across a
Network for the Detection of Atmospheric Composition Change (NDACC)
, state and local government web resources and services. Home > Network for the Detection of and troposphere, and establishing links between climate change and atmospheric composition. Following
Detecting the effects of forest harvesting on streamflow using hydrologic model change detection
Nicolas P. Zegre; Nicholas A. Som
2011-01-01
Knowledge of the effects of forest management on hydrology primarily comes from paired-catchment study experiments. This approach has contributed fundamental knowledge of the effects of forest management on hydrology, but results from these studies lack insight into catchment processes. Outlined in this study is an alternative method of change detection that uses a...
Liu, Xiao; Xu, Yinyin; Liang, Dequan; Gao, Peng; Sun, Yepeng; Gifford, Benjamin; D’Ascenzo, Mark; Liu, Xiaomin; Tellier, Laurent C. A. M.; Yang, Fang; Tong, Xin; Chen, Dan; Zheng, Jing; Li, Weiyang; Richmond, Todd; Xu, Xun; Wang, Jun; Li, Yingrui
2013-01-01
The major histocompatibility complex (MHC) is one of the most variable and gene-dense regions of the human genome. Most studies of the MHC, and associated regions, focus on minor variants and HLA typing, many of which have been demonstrated to be associated with human disease susceptibility and metabolic pathways. However, the detection of variants in the MHC region, and diagnostic HLA typing, still lacks a coherent, standardized, cost effective and high coverage protocol of clinical quality and reliability. In this paper, we presented such a method for the accurate detection of minor variants and HLA types in the human MHC region, using high-throughput, high-coverage sequencing of target regions. A probe set was designed to template upon the 8 annotated human MHC haplotypes, and to encompass the 5 megabases (Mb) of the extended MHC region. We deployed our probes upon three, genetically diverse human samples for probe set evaluation, and sequencing data show that ∼97% of the MHC region, and over 99% of the genes in MHC region, are covered with sufficient depth and good evenness. 98% of genotypes called by this capture sequencing prove consistent with established HapMap genotypes. We have concurrently developed a one-step pipeline for calling any HLA type referenced in the IMGT/HLA database from this target capture sequencing data, which shows over 96% typing accuracy when deployed at 4 digital resolution. This cost-effective and highly accurate approach for variant detection and HLA typing in the MHC region may lend further insight into immune-mediated diseases studies, and may find clinical utility in transplantation medicine research. This one-step pipeline is released for general evaluation and use by the scientific community. PMID:23894464
The Effect of Concurrent Music Reading and Performance on the Ability to Detect Tempo Change.
ERIC Educational Resources Information Center
Ellis, Mark Carlton
1989-01-01
Measures the ability of three groups of musicians to detect tempo change while reading and performing music. Compares this ability with that of the same musicians to detect tempo change while listening only. Found that for all groups the ability to detect tempo changes was inhibited by the playing task, although to different degrees for each…
Robust Detection of Examinees with Aberrant Answer Changes
ERIC Educational Resources Information Center
Belov, Dmitry I.
2015-01-01
The statistical analysis of answer changes (ACs) has uncovered multiple testing irregularities on large-scale assessments and is now routinely performed at testing organizations. However, AC data has an uncertainty caused by technological or human factors. Therefore, existing statistics (e.g., number of wrong-to-right ACs) used to detect examinees…
NASA Astrophysics Data System (ADS)
Legeais, JeanFrancois; Cazenave, Anny; Ablain, Michael; Larnicol, Gilles; Benveniste, Jerome; Johannessen, Johnny; Timms, Gary; Andersen, Ole; Cipollini, Paolo; Roca, Monica; Rudenko, Sergei; Fernandes, Joana; Balmaseda, Magdalena; Quartly, Graham; Fenoglio-Marc, Luciana; Meyssignac, Benoit; Scharffenberg, Martin
2016-04-01
Sea level is a very sensitive index of climate change and variability. Sea level integrates the ocean warming, mountain glaciers and ice sheet melting. Understanding the sea level variability and changes implies an accurate monitoring of the sea level variable at climate scales, in addition to understanding the ocean variability and the exchanges between ocean, land, cryosphere, and atmosphere. That is why Sea Level is one of the Essential Climate Variables (ECV) selected in the frame of the ESA Climate Change Initiative (CCI) program. It aims at providing long-term monitoring of the sea level ECV with regular updates, as required for climate studies. The program is now in its second phase of 3 year (following phase I during 2011-2013). The objectives are firstly to involve the climate research community, to refine their needs and collect their feedbacks on product quality. And secondly to develop, test and select the best algorithms and standards to generate an updated climate time series and to produce and validate the Sea Level ECV product. This will better answer the climate user needs by improving the quality of the Sea Level products and maintain a sustain service for an up-to-date production. This has led to the production of the Sea Level ECV which has benefited from yearly extensions and now covers the period 1993-2014. We will firstly present the main achievements of the ESA CCI Sea Level Project. On the one hand, the major steps required to produce the 22 years climate time series are briefly described: collect and refine the user requirements, development of adapted algorithms for climate applications and specification of the production system. On the other hand, the product characteristics are described as well as the results from product validation, performed by several groups of the ocean and climate modeling community. At last, new altimeter standards have been developed and the best one have been recently selected in order to produce a full
NASA Astrophysics Data System (ADS)
Legeais, JeanFrancois; Benveniste, Jérôme
2016-07-01
Sea level is a very sensitive index of climate change and variability. Sea level integrates the ocean warming, mountain glaciers and ice sheet melting. Understanding the sea level variability and changes implies an accurate monitoring of the sea level variable at climate scales, in addition to understanding the ocean variability and the exchanges between ocean, land, cryosphere, and atmosphere. That is why Sea Level is one of the Essential Climate Variables (ECV) selected in the frame of the ESA Climate Change Initiative (CCI) program. It aims at providing long-term monitoring of the sea level ECV with regular updates, as required for climate studies. The program is now in its second phase of 3 year (following phase I during 2011-2013). The objectives are firstly to involve the climate research community, to refine their needs and collect their feedbacks on product quality. And secondly to develop, test and select the best algorithms and standards to generate an updated climate time series and to produce and validate the Sea Level ECV product. This will better answer the climate user needs by improving the quality of the Sea Level products and maintain a sustain service for an up-to-date production. This has led to the production of a first version of the Sea Level ECV which has benefited from yearly extensions and now covers the period 1993-2014. Within phase II, new altimeter standards have been developed and tested in order to reprocess the dataset with the best standards for climate studies. The reprocessed ECV will be released in summer 2016. We will present the main achievements of the ESA CCI Sea Level Project. On the one hand, the major steps required to produce the 22 years climate time series are briefly described: collect and refine the user requirements, development of adapted algorithms for climate applications and specification of the production system. On the other hand, the product characteristics are described as well as the results from product
NASA Astrophysics Data System (ADS)
Nguyen, Hoang Hai; Tran, Hien; Sunwoo, Wooyeon; Yi, Jong-hyuk; Kim, Dongkyun; Choi, Minha
2017-04-01
A series of multispectral high-resolution Korean Multi-Purpose Satellite (KOMPSAT) images was used to detect the geographical changes in four different tidal flats between the Yellow Sea and the west coast of South Korea. The method of unsupervised classification was used to generate a series of land use/land cover (LULC) maps from satellite images, which were then used as input for temporal trajectory analysis to detect the temporal change of coastal wetlands and its association with natural and anthropogenic activities. The accurately classified LULC maps of KOMPSAT images, with overall accuracy ranging from 83.34% to 95.43%, indicate that these multispectral high-resolution satellite data are highly applicable to the generation of high-quality thematic maps for extracting wetlands. The result of the trajectory analysis showed that, while the variation of the tidal flats in the Gyeonggi and Jeollabuk provinces was well correlated with the regular tidal regimes, the reductive trajectory of the wetland areas belonging to the Saemangeum province was caused by a high degree of human-induced activities including large reclamation and urbanization. The conservation of the Jeungdo Wetland Protected Area in the Jeollanam province revealed that effective social and environmental policies could help in protecting coastal wetlands from degradation.
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.
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
NASA Astrophysics Data System (ADS)
Candela, S. G.; Howat, I.; Noh, M. J.; Porter, C. C.; Morin, P. J.
2016-12-01
In the last decade, high resolution satellite imagery has become an increasingly accessible tool for geoscientists to quantify changes in the Arctic land surface due to geophysical, ecological and anthropomorphic processes. However, the trade off between spatial coverage and spatial-temporal resolution has limited detailed, process-level change detection over large (i.e. continental) scales. The ArcticDEM project utilized over 300,000 Worldview image pairs to produce a nearly 100% coverage elevation model (above 60°N) offering the first polar, high spatial - high resolution (2-8m by region) dataset, often with multiple repeats in areas of particular interest to geo-scientists. A dataset of this size (nearly 250 TB) offers endless new avenues of scientific inquiry, but quickly becomes unmanageable computationally and logistically for the computing resources available to the average scientist. Here we present TopoDiff, a framework for a generalized. automated workflow that requires minimal input from the end user about a study site, and utilizes cloud computing resources to provide a temporally sorted and differenced dataset, ready for geostatistical analysis. This hands-off approach allows the end user to focus on the science, without having to manage thousands of files, or petabytes of data. At the same time, TopoDiff provides a consistent and accurate workflow for image sorting, selection, and co-registration enabling cross-comparisons between research projects.
The Dynamic Range Paradox: A Central Auditory Model of Intensity Change Detection
Simpson, Andrew J.R.; Reiss, Joshua D.
2013-01-01
In this paper we use empirical loudness modeling to explore a perceptual sub-category of the dynamic range problem of auditory neuroscience. Humans are able to reliably report perceived intensity (loudness), and discriminate fine intensity differences, over a very large dynamic range. It is usually assumed that loudness and intensity change detection operate upon the same neural signal, and that intensity change detection may be predicted from loudness data and vice versa. However, while loudness grows as intensity is increased, improvement in intensity discrimination performance does not follow the same trend and so dynamic range estimations of the underlying neural signal from loudness data contradict estimations based on intensity just-noticeable difference (JND) data. In order to account for this apparent paradox we draw on recent advances in auditory neuroscience. We test the hypothesis that a central model, featuring central adaptation to the mean loudness level and operating on the detection of maximum central-loudness rate of change, can account for the paradoxical data. We use numerical optimization to find adaptation parameters that fit data for continuous-pedestal intensity change detection over a wide dynamic range. The optimized model is tested on a selection of equivalent pseudo-continuous intensity change detection data. We also report a supplementary experiment which confirms the modeling assumption that the detection process may be modeled as rate-of-change. Data are obtained from a listening test (N = 10) using linearly ramped increment-decrement envelopes applied to pseudo-continuous noise with an overall level of 33 dB SPL. Increments with half-ramp durations between 5 and 50,000 ms are used. The intensity JND is shown to increase towards long duration ramps (p<10−6). From the modeling, the following central adaptation parameters are derived; central dynamic range of 0.215 sones, 95% central normalization, and a central loudness JND
The Decay of Motor Memories Is Independent of Context Change Detection
Brennan, Andrew E.; Smith, Maurice A.
2015-01-01
When the error signals that guide human motor learning are withheld following training, recently-learned motor memories systematically regress toward untrained performance. It has previously been hypothesized that this regression results from an intrinsic volatility in these memories, resulting in an inevitable decay in the absence of ongoing error signals. However, a recently-proposed alternative posits that even recently-acquired motor memories are intrinsically stable, decaying only if a change in context is detected. This new theory, the context-dependent decay hypothesis, makes two key predictions: (1) after error signals are withheld, decay onset should be systematically delayed until the context change is detected; and (2) manipulations that impair detection by masking context changes should result in prolonged delays in decay onset and reduced decay amplitude at any given time. Here we examine the decay of motor adaptation following the learning of novel environmental dynamics in order to carefully evaluate this hypothesis. To account for potential issues in previous work that supported the context-dependent decay hypothesis, we measured decay using a balanced and baseline-referenced experimental design that allowed for direct comparisons between analogous masked and unmasked context changes. Using both an unbiased variant of the previous decay onset analysis and a novel highly-powered group-level version of this analysis, we found no evidence for systematically delayed decay onset nor for the masked context change affecting decay amplitude or its onset time. We further show how previous estimates of decay onset latency can be substantially biased in the presence of noise, and even more so with correlated noise, explaining the discrepancy between the previous results and our findings. Our results suggest that the decay of motor memories is an intrinsic feature of error-based learning that does not depend on context change detection. PMID:26111244
DOE Office of Scientific and Technical Information (OSTI.GOV)
Rozario, T; Bereg, S; Chiu, T
Purpose: In order to locate lung tumors on projection images without internal markers, digitally reconstructed radiograph (DRR) is created and compared with projection images. Since lung tumors always move and their locations change on projection images while they are static on DRRs, a special DRR (background DRR) is generated based on modified anatomy from which lung tumors are removed. In addition, global discrepancies exist between DRRs and projections due to their different image originations, scattering, and noises. This adversely affects comparison accuracy. A simple but efficient comparison algorithm is reported. Methods: This method divides global images into a matrix ofmore » small tiles and similarities will be evaluated by calculating normalized cross correlation (NCC) between corresponding tiles on projections and DRRs. The tile configuration (tile locations) will be automatically optimized to keep the tumor within a single tile which has bad matching with the corresponding DRR tile. A pixel based linear transformation will be determined by linear interpolations of tile transformation results obtained during tile matching. The DRR will be transformed to the projection image level and subtracted from it. The resulting subtracted image now contains only the tumor. A DRR of the tumor is registered to the subtracted image to locate the tumor. Results: This method has been successfully applied to kV fluoro images (about 1000 images) acquired on a Vero (Brainlab) for dynamic tumor tracking on phantom studies. Radiation opaque markers are implanted and used as ground truth for tumor positions. Although, other organs and bony structures introduce strong signals superimposed on tumors at some angles, this method accurately locates tumors on every projection over 12 gantry angles. The maximum error is less than 2.6 mm while the total average error is 1.0 mm. Conclusion: This algorithm is capable of detecting tumor without markers despite strong background
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.
Aoyama, Atsushi; Haruyama, Tomohiro; Kuriki, Shinya
2013-09-01
Unconscious monitoring of multimodal stimulus changes enables humans to effectively sense the external environment. Such automatic change detection is thought to be reflected in auditory and visual mismatch negativity (MMN) and mismatch negativity fields (MMFs). These are event-related potentials and magnetic fields, respectively, evoked by deviant stimuli within a sequence of standard stimuli, and both are typically studied during irrelevant visual tasks that cause the stimuli to be ignored. Due to the sensitivity of MMN/MMF to potential effects of explicit attention to vision, however, it is unclear whether multisensory co-occurring changes can purely facilitate early sensory change detection reciprocally across modalities. We adopted a tactile task involving the reading of Braille patterns as a neutral ignore condition, while measuring magnetoencephalographic responses to concurrent audiovisual stimuli that were infrequently deviated either in auditory, visual, or audiovisual dimensions; 1000-Hz standard tones were switched to 1050-Hz deviant tones and/or two-by-two standard check patterns displayed on both sides of visual fields were switched to deviant reversed patterns. The check patterns were set to be faint enough so that the reversals could be easily ignored even during Braille reading. While visual MMFs were virtually undetectable even for visual and audiovisual deviants, significant auditory MMFs were observed for auditory and audiovisual deviants, originating from bilateral supratemporal auditory areas. Notably, auditory MMFs were significantly enhanced for audiovisual deviants from about 100 ms post-stimulus, as compared with the summation responses for auditory and visual deviants or for each of the unisensory deviants recorded in separate sessions. Evidenced by high tactile task performance with unawareness of visual changes, we conclude that Braille reading can successfully suppress explicit attention and that simultaneous multisensory changes can
Ice Sheet Change Detection by Satellite Image Differencing
NASA Technical Reports Server (NTRS)
Bindschadler, Robert A.; Scambos, Ted A.; Choi, Hyeungu; Haran, Terry M.
2010-01-01
Differencing of digital satellite image pairs highlights subtle changes in near-identical scenes of Earth surfaces. Using the mathematical relationships relevant to photoclinometry, we examine the effectiveness of this method for the study of localized ice sheet surface topography changes using numerical experiments. We then test these results by differencing images of several regions in West Antarctica, including some where changes have previously been identified in altimeter profiles. The technique works well with coregistered images having low noise, high radiometric sensitivity, and near-identical solar illumination geometry. Clouds and frosts detract from resolving surface features. The ETM(plus) sensor on Landsat-7, ALI sensor on EO-1, and MODIS sensor on the Aqua and Terra satellite platforms all have potential for detecting localized topographic changes such as shifting dunes, surface inflation and deflation features associated with sub-glacial lake fill-drain events, or grounding line changes. Availability and frequency of MODIS images favor this sensor for wide application, and using it, we demonstrate both qualitative identification of changes in topography and quantitative mapping of slope and elevation changes.
Intravital phosphorescence lifetime imaging of the renal cortex accurately measures renal hypoxia.
Hirakawa, Yosuke; Mizukami, Kiichi; Yoshihara, Toshitada; Takahashi, Ippei; Khulan, Purevsuren; Honda, Tomoko; Mimura, Imari; Tanaka, Tetsuhiro; Tobita, Seiji; Nangaku, Masaomi
2018-06-01
Renal tubulointerstitial hypoxia is recognized as a final common pathway of chronic kidney disease and is considered a promising drug target. However, hypoxia in the tubules is not well examined because of limited detection methods. Here, we devised a method to visualize renal tubular oxygen tension with spatial resolution at a cellular level using the cell-penetrating phosphorescent probe, BTPDM1 (an iridium-based cationic lipophilic dye), and confocal phosphorescence lifetime imaging microscopy to precisely assess renal hypoxia. Imaging with BTPDM1 revealed an oxygen gradient between S1 and S2 segments in mouse kidney. We also demonstrated that our microscopy system can detect subtle changes of hypoxemia and reoxygenation, and the acquired phosphorescence lifetime can be converted to partial pressure of oxygen. This new method allows, for the first time, visualization of intravital oxygen gradients at the renal surface with high spatial resolution. Thus, the confocal phosphorescence lifetime imaging microscopy platform, combined with BTPDM1, will promote an accurate understanding of tissue hypoxia, including renal hypoxia. Copyright © 2018 International Society of Nephrology. Published by Elsevier Inc. All rights reserved.
Deceiving Oneself about Being in Control: Conscious Detection of Changes in Visuomotor Coupling
ERIC Educational Resources Information Center
Knoblich, Gunther; Kircher, Tilo T. J.
2004-01-01
Previous research has demonstrated that compensatory movements for changes in visuomotor coupling often are not consciously detected. But what factors affect the conscious detection of such changes? This issue was addressed in 4 experiments. Participants carried out a drawing task in which the relative velocity between the actual movement and its…
NASA Astrophysics Data System (ADS)
Gliese, U.; Avanov, L. A.; Barrie, A.; Kujawski, J. T.; Mariano, A. J.; Tucker, C. J.; Chornay, D. J.; Cao, N. T.; Zeuch, M.; Pollock, C. J.; Jacques, A. D.
2013-12-01
The Fast Plasma Investigation (FPI) of the NASA Magnetospheric MultiScale (MMS) mission employs 16 Dual Electron Spectrometers (DESs) and 16 Dual Ion Spectrometers (DISs) with 4 of each type on each of 4 spacecraft to enable fast (30ms for electrons; 150ms for ions) and spatially differentiated measurements of full the 3D particle velocity distributions. This approach presents a new and challenging aspect to the calibration and operation of these instruments on ground and in flight. The response uniformity and reliability of their calibration and the approach to handling any temporal evolution of these calibrated characteristics all assume enhanced importance in this application, where we attempt to understand the meaning of particle distributions within the ion and electron diffusion regions. Traditionally, the micro-channel plate (MCP) based detection systems for electrostatic particle spectrometers have been calibrated by setting a fixed detection threshold and, subsequently, measuring a detection system count rate plateau curve to determine the MCP voltage that ensures the count rate has reached a constant value independent of further variation in the MCP voltage. This is achieved when most of the MCP pulse height distribution (PHD) is located at higher values (larger pulses) than the detection amplifier threshold. This method is adequate in single-channel detection systems and in multi-channel detection systems with very low crosstalk between channels. However, in dense multi-channel systems, it can be inadequate. Furthermore, it fails to fully and individually characterize each of the fundamental parameters of the detection system. We present a new detection system calibration method that enables accurate and repeatable measurement and calibration of MCP gain, MCP efficiency, signal loss due to variation in gain and efficiency, crosstalk from effects both above and below the MCP, noise margin, and stability margin in one single measurement. The fundamental
Variable threshold method for ECG R-peak detection.
Kew, Hsein-Ping; Jeong, Do-Un
2011-10-01
In this paper, a wearable belt-type ECG electrode worn around the chest by measuring the real-time ECG is produced in order to minimize the inconvenient in wearing. ECG signal is detected using a potential instrument system. The measured ECG signal is transmits via an ultra low power consumption wireless data communications unit to personal computer using Zigbee-compatible wireless sensor node. ECG signals carry a lot of clinical information for a cardiologist especially the R-peak detection in ECG. R-peak detection generally uses the threshold value which is fixed. There will be errors in peak detection when the baseline changes due to motion artifacts and signal size changes. Preprocessing process which includes differentiation process and Hilbert transform is used as signal preprocessing algorithm. Thereafter, variable threshold method is used to detect the R-peak which is more accurate and efficient than fixed threshold value method. R-peak detection using MIT-BIH databases and Long Term Real-Time ECG is performed in this research in order to evaluate the performance analysis.
Change Detection by Rhesus Monkeys (Macaca mulatta) and Pigeons (Columba livia)
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
Warren B. Cohen; Zhiqiang Yang; Robert Kennedy
2010-01-01
Availability of free, high quality Landsat data portends a new era in remote sensing change detection. Using dense (~annual) Landsat time series (LTS), we can now characterize vegetation change over large areas at an annual time step and at the spatial grain of anthropogenic disturbance. Additionally, we expect more accurate detection of subtle disturbances and...
NASA Astrophysics Data System (ADS)
Akay, A. E.; Gencal, B.; Taş, İ.
2017-11-01
This short paper aims to detect spatiotemporal detection of land use/land cover change within Karacabey Flooded Forest region. Change detection analysis applied to Landsat 5 TM images representing July 2000 and a Landsat 8 OLI representing June 2017. Various image processing tools were implemented using ERDAS 9.2, ArcGIS 10.4.1, and ENVI programs to conduct spatiotemporal change detection over these two images such as band selection, corrections, subset, classification, recoding, accuracy assessment, and change detection analysis. Image classification revealed that there are five significant land use/land cover types, including forest, flooded forest, swamp, water, and other lands (i.e. agriculture, sand, roads, settlement, and open areas). The results indicated that there was increase in flooded forest, water, and other lands, while the cover of forest and swamp decreased.
DOE Office of Scientific and Technical Information (OSTI.GOV)
Moody, Daniela Irina
An approach for land cover classification, seasonal and yearly change detection and monitoring, and identification of changes in man-made features may use a clustering of sparse approximations (CoSA) on sparse representations in learned dictionaries. A Hebbian learning rule may be used to build multispectral or hyperspectral, multiresolution dictionaries that are adapted to regional satellite image data. Sparse image representations of pixel patches over the learned dictionaries may be used to perform unsupervised k-means clustering into land cover categories. The clustering process behaves as a classifier in detecting real variability. This approach may combine spectral and spatial textural characteristics to detectmore » geologic, vegetative, hydrologic, and man-made features, as well as changes in these features over time.« less
Nika, Varvara; Babyn, Paul; Zhu, Hongmei
2014-07-01
Automatic change detection methods for identifying the changes of serial MR images taken at different times are of great interest to radiologists. The majority of existing change detection methods in medical imaging, and those of brain images in particular, include many preprocessing steps and rely mostly on statistical analysis of magnetic resonance imaging (MRI) scans. Although most methods utilize registration software, tissue classification remains a difficult and overwhelming task. Recently, dictionary learning techniques are being used in many areas of image processing, such as image surveillance, face recognition, remote sensing, and medical imaging. We present an improved version of the EigenBlockCD algorithm, named the EigenBlockCD-2. The EigenBlockCD-2 algorithm performs an initial global registration and identifies the changes between serial MR images of the brain. Blocks of pixels from a baseline scan are used to train local dictionaries to detect changes in the follow-up scan. We use PCA to reduce the dimensionality of the local dictionaries and the redundancy of data. Choosing the appropriate distance measure significantly affects the performance of our algorithm. We examine the differences between [Formula: see text] and [Formula: see text] norms as two possible similarity measures in the improved EigenBlockCD-2 algorithm. We show the advantages of the [Formula: see text] norm over the [Formula: see text] norm both theoretically and numerically. We also demonstrate the performance of the new EigenBlockCD-2 algorithm for detecting changes of MR images and compare our results with those provided in the recent literature. Experimental results with both simulated and real MRI scans show that our improved EigenBlockCD-2 algorithm outperforms the previous methods. It detects clinical changes while ignoring the changes due to the patient's position and other acquisition artifacts.
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
Continuous Change Detection and Classification (CCDC) of Land Cover Using All Available Landsat Data
NASA Astrophysics Data System (ADS)
Zhu, Z.; Woodcock, C. E.
2012-12-01
A new algorithm for Continuous Change Detection and Classification (CCDC) of land cover using all available Landsat data is developed. This new algorithm is capable of detecting many kinds of land cover change as new images are collected and at the same time provide land cover maps for any given time. To better identify land cover change, a two step cloud, cloud shadow, and snow masking algorithm is used for eliminating "noisy" observations. Next, a time series model that has components of seasonality, trend, and break estimates the surface reflectance and temperature. The time series model is updated continuously with newly acquired observations. Due to the high variability in spectral response for different kinds of land cover change, the CCDC algorithm uses a data-driven threshold derived from all seven Landsat bands. When the difference between observed and predicted exceeds the thresholds three consecutive times, a pixel is identified as land cover change. Land cover classification is done after change detection. Coefficients from the time series models and the Root Mean Square Error (RMSE) from model fitting are used as classification inputs for the Random Forest Classifier (RFC). We applied this new algorithm for one Landsat scene (Path 12 Row 31) that includes all of Rhode Island as well as much of Eastern Massachusetts and parts of Connecticut. A total of 532 Landsat images acquired between 1982 and 2011 were processed. During this period, 619,924 pixels were detected to change once (91% of total changed pixels) and 60,199 pixels were detected to change twice (8% of total changed pixels). The most frequent land cover change category is from mixed forest to low density residential which occupies more than 8% of total land cover change pixels.
Activatable Optical Probes for the Detection of Enzymes
Drake, Christopher R.; Miller, David C.; Jones, Ella F.
2013-01-01
The early detection of many human diseases is crucial if they are to be treated successfully. Therefore, the development of imaging techniques that can facilitate early detection of disease is of high importance. Changes in the levels of enzyme expression are known to occur in many diseases, making their accurate detection at low concentrations an area of considerable active research. Activatable fluorescent probes show immense promise in this area. If properly designed they should exhibit no signal until they interact with their target enzyme, reducing the level of background fluorescence and potentially endowing them with greater sensitivity. The mechanisms of fluorescence changes in activatable probes vary. This review aims to survey the field of activatable probes, focusing on their mechanisms of action as well as illustrating some of the in vitro and in vivo settings in which they have been employed. PMID:23519774
Trajectory-based change detection for automated characterization of forest disturbance dynamics
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 (...
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
Robust Face Detection from Still Images
2014-01-01
significant change in false acceptance rates. Keywords— face detection; illumination; skin color variation; Haar-like features; OpenCV I. INTRODUCTION... OpenCV and an algorithm which used histogram equalization. The test is performed against 17 subjects under 576 viewing conditions from the extended Yale...original OpenCV algorithm proved the least accurate, having a hit rate of only 75.6%. It also had the lowest FAR but only by a slight margin at 25.2
Experimental application of simulation tools for evaluating UAV video change detection
NASA Astrophysics Data System (ADS)
Saur, Günter; Bartelsen, Jan
2015-10-01
Change detection is one of the most important tasks when unmanned aerial vehicles (UAV) are used for video reconnaissance and surveillance. In this paper, we address changes on short time scale, i.e. the observations are taken within time distances of a few hours. Each observation is a short video sequence corresponding to the near-nadir overflight of the UAV above the interesting area and the relevant changes are e.g. recently added or removed objects. The change detection algorithm has to distinguish between relevant and non-relevant changes. Examples for non-relevant changes are versatile objects like trees and compression or transmission artifacts. To enable the usage of an automatic change detection within an interactive workflow of an UAV video exploitation system, an evaluation and assessment procedure has to be performed. Large video data sets which contain many relevant objects with varying scene background and altering influence parameters (e.g. image quality, sensor and flight parameters) including image metadata and ground truth data are necessary for a comprehensive evaluation. Since the acquisition of real video data is limited by cost and time constraints, from our point of view, the generation of synthetic data by simulation tools has to be considered. In this paper the processing chain of Saur et al. (2014) [1] and the interactive workflow for video change detection is described. We have selected the commercial simulation environment Virtual Battle Space 3 (VBS3) to generate synthetic data. For an experimental setup, an example scenario "road monitoring" has been defined and several video clips have been produced with varying flight and sensor parameters and varying objects in the scene. Image registration and change mask extraction, both components of the processing chain, are applied to corresponding frames of different video clips. For the selected examples, the images could be registered, the modelled changes could be extracted and the
In situ detection of porosity initiation during aluminum thin film anodizing
NASA Astrophysics Data System (ADS)
Van Overmeere, Quentin; Nysten, Bernard; Proost, Joris
2009-02-01
High-resolution curvature measurements have been performed in situ during aluminum thin film anodizing in sulfuric acid. A well-defined transition in the rate of internal stress-induced curvature change is shown to allow for the accurate, real-time detection of porosity initiation. The validity of this in situ diagnostic tool was confirmed by a quantitative analysis of the spectral density distributions of the anodized surfaces. These were obtained by analyzing ex situ atomic force microscopy images of surfaces anodized for different times, and allowed to correlate the in situ detected transition in the rate of curvature change with the appearance of porosity.
Interoperable cross-domain semantic and geospatial framework for automatic change detection
NASA Astrophysics Data System (ADS)
Kuo, Chiao-Ling; Hong, Jung-Hong
2016-01-01
With the increasingly diverse types of geospatial data established over the last few decades, semantic interoperability in integrated applications has attracted much interest in the field of Geographic Information System (GIS). This paper proposes a new strategy and framework to process cross-domain geodata at the semantic level. This framework leverages the semantic equivalence of concepts between domains through bridge ontology and facilitates the integrated use of different domain data, which has been long considered as an essential superiority of GIS, but is impeded by the lack of understanding about the semantics implicitly hidden in the data. We choose the task of change detection to demonstrate how the introduction of ontology concept can effectively make the integration possible. We analyze the common properties of geodata and change detection factors, then construct rules and summarize possible change scenario for making final decisions. The use of topographic map data to detect changes in land use shows promising success, as far as the improvement of efficiency and level of automation is concerned. We believe the ontology-oriented approach will enable a new way for data integration across different domains from the perspective of semantic interoperability, and even open a new dimensionality for the future GIS.
NASA Technical Reports Server (NTRS)
Gliese, U.; Avanov, L. A.; Barrie, A. C.; Kujawski, J. T.; Mariano, A. J.; Tucker, C. J.; Chornay, D. J.; Cao, N. T.; Gershman, D. J.; Dorelli, J. C.;
2015-01-01
system calibration method that enables accurate and repeatable measurement and calibration of MCP gain, MCP efficiency, signal loss due to variation in gain and efficiency, crosstalk from effects both above and below the MCP, noise margin, and stability margin in one single measurement. More precise calibration is highly desirable as the instruments will produce higher quality raw data that will require less post-acquisition data correction using results from in-flight pitch angle distribution measurements and ground calibration measurements. The detection system description and the fundamental concepts of this new calibration method, named threshold scan, will be presented. It will be shown how to derive all the individual detection system parameters and how to choose the optimum detection system operating point. This new method has been successfully applied to achieve a highly accurate calibration of the DESs and DISs of the MMS mission. The practical application of the method will be presented together with the achieved calibration results and their significance. Finally, it will be shown that, with further detailed modeling, this method can be extended for use in flight to achieve and maintain a highly accurate detection system calibration across a large number of instruments during the mission.
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
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.
Remote Sensing Image Change Detection Based on NSCT-HMT Model and Its Application.
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
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
How do we watch images? A case of change detection and quality estimation
NASA Astrophysics Data System (ADS)
Radun, Jenni; Leisti, Tuomas; Virtanen, Toni; Nyman, Göte
2012-01-01
The most common tasks in subjective image estimation are change detection (a detection task) and image quality estimation (a preference task). We examined how the task influences the gaze behavior when comparing detection and preference tasks. The eye movements of 16 naïve observers were recorded with 8 observers in both tasks. The setting was a flicker paradigm, where the observers see a non-manipulated image, a manipulated version of the image and again the non-manipulated image and estimate the difference they perceived in them. The material was photographic material with different image distortions and contents. To examine the spatial distribution of fixations, we defined the regions of interest using a memory task and calculated information entropy to estimate how concentrated the fixations were on the image plane. The quality task was faster and needed fewer fixations and the first eight fixations were more concentrated on certain image areas than the change detection task. The bottom-up influences of the image also caused more variation to the gaze behavior in the quality estimation task than in the change detection task The results show that the quality estimation is faster and the regions of interest are emphasized more on certain images compared with the change detection task that is a scan task where the whole image is always thoroughly examined. In conclusion, in subjective image estimation studies it is important to think about the task.
Detecting past changes of effective population size
Nikolic, Natacha; Chevalet, Claude
2014-01-01
Understanding and predicting population abundance is a major challenge confronting scientists. Several genetic models have been developed using microsatellite markers to estimate the present and ancestral effective population sizes. However, to get an overview on the evolution of population requires that past fluctuation of population size be traceable. To address the question, we developed a new model estimating the past changes of effective population size from microsatellite by resolving coalescence theory and using approximate likelihoods in a Monte Carlo Markov Chain approach. The efficiency of the model and its sensitivity to gene flow and to assumptions on the mutational process were checked using simulated data and analysis. The model was found especially useful to provide evidence of transient changes of population size in the past. The times at which some past demographic events cannot be detected because they are too ancient and the risk that gene flow may suggest the false detection of a bottleneck are discussed considering the distribution of coalescence times. The method was applied on real data sets from several Atlantic salmon populations. The method called VarEff (Variation of Effective size) was implemented in the R package VarEff and is made available at https://qgsp.jouy.inra.fr and at http://cran.r-project.org/web/packages/VarEff. PMID:25067949
Volumetric Forest Change Detection Through Vhr Satellite Imagery
NASA Astrophysics Data System (ADS)
Akca, Devrim; Stylianidis, Efstratios; Smagas, Konstantinos; Hofer, Martin; Poli, Daniela; Gruen, Armin; Sanchez Martin, Victor; Altan, Orhan; Walli, Andreas; Jimeno, Elisa; Garcia, Alejandro
2016-06-01
Quick and economical ways of detecting of planimetric and volumetric changes of forest areas are in high demand. A research platform, called FORSAT (A satellite processing platform for high resolution forest assessment), was developed for the extraction of 3D geometric information from VHR (very-high resolution) imagery from satellite optical sensors and automatic change detection. This 3D forest information solution was developed during a Eurostars project. FORSAT includes two main units. The first one is dedicated to the geometric and radiometric processing of satellite optical imagery and 2D/3D information extraction. This includes: image radiometric pre-processing, image and ground point measurement, improvement of geometric sensor orientation, quasiepipolar image generation for stereo measurements, digital surface model (DSM) extraction by using a precise and robust image matching approach specially designed for VHR satellite imagery, generation of orthoimages, and 3D measurements in single images using mono-plotting and in stereo images as well as triplets. FORSAT supports most of the VHR optically imagery commonly used for civil applications: IKONOS, OrbView - 3, SPOT - 5 HRS, SPOT - 5 HRG, QuickBird, GeoEye-1, WorldView-1/2, Pléiades 1A/1B, SPOT 6/7, and sensors of similar type to be expected in the future. The second unit of FORSAT is dedicated to 3D surface comparison for change detection. It allows users to import digital elevation models (DEMs), align them using an advanced 3D surface matching approach and calculate the 3D differences and volume changes between epochs. To this end our 3D surface matching method LS3D is being used. FORSAT is a single source and flexible forest information solution with a very competitive price/quality ratio, allowing expert and non-expert remote sensing users to monitor forests in three and four dimensions from VHR optical imagery for many forest information needs. The capacity and benefits of FORSAT have been tested in
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.
Clark, Kait; Fleck, Mathias S; Mitroff, Stephen R
2011-01-01
Recent research has shown that avid action video game players (VGPs) outperform non-video game players (NVGPs) on a variety of attentional and perceptual tasks. However, it remains unknown exactly why and how such differences arise; while some prior research has demonstrated that VGPs' improvements stem from enhanced basic perceptual processes, other work indicates that they can stem from enhanced attentional control. The current experiment used a change-detection task to explore whether top-down strategies can contribute to VGPs' improved abilities. Participants viewed alternating presentations of an image and a modified version of the image and were tasked with detecting and localizing the changed element. Consistent with prior claims of enhanced perceptual abilities, VGPs were able to detect the changes while requiring less exposure to the change than NVGPs. Further analyses revealed this improved change detection performance may result from altered strategy use; VGPs employed broader search patterns when scanning scenes for potential changes. These results complement prior demonstrations of VGPs' enhanced bottom-up perceptual benefits by providing new evidence of VGPs' potentially enhanced top-down strategic benefits. Copyright © 2010 Elsevier B.V. All rights reserved.
Detecting and Understanding Changing Arctic Carbon Emissions
NASA Astrophysics Data System (ADS)
Bruhwiler, L.
2017-12-01
Warming in the Arctic has proceeded faster than anyplace on Earth. Our current understanding of biogeochemistry suggests that we can expect feedbacks between climate and carbon in the Arctic. Changes in terrestrial fluxes of carbon can be expected as the Arctic warms, and the vast stores of organic carbon frozen in Arctic soils could be mobilized to the atmosphere, with possible significant impacts on global climate. Quantifying trends in Arctic carbon exchanges is important for policymaking because greater reductions in anthropogenic emissions may be required to meet climate goals. Observations of greenhouse gases in the Arctic and globally have been collected for several decades. Analysis of this data does not currently support significantly changed Arctic emissions of CH4, however it is difficult to detect changes in Arctic emissions because of transport from lower latitudes and large inter-annual variability. Unfortunately, current space-based remote sensing systems have limitations at Arctic latitudes. Modeling systems can help untangle the Arctic budget of greenhouse gases, but they are dependent on underlying prior fluxes, wetland distributions and global anthropogenic emissions. Also, atmospheric transport models may have significant biases and errors. For example, unrealistic near-surface stability can lead to underestimation of emissions in atmospheric inversions. We discuss our current understanding of the Arctic carbon budget from both top-down and bottom-up approaches. We show that current atmospheric inversions agree well on the CH4 budget. On the other hand, bottom-up models vary widely in their predictions of natural emissions, with some models predicting emissions too large to be accommodated by the budget implied by global observations. Large emissions from the shallow Arctic ocean are also inconsistent with atmospheric observations. We also discuss the sensitivity of the current atmospheric network to what is likely small, gradual increases in
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.
[Early detection of cervical cancer in Chile: time for change].
Léniz Martelli, Javiera; Van De Wyngard, Vanessa; Lagos, Marcela; Barriga, María Isabel; Puschel Illanes, Klaus; Ferreccio Readi, Catterina
2014-08-01
Mortality rates for cervical cancer (CC) in Chile are higher than those of developed countries and it has an unequal socioeconomic distribution. The recognition of human papilloma virus (HPV) as the causal agent of cervical cancer in the early 80's changed the prevention paradigms. Current goals are to prevent HPV infection by vaccination before the onset of sexual activity and to detect HPV infection in women older than 30 years. This article reviews CC prevention and early detection methods, discusses relevant evidence to support a change in Chile and presents an innovation proposal. A strategy of primary screening based on HPV detection followed by triage of HPV-positive women by colposcopy in primary care or by cytological or molecular reflex testing is proposed. Due to the existence in Chile of a well-organized nationwide CC prevention program, the replacement of a low-sensitivity screening test such as the Papanicolau test with a highly sensitive one such as HPV detection, could quickly improve the effectiveness of the program. The program also has a network of personnel qualified to conduct naked-eye inspections of the cervix, who could easily be trained to perform triage colposcopy. The incorporation of new prevention strategies could reduce the deaths of Chilean women and correct inequities.
SuBSENSE: a universal change detection method with local adaptive sensitivity.
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.
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
McAnally, Ken I.; Morris, Adam P.; Best, Christopher
2017-01-01
Metacognitive monitoring and control of situation awareness (SA) are important for a range of safety-critical roles (e.g., air traffic control, military command and control). We examined the factors affecting these processes using a visual change detection task that included representative tactical displays. SA was assessed by asking novice observers to detect changes to a tactical display. Metacognitive monitoring was assessed by asking observers to estimate the probability that they would correctly detect a change, either after study of the display and before the change (judgement of learning; JOL) or after the change and detection response (judgement of performance; JOP). In Experiment 1, observers failed to detect some changes to the display, indicating imperfect SA, but JOPs were reasonably well calibrated to objective performance. Experiment 2 examined JOLs and JOPs in two task contexts: with study-time limits imposed by the task or with self-pacing to meet specified performance targets. JOPs were well calibrated in both conditions as were JOLs for high performance targets. In summary, observers had limited SA, but good insight about their performance and learning for high performance targets and allocated study time appropriately. PMID:28915244
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.
Monitoring gypsy moth defoliation by applying change detection techniques to Landsat imagery
NASA Technical Reports Server (NTRS)
Williams, D. L.; Stauffer, M. L.
1978-01-01
The overall objective of a research effort at NASA's Goddard Space Flight Center is to develop and evaluate digital image processing techniques that will facilitate the assessment of the intensity and spatial distribution of forest insect damage in Northeastern U.S. forests using remotely sensed data from Landsats 1, 2 and C. Automated change detection techniques are presently being investigated as a method of isolating the areas of change in the forest canopy resulting from pest outbreaks. In order to follow the change detection approach, Landsat scene correction and overlay capabilities are utilized to provide multispectral/multitemporal image files of 'defoliation' and 'nondefoliation' forest stand conditions.
Fingerprint-Inspired Flexible Tactile Sensor for Accurately Discerning Surface Texture.
Cao, Yudong; Li, Tie; Gu, Yang; Luo, Hui; Wang, Shuqi; Zhang, Ting
2018-04-01
Inspired by the epidermal-dermal and outer microstructures of the human fingerprint, a novel flexible sensor device is designed to improve haptic perception and surface texture recognition, which is consisted of single-walled carbon nanotubes, polyethylene, and polydimethylsiloxane with interlocked and outer micropyramid arrays. The sensor shows high pressure sensitivity (-3.26 kPa -1 in the pressure range of 0-300 Pa), and it can detect the shear force changes induced by the dynamic interaction between the outer micropyramid structure on the sensor and the tested material surface, and the minimum dimension of the microstripe that can be discerned is as low as 15 µm × 15 µm (interval × width). To demonstrate the texture discrimination capability, the sensors are tested for accurately discerning various surface textures, such as the textures of different fabrics, Braille characters, the inverted pyramid patterns, which will have great potential in robot skins and haptic perception, etc. © 2018 WILEY-VCH Verlag GmbH & Co. KGaA, Weinheim.
Sharma, Kakali; Sharma, Shiba P; Lahiri, Sujit C
2013-01-01
Phenolphthalein, an acid-base indicator and laxative, is important as a constituent of widely used weight-reducing multicomponent food formulations. Phenolphthalein is an useful reagent in forensic science for the identification of blood stains of suspected victims and for apprehending erring officials accepting bribes in graft or trap cases. The pink-colored alkaline hand washes originating from the phenolphthalein-smeared notes can easily be determined spectrophotometrically. But in many cases, colored solution turns colorless with time, which renders the genuineness of bribe cases doubtful to the judiciary. No method is known till now for the detection and identification of phenolphthalein in colorless forensic exhibits with positive proof. Liquid chromatography-tandem mass spectrometry had been found to be most sensitive, accurate method capable of detection and quantitation of trace phenolphthalein in commercial formulations and colorless forensic exhibits with positive proof. The detection limit of phenolphthalein was found to be 1.66 pg/L or ng/mL, and the calibration curve shows good linearity (r(2) = 0.9974). © 2012 American Academy of Forensic Sciences.
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.
Phytochrome-Mediated Detection of Changes in Reflected Light
Mancinelli, Alberto L.
1991-01-01
Measurements of phytochrome photoequilibria and photoconversion rates in vivo, in seedlings of Cucurbita pepo L. exposed to light in growth chambers, indicate that significant changes in the state of phytochrome can be brought about by changes in the quality and quantity of the light reflected from the walls of the growth chambers. The changes in reflected light, although large, were small in terms of the total radiation (direct light from the lamps plus wall-reflected light) to which the seedlings were exposed. The conditions used were approximate simulations of direct and reflected sunlight conditions in the natural environment. Keeping in mind the limitations imposed by the approximation of the simulations, the results from this study are consistent with the hypothesis that, in the natural environment, a plant might be capable of detecting the presence of nearby plants, before being shaded by them, through the phytochrome-mediated perception of changes in reflected light. PMID:16667942
A rapid beverage intake questionnaire can detect changes in beverage intake.
Hedrick, Valisa E; Comber, Dana L; Ferguson, Katherine E; Estabrooks, Paul A; Savla, Jyoti; Dietrich, Andrea M; Serrano, Elena; Davy, Brenda M
2013-01-01
Attention on beverage intake, specifically sugar-sweetened beverages (SSB), has increased in recent years. A brief valid, reliable and sensitive assessment tool for quantifying beverage consumption and determining its influence on weight status could help to advance research on this topic. The valid and reliable 15-item beverage questionnaire (BEVQ-15) estimates mean daily intake of water, SSB and total beverages (g, kcal) across multiple beverage categories. to determine the ability of the BEVQ-15 to detect changes in beverage intake over time. Participants (n=70; age=37±2 yr; BMI=24.5±0.4 kg/m(2)) underwent two randomly assigned 30-day periods (intervention, increased water and fruit juice consumption; control, increased solid fruit consumption), with a 30-day washout phase between feeding periods. The BEVQ-15 was administered at the beginning and end of each period. Reliability was assessed by Pearson's correlations, paired sample t tests and Cronbach's alpha. Paired sample t tests and repeated measures ANOVA were used to evaluate sensitivity to change. Sixty-nine participants completed all study sessions. Reliability was acceptable for most beverages (range: R(2)=0.52-0.95, P<0.001), but not for energy drinks. Increases in water (g), juice (kcal, g) and total beverage (g) were detected during the intervention period (P<0.001); no changes in these variables were detected in the control period. The BEVQ-15 demonstrates the ability to detect changes in beverage intake over time. This brief (~2 min), self-administered, valid, reliable and sensitive beverage intake assessment tool may be used by researchers and practitioners who evaluate and intervene upon beverage intake patterns in adults. Published by Elsevier Ltd.
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.
Irsik, Vanessa C; Vanden Bosch der Nederlanden, Christina M; Snyder, Joel S
2016-11-01
Attention and other processing constraints limit the perception of objects in complex scenes, which has been studied extensively in the visual sense. We used a change deafness paradigm to examine how attention to particular objects helps and hurts the ability to notice changes within complex auditory scenes. In a counterbalanced design, we examined how cueing attention to particular objects affected performance in an auditory change-detection task through the use of valid or invalid cues and trials without cues (Experiment 1). We further examined how successful encoding predicted change-detection performance using an object-encoding task and we addressed whether performing the object-encoding task along with the change-detection task affected performance overall (Experiment 2). Participants had more error for invalid compared to valid and uncued trials, but this effect was reduced in Experiment 2 compared to Experiment 1. When the object-encoding task was present, listeners who completed the uncued condition first had less overall error than those who completed the cued condition first. All participants showed less change deafness when they successfully encoded change-relevant compared to irrelevant objects during valid and uncued trials. However, only participants who completed the uncued condition first also showed this effect during invalid cue trials, suggesting a broader scope of attention. These findings provide converging evidence that attention to change-relevant objects is crucial for successful detection of acoustic changes and that encouraging broad attention to multiple objects is the best way to reduce change deafness. (PsycINFO Database Record (c) 2016 APA, all rights reserved).
Optical imaging the redox status change during cell apoptosis
NASA Astrophysics Data System (ADS)
Su, Ting; Zhang, Zhihong; Lin, Juqiang; Luo, Qingming
2007-02-01
Many cellular events involve the alteration in redox equilibrium, globally or locally. In many cases, excessive reactive oxygen species (ROS) production is the underlying cause. Several green fluoresecence protein based indicators are constructed to measure redox status in cells, e.g, rxYFP and roGFPs, which allow real time detection. reduction and oxidization-sensitive GFP (RoGFPs) are more useful due to ratiometric variation by excitation, making the measurement more accurate. Utilizing one of those roGFPs called roGFP1, we establish a mitochondrial redox state probing platform in HeLa cells with laser scan confocal microscopy (LSCM) as detection system. Control experiments confirmed that our platform could produce stable ratiometric values, which made the data more accurately reflect the real environmental changes of redox status that roGFP1 probed. Using exogenous H IIO II and DTT, we evaluated the reactivity and reversibility of roGFP1. The minimal hydrogen peroxide concentration that roGFP1 could show detectable ratiometric changes in our system was about 200μM. Preliminarily applying our platform to exploring the redox status during apoptosis, we observed an increase in ratiometric, suggesting an excessive ROS production.
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.
Protein detection by Simple Western™ analysis.
Harris, Valerie M
2015-01-01
Protein Simple© has taken a well-known protein detection method, the western blot, and revolutionized it. The Simple Western™ system uses capillary electrophoresis to identify and quantitate a protein of interest. Protein Simple© provides multiple detection apparatuses (Wes, Sally Sue, or Peggy Sue) that are suggested to save scientists valuable time by allowing the researcher to prepare the protein sample, load it along with necessary antibodies and substrates, and walk away. Within 3-5 h the protein will be separated by size, or charge, immuno-detection of target protein will be accurately quantitated, and results will be immediately made available. Using the Peggy Sue instrument, one study recently examined changes in MAPK signaling proteins in the sex-determining stage of gonadal development. Here the methodology is described.
Evaluation of Landsat-7 SLC-off image products for forest change detection
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.
Arctic Change Detection: Multiple Observations and Recent Explanations
NASA Astrophysics Data System (ADS)
Soreide, N. N.; Overland, J. E.; Calder, J.
2004-12-01
is a comprehensive Arctic Change Detection product which builds upon the ACIA report with regularly updated information. Credibility is based on multiple lines of evidence and cooperation of scientists. The Arctic Change Detection project provides a near-realtime suite of indicators, their potential impacts, recent events, news items, and scientific publications, in an understandable format at www.arctic.noaa.gov. This website makes information about the current status of the Arctic available to a wide audience.
FAS 33: accurately recording effects of changing prices.
Sage, L G
1987-02-01
FAS 33 addresses the problem of distortion in conventional historical cost financial statements because of changing prices. It requires 1300 business enterprises to report selected changing price data on a supplementary basis. It has been demonstrated that it is also feasible and beneficial for hospitals to present price disclosures as supplementary information to their financial statements. The possible application of FAS 33 is supported on the basis that the accounting and reporting methods of healthcare institutions are similar to the accounting and reporting practices of profit-seeking entities.
Detecting activity-evoked pH changes in human brain
Magnotta, Vincent A.; Heo, Hye-Young; Dlouhy, Brian J.; Dahdaleh, Nader S.; Follmer, Robin L.; Thedens, Daniel R.; Welsh, Michael J.; Wemmie, John A.
2012-01-01
Localized pH changes have been suggested to occur in the brain during normal function. However, the existence of such pH changes has also been questioned. Lack of methods for noninvasively measuring pH with high spatial and temporal resolution has limited insight into this issue. Here we report that a magnetic resonance imaging (MRI) strategy, T1 relaxation in the rotating frame (T1ρ), is sufficiently sensitive to detect widespread pH changes in the mouse and human brain evoked by systemically manipulating carbon dioxide or bicarbonate. Moreover, T1ρ detected a localized acidosis in the human visual cortex induced by a flashing checkerboard. Lactate measurements and pH-sensitive 31P spectroscopy at the same site also identified a localized acidosis. Consistent with the established role for pH in blood flow recruitment, T1ρ correlated with blood oxygenation level-dependent contrast commonly used in functional MRI. However, T1ρ was not directly sensitive to blood oxygen content. These observations indicate that localized pH fluctuations occur in the human brain during normal function. Furthermore, they suggest a unique functional imaging strategy based on pH that is independent of traditional functional MRI contrast mechanisms. PMID:22566645
PRESENTATION ON--LAND-COVER CHANGE DETECTION USING MULTI-TEMPORAL MODIS NDVI DATA
Monitoring the locations and distributions of land-cover changes is important for establishing linkages between policy decisions, regulatory actions and subsequent landuse activities. Past efforts incorporating two-date change detection using moderate resolution data (e.g., Lands...
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.
A New CCI ECV Release (v2.0) to Accurately Measure the Sea Level Change from space (1993-2015)
NASA Astrophysics Data System (ADS)
Legeais, Jean-Francois; Benveniste, Jérôme
2017-04-01
Accurate monitoring of the sea level is required to better understand its variability and changes. Sea level is one of the Essential Climate Variables (ECV) selected in the frame of the ESA Climate Change Initiative (CCI) program. It aims at providing a long-term homogeneous and accurate sea level record. The needs and feedback of the climate research community have been collected so that the development of the products is adapted to the users. A first version of the sea level ECV product has been generated during phase I of the project (2011-2013). Within phase II (2014-2016), the 15 partner consortium has prepared the production of a new reprocessed homogeneous and accurate altimeter sea level record which is now available (see http://www.esa-sealevel-cci.org/products ). New level 2 altimeter standards developed and tested within the project as well as external contributions have been identified, processed and evaluated by comparison with a reference for different altimeter missions (TOPEX/Poseidon, Jason-1 & 2, ERS-1 & 2, Envisat, GFO, SARAL/AltiKa and CryoSat-2). The main evolutions are associated with the wet troposphere correction (based on the GPD+ algorithm including inter calibration with respect to external sensors) but also to the orbit solutions (POE-E and GFZ15), the ERA-Interim based atmospheric corrections and the FES2014 ocean tide model. A new pole tide solution is used and anomalies are referenced to the MSS DTU15. The presentation will focus on the main achievements of the ESA CCI Sea Level project and on the description of the new SL_cci ECV release covering 1993-2015. The major steps required to produce the reprocessed 23 year climate time series will be described. The impacts of the selected level 2 altimeter standards on the SL_cci ECV have been assessed on different spatial scales (global, regional, mesoscale) and temporal scales (long-term, inter-annual, periodic signals). A significant improvement is observed compared to the current v1
Detection of Functional Change Using Cluster Trend Analysis in Glaucoma.
Gardiner, Stuart K; Mansberger, Steven L; Demirel, Shaban
2017-05-01
Global analyses using mean deviation (MD) assess visual field progression, but can miss localized changes. Pointwise analyses are more sensitive to localized progression, but more variable so require confirmation. This study assessed whether cluster trend analysis, averaging information across subsets of locations, could improve progression detection. A total of 133 test-retest eyes were tested 7 to 10 times. Rates of change and P values were calculated for possible re-orderings of these series to generate global analysis ("MD worsening faster than x dB/y with P < y"), pointwise and cluster analyses ("n locations [or clusters] worsening faster than x dB/y with P < y") with specificity exactly 95%. These criteria were applied to 505 eyes tested over a mean of 10.5 years, to find how soon each detected "deterioration," and compared using survival models. This was repeated including two subsequent visual fields to determine whether "deterioration" was confirmed. The best global criterion detected deterioration in 25% of eyes in 5.0 years (95% confidence interval [CI], 4.7-5.3 years), compared with 4.8 years (95% CI, 4.2-5.1) for the best cluster analysis criterion, and 4.1 years (95% CI, 4.0-4.5) for the best pointwise criterion. However, for pointwise analysis, only 38% of these changes were confirmed, compared with 61% for clusters and 76% for MD. The time until 25% of eyes showed subsequently confirmed deterioration was 6.3 years (95% CI, 6.0-7.2) for global, 6.3 years (95% CI, 6.0-7.0) for pointwise, and 6.0 years (95% CI, 5.3-6.6) for cluster analyses. Although the specificity is still suboptimal, cluster trend analysis detects subsequently confirmed deterioration sooner than either global or pointwise analyses.
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
Detecting glaucomatous change in visual fields: Analysis with an optimization framework.
Yousefi, Siamak; Goldbaum, Michael H; Varnousfaderani, Ehsan S; Belghith, Akram; Jung, Tzyy-Ping; Medeiros, Felipe A; Zangwill, Linda M; Weinreb, Robert N; Liebmann, Jeffrey M; Girkin, Christopher A; Bowd, Christopher
2015-12-01
Detecting glaucomatous progression is an important aspect of glaucoma management. The assessment of longitudinal series of visual fields, measured using Standard Automated Perimetry (SAP), is considered the reference standard for this effort. We seek efficient techniques for determining progression from longitudinal visual fields by formulating the problem as an optimization framework, learned from a population of glaucoma data. The longitudinal data from each patient's eye were used in a convex optimization framework to find a vector that is representative of the progression direction of the sample population, as a whole. Post-hoc analysis of longitudinal visual fields across the derived vector led to optimal progression (change) detection. The proposed method was compared to recently described progression detection methods and to linear regression of instrument-defined global indices, and showed slightly higher sensitivities at the highest specificities than other methods (a clinically desirable result). The proposed approach is simpler, faster, and more efficient for detecting glaucomatous changes, compared to our previously proposed machine learning-based methods, although it provides somewhat less information. This approach has potential application in glaucoma clinics for patient monitoring and in research centers for classification of study participants. Copyright © 2015 Elsevier Inc. All rights reserved.
Normalizing Landsat and ASTER Data Using MODIS Data Products for Forest Change Detection
NASA Technical Reports Server (NTRS)
Gao, Feng; Masek, Jeffrey G.; Wolfe, Robert E.; Tan, Bin
2010-01-01
Monitoring forest cover and its changes are a major application for optical remote sensing. In this paper, we present an approach to integrate Landsat, ASTER and MODIS data for forest change detection. Moderate resolution (10-100m) images (e.g. Landsat and ASTER) acquired from different seasons and times are normalized to one "standard" date using MODIS data products as reference. The normalized data are then used to compute forest disturbance index for forest change detection. Comparing to the results from original data, forest disturbance index from the normalized images is more consistent spatially and temporally. This work demonstrates an effective approach for mapping forest change over a large area from multiple moderate resolution sensors on various acquisition dates.
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.
Seo, M H; Won, E J; Hong, Y J; Chun, S; Kwon, J R; Choi, Y S; Kim, J N; Lee, S A; Lim, A H; Kim, S H; Park, K U; Cho, D
2016-11-01
The purpose of this study was to provide an effective RHD genotyping strategy for the East Asian blood donors. RhD phenotyping, weak D testing and RhCE phenotyping were performed on 110 samples from members of the RhD-negative club, private organization composed of RhD-negative blood donors, in the GwangJu-Chonnam region of Korea. The RHD promoter, intron 4, and exons 7 and 10 were analysed by real-time PCR. Two nucleotide changes (c.1227 G>A, and c.1222 T>C) in exon 9 were analysed by sequencing. Of 110 RhD-negative club members, 79 (71·8%) showed complete deletion of the RHD gene, 10 (9·1%) showed results consistent with RHD-CE-D hybrid, and 21 (19·1%) showed amplification of RHD promoter, intron 4, and exons 7 and 10. Of the latter group, 16 (14·5%) were in the DEL blood group including c.1227 G>A (N = 14) and c.1222 T>C (N = 2), 2 (1·8%) were weak D, 1(0·9%) was partial D, and 2 (1·8%) were undetermined. The RhD-negative phenotype samples consisted of 58 C-E-c+e+, 19 C-E+c+e+, 3 C-E+c+e-, 21 C+E-c+e-, 6 C+E-c+e+ and 3 C+E-c-e + . Notably, all 58 samples with the C-E-c+e+ phenotype were revealed to have complete deletion of the RHD gene. The C-E-c+e+ phenotype showed 100% positive predictive value for detecting D-negative cases. RHD genotyping is not required in half of D-negative cases. We suggest here an effective RHD genotyping strategy for accurate detection of RhD variants in apparently RhD-negative blood donors in East Asia. © 2016 International Society of Blood Transfusion.
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.
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.
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
DOE Office of Scientific and Technical Information (OSTI.GOV)
Cui, Yi; Hu, Dehong; Markillie, Lye Meng
Quantitative gene expression analysis in intact single cells can be achieved using single molecule- based fluorescence in situ hybridization (smFISH). This approach relies on fluorescence intensity to distinguish between true signals, emitted from an RNA copy hybridized with multiple FISH sub-probes, and background noise. Thus, the precision in smFISH is often compromised by partial or nonspecific binding of sub-probes and tissue autofluorescence, limiting its accuracy. Here we provide an accurate approach for setting quantitative thresholds between true and false signals, which relies on blinking frequencies of photoswitchable dyes. This fluctuation localization imaging-based FISH (fliFISH) uses blinking frequency patterns, emitted frommore » a transcript bound to multiple sub-probes, which are distinct from blinking patterns emitted from partial or nonspecifically bound sub-probes and autofluorescence. Using multicolor fliFISH, we identified radial gene expression patterns in mouse pancreatic islets for insulin, the transcription factor, NKX2-2, and their ratio (Nkx2-2/Ins2). These radial patterns, showing higher values in β cells at the islet core and lower values in peripheral cells, were lost in diabetic mouse islets. In summary, fliFISH provides an accurate, quantitative approach for detecting and counting true RNA copies and rejecting false signals by their distinct blinking frequency patterns, laying the foundation for reliable single-cell transcriptomics.« less
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.
Detecting gradual visual changes in colour and brightness agnosia: a double dissociation.
Nijboer, Tanja C W; te Pas, Susan F; van der Smagt, Maarten J
2011-03-09
Two patients, one with colour agnosia and one with brightness agnosia, performed a task that required the detection of gradual temporal changes in colour and brightness. The results for these patients, who showed anaverage or an above-average performance on several tasks designed to test low-level colour and luminance (contrast) perception in the spatial domain, yielded a double dissociation; the brightness agnosic patient was within the normal range for the coloured stimuli, but much slower to detect brightness differences, whereas the colour agnosic patient was within the normal range for the achromatic stimuli, but much slower for the coloured stimuli. These results suggest that a modality-specific impairment in the detection of gradual temporal changes might be related to, if not underlie, the phenomenon of visual agnosia.
Interannual Change Detection of Mediterranean Seagrasses Using RapidEye Image Time Series
Traganos, Dimosthenis; Reinartz, Peter
2018-01-01
Recent research studies have highlighted the decrease in the coverage of Mediterranean seagrasses due to mainly anthropogenic activities. The lack of data on the distribution of these significant aquatic plants complicates the quantification of their decreasing tendency. While Mediterranean seagrasses are declining, satellite remote sensing technology is growing at an unprecedented pace, resulting in a wealth of spaceborne image time series. Here, we exploit recent advances in high spatial resolution sensors and machine learning to study Mediterranean seagrasses. We process a multispectral RapidEye time series between 2011 and 2016 to detect interannual seagrass dynamics in 888 submerged hectares of the Thermaikos Gulf, NW Aegean Sea, Greece (eastern Mediterranean Sea). We assess the extent change of two Mediterranean seagrass species, the dominant Posidonia oceanica and Cymodocea nodosa, following atmospheric and analytical water column correction, as well as machine learning classification, using Random Forests, of the RapidEye time series. Prior corrections are necessary to untangle the initially weak signal of the submerged seagrass habitats from satellite imagery. The central results of this study show that P. oceanica seagrass area has declined by 4.1%, with a trend of −11.2 ha/yr, while C. nodosa seagrass area has increased by 17.7% with a trend of +18 ha/yr throughout the 5-year study period. Trends of change in spatial distribution of seagrasses in the Thermaikos Gulf site are in line with reported trends in the Mediterranean. Our presented methodology could be a time- and cost-effective method toward the quantitative ecological assessment of seagrass dynamics elsewhere in the future. From small meadows to whole coastlines, knowledge of aquatic plant dynamics could resolve decline or growth trends and accurately highlight key units for future restoration, management, and conservation. PMID:29467777
Interannual Change Detection of Mediterranean Seagrasses Using RapidEye Image Time Series.
Traganos, Dimosthenis; Reinartz, Peter
2018-01-01
Recent research studies have highlighted the decrease in the coverage of Mediterranean seagrasses due to mainly anthropogenic activities. The lack of data on the distribution of these significant aquatic plants complicates the quantification of their decreasing tendency. While Mediterranean seagrasses are declining, satellite remote sensing technology is growing at an unprecedented pace, resulting in a wealth of spaceborne image time series. Here, we exploit recent advances in high spatial resolution sensors and machine learning to study Mediterranean seagrasses. We process a multispectral RapidEye time series between 2011 and 2016 to detect interannual seagrass dynamics in 888 submerged hectares of the Thermaikos Gulf, NW Aegean Sea, Greece (eastern Mediterranean Sea). We assess the extent change of two Mediterranean seagrass species, the dominant Posidonia oceanica and Cymodocea nodosa , following atmospheric and analytical water column correction, as well as machine learning classification, using Random Forests, of the RapidEye time series. Prior corrections are necessary to untangle the initially weak signal of the submerged seagrass habitats from satellite imagery. The central results of this study show that P. oceanica seagrass area has declined by 4.1%, with a trend of -11.2 ha/yr, while C. nodosa seagrass area has increased by 17.7% with a trend of +18 ha/yr throughout the 5-year study period. Trends of change in spatial distribution of seagrasses in the Thermaikos Gulf site are in line with reported trends in the Mediterranean. Our presented methodology could be a time- and cost-effective method toward the quantitative ecological assessment of seagrass dynamics elsewhere in the future. From small meadows to whole coastlines, knowledge of aquatic plant dynamics could resolve decline or growth trends and accurately highlight key units for future restoration, management, and conservation.
Improved Calibration through SMAP RFI Change Detection
NASA Technical Reports Server (NTRS)
Piepmeier, Jeffrey; De Amici, Giovanni; Mohammed, Priscilla; Peng, Jinzheng
2017-01-01
Anthropogenic Radio-Frequency Interference (RFI) drove both the SMAP (Soil Moisture Active Passive) microwave radiometer hardware and Level 1 science algorithm designs to use new technology and techniques for the first time on a spaceflight project. Care was taken to provide special features allowing the detection and removal of harmful interference in order to meet the error budget. Nonetheless, the project accepted a risk that RFI and its mitigation would exceed the 1.3-K error budget. Thus, RFI will likely remain a challenge afterwards due to its changing and uncertain nature. To address the challenge, we seek to answer the following questions: How does RFI evolve over the SMAP lifetime? What calibration error does the changing RFI environment cause? Can time series information be exploited to reduce these errors and improve calibration for all science products reliant upon SMAP radiometer data? In this talk, we address the first question.
Correlation based efficient face recognition and color change detection
NASA Astrophysics Data System (ADS)
Elbouz, M.; Alfalou, A.; Brosseau, C.; Alam, M. S.; Qasmi, S.
2013-01-01
Identifying the human face via correlation is a topic attracting widespread interest. At the heart of this technique lies the comparison of an unknown target image to a known reference database of images. However, the color information in the target image remains notoriously difficult to interpret. In this paper, we report a new technique which: (i) is robust against illumination change, (ii) offers discrimination ability to detect color change between faces having similar shape, and (iii) is specifically designed to detect red colored stains (i.e. facial bleeding). We adopt the Vanderlugt correlator (VLC) architecture with a segmented phase filter and we decompose the color target image using normalized red, green, and blue (RGB), and hue, saturation, and value (HSV) scales. We propose a new strategy to effectively utilize color information in signatures for further increasing the discrimination ability. The proposed algorithm has been found to be very efficient for discriminating face subjects with different skin colors, and those having color stains in different areas of the facial image.
Detecting Evidence of Climate Change in the Forests of the Eastern United States
Jones, John W.; Osborne, Jesse D.
2008-01-01
Changes in land use or disturbances such as defoliation by insects, disease, or fire all affect the composition and amount of tree canopy in a forest. These changes are easy to detect. Noticing and understanding the complex ways that global or regional-scale climate change combines with these disturbances to affect forest growth patterns and succession is difficult. This is particularly true for regions where changes in climate are not the most extreme, such as the mid-latitude forests of the Eastern United States. If land and water resources are to be managed responsibly, it is important to know how well the impacts of climate change on these forests can be measured in order to provide the best information possible to respond to any future changes. The goal of this study is to test whether climate-induced changes in forests in the Eastern United States can be detected and characterized using satellite imagery.
Zhang, Yunfeng; Paik, Jaehyon; Pirolli, Peter
2015-04-01
Animals routinely adapt to changes in the environment in order to survive. Though reinforcement learning may play a role in such adaptation, it is not clear that it is the only mechanism involved, as it is not well suited to producing rapid, relatively immediate changes in strategies in response to environmental changes. This research proposes that counterfactual reasoning might be an additional mechanism that facilitates change detection. An experiment is conducted in which a task state changes over time and the participants had to detect the changes in order to perform well and gain monetary rewards. A cognitive model is constructed that incorporates reinforcement learning with counterfactual reasoning to help quickly adjust the utility of task strategies in response to changes. The results show that the model can accurately explain human data and that counterfactual reasoning is key to reproducing the various effects observed in this change detection paradigm. Copyright © 2015 Cognitive Science Society, Inc.
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
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.
Active doublet method for measuring small changes in physical properties
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.
NASA Technical Reports Server (NTRS)
Kim, Sungwan
1994-01-01
System parameters should be tracked on-line to build a reconfigurable control system even though there exists an abrupt change. For this purpose, a new performance index that we are studying is the speed of adaptation- how quickly does the system determine that a change has occurred? In this paper, a new, robust algorithm that is optimized to minimize the time delay in detecting a change for fixed false alarm probability is proposed. Simulation results for the aircraft lateral motion with a known or unknown change in control gain matrices, in the presence of doublet input, indicate that the algorithm works fairly well. One of its distinguishing properties is that detection delay of this algorithm is superior to that of Whiteness Test.
Quantification of Forecasting and Change-Point Detection Methods for Predictive Maintenance
2015-08-19
industries to manage the service life of equipment, and also to detect precursors to the failure of components found in nuclear power plants, wind turbines ...detection methods for predictive maintenance 5a. CONTRACT NUMBER FA2386-14-1-4096 5b. GRANT NUMBER Grant 14IOA015 AOARD-144096 5c. PROGRAM ELEMENT...sensitive to changes related to abnormality. 15. SUBJECT TERMS predictive maintenance , predictive maintenance , forecasting 16
Li, Cuixia; Zuo, Jing; Zhang, Li; Chang, Yulei; Zhang, Youlin; Tu, Langping; Liu, Xiaomin; Xue, Bin; Li, Qiqing; Zhao, Huiying; Zhang, Hong; Kong, Xianggui
2016-12-09
Accurate quantitation of intracellular pH (pH i ) is of great importance in revealing the cellular activities and early warning of diseases. A series of fluorescence-based nano-bioprobes composed of different nanoparticles or/and dye pairs have already been developed for pH i sensing. Till now, biological auto-fluorescence background upon UV-Vis excitation and severe photo-bleaching of dyes are the two main factors impeding the accurate quantitative detection of pH i . Herein, we have developed a self-ratiometric luminescence nanoprobe based on förster resonant energy transfer (FRET) for probing pH i , in which pH-sensitive fluorescein isothiocyanate (FITC) and upconversion nanoparticles (UCNPs) were served as energy acceptor and donor, respectively. Under 980 nm excitation, upconversion emission bands at 475 nm and 645 nm of NaYF 4 :Yb 3+ , Tm 3+ UCNPs were used as pH i response and self-ratiometric reference signal, respectively. This direct quantitative sensing approach has circumvented the traditional software-based subsequent processing of images which may lead to relatively large uncertainty of the results. Due to efficient FRET and fluorescence background free, a highly-sensitive and accurate sensing has been achieved, featured by 3.56 per unit change in pH i value 3.0-7.0 with deviation less than 0.43. This approach shall facilitate the researches in pH i related areas and development of the intracellular drug delivery systems.
NASA Astrophysics Data System (ADS)
Li, Cuixia; Zuo, Jing; Zhang, Li; Chang, Yulei; Zhang, Youlin; Tu, Langping; Liu, Xiaomin; Xue, Bin; Li, Qiqing; Zhao, Huiying; Zhang, Hong; Kong, Xianggui
2016-12-01
Accurate quantitation of intracellular pH (pHi) is of great importance in revealing the cellular activities and early warning of diseases. A series of fluorescence-based nano-bioprobes composed of different nanoparticles or/and dye pairs have already been developed for pHi sensing. Till now, biological auto-fluorescence background upon UV-Vis excitation and severe photo-bleaching of dyes are the two main factors impeding the accurate quantitative detection of pHi. Herein, we have developed a self-ratiometric luminescence nanoprobe based on förster resonant energy transfer (FRET) for probing pHi, in which pH-sensitive fluorescein isothiocyanate (FITC) and upconversion nanoparticles (UCNPs) were served as energy acceptor and donor, respectively. Under 980 nm excitation, upconversion emission bands at 475 nm and 645 nm of NaYF4:Yb3+, Tm3+ UCNPs were used as pHi response and self-ratiometric reference signal, respectively. This direct quantitative sensing approach has circumvented the traditional software-based subsequent processing of images which may lead to relatively large uncertainty of the results. Due to efficient FRET and fluorescence background free, a highly-sensitive and accurate sensing has been achieved, featured by 3.56 per unit change in pHi value 3.0-7.0 with deviation less than 0.43. This approach shall facilitate the researches in pHi related areas and development of the intracellular drug delivery systems.
de Bruin, Elza C.; Whiteley, Jessica L.; Corcoran, Claire; Kirk, Pauline M.; Fox, Jayne C.; Armisen, Javier; Lindemann, Justin P. O.; Schiavon, Gaia; Ambrose, Helen J.; Kohlmann, Alexander
2017-01-01
Personalized healthcare relies on accurate companion diagnostic assays that enable the most appropriate treatment decision for cancer patients. Extensive assay validation prior to use in a clinical setting is essential for providing a reliable test result. This poses a challenge for low prevalence mutations with limited availability of appropriate clinical samples harboring the mutation. To enable prospective screening for the low prevalence AKT1 E17K mutation, we have developed and validated a competitive allele-specific TaqMan® PCR (castPCR™) assay for mutation detection in formalin-fixed paraffin-embedded (FFPE) tumor tissue. Analysis parameters of the castPCR™ assay were established using an FFPE DNA reference standard and its analytical performance was assessed using 338 breast cancer and gynecological cancer FFPE samples. With recent technical advances for minimally invasive mutation detection in circulating tumor DNA (ctDNA), we subsequently also evaluated the OncoBEAM™ assay to enable plasma specimens as additional diagnostic opportunity for AKT1 E17K mutation testing. The analysis performance of the OncoBEAM™ test was evaluated using a novel AKT1 E17K ctDNA reference standard consisting of sheared genomic DNA spiked into human plasma. Both assays are employed at centralized testing laboratories operating according to quality standards for prospective identification of the AKT1 E17K mutation in ER+ breast cancer patients in the context of a clinical trial evaluating the AKT inhibitor AZD5363 in combination with endocrine (fulvestrant) therapy. PMID:28472036
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.
Effects of spatial cues on color-change detection in humans
Herman, James P.; Bogadhi, Amarender R.; Krauzlis, Richard J.
2015-01-01
Studies of covert spatial attention have largely used motion, orientation, and contrast stimuli as these features are fundamental components of vision. The feature dimension of color is also fundamental to visual perception, particularly for catarrhine primates, and yet very little is known about the effects of spatial attention on color perception. Here we present results using novel dynamic color stimuli in both discrimination and color-change detection tasks. We find that our stimuli yield comparable discrimination thresholds to those obtained with static stimuli. Further, we find that an informative spatial cue improves performance and speeds response time in a color-change detection task compared with an uncued condition, similar to what has been demonstrated for motion, orientation, and contrast stimuli. Our results demonstrate the use of dynamic color stimuli for an established psychophysical task and show that color stimuli are well suited to the study of spatial attention. PMID:26047359
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.
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
P.S. Homann; B.T. Bormann; J.R. Boyle; R.L. Darbyshire; R. Bigley
2008-01-01
Detecting changes in forest soil C and N is vital to the study of global budgets and long-term ecosystem productivity. Identifying differences among land-use practices may guide future management. Our objective was to determine the relation of minimum detectable changes (MDCs) and minimum detectable differences between treatments (MDDs) to soil C and N variability at...
Detecting dynamical changes in time series by using the Jensen Shannon divergence
NASA Astrophysics Data System (ADS)
Mateos, D. M.; Riveaud, L. E.; Lamberti, P. W.
2017-08-01
Most of the time series in nature are a mixture of signals with deterministic and random dynamics. Thus the distinction between these two characteristics becomes important. Distinguishing between chaotic and aleatory signals is difficult because they have a common wide band power spectrum, a delta like autocorrelation function, and share other features as well. In general, signals are presented as continuous records and require to be discretized for being analyzed. In this work, we introduce different schemes for discretizing and for detecting dynamical changes in time series. One of the main motivations is to detect transitions between the chaotic and random regime. The tools here used here originate from the Information Theory. The schemes proposed are applied to simulated and real life signals, showing in all cases a high proficiency for detecting changes in the dynamics of the associated time series.
Statistical methods for change-point detection in surface temperature records
NASA Astrophysics Data System (ADS)
Pintar, A. L.; Possolo, A.; Zhang, N. F.
2013-09-01
We describe several statistical methods to detect possible change-points in a time series of values of surface temperature measured at a meteorological station, and to assess the statistical significance of such changes, taking into account the natural variability of the measured values, and the autocorrelations between them. These methods serve to determine whether the record may suffer from biases unrelated to the climate signal, hence whether there may be a need for adjustments as considered by M. J. Menne and C. N. Williams (2009) "Homogenization of Temperature Series via Pairwise Comparisons", Journal of Climate 22 (7), 1700-1717. We also review methods to characterize patterns of seasonality (seasonal decomposition using monthly medians or robust local regression), and explain the role they play in the imputation of missing values, and in enabling robust decompositions of the measured values into a seasonal component, a possible climate signal, and a station-specific remainder. The methods for change-point detection that we describe include statistical process control, wavelet multi-resolution analysis, adaptive weights smoothing, and a Bayesian procedure, all of which are applicable to single station records.
Testing hypotheses on distribution shifts and changes in phenology of imperfectly detectable species
Chambert, Thierry A.; Kendall, William L.; Hines, James E.; Nichols, James D.; Pedrini, Paolo; Waddle, J. Hardin; Tavecchia, Giacomo; Walls, Susan C.; Tenan, Simone
2015-01-01
With ongoing climate change, many species are expected to shift their spatial and temporal distributions. To document changes in species distribution and phenology, detection/non-detection data have proven very useful. Occupancy models provide a robust way to analyse such data, but inference is usually focused on species spatial distribution, not phenology.We present a multi-season extension of the staggered-entry occupancy model of Kendall et al. (2013, Ecology, 94, 610), which permits inference about the within-season patterns of species arrival and departure at sampling sites. The new model presented here allows investigation of species phenology and spatial distribution across years, as well as site extinction/colonization dynamics.We illustrate the model with two data sets on European migratory passerines and one data set on North American treefrogs. We show how to derive several additional phenological parameters, such as annual mean arrival and departure dates, from estimated arrival and departure probabilities.Given the extent of detection/non-detection data that are available, we believe that this modelling approach will prove very useful to further understand and predict species responses to climate change.
Optical sensor for rapid microbial detection
NASA Astrophysics Data System (ADS)
Al-Adhami, Mustafa; Tilahun, Dagmawi; Rao, Govind; Kostov, Yordan
2016-05-01
In biotechnology, the ability to instantly detect contaminants is key to running a reliable bioprocess. Bioprocesses are prone to be contaminated by cells that are abundant in our environment; detection and quantification of these cells would aid in the preservation of the bioprocess product. This paper discusses the design and development of a portable kinetics fluorometer which acts as a single-excitation, single-emission photometer that continuously measures fluorescence intensity of an indicator dye, and plots it. Resazurin is used as an indicator dye since the viable contaminant cells reduce Resazurin toResorufin, the latter being strongly fluorescent. A photodiode detects fluorescence change by generating current proportional to the intensity of the light that reached it, and a trans-impedance differential op-amp ensures amplification of the photodiodes' signal. A microfluidic chip was designed specifically for the device. It acts as a fully enclosed cuvette, which enhances the Resazurin reduction rate. E. coli in LB media, along with Resazurin were injected into the microfluidic chip. The optical sensor detected the presence of E. coli in the media based on the fluorescence change that occurred in the indicator dye in concentrations as low as 10 CFU/ml. A method was devised to detect and determine an approximate amount of contamination with this device. This paper discusses application of this method to detect and estimate sample contamination. This device provides fast, accurate, and inexpensive means to optically detect the presence of viable cells.
Detection of a sudden change of the field time series based on the Lorenz system
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
Detection of a sudden change of the field time series based on the Lorenz system.
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.
Levin, Daniel T; Drivdahl, Sarah B; Momen, Nausheen; Beck, Melissa R
2002-12-01
Recently, a number of experiments have emphasized the degree to which subjects fail to detect large changes in visual scenes. This finding, referred to as "change blindness," is often considered surprising because many people have the intuition that such changes should be easy to detect. documented this intuition by showing that the majority of subjects believe they would notice changes that are actually very rarely detected. Thus subjects exhibit a metacognitive error we refer to as "change blindness blindness." Here, we test whether CBB is caused by a misestimation of the perceptual experience associated with visual changes and show that it persists even when the pre- and postchange views are separated by long delays. In addition, subjects overestimate their change detection ability both when the relevant changes are illustrated by still pictures, and when they are illustrated using videos showing the changes occurring in real time. We conclude that CBB is a robust phenomenon that cannot be accounted for by failure to understand the specific perceptual experience associated with a change. Copyright 2002 Elsevier Science (USA)
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...
Building Change Detection from Harvey using Unmanned Aerial System (UAS)
NASA Astrophysics Data System (ADS)
Chang, A.; Yeom, J.; Jung, J.; Choi, I.
2017-12-01
Unmanned Aerial System (UAS) is getting to be the most important technique in recent days since the fine spatial and high temporal resolution data previously unobtainable from traditional remote sensing platforms. Advanced UAS data can provide a great opportunity for disaster monitoring. Especially, building change detection is the one of the most important topics for damage assessment and recovery from disasters. This study is proposing a method to monitor building change with UAS data for Holiday Beach in Texas, where was directly hit by Harvey on 25 August 2017. This study adopted 3D change detection to monitor building damage and recovery levels with building height as well as natural color information. We used a rotorcraft UAS to collect RGB data twice on 9 September and 18 October 2017 after the hurricane. The UAS data was processed using Agisoft Photoscan Pro Software to generate super high resolution dataset including orthomosaic, DSM (Digital Surface Model), and 3D point cloud. We compared the processed dataset with an airborne image considerable as before-hurricane data, which was acquired on January 2016. Building damage and recovery levels were determined by height and color change. The result will show that UAS data is useful to assess building damage and recovery for affected area by the natural disaster such as Harvey.
Extracting Time-Accurate Acceleration Vectors From Nontrivial Accelerometer Arrangements.
Franck, Jennifer A; Blume, Janet; Crisco, Joseph J; Franck, Christian
2015-09-01
Sports-related concussions are of significant concern in many impact sports, and their detection relies on accurate measurements of the head kinematics during impact. Among the most prevalent recording technologies are videography, and more recently, the use of single-axis accelerometers mounted in a helmet, such as the HIT system. Successful extraction of the linear and angular impact accelerations depends on an accurate analysis methodology governed by the equations of motion. Current algorithms are able to estimate the magnitude of acceleration and hit location, but make assumptions about the hit orientation and are often limited in the position and/or orientation of the accelerometers. The newly formulated algorithm presented in this manuscript accurately extracts the full linear and rotational acceleration vectors from a broad arrangement of six single-axis accelerometers directly from the governing set of kinematic equations. The new formulation linearizes the nonlinear centripetal acceleration term with a finite-difference approximation and provides a fast and accurate solution for all six components of acceleration over long time periods (>250 ms). The approximation of the nonlinear centripetal acceleration term provides an accurate computation of the rotational velocity as a function of time and allows for reconstruction of a multiple-impact signal. Furthermore, the algorithm determines the impact location and orientation and can distinguish between glancing, high rotational velocity impacts, or direct impacts through the center of mass. Results are shown for ten simulated impact locations on a headform geometry computed with three different accelerometer configurations in varying degrees of signal noise. Since the algorithm does not require simplifications of the actual impacted geometry, the impact vector, or a specific arrangement of accelerometer orientations, it can be easily applied to many impact investigations in which accurate kinematics need
DOE Office of Scientific and Technical Information (OSTI.GOV)
Nakhleh, Luay
I proposed to develop computationally efficient tools for accurate detection and reconstruction of microbes' complex evolutionary mechanisms, thus enabling rapid and accurate annotation, analysis and understanding of their genomes. To achieve this goal, I proposed to address three aspects. (1) Mathematical modeling. A major challenge facing the accurate detection of HGT is that of distinguishing between these two events on the one hand and other events that have similar "effects." I proposed to develop a novel mathematical approach for distinguishing among these events. Further, I proposed to develop a set of novel optimization criteria for the evolutionary analysis of microbialmore » genomes in the presence of these complex evolutionary events. (2) Algorithm design. In this aspect of the project, I proposed to develop an array of e cient and accurate algorithms for analyzing microbial genomes based on the formulated optimization criteria. Further, I proposed to test the viability of the criteria and the accuracy of the algorithms in an experimental setting using both synthetic as well as biological data. (3) Software development. I proposed the nal outcome to be a suite of software tools which implements the mathematical models as well as the algorithms developed.« less
NASA Astrophysics Data System (ADS)
Heathfield, D.; Walker, I. J.; Grilliot, M. J.
2016-12-01
The recent emergence of terrestrial laser scanning (TLS) and unmanned aerial systems (UAS) as mapping platforms in geomorphology research has allowed for expedited acquisition of high spatial and temporal resolution, three-dimensional topographic datasets. TLS provides dense 3D `point cloud' datasets that require careful acquisition strategies and appreciable post-processing to produce accurate digital elevation models (DEMs). UAS provide overlapping nadir and oblique imagery that can be analysed using Structure from Motion (SfM) photogrammetry software to provide accurate, high-resolution orthophoto mosaics and accurate digital surface models (DSMs). Both methods yield centimeter to decimeter scale accuracy, depending on various hardware and field acquisition considerations (e.g., camera resolution, flight height, on-site GNSS control, etc.). Combined, the UAS-SfM workflow provides a comparable and more affordable solution to the more expensive TLS or aerial LiDAR methods. This paper compares and contrasts SfM and TLS survey methodologies and related workflow costs and benefits as used to quantify and examine seasonal beach-dune erosion and recovery processes at a site (Calvert Island) on British Columbia's central coast in western Canada. Seasonal SfM- and TLS-derived DEMs were used to quantify spatial patterns of surface elevation change, geomorphic responses, and related significant sediment volume changes. Cluster maps of positive (depositional) and negative (erosional) change are analysed to detect and interpret the geomorphic and sediment budget responses following an erosive water level event during winter 2016 season (Oct. 2015 - Apr. 2016). Vantage cameras also provided qualitative data on the frequency and magnitude of environmental drivers (e.g., tide, wave, wind forcing) of erosion and deposition events during the observation period. In addition, we evaluate the costs, time expenditures, and accuracy considerations for both SfM and TLS methodologies.
How Accurately Can Your Wrist Device Recognize Daily Activities and Detect Falls?
Gjoreski, Martin; Gjoreski, Hristijan; Luštrek, Mitja; Gams, Matjaž
2016-01-01
Although wearable accelerometers can successfully recognize activities and detect falls, their adoption in real life is low because users do not want to wear additional devices. A possible solution is an accelerometer inside a wrist device/smartwatch. However, wrist placement might perform poorly in terms of accuracy due to frequent random movements of the hand. In this paper we perform a thorough, large-scale evaluation of methods for activity recognition and fall detection on four datasets. On the first two we showed that the left wrist performs better compared to the dominant right one, and also better compared to the elbow and the chest, but worse compared to the ankle, knee and belt. On the third (Opportunity) dataset, our method outperformed the related work, indicating that our feature-preprocessing creates better input data. And finally, on a real-life unlabeled dataset the recognized activities captured the subject’s daily rhythm and activities. Our fall-detection method detected all of the fast falls and minimized the false positives, achieving 85% accuracy on the first dataset. Because the other datasets did not contain fall events, only false positives were evaluated, resulting in 9 for the second, 1 for the third and 15 for the real-life dataset (57 days data). PMID:27258282
Oxygen detection using the laser diode absorption technique
NASA Technical Reports Server (NTRS)
Disimile, P. J.; Fox, C. W.
1991-01-01
Accurate measurement of the concentration and flow rate of gaseous oxygen is becoming of greater importance. The detection technique presented is based on the principal of light absorption by the Oxygen A-Band. Oxygen molecules have characteristics which attenuate radiation in the 759-770 nm wavelength range. With an ability to measure changes in the relative light transmission to less than 0.01 percent, a sensitive optical gas detection system was configured. This system is smaller in size and light in weight, has low energy requirements and has a rapid response time. In this research program, the application of temperature tuning laser diodes and their ability to be wavelength shifted to a selected absorption spectral peak has allowed concentrations as low as 1300 ppm to be detected.
Detecting and isolating abrupt changes in linear switching systems
NASA Astrophysics Data System (ADS)
Nazari, Sohail; Zhao, Qing; Huang, Biao
2015-04-01
In this paper, a novel fault detection and isolation (FDI) method for switching linear systems is developed. All input and output signals are assumed to be corrupted with measurement noises. In the proposed method, a 'lifted' linear model named as stochastic hybrid decoupling polynomial (SHDP) is introduced. The SHDP model governs the dynamics of the switching linear system with all different modes, and is independent of the switching sequence. The error-in-variable (EIV) representation of SHDP is derived, and is used for the fault residual generation and isolation following the well-adopted local approach. The proposed FDI method can detect and isolate the fault-induced abrupt changes in switching models' parameters without estimating the switching modes. Furthermore, in this paper, the analytical expressions of the gradient vector and Hessian matrix are obtained based on the EIV SHDP formulation, so that they can be used to implement the online fault detection scheme. The performance of the proposed method is then illustrated by simulation examples.
Change detection on LOD 2 building models with very high resolution spaceborne stereo imagery
NASA Astrophysics Data System (ADS)
Qin, Rongjun
2014-10-01
Due to the fast development of the urban environment, the need for efficient maintenance and updating of 3D building models is ever increasing. Change detection is an essential step to spot the changed area for data (map/3D models) updating and urban monitoring. Traditional methods based on 2D images are no longer suitable for change detection in building scale, owing to the increased spectral variability of the building roofs and larger perspective distortion of the very high resolution (VHR) imagery. Change detection in 3D is increasingly being investigated using airborne laser scanning data or matched Digital Surface Models (DSM), but rare study has been conducted regarding to change detection on 3D city models with VHR images, which is more informative but meanwhile more complicated. This is due to the fact that the 3D models are abstracted geometric representation of the urban reality, while the VHR images record everything. In this paper, a novel method is proposed to detect changes directly on LOD (Level of Detail) 2 building models with VHR spaceborne stereo images from a different date, with particular focus on addressing the special characteristics of the 3D models. In the first step, the 3D building models are projected onto a raster grid, encoded with building object, terrain object, and planar faces. The DSM is extracted from the stereo imagery by hierarchical semi-global matching (SGM). In the second step, a multi-channel change indicator is extracted between the 3D models and stereo images, considering the inherent geometric consistency (IGC), height difference, and texture similarity for each planar face. Each channel of the indicator is then clustered with the Self-organizing Map (SOM), with "change", "non-change" and "uncertain change" status labeled through a voting strategy. The "uncertain changes" are then determined with a Markov Random Field (MRF) analysis considering the geometric relationship between faces. In the third step, buildings are
Detection of non-natural springtime precipitation change over northern South America
NASA Astrophysics Data System (ADS)
Barkhordarian, A.; Behrangi, A.; Mechoso, C. R.
2017-12-01
Here we determine whether the climate over South America has changed as a result of human activity since the beginning of the industrial revolution. To this end, we assess whether the observed changes are likely to have been due to natural (internal) variability alone, and if not, whether they are consistent with what models simulate as response to anthropogenic and natural forcing. Internal variability is estimated using 12,000-year control runs derived from CMIP5 archive. Results indicate that, in the past decades, trends in springtime (ASO, August-October) precipitation over South America have a magnitude that is beyond the estimated range due to natural (internal) variability or natural forcings alone. Evidence for the presence of an external driving factor is clearly detectable in the observed precipitation record (with less than 5% risk of error). The regression results illustrate the concerted emergence of an anthropogenic signal consistent with greenhouse gas (GHG) in observed decreasing 30-year trends of precipitation ending in 1998 and later on. In addition, the fingerprint of land-use-change signal is detectable in the observed precipitation decrease over 1983-2012. While the influence of GHG signal is detectable in precipitation, an observed decrease up to 10 mm/decade drying over the Amazon region, is much larger than the changes simulated by global and regional climate models as response to GHG forcing. We further show that the projected increasing trend of vapor pressure deficit (VPD), an indicator of background aridity, by the climate models with GHG forcing is much smaller than that observed over the Amazon rainforest. This may imply that models may underestimate the resulting reductions in forest CO2 uptake that could function as a positive feedback to rising temperature and reducing precipitation. Taking the ensemble of 23 IPCC models as a crude metric of probabilities, we further show that with 19 out of 24 models the effect of GS signal
Preliminary study of detection of buried landmines using a programmable hyperspectral imager
NASA Astrophysics Data System (ADS)
McFee, John E.; Ripley, Herb T.; Buxton, Roger; Thriscutt, Andrew M.
1996-05-01
Experiments were conducted to determine if buried mines could be detected by measuring the change in reflectance spectra of vegetation above mine burial sites. Mines were laid using hand methods and simulated mechanical methods and spectral images were obtained over a three month period using a casi hyperspectral imager scanned from a personnel lift. Mines were not detectable by measurement of the shift of the red edge of vegetative spectra. By calculating the linear correlation coefficient image, some mines in light vegetative cover (grass, grass/blueberries) were apparently detected, but mines buried in heavy vegetation cover (deep ferns) were not detectable. Due to problems with ground truthing, accurate probabilities of detection and false alarm rates were not obtained.
NASA Astrophysics Data System (ADS)
Liu, Tao; Li, Ying; Cao, Ying; Shen, Qiang
2017-10-01
This paper proposes a model of dual-channel convolutional neural network (CNN) that is designed for change detection in SAR images, in an effort to acquire higher detection accuracy and lower misclassification rate. This network model contains two parallel CNN channels, which can extract deep features from two multitemporal SAR images. For comparison and validation, the proposed method is tested along with other change detection algorithms on both simulated SAR images and real-world SAR images captured by different sensors. The experimental results demonstrate that the presented method outperforms the state-of-the-art techniques by a considerable margin.
Automatic Detection of Changes on Mars Surface from High-Resolution Orbital Images
NASA Astrophysics Data System (ADS)
Sidiropoulos, Panagiotis; Muller, Jan-Peter
2017-04-01
Over the last 40 years Mars has been extensively mapped by several NASA and ESA orbital missions, generating a large image dataset comprised of approximately 500,000 high-resolution images (of <100m resolution). The overall area mapped from orbital imagery is approximately 6 times the overall surface of Mars [1]. The multi-temporal coverage of Martian surface allows a visual inspection of the surface to identify dynamic phenomena, i.e. surface features that change over time, such as slope streaks [2], recurring slope lineae [3], new impact craters [4], etc. However, visual inspection for change detection is a limited approach, since it requires extensive use of human resources, which is very difficult to achieve when dealing with a rapidly increasing volume of data. Although citizen science can be employed for training and verification it is unsuitable for planetwide systematic change detection. In this work, we introduce a novel approach in planetary image change detection, which involves a batch-mode automatic change detection pipeline that identifies regions that have changed. This is tested in anger, on tens of thousands of high-resolution images over the MC11 quadrangle [5], acquired by CTX, HRSC, THEMIS-VIS and MOC-NA instruments [1]. We will present results which indicate a substantial level of activity in this region of Mars, including instances of dynamic natural phenomena that haven't been cataloged in the planetary science literature before. We will demonstrate the potential and usefulness of such an automatic approach in planetary science change detection. Acknowledgments: The research leading to these results has received funding from the STFC "MSSL Consolidated Grant" ST/K000977/1 and partial support from the European Union's Seventh Framework Programme (FP7/2007-2013) under iMars grant agreement n° 607379. References: [1] P. Sidiropoulos and J. - P. Muller (2015) On the status of orbital high-resolution repeat imaging of Mars for the observation of
Building Change Detection from Bi-Temporal Dense-Matching Point Clouds and Aerial Images.
Pang, Shiyan; Hu, Xiangyun; Cai, Zhongliang; Gong, Jinqi; Zhang, Mi
2018-03-24
In this work, a novel building change detection method from bi-temporal dense-matching point clouds and aerial images is proposed to address two major problems, namely, the robust acquisition of the changed objects above ground and the automatic classification of changed objects into buildings or non-buildings. For the acquisition of changed objects above ground, the change detection problem is converted into a binary classification, in which the changed area above ground is regarded as the foreground and the other area as the background. For the gridded points of each period, the graph cuts algorithm is adopted to classify the points into foreground and background, followed by the region-growing algorithm to form candidate changed building objects. A novel structural feature that was extracted from aerial images is constructed to classify the candidate changed building objects into buildings and non-buildings. The changed building objects are further classified as "newly built", "taller", "demolished", and "lower" by combining the classification and the digital surface models of two periods. Finally, three typical areas from a large dataset are used to validate the proposed method. Numerous experiments demonstrate the effectiveness of the proposed algorithm.
A pH-based biosensor for detection of arsenic in drinking water.
de Mora, K; Joshi, N; Balint, B L; Ward, F B; Elfick, A; French, C E
2011-05-01
Arsenic contaminated groundwater is estimated to affect over 100 million people worldwide, with Bangladesh and West Bengal being among the worst affected regions. A simple, cheap, accurate and disposable device is required for arsenic field testing. We have previously described a novel biosensor for arsenic in which the output is a change in pH, which can be detected visually as a colour change by the use of a pH indicator. Here, we present an improved formulation allowing sensitive and accurate detection of less than 10 ppb arsenate with static overnight incubation. Furthermore, we describe a cheap and simple high-throughput system for simultaneous monitoring of pH in multiple assays over time. Up to 50 samples can be monitored continuously over the desired time period. Cells can be stored and distributed in either air-dried or freeze-dried form. This system was successfully tested on arsenic-contaminated groundwater samples from the South East region of Hungary. We hope to continue to develop this sensor to produce a device suitable for field trials.
NASA Astrophysics Data System (ADS)
Adal, Kedir M.; van Etten, Peter G.; Martinez, Jose P.; Rouwen, Kenneth; Vermeer, Koenraad A.; van Vliet, Lucas J.
2017-03-01
Automated detection and quantification of spatio-temporal retinal changes is an important step to objectively assess disease progression and treatment effects for dynamic retinal diseases such as diabetic retinopathy (DR). However, detecting retinal changes caused by early DR lesions such as microaneurysms and dot hemorrhages from longitudinal pairs of fundus images is challenging due to intra and inter-image illumination variation between fundus images. This paper explores a method for automated detection of retinal changes from illumination normalized fundus images using a deep convolutional neural network (CNN), and compares its performance with two other CNNs trained separately on color and green channel fundus images. Illumination variation was addressed by correcting for the variability in the luminosity and contrast estimated from a large scale retinal regions. The CNN models were trained and evaluated on image patches extracted from a registered fundus image set collected from 51 diabetic eyes that were screened at two different time-points. The results show that using normalized images yield better performance than color and green channel images, suggesting that illumination normalization greatly facilitates CNNs to quickly and correctly learn distinctive local image features of DR related retinal changes.
Detection of emetic activity in the cat by monitoring venous pressure and audio signals
NASA Technical Reports Server (NTRS)
Nagahara, A.; Fox, Robert A.; Daunton, Nancy G.; Elfar, S.
1991-01-01
To investigate the use of audio signals as a simple, noninvasive measure of emetic activity, the relationship between the somatic events and sounds associated with retching and vomiting was studied. Thoracic venous pressure obtained from an implanted external jugular catheter was shown to provide a precise measure of the somatic events associated with retching and vomiting. Changes in thoracic venous pressure monitored through an indwelling external jugular catheter with audio signals, obtained from a microphone located above the animal in a test chamber, were compared. In addition, two independent observers visually monitored emetic episodes. Retching and vomiting were induced by injection of xylazine (0.66mg/kg s.c.), or by motion. A unique audio signal at a frequency of approximately 250 Hz is produced at the time of the negative thoracic venous pressure change associated with retching. Sounds with higher frequencies (around 2500 Hz) occur in conjunction with the positive pressure changes associated with vomiting. These specific signals could be discriminated reliably by individuals reviewing the audio recordings of the sessions. Retching and those emetic episodes associated with positive venous pressure changes were detected accurately by audio monitoring, with 90 percent of retches and 100 percent of emetic episodes correctly identified. Retching was detected more accurately (p is less than .05) by audio monitoring than by direct visual observation. However, with visual observation a few incidents in which stomach contents were expelled in the absence of positive pressure changes or detectable sounds were identified. These data suggest that in emetic situations, the expulsion of stomach contents may be accomplished by more than one neuromuscular system and that audio signals can be used to detect emetic episodes associated with thoracic venous pressure changes.
Detection ratios on winter surveys of Rocky Mountain Trumpeter Swans Cygnus buccinator
Bart, J.; Mitchell, C.D.; Fisher, M.N.; Dubovsky, J.A.
2007-01-01
We estimated the detection ratio for Rocky Mountain Trumpeter Swans Cygnus buccinator that were counted during aerial surveys made in winter. The standard survey involved counting white or grey birds on snow and ice and thus might be expected to have had low detection ratios. On the other hand, observers were permitted to circle areas where the birds were concentrated multiple times to obtain accurate counts. Actual numbers present were estimated by conducting additional intensive aerial counts either immediately before or immediately after the standard count. Surveyors continued the intensive surveys at each area until consecutive counts were identical. The surveys were made at 10 locations in 2006 and at 19 locations in 2007. A total of 2,452 swans were counted on the intensive surveys. Detection ratios did not vary detectably with year, observer, which survey was conducted first, age of the swans, or the number of swans present. The overall detection ratio was 0.93 (90% confidence interval 0.82-1.04), indicating that the counts were quite accurate. Results are used to depict changes in population size for Rocky Mountain Trumpeter Swans from 1974-2007. ?? Wildfowl & Wetlands Trust.
Cui, Yi; Hu, Dehong; Markillie, Lye Meng; ...
2017-10-04
Here, quantitative gene expression analysis in intact single cells can be achieved using single molecule-based fluorescence in situ hybridization (smFISH). This approach relies on fluorescence intensity to distinguish between true signals, emitted from an RNA copy hybridized with multiple oligonucleotide probes, and background noise. Thus, the precision in smFISH is often compromised by partial or nonspecific probe binding and tissue autofluorescence, especially when only a small number of probes can be fitted to the target transcript. Here we provide an accurate approach for setting quantitative thresholds between true and false signals, which relies on on-off duty cycles of photoswitchable dyes.more » This fluctuation localization imaging-based FISH (fliFISH) uses on-time fractions (measured over a series of exposures) collected from transcripts bound to as low as 8 probes, which are distinct from on-time fractions collected from nonspecifically bound probes or autofluorescence. Using multicolor fliFISH, we identified radial gene expression patterns in mouse pancreatic islets for insulin, the transcription factor, NKX2-2 and their ratio ( Nkx2- 2/Ins2). These radial patterns, showing higher values in β cells at the islet core and lower values in peripheral cells, were lost in diabetic mouse islets. In summary, fliFISH provides an accurate, quantitative approach for detecting and counting true RNA copies and rejecting false signals by their distinct on-time fractions, laying the foundation for reliable single-cell transcriptomics.« less
DOE Office of Scientific and Technical Information (OSTI.GOV)
Cui, Yi; Hu, Dehong; Markillie, Lye Meng
Here, quantitative gene expression analysis in intact single cells can be achieved using single molecule-based fluorescence in situ hybridization (smFISH). This approach relies on fluorescence intensity to distinguish between true signals, emitted from an RNA copy hybridized with multiple oligonucleotide probes, and background noise. Thus, the precision in smFISH is often compromised by partial or nonspecific probe binding and tissue autofluorescence, especially when only a small number of probes can be fitted to the target transcript. Here we provide an accurate approach for setting quantitative thresholds between true and false signals, which relies on on-off duty cycles of photoswitchable dyes.more » This fluctuation localization imaging-based FISH (fliFISH) uses on-time fractions (measured over a series of exposures) collected from transcripts bound to as low as 8 probes, which are distinct from on-time fractions collected from nonspecifically bound probes or autofluorescence. Using multicolor fliFISH, we identified radial gene expression patterns in mouse pancreatic islets for insulin, the transcription factor, NKX2-2 and their ratio ( Nkx2- 2/Ins2). These radial patterns, showing higher values in β cells at the islet core and lower values in peripheral cells, were lost in diabetic mouse islets. In summary, fliFISH provides an accurate, quantitative approach for detecting and counting true RNA copies and rejecting false signals by their distinct on-time fractions, laying the foundation for reliable single-cell transcriptomics.« less
Probability of detection of clinical seizures using heart rate changes.
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.
Farrell, Mikella E; Holthoff, Ellen L; Pellegrino, Paul M
2014-01-01
The United States Army and the first responder community are increasingly focusing efforts on energetic materials detection and identification. Main hazards encountered in theater include homemade explosives and improvised explosive devices, in part fabricated from simple components like ammonium nitrate (AN). In order to accurately detect and identify these unknowns (energetic or benign), fielded detection systems must be accurately trained using well-understood universal testing substrates. These training substrates must contain target species at known concentrations and recognized polymorphic phases. Ammonium nitrate is an explosive precursor material that demonstrates several different polymorphic phases dependent upon how the material is deposited onto testing substrates. In this paper, known concentrations of AN were uniformly deposited onto commercially available surface-enhanced Raman scattering (SERS) substrates using a drop-on-demand inkjet printing system. The phase changes observed after the deposition of AN under several solvent conditions are investigated. Characteristics of the collected SERS spectra of AN are discussed, and it is demonstrated that an understanding of the exact nature of the AN samples deposited will result in an increased ability to accurately and reliably "train" hazard detection systems.
A novel framework for change detection in bi-temporal polarimetric SAR images
NASA Astrophysics Data System (ADS)
Pirrone, Davide; Bovolo, Francesca; Bruzzone, Lorenzo
2016-10-01
Last years have seen relevant increase of polarimetric Synthetic Aperture Radar (SAR) data availability, thanks to satellite sensors like Sentinel-1 or ALOS-2 PALSAR-2. The augmented information lying in the additional polarimetric channels represents a possibility for better discriminate different classes of changes in change detection (CD) applications. This work aims at proposing a framework for CD in multi-temporal multi-polarization SAR data. The framework includes both a tool for an effective visual representation of the change information and a method for extracting the multiple-change information. Both components are designed to effectively handle the multi-dimensionality of polarimetric data. In the novel representation, multi-temporal intensity SAR data are employed to compute a polarimetric log-ratio. The multitemporal information of the polarimetric log-ratio image is represented in a multi-dimensional features space, where changes are highlighted in terms of magnitude and direction. This representation is employed to design a novel unsupervised multi-class CD approach. This approach considers a sequential two-step analysis of the magnitude and the direction information for separating non-changed and changed samples. The proposed approach has been validated on a pair of Sentinel-1 data acquired before and after the flood in Tamil-Nadu in 2015. Preliminary results demonstrate that the representation tool is effective and that the use of polarimetric SAR data is promising in multi-class change detection applications.
DOE Office of Scientific and Technical Information (OSTI.GOV)
Hansen, D.J.; Ostler, W.K.
2000-02-01
Research funded by the US Department of Defense, US Department of Energy, and the US Environmental Protection Agency as part of Project CS-1131 of the Strategic Environmental Research and Development Program evaluated novel techniques for collecting high-resolution images in the Mojave Desert using helicopters, helium-filled blimps, kites, and hand-held telescoping poles at heights from 1 to 150 meters. Several camera types, lens, films, and digital techniques were evaluated on the basis of their ability to correctly estimate canopy cover of shrubs. A high degree of accuracy was obtained with photo scales of 1:4,000 or larger and flatbed scanning rates frommore » films or prints of 300 lines per inch or larger. Smaller scale images were of value in detecting retrospective changes in cover of large shrubs, but failed to detect smaller shrubs. Excellent results were obtained using inexpensive 35-millimeter cameras and new super-fine grain film such as Kodak's Royal Gold{trademark} (ASA 100) film or megapixel digital cameras. New image-processing software, such as SigmaScan Pro{trademark}, makes it possible to accurately measure areas up to 1 hectare in size for total cover and density in 10 minutes compared to several hours or days of field work. In photographs with scales of 1:1,000 and 1:2,000, it was possible to detect cover and density of up to four dominant shrub species. Canopy cover and other parameters such as width, length, feet diameter, and shape factors can be nearly instantaneously measured for each individual shrub yielding size distribution histograms and other statistical data on plant community structure. Use of the technique is being evaluated in a four-year study of military training impacts at Fort Irwin, California, and results compared with image processing using conventional aerial photography and satellite imagery, including the new 1-meter pixel IKONOS images. The technique is a valuable new emerging tool to accurately assess vegetation
Uncertainties in detecting decadal change in extractable soil elements in Northern Forests
NASA Astrophysics Data System (ADS)
Bartlett, O.; Bailey, S. W.; Ducey, M. J.
2016-12-01
Northern Forest ecosystems have been or are being impacted by land use change, forest harvesting, acid deposition, atmospheric CO2 enrichment, and climate change. Each of these has the potential to modify soil forming processes, and the resulting chemical stocks. Horizontal and vertical variations in concentrations complicate determination of temporal change. This study evaluates sample design, sample size, and differences among observers as sources of uncertainty when quantifying soil temporal change over regional scales. Forty permanent, northern hardwood, monitoring plots were established on the White Mountain National Forest in central New Hampshire and western Maine. Soil pits were characterized and sampled by genetic horizon at plot center in 2001 and resampled again in 2014 two-meters on contour from the original sampling location. Each soil horizon was characterized by depth, color, texture, structure, consistency, boundaries, coarse fragments, and roots from the forest floor to the upper C horizon, the relatively unaltered glacial till parent material. Laboratory analyses included pH in 0.01 M CaCl2 solution and extractable Ca, Mg, Na, K, Al, Mn, and P in 1 M NH4OAc solution buffered at pH 4.8. Significant elemental differences were identified by genetic horizon from paired t-tests (p ≤ 0.05) indicate temporal change across the study region. Power analysis, 0.9 power (α = 0.05), revealed sampling size was appropriate within this region to detect concentration change by genetic horizon using a stratified sample design based on topographic metrics. There were no significant differences between observers' descriptions of physical properties. As physical properties would not be expected to change over a decade, this suggests spatial variation in physical properties between the pairs of sampling pits did not detract from our ability to detect temporal change. These results suggest that resampling efforts within a site, repeated across a region, to quantify
NASA Astrophysics Data System (ADS)
Bai, Ting; Sun, Kaimin; Deng, Shiquan; Chen, Yan
2018-03-01
High resolution image change detection is one of the key technologies of remote sensing application, which is of great significance for resource survey, environmental monitoring, fine agriculture, military mapping and battlefield environment detection. In this paper, for high-resolution satellite imagery, Random Forest (RF), Support Vector Machine (SVM), Deep belief network (DBN), and Adaboost models were established to verify the possibility of different machine learning applications in change detection. In order to compare detection accuracy of four machine learning Method, we applied these four machine learning methods for two high-resolution images. The results shows that SVM has higher overall accuracy at small samples compared to RF, Adaboost, and DBN for binary and from-to change detection. With the increase in the number of samples, RF has higher overall accuracy compared to Adaboost, SVM and DBN.
The detection of 'virtual' objects using echoes by humans: Spectral cues.
Rowan, Daniel; Papadopoulos, Timos; Archer, Lauren; Goodhew, Amanda; Cozens, Hayley; Lopez, Ricardo Guzman; Edwards, David; Holmes, Hannah; Allen, Robert
2017-07-01
Some blind people use echoes to detect discrete, silent objects to support their spatial orientation/navigation, independence, safety and wellbeing. The acoustical features that people use for this are not well understood. Listening to changes in spectral shape due to the presence of an object could be important for object detection and avoidance, especially at short range, although it is currently not known whether it is possible with echolocation-related sounds. Bands of noise were convolved with recordings of binaural impulse responses of objects in an anechoic chamber to create 'virtual objects', which were analysed and played to sighted and blind listeners inexperienced in echolocation. The sounds were also manipulated to remove cues unrelated to spectral shape. Most listeners could accurately detect hard flat objects using changes in spectral shape. The useful spectral changes for object detection occurred above approximately 3 kHz, as with object localisation. However, energy in the sounds below 3 kHz was required to exploit changes in spectral shape for object detection, whereas energy below 3 kHz impaired object localisation. Further recordings showed that the spectral changes were diminished by room reverberation. While good high-frequency hearing is generally important for echolocation, the optimal echo-generating stimulus will probably depend on the task. Copyright © 2017 The Authors. Published by Elsevier B.V. All rights reserved.
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.
Kasvis, Popi; Cohen, Tamara R; Loiselle, Sarah-Ève; Kim, Nicolas; Hazell, Tom J; Vanstone, Catherine A; Rodd, Celia; Plourde, Hugues; Weiler, Hope A
2015-03-01
Body composition measurements are valuable when evaluating pediatric obesity interventions. We hypothesized that foot-to-foot bioelectrical impedance analysis (BIA) will accurately track the direction of adiposity change, but not magnitude, in part due to differences in fat patterning. The purposes of this study were to examine the accuracy of body composition measurements of overweight and obese children over time using dual-energy x-ray absorptiometry (DXA) and BIA and to determine if BIA accuracy was affected by fat patterning. Eighty-nine overweight or obese children (48 girls, 41 boys, age 7-13 years) participating in a randomized controlled trial providing a family-centered, lifestyle intervention, underwent DXA and BIA measurements every 3 months. Bland-Altman plots showed a poor level of agreement between devices for baseline percent body fat (%BF; mean, 0.398%; +2SD, 8.685%; -2SD, -7.889%). There was overall agreement between DXA and BIA in the direction of change over time for %BF (difference between visits 3 and 1: DXA -0.8 ± 0.5%, BIA -0.7 ± 0.5%; P = 1.000) and fat mass (FM; difference between visits 3 and 1: DXA 0.7 ± 0.5 kg, BIA 0.6 ± 0.5 kg; P = 1.000). Bioelectrical impedance analysis measurements of %BF and FM at baseline were significantly different in those with android and gynoid fat (%BF: 35.9% ± 1.4%, 32.2% ± 1.4%, P < .003; FM: 20.1 ± 0.8 kg, 18.4 ± 0.8, P < .013). Bioelectrical impedance analysis accurately reports the direction of change in FM and FFM in overweight and obese children; inaccuracy in the magnitude of BIA measurements may be a result of fat patterning differences. Copyright © 2015 Elsevier Inc. All rights reserved.
Harrison, Samantha L; Horton, Elizabeth J; Smith, Robert; Sandland, Carolyn J; Steiner, Michael C; Morgan, Mike D L; Singh, Sally J
2013-01-01
To test the accuracy of a multi-sensor activity monitor (SWM) in detecting slow walking speeds in patients with chronic obstructive pulmonary disease (COPD). Concerns have been expressed regarding the use of pedometers in patient populations. Although activity monitors are more sophisticated devices, their accuracy at detecting slow walking speeds common in patients with COPD has yet to be proven. A prospective observational study design was employed. An incremental shuttle walk test (ISWT) was completed by 57 patients with COPD wearing an SWM. The ISWT was repeated by 20 patients wearing the same SWM. Differences were identified between metabolic equivalents (METS) and between step-count across five levels of the ISWT (p < 0.001). Good within monitor reproducibility between two ISWT was identified for total energy expenditure and step-count (p < 0.001). The SWM is able to detect slow (standardized) speeds of walking and is an acceptable method for measuring physical activity in individuals disabled by COPD. Copyright © 2013 Elsevier Inc. All rights reserved.
Evaluate ERTS imagery for mapping and detection of changes of snowcover on land and on glaciers
NASA Technical Reports Server (NTRS)
Meier, M. F. (Principal Investigator)
1973-01-01
The author has identified the following significant results. A possibly more accurate method to determine snowcover area change has been tried; snowcover area change over periods of an ERTS-1 cycle are very useful in determining energy balances over regional areas and to determine snow depth as a function of altitude. Also since shadow and cloud cover areas are highlighted this method may be a step toward more complete machine processing.
Towards Sensor-Free Affect Detection in Cognitive Tutor Algebra
ERIC Educational Resources Information Center
Baker, Ryan S. J. d.; Gowda, Sujith M.; Wixon, Michael; Kalka, Jessica; Wagner, Angela Z.; Salvi, Aatish; Aleven, Vincent; Kusbit, Gail W.; Ocumpaugh, Jaclyn; Rossi, Lisa
2012-01-01
In recent years, the usefulness of affect detection for educational software has become clear. Accurate detection of student affect can support a wide range of interventions with the potential to improve student affect, increase engagement, and improve learning. In addition, accurate detection of student affect could play an essential role in…
Energy Change Detection to Assist in Tactical Intelligence Production
2009-06-01
COVERED Master’s Thesis 4. TITLE AND SUBTITLE Energy Change Detection to Assist in Tactical Intelligence Production 6. AUTHOR( S ) Derek Anthony Filipe...5. FUNDING NUMBERS 7. PERFORMING ORGANIZATION NAME( S ) AND ADDRESS(ES) Naval Postgraduate School Monterey, CA 93943-5000 8. PERFORMING...ORGANIZATION REPORT NUMBER 9. SPONSORING /MONITORING AGENCY NAME( S ) AND ADDRESS(ES) N/A 10. SPONSORING/MONITORING AGENCY REPORT NUMBER 11
Collins, Brian D.; Minasian, Diane L.; Kayen, Robert
2009-01-01
Topographic change of archeological sites within the Colorado River corridor of Grand Canyon National Park (GCNP) is a subject of interest to National Park Service managers and other stakeholders in the Glen Canyon Dam Adaptive Management Program. Although long-term topographic change resulting from a variety of natural processes is typical in the Grand Canyon region, a continuing debate exists on whether and how controlled releases from Glen Canyon Dam, located immediately upstream of GCNP, are impacting rates of site erosion, artifact transport, and the preservation of archeological resources. Continued erosion of archeological sites threatens both the archeological resources and our future ability to study evidence of past cultural habitation. Understanding the causes and effects of archaeological site erosion requires a knowledge of several factors including the location and magnitude of the changes occurring in relation to archeological resources, the rate of the changes, and the relative contribution of several potential causes, including sediment depletion associated with managed flows from Glen Canyon Dam, site-specific weather patterns, visitor impacts, and long-term climate change. To obtain this information, highly accurate, spatially specific data are needed from sites undergoing change. Using terrestrial lidar data collection techniques and novel TIN- and GRID-based change-detection post-processing methods, we analyzed topographic data for nine archeological sites. The data were collected using three separate data collection efforts spanning 16 months (May 2006 to September 2007). Our results documented positive evidence of erosion, deposition, or both at six of the nine sites investigated during this time interval. In addition, we observed possible signs of change at two of the other sites. Erosion was concentrated in established gully drainages and averaged 12 cm to 17 cm in depth with maximum depths of 50 cm. Deposition was concentrated at specific
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 a trend change in cross-border epidemic transmission
NASA Astrophysics Data System (ADS)
Maeno, Yoshiharu
2016-09-01
A method for a system of Langevin equations is developed for detecting a trend change in cross-border epidemic transmission. The equations represent a standard epidemiological SIR compartment model and a meta-population network model. The method analyzes a time series of the number of new cases reported in multiple geographical regions. The method is applicable to investigating the efficacy of the implemented public health intervention in managing infectious travelers across borders. It is found that the change point of the probability of travel movements was one week after the WHO worldwide alert on the SARS outbreak in 2003. The alert was effective in managing infectious travelers. On the other hand, it is found that the probability of travel movements did not change at all for the flu pandemic in 2009. The pandemic did not affect potential travelers despite the WHO alert.
Characterization of normality of chaotic systems including prediction and detection of anomalies
NASA Astrophysics Data System (ADS)
Engler, Joseph John
Accurate prediction and control pervades domains such as engineering, physics, chemistry, and biology. Often, it is discovered that the systems under consideration cannot be well represented by linear, periodic nor random data. It has been shown that these systems exhibit deterministic chaos behavior. Deterministic chaos describes systems which are governed by deterministic rules but whose data appear to be random or quasi-periodic distributions. Deterministically chaotic systems characteristically exhibit sensitive dependence upon initial conditions manifested through rapid divergence of states initially close to one another. Due to this characterization, it has been deemed impossible to accurately predict future states of these systems for longer time scales. Fortunately, the deterministic nature of these systems allows for accurate short term predictions, given the dynamics of the system are well understood. This fact has been exploited in the research community and has resulted in various algorithms for short term predictions. Detection of normality in deterministically chaotic systems is critical in understanding the system sufficiently to able to predict future states. Due to the sensitivity to initial conditions, the detection of normal operational states for a deterministically chaotic system can be challenging. The addition of small perturbations to the system, which may result in bifurcation of the normal states, further complicates the problem. The detection of anomalies and prediction of future states of the chaotic system allows for greater understanding of these systems. The goal of this research is to produce methodologies for determining states of normality for deterministically chaotic systems, detection of anomalous behavior, and the more accurate prediction of future states of the system. Additionally, the ability to detect subtle system state changes is discussed. The dissertation addresses these goals by proposing new representational
Examining change detection approaches for tropical mangrove monitoring
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.
The Right Hemisphere Advantage in Visual Change Detection Depends on Temporal Factors
ERIC Educational Resources Information Center
Spotorno, Sara; Faure, Sylvane
2011-01-01
What accounts for the Right Hemisphere (RH) functional superiority in visual change detection? An original task which combines one-shot and divided visual field paradigms allowed us to direct change information initially to the RH or the Left Hemisphere (LH) by deleting, respectively, an object included in the left or right half of a scene…
NASA Astrophysics Data System (ADS)
Orellana, G.; Nagar, N. M.; Isaak, K. G.; Priddey, R.; Maiolino, R.; McMahon, R.; Marconi, A.; Oliva, E.
2011-07-01
Context. We present near-IR spectroscopy of a sample of luminous (MB - 27.5; Lbol > 1014 L⊙), sub-millimeter-detected, dusty (Md ~ 109 M⊙), radio-quiet quasi-stellar objects (QSOs) at z ~ 2. Aims: A primary aim is to provide a more accurate QSO redshift determination in order to trace kinematics and inflows/outflows in these sub-mm bright QSOs. Additionally, the Hα and continuum properties allow an estimation of the black hole mass and accretion rate, offering insights into the starburst-AGN connection in sub-mm bright QSOs. Methods: We measure the redshift, width, and luminosity of the Hα line, and the continuum luminosity near Hα. Relative velocity differences between Hα and rest-frame UV emission lines are used to study the presence and strength of outflows/inflows. Luminosities and line widths are used to estimate the black hole masses, bolometric luminosities, Eddington fractions, and accretion rates; these are compared to the star-formation-rate (SFR), estimated from the sub-mm derived far-infrared (FIR) luminosity. Finally our sub-mm-bright QSO sample is compared with other QSO samples at similar redshifts. Results: The Hα emission line was strongly detected in all sources. Two components - a very broad (≳5000 km s-1) Gaussian and an intermediate-width (≳1500 km s-1) Gaussian, were required to fit the Hα profile of all observed QSOs. Narrow (≲1000 km s-1) lines were not detected in the sample QSOs. The rest-frame UV emission lines in these sub-mm bright QSOs show larger than average blue-shifted velocities, potentially tracing strong - up to 3000 km s-1 - outflows in the broad line region. With the exception of the one QSO which shows exceptionally broad Hα lines, the black hole masses of the QSO sample are in the range log MBH = 9.0-9.7 and the Eddington fractions are between 0.5 and ~1. In black hole mass and accretion rate, this sub-mm bright QSO sample is indistinguishable from the Shemmer et al. (2004, ApJ, 614, 547) optically
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.
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.
Dascalu, A M; Cherecheanu, A P; Stana, D; Voinea, L; Ciuluvica, R; Savlovschi, C; Serban, D
2014-01-01
to investigate the sensitivity and specificity of the stereometric parameters change analysis vs. Topographic Change Analysis in early detection of glaucoma progression. 81 patients with POAG were monitored for 4 years (GAT monthly, SAP at every 6 months, optic disc photographs and HRT3 yearly). The exclusion criteria were other optic disc or retinal pathology; topographic standard deviation (TSD>30; inter-test variation of reference height>25 μm. The criterion for structural progression was the following: at least 20 adjacent super-pixels with a clinically significant decrease in height (>5%). 16 patients of the total 81 presented structural progression on TCA. The most useful stereometric parameters for the early detection of glaucoma progression were the following: Rim Area change (sensitivity 100%, specificity 74.2% for a "cut-off " value of -0.05), C/D Area change (sensitivity 85.7%, specificity 71.5% for a "cut off " value of 0.02), C/D linear change (sensitivity 85.7%, specificity 71.5% for a "cut-off " value of 0.02), Rim Volume change (sensitivity 71.4%, specificity 88.8% for a "cut-off " value of -0.04). RNFL Thickness change (<0) was highly sensitive (82%), but less specific for glaucoma progression (45,2%). Changes of the other stereometric parameters have a limited diagnostic value for the early detection of glaucoma progression. TCA is a valuable tool for the assessment of the structural progression in glaucoma patients and its inter-test variability is low. On long-term, the quantitative analysis according to stereometric parameters change is also very important. The most relevant parameters to detect progression are RA, C/D Area, Linear C/D and RV.
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
Change and Anomaly Detection in Real-Time GPS Data
NASA Astrophysics Data System (ADS)
Granat, R.; Pierce, M.; Gao, X.; Bock, Y.
2008-12-01
The California Real-Time Network (CRTN) is currently generating real-time GPS position data at a rate of 1-2Hz at over 80 locations. The CRTN data presents the possibility of studying dynamical solid earth processes in a way that complements existing seismic networks. To realize this possibility we have developed a prototype system for detecting changes and anomalies in the real-time data. Through this system, we can can correlate changes in multiple stations in order to detect signals with geographical extent. Our approach involves developing a statistical model for each GPS station in the network, and then using those models to segment the time series into a number of discrete states described by the model. We use a hidden Markov model (HMM) to describe the behavior of each station; fitting the model to the data requires neither labeled training examples nor a priori information about the system. As such, HMMs are well suited to this problem domain, in which the data remains largely uncharacterized. There are two main components to our approach. The first is the model fitting algorithm, regularized deterministic annealing expectation- maximization (RDAEM), which provides robust, high-quality results. The second is a web service infrastructure that connects the data to the statistical modeling analysis and allows us to easily present the results of that analysis through a web portal interface. This web service approach facilitates the automatic updating of station models to keep pace with dynamical changes in the data. Our web portal interface is critical to the process of interpreting the data. A Google Maps interface allows users to visually interpret state changes not only on individual stations but across the entire network. Users can drill down from the map interface to inspect detailed results for individual stations, download the time series data, and inspect fitted models. Alternatively, users can use the web portal look at the evolution of changes on the
Accurate sub-millimetre rest frequencies for HOCO+ and DOCO+ ions
NASA Astrophysics Data System (ADS)
Bizzocchi, L.; Lattanzi, V.; Laas, J.; Spezzano, S.; Giuliano, B. M.; Prudenzano, D.; Endres, C.; Sipilä, O.; Caselli, P.
2017-06-01
Context. HOCO+ is a polar molecule that represents a useful proxy for its parent molecule CO2, which is not directly observable in the cold interstellar medium. This cation has been detected towards several lines of sight, including massive star forming regions, protostars, and cold cores. Despite the obvious astrochemical relevance, protonated CO2 and its deuterated variant, DOCO+, still lack an accurate spectroscopic characterisation. Aims: The aim of this work is to extend the study of the ground-state pure rotational spectra of HOCO+ and DOCO+ well into the sub-millimetre region. Methods: Ground-state transitions have been recorded in the laboratory using a frequency-modulation absorption spectrometer equipped with a free-space glow-discharge cell. The ions were produced in a low-density, magnetically confined plasma generated in a suitable gas mixture. The ground-state spectra of HOCO+ and DOCO+ have been investigated in the 213-967 GHz frequency range; 94 new rotational transitions have been detected. Additionally, 46 line positions taken from the literature have been accurately remeasured. Results: The newly measured lines have significantly enlarged the available data sets for HOCO+ and DOCO+, thus enabling the determination of highly accurate rotational and centrifugal distortion parameters. Our analysis shows that all HOCO+ lines with Ka ≥ 3 are perturbed by a ro-vibrational interaction that couples the ground state with the v5 = 1 vibrationally excited state. This resonance has been explicitly treated in the analysis in order to obtain molecular constants with clear physical meaning. Conclusions: The improved sets of spectroscopic parameters provide enhanced lists of very accurate sub-millimetre rest frequencies of HOCO+ and DOCO+ for astrophysical applications. These new data challenge a recent tentative identification of DOCO+ towards a pre-stellar core. Supplementary tables are only available at the CDS via anonymous ftp to http
Applications of Graph-Theoretic Tests to Online Change Detection
2014-05-09
NUMBER 5f. WORK UNIT NUMBER 7. PERFORMING ORGANIZATION NAME(S) AND ADDRESS(ES) AND ADDRESS(ES) 8. PERFORMING ORGANIZATION REPORT ...assessment, crime investigation, and environmental field analysis. Our work offers a new tool for change detection that can be employed in real- time in very...this paper such MSTs and bipartite matchings. Ruth (2009) reports run times for MNBM ensembles created using Derigs’ (1998) algorithm on the order of
Telegrafo, Michele; Rella, Leonarda; Stabile Ianora, Amato Antonio; Angelelli, Giuseppe; Moschetta, Marco
2015-10-01
To assess the role of STIR, T2-weighted TSE and DWIBS sequences for detecting and characterizing breast lesions and to compare unenhanced (UE)-MRI results with contrast-enhanced (CE)-MRI and histological findings, having the latter as the reference standard. Two hundred eighty consecutive patients (age range, 27-73 years; mean age±standard deviation (SD), 48.8±9.8years) underwent MR examination with a diagnostic protocol including STIR, T2-weighted TSE, THRIVE and DWIBS sequences. Two radiologists blinded to both dynamic sequences and histological findings evaluated in consensus STIR, T2-weighted TSE and DWIBS sequences and after two weeks CE-MRI images searching for breast lesions. Sensitivity, specificity, positive predictive value (PPV), negative predictive value (NPV), and diagnostic accuracy for UE-MRI and CE-MRI were calculated. UE-MRI results were also compared with CE- MRI. UE-MRI sequences obtained sensitivity, specificity, diagnostic accuracy, PPV and NPV values of 94%, 79%, 86%, 79% and 94%, respectively. CE-MRI sequences obtained sensitivity, specificity, diagnostic accuracy, PPV and NPV values of 98%, 83%, 90%, 84% and 98%, respectively. No statistically significant difference between UE-MRI and CE-MRI was found. Breast UE-MRI could represent an accurate diagnostic tool and a valid alternative to CE-MRI for evaluating breast lesions. STIR and DWIBS sequences allow to detect breast lesions while T2-weighted TSE sequences and ADC values could be useful for lesion characterization. Copyright © 2015 Elsevier Inc. All rights reserved.
Collins, Brian D.; Corbett, Skye C.; Fairley, Helen C.; Minasian, Diane L.; Kayen, Robert; Dealy, Timothy P.; Bedford, David R.
2012-01-01
Human occupation in Grand Canyon, Arizona, dates from at least 11,000 years before present to the modern era. For most of this period, the only evidence of human occupation in this iconic landscape is provided by archeological sites. Because of the dynamic nature of this environment, many archeological sites are subject to relatively rapid topographic change. Quantifying the extent, magnitude, and cause of such change is important for monitoring and managing these archeological sites. Such quantification is necessary to help inform the continuing debate on whether and how controlled releases from Glen Canyon Dam, located immediately upstream of Grand Canyon National Park, are affecting site erosion rates, artifact transport, and archeological resource preservation along the Colorado River in Grand Canyon. Although long-term topographic change resulting from a variety of natural processes is inherent in the Grand Canyon region, continued erosion of archeological sites threatens both the archeological resources and our future ability to study evidence of past cultural habitation. Thus, this subject is of considerable interest to National Park Service managers and other stakeholders in the Glen Canyon Dam Adaptive Management Program. Understanding the causes and effects of archeological site erosion requires a knowledge of several factors, including the location, timing, and magnitude of the changes occurring in relation to archeological resources, the rates of change, and the relative contribution of potential causes. These potential causes include sediment depletion associated with managed flows from Glen Canyon Dam, site-specific weather and overland flow patterns, visitor impacts, and long-term regional climate change. To obtain this information, highly accurate, spatially specific data are needed from sites undergoing change. Using terrestrial lidar techniques, and building upon three previous surveys of archeological sites performed in 2006 and 2007, we
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.
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.
Nicol, Samuel; Roach, Jennifer K.; Griffith, Brad
2013-01-01
Over the past 50 years, the number and size of high-latitude lakes have decreased throughout many regions; however, individual lake trends have been variable in direction and magnitude. This spatial heterogeneity in lake change makes statistical detection of temporal trends challenging, particularly in small analysis areas where weak trends are difficult to separate from inter- and intra-annual variability. Factors affecting trend detection include inherent variability, trend magnitude, and sample size. In this paper, we investigated how the statistical power to detect average linear trends in lake size of 0.5, 1.0 and 2.0 %/year was affected by the size of the analysis area and the number of years of monitoring in National Wildlife Refuges in Alaska. We estimated power for large (930–4,560 sq km) study areas within refuges and for 2.6, 12.9, and 25.9 sq km cells nested within study areas over temporal extents of 4–50 years. We found that: (1) trends in study areas could be detected within 5–15 years, (2) trends smaller than 2.0 %/year would take >50 years to detect in cells within study areas, and (3) there was substantial spatial variation in the time required to detect change among cells. Power was particularly low in the smallest cells which typically had the fewest lakes. Because small but ecologically meaningful trends may take decades to detect, early establishment of long-term monitoring will enhance power to detect change. Our results have broad applicability and our method is useful for any study involving change detection among variable spatial and temporal extents.
A Fast Framework for Abrupt Change Detection Based on Binary Search Trees and Kolmogorov Statistic
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.
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.
Recent advances in targeted endoscopic imaging: Early detection of gastrointestinal neoplasms
Kwon, Yong-Soo; Cho, Young-Seok; Yoon, Tae-Jong; Kim, Ho-Shik; Choi, Myung-Gyu
2012-01-01
Molecular imaging has emerged as a new discipline in gastrointestinal endoscopy. This technology encompasses modalities that can visualize disease-specific morphological or functional tissue changes based on the molecular signature of individual cells. Molecular imaging has several advantages including minimal damage to tissues, repetitive visualization, and utility for conducting quantitative analyses. Advancements in basic science coupled with endoscopy have made early detection of gastrointestinal cancer possible. Molecular imaging during gastrointestinal endoscopy requires the development of safe biomarkers and exogenous probes to detect molecular changes in cells with high specificity anda high signal-to-background ratio. Additionally, a high-resolution endoscope with an accurate wide-field viewing capability must be developed. Targeted endoscopic imaging is expected to improve early diagnosis and individual therapy of gastrointestinal cancer. PMID:22442742
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 accurate nephelometric titrimetry.
Zhan, Xiancheng; Li, Chengrong; Li, Zhiyi; Yang, Xiucen; Zhong, Shuguang; Yi, Tao
2004-02-01
A method that accurately indicates the end-point of precipitation reactions by the measurement of the relative intensity of the scattered light in the titrate is presented. A new nephelometric titrator with an internal nephelometric sensor has been devised. The work of the titrator including the sensor and change in the turbidity of the titrate and intensity of the scattered light are described. The accuracy of the nephelometric titrimetry is discussed theoretically. The titration of NaCl with AgNO(3) serves as a model. A relative error as well as deviation is within 0.2% under the experimental conditions. The applicability of the titrimetry in pharmaceutical analyses, for example, phenytoin sodium and procaine hydrochloride, is generally illustrated. Copyright 2004 Wiley-Liss, Inc. and the American Pharmacists Association
Stuhne-Sekalec, L; Stanacev, N Z
1977-02-01
Several spin-labelled phospholipids carrying covalently bound 5-doxylstearic acid (2-(3-carboxydecyl)-2-hexyl-4,4-dimethyl-3-oxazolidinoxyl) were intercalated in liposomes of saturated and unsaturated lecithins. Temperature-induced changes of these liposomes, detected by the spin-labelled phospholipids, were found to be in agreement with the previously described transitions of hydrocarbon chains of host lecithins detected by different probes and different techniques, establishing that spin-labelled phosopholipids are sensitive probes for the detection of temperature-induced changes in lecithin model membranes. In addition to the detection of already-known transitions in lecithin liposomes, the coexistence of two distinctly different enviroments was observed above the characteristic transition temperature. This phenomenon was tentatively attributed to the influence of the lecithin polar group on the fluidity of fatty acyl chains near the polar group. Combined with other results from the literature, the coexistence of two environments could be associated with the coexistence of two conformational isomers of lecithin, differing in the orientation of the polar head group with respect to the plane of bilayer. These findings have been discussed in view of the present state of knowledge regarding temperature-induced changes in model membranes.
Determining accurate distances to nearby galaxies
NASA Astrophysics Data System (ADS)
Bonanos, Alceste Zoe
2005-11-01
Determining accurate distances to nearby or distant galaxies is a very simple conceptually, yet complicated in practice, task. Presently, distances to nearby galaxies are only known to an accuracy of 10-15%. The current anchor galaxy of the extragalactic distance scale is the Large Magellanic Cloud, which has large (10-15%) systematic uncertainties associated with it, because of its morphology, its non-uniform reddening and the unknown metallicity dependence of the Cepheid period-luminosity relation. This work aims to determine accurate distances to some nearby galaxies, and subsequently help reduce the error in the extragalactic distance scale and the Hubble constant H 0 . In particular, this work presents the first distance determination of the DIRECT Project to M33 with detached eclipsing binaries. DIRECT aims to obtain a new anchor galaxy for the extragalactic distance scale by measuring direct, accurate (to 5%) distances to two Local Group galaxies, M31 and M33, with detached eclipsing binaries. It involves a massive variability survey of these galaxies and subsequent photometric and spectroscopic follow-up of the detached binaries discovered. In this work, I also present a catalog of variable stars discovered in one of the DIRECT fields, M31Y, which includes 41 eclipsing binaries. Additionally, we derive the distance to the Draco Dwarf Spheroidal galaxy, with ~100 RR Lyrae found in our first CCD variability study of this galaxy. A "hybrid" method of discovering Cepheids with ground-based telescopes is described next. It involves applying the image subtraction technique on the images obtained from ground-based telescopes and then following them up with the Hubble Space Telescope to derive Cepheid period-luminosity distances. By re-analyzing ESO Very Large Telescope data on M83 (NGC 5236), we demonstrate that this method is much more powerful for detecting variability, especially in crowded fields. I finally present photometry for the Wolf-Rayet binary WR 20a
Differential equation based method for accurate approximations in optimization
NASA Technical Reports Server (NTRS)
Pritchard, Jocelyn I.; Adelman, Howard M.
1990-01-01
This paper describes a method to efficiently and accurately approximate the effect of design changes on structural response. The key to this new method is to interpret sensitivity equations as differential equations that may be solved explicitly for closed form approximations, hence, the method is denoted the Differential Equation Based (DEB) method. Approximations were developed for vibration frequencies, mode shapes and static displacements. The DEB approximation method was applied to a cantilever beam and results compared with the commonly-used linear Taylor series approximations and exact solutions. The test calculations involved perturbing the height, width, cross-sectional area, tip mass, and bending inertia of the beam. The DEB method proved to be very accurate, and in msot cases, was more accurate than the linear Taylor series approximation. The method is applicable to simultaneous perturbation of several design variables. Also, the approximations may be used to calculate other system response quantities. For example, the approximations for displacement are used to approximate bending stresses.
Differential equation based method for accurate approximations in optimization
NASA Technical Reports Server (NTRS)
Pritchard, Jocelyn I.; Adelman, Howard M.
1990-01-01
A method to efficiently and accurately approximate the effect of design changes on structural response is described. The key to this method is to interpret sensitivity equations as differential equations that may be solved explicitly for closed form approximations, hence, the method is denoted the Differential Equation Based (DEB) method. Approximations were developed for vibration frequencies, mode shapes and static displacements. The DEB approximation method was applied to a cantilever beam and results compared with the commonly-used linear Taylor series approximations and exact solutions. The test calculations involved perturbing the height, width, cross-sectional area, tip mass, and bending inertia of the beam. The DEB method proved to be very accurate, and in most cases, was more accurate than the linear Taylor series approximation. The method is applicable to simultaneous perturbation of several design variables. Also, the approximations may be used to calculate other system response quantities. For example, the approximations for displacements are used to approximate bending stresses.
2011-09-01
36 b. Designing for Vigilance .............................................. 38 viii c. Landmarks and Spatial Navigation... DESIGNING AND EXECUTING CHANGE DETECTION SCENARIO TRAINING ............................................................................... 153 A...SCENARIO INSTRUCTIONS FOR EDITORS ................................. 153 B. HOW TO DESIGN A CHANGE DETECTION SCENARIO .............. 157 APPENDIX K
Visual Salience in the Change Detection Paradigm: The Special Role of Object Onset
ERIC Educational Resources Information Center
Cole, Geoff G.; Kentridge, Robert W.; Heywood, Charles A.
2004-01-01
The relative efficacy with which appearance of a new object orients visual attention was investigated. At issue is whether the visual system treats onset as being of particular importance or only 1 of a number of stimulus events equally likely to summon attention. Using the 1-shot change detection paradigm, the authors compared detectability of…
Powell, Catherine; Blighe, Alan; Froggatt, Katherine; McCormack, Brendan; Woodward-Carlton, Barbara; Young, John; Robinson, Louise; Downs, Murna
2018-01-01
To explore family perspectives on their involvement in the timely detection of changes in their relatives' health in UK nursing homes. Increasingly, policy attention is being paid to the need to reduce hospitalisations for conditions that, if detected and treated in time, could be managed in the community. We know that family continue to be involved in the care of their family members once they have moved into a nursing home. Little is known, however, about family involvement in the timely detection of changes in health in nursing home residents. Qualitative exploratory study with thematic analysis. A purposive sampling strategy was applied. Fourteen semi-structured one-to-one interviews with family members of people living in 13 different UK nursing homes. Data were collected from November 2015-March 2016. Families were involved in the timely detection of changes in health in three key ways: noticing signs of changes in health, informing care staff about what they noticed and educating care staff about their family members' changes in health. Families suggested they could be supported to detect timely changes in health by developing effective working practices with care staff. Families can provide a special contribution to the process of timely detection in nursing homes. Their involvement needs to be negotiated, better supported, as well as given more legitimacy and structure within the nursing home. Families could provide much needed support to nursing home nurses, care assistants and managers in timely detection of changes in health. This may be achieved through communication about their preferred involvement on a case-by-case basis as well as providing appropriate support or services. © 2017 The Authors. Journal of Clinical Nursing Published by John Wiley & Sons Ltd.
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.
Detection of urban expansion in an urban-rural landscape with multitemporal QuickBird images
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
NASA Astrophysics Data System (ADS)
Missif, Lial Raja; Kadhum, Mohammad M.
2017-09-01
Wireless Sensor Network (WSN) has been widely used for monitoring where sensors are deployed to operate independently to sense abnormal phenomena. Most of the proposed environmental monitoring systems are designed based on a predetermined sensing range which does not reflect the sensor reliability, event characteristics, and the environment conditions. Measuring of the capability of a sensor node to accurately detect an event within a sensing field is of great important for monitoring applications. This paper presents an efficient mechanism for even detection based on probabilistic sensing model. Different models have been presented theoretically in this paper to examine their adaptability and applicability to the real environment applications. The numerical results of the experimental evaluation have showed that the probabilistic sensing model provides accurate observation and delectability of an event, and it can be utilized for different environment scenarios.
2013-01-01
The formalin-fixed, paraffin-embedded (FFPE) biopsy is a challenging sample for molecular assays such as targeted next-generation sequencing (NGS). We compared three methods for FFPE DNA quantification, including a novel PCR assay (‘QFI-PCR’) that measures the absolute copy number of amplifiable DNA, across 165 residual clinical specimens. The results reveal the limitations of commonly used approaches, and demonstrate the value of an integrated workflow using QFI-PCR to improve the accuracy of NGS mutation detection and guide changes in input that can rescue low quality FFPE DNA. These findings address a growing need for improved quality measures in NGS-based patient testing. PMID:24001039
An Accurate Direction Finding Scheme Using Virtual Antenna Array via Smartphones
Wang, Xiaopu; Xiong, Yan; Huang, Wenchao
2016-01-01
With the development of localization technologies, researchers solve the indoor localization problems using diverse methods and equipment. Most localization techniques require either specialized devices or fingerprints, which are inconvenient for daily use. Therefore, we propose and implement an accurate, efficient and lightweight system for indoor direction finding using common smartphones and loudspeakers. Our method is derived from a key insight: By moving a smartphone in regular patterns, we can effectively emulate the sensitivity and functionality of a Uniform Antenna Array to estimate the angle of arrival of the target signal. Specifically, a user only needs to hold his smartphone still in front of him, and then rotate his body around 360∘ duration with the smartphone at an approximate constant velocity. Then, our system can provide accurate directional guidance and lead the user to their destinations (normal loudspeakers we preset in the indoor environment transmitting high frequency acoustic signals) after a few measurements. Major challenges in implementing our system are not only imitating a virtual antenna array by ordinary smartphones but also overcoming the detection difficulties caused by the complex indoor environment. In addition, we leverage the gyroscope of the smartphone to reduce the impact of a user’s motion pattern change to the accuracy of our system. In order to get rid of the multipath effect, we leverage multiple signal classification to calculate the direction of the target signal, and then design and deploy our system in various indoor scenes. Extensive comparative experiments show that our system is reliable under various circumstances. PMID:27801866
An Accurate Direction Finding Scheme Using Virtual Antenna Array via Smartphones.
Wang, Xiaopu; Xiong, Yan; Huang, Wenchao
2016-10-29
With the development of localization technologies, researchers solve the indoor localization problems using diverse methods and equipment. Most localization techniques require either specialized devices or fingerprints, which are inconvenient for daily use. Therefore, we propose and implement an accurate, efficient and lightweight system for indoor direction finding using common smartphones and loudspeakers. Our method is derived from a key insight: By moving a smartphone in regular patterns, we can effectively emulate the sensitivity and functionality of a Uniform Antenna Array to estimate the angle of arrival of the target signal. Specifically, a user only needs to hold his smartphone still in front of him, and then rotate his body around 360 ∘ duration with the smartphone at an approximate constant velocity. Then, our system can provide accurate directional guidance and lead the user to their destinations (normal loudspeakers we preset in the indoor environment transmitting high frequency acoustic signals) after a few measurements. Major challenges in implementing our system are not only imitating a virtual antenna array by ordinary smartphones but also overcoming the detection difficulties caused by the complex indoor environment. In addition, we leverage the gyroscope of the smartphone to reduce the impact of a user's motion pattern change to the accuracy of our system. In order to get rid of the multipath effect, we leverage multiple signal classification to calculate the direction of the target signal, and then design and deploy our system in various indoor scenes. Extensive comparative experiments show that our system is reliable under various circumstances.
Detection of motion and posture change using an IR-UWB radar.
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.
Molina, Iñigo; Martinez, Estibaliz; Arquero, Agueda; Pajares, Gonzalo; Sanchez, Javier
2012-01-01
Landcover is subject to continuous changes on a wide variety of temporal and spatial scales. Those changes produce significant effects in human and natural activities. Maintaining an updated spatial database with the occurred changes allows a better monitoring of the Earth’s resources and management of the environment. Change detection (CD) techniques using images from different sensors, such as satellite imagery, aerial photographs, etc., have proven to be suitable and secure data sources from which updated information can be extracted efficiently, so that changes can also be inventoried and monitored. In this paper, a multisource CD methodology for multiresolution datasets is applied. First, different change indices are processed, then different thresholding algorithms for change/no_change are applied to these indices in order to better estimate the statistical parameters of these categories, finally the indices are integrated into a change detection multisource fusion process, which allows generating a single CD result from several combination of indices. This methodology has been applied to datasets with different spectral and spatial resolution properties. Then, the obtained results are evaluated by means of a quality control analysis, as well as with complementary graphical representations. The suggested methodology has also been proved efficiently for identifying the change detection index with the higher contribution. PMID:22737023
Molina, Iñigo; Martinez, Estibaliz; Arquero, Agueda; Pajares, Gonzalo; Sanchez, Javier
2012-01-01
Landcover is subject to continuous changes on a wide variety of temporal and spatial scales. Those changes produce significant effects in human and natural activities. Maintaining an updated spatial database with the occurred changes allows a better monitoring of the Earth's resources and management of the environment. Change detection (CD) techniques using images from different sensors, such as satellite imagery, aerial photographs, etc., have proven to be suitable and secure data sources from which updated information can be extracted efficiently, so that changes can also be inventoried and monitored. In this paper, a multisource CD methodology for multiresolution datasets is applied. First, different change indices are processed, then different thresholding algorithms for change/no_change are applied to these indices in order to better estimate the statistical parameters of these categories, finally the indices are integrated into a change detection multisource fusion process, which allows generating a single CD result from several combination of indices. This methodology has been applied to datasets with different spectral and spatial resolution properties. Then, the obtained results are evaluated by means of a quality control analysis, as well as with complementary graphical representations. The suggested methodology has also been proved efficiently for identifying the change detection index with the higher contribution.