Online attitude determination of a passively magnetically stabilized spacecraft

NASA Astrophysics Data System (ADS)

Burton, R.; Rock, S.; Springmann, J.; Cutler, J.

2017-04-01

An online attitude determination filter is developed for a nano satellite that has no onboard attitude sensors or gyros. Specifically, the attitude of NASA Ames Research Center's O/OREOS, a passively magnetically stabilized 3U CubeSat, is determined using only an estimate of the solar vector obtained from solar panel currents. The filter is based upon the existing multiplicative extended Kalman filter (MEKF) but instead of relying on gyros to drive the motion model, the filter instead incorporates a model of the spacecraft's attitude dynamics in the motion model. An attitude determination accuracy of five degrees is demonstrated, a performance verified using flight data from the University of Michigan's RAX-1. Although the filter was designed for the specific problem of a satellite without gyros or attitude determination it could also be used to provide smoothing of noisy gyro signals or to provide a backup in the event of gyro failures.

Personalized Recommender System for Digital Libraries

ERIC Educational Resources Information Center

Omisore, M. O.; Samuel, O. W.

2014-01-01

The huge amount of information available online has given rise to personalization and filtering systems. Recommender systems (RS) constitute a specific type of information filtering technique that present items according to user's interests. In this research, a web-based personalized recommender system capable of providing learners with books that…

On-line estimation and compensation of measurement delay in GPS/SINS integration

NASA Astrophysics Data System (ADS)

Yang, Tao; Wang, Wei

2008-10-01

The chief aim of this paper is to propose a simple on-line estimation and compensation method of GPS/SINS measurement delay. The causes of time delay for GPS/SINS integration are analyzed in this paper. New Kalman filter state equations augmented by measurement delay and modified measurement equations are derived. Based on an open-loop Kalman filter, several simulations are run, results of which show that by the proposed method, the estimation and compensation error of measurement delay is below 0.1s.

Information filtering on coupled social networks.

PubMed

Nie, Da-Cheng; Zhang, Zi-Ke; Zhou, Jun-Lin; Fu, Yan; Zhang, Kui

2014-01-01

In this paper, based on the coupled social networks (CSN), we propose a hybrid algorithm to nonlinearly integrate both social and behavior information of online users. Filtering algorithm, based on the coupled social networks, considers the effects of both social similarity and personalized preference. Experimental results based on two real datasets, Epinions and Friendfeed, show that the hybrid pattern can not only provide more accurate recommendations, but also enlarge the recommendation coverage while adopting global metric. Further empirical analyses demonstrate that the mutual reinforcement and rich-club phenomenon can also be found in coupled social networks where the identical individuals occupy the core position of the online system. This work may shed some light on the in-depth understanding of the structure and function of coupled social networks.

Online particle detection with Neural Networks based on topological calorimetry information

NASA Astrophysics Data System (ADS)

Ciodaro, T.; Deva, D.; de Seixas, J. M.; Damazio, D.

2012-06-01

This paper presents the latest results from the Ringer algorithm, which is based on artificial neural networks for the electron identification at the online filtering system of the ATLAS particle detector, in the context of the LHC experiment at CERN. The algorithm performs topological feature extraction using the ATLAS calorimetry information (energy measurements). The extracted information is presented to a neural network classifier. Studies showed that the Ringer algorithm achieves high detection efficiency, while keeping the false alarm rate low. Optimizations, guided by detailed analysis, reduced the algorithm execution time by 59%. Also, the total memory necessary to store the Ringer algorithm information represents less than 6.2 percent of the total filtering system amount.

Impact imaging of aircraft composite structure based on a model-independent spatial-wavenumber filter.

PubMed

Qiu, Lei; Liu, Bin; Yuan, Shenfang; Su, Zhongqing

2016-01-01

The spatial-wavenumber filtering technique is an effective approach to distinguish the propagating direction and wave mode of Lamb wave in spatial-wavenumber domain. Therefore, it has been gradually studied for damage evaluation in recent years. But for on-line impact monitoring in practical application, the main problem is how to realize the spatial-wavenumber filtering of impact signal when the wavenumber of high spatial resolution cannot be measured or the accurate wavenumber curve cannot be modeled. In this paper, a new model-independent spatial-wavenumber filter based impact imaging method is proposed. In this method, a 2D cross-shaped array constructed by two linear piezoelectric (PZT) sensor arrays is used to acquire impact signal on-line. The continuous complex Shannon wavelet transform is adopted to extract the frequency narrowband signals from the frequency wideband impact response signals of the PZT sensors. A model-independent spatial-wavenumber filter is designed based on the spatial-wavenumber filtering technique. Based on the designed filter, a wavenumber searching and best match mechanism is proposed to implement the spatial-wavenumber filtering of the frequency narrowband signals without modeling, which can be used to obtain a wavenumber-time image of the impact relative to a linear PZT sensor array. By using the two wavenumber-time images of the 2D cross-shaped array, the impact direction can be estimated without blind angle. The impact distance relative to the 2D cross-shaped array can be calculated by using the difference of time-of-flight between the frequency narrowband signals of two different central frequencies and the corresponding group velocities. The validations performed on a carbon fiber composite laminate plate and an aircraft composite oil tank show a good impact localization accuracy of the model-independent spatial-wavenumber filter based impact imaging method. Copyright © 2015 Elsevier B.V. All rights reserved.

Efficient Online Learning Algorithms Based on LSTM Neural Networks.

PubMed

Ergen, Tolga; Kozat, Suleyman Serdar

2017-09-13

We investigate online nonlinear regression and introduce novel regression structures based on the long short term memory (LSTM) networks. For the introduced structures, we also provide highly efficient and effective online training methods. To train these novel LSTM-based structures, we put the underlying architecture in a state space form and introduce highly efficient and effective particle filtering (PF)-based updates. We also provide stochastic gradient descent and extended Kalman filter-based updates. Our PF-based training method guarantees convergence to the optimal parameter estimation in the mean square error sense provided that we have a sufficient number of particles and satisfy certain technical conditions. More importantly, we achieve this performance with a computational complexity in the order of the first-order gradient-based methods by controlling the number of particles. Since our approach is generic, we also introduce a gated recurrent unit (GRU)-based approach by directly replacing the LSTM architecture with the GRU architecture, where we demonstrate the superiority of our LSTM-based approach in the sequential prediction task via different real life data sets. In addition, the experimental results illustrate significant performance improvements achieved by the introduced algorithms with respect to the conventional methods over several different benchmark real life data sets.

Optimal Tuner Selection for Kalman Filter-Based Aircraft Engine Performance Estimation

NASA Technical Reports Server (NTRS)

Simon, Donald L.; Garg, Sanjay

2010-01-01

A linear point design methodology for minimizing the error in on-line Kalman filter-based aircraft engine performance estimation applications is presented. This technique specifically addresses the underdetermined estimation problem, where there are more unknown parameters than available sensor measurements. A systematic approach is applied to produce a model tuning parameter vector of appropriate dimension to enable estimation by a Kalman filter, while minimizing the estimation error in the parameters of interest. Tuning parameter selection is performed using a multi-variable iterative search routine which seeks to minimize the theoretical mean-squared estimation error. This paper derives theoretical Kalman filter estimation error bias and variance values at steady-state operating conditions, and presents the tuner selection routine applied to minimize these values. Results from the application of the technique to an aircraft engine simulation are presented and compared to the conventional approach of tuner selection. Experimental simulation results are found to be in agreement with theoretical predictions. The new methodology is shown to yield a significant improvement in on-line engine performance estimation accuracy

Information Filtering on Coupled Social Networks

PubMed Central

Nie, Da-Cheng; Zhang, Zi-Ke; Zhou, Jun-Lin; Fu, Yan; Zhang, Kui

2014-01-01

In this paper, based on the coupled social networks (CSN), we propose a hybrid algorithm to nonlinearly integrate both social and behavior information of online users. Filtering algorithm, based on the coupled social networks, considers the effects of both social similarity and personalized preference. Experimental results based on two real datasets, Epinions and Friendfeed, show that the hybrid pattern can not only provide more accurate recommendations, but also enlarge the recommendation coverage while adopting global metric. Further empirical analyses demonstrate that the mutual reinforcement and rich-club phenomenon can also be found in coupled social networks where the identical individuals occupy the core position of the online system. This work may shed some light on the in-depth understanding of the structure and function of coupled social networks. PMID:25003525

Nonlinear system identification based on Takagi-Sugeno fuzzy modeling and unscented Kalman filter.

PubMed

Vafamand, Navid; Arefi, Mohammad Mehdi; Khayatian, Alireza

2018-03-01

This paper proposes two novel Kalman-based learning algorithms for an online Takagi-Sugeno (TS) fuzzy model identification. The proposed approaches are designed based on the unscented Kalman filter (UKF) and the concept of dual estimation. Contrary to the extended Kalman filter (EKF) which utilizes derivatives of nonlinear functions, the UKF employs the unscented transformation. Consequently, non-differentiable membership functions can be considered in the structure of the TS models. This makes the proposed algorithms to be applicable for the online parameter calculation of wider classes of TS models compared to the recently published papers concerning the same issue. Furthermore, because of the great capability of the UKF in handling severe nonlinear dynamics, the proposed approaches can effectively approximate the nonlinear systems. Finally, numerical and practical examples are provided to show the advantages of the proposed approaches. Simulation results reveal the effectiveness of the proposed methods and performance improvement based on the root mean square (RMS) of the estimation error compared to the existing results. Copyright © 2018 ISA. Published by Elsevier Ltd. All rights reserved.

NIRS-EEG joint imaging during transcranial direct current stimulation: Online parameter estimation with an autoregressive model.

PubMed

Sood, Mehak; Besson, Pierre; Muthalib, Makii; Jindal, Utkarsh; Perrey, Stephane; Dutta, Anirban; Hayashibe, Mitsuhiro

2016-12-01

Transcranial direct current stimulation (tDCS) has been shown to perturb both cortical neural activity and hemodynamics during (online) and after the stimulation, however mechanisms of these tDCS-induced online and after-effects are not known. Here, online resting-state spontaneous brain activation may be relevant to monitor tDCS neuromodulatory effects that can be measured using electroencephalography (EEG) in conjunction with near-infrared spectroscopy (NIRS). We present a Kalman Filter based online parameter estimation of an autoregressive (ARX) model to track the transient coupling relation between the changes in EEG power spectrum and NIRS signals during anodal tDCS (2mA, 10min) using a 4×1 ring high-definition montage. Our online ARX parameter estimation technique using the cross-correlation between log (base-10) transformed EEG band-power (0.5-11.25Hz) and NIRS oxy-hemoglobin signal in the low frequency (≤0.1Hz) range was shown in 5 healthy subjects to be sensitive to detect transient EEG-NIRS coupling changes in resting-state spontaneous brain activation during anodal tDCS. Conventional sliding window cross-correlation calculations suffer a fundamental problem in computing the phase relationship as the signal in the window is considered time-invariant and the choice of the window length and step size are subjective. Here, Kalman Filter based method allowed online ARX parameter estimation using time-varying signals that could capture transients in the coupling relationship between EEG and NIRS signals. Our new online ARX model based tracking method allows continuous assessment of the transient coupling between the electrophysiological (EEG) and the hemodynamic (NIRS) signals representing resting-state spontaneous brain activation during anodal tDCS. Published by Elsevier B.V.

Optimized tuner selection for engine performance estimation

NASA Technical Reports Server (NTRS)

Simon, Donald L. (Inventor); Garg, Sanjay (Inventor)

2013-01-01

A methodology for minimizing the error in on-line Kalman filter-based aircraft engine performance estimation applications is presented. This technique specifically addresses the underdetermined estimation problem, where there are more unknown parameters than available sensor measurements. A systematic approach is applied to produce a model tuning parameter vector of appropriate dimension to enable estimation by a Kalman filter, while minimizing the estimation error in the parameters of interest. Tuning parameter selection is performed using a multi-variable iterative search routine which seeks to minimize the theoretical mean-squared estimation error. Theoretical Kalman filter estimation error bias and variance values are derived at steady-state operating conditions, and the tuner selection routine is applied to minimize these values. The new methodology yields an improvement in on-line engine performance estimation accuracy.

An object tracking method based on guided filter for night fusion image

NASA Astrophysics Data System (ADS)

Qian, Xiaoyan; Wang, Yuedong; Han, Lei

2016-01-01

Online object tracking is a challenging problem as it entails learning an effective model to account for appearance change caused by intrinsic and extrinsic factors. In this paper, we propose a novel online object tracking with guided image filter for accurate and robust night fusion image tracking. Firstly, frame difference is applied to produce the coarse target, which helps to generate observation models. Under the restriction of these models and local source image, guided filter generates sufficient and accurate foreground target. Then accurate boundaries of the target can be extracted from detection results. Finally timely updating for observation models help to avoid tracking shift. Both qualitative and quantitative evaluations on challenging image sequences demonstrate that the proposed tracking algorithm performs favorably against several state-of-art methods.

Online Sensor Fault Detection Based on an Improved Strong Tracking Filter

PubMed Central

Wang, Lijuan; Wu, Lifeng; Guan, Yong; Wang, Guohui

2015-01-01

We propose a method for online sensor fault detection that is based on the evolving Strong Tracking Filter (STCKF). The cubature rule is used to estimate states to improve the accuracy of making estimates in a nonlinear case. A residual is the difference in value between an estimated value and the true value. A residual will be regarded as a signal that includes fault information. The threshold is set at a reasonable level, and will be compared with residuals to determine whether or not the sensor is faulty. The proposed method requires only a nominal plant model and uses STCKF to estimate the original state vector. The effectiveness of the algorithm is verified by simulation on a drum-boiler model. PMID:25690553

urCF: An Approach to Integrating User Reviews into Memory-Based Collaborative Filtering

ERIC Educational Resources Information Center

Zhang, Zhenxue

2013-01-01

Blessed by the Internet age, many online retailers (e.g., Amazon.com) have deployed recommender systems to help their customers identify products that may be of their interest in order to improve cross-selling and enhance customer loyalty. Collaborative Filtering (CF) is the most successful technique among different approaches to generating…

Adaptive Spatial Filter Based on Similarity Indices to Preserve the Neural Information on EEG Signals during On-Line Processing

PubMed Central

Villa-Parra, Ana Cecilia; Bastos-Filho, Teodiano; López-Delis, Alberto; Frizera-Neto, Anselmo; Krishnan, Sridhar

2017-01-01

This work presents a new on-line adaptive filter, which is based on a similarity analysis between standard electrode locations, in order to reduce artifacts and common interferences throughout electroencephalography (EEG) signals, but preserving the useful information. Standard deviation and Concordance Correlation Coefficient (CCC) between target electrodes and its correspondent neighbor electrodes are analyzed on sliding windows to select those neighbors that are highly correlated. Afterwards, a model based on CCC is applied to provide higher values of weight to those correlated electrodes with lower similarity to the target electrode. The approach was applied to brain computer-interfaces (BCIs) based on Canonical Correlation Analysis (CCA) to recognize 40 targets of steady-state visual evoked potential (SSVEP), providing an accuracy (ACC) of 86.44 ± 2.81%. In addition, also using this approach, features of low frequency were selected in the pre-processing stage of another BCI to recognize gait planning. In this case, the recognition was significantly (p<0.01) improved for most of the subjects (ACC≥74.79%), when compared with other BCIs based on Common Spatial Pattern, Filter Bank-Common Spatial Pattern, and Riemannian Geometry. PMID:29186848

Exploring Potential ADS-B Vulnerabilites in the FAA’s Nextgen Air Transportation System

DTIC Science & Technology

2011-06-01

an online database of aircraft and the camera is used to filter the data, based on field of view, for display on the phone’s screen. The developers...34 December 28, 2009. [Online]. abcnews.go.com [5] P. Dempsey and L. Gesell , Air Transportation: Foundations for the 21st Century. Arizona: Coast Aire

On-line crack prognosis in attachment lug using Lamb wave-deterministic resampling particle filter-based method

NASA Astrophysics Data System (ADS)

Yuan, Shenfang; Chen, Jian; Yang, Weibo; Qiu, Lei

2017-08-01

Fatigue crack growth prognosis is important for prolonging service time, improving safety, and reducing maintenance cost in many safety-critical systems, such as in aircraft, wind turbines, bridges, and nuclear plants. Combining fatigue crack growth models with the particle filter (PF) method has proved promising to deal with the uncertainties during fatigue crack growth and reach a more accurate prognosis. However, research on prognosis methods integrating on-line crack monitoring with the PF method is still lacking, as well as experimental verifications. Besides, the PF methods adopted so far are almost all sequential importance resampling-based PFs, which usually encounter sample impoverishment problems, and hence performs poorly. To solve these problems, in this paper, the piezoelectric transducers (PZTs)-based active Lamb wave method is adopted for on-line crack monitoring. The deterministic resampling PF (DRPF) is proposed to be used in fatigue crack growth prognosis, which can overcome the sample impoverishment problem. The proposed method is verified through fatigue tests of attachment lugs, which are a kind of important joint component in aerospace systems.

Managing Trust in Online Social Networks

NASA Astrophysics Data System (ADS)

Bhuiyan, Touhid; Josang, Audun; Xu, Yue

In recent years, there is a dramatic growth in number and popularity of online social networks. There are many networks available with more than 100 million registered users such as Facebook, MySpace, QZone, Windows Live Spaces etc. People may connect, discover and share by using these online social networks. The exponential growth of online communities in the area of social networks attracts the attention of the researchers about the importance of managing trust in online environment. Users of the online social networks may share their experiences and opinions within the networks about an item which may be a product or service. The user faces the problem of evaluating trust in a service or service provider before making a choice. Recommendations may be received through a chain of friends network, so the problem for the user is to be able to evaluate various types of trust opinions and recommendations. This opinion or recommendation has a great influence to choose to use or enjoy the item by the other user of the community. Collaborative filtering system is the most popular method in recommender system. The task in collaborative filtering is to predict the utility of items to a particular user based on a database of user rates from a sample or population of other users. Because of the different taste of different people, they rate differently according to their subjective taste. If two people rate a set of items similarly, they share similar tastes. In the recommender system, this information is used to recommend items that one participant likes, to other persons in the same cluster. But the collaborative filtering system performs poor when there is insufficient previous common rating available between users; commonly known as cost start problem. To overcome the cold start problem and with the dramatic growth of online social networks, trust based approach to recommendation has emerged. This approach assumes a trust network among users and makes recommendations based on the ratings of the users that are directly or indirectly trusted by the target user.

Accurate mask-based spatially regularized correlation filter for visual tracking

NASA Astrophysics Data System (ADS)

Gu, Xiaodong; Xu, Xinping

2017-01-01

Recently, discriminative correlation filter (DCF)-based trackers have achieved extremely successful results in many competitions and benchmarks. These methods utilize a periodic assumption of the training samples to efficiently learn a classifier. However, this assumption will produce unwanted boundary effects, which severely degrade the tracking performance. Correlation filters with limited boundaries and spatially regularized DCFs were proposed to reduce boundary effects. However, their methods used the fixed mask or predesigned weights function, respectively, which was unsuitable for large appearance variation. We propose an accurate mask-based spatially regularized correlation filter for visual tracking. Our augmented objective can reduce the boundary effect even in large appearance variation. In our algorithm, the masking matrix is converted into the regularized function that acts on the correlation filter in frequency domain, which makes the algorithm fast convergence. Our online tracking algorithm performs favorably against state-of-the-art trackers on OTB-2015 Benchmark in terms of efficiency, accuracy, and robustness.

On-Line Modal State Monitoring of Slowly Time-Varying Structures

NASA Technical Reports Server (NTRS)

Johnson, Erik A.; Bergman, Lawrence A.; Voulgaris, Petros G.

1997-01-01

Monitoring the dynamic response of structures is often performed for a variety of reasons. These reasons include condition-based maintenance, health monitoring, performance improvements, and control. In many cases the data analysis that is performed is part of a repetitive decision-making process, and in these cases the development of effective on-line monitoring schemes help to speed the decision-making process and reduce the risk of erroneous decisions. This report investigates the use of spatial modal filters for tracking the dynamics of slowly time-varying linear structures. The report includes an overview of modal filter theory followed by an overview of several structural system identification methods. Included in this discussion and comparison are H-infinity, eigensystem realization, and several time-domain least squares approaches. Finally, a two-stage adaptive on-line monitoring scheme is developed and evaluated.

Linear theory for filtering nonlinear multiscale systems with model error

PubMed Central

Berry, Tyrus; Harlim, John

2014-01-01

In this paper, we study filtering of multiscale dynamical systems with model error arising from limitations in resolving the smaller scale processes. In particular, the analysis assumes the availability of continuous-time noisy observations of all components of the slow variables. Mathematically, this paper presents new results on higher order asymptotic expansion of the first two moments of a conditional measure. In particular, we are interested in the application of filtering multiscale problems in which the conditional distribution is defined over the slow variables, given noisy observation of the slow variables alone. From the mathematical analysis, we learn that for a continuous time linear model with Gaussian noise, there exists a unique choice of parameters in a linear reduced model for the slow variables which gives the optimal filtering when only the slow variables are observed. Moreover, these parameters simultaneously give the optimal equilibrium statistical estimates of the underlying system, and as a consequence they can be estimated offline from the equilibrium statistics of the true signal. By examining a nonlinear test model, we show that the linear theory extends in this non-Gaussian, nonlinear configuration as long as we know the optimal stochastic parametrization and the correct observation model. However, when the stochastic parametrization model is inappropriate, parameters chosen for good filter performance may give poor equilibrium statistical estimates and vice versa; this finding is based on analytical and numerical results on our nonlinear test model and the two-layer Lorenz-96 model. Finally, even when the correct stochastic ansatz is given, it is imperative to estimate the parameters simultaneously and to account for the nonlinear feedback of the stochastic parameters into the reduced filter estimates. In numerical experiments on the two-layer Lorenz-96 model, we find that the parameters estimated online, as part of a filtering procedure, simultaneously produce accurate filtering and equilibrium statistical prediction. In contrast, an offline estimation technique based on a linear regression, which fits the parameters to a training dataset without using the filter, yields filter estimates which are worse than the observations or even divergent when the slow variables are not fully observed. This finding does not imply that all offline methods are inherently inferior to the online method for nonlinear estimation problems, it only suggests that an ideal estimation technique should estimate all parameters simultaneously whether it is online or offline. PMID:25002829

Responding to Concerns about Online Radicalization in U.K. Schools through a Radicalization Critical Digital Literacy Approach

ERIC Educational Resources Information Center

McNicol, Sarah

2016-01-01

This article discusses the two main strategies commonly used to safeguard children and young people online; namely, Internet filtering and digital literacy education. In recent U.K. government guidance, both are identified as means to prevent online radicalization in schools. However, despite the inadequacies of filtering, more attention is…

Random Weighting, Strong Tracking, and Unscented Kalman Filter for Soft Tissue Characterization.

PubMed

Shin, Jaehyun; Zhong, Yongmin; Oetomo, Denny; Gu, Chengfan

2018-05-21

This paper presents a new nonlinear filtering method based on the Hunt-Crossley model for online nonlinear soft tissue characterization. This method overcomes the problem of performance degradation in the unscented Kalman filter due to contact model error. It adopts the concept of Mahalanobis distance to identify contact model error, and further incorporates a scaling factor in predicted state covariance to compensate identified model error. This scaling factor is determined according to the principle of innovation orthogonality to avoid the cumbersome computation of Jacobian matrix, where the random weighting concept is adopted to improve the estimation accuracy of innovation covariance. A master-slave robotic indentation system is developed to validate the performance of the proposed method. Simulation and experimental results as well as comparison analyses demonstrate that the efficacy of the proposed method for online characterization of soft tissue parameters in the presence of contact model error.

H-SLAM: Rao-Blackwellized Particle Filter SLAM Using Hilbert Maps.

PubMed

Vallicrosa, Guillem; Ridao, Pere

2018-05-01

Occupancy Grid maps provide a probabilistic representation of space which is important for a variety of robotic applications like path planning and autonomous manipulation. In this paper, a SLAM (Simultaneous Localization and Mapping) framework capable of obtaining this representation online is presented. The H-SLAM (Hilbert Maps SLAM) is based on Hilbert Map representation and uses a Particle Filter to represent the robot state. Hilbert Maps offer a continuous probabilistic representation with a small memory footprint. We present a series of experimental results carried both in simulation and with real AUVs (Autonomous Underwater Vehicles). These results demonstrate that our approach is able to represent the environment more consistently while capable of running online.

Using Collaborative Filtering to Support College Students' Use of Online Forum for English Learning

ERIC Educational Resources Information Center

Wang, Pei-Yu; Yang, Hui-Chun

2012-01-01

This study examined the impact of collaborative filtering (the so-called recommender) on college students' use of an online forum for English learning. The forum was created with an open-source software, Drupal, and its extended recommender module. This study was guided by three main questions: 1) Is there any difference in online behaviors…

Bilinear modeling and nonlinear estimation

NASA Technical Reports Server (NTRS)

Dwyer, Thomas A. W., III; Karray, Fakhreddine; Bennett, William H.

1989-01-01

New methods are illustrated for online nonlinear estimation applied to the lateral deflection of an elastic beam on board measurements of angular rates and angular accelerations. The development of the filter equations, together with practical issues of their numerical solution as developed from global linearization by nonlinear output injection are contrasted with the usual method of the extended Kalman filter (EKF). It is shown how nonlinear estimation due to gyroscopic coupling can be implemented as an adaptive covariance filter using off-the-shelf Kalman filter algorithms. The effect of the global linearization by nonlinear output injection is to introduce a change of coordinates in which only the process noise covariance is to be updated in online implementation. This is in contrast to the computational approach which arises in EKF methods arising by local linearization with respect to the current conditional mean. Processing refinements for nonlinear estimation based on optimal, nonlinear interpolation between observations are also highlighted. In these methods the extrapolation of the process dynamics between measurement updates is obtained by replacing a transition matrix with an operator spline that is optimized off-line from responses to selected test inputs.

Online Kinematic and Dynamic-State Estimation for Constrained Multibody Systems Based on IMUs

PubMed Central

Torres-Moreno, José Luis; Blanco-Claraco, José Luis; Giménez-Fernández, Antonio; Sanjurjo, Emilio; Naya, Miguel Ángel

2016-01-01

This article addresses the problems of online estimations of kinematic and dynamic states of a mechanism from a sequence of noisy measurements. In particular, we focus on a planar four-bar linkage equipped with inertial measurement units (IMUs). Firstly, we describe how the position, velocity, and acceleration of all parts of the mechanism can be derived from IMU signals by means of multibody kinematics. Next, we propose the novel idea of integrating the generic multibody dynamic equations into two variants of Kalman filtering, i.e., the extended Kalman filter (EKF) and the unscented Kalman filter (UKF), in a way that enables us to handle closed-loop, constrained mechanisms, whose state space variables are not independent and would normally prevent the direct use of such estimators. The proposal in this work is to apply those estimators over the manifolds of allowed positions and velocities, by means of estimating a subset of independent coordinates only. The proposed techniques are experimentally validated on a testbed equipped with encoders as a means of establishing the ground-truth. Estimators are run online in real-time, a feature not matched by any previous procedure of those reported in the literature on multibody dynamics. PMID:26959027

Development and Validation of Search Filters to Identify Articles on Family Medicine in Online Medical Databases

PubMed Central

Pols, David H.J.; Bramer, Wichor M.; Bindels, Patrick J.E.; van de Laar, Floris A.; Bohnen, Arthur M.

2015-01-01

Physicians and researchers in the field of family medicine often need to find relevant articles in online medical databases for a variety of reasons. Because a search filter may help improve the efficiency and quality of such searches, we aimed to develop and validate search filters to identify research studies of relevance to family medicine. Using a new and objective method for search filter development, we developed and validated 2 search filters for family medicine. The sensitive filter had a sensitivity of 96.8% and a specificity of 74.9%. The specific filter had a specificity of 97.4% and a sensitivity of 90.3%. Our new filters should aid literature searches in the family medicine field. The sensitive filter may help researchers conducting systematic reviews, whereas the specific filter may help family physicians find answers to clinical questions at the point of care when time is limited. PMID:26195683

On-board adaptive model for state of charge estimation of lithium-ion batteries based on Kalman filter with proportional integral-based error adjustment

NASA Astrophysics Data System (ADS)

Wei, Jingwen; Dong, Guangzhong; Chen, Zonghai

2017-10-01

With the rapid development of battery-powered electric vehicles, the lithium-ion battery plays a critical role in the reliability of vehicle system. In order to provide timely management and protection for battery systems, it is necessary to develop a reliable battery model and accurate battery parameters estimation to describe battery dynamic behaviors. Therefore, this paper focuses on an on-board adaptive model for state-of-charge (SOC) estimation of lithium-ion batteries. Firstly, a first-order equivalent circuit battery model is employed to describe battery dynamic characteristics. Then, the recursive least square algorithm and the off-line identification method are used to provide good initial values of model parameters to ensure filter stability and reduce the convergence time. Thirdly, an extended-Kalman-filter (EKF) is applied to on-line estimate battery SOC and model parameters. Considering that the EKF is essentially a first-order Taylor approximation of battery model, which contains inevitable model errors, thus, a proportional integral-based error adjustment technique is employed to improve the performance of EKF method and correct model parameters. Finally, the experimental results on lithium-ion batteries indicate that the proposed EKF with proportional integral-based error adjustment method can provide robust and accurate battery model and on-line parameter estimation.

Estimation of tool wear during CNC milling using neural network-based sensor fusion

NASA Astrophysics Data System (ADS)

Ghosh, N.; Ravi, Y. B.; Patra, A.; Mukhopadhyay, S.; Paul, S.; Mohanty, A. R.; Chattopadhyay, A. B.

2007-01-01

Cutting tool wear degrades the product quality in manufacturing processes. Monitoring tool wear value online is therefore needed to prevent degradation in machining quality. Unfortunately there is no direct way of measuring the tool wear online. Therefore one has to adopt an indirect method wherein the tool wear is estimated from several sensors measuring related process variables. In this work, a neural network-based sensor fusion model has been developed for tool condition monitoring (TCM). Features extracted from a number of machining zone signals, namely cutting forces, spindle vibration, spindle current, and sound pressure level have been fused to estimate the average flank wear of the main cutting edge. Novel strategies such as, signal level segmentation for temporal registration, feature space filtering, outlier removal, and estimation space filtering have been proposed. The proposed approach has been validated by both laboratory and industrial implementations.

Nonlinear Bayesian filtering and learning: a neuronal dynamics for perception.

PubMed

Kutschireiter, Anna; Surace, Simone Carlo; Sprekeler, Henning; Pfister, Jean-Pascal

2017-08-18

The robust estimation of dynamical hidden features, such as the position of prey, based on sensory inputs is one of the hallmarks of perception. This dynamical estimation can be rigorously formulated by nonlinear Bayesian filtering theory. Recent experimental and behavioral studies have shown that animals' performance in many tasks is consistent with such a Bayesian statistical interpretation. However, it is presently unclear how a nonlinear Bayesian filter can be efficiently implemented in a network of neurons that satisfies some minimum constraints of biological plausibility. Here, we propose the Neural Particle Filter (NPF), a sampling-based nonlinear Bayesian filter, which does not rely on importance weights. We show that this filter can be interpreted as the neuronal dynamics of a recurrently connected rate-based neural network receiving feed-forward input from sensory neurons. Further, it captures properties of temporal and multi-sensory integration that are crucial for perception, and it allows for online parameter learning with a maximum likelihood approach. The NPF holds the promise to avoid the 'curse of dimensionality', and we demonstrate numerically its capability to outperform weighted particle filters in higher dimensions and when the number of particles is limited.

An automated and fast approach to detect single-trial visual evoked potentials with application to brain-computer interface.

PubMed

Tu, Yiheng; Hung, Yeung Sam; Hu, Li; Huang, Gan; Hu, Yong; Zhang, Zhiguo

2014-12-01

This study aims (1) to develop an automated and fast approach for detecting visual evoked potentials (VEPs) in single trials and (2) to apply the single-trial VEP detection approach in designing a real-time and high-performance brain-computer interface (BCI) system. The single-trial VEP detection approach uses common spatial pattern (CSP) as a spatial filter and wavelet filtering (WF) a temporal-spectral filter to jointly enhance the signal-to-noise ratio (SNR) of single-trial VEPs. The performance of the joint spatial-temporal-spectral filtering approach was assessed in a four-command VEP-based BCI system. The offline classification accuracy of the BCI system was significantly improved from 67.6±12.5% (raw data) to 97.3±2.1% (data filtered by CSP and WF). The proposed approach was successfully implemented in an online BCI system, where subjects could make 20 decisions in one minute with classification accuracy of 90%. The proposed single-trial detection approach is able to obtain robust and reliable VEP waveform in an automatic and fast way and it is applicable in VEP based online BCI systems. This approach provides a real-time and automated solution for single-trial detection of evoked potentials or event-related potentials (EPs/ERPs) in various paradigms, which could benefit many applications such as BCI and intraoperative monitoring. Copyright © 2014 International Federation of Clinical Neurophysiology. Published by Elsevier Ireland Ltd. All rights reserved.

Navigation in GPS Denied Environments: Feature-Aided Inertial Systems

DTIC Science & Technology

2010-03-01

21] Brown , R. G. and Hwang , P. Y. C., Introduction to Random Signals and Applied Kalman Filtering, Third Edition, John Wiley & Sons, Inc., New York...knowledge is provided in [17]. An online (extended Kalman filter-based) method for calculating a trajectory by tracking features at an unknown location on...Finally, the trajectory error is estimated using these associated features in a Kalman estimator. The next couple of paragraphs will explain these

Online frequency estimation with applications to engine and generator sets

NASA Astrophysics Data System (ADS)

Manngård, Mikael; Böling, Jari M.

2017-07-01

Frequency and spectral analysis based on the discrete Fourier transform is a fundamental task in signal processing and machine diagnostics. This paper aims at presenting computationally efficient methods for real-time estimation of stationary and time-varying frequency components in signals. A brief survey of the sliding time window discrete Fourier transform and Goertzel filter is presented, and two filter banks consisting of: (i) sliding time window Goertzel filters (ii) infinite impulse response narrow bandpass filters are proposed for estimating instantaneous frequencies. The proposed methods show excellent results on both simulation studies and on a case study using angular speed data measurements of the crankshaft of a marine diesel engine-generator set.

State of Charge estimation of lithium ion battery based on extended Kalman filtering algorithm

NASA Astrophysics Data System (ADS)

Yang, Fan; Feng, Yiming; Pan, Binbiao; Wan, Renzhuo; Wang, Jun

2017-08-01

Accurate estimation of state-of-charge (SOC) for lithium ion battery is crucial for real-time diagnosis and prognosis in green energy vehicles. In this paper, a state space model of the battery based on Thevenin model is adopted. The strategy of estimating state of charge (SOC) based on extended Kalman fil-ter is presented, as well as to combine with ampere-hour counting (AH) and open circuit voltage (OCV) methods. The comparison between simulation and experiments indicates that the model’s performance matches well with that of lithium ion battery. The algorithm of extended Kalman filter keeps a good accura-cy precision and less dependent on its initial value in full range of SOC, which is proved to be suitable for online SOC estimation.

Adaptive filter design using recurrent cerebellar model articulation controller.

PubMed

Lin, Chih-Min; Chen, Li-Yang; Yeung, Daniel S

2010-07-01

A novel adaptive filter is proposed using a recurrent cerebellar-model-articulation-controller (CMAC). The proposed locally recurrent globally feedforward recurrent CMAC (RCMAC) has favorable properties of small size, good generalization, rapid learning, and dynamic response, thus it is more suitable for high-speed signal processing. To provide fast training, an efficient parameter learning algorithm based on the normalized gradient descent method is presented, in which the learning rates are on-line adapted. Then the Lyapunov function is utilized to derive the conditions of the adaptive learning rates, so the stability of the filtering error can be guaranteed. To demonstrate the performance of the proposed adaptive RCMAC filter, it is applied to a nonlinear channel equalization system and an adaptive noise cancelation system. The advantages of the proposed filter over other adaptive filters are verified through simulations.

Online vegetation parameter estimation using passive microwave remote sensing observations

USDA-ARS?s Scientific Manuscript database

In adaptive system identification the Kalman filter can be used to identify the coefficient of the observation operator of a linear system. Here the ensemble Kalman filter is tested for adaptive online estimation of the vegetation opacity parameter of a radiative transfer model. A state augmentatio...

Multiple UAV Cooperation for Wildfire Monitoring

NASA Astrophysics Data System (ADS)

Lin, Zhongjie

Wildfires have been a major factor in the development and management of the world's forest. An accurate assessment of wildfire status is imperative for fire management. This thesis is dedicated to the topic of utilizing multiple unmanned aerial vehicles (UAVs) to cooperatively monitor a large-scale wildfire. This is achieved through wildfire spreading situation estimation based on on-line measurements and wise cooperation strategy to ensure efficiency. First, based on the understanding of the physical characteristics of the wildfire propagation behavior, a wildfire model and a Kalman filter-based method are proposed to estimate the wildfire rate of spread and the fire front contour profile. With the enormous on-line measurements from on-board sensors of UAVs, the proposed method allows a wildfire monitoring mission to benefit from on-line information updating, increased flexibility, and accurate estimation. An independent wildfire simulator is utilized to verify the effectiveness of the proposed method. Second, based on the filter analysis, wildfire spreading situation and vehicle dynamics, the influence of different cooperation strategies of UAVs to the overall mission performance is studied. The multi-UAV cooperation problem is formulated in a distributed network. A consensus-based method is proposed to help address the problem. The optimal cooperation strategy of UAVs is obtained through mathematical analysis. The derived optimal cooperation strategy is then verified in an independent fire simulation environment to verify its effectiveness.

Development and Validation of Search Filters to Identify Articles on Family Medicine in Online Medical Databases.

PubMed

Pols, David H J; Bramer, Wichor M; Bindels, Patrick J E; van de Laar, Floris A; Bohnen, Arthur M

2015-01-01

Physicians and researchers in the field of family medicine often need to find relevant articles in online medical databases for a variety of reasons. Because a search filter may help improve the efficiency and quality of such searches, we aimed to develop and validate search filters to identify research studies of relevance to family medicine. Using a new and objective method for search filter development, we developed and validated 2 search filters for family medicine. The sensitive filter had a sensitivity of 96.8% and a specificity of 74.9%. The specific filter had a specificity of 97.4% and a sensitivity of 90.3%. Our new filters should aid literature searches in the family medicine field. The sensitive filter may help researchers conducting systematic reviews, whereas the specific filter may help family physicians find answers to clinical questions at the point of care when time is limited. © 2015 Annals of Family Medicine, Inc.

An Integrated Approach for Aircraft Engine Performance Estimation and Fault Diagnostics

NASA Technical Reports Server (NTRS)

imon, Donald L.; Armstrong, Jeffrey B.

2012-01-01

A Kalman filter-based approach for integrated on-line aircraft engine performance estimation and gas path fault diagnostics is presented. This technique is specifically designed for underdetermined estimation problems where there are more unknown system parameters representing deterioration and faults than available sensor measurements. A previously developed methodology is applied to optimally design a Kalman filter to estimate a vector of tuning parameters, appropriately sized to enable estimation. The estimated tuning parameters can then be transformed into a larger vector of health parameters representing system performance deterioration and fault effects. The results of this study show that basing fault isolation decisions solely on the estimated health parameter vector does not provide ideal results. Furthermore, expanding the number of the health parameters to address additional gas path faults causes a decrease in the estimation accuracy of those health parameters representative of turbomachinery performance deterioration. However, improved fault isolation performance is demonstrated through direct analysis of the estimated tuning parameters produced by the Kalman filter. This was found to provide equivalent or superior accuracy compared to the conventional fault isolation approach based on the analysis of sensed engine outputs, while simplifying online implementation requirements. Results from the application of these techniques to an aircraft engine simulation are presented and discussed.

Internet filters and entry pages do not protect children from online alcohol marketing.

PubMed

Jones, Sandra C; Thom, Jeffrey A; Davoren, Sondra; Barrie, Lance

2014-02-01

We review programs and policies to prevent children from accessing alcohol marketing online. To update the literature, we present our recent studies that assess (i) in-built barriers to underage access to alcohol brand websites and (ii) commercial internet filters. Alcohol websites typically had poor filter systems for preventing entry of underage persons; only half of the sites required the user to provide a date of birth, and none had any means of preventing users from trying again. Even the most effective commercial internet filters allowed access to one-third of the sites we examined.

Online Wavelet Complementary velocity Estimator.

PubMed

Righettini, Paolo; Strada, Roberto; KhademOlama, Ehsan; Valilou, Shirin

2018-02-01

In this paper, we have proposed a new online Wavelet Complementary velocity Estimator (WCE) over position and acceleration data gathered from an electro hydraulic servo shaking table. This is a batch estimator type that is based on the wavelet filter banks which extract the high and low resolution of data. The proposed complementary estimator combines these two resolutions of velocities which acquired from numerical differentiation and integration of the position and acceleration sensors by considering a fixed moving horizon window as input to wavelet filter. Because of using wavelet filters, it can be implemented in a parallel procedure. By this method the numerical velocity is estimated without having high noise of differentiators, integration drifting bias and with less delay which is suitable for active vibration control in high precision Mechatronics systems by Direct Velocity Feedback (DVF) methods. This method allows us to make velocity sensors with less mechanically moving parts which makes it suitable for fast miniature structures. We have compared this method with Kalman and Butterworth filters over stability, delay and benchmarked them by their long time velocity integration for getting back the initial position data. Copyright © 2017 ISA. Published by Elsevier Ltd. All rights reserved.

Adaptive estimation of state of charge and capacity with online identified battery model for vanadium redox flow battery

NASA Astrophysics Data System (ADS)

Wei, Zhongbao; Tseng, King Jet; Wai, Nyunt; Lim, Tuti Mariana; Skyllas-Kazacos, Maria

2016-11-01

Reliable state estimate depends largely on an accurate battery model. However, the parameters of battery model are time varying with operating condition variation and battery aging. The existing co-estimation methods address the model uncertainty by integrating the online model identification with state estimate and have shown improved accuracy. However, the cross interference may arise from the integrated framework to compromise numerical stability and accuracy. Thus this paper proposes the decoupling of model identification and state estimate to eliminate the possibility of cross interference. The model parameters are online adapted with the recursive least squares (RLS) method, based on which a novel joint estimator based on extended Kalman Filter (EKF) is formulated to estimate the state of charge (SOC) and capacity concurrently. The proposed joint estimator effectively compresses the filter order which leads to substantial improvement in the computational efficiency and numerical stability. Lab scale experiment on vanadium redox flow battery shows that the proposed method is highly authentic with good robustness to varying operating conditions and battery aging. The proposed method is further compared with some existing methods and shown to be superior in terms of accuracy, convergence speed, and computational cost.

IMU-based online kinematic calibration of robot manipulator.

PubMed

Du, Guanglong; Zhang, Ping

2013-01-01

Robot calibration is a useful diagnostic method for improving the positioning accuracy in robot production and maintenance. An online robot self-calibration method based on inertial measurement unit (IMU) is presented in this paper. The method requires that the IMU is rigidly attached to the robot manipulator, which makes it possible to obtain the orientation of the manipulator with the orientation of the IMU in real time. This paper proposed an efficient approach which incorporates Factored Quaternion Algorithm (FQA) and Kalman Filter (KF) to estimate the orientation of the IMU. Then, an Extended Kalman Filter (EKF) is used to estimate kinematic parameter errors. Using this proposed orientation estimation method will result in improved reliability and accuracy in determining the orientation of the manipulator. Compared with the existing vision-based self-calibration methods, the great advantage of this method is that it does not need the complex steps, such as camera calibration, images capture, and corner detection, which make the robot calibration procedure more autonomous in a dynamic manufacturing environment. Experimental studies on a GOOGOL GRB3016 robot show that this method has better accuracy, convenience, and effectiveness than vision-based methods.

Consistently Sampled Correlation Filters with Space Anisotropic Regularization for Visual Tracking

PubMed Central

Shi, Guokai; Xu, Tingfa; Luo, Jiqiang; Li, Yuankun

2017-01-01

Most existing correlation filter-based tracking algorithms, which use fixed patches and cyclic shifts as training and detection measures, assume that the training samples are reliable and ignore the inconsistencies between training samples and detection samples. We propose to construct and study a consistently sampled correlation filter with space anisotropic regularization (CSSAR) to solve these two problems simultaneously. Our approach constructs a spatiotemporally consistent sample strategy to alleviate the redundancies in training samples caused by the cyclical shifts, eliminate the inconsistencies between training samples and detection samples, and introduce space anisotropic regularization to constrain the correlation filter for alleviating drift caused by occlusion. Moreover, an optimization strategy based on the Gauss-Seidel method was developed for obtaining robust and efficient online learning. Both qualitative and quantitative evaluations demonstrate that our tracker outperforms state-of-the-art trackers in object tracking benchmarks (OTBs). PMID:29231876

Application of wavelet-based multi-model Kalman filters to real-time flood forecasting

NASA Astrophysics Data System (ADS)

Chou, Chien-Ming; Wang, Ru-Yih

2004-04-01

This paper presents the application of a multimodel method using a wavelet-based Kalman filter (WKF) bank to simultaneously estimate decomposed state variables and unknown parameters for real-time flood forecasting. Applying the Haar wavelet transform alters the state vector and input vector of the state space. In this way, an overall detail plus approximation describes each new state vector and input vector, which allows the WKF to simultaneously estimate and decompose state variables. The wavelet-based multimodel Kalman filter (WMKF) is a multimodel Kalman filter (MKF), in which the Kalman filter has been substituted for a WKF. The WMKF then obtains M estimated state vectors. Next, the M state-estimates, each of which is weighted by its possibility that is also determined on-line, are combined to form an optimal estimate. Validations conducted for the Wu-Tu watershed, a small watershed in Taiwan, have demonstrated that the method is effective because of the decomposition of wavelet transform, the adaptation of the time-varying Kalman filter and the characteristics of the multimodel method. Validation results also reveal that the resulting method enhances the accuracy of the runoff prediction of the rainfall-runoff process in the Wu-Tu watershed.

Bio-knowledge based filters improve residue-residue contact prediction accuracy.

PubMed

Wozniak, P P; Pelc, J; Skrzypecki, M; Vriend, G; Kotulska, M

2018-05-29

Residue-residue contact prediction through direct coupling analysis has reached impressive accuracy, but yet higher accuracy will be needed to allow for routine modelling of protein structures. One way to improve the prediction accuracy is to filter predicted contacts using knowledge about the particular protein of interest or knowledge about protein structures in general. We focus on the latter and discuss a set of filters that can be used to remove false positive contact predictions. Each filter depends on one or a few cut-off parameters for which the filter performance was investigated. Combining all filters while using default parameters resulted for a test-set of 851 protein domains in the removal of 29% of the predictions of which 92% were indeed false positives. All data and scripts are available from http://comprec-lin.iiar.pwr.edu.pl/FPfilter/. malgorzata.kotulska@pwr.edu.pl. Supplementary data are available at Bioinformatics online.

FPGA-based fused smart sensor for dynamic and vibration parameter extraction in industrial robot links.

PubMed

Rodriguez-Donate, Carlos; Morales-Velazquez, Luis; Osornio-Rios, Roque Alfredo; Herrera-Ruiz, Gilberto; de Jesus Romero-Troncoso, Rene

2010-01-01

Intelligent robotics demands the integration of smart sensors that allow the controller to efficiently measure physical quantities. Industrial manipulator robots require a constant monitoring of several parameters such as motion dynamics, inclination, and vibration. This work presents a novel smart sensor to estimate motion dynamics, inclination, and vibration parameters on industrial manipulator robot links based on two primary sensors: an encoder and a triaxial accelerometer. The proposed smart sensor implements a new methodology based on an oversampling technique, averaging decimation filters, FIR filters, finite differences and linear interpolation to estimate the interest parameters, which are computed online utilizing digital hardware signal processing based on field programmable gate arrays (FPGA).

FPGA-Based Fused Smart Sensor for Dynamic and Vibration Parameter Extraction in Industrial Robot Links

PubMed Central

Rodriguez-Donate, Carlos; Morales-Velazquez, Luis; Osornio-Rios, Roque Alfredo; Herrera-Ruiz, Gilberto; de Jesus Romero-Troncoso, Rene

2010-01-01

Intelligent robotics demands the integration of smart sensors that allow the controller to efficiently measure physical quantities. Industrial manipulator robots require a constant monitoring of several parameters such as motion dynamics, inclination, and vibration. This work presents a novel smart sensor to estimate motion dynamics, inclination, and vibration parameters on industrial manipulator robot links based on two primary sensors: an encoder and a triaxial accelerometer. The proposed smart sensor implements a new methodology based on an oversampling technique, averaging decimation filters, FIR filters, finite differences and linear interpolation to estimate the interest parameters, which are computed online utilizing digital hardware signal processing based on field programmable gate arrays (FPGA). PMID:22319345

A Multi-Agent System for Intelligent Online Education.

ERIC Educational Resources Information Center

O'Riordan, Colm; Griffith, Josephine

1999-01-01

Describes the system architecture of an intelligent Web-based education system that includes user modeling agents, information filtering agents for automatic information gathering, and the multi-agent interaction. Discusses information management; user interaction; support for collaborative peer-peer learning; implementation; testing; and future…

Establishing a Link Between Prescription Drug Abuse and Illicit Online Pharmacies: Analysis of Twitter Data.

PubMed

Katsuki, Takeo; Mackey, Tim Ken; Cuomo, Raphael

2015-12-16

Youth and adolescent non-medical use of prescription medications (NUPM) has become a national epidemic. However, little is known about the association between promotion of NUPM behavior and access via the popular social media microblogging site, Twitter, which is currently used by a third of all teens. In order to better assess NUPM behavior online, this study conducts surveillance and analysis of Twitter data to characterize the frequency of NUPM-related tweets and also identifies illegal access to drugs of abuse via online pharmacies. Tweets were collected over a 2-week period from April 1-14, 2015, by applying NUPM keyword filters for both generic/chemical and street names associated with drugs of abuse using the Twitter public streaming application programming interface. Tweets were then analyzed for relevance to NUPM and whether they promoted illegal online access to prescription drugs using a protocol of content coding and supervised machine learning. A total of 2,417,662 tweets were collected and analyzed for this study. Tweets filtered for generic drugs names comprised 232,108 tweets, including 22,174 unique associated uniform resource locators (URLs), and 2,185,554 tweets (376,304 unique URLs) filtered for street names. Applying an iterative process of manual content coding and supervised machine learning, 81.72% of the generic and 12.28% of the street NUPM datasets were predicted as having content relevant to NUPM respectively. By examining hyperlinks associated with NUPM relevant content for the generic Twitter dataset, we discovered that 75.72% of the tweets with URLs included a hyperlink to an online marketing affiliate that directly linked to an illicit online pharmacy advertising the sale of Valium without a prescription. This study examined the association between Twitter content, NUPM behavior promotion, and online access to drugs using a broad set of prescription drug keywords. Initial results are concerning, as our study found over 45,000 tweets that directly promoted NUPM by providing a URL that actively marketed the illegal online sale of prescription drugs of abuse. Additional research is needed to further establish the link between Twitter content and NUPM, as well as to help inform future technology-based tools, online health promotion activities, and public policy to combat NUPM online.

Establishing a Link Between Prescription Drug Abuse and Illicit Online Pharmacies: Analysis of Twitter Data

PubMed Central

Cuomo, Raphael

2015-01-01

Background Youth and adolescent non-medical use of prescription medications (NUPM) has become a national epidemic. However, little is known about the association between promotion of NUPM behavior and access via the popular social media microblogging site, Twitter, which is currently used by a third of all teens. Objective In order to better assess NUPM behavior online, this study conducts surveillance and analysis of Twitter data to characterize the frequency of NUPM-related tweets and also identifies illegal access to drugs of abuse via online pharmacies. Methods Tweets were collected over a 2-week period from April 1-14, 2015, by applying NUPM keyword filters for both generic/chemical and street names associated with drugs of abuse using the Twitter public streaming application programming interface. Tweets were then analyzed for relevance to NUPM and whether they promoted illegal online access to prescription drugs using a protocol of content coding and supervised machine learning. Results A total of 2,417,662 tweets were collected and analyzed for this study. Tweets filtered for generic drugs names comprised 232,108 tweets, including 22,174 unique associated uniform resource locators (URLs), and 2,185,554 tweets (376,304 unique URLs) filtered for street names. Applying an iterative process of manual content coding and supervised machine learning, 81.72% of the generic and 12.28% of the street NUPM datasets were predicted as having content relevant to NUPM respectively. By examining hyperlinks associated with NUPM relevant content for the generic Twitter dataset, we discovered that 75.72% of the tweets with URLs included a hyperlink to an online marketing affiliate that directly linked to an illicit online pharmacy advertising the sale of Valium without a prescription. Conclusions This study examined the association between Twitter content, NUPM behavior promotion, and online access to drugs using a broad set of prescription drug keywords. Initial results are concerning, as our study found over 45,000 tweets that directly promoted NUPM by providing a URL that actively marketed the illegal online sale of prescription drugs of abuse. Additional research is needed to further establish the link between Twitter content and NUPM, as well as to help inform future technology-based tools, online health promotion activities, and public policy to combat NUPM online. PMID:26677966

Fast Estimation of Defect Profiles from the Magnetic Flux Leakage Signal Based on a Multi-Power Affine Projection Algorithm

PubMed Central

Han, Wenhua; Shen, Xiaohui; Xu, Jun; Wang, Ping; Tian, Guiyun; Wu, Zhengyang

2014-01-01

Magnetic flux leakage (MFL) inspection is one of the most important and sensitive nondestructive testing approaches. For online MFL inspection of a long-range railway track or oil pipeline, a fast and effective defect profile estimating method based on a multi-power affine projection algorithm (MAPA) is proposed, where the depth of a sampling point is related with not only the MFL signals before it, but also the ones after it, and all of the sampling points related to one point appear as serials or multi-power. Defect profile estimation has two steps: regulating a weight vector in an MAPA filter and estimating a defect profile with the MAPA filter. Both simulation and experimental data are used to test the performance of the proposed method. The results demonstrate that the proposed method exhibits high speed while maintaining the estimated profiles clearly close to the desired ones in a noisy environment, thereby meeting the demand of accurate online inspection. PMID:25192314

Fast estimation of defect profiles from the magnetic flux leakage signal based on a multi-power affine projection algorithm.

PubMed

Han, Wenhua; Shen, Xiaohui; Xu, Jun; Wang, Ping; Tian, Guiyun; Wu, Zhengyang

2014-09-04

Magnetic flux leakage (MFL) inspection is one of the most important and sensitive nondestructive testing approaches. For online MFL inspection of a long-range railway track or oil pipeline, a fast and effective defect profile estimating method based on a multi-power affine projection algorithm (MAPA) is proposed, where the depth of a sampling point is related with not only the MFL signals before it, but also the ones after it, and all of the sampling points related to one point appear as serials or multi-power. Defect profile estimation has two steps: regulating a weight vector in an MAPA filter and estimating a defect profile with the MAPA filter. Both simulation and experimental data are used to test the performance of the proposed method. The results demonstrate that the proposed method exhibits high speed while maintaining the estimated profiles clearly close to the desired ones in a noisy environment, thereby meeting the demand of accurate online inspection.

Application of an Optimal Tuner Selection Approach for On-Board Self-Tuning Engine Models

NASA Technical Reports Server (NTRS)

Simon, Donald L.; Armstrong, Jeffrey B.; Garg, Sanjay

2012-01-01

An enhanced design methodology for minimizing the error in on-line Kalman filter-based aircraft engine performance estimation applications is presented in this paper. It specific-ally addresses the under-determined estimation problem, in which there are more unknown parameters than available sensor measurements. This work builds upon an existing technique for systematically selecting a model tuning parameter vector of appropriate dimension to enable estimation by a Kalman filter, while minimizing the estimation error in the parameters of interest. While the existing technique was optimized for open-loop engine operation at a fixed design point, in this paper an alternative formulation is presented that enables the technique to be optimized for an engine operating under closed-loop control throughout the flight envelope. The theoretical Kalman filter mean squared estimation error at a steady-state closed-loop operating point is derived, and the tuner selection approach applied to minimize this error is discussed. A technique for constructing a globally optimal tuning parameter vector, which enables full-envelope application of the technology, is also presented, along with design steps for adjusting the dynamic response of the Kalman filter state estimates. Results from the application of the technique to linear and nonlinear aircraft engine simulations are presented and compared to the conventional approach of tuner selection. The new methodology is shown to yield a significant improvement in on-line Kalman filter estimation accuracy.

Filter-Based Phase Shifts Distort Neuronal Timing Information.

PubMed

Yael, Dorin; Vecht, Jacob J; Bar-Gad, Izhar

2018-01-01

Filters are widely used for the modulation, typically attenuation, of amplitudes of different frequencies within neurophysiological signals. Filters, however, also induce changes in the phases of different frequencies whose amplitude is unmodulated. These phase shifts cause time lags in the filtered signals, leading to a disruption of the timing information between different frequencies within the same signal and between different signals. The emerging time lags can be either constant in the case of linear phase (LP) filters or vary as a function of the frequency in the more common case of non-LP (NLP) filters. Since filters are used ubiquitously online in the early stages of data acquisition, the vast majority of neurophysiological signals thus suffer from distortion of the timing information even prior to their sampling. This distortion is often exacerbated by further multiple offline filtering stages of the sampled signal. The distortion of timing information may cause misinterpretation of the results and lead to erroneous conclusions. Here we present a variety of typical examples of filter-induced phase distortions and discuss the evaluation and restoration of the timing information underlying the original signal.

Filter-Based Phase Shifts Distort Neuronal Timing Information

PubMed Central

Yael, Dorin; Vecht, Jacob J.

2018-01-01

Filters are widely used for the modulation, typically attenuation, of amplitudes of different frequencies within neurophysiological signals. Filters, however, also induce changes in the phases of different frequencies whose amplitude is unmodulated. These phase shifts cause time lags in the filtered signals, leading to a disruption of the timing information between different frequencies within the same signal and between different signals. The emerging time lags can be either constant in the case of linear phase (LP) filters or vary as a function of the frequency in the more common case of non-LP (NLP) filters. Since filters are used ubiquitously online in the early stages of data acquisition, the vast majority of neurophysiological signals thus suffer from distortion of the timing information even prior to their sampling. This distortion is often exacerbated by further multiple offline filtering stages of the sampled signal. The distortion of timing information may cause misinterpretation of the results and lead to erroneous conclusions. Here we present a variety of typical examples of filter-induced phase distortions and discuss the evaluation and restoration of the timing information underlying the original signal. PMID:29766044

Filters: It's Not about Porn, Stupid!

ERIC Educational Resources Information Center

Schuyler, Michael

1997-01-01

Discusses libraries' uses of filters to prevent access to objectionable sites on the Internet. Highlights include the American Library Association's resolution against filters as a violation of First Amendment rights; patron's use of terminals for e-mail or games; using filters for collection management; and listservs and online resources…

Modeling, Monitoring and Fault Diagnosis of Spacecraft Air Contaminants

NASA Technical Reports Server (NTRS)

Ramirez, W. Fred; Skliar, Mikhail; Narayan, Anand; Morgenthaler, George W.; Smith, Gerald J.

1996-01-01

Progress and results in the development of an integrated air quality modeling, monitoring, fault detection, and isolation system are presented. The focus was on development of distributed models of the air contaminants transport, the study of air quality monitoring techniques based on the model of transport process and on-line contaminant concentration measurements, and sensor placement. Different approaches to the modeling of spacecraft air contamination are discussed, and a three-dimensional distributed parameter air contaminant dispersion model applicable to both laminar and turbulent transport is proposed. A two-dimensional approximation of a full scale transport model is also proposed based on the spatial averaging of the three dimensional model over the least important space coordinate. A computer implementation of the transport model is considered and a detailed development of two- and three-dimensional models illustrated by contaminant transport simulation results is presented. The use of a well established Kalman filtering approach is suggested as a method for generating on-line contaminant concentration estimates based on both real time measurements and the model of contaminant transport process. It is shown that high computational requirements of the traditional Kalman filter can render difficult its real-time implementation for high-dimensional transport model and a novel implicit Kalman filtering algorithm is proposed which is shown to lead to an order of magnitude faster computer implementation in the case of air quality monitoring.

Automating "Word of Mouth" to Recommend Classes to Students: An Application of Social Information Filtering Algorithms

ERIC Educational Resources Information Center

Booker, Queen Esther

2009-01-01

An approach used to tackle the problem of helping online students find the classes they want and need is a filtering technique called "social information filtering," a general approach to personalized information filtering. Social information filtering essentially automates the process of "word-of-mouth" recommendations: items are recommended to a…

MIIC online: a web server to reconstruct causal or non-causal networks from non-perturbative data.

PubMed

Sella, Nadir; Verny, Louis; Uguzzoni, Guido; Affeldt, Séverine; Isambert, Hervé

2018-07-01

We present a web server running the MIIC algorithm, a network learning method combining constraint-based and information-theoretic frameworks to reconstruct causal, non-causal or mixed networks from non-perturbative data, without the need for an a priori choice on the class of reconstructed network. Starting from a fully connected network, the algorithm first removes dispensable edges by iteratively subtracting the most significant information contributions from indirect paths between each pair of variables. The remaining edges are then filtered based on their confidence assessment or oriented based on the signature of causality in observational data. MIIC online server can be used for a broad range of biological data, including possible unobserved (latent) variables, from single-cell gene expression data to protein sequence evolution and outperforms or matches state-of-the-art methods for either causal or non-causal network reconstruction. MIIC online can be freely accessed at https://miic.curie.fr. Supplementary data are available at Bioinformatics online.

An Improved Sparse Representation over Learned Dictionary Method for Seizure Detection.

PubMed

Li, Junhui; Zhou, Weidong; Yuan, Shasha; Zhang, Yanli; Li, Chengcheng; Wu, Qi

2016-02-01

Automatic seizure detection has played an important role in the monitoring, diagnosis and treatment of epilepsy. In this paper, a patient specific method is proposed for seizure detection in the long-term intracranial electroencephalogram (EEG) recordings. This seizure detection method is based on sparse representation with online dictionary learning and elastic net constraint. The online learned dictionary could sparsely represent the testing samples more accurately, and the elastic net constraint which combines the 11-norm and 12-norm not only makes the coefficients sparse but also avoids over-fitting problem. First, the EEG signals are preprocessed using wavelet filtering and differential filtering, and the kernel function is applied to make the samples closer to linearly separable. Then the dictionaries of seizure and nonseizure are respectively learned from original ictal and interictal training samples with online dictionary optimization algorithm to compose the training dictionary. After that, the test samples are sparsely coded over the learned dictionary and the residuals associated with ictal and interictal sub-dictionary are calculated, respectively. Eventually, the test samples are classified as two distinct categories, seizure or nonseizure, by comparing the reconstructed residuals. The average segment-based sensitivity of 95.45%, specificity of 99.08%, and event-based sensitivity of 94.44% with false detection rate of 0.23/h and average latency of -5.14 s have been achieved with our proposed method.

From tiger to panda: animal head detection.

PubMed

Zhang, Weiwei; Sun, Jian; Tang, Xiaoou

2011-06-01

Robust object detection has many important applications in real-world online photo processing. For example, both Google image search and MSN live image search have integrated human face detector to retrieve face or portrait photos. Inspired by the success of such face filtering approach, in this paper, we focus on another popular online photo category--animal, which is one of the top five categories in the MSN live image search query log. As a first attempt, we focus on the problem of animal head detection of a set of relatively large land animals that are popular on the internet, such as cat, tiger, panda, fox, and cheetah. First, we proposed a new set of gradient oriented feature, Haar of Oriented Gradients (HOOG), to effectively capture the shape and texture features on animal head. Then, we proposed two detection algorithms, namely Bruteforce detection and Deformable detection, to effectively exploit the shape feature and texture feature simultaneously. Experimental results on 14,379 well labeled animals images validate the superiority of the proposed approach. Additionally, we apply the animal head detector to improve the image search result through text based online photo search result filtering.

Online Estimation of Allan Variance Coefficients Based on a Neural-Extended Kalman Filter

PubMed Central

Miao, Zhiyong; Shen, Feng; Xu, Dingjie; He, Kunpeng; Tian, Chunmiao

2015-01-01

As a noise analysis method for inertial sensors, the traditional Allan variance method requires the storage of a large amount of data and manual analysis for an Allan variance graph. Although the existing online estimation methods avoid the storage of data and the painful procedure of drawing slope lines for estimation, they require complex transformations and even cause errors during the modeling of dynamic Allan variance. To solve these problems, first, a new state-space model that directly models the stochastic errors to obtain a nonlinear state-space model was established for inertial sensors. Then, a neural-extended Kalman filter algorithm was used to estimate the Allan variance coefficients. The real noises of an ADIS16405 IMU and fiber optic gyro-sensors were analyzed by the proposed method and traditional methods. The experimental results show that the proposed method is more suitable to estimate the Allan variance coefficients than the traditional methods. Moreover, the proposed method effectively avoids the storage of data and can be easily implemented using an online processor. PMID:25625903

Online sales: profit without question.

PubMed

Bryant, J A; Cody, M J; Murphy, S T

2002-09-01

To examine the ease with which underage smokers can purchase cigarettes online using money orders and to evaluate the effectiveness of internet filtering programs in blocking access to internet cigarette vendors (ICVs). Four young people purchased 32 money orders using 32 different names to buy one carton of cigarettes for each named individual. Each money order was subsequently mailed to a different ICV in the USA. No age related information accompanied these online orders. Two internet filtering programs ("Bess" and filtertobacco.org) were tested for their relative efficacy in blocking access to ICV sites. Of the 32 orders placed, four orders never reached the intended ICV. Of the remaining 28 orders, 20 (71%) were filled despite a lack of age verification. Only four (14%) of the orders received were rejected because they lacked proof of age. "Bess" blocked access to 84% and filtertobacco.org to 94% of the ICV sites. Although underage smokers can easily purchase cigarettes online using money orders, access to these sites can be largely blocked if appropriate filtering devices are installed.

IMU-Based Online Kinematic Calibration of Robot Manipulator

PubMed Central

2013-01-01

Robot calibration is a useful diagnostic method for improving the positioning accuracy in robot production and maintenance. An online robot self-calibration method based on inertial measurement unit (IMU) is presented in this paper. The method requires that the IMU is rigidly attached to the robot manipulator, which makes it possible to obtain the orientation of the manipulator with the orientation of the IMU in real time. This paper proposed an efficient approach which incorporates Factored Quaternion Algorithm (FQA) and Kalman Filter (KF) to estimate the orientation of the IMU. Then, an Extended Kalman Filter (EKF) is used to estimate kinematic parameter errors. Using this proposed orientation estimation method will result in improved reliability and accuracy in determining the orientation of the manipulator. Compared with the existing vision-based self-calibration methods, the great advantage of this method is that it does not need the complex steps, such as camera calibration, images capture, and corner detection, which make the robot calibration procedure more autonomous in a dynamic manufacturing environment. Experimental studies on a GOOGOL GRB3016 robot show that this method has better accuracy, convenience, and effectiveness than vision-based methods. PMID:24302854

Feasibility of granular bed filtration of an aerosol of ultrafine metallic particles including a pressure drop regeneration system.

PubMed

Bémer, D; Wingert, L; Morele, Y; Subra, I

2015-09-01

A process for filtering an aerosol of ultrafine metallic particles (UFP) has been designed and tested, based on the principle of a multistage granular bed. The filtration system comprised a succession of granular beds of varying thickness composed of glass beads of different diameters. This system allows the pressure drop to be regenerated during filtration ("on-line" mode) using a vibrating probe. Tests monitoring the pressure drop were conducted on a "10-L/min" low airflow rate device and on a "100-m(3)/hr" prototype. Granular bed unclogging is automated on the latter. The cyclic operation and filtration performances are similar to that of filter medium-based industrial dust collectors. Filtration of ultrafine metallic particles generated by different industrial processes such as arc welding, metal cutting, or spraying constitutes a difficult problem due to the high filter clogging properties of these particles and to the high temperatures generally encountered. Granular beds represent an advantageous means of filtering these aerosols with difficult properties.

FOG Random Drift Signal Denoising Based on the Improved AR Model and Modified Sage-Husa Adaptive Kalman Filter.

PubMed

Sun, Jin; Xu, Xiaosu; Liu, Yiting; Zhang, Tao; Li, Yao

2016-07-12

In order to reduce the influence of fiber optic gyroscope (FOG) random drift error on inertial navigation systems, an improved auto regressive (AR) model is put forward in this paper. First, based on real-time observations at each restart of the gyroscope, the model of FOG random drift can be established online. In the improved AR model, the FOG measured signal is employed instead of the zero mean signals. Then, the modified Sage-Husa adaptive Kalman filter (SHAKF) is introduced, which can directly carry out real-time filtering on the FOG signals. Finally, static and dynamic experiments are done to verify the effectiveness. The filtering results are analyzed with Allan variance. The analysis results show that the improved AR model has high fitting accuracy and strong adaptability, and the minimum fitting accuracy of single noise is 93.2%. Based on the improved AR(3) model, the denoising method of SHAKF is more effective than traditional methods, and its effect is better than 30%. The random drift error of FOG is reduced effectively, and the precision of the FOG is improved.

Development of online NIR urine analyzing system based on AOTF

NASA Astrophysics Data System (ADS)

Wan, Feng; Sun, Zhendong; Li, Xiaoxia

2006-09-01

In this paper, some key techniques on development of on-line MR urine analyzing system based on AOTF (Acousto - Optics Tunable Filter) are introduced. Problems about designing the optical system including collimation of incident light and working distance (the shortest distance for separating incident light and diffracted light) are analyzed and researched. DDS (Direct Digital Synthesizer) controlled by microprocessor is used to realize the wavelength scan. The experiment results show that this MR urine analyzing system based on. AOTF has 10000 - 4000cm -1 wavelength range and O.3ms wavelength transfer rate. Compare with the conventional Fourier Transform NIP. spectrophotometer for analyzing multi-components in urine, this system features low cost, small volume and on-line measurement function. Unscrambler software (multivariate statistical software by CAMO Inc. Norway) is selected as the software for processing the data. This system can realize on line quantitative analysis of protein, urea and creatinine in urine.

Choosing and using methodological search filters: searchers' views.

PubMed

Beale, Sophie; Duffy, Steven; Glanville, Julie; Lefebvre, Carol; Wright, Dianne; McCool, Rachael; Varley, Danielle; Boachie, Charles; Fraser, Cynthia; Harbour, Jenny; Smith, Lynne

2014-06-01

Search filters or hedges are search strategies developed to assist information specialists and librarians to retrieve different types of evidence from bibliographic databases. The objectives of this project were to learn about searchers' filter use, how searchers choose search filters and what information they would like to receive to inform their choices. Interviews with information specialists working in, or for, the National Institute for Health and Care Excellence (NICE) were conducted. An online questionnaire survey was also conducted and advertised via a range of email lists. Sixteen interviews were undertaken and 90 completed questionnaires were received. The use of search filters tends to be linked to reducing a large amount of literature, introducing focus and assisting with searches that are based on a single study type. Respondents use numerous ways to identify search filters and can find choosing between different filters problematic because of knowledge gaps and lack of time. Search filters are used mainly for reducing large result sets (introducing focus) and assisting with searches focused on a single study type. Features that would help with choosing filters include making information about filters less technical, offering ratings and providing more detail about filter validation strategies and filter provenance. © 2014 The authors. Health Information and Libraries Journal © 2014 Health Libraries Group.

SU-E-J-127: Implementation of An Online Replanning Tool for VMAT Using Flattening Filter-Free Beams

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

Ates, O; Ahunbay, E; Li, X

2015-06-15

Purpose: This is to report the implementation of an online replanning tool based on segment aperture morphing (SAM) for VMAT with flattening filter free (FFF) beams. Methods: Previously reported SAM algorithm modified to accommodate VMAT with FFF beams was implemented in a tool that was interfaced with a treatment planning system (Monaco, Elekta). The tool allows (1) to output the beam parameters of the original VMAT plan from Monaco, and (2) to input the apertures generated from the SAM algorithm into Monaco for the dose calculation on daily CT/CBCT/MRI in the following steps:(1) Quickly generating target contour based on themore » image of the day, using an auto-segmentation tool (ADMIRE, Elekta) with manual editing if necessary; (2) Morphing apertures based on the SAM in the original VMAT plan to account for the interfractional change of the target from the planning to the daily images; (3) Calculating dose distribution for new apertures with the same numbers of MU as in the original plan; (4) Transferring the new plan into a record & verify system (MOSAIQ, Elekta); (5) Performing a pre-delivery QA based on software; (6) Delivering the adaptive plan for the fraction.This workflow was implemented on a 16-CPU (2.6 GHz dual-core) hardware with GPU and was tested for sample cases of prostate, pancreas and lung tumors. Results: The online replanning process can be completed within 10 minutes. The adaptive plans generally have improved the plan quality when compared to the IGRT repositioning plans. The adaptive plans with FFF beams have better normal tissue sparing as compared with those of FF beams. Conclusion: The online replanning tool based on SAM can quickly generate adaptive VMAT plans using FFF beams with improved plan quality than those from the IGRT repositioning plans based on daily CT/CBCT/MRI and can be used clinically. This research was supported by Elekta Inc. (Crawley, UK)« less

Enabling User to User Interactions in Web Lectures with History-Aware User Awareness

ERIC Educational Resources Information Center

Ketterl, Markus; Mertens, Robert; Wiesen, Christoph; Vornberger, Oliver

2011-01-01

Purpose: The purpose of this paper is to present a user interface for web lectures for engaging with other users while working with video based learning content. The application allows its users to ask questions about the content and to get answers from those users that currently online are more familiar with it. The filtering is based on the…

Using Biographical Prompts to Build Community in an Online Graduate Course: An Adult Learning Perspective

ERIC Educational Resources Information Center

Lohr, Kathy D.; Haley, Karen J.

2018-01-01

Adult learners are taking advantage of the availability and convenience of online education. Mature learners in online higher education classrooms bring a wealth of experience filtered through cultural, generational, and socioeconomical differences. The purpose of this research was to explore community building in an online graduate course by…

Predicting online ratings based on the opinion spreading process

NASA Astrophysics Data System (ADS)

He, Xing-Sheng; Zhou, Ming-Yang; Zhuo, Zhao; Fu, Zhong-Qian; Liu, Jian-Guo

2015-10-01

Predicting users' online ratings is always a challenge issue and has drawn lots of attention. In this paper, we present a rating prediction method by combining the user opinion spreading process with the collaborative filtering algorithm, where user similarity is defined by measuring the amount of opinion a user transfers to another based on the primitive user-item rating matrix. The proposed method could produce a more precise rating prediction for each unrated user-item pair. In addition, we introduce a tunable parameter λ to regulate the preferential diffusion relevant to the degree of both opinion sender and receiver. The numerical results for Movielens and Netflix data sets show that this algorithm has a better accuracy than the standard user-based collaborative filtering algorithm using Cosine and Pearson correlation without increasing computational complexity. By tuning λ, our method could further boost the prediction accuracy when using Mean Absolute Error (MAE) and Root Mean Squared Error (RMSE) as measurements. In the optimal cases, on Movielens and Netflix data sets, the corresponding algorithmic accuracy (MAE and RMSE) are improved 11.26% and 8.84%, 13.49% and 10.52% compared to the item average method, respectively.

Real Time Data Acquisition and Online Signal Processing for Magnetoencephalography

NASA Astrophysics Data System (ADS)

Rongen, H.; Hadamschek, V.; Schiek, M.

2006-06-01

To establish improved therapies for patients suffering from severe neurological and psychiatric diseases, a demand controlled and desynchronizing brain-pacemaker has been developed with techniques from statistical physics and nonlinear dynamics. To optimize the novel therapeutic approach, brain activity is investigated with a Magnetoencephalography (MEG) system prior to surgery. For this, a real time data acquisition system for a 148 channel MEG and online signal processing for artifact rejection, filtering, cross trial phase resetting analysis and three-dimensional (3-D) reconstruction of the cerebral current sources was developed. The developed PCI bus hardware is based on a FPGA and DSP design, using the benefits from both architectures. The reconstruction and visualization of the 3-D volume data is done by the PC which hosts the real time DAQ and pre-processing board. The framework of the MEG-online system is introduced and the architecture of the real time DAQ board and online reconstruction is described. In addition we show first results with the MEG-Online system for the investigation of dynamic brain activities in relation to external visual stimulation, based on test data sets.

Progress in mental workload measurement

NASA Technical Reports Server (NTRS)

Moray, Neville; Turksen, Burhan; Aidie, Paul; Drascic, David; Eisen, Paul

1986-01-01

Two new techniques are described, one using subjective, the other physiological data for the measurement of workload in complex tasks. The subjective approach uses fuzzy measurement to analyze and predict the difficulty of combinations of skill based and rule based behavior from the difficulty of skill based behavior and rule based behavior measured separately. The physiological technique offers an on-line real-time filter for measuring the Mulder signal at 0.1 Hz in the heart rate variability spectrum.

Non-Dispersive Infrared Sensor for Online Condition Monitoring of Gearbox Oil.

PubMed

Rauscher, Markus S; Tremmel, Anton J; Schardt, Michael; Koch, Alexander W

2017-02-18

The condition of lubricating oil used in automotive and industrial gearboxes must be controlled in order to guarantee optimum performance and prevent damage to machinery parts. In normal practice, this is done by regular oil change intervals and routine laboratory analysis, both of which involve considerable operating costs. In this paper, we present a compact and robust optical sensor that can be installed in the lubrication circuit to provide quasi-continuous information about the condition of the oil. The measuring principle is based on non-dispersive infrared spectroscopy. The implemented sensor setup consists of an optical measurement cell, two thin-film infrared emitters, and two four-channel pyroelectric detectors equipped with optical bandpass filters. We present a method based on multivariate partial least squares regression to select appropriate optical bandpass filters for monitoring the oxidation, water content, and acid number of the oil. We perform a ray tracing analysis to analyze and correct the influence of the light path in the optical setup on the optical parameters of the bandpass filters. The measurement values acquired with the sensor for three different gearbox oil types show high correlation with laboratory reference data for the oxidation, water content, and acid number. The presented sensor can thus be a useful supplementary tool for the online condition monitoring of lubricants when integrated into a gearbox oil circuit.

Non-Dispersive Infrared Sensor for Online Condition Monitoring of Gearbox Oil

PubMed Central

Rauscher, Markus S.; Tremmel, Anton J.; Schardt, Michael; Koch, Alexander W.

2017-01-01

The condition of lubricating oil used in automotive and industrial gearboxes must be controlled in order to guarantee optimum performance and prevent damage to machinery parts. In normal practice, this is done by regular oil change intervals and routine laboratory analysis, both of which involve considerable operating costs. In this paper, we present a compact and robust optical sensor that can be installed in the lubrication circuit to provide quasi-continuous information about the condition of the oil. The measuring principle is based on non-dispersive infrared spectroscopy. The implemented sensor setup consists of an optical measurement cell, two thin-film infrared emitters, and two four-channel pyroelectric detectors equipped with optical bandpass filters. We present a method based on multivariate partial least squares regression to select appropriate optical bandpass filters for monitoring the oxidation, water content, and acid number of the oil. We perform a ray tracing analysis to analyze and correct the influence of the light path in the optical setup on the optical parameters of the bandpass filters. The measurement values acquired with the sensor for three different gearbox oil types show high correlation with laboratory reference data for the oxidation, water content, and acid number. The presented sensor can thus be a useful supplementary tool for the online condition monitoring of lubricants when integrated into a gearbox oil circuit. PMID:28218701

Toward On-line Parameter Estimation of Concentric Tube Robots Using a Mechanics-based Kinematic Model

PubMed Central

Jang, Cheongjae; Ha, Junhyoung; Dupont, Pierre E.; Park, Frank Chongwoo

2017-01-01

Although existing mechanics-based models of concentric tube robots have been experimentally demonstrated to approximate the actual kinematics, determining accurate estimates of model parameters remains difficult due to the complex relationship between the parameters and available measurements. Further, because the mechanics-based models neglect some phenomena like friction, nonlinear elasticity, and cross section deformation, it is also not clear if model error is due to model simplification or to parameter estimation errors. The parameters of the superelastic materials used in these robots can be slowly time-varying, necessitating periodic re-estimation. This paper proposes a method for estimating the mechanics-based model parameters using an extended Kalman filter as a step toward on-line parameter estimation. Our methodology is validated through both simulation and experiments. PMID:28717554

Neural adaptive observer-based sensor and actuator fault detection in nonlinear systems: Application in UAV.

PubMed

Abbaspour, Alireza; Aboutalebi, Payam; Yen, Kang K; Sargolzaei, Arman

2017-03-01

A new online detection strategy is developed to detect faults in sensors and actuators of unmanned aerial vehicle (UAV) systems. In this design, the weighting parameters of the Neural Network (NN) are updated by using the Extended Kalman Filter (EKF). Online adaptation of these weighting parameters helps to detect abrupt, intermittent, and incipient faults accurately. We apply the proposed fault detection system to a nonlinear dynamic model of the WVU YF-22 unmanned aircraft for its evaluation. The simulation results show that the new method has better performance in comparison with conventional recurrent neural network-based fault detection strategies. Copyright © 2016 ISA. Published by Elsevier Ltd. All rights reserved.

Proportional-Integral-Derivative (PID) Temperature Control and Data Acquisition System for Faraday Filter based Sodium Spectrometer

NASA Astrophysics Data System (ADS)

Semerjyan, Vardan; Yuan, Tao

2011-04-01

Sodium (Na) Faraday filters based spectrometer is a relatively new instrument to study sodium nightglow as well as sodium and oxygen chemistry in the mesopause region. Successful spectrometer measurement demands highly accurate control of filter temperature. The ideal, long-term operation site for the Na spectrometer is an isolated location with minimum nocturnal sky background. Thus, the remote control of the filter temperature is a requirement for such operation, whereas current temperature controllers can only be operated manually. The proposed approach is aimed to not only enhance the temperature control, but also achieve spectrometer's remote and autonomous operation. In the meantime, the redesign should relief the burden of the cost for multi temperature controllers. The program will give to the operator flexibility in setting the operation temperatures of the Faraday filters, monitoring the temperature variations, and logging the data during the operation. Research will make diligent efforts to attach preliminary data analysis subroutine to the main control program. The real-time observation results will be posted online after the observation is completed. This approach also can be a good substitute for the temperature control system currently used to run the Lidar system at Utah State University (USU).

Twitter-Based Detection of Illegal Online Sale of Prescription Opioid.

PubMed

Mackey, Tim K; Kalyanam, Janani; Katsuki, Takeo; Lanckriet, Gert

2017-12-01

To deploy a methodology accurately identifying tweets marketing the illegal online sale of controlled substances. We first collected tweets from the Twitter public application program interface stream filtered for prescription opioid keywords. We then used unsupervised machine learning (specifically, topic modeling) to identify topics associated with illegal online marketing and sales. Finally, we conducted Web forensic analyses to characterize different types of online vendors. We analyzed 619 937 tweets containing the keywords codeine, Percocet, fentanyl, Vicodin, Oxycontin, oxycodone, and hydrocodone over a 5-month period from June to November 2015. A total of 1778 tweets (< 1%) were identified as marketing the sale of controlled substances online; 90% had imbedded hyperlinks, but only 46 were "live" at the time of the evaluation. Seven distinct URLs linked to Web sites marketing or illegally selling controlled substances online. Our methodology can identify illegal online sale of prescription opioids from large volumes of tweets. Our results indicate that controlled substances are trafficked online via different strategies and vendors. Public Health Implications. Our methodology can be used to identify illegal online sellers in criminal violation of the Ryan Haight Online Pharmacy Consumer Protection Act.

Filter bank canonical correlation analysis for implementing a high-speed SSVEP-based brain-computer interface

NASA Astrophysics Data System (ADS)

Chen, Xiaogang; Wang, Yijun; Gao, Shangkai; Jung, Tzyy-Ping; Gao, Xiaorong

2015-08-01

Objective. Recently, canonical correlation analysis (CCA) has been widely used in steady-state visual evoked potential (SSVEP)-based brain-computer interfaces (BCIs) due to its high efficiency, robustness, and simple implementation. However, a method with which to make use of harmonic SSVEP components to enhance the CCA-based frequency detection has not been well established. Approach. This study proposed a filter bank canonical correlation analysis (FBCCA) method to incorporate fundamental and harmonic frequency components to improve the detection of SSVEPs. A 40-target BCI speller based on frequency coding (frequency range: 8-15.8 Hz, frequency interval: 0.2 Hz) was used for performance evaluation. To optimize the filter bank design, three methods (M1: sub-bands with equally spaced bandwidths; M2: sub-bands corresponding to individual harmonic frequency bands; M3: sub-bands covering multiple harmonic frequency bands) were proposed for comparison. Classification accuracy and information transfer rate (ITR) of the three FBCCA methods and the standard CCA method were estimated using an offline dataset from 12 subjects. Furthermore, an online BCI speller adopting the optimal FBCCA method was tested with a group of 10 subjects. Main results. The FBCCA methods significantly outperformed the standard CCA method. The method M3 achieved the highest classification performance. At a spelling rate of ˜33.3 characters/min, the online BCI speller obtained an average ITR of 151.18 ± 20.34 bits min-1. Significance. By incorporating the fundamental and harmonic SSVEP components in target identification, the proposed FBCCA method significantly improves the performance of the SSVEP-based BCI, and thereby facilitates its practical applications such as high-speed spelling.

Model-Based Reinforcement of Kinect Depth Data for Human Motion Capture Applications

PubMed Central

Calderita, Luis Vicente; Bandera, Juan Pedro; Bustos, Pablo; Skiadopoulos, Andreas

2013-01-01

Motion capture systems have recently experienced a strong evolution. New cheap depth sensors and open source frameworks, such as OpenNI, allow for perceiving human motion on-line without using invasive systems. However, these proposals do not evaluate the validity of the obtained poses. This paper addresses this issue using a model-based pose generator to complement the OpenNI human tracker. The proposed system enforces kinematics constraints, eliminates odd poses and filters sensor noise, while learning the real dimensions of the performer's body. The system is composed by a PrimeSense sensor, an OpenNI tracker and a kinematics-based filter and has been extensively tested. Experiments show that the proposed system improves pure OpenNI results at a very low computational cost. PMID:23845933

Online measurement of bead geometry in GMAW-based additive manufacturing using passive vision

NASA Astrophysics Data System (ADS)

Xiong, Jun; Zhang, Guangjun

2013-11-01

Additive manufacturing based on gas metal arc welding is an advanced technique for depositing fully dense components with low cost. Despite this fact, techniques to achieve accurate control and automation of the process have not yet been perfectly developed. The online measurement of the deposited bead geometry is a key problem for reliable control. In this work a passive vision-sensing system, comprising two cameras and composite filtering techniques, was proposed for real-time detection of the bead height and width through deposition of thin walls. The nozzle to the top surface distance was monitored for eliminating accumulated height errors during the multi-layer deposition process. Various image processing algorithms were applied and discussed for extracting feature parameters. A calibration procedure was presented for the monitoring system. Validation experiments confirmed the effectiveness of the online measurement system for bead geometry in layered additive manufacturing.

Sequential Monte Carlo filter for state estimation of LiFePO4 batteries based on an online updated model

NASA Astrophysics Data System (ADS)

Li, Jiahao; Klee Barillas, Joaquin; Guenther, Clemens; Danzer, Michael A.

2014-02-01

Battery state monitoring is one of the key techniques in battery management systems e.g. in electric vehicles. An accurate estimation can help to improve the system performance and to prolong the battery remaining useful life. Main challenges for the state estimation for LiFePO4 batteries are the flat characteristic of open-circuit-voltage over battery state of charge (SOC) and the existence of hysteresis phenomena. Classical estimation approaches like Kalman filtering show limitations to handle nonlinear and non-Gaussian error distribution problems. In addition, uncertainties in the battery model parameters must be taken into account to describe the battery degradation. In this paper, a novel model-based method combining a Sequential Monte Carlo filter with adaptive control to determine the cell SOC and its electric impedance is presented. The applicability of this dual estimator is verified using measurement data acquired from a commercial LiFePO4 cell. Due to a better handling of the hysteresis problem, results show the benefits of the proposed method against the estimation with an Extended Kalman filter.

FilterGate, or Knowing What We're Walling In or Walling Out.

ERIC Educational Resources Information Center

Wolinsky, Art

2001-01-01

Discusses problems with Internet filtering when it results in erroneously blocked Web sites. Topics include the Children's Online Protection Act (CIPA); blocking all sites on an Internet Service Provider (ISP) through Round Robin DNS; blocking by URL or by IP number; and questioning the need for filters. (LRW)

Detection of illicit online sales of fentanyls via Twitter

PubMed Central

Mackey, Tim K.; Kalyanam, Janani

2017-01-01

A counterfeit fentanyl crisis is currently underway in the United States.  Counterfeit versions of commonly abused prescription drugs laced with fentanyl are being manufactured, distributed, and sold globally, leading to an increase in overdose and death in countries like the United States and Canada.  Despite concerns from the U.S. Drug Enforcement Agency regarding covert and overt sale of fentanyls online, no study has examined the role of the Internet and social media on fentanyl illegal marketing and direct-to-consumer access.  In response, this study collected and analyzed five months of Twitter data (from June-November 2015) filtered for the keyword “fentanyl” using Amazon Web Services.  We then analyzed 28,711 fentanyl-related tweets using text filtering and a machine learning approach called a Biterm Topic Model (BTM) to detect underlying latent patterns or “topics” present in the corpus of tweets.  Using this approach we detected a subset of 771 tweets marketing the sale of fentanyls online and then filtered this down to nine unique tweets containing hyperlinks to external websites.  Six hyperlinks were associated with online fentanyl classified ads, 2 with illicit online pharmacies, and 1 could not be classified due to traffic redirection.  Importantly, the one illicit online pharmacy detected was still accessible and offered the sale of fentanyls and other controlled substances direct-to-consumers with no prescription required at the time of publication of this study.   Overall, we detected a relatively small sample of Tweets promoting illegal online sale of fentanyls.  However, the detection of even a few online sellers represents a public health danger and a direct violation of law that demands further study. PMID:29259769

Detection of illicit online sales of fentanyls via Twitter.

PubMed

Mackey, Tim K; Kalyanam, Janani

2017-01-01

A counterfeit fentanyl crisis is currently underway in the United States.  Counterfeit versions of commonly abused prescription drugs laced with fentanyl are being manufactured, distributed, and sold globally, leading to an increase in overdose and death in countries like the United States and Canada.  Despite concerns from the U.S. Drug Enforcement Agency regarding covert and overt sale of fentanyls online, no study has examined the role of the Internet and social media on fentanyl illegal marketing and direct-to-consumer access.  In response, this study collected and analyzed five months of Twitter data (from June-November 2015) filtered for the keyword "fentanyl" using Amazon Web Services.  We then analyzed 28,711 fentanyl-related tweets using text filtering and a machine learning approach called a Biterm Topic Model (BTM) to detect underlying latent patterns or "topics" present in the corpus of tweets.  Using this approach we detected a subset of 771 tweets marketing the sale of fentanyls online and then filtered this down to nine unique tweets containing hyperlinks to external websites.  Six hyperlinks were associated with online fentanyl classified ads, 2 with illicit online pharmacies, and 1 could not be classified due to traffic redirection.  Importantly, the one illicit online pharmacy detected was still accessible and offered the sale of fentanyls and other controlled substances direct-to-consumers with no prescription required at the time of publication of this study.   Overall, we detected a relatively small sample of Tweets promoting illegal online sale of fentanyls.  However, the detection of even a few online sellers represents a public health danger and a direct violation of law that demands further study.

Migrating an Online Service to WAP - A Case Study.

ERIC Educational Resources Information Center

Klasen, Lars

2002-01-01

Discusses mobile access via wireless application protocol (WAP) to online services that is offered in Sweden through InfoTorg. Topics include the Swedish online market; filtering HTML data from an Internet/Web server into WML (wireless markup language); mobile phone technology; microbrowsers; WAP protocol; and future possibilities. (LRW)

On-line range prediction system, part 2

NASA Technical Reports Server (NTRS)

Levan, Nhan

1988-01-01

The on-line range prediction system is designed for providing a prediction of the target range in the case of a laser data dropout. It consists of real time implementation of a Kalman filter on an IBM PC/AT equipped with necessary hardware. The system was set up and tested at Crows Landing in the Fall of 1987. The improvements made on the on-line range prediction system during 1988 are examined. Solutions are proposed and discussed to the several problems encountered during system tests. Then, the improvements made on the filter software are explained, namely, accounting for the time lag and providing data continously. Finally, the ideas are mentioned that can be considered in the future.

Online fingerprint verification.

PubMed

Upendra, K; Singh, S; Kumar, V; Verma, H K

2007-01-01

As organizations search for more secure authentication methods for user access, e-commerce, and other security applications, biometrics is gaining increasing attention. With an increasing emphasis on the emerging automatic personal identification applications, fingerprint based identification is becoming more popular. The most widely used fingerprint representation is the minutiae based representation. The main drawback with this representation is that it does not utilize a significant component of the rich discriminatory information available in the fingerprints. Local ridge structures cannot be completely characterized by minutiae. Also, it is difficult quickly to match two fingerprint images containing different number of unregistered minutiae points. In this study filter bank based representation, which eliminates these weakness, is implemented and the overall performance of the developed system is tested. The results have shown that this system can be used effectively for secure online verification applications.

Translation of EEG Spatial Filters from Resting to Motor Imagery Using Independent Component Analysis

PubMed Central

Wang, Yijun; Wang, Yu-Te; Jung, Tzyy-Ping

2012-01-01

Electroencephalogram (EEG)-based brain-computer interfaces (BCIs) often use spatial filters to improve signal-to-noise ratio of task-related EEG activities. To obtain robust spatial filters, large amounts of labeled data, which are often expensive and labor-intensive to obtain, need to be collected in a training procedure before online BCI control. Several studies have recently developed zero-training methods using a session-to-session scenario in order to alleviate this problem. To our knowledge, a state-to-state translation, which applies spatial filters derived from one state to another, has never been reported. This study proposes a state-to-state, zero-training method to construct spatial filters for extracting EEG changes induced by motor imagery. Independent component analysis (ICA) was separately applied to the multi-channel EEG in the resting and the motor imagery states to obtain motor-related spatial filters. The resultant spatial filters were then applied to single-trial EEG to differentiate left- and right-hand imagery movements. On a motor imagery dataset collected from nine subjects, comparable classification accuracies were obtained by using ICA-based spatial filters derived from the two states (motor imagery: 87.0%, resting: 85.9%), which were both significantly higher than the accuracy achieved by using monopolar scalp EEG data (80.4%). The proposed method considerably increases the practicality of BCI systems in real-world environments because it is less sensitive to electrode misalignment across different sessions or days and does not require annotated pilot data to derive spatial filters. PMID:22666377

An optimal search filter for retrieving systematic reviews and meta-analyses

PubMed Central

2012-01-01

Background Health-evidence.ca is an online registry of systematic reviews evaluating the effectiveness of public health interventions. Extensive searching of bibliographic databases is required to keep the registry up to date. However, search filters have been developed to assist in searching the extensive amount of published literature indexed. Search filters can be designed to find literature related to a certain subject (i.e. content-specific filter) or particular study designs (i.e. methodological filter). The objective of this paper is to describe the development and validation of the health-evidence.ca Systematic Review search filter and to compare its performance to other available systematic review filters. Methods This analysis of search filters was conducted in MEDLINE, EMBASE, and CINAHL. The performance of thirty-one search filters in total was assessed. A validation data set of 219 articles indexed between January 2004 and December 2005 was used to evaluate performance on sensitivity, specificity, precision and the number needed to read for each filter. Results Nineteen of 31 search filters were effective in retrieving a high level of relevant articles (sensitivity scores greater than 85%). The majority achieved a high degree of sensitivity at the expense of precision and yielded large result sets. The main advantage of the health-evidence.ca Systematic Review search filter in comparison to the other filters was that it maintained the same level of sensitivity while reducing the number of articles that needed to be screened. Conclusions The health-evidence.ca Systematic Review search filter is a useful tool for identifying published systematic reviews, with further screening to identify those evaluating the effectiveness of public health interventions. The filter that narrows the focus saves considerable time and resources during updates of this online resource, without sacrificing sensitivity. PMID:22512835

Real-time monitoring of capacity loss for vanadium redox flow battery

NASA Astrophysics Data System (ADS)

Wei, Zhongbao; Bhattarai, Arjun; Zou, Changfu; Meng, Shujuan; Lim, Tuti Mariana; Skyllas-Kazacos, Maria

2018-06-01

The long-term operation of the vanadium redox flow battery is accompanied by ion diffusion across the separator and side reactions, which can lead to electrolyte imbalance and capacity loss. The accurate online monitoring of capacity loss is therefore valuable for the reliable and efficient operation of vanadium redox flow battery system. In this paper, a model-based online monitoring method is proposed to detect capacity loss in the vanadium redox flow battery in real time. A first-order equivalent circuit model is built to capture the dynamics of the vanadium redox flow battery. The model parameters are online identified from the onboard measureable signals with the recursive least squares, in seeking to keep a high modeling accuracy and robustness under a wide range of working scenarios. Based on the online adapted model, an observer is designed with the extended Kalman Filter to keep tracking both the capacity and state of charge of the battery in real time. Experiments are conducted on a lab-scale battery system. Results suggest that the online adapted model is able to simulate the battery behavior with high accuracy. The capacity loss as well as the state of charge can be estimated accurately in a real-time manner.

Using Online Role-Play to Promote Collaborative Argument and Collective Action

ERIC Educational Resources Information Center

Doerr-Stevens, Candance; Beach, Richard; Boeser, Elizabeth

2011-01-01

This article discusses how students use online role-play to collaborate and change real school policy. Playing different characters in an online role-play, students explore controversial aspects of Internet filtering and adopt a plan to change their school's policy. Through engaging in collaborative argumentation during their role-play, students…

Online Detection of Broken Rotor Bar Fault in Induction Motors by Combining Estimation of Signal Parameters via Min-norm Algorithm and Least Square Method

NASA Astrophysics Data System (ADS)

Wang, Pan-Pan; Yu, Qiang; Hu, Yong-Jun; Miao, Chang-Xin

2017-11-01

Current research in broken rotor bar (BRB) fault detection in induction motors is primarily focused on a high-frequency resolution analysis of the stator current. Compared with a discrete Fourier transformation, the parametric spectrum estimation technique has a higher frequency accuracy and resolution. However, the existing detection methods based on parametric spectrum estimation cannot realize online detection, owing to the large computational cost. To improve the efficiency of BRB fault detection, a new detection method based on the min-norm algorithm and least square estimation is proposed in this paper. First, the stator current is filtered using a band-pass filter and divided into short overlapped data windows. The min-norm algorithm is then applied to determine the frequencies of the fundamental and fault characteristic components with each overlapped data window. Next, based on the frequency values obtained, a model of the fault current signal is constructed. Subsequently, a linear least squares problem solved through singular value decomposition is designed to estimate the amplitudes and phases of the related components. Finally, the proposed method is applied to a simulated current and an actual motor, the results of which indicate that, not only parametric spectrum estimation technique.

Evaluation of Keyphrase Extraction Algorithm and Tiling Process for a Document/Resource Recommender within E-Learning Environments

ERIC Educational Resources Information Center

Mangina, Eleni; Kilbride, John

2008-01-01

The research presented in this paper is an examination of the applicability of IUI techniques in an online e-learning environment. In particular we make use of user modeling techniques, information retrieval and extraction mechanisms and collaborative filtering methods. The domains of e-learning, web-based training and instruction and intelligent…

Sequential Geoacoustic Filtering and Geoacoustic Inversion

DTIC Science & Technology

2015-09-30

and online algorithms. We show here that CS obtains higher resolution than MVDR, even in scenarios, which favor classical high-resolution methods...windows actually performs better than conventional beamforming and MVDR/ MUSIC (see Figs. 1-2). Compressive geoacoustic inversion Geoacoustic...histograms based on 100 Monte Carlo simulations, and c)(CS, exhaustive-search, CBF, MVDR, and MUSIC performance versus SNR. The true source positions

VizieR Online Data Catalog: Synthetic spectrophotometry for C-rich giants (Aringer+, 2009)

NASA Astrophysics Data System (ADS)

Aringer, B.; Girardi, L.; Nowotny, W.; Marigo, P.; Lederer, M. T.

2009-09-01

The following bolometric correction tables have been calculated from COMA07/SPHREAD03 spectra based on COMARCS04 hydrostatic models. Reddening due to circumstellar dust is not included. Each of the 36 tables contains a photometric system including different filters. If a certain filter curve is not covered by the synthetic spectra (below 4444 and above 250000A) a value of -999999 is listed for the bolometric correction. Details concerning the photometric systems are given in Marigo, P., Girardi, L., Bressan, A., et al., 2008A&A...482..883M and in http://stev.oapd.inaf.it/~lgirardi/cmd_2.1/photsys.html . (4 data files).

Tracking of multiple targets using online learning for reference model adaptation.

PubMed

Pernkopf, Franz

2008-12-01

Recently, much work has been done in multiple object tracking on the one hand and on reference model adaptation for a single-object tracker on the other side. In this paper, we do both tracking of multiple objects (faces of people) in a meeting scenario and online learning to incrementally update the models of the tracked objects to account for appearance changes during tracking. Additionally, we automatically initialize and terminate tracking of individual objects based on low-level features, i.e., face color, face size, and object movement. Many methods unlike our approach assume that the target region has been initialized by hand in the first frame. For tracking, a particle filter is incorporated to propagate sample distributions over time. We discuss the close relationship between our implemented tracker based on particle filters and genetic algorithms. Numerous experiments on meeting data demonstrate the capabilities of our tracking approach. Additionally, we provide an empirical verification of the reference model learning during tracking of indoor and outdoor scenes which supports a more robust tracking. Therefore, we report the average of the standard deviation of the trajectories over numerous tracking runs depending on the learning rate.

A Soft Sensor for Bioprocess Control Based on Sequential Filtering of Metabolic Heat Signals

PubMed Central

Paulsson, Dan; Gustavsson, Robert; Mandenius, Carl-Fredrik

2014-01-01

Soft sensors are the combination of robust on-line sensor signals with mathematical models for deriving additional process information. Here, we apply this principle to a microbial recombinant protein production process in a bioreactor by exploiting bio-calorimetric methodology. Temperature sensor signals from the cooling system of the bioreactor were used for estimating the metabolic heat of the microbial culture and from that the specific growth rate and active biomass concentration were derived. By applying sequential digital signal filtering, the soft sensor was made more robust for industrial practice with cultures generating low metabolic heat in environments with high noise level. The estimated specific growth rate signal obtained from the three stage sequential filter allowed controlled feeding of substrate during the fed-batch phase of the production process. The biomass and growth rate estimates from the soft sensor were also compared with an alternative sensor probe and a capacitance on-line sensor, for the same variables. The comparison showed similar or better sensitivity and lower variability for the metabolic heat soft sensor suggesting that using permanent temperature sensors of a bioreactor is a realistic and inexpensive alternative for monitoring and control. However, both alternatives are easy to implement in a soft sensor, alone or in parallel. PMID:25264951

A soft sensor for bioprocess control based on sequential filtering of metabolic heat signals.

PubMed

Paulsson, Dan; Gustavsson, Robert; Mandenius, Carl-Fredrik

2014-09-26

Soft sensors are the combination of robust on-line sensor signals with mathematical models for deriving additional process information. Here, we apply this principle to a microbial recombinant protein production process in a bioreactor by exploiting bio-calorimetric methodology. Temperature sensor signals from the cooling system of the bioreactor were used for estimating the metabolic heat of the microbial culture and from that the specific growth rate and active biomass concentration were derived. By applying sequential digital signal filtering, the soft sensor was made more robust for industrial practice with cultures generating low metabolic heat in environments with high noise level. The estimated specific growth rate signal obtained from the three stage sequential filter allowed controlled feeding of substrate during the fed-batch phase of the production process. The biomass and growth rate estimates from the soft sensor were also compared with an alternative sensor probe and a capacitance on-line sensor, for the same variables. The comparison showed similar or better sensitivity and lower variability for the metabolic heat soft sensor suggesting that using permanent temperature sensors of a bioreactor is a realistic and inexpensive alternative for monitoring and control. However, both alternatives are easy to implement in a soft sensor, alone or in parallel.

Online two-stage association method for robust multiple people tracking

NASA Astrophysics Data System (ADS)

Lv, Jingqin; Fang, Jiangxiong; Yang, Jie

2011-07-01

Robust multiple people tracking is very important for many applications. It is a challenging problem due to occlusion and interaction in crowded scenarios. This paper proposes an online two-stage association method for robust multiple people tracking. In the first stage, short tracklets generated by linking people detection responses grow longer by particle filter based tracking, with detection confidence embedded into the observation model. And, an examining scheme runs at each frame for the reliability of tracking. In the second stage, multiple people tracking is achieved by linking tracklets to generate trajectories. An online tracklet association method is proposed to solve the linking problem, which allows applications in time-critical scenarios. This method is evaluated on the popular CAVIAR dataset. The experimental results show that our two-stage method is robust.

Online Denoising Based on the Second-Order Adaptive Statistics Model.

PubMed

Yi, Sheng-Lun; Jin, Xue-Bo; Su, Ting-Li; Tang, Zhen-Yun; Wang, Fa-Fa; Xiang, Na; Kong, Jian-Lei

2017-07-20

Online denoising is motivated by real-time applications in the industrial process, where the data must be utilizable soon after it is collected. Since the noise in practical process is usually colored, it is quite a challenge for denoising techniques. In this paper, a novel online denoising method was proposed to achieve the processing of the practical measurement data with colored noise, and the characteristics of the colored noise were considered in the dynamic model via an adaptive parameter. The proposed method consists of two parts within a closed loop: the first one is to estimate the system state based on the second-order adaptive statistics model and the other is to update the adaptive parameter in the model using the Yule-Walker algorithm. Specifically, the state estimation process was implemented via the Kalman filter in a recursive way, and the online purpose was therefore attained. Experimental data in a reinforced concrete structure test was used to verify the effectiveness of the proposed method. Results show the proposed method not only dealt with the signals with colored noise, but also achieved a tradeoff between efficiency and accuracy.

Parenting the Internet: Resources for Parents and Children.

ERIC Educational Resources Information Center

Bushong, Sara

2002-01-01

Presents general statistical findings of Internet use by children, discusses the recent Children's Online Privacy Protection Act (COPPA), and summarizes online child safety considerations. Considers filtering and includes current resources for parents and children. (Author/LRW)

Human tracking in thermal images using adaptive particle filters with online random forest learning

NASA Astrophysics Data System (ADS)

Ko, Byoung Chul; Kwak, Joon-Young; Nam, Jae-Yeal

2013-11-01

This paper presents a fast and robust human tracking method to use in a moving long-wave infrared thermal camera under poor illumination with the existence of shadows and cluttered backgrounds. To improve the human tracking performance while minimizing the computation time, this study proposes an online learning of classifiers based on particle filters and combination of a local intensity distribution (LID) with oriented center-symmetric local binary patterns (OCS-LBP). Specifically, we design a real-time random forest (RF), which is the ensemble of decision trees for confidence estimation, and confidences of the RF are converted into a likelihood function of the target state. First, the target model is selected by the user and particles are sampled. Then, RFs are generated using the positive and negative examples with LID and OCS-LBP features by online learning. The learned RF classifiers are used to detect the most likely target position in the subsequent frame in the next stage. Then, the RFs are learned again by means of fast retraining with the tracked object and background appearance in the new frame. The proposed algorithm is successfully applied to various thermal videos as tests and its tracking performance is better than those of other methods.

Emotional Mining: Tagging Emoticons to Online News

NASA Astrophysics Data System (ADS)

Kasinathan, Vinothini; Mustapha, Aida; Zhi Yong, Lee; Aida Zamnah, Z. A.

2017-08-01

This paper presents an emotion mining system, which assigns emoticons to newspaper articles into a pre-defined emotion category based on the underlying emotion in the news. Next, the system makes recommendation to the reader by tagging the news headline with the respective emoticons. Users are then able to decide whether to read the news based on the emoticons provided. The system also provides a filter for the users to choose the category of news to read following the emoticons.

Architecture for knowledge-based and federated search of online clinical evidence.

PubMed

Coiera, Enrico; Walther, Martin; Nguyen, Ken; Lovell, Nigel H

2005-10-24

It is increasingly difficult for clinicians to keep up-to-date with the rapidly growing biomedical literature. Online evidence retrieval methods are now seen as a core tool to support evidence-based health practice. However, standard search engine technology is not designed to manage the many different types of evidence sources that are available or to handle the very different information needs of various clinical groups, who often work in widely different settings. The objectives of this paper are (1) to describe the design considerations and system architecture of a wrapper-mediator approach to federate search system design, including the use of knowledge-based, meta-search filters, and (2) to analyze the implications of system design choices on performance measurements. A trial was performed to evaluate the technical performance of a federated evidence retrieval system, which provided access to eight distinct online resources, including e-journals, PubMed, and electronic guidelines. The Quick Clinical system architecture utilized a universal query language to reformulate queries internally and utilized meta-search filters to optimize search strategies across resources. We recruited 227 family physicians from across Australia who used the system to retrieve evidence in a routine clinical setting over a 4-week period. The total search time for a query was recorded, along with the duration of individual queries sent to different online resources. Clinicians performed 1662 searches over the trial. The average search duration was 4.9 +/- 3.2 s (N = 1662 searches). Mean search duration to the individual sources was between 0.05 s and 4.55 s. Average system time (ie, system overhead) was 0.12 s. The relatively small system overhead compared to the average time it takes to perform a search for an individual source shows that the system achieves a good trade-off between performance and reliability. Furthermore, despite the additional effort required to incorporate the capabilities of each individual source (to improve the quality of search results), system maintenance requires only a small additional overhead.

Real-time experimental demonstrations of software reconfigurable optical OFDM transceivers utilizing DSP-based digital orthogonal filters for SDN PONs.

PubMed

Duan, X; Giddings, R P; Bolea, M; Ling, Y; Cao, B; Mansoor, S; Tang, J M

2014-08-11

Real-time optical OFDM (OOFDM) transceivers with on-line software-controllable channel reconfigurability and transmission performance adaptability are experimentally demonstrated, for the first time, utilizing Hilbert-pair-based 32-tap digital orthogonal filters implemented in FPGAs. By making use of an 8-bit DAC/ADC operating at 2GS/s, an oversampling factor of 2 and an EML intensity modulator, the demonstrated RF conversion-free transceiver supports end-to-end real-time simultaneous adaptive transmissions, within a 1GHz signal spectrum region, of a 2.03Gb/s in-phase OOFDM channel and a 1.41Gb/s quadrature-phase OOFDM channel over a 25km SSMF IMDD system. In addition, detailed experimental explorations are also undertaken of key physical mechanisms limiting the maximum achievable transmission performance, impacts of transceiver's channel multiplexing/demultiplexing operations on the system BER performance, and the feasibility of utilizing adaptive modulation to combat impairments associated with low-complexity digital filter designs. Furthermore, experimental results indicate that the transceiver incorporating a fixed digital orthogonal filter DSP architecture can be made transparent to various signal modulation formats up to 64-QAM.

Collaborative Filtering Recommendation on Users' Interest Sequences.

PubMed

Cheng, Weijie; Yin, Guisheng; Dong, Yuxin; Dong, Hongbin; Zhang, Wansong

2016-01-01

As an important factor for improving recommendations, time information has been introduced to model users' dynamic preferences in many papers. However, the sequence of users' behaviour is rarely studied in recommender systems. Due to the users' unique behavior evolution patterns and personalized interest transitions among items, users' similarity in sequential dimension should be introduced to further distinguish users' preferences and interests. In this paper, we propose a new collaborative filtering recommendation method based on users' interest sequences (IS) that rank users' ratings or other online behaviors according to the timestamps when they occurred. This method extracts the semantics hidden in the interest sequences by the length of users' longest common sub-IS (LCSIS) and the count of users' total common sub-IS (ACSIS). Then, these semantics are utilized to obtain users' IS-based similarities and, further, to refine the similarities acquired from traditional collaborative filtering approaches. With these updated similarities, transition characteristics and dynamic evolution patterns of users' preferences are considered. Our new proposed method was compared with state-of-the-art time-aware collaborative filtering algorithms on datasets MovieLens, Flixster and Ciao. The experimental results validate that the proposed recommendation method is effective and outperforms several existing algorithms in the accuracy of rating prediction.

Collaborative Filtering Recommendation on Users’ Interest Sequences

PubMed Central

Cheng, Weijie; Yin, Guisheng; Dong, Yuxin; Dong, Hongbin; Zhang, Wansong

2016-01-01

As an important factor for improving recommendations, time information has been introduced to model users’ dynamic preferences in many papers. However, the sequence of users’ behaviour is rarely studied in recommender systems. Due to the users’ unique behavior evolution patterns and personalized interest transitions among items, users’ similarity in sequential dimension should be introduced to further distinguish users’ preferences and interests. In this paper, we propose a new collaborative filtering recommendation method based on users’ interest sequences (IS) that rank users’ ratings or other online behaviors according to the timestamps when they occurred. This method extracts the semantics hidden in the interest sequences by the length of users’ longest common sub-IS (LCSIS) and the count of users’ total common sub-IS (ACSIS). Then, these semantics are utilized to obtain users’ IS-based similarities and, further, to refine the similarities acquired from traditional collaborative filtering approaches. With these updated similarities, transition characteristics and dynamic evolution patterns of users’ preferences are considered. Our new proposed method was compared with state-of-the-art time-aware collaborative filtering algorithms on datasets MovieLens, Flixster and Ciao. The experimental results validate that the proposed recommendation method is effective and outperforms several existing algorithms in the accuracy of rating prediction. PMID:27195787

Kalman filter-based EM-optical sensor fusion for needle deflection estimation.

PubMed

Jiang, Baichuan; Gao, Wenpeng; Kacher, Daniel; Nevo, Erez; Fetics, Barry; Lee, Thomas C; Jayender, Jagadeesan

2018-04-01

In many clinical procedures such as cryoablation that involves needle insertion, accurate placement of the needle's tip at the desired target is the major issue for optimizing the treatment and minimizing damage to the neighboring anatomy. However, due to the interaction force between the needle and tissue, considerable error in intraoperative tracking of the needle tip can be observed as needle deflects. In this paper, measurements data from an optical sensor at the needle base and a magnetic resonance (MR) gradient field-driven electromagnetic (EM) sensor placed 10 cm from the needle tip are used within a model-integrated Kalman filter-based sensor fusion scheme. Bending model-based estimations and EM-based direct estimation are used as the measurement vectors in the Kalman filter, thus establishing an online estimation approach. Static tip bending experiments show that the fusion method can reduce the mean error of the tip position estimation from 29.23 mm of the optical sensor-based approach to 3.15 mm of the fusion-based approach and from 39.96 to 6.90 mm, at the MRI isocenter and the MRI entrance, respectively. This work established a novel sensor fusion scheme that incorporates model information, which enables real-time tracking of needle deflection with MRI compatibility, in a free-hand operating setup.

Filtering Access to Internet Content at Higher Education Institutions: Stakeholder Perceptions and Their Impact on Research and Academic Freedom

ERIC Educational Resources Information Center

Orenstein, David I.

2009-01-01

Hardware and software filters, which sift through keywords placed in Internet search engines and online databases, work to limit the return of information from these sources. By their very purpose, filters exist to decrease the amount of information researchers can access. The purpose of this study is to gain insight into the perceptions key…

New-style defect inspection system of film

NASA Astrophysics Data System (ADS)

Liang, Yan; Liu, Wenyao; Liu, Ming; Lee, Ronggang

2002-09-01

An inspection system has been developed for on-line detection of film defects, which bases on combination of photoelectric imaging and digital image processing. The system runs in high speed of maximum 60m/min. Moving film is illuminated by LED array which emits even infrared (peak wavelength λp=940nm), and infrared images are obtained with a high quality and high speed CCD camera. The application software based on Visual C++6.0 under Windows processes images in real time by means of such algorithms as median filter, edge detection and projection, etc. The system is made up of four modules, which are introduced in detail in the paper. On-line experiment results shows that the inspection system can recognize defects precisely in high speed and run reliably in practical application.

Binaural noise reduction via cue-preserving MMSE filter and adaptive-blocking-based noise PSD estimation

NASA Astrophysics Data System (ADS)

Azarpour, Masoumeh; Enzner, Gerald

2017-12-01

Binaural noise reduction, with applications for instance in hearing aids, has been a very significant challenge. This task relates to the optimal utilization of the available microphone signals for the estimation of the ambient noise characteristics and for the optimal filtering algorithm to separate the desired speech from the noise. The additional requirements of low computational complexity and low latency further complicate the design. A particular challenge results from the desired reconstruction of binaural speech input with spatial cue preservation. The latter essentially diminishes the utility of multiple-input/single-output filter-and-sum techniques such as beamforming. In this paper, we propose a comprehensive and effective signal processing configuration with which most of the aforementioned criteria can be met suitably. This relates especially to the requirement of efficient online adaptive processing for noise estimation and optimal filtering while preserving the binaural cues. Regarding noise estimation, we consider three different architectures: interaural (ITF), cross-relation (CR), and principal-component (PCA) target blocking. An objective comparison with two other noise PSD estimation algorithms demonstrates the superiority of the blocking-based noise estimators, especially the CR-based and ITF-based blocking architectures. Moreover, we present a new noise reduction filter based on minimum mean-square error (MMSE), which belongs to the class of common gain filters, hence being rigorous in terms of spatial cue preservation but also efficient and competitive for the acoustic noise reduction task. A formal real-time subjective listening test procedure is also developed in this paper. The proposed listening test enables a real-time assessment of the proposed computationally efficient noise reduction algorithms in a realistic acoustic environment, e.g., considering time-varying room impulse responses and the Lombard effect. The listening test outcome reveals that the signals processed by the blocking-based algorithms are significantly preferred over the noisy signal in terms of instantaneous noise attenuation. Furthermore, the listening test data analysis confirms the conclusions drawn based on the objective evaluation.

Online Artifact Removal for Brain-Computer Interfaces Using Support Vector Machines and Blind Source Separation

PubMed Central

Halder, Sebastian; Bensch, Michael; Mellinger, Jürgen; Bogdan, Martin; Kübler, Andrea; Birbaumer, Niels; Rosenstiel, Wolfgang

2007-01-01

We propose a combination of blind source separation (BSS) and independent component analysis (ICA) (signal decomposition into artifacts and nonartifacts) with support vector machines (SVMs) (automatic classification) that are designed for online usage. In order to select a suitable BSS/ICA method, three ICA algorithms (JADE, Infomax, and FastICA) and one BSS algorithm (AMUSE) are evaluated to determine their ability to isolate electromyographic (EMG) and electrooculographic (EOG) artifacts into individual components. An implementation of the selected BSS/ICA method with SVMs trained to classify EMG and EOG artifacts, which enables the usage of the method as a filter in measurements with online feedback, is described. This filter is evaluated on three BCI datasets as a proof-of-concept of the method. PMID:18288259

Online artifact removal for brain-computer interfaces using support vector machines and blind source separation.

PubMed

Halder, Sebastian; Bensch, Michael; Mellinger, Jürgen; Bogdan, Martin; Kübler, Andrea; Birbaumer, Niels; Rosenstiel, Wolfgang

2007-01-01

We propose a combination of blind source separation (BSS) and independent component analysis (ICA) (signal decomposition into artifacts and nonartifacts) with support vector machines (SVMs) (automatic classification) that are designed for online usage. In order to select a suitable BSS/ICA method, three ICA algorithms (JADE, Infomax, and FastICA) and one BSS algorithm (AMUSE) are evaluated to determine their ability to isolate electromyographic (EMG) and electrooculographic (EOG) artifacts into individual components. An implementation of the selected BSS/ICA method with SVMs trained to classify EMG and EOG artifacts, which enables the usage of the method as a filter in measurements with online feedback, is described. This filter is evaluated on three BCI datasets as a proof-of-concept of the method.

An e-Learning Collaborative Filtering Approach to Suggest Problems to Solve in Programming Online Judges

ERIC Educational Resources Information Center

Toledo, Raciel Yera; Mota, Yailé Caballero

2014-01-01

The paper proposes a recommender system approach to cover online judge's domains. Online judges are e-learning tools that support the automatic evaluation of programming tasks done by individual users, and for this reason they are usually used for training students in programming contest and for supporting basic programming teachings. The…

TomoEED: Fast Edge-Enhancing Denoising of Tomographic Volumes.

PubMed

Moreno, J J; Martínez-Sánchez, A; Martínez, J A; Garzón, E M; Fernández, J J

2018-05-29

TomoEED is an optimized software tool for fast feature-preserving noise filtering of large 3D tomographic volumes on CPUs and GPUs. The tool is based on the anisotropic nonlinear diffusion method. It has been developed with special emphasis in the reduction of the computational demands by using different strategies, from the algorithmic to the high performance computing perspectives. TomoEED manages to filter large volumes in a matter of minutes in standard computers. TomoEED has been developed in C. It is available for Linux platforms at http://www.cnb.csic.es/%7ejjfernandez/tomoeed. gmartin@ual.es, JJ.Fernandez@csic.es. Supplementary data are available at Bioinformatics online.

An FPGA-based demodulation system for fiber Bragg grating sensing

NASA Astrophysics Data System (ADS)

Li, Yongqian; He, Haitao; Yao, Guozhen

2010-11-01

This paper introduces the principle of fiber Bragg grating (FBG) sensor, designs and realizes a compact wavelength demodulation system for FBG sensing using a Fabry-Perot (F-P) filter. FPGA is adopted as a main controller to control a D/A converter to produce a sawtooth wave for driving the F-P filter, and to design the data acquisition circuit for collecting the output signals of photoelectric detector. The collected data is processed after transmitting to PC through the data transmission circuit, and then the demodulation of FBG wavelength is completed finally. This compact FBG wavelength demodulation system is expected to have wide applications in on-line monitoring of electric power equipment and large structures.

A sensor fusion method for tracking vertical velocity and height based on inertial and barometric altimeter measurements.

PubMed

Sabatini, Angelo Maria; Genovese, Vincenzo

2014-07-24

A sensor fusion method was developed for vertical channel stabilization by fusing inertial measurements from an Inertial Measurement Unit (IMU) and pressure altitude measurements from a barometric altimeter integrated in the same device (baro-IMU). An Extended Kalman Filter (EKF) estimated the quaternion from the sensor frame to the navigation frame; the sensed specific force was rotated into the navigation frame and compensated for gravity, yielding the vertical linear acceleration; finally, a complementary filter driven by the vertical linear acceleration and the measured pressure altitude produced estimates of height and vertical velocity. A method was also developed to condition the measured pressure altitude using a whitening filter, which helped to remove the short-term correlation due to environment-dependent pressure changes from raw pressure altitude. The sensor fusion method was implemented to work on-line using data from a wireless baro-IMU and tested for the capability of tracking low-frequency small-amplitude vertical human-like motions that can be critical for stand-alone inertial sensor measurements. Validation tests were performed in different experimental conditions, namely no motion, free-fall motion, forced circular motion and squatting. Accurate on-line tracking of height and vertical velocity was achieved, giving confidence to the use of the sensor fusion method for tracking typical vertical human motions: velocity Root Mean Square Error (RMSE) was in the range 0.04-0.24 m/s; height RMSE was in the range 5-68 cm, with statistically significant performance gains when the whitening filter was used by the sensor fusion method to track relatively high-frequency vertical motions.

Unscented Kalman Filter-Trained Neural Networks for Slip Model Prediction

PubMed Central

Li, Zhencai; Wang, Yang; Liu, Zhen

2016-01-01

The purpose of this work is to investigate the accurate trajectory tracking control of a wheeled mobile robot (WMR) based on the slip model prediction. Generally, a nonholonomic WMR may increase the slippage risk, when traveling on outdoor unstructured terrain (such as longitudinal and lateral slippage of wheels). In order to control a WMR stably and accurately under the effect of slippage, an unscented Kalman filter and neural networks (NNs) are applied to estimate the slip model in real time. This method exploits the model approximating capabilities of nonlinear state–space NN, and the unscented Kalman filter is used to train NN’s weights online. The slip parameters can be estimated and used to predict the time series of deviation velocity, which can be used to compensate control inputs of a WMR. The results of numerical simulation show that the desired trajectory tracking control can be performed by predicting the nonlinear slip model. PMID:27467703

An algorithm of adaptive scale object tracking in occlusion

NASA Astrophysics Data System (ADS)

Zhao, Congmei

2017-05-01

Although the correlation filter-based trackers achieve the competitive results both on accuracy and robustness, there are still some problems in handling scale variations, object occlusion, fast motions and so on. In this paper, a multi-scale kernel correlation filter algorithm based on random fern detector was proposed. The tracking task was decomposed into the target scale estimation and the translation estimation. At the same time, the Color Names features and HOG features were fused in response level to further improve the overall tracking performance of the algorithm. In addition, an online random fern classifier was trained to re-obtain the target after the target was lost. By comparing with some algorithms such as KCF, DSST, TLD, MIL, CT and CSK, experimental results show that the proposed approach could estimate the object state accurately and handle the object occlusion effectively.

On-line estimation and detection of abnormal substrate concentrations in WWTPs using a software sensor: a benchmark study.

PubMed

Benazzi, F; Gernaey, K V; Jeppsson, U; Katebi, R

2007-08-01

In this paper, a new approach for on-line monitoring and detection of abnormal readily biodegradable substrate (S(s)) and slowly biodegradable substrate (X(s)) concentrations, for example due to input of toxic loads from the sewer, or due to influent substrate shock load, is proposed. Considering that measurements of S(s) and X(s) concentrations are not available in real wastewater treatment plants, the S(s) / X(s) software sensor can activate an alarm with a response time of about 60 and 90 minutes, respectively, based on the dissolved oxygen measurement. The software sensor implementation is based on an extended Kalman filter observer and disturbances are modelled using fast Fourier transform and spectrum analyses. Three case studies are described. The first one illustrates the fast and accurate convergence of the extended Kalman filter algorithm, which is achieved in less than 2 hours. Furthermore, the difficulties of estimating X(s) when off-line analysis is not available are depicted, and the S(s) / X(s) software sensor performances when no measurements of S(s) and X(s) are available are illustrated. Estimation problems related to the death-regeneration concept of the activated sludge model no.1 and possible application of the software sensor in wastewater monitoring are discussed.

A GPU-Based Wide-Band Radio Spectrometer

NASA Astrophysics Data System (ADS)

Chennamangalam, Jayanth; Scott, Simon; Jones, Glenn; Chen, Hong; Ford, John; Kepley, Amanda; Lorimer, D. R.; Nie, Jun; Prestage, Richard; Roshi, D. Anish; Wagner, Mark; Werthimer, Dan

2014-12-01

The graphics processing unit has become an integral part of astronomical instrumentation, enabling high-performance online data reduction and accelerated online signal processing. In this paper, we describe a wide-band reconfigurable spectrometer built using an off-the-shelf graphics processing unit card. This spectrometer, when configured as a polyphase filter bank, supports a dual-polarisation bandwidth of up to 1.1 GHz (or a single-polarisation bandwidth of up to 2.2 GHz) on the latest generation of graphics processing units. On the other hand, when configured as a direct fast Fourier transform, the spectrometer supports a dual-polarisation bandwidth of up to 1.4 GHz (or a single-polarisation bandwidth of up to 2.8 GHz).

VizieR Online Data Catalog: Flux conversion factors for the Swift/UVOT filters (Brown+, 2016)

NASA Astrophysics Data System (ADS)

Brown, P. J.; Breeveld, A.; Roming, P. W. A.; Siegel, M.

2016-10-01

The conversion of observed magnitudes (or the actual observed photon or electron count rates) to a flux density is one of the most fundamental calculations. The flux conversions factors for the six Swift/UVOT filters are tabulated in Table1. (1 data file).

Inference of Spatio-Temporal Functions Over Graphs via Multikernel Kriged Kalman Filtering

NASA Astrophysics Data System (ADS)

Ioannidis, Vassilis N.; Romero, Daniel; Giannakis, Georgios B.

2018-06-01

Inference of space-time varying signals on graphs emerges naturally in a plethora of network science related applications. A frequently encountered challenge pertains to reconstructing such dynamic processes, given their values over a subset of vertices and time instants. The present paper develops a graph-aware kernel-based kriged Kalman filter that accounts for the spatio-temporal variations, and offers efficient online reconstruction, even for dynamically evolving network topologies. The kernel-based learning framework bypasses the need for statistical information by capitalizing on the smoothness that graph signals exhibit with respect to the underlying graph. To address the challenge of selecting the appropriate kernel, the proposed filter is combined with a multi-kernel selection module. Such a data-driven method selects a kernel attuned to the signal dynamics on-the-fly within the linear span of a pre-selected dictionary. The novel multi-kernel learning algorithm exploits the eigenstructure of Laplacian kernel matrices to reduce computational complexity. Numerical tests with synthetic and real data demonstrate the superior reconstruction performance of the novel approach relative to state-of-the-art alternatives.

VariantBam: filtering and profiling of next-generational sequencing data using region-specific rules.

PubMed

Wala, Jeremiah; Zhang, Cheng-Zhong; Meyerson, Matthew; Beroukhim, Rameen

2016-07-01

We developed VariantBam, a C ++ read filtering and profiling tool for use with BAM, CRAM and SAM sequencing files. VariantBam provides a flexible framework for extracting sequencing reads or read-pairs that satisfy combinations of rules, defined by any number of genomic intervals or variant sites. We have implemented filters based on alignment data, sequence motifs, regional coverage and base quality. For example, VariantBam achieved a median size reduction ratio of 3.1:1 when applied to 10 lung cancer whole genome BAMs by removing large tags and selecting for only high-quality variant-supporting reads and reads matching a large dictionary of sequence motifs. Thus VariantBam enables efficient storage of sequencing data while preserving the most relevant information for downstream analysis. VariantBam and full documentation are available at github.com/jwalabroad/VariantBam rameen@broadinstitute.org Supplementary data are available at Bioinformatics online. © The Author 2016. Published by Oxford University Press. All rights reserved. For Permissions, please e-mail: journals.permissions@oup.com.

Multisensor-based real-time quality monitoring by means of feature extraction, selection and modeling for Al alloy in arc welding

NASA Astrophysics Data System (ADS)

Zhang, Zhifen; Chen, Huabin; Xu, Yanling; Zhong, Jiyong; Lv, Na; Chen, Shanben

2015-08-01

Multisensory data fusion-based online welding quality monitoring has gained increasing attention in intelligent welding process. This paper mainly focuses on the automatic detection of typical welding defect for Al alloy in gas tungsten arc welding (GTAW) by means of analzing arc spectrum, sound and voltage signal. Based on the developed algorithms in time and frequency domain, 41 feature parameters were successively extracted from these signals to characterize the welding process and seam quality. Then, the proposed feature selection approach, i.e., hybrid fisher-based filter and wrapper was successfully utilized to evaluate the sensitivity of each feature and reduce the feature dimensions. Finally, the optimal feature subset with 19 features was selected to obtain the highest accuracy, i.e., 94.72% using established classification model. This study provides a guideline for feature extraction, selection and dynamic modeling based on heterogeneous multisensory data to achieve a reliable online defect detection system in arc welding.

Remaining useful life assessment of lithium-ion batteries in implantable medical devices

NASA Astrophysics Data System (ADS)

Hu, Chao; Ye, Hui; Jain, Gaurav; Schmidt, Craig

2018-01-01

This paper presents a prognostic study on lithium-ion batteries in implantable medical devices, in which a hybrid data-driven/model-based method is employed for remaining useful life assessment. The method is developed on and evaluated against data from two sets of lithium-ion prismatic cells used in implantable applications exhibiting distinct fade performance: 1) eight cells from Medtronic, PLC whose rates of capacity fade appear to be stable and gradually decrease over a 10-year test duration; and 2) eight cells from Manufacturer X whose rates appear to be greater and show sharp increase after some period over a 1.8-year test duration. The hybrid method enables online prediction of remaining useful life for predictive maintenance/control. It consists of two modules: 1) a sparse Bayesian learning module (data-driven) for inferring capacity from charge-related features; and 2) a recursive Bayesian filtering module (model-based) for updating empirical capacity fade models and predicting remaining useful life. A generic particle filter is adopted to implement recursive Bayesian filtering for the cells from the first set, whose capacity fade behavior can be represented by a single fade model; a multiple model particle filter with fixed-lag smoothing is proposed for the cells from the second data set, whose capacity fade behavior switches between multiple fade models.

Unscented predictive variable structure filter for satellite attitude estimation with model errors when using low precision sensors

NASA Astrophysics Data System (ADS)

Cao, Lu; Li, Hengnian

2016-10-01

For the satellite attitude estimation problem, the serious model errors always exist and hider the estimation performance of the Attitude Determination and Control System (ACDS), especially for a small satellite with low precision sensors. To deal with this problem, a new algorithm for the attitude estimation, referred to as the unscented predictive variable structure filter (UPVSF) is presented. This strategy is proposed based on the variable structure control concept and unscented transform (UT) sampling method. It can be implemented in real time with an ability to estimate the model errors on-line, in order to improve the state estimation precision. In addition, the model errors in this filter are not restricted only to the Gaussian noises; therefore, it has the advantages to deal with the various kinds of model errors or noises. It is anticipated that the UT sampling strategy can further enhance the robustness and accuracy of the novel UPVSF. Numerical simulations show that the proposed UPVSF is more effective and robustness in dealing with the model errors and low precision sensors compared with the traditional unscented Kalman filter (UKF).

Hierarchical detection of red lesions in retinal images by multiscale correlation filtering

NASA Astrophysics Data System (ADS)

Zhang, Bob; Wu, Xiangqian; You, Jane; Li, Qin; Karray, Fakhri

2009-02-01

This paper presents an approach to the computer aided diagnosis (CAD) of diabetic retinopathy (DR) -- a common and severe complication of long-term diabetes which damages the retina and cause blindness. Since red lesions are regarded as the first signs of DR, there has been extensive research on effective detection and localization of these abnormalities in retinal images. In contrast to existing algorithms, a new approach based on Multiscale Correlation Filtering (MSCF) and dynamic thresholding is developed. This consists of two levels, Red Lesion Candidate Detection (coarse level) and True Red Lesion Detection (fine level). The approach was evaluated using data from Retinopathy On-line Challenge (ROC) competition website and we conclude our method to be effective and efficient.

Autonomous detection of crowd anomalies in multiple-camera surveillance feeds

NASA Astrophysics Data System (ADS)

Nordlöf, Jonas; Andersson, Maria

2016-10-01

A novel approach for autonomous detection of anomalies in crowded environments is presented in this paper. The proposed models uses a Gaussian mixture probability hypothesis density (GM-PHD) filter as feature extractor in conjunction with different Gaussian mixture hidden Markov models (GM-HMMs). Results, based on both simulated and recorded data, indicate that this method can track and detect anomalies on-line in individual crowds through multiple camera feeds in a crowded environment.

TH-E-BRE-04: An Online Replanning Algorithm for VMAT

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

Ahunbay, E; Li, X; Moreau, M

2014-06-15

Purpose: To develop a fast replanning algorithm based on segment aperture morphing (SAM) for online replanning of volumetric modulated arc therapy (VMAT) with flattening filtered (FF) and flattening filter free (FFF) beams. Methods: A software tool was developed to interface with a VMAT planning system ((Monaco, Elekta), enabling the output of detailed beam/machine parameters of original VMAT plans generated based on planning CTs for FF or FFF beams. A SAM algorithm, previously developed for fixed-beam IMRT, was modified to allow the algorithm to correct for interfractional variations (e.g., setup error, organ motion and deformation) by morphing apertures based on themore » geometric relationship between the beam's eye view of the anatomy from the planning CT and that from the daily CT for each control point. The algorithm was tested using daily CTs acquired using an in-room CT during daily IGRT for representative prostate cancer cases along with their planning CTs. The algorithm allows for restricted MLC leaf travel distance between control points of the VMAT delivery to prevent SAM from increasing leaf travel, and therefore treatment delivery time. Results: The VMAT plans adapted to the daily CT by SAM were found to improve the dosimetry relative to the IGRT repositioning plans for both FF and FFF beams. For the adaptive plans, the changes in leaf travel distance between control points were < 1cm for 80% of the control points with no restriction. When restricted to the original plans' maximum travel distance, the dosimetric effect was minimal. The adaptive plans were delivered successfully with similar delivery times as the original plans. The execution of the SAM algorithm was < 10 seconds. Conclusion: The SAM algorithm can quickly generate deliverable online-adaptive VMAT plans based on the anatomy of the day for both FF and FFF beams.« less

Technical Note: A fast online adaptive replanning method for VMAT using flattening filter free beams

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

Ates, Ozgur; Ahunbay, Ergun E.; Li, X. Allen, E-mail: ali@mcw.edu

Purpose: To develop a fast replanning algorithm based on segment aperture morphing (SAM) for online replanning of volumetric modulated arc therapy (VMAT) with flattening filter free (FFF) beams. Methods: A software tool was developed to interface with a VMAT research planning system, which enables the input and output of beam and machine parameters of VMAT plans. The SAM algorithm was used to modify multileaf collimator positions for each segment aperture based on the changes of the target from the planning (CT/MR) to daily image [CT/CBCT/magnetic resonance imaging (MRI)]. The leaf travel distance was controlled for large shifts to prevent themore » increase of VMAT delivery time. The SAM algorithm was tested for 11 patient cases including prostate, pancreatic, and lung cancers. For each daily image set, three types of VMAT plans, image-guided radiation therapy (IGRT) repositioning, SAM adaptive, and full-scope reoptimization plans, were generated and compared. Results: The SAM adaptive plans were found to have improved the plan quality in target and/or critical organs when compared to the IGRT repositioning plans and were comparable to the reoptimization plans based on the data of planning target volume (PTV)-V100 (volume covered by 100% of prescription dose). For the cases studied, the average PTV-V100 was 98.85% ± 1.13%, 97.61% ± 1.45%, and 92.84% ± 1.61% with FFF beams for the reoptimization, SAM adaptive, and repositioning plans, respectively. The execution of the SAM algorithm takes less than 10 s using 16-CPU (2.6 GHz dual core) hardware. Conclusions: The SAM algorithm can generate adaptive VMAT plans using FFF beams with comparable plan qualities as those from the full-scope reoptimization plans based on daily CT/CBCT/MRI and can be used for online replanning to address interfractional variations.« less

An effective trust-based recommendation method using a novel graph clustering algorithm

NASA Astrophysics Data System (ADS)

Moradi, Parham; Ahmadian, Sajad; Akhlaghian, Fardin

2015-10-01

Recommender systems are programs that aim to provide personalized recommendations to users for specific items (e.g. music, books) in online sharing communities or on e-commerce sites. Collaborative filtering methods are important and widely accepted types of recommender systems that generate recommendations based on the ratings of like-minded users. On the other hand, these systems confront several inherent issues such as data sparsity and cold start problems, caused by fewer ratings against the unknowns that need to be predicted. Incorporating trust information into the collaborative filtering systems is an attractive approach to resolve these problems. In this paper, we present a model-based collaborative filtering method by applying a novel graph clustering algorithm and also considering trust statements. In the proposed method first of all, the problem space is represented as a graph and then a sparsest subgraph finding algorithm is applied on the graph to find the initial cluster centers. Then, the proposed graph clustering algorithm is performed to obtain the appropriate users/items clusters. Finally, the identified clusters are used as a set of neighbors to recommend unseen items to the current active user. Experimental results based on three real-world datasets demonstrate that the proposed method outperforms several state-of-the-art recommender system methods.

An Examination of Internet Filtering and Safety Policy Trends and Issues in South Carolina's K-12 Public Schools

ERIC Educational Resources Information Center

Vicks, Mary E.

2013-01-01

School districts have implemented filtering and safety policies in response to legislative and social mandates to protect students from the proliferation of objectionable online content. Subject related literature suggests these policies are more restrictive than legal mandates require and are adversely affecting information access and…

A Sensor Fusion Method for Tracking Vertical Velocity and Height Based on Inertial and Barometric Altimeter Measurements

PubMed Central

Sabatini, Angelo Maria; Genovese, Vincenzo

2014-01-01

A sensor fusion method was developed for vertical channel stabilization by fusing inertial measurements from an Inertial Measurement Unit (IMU) and pressure altitude measurements from a barometric altimeter integrated in the same device (baro-IMU). An Extended Kalman Filter (EKF) estimated the quaternion from the sensor frame to the navigation frame; the sensed specific force was rotated into the navigation frame and compensated for gravity, yielding the vertical linear acceleration; finally, a complementary filter driven by the vertical linear acceleration and the measured pressure altitude produced estimates of height and vertical velocity. A method was also developed to condition the measured pressure altitude using a whitening filter, which helped to remove the short-term correlation due to environment-dependent pressure changes from raw pressure altitude. The sensor fusion method was implemented to work on-line using data from a wireless baro-IMU and tested for the capability of tracking low-frequency small-amplitude vertical human-like motions that can be critical for stand-alone inertial sensor measurements. Validation tests were performed in different experimental conditions, namely no motion, free-fall motion, forced circular motion and squatting. Accurate on-line tracking of height and vertical velocity was achieved, giving confidence to the use of the sensor fusion method for tracking typical vertical human motions: velocity Root Mean Square Error (RMSE) was in the range 0.04–0.24 m/s; height RMSE was in the range 5–68 cm, with statistically significant performance gains when the whitening filter was used by the sensor fusion method to track relatively high-frequency vertical motions. PMID:25061835

Spacecraft attitude determination using a second-order nonlinear filter

NASA Technical Reports Server (NTRS)

Vathsal, S.

1987-01-01

The stringent attitude determination accuracy and faster slew maneuver requirements demanded by present-day spacecraft control systems motivate the development of recursive nonlinear filters for attitude estimation. This paper presents the second-order filter development for the estimation of attitude quaternion using three-axis gyro and star tracker measurement data. Performance comparisons have been made by computer simulation of system models and filter mechanization. It is shown that the second-order filter consistently performs better than the extended Kalman filter when the performance index of the root sum square estimation error of the quaternion vector is compared. The second-order filter identifies the gyro drift rates faster than the extended Kalman filter. The uniqueness of this algorithm is the online generation of the time-varying process and measurement noise covariance matrices, derived as a function or the process and measurement nonlinearity, respectively.

DEAP-3600 Data Acquisition System

NASA Astrophysics Data System (ADS)

Lindner, Thomas

2015-12-01

DEAP-3600 is a dark matter experiment using liquid argon to detect Weakly Interacting Massive Particles (WIMPs). The DEAP-3600 Data Acquisition (DAQ) has been built using a combination of commercial and custom electronics, organized using the MIDAS framework. The DAQ system needs to suppress a high rate of background events from 39Ar beta decays. This suppression is implemented using a combination of online firmware and software-based event filtering. We will report on progress commissioning the DAQ system, as well as the development of the web-based user interface.

Interactive Visualization of DGA Data Based on Multiple Views

NASA Astrophysics Data System (ADS)

Geng, Yujie; Lin, Ying; Ma, Yan; Guo, Zhihong; Gu, Chao; Wang, Mingtao

2017-01-01

The commission and operation of dissolved gas analysis (DGA) online monitoring makes up for the weakness of traditional DGA method. However, volume and high-dimensional DGA data brings a huge challenge for monitoring and analysis. In this paper, we present a novel interactive visualization model of DGA data based on multiple views. This model imitates multi-angle analysis by combining parallel coordinates, scatter plot matrix and data table. By offering brush, collaborative filter and focus + context technology, this model provides a convenient and flexible interactive way to analyze and understand the DGA data.

Active impulsive noise control using maximum correntropy with adaptive kernel size

NASA Astrophysics Data System (ADS)

Lu, Lu; Zhao, Haiquan

2017-03-01

The active noise control (ANC) based on the principle of superposition is an attractive method to attenuate the noise signals. However, the impulsive noise in the ANC systems will degrade the performance of the controller. In this paper, a filtered-x recursive maximum correntropy (FxRMC) algorithm is proposed based on the maximum correntropy criterion (MCC) to reduce the effect of outliers. The proposed FxRMC algorithm does not requires any priori information of the noise characteristics and outperforms the filtered-x least mean square (FxLMS) algorithm for impulsive noise. Meanwhile, in order to adjust the kernel size of FxRMC algorithm online, a recursive approach is proposed through taking into account the past estimates of error signals over a sliding window. Simulation and experimental results in the context of active impulsive noise control demonstrate that the proposed algorithms achieve much better performance than the existing algorithms in various noise environments.

Ensembles of adaptive spatial filters increase BCI performance: an online evaluation

NASA Astrophysics Data System (ADS)

Sannelli, Claudia; Vidaurre, Carmen; Müller, Klaus-Robert; Blankertz, Benjamin

2016-08-01

Objective: In electroencephalographic (EEG) data, signals from distinct sources within the brain are widely spread by volume conduction and superimposed such that sensors receive mixtures of a multitude of signals. This reduction of spatial information strongly hampers single-trial analysis of EEG data as, for example, required for brain-computer interfacing (BCI) when using features from spontaneous brain rhythms. Spatial filtering techniques are therefore greatly needed to extract meaningful information from EEG. Our goal is to show, in online operation, that common spatial pattern patches (CSPP) are valuable to counteract this problem. Approach: Even though the effect of spatial mixing can be encountered by spatial filters, there is a trade-off between performance and the requirement of calibration data. Laplacian derivations do not require calibration data at all, but their performance for single-trial classification is limited. Conversely, data-driven spatial filters, such as common spatial patterns (CSP), can lead to highly distinctive features; however they require a considerable amount of training data. Recently, we showed in an offline analysis that CSPP can establish a valuable compromise. In this paper, we confirm these results in an online BCI study. In order to demonstrate the paramount feature that CSPP requires little training data, we used them in an adaptive setting with 20 participants and focused on users who did not have success with previous BCI approaches. Main results: The results of the study show that CSPP adapts faster and thereby allows users to achieve better feedback within a shorter time than previous approaches performed with Laplacian derivations and CSP filters. The success of the experiment highlights that CSPP has the potential to further reduce BCI inefficiency. Significance: CSPP are a valuable compromise between CSP and Laplacian filters. They allow users to attain better feedback within a shorter time and thus reduce BCI inefficiency to one-fourth in comparison to previous non-adaptive paradigms.

Ensembles of adaptive spatial filters increase BCI performance: an online evaluation.

PubMed

Sannelli, Claudia; Vidaurre, Carmen; Müller, Klaus-Robert; Blankertz, Benjamin

2016-08-01

In electroencephalographic (EEG) data, signals from distinct sources within the brain are widely spread by volume conduction and superimposed such that sensors receive mixtures of a multitude of signals. This reduction of spatial information strongly hampers single-trial analysis of EEG data as, for example, required for brain-computer interfacing (BCI) when using features from spontaneous brain rhythms. Spatial filtering techniques are therefore greatly needed to extract meaningful information from EEG. Our goal is to show, in online operation, that common spatial pattern patches (CSPP) are valuable to counteract this problem. Even though the effect of spatial mixing can be encountered by spatial filters, there is a trade-off between performance and the requirement of calibration data. Laplacian derivations do not require calibration data at all, but their performance for single-trial classification is limited. Conversely, data-driven spatial filters, such as common spatial patterns (CSP), can lead to highly distinctive features; however they require a considerable amount of training data. Recently, we showed in an offline analysis that CSPP can establish a valuable compromise. In this paper, we confirm these results in an online BCI study. In order to demonstrate the paramount feature that CSPP requires little training data, we used them in an adaptive setting with 20 participants and focused on users who did not have success with previous BCI approaches. The results of the study show that CSPP adapts faster and thereby allows users to achieve better feedback within a shorter time than previous approaches performed with Laplacian derivations and CSP filters. The success of the experiment highlights that CSPP has the potential to further reduce BCI inefficiency. CSPP are a valuable compromise between CSP and Laplacian filters. They allow users to attain better feedback within a shorter time and thus reduce BCI inefficiency to one-fourth in comparison to previous non-adaptive paradigms.

Architecture for Knowledge-Based and Federated Search of Online Clinical Evidence

PubMed Central

Walther, Martin; Nguyen, Ken; Lovell, Nigel H

2005-01-01

Background It is increasingly difficult for clinicians to keep up-to-date with the rapidly growing biomedical literature. Online evidence retrieval methods are now seen as a core tool to support evidence-based health practice. However, standard search engine technology is not designed to manage the many different types of evidence sources that are available or to handle the very different information needs of various clinical groups, who often work in widely different settings. Objectives The objectives of this paper are (1) to describe the design considerations and system architecture of a wrapper-mediator approach to federate search system design, including the use of knowledge-based, meta-search filters, and (2) to analyze the implications of system design choices on performance measurements. Methods A trial was performed to evaluate the technical performance of a federated evidence retrieval system, which provided access to eight distinct online resources, including e-journals, PubMed, and electronic guidelines. The Quick Clinical system architecture utilized a universal query language to reformulate queries internally and utilized meta-search filters to optimize search strategies across resources. We recruited 227 family physicians from across Australia who used the system to retrieve evidence in a routine clinical setting over a 4-week period. The total search time for a query was recorded, along with the duration of individual queries sent to different online resources. Results Clinicians performed 1662 searches over the trial. The average search duration was 4.9 ± 3.2 s (N = 1662 searches). Mean search duration to the individual sources was between 0.05 s and 4.55 s. Average system time (ie, system overhead) was 0.12 s. Conclusions The relatively small system overhead compared to the average time it takes to perform a search for an individual source shows that the system achieves a good trade-off between performance and reliability. Furthermore, despite the additional effort required to incorporate the capabilities of each individual source (to improve the quality of search results), system maintenance requires only a small additional overhead. PMID:16403716

Understanding user intents in online health forums.

PubMed

Zhang, Thomas; Cho, Jason H D; Zhai, Chengxiang

2015-07-01

Online health forums provide a convenient way for patients to obtain medical information and connect with physicians and peers outside of clinical settings. However, large quantities of unstructured and diversified content generated on these forums make it difficult for users to digest and extract useful information. Understanding user intents would enable forums to find and recommend relevant information to users by filtering out threads that do not match particular intents. In this paper, we derive a taxonomy of intents to capture user information needs in online health forums and propose novel pattern-based features for use with a multiclass support vector machine (SVM) classifier to classify original thread posts according to their underlying intents. Since no dataset existed for this task, we employ three annotators to manually label a dataset of 1192 HealthBoards posts spanning four forum topics. Experimental results show that a SVM using pattern-based features is highly capable of identifying user intents in forum posts, reaching a maximum precision of 75%, and that a SVM-based hierarchical classifier using both pattern and word features outperforms its SVM counterpart that uses only word features. Furthermore, comparable classification performance can be achieved by training and testing on posts from different forum topics.

Combined micro-droplet and thin-film-assisted pre-concentration of lead traces for on-line monitoring using anodic stripping voltammetry.

PubMed

Belostotsky, Inessa; Gridin, Vladimir V; Schechter, Israel; Yarnitzky, Chaim N

2003-02-01

An improved analytical method for airborne lead traces is reported. It is based on using a Venturi scrubber sampling device for simultaneous thin-film stripping and droplet entrapment of aerosol influxes. At least threefold enhancement of the lead-trace pre-concentration is achieved. The sampled traces are analyzed by square-wave anodic stripping voltammetry. The method was tested by a series of pilot experiments. These were performed using contaminant-controlled air intakes. Reproducible calibration plots were obtained. The data were validated by traditional analysis using filter sampling. LODs are comparable with the conventional techniques. The method was successfully applied to on-line and in situ environmental monitoring of lead.

Particle identification algorithms for the PANDA Endcap Disc DIRC

NASA Astrophysics Data System (ADS)

Schmidt, M.; Ali, A.; Belias, A.; Dzhygadlo, R.; Gerhardt, A.; Götzen, K.; Kalicy, G.; Krebs, M.; Lehmann, D.; Nerling, F.; Patsyuk, M.; Peters, K.; Schepers, G.; Schmitt, L.; Schwarz, C.; Schwiening, J.; Traxler, M.; Böhm, M.; Eyrich, W.; Lehmann, A.; Pfaffinger, M.; Uhlig, F.; Düren, M.; Etzelmüller, E.; Föhl, K.; Hayrapetyan, A.; Kreutzfeld, K.; Merle, O.; Rieke, J.; Wasem, T.; Achenbach, P.; Cardinali, M.; Hoek, M.; Lauth, W.; Schlimme, S.; Sfienti, C.; Thiel, M.

2017-12-01

The Endcap Disc DIRC has been developed to provide an excellent particle identification for the future PANDA experiment by separating pions and kaons up to a momentum of 4 GeV/c with a separation power of 3 standard deviations in the polar angle region from 5o to 22o. This goal will be achieved using dedicated particle identification algorithms based on likelihood methods and will be applied in an offline analysis and online event filtering. This paper evaluates the resulting PID performance using Monte-Carlo simulations to study basic single track PID as well as the analysis of complex physics channels. The online reconstruction algorithm has been tested with a Virtex4 FGPA card and optimized regarding the resulting constraints.

Visual identification and similarity measures used for on-line motion planning of autonomous robots in unknown environments

NASA Astrophysics Data System (ADS)

Martínez, Fredy; Martínez, Fernando; Jacinto, Edwar

2017-02-01

In this paper we propose an on-line motion planning strategy for autonomous robots in dynamic and locally observable environments. In this approach, we first visually identify geometric shapes in the environment by filtering images. Then, an ART-2 network is used to establish the similarity between patterns. The proposed algorithm allows that a robot establish its relative location in the environment, and define its navigation path based on images of the environment and its similarity to reference images. This is an efficient and minimalist method that uses the similarity of landmark view patterns to navigate to the desired destination. Laboratory tests on real prototypes demonstrate the performance of the algorithm.

Empirical Analysis of Exploiting Review Helpfulness for Extractive Summarization of Online Reviews

ERIC Educational Resources Information Center

Xiong, Wenting; Litman, Diane

2014-01-01

We propose a novel unsupervised extractive approach for summarizing online reviews by exploiting review helpfulness ratings. In addition to using the helpfulness ratings for review-level filtering, we suggest using them as the supervision of a topic model for sentence-level content scoring. The proposed method is metadata-driven, requiring no…

Long-term object tracking combined offline with online learning

NASA Astrophysics Data System (ADS)

Hu, Mengjie; Wei, Zhenzhong; Zhang, Guangjun

2016-04-01

We propose a simple yet effective method for long-term object tracking. Different from the traditional visual tracking method, which mainly depends on frame-to-frame correspondence, we combine high-level semantic information with low-level correspondences. Our framework is formulated in a confidence selection framework, which allows our system to recover from drift and partly deal with occlusion. To summarize, our algorithm can be roughly decomposed into an initialization stage and a tracking stage. In the initialization stage, an offline detector is trained to get the object appearance information at the category level, which is used for detecting the potential target and initializing the tracking stage. The tracking stage consists of three modules: the online tracking module, detection module, and decision module. A pretrained detector is used for maintaining drift of the online tracker, while the online tracker is used for filtering out false positive detections. A confidence selection mechanism is proposed to optimize the object location based on the online tracker and detection. If the target is lost, the pretrained detector is utilized to reinitialize the whole algorithm when the target is relocated. During experiments, we evaluate our method on several challenging video sequences, and it demonstrates huge improvement compared with detection and online tracking only.

Mississippi State University Center for Air Sea Technology. FY93 and FY 94 Research Program in Navy Ocean Modeling and Prediction

DTIC Science & Technology

1994-09-30

relational versus object oriented DBMS, knowledge discovery, data models, rnetadata, data filtering, clustering techniques, and synthetic data. A secondary...The first was the investigation of Al/ES Lapplications (knowledge discovery, data mining, and clustering ). Here CAST collabo.rated with Dr. Fred Petry...knowledge discovery system based on clustering techniques; implemented an on-line data browser to the DBMS; completed preliminary efforts to apply object

Sensor Fusion Based on an Integrated Neural Network and Probability Density Function (PDF) Dual Kalman Filter for On-Line Estimation of Vehicle Parameters and States.

PubMed

Vargas-Melendez, Leandro; Boada, Beatriz L; Boada, Maria Jesus L; Gauchia, Antonio; Diaz, Vicente

2017-04-29

Vehicles with a high center of gravity (COG), such as light trucks and heavy vehicles, are prone to rollover. This kind of accident causes nearly 33 % of all deaths from passenger vehicle crashes. Nowadays, these vehicles are incorporating roll stability control (RSC) systems to improve their safety. Most of the RSC systems require the vehicle roll angle as a known input variable to predict the lateral load transfer. The vehicle roll angle can be directly measured by a dual antenna global positioning system (GPS), but it is expensive. For this reason, it is important to estimate the vehicle roll angle from sensors installed onboard in current vehicles. On the other hand, the knowledge of the vehicle's parameters values is essential to obtain an accurate vehicle response. Some of vehicle parameters cannot be easily obtained and they can vary over time. In this paper, an algorithm for the simultaneous on-line estimation of vehicle's roll angle and parameters is proposed. This algorithm uses a probability density function (PDF)-based truncation method in combination with a dual Kalman filter (DKF), to guarantee that both vehicle's states and parameters are within bounds that have a physical meaning, using the information obtained from sensors mounted on vehicles. Experimental results show the effectiveness of the proposed algorithm.

Sensor Fusion Based on an Integrated Neural Network and Probability Density Function (PDF) Dual Kalman Filter for On-Line Estimation of Vehicle Parameters and States

PubMed Central

Vargas-Melendez, Leandro; Boada, Beatriz L.; Boada, Maria Jesus L.; Gauchia, Antonio; Diaz, Vicente

2017-01-01

Vehicles with a high center of gravity (COG), such as light trucks and heavy vehicles, are prone to rollover. This kind of accident causes nearly 33% of all deaths from passenger vehicle crashes. Nowadays, these vehicles are incorporating roll stability control (RSC) systems to improve their safety. Most of the RSC systems require the vehicle roll angle as a known input variable to predict the lateral load transfer. The vehicle roll angle can be directly measured by a dual antenna global positioning system (GPS), but it is expensive. For this reason, it is important to estimate the vehicle roll angle from sensors installed onboard in current vehicles. On the other hand, the knowledge of the vehicle’s parameters values is essential to obtain an accurate vehicle response. Some of vehicle parameters cannot be easily obtained and they can vary over time. In this paper, an algorithm for the simultaneous on-line estimation of vehicle’s roll angle and parameters is proposed. This algorithm uses a probability density function (PDF)-based truncation method in combination with a dual Kalman filter (DKF), to guarantee that both vehicle’s states and parameters are within bounds that have a physical meaning, using the information obtained from sensors mounted on vehicles. Experimental results show the effectiveness of the proposed algorithm. PMID:28468252

Continuous single pulse resolved measurement of beam diameters at 200 kHz using optical transmission filters

NASA Astrophysics Data System (ADS)

Fruechtenicht, Johannes; Letsch, Andreas; Voss, Andreas; Abdou Ahmed, Marwan; Graf, Thomas

2012-02-01

We present a novel laser beam measurement setup which allows the determination of the beam diameter for each single pulse of a pulsed laser beam at repetition rates of up to 200 kHz. This is useful for online process-parameter control e.g. in micromachining or for laser source characterization. Basically, the developed instrument combines spatial transmission filters specially designed for instantaneous optical determination of the second order moments of the lateral intensity distribution of the light beam and photodiodes coupled to customized electronics. The acquisition is computer-based, enabling real-time operation for online monitoring or control. It also allows data storage for a later analysis and visualization of the measurement results. The single-pulse resolved beam diameter can be measured and recorded without any interruption for an unlimited number of pulses. It is only limited by the capacity of the data storage means. In our setup a standard PC and hard-disk provided 2 hours uninterrupted operation and recording of varying beam diameters at 200 kHz. This is about three orders of magnitude faster than other systems. To calibrate our device we performed experiments in cw and pulsed regimes and the obtained results were compared to those obtained with a commercial camera based system. Only minor deviations of the beam diameter values between the two instruments were observed, proving the reliability of our approach.

Kalman and particle filtering methods for full vehicle and tyre identification

NASA Astrophysics Data System (ADS)

Bogdanski, Karol; Best, Matthew C.

2018-05-01

This paper considers identification of all significant vehicle handling dynamics of a test vehicle, including identification of a combined-slip tyre model, using only those sensors currently available on most vehicle controller area network buses. Using an appropriately simple but efficient model structure, all of the independent parameters are found from test vehicle data, with the resulting model accuracy demonstrated on independent validation data. The paper extends previous work on augmented Kalman Filter state estimators to concentrate wholly on parameter identification. It also serves as a review of three alternative filtering methods; identifying forms of the unscented Kalman filter, extended Kalman filter and particle filter are proposed and compared for effectiveness, complexity and computational efficiency. All three filters are suited to applications of system identification and the Kalman Filters can also operate in real-time in on-line model predictive controllers or estimators.

An adaptive morphological gradient lifting wavelet for detecting bearing defects

NASA Astrophysics Data System (ADS)

Li, Bing; Zhang, Pei-lin; Mi, Shuang-shan; Hu, Ren-xi; Liu, Dong-sheng

2012-05-01

This paper presents a novel wavelet decomposition scheme, named adaptive morphological gradient lifting wavelet (AMGLW), for detecting bearing defects. The adaptability of the AMGLW consists in that the scheme can select between two filters, mean the average filter and morphological gradient filter, to update the approximation signal based on the local gradient of the analyzed signal. Both a simulated signal and vibration signals acquired from bearing are employed to evaluate and compare the proposed AMGLW scheme with the traditional linear wavelet transform (LWT) and another adaptive lifting wavelet (ALW) developed in literature. Experimental results reveal that the AMGLW outperforms the LW and ALW obviously for detecting bearing defects. The impulsive components can be enhanced and the noise can be depressed simultaneously by the presented AMGLW scheme. Thus the fault characteristic frequencies of bearing can be clearly identified. Furthermore, the AMGLW gets an advantage over LW in computation efficiency. It is quite suitable for online condition monitoring of bearings and other rotating machineries.

Innovative Monitoring of Atmospheric Gaseous Hydrogen Fluoride

PubMed Central

Bonari, Alessandro; Pompilio, Ilenia; Monti, Alessandro; Arcangeli, Giulio

2016-01-01

Hydrogen fluoride (HF) is a basic raw material for a wide variety of industrial products, with a worldwide production capacity of more than three million metric tonnes. A novel method for determining particulate fluoride and gaseous hydrogen fluoride in air is presented herewith. Air was sampled using miniaturised 13 mm Swinnex two-stage filter holders in a medium-flow pumping system and through the absorption of particulate fluoride and HF vapours on cellulose ester filters uncoated or impregnated with sodium carbonate. Furthermore, filter desorption from the holders and the extraction of the pentafluorobenzyl ester derivative based on solid-phase microextraction were performed using an innovative robotic system installed on an xyz autosampler on-line with gas chromatography (GC)/mass spectrometry (MS). After generating atmospheres of a known concentration of gaseous HF, we evaluated the agreement between the results of our sampling method and those of the conventional preassembled 37 mm cassette (±8.10%; correlation coefficient: 0.90). In addition, precision (relative standard deviation for n = 10, 4.3%), sensitivity (0.2 μg/filter), and linearity (2.0–4000 μg/filter; correlation coefficient: 0.9913) were also evaluated. This procedure combines the efficiency of GC/MS systems with the high throughput (96 samples/day) and the quantitative accuracy of pentafluorobenzyl bromide on-sample derivatisation. PMID:27829835

Adaptive optimal stochastic state feedback control of resistive wall modes in tokamaks

NASA Astrophysics Data System (ADS)

Sun, Z.; Sen, A. K.; Longman, R. W.

2006-01-01

An adaptive optimal stochastic state feedback control is developed to stabilize the resistive wall mode (RWM) instability in tokamaks. The extended least-square method with exponential forgetting factor and covariance resetting is used to identify (experimentally determine) the time-varying stochastic system model. A Kalman filter is used to estimate the system states. The estimated system states are passed on to an optimal state feedback controller to construct control inputs. The Kalman filter and the optimal state feedback controller are periodically redesigned online based on the identified system model. This adaptive controller can stabilize the time-dependent RWM in a slowly evolving tokamak discharge. This is accomplished within a time delay of roughly four times the inverse of the growth rate for the time-invariant model used.

Adaptive Optimal Stochastic State Feedback Control of Resistive Wall Modes in Tokamaks

NASA Astrophysics Data System (ADS)

Sun, Z.; Sen, A. K.; Longman, R. W.

2007-06-01

An adaptive optimal stochastic state feedback control is developed to stabilize the resistive wall mode (RWM) instability in tokamaks. The extended least square method with exponential forgetting factor and covariance resetting is used to identify the time-varying stochastic system model. A Kalman filter is used to estimate the system states. The estimated system states are passed on to an optimal state feedback controller to construct control inputs. The Kalman filter and the optimal state feedback controller are periodically redesigned online based on the identified system model. This adaptive controller can stabilize the time dependent RWM in a slowly evolving tokamak discharge. This is accomplished within a time delay of roughly four times the inverse of the growth rate for the time-invariant model used.

Robust Indoor Human Activity Recognition Using Wireless Signals.

PubMed

Wang, Yi; Jiang, Xinli; Cao, Rongyu; Wang, Xiyang

2015-07-15

Wireless signals-based activity detection and recognition technology may be complementary to the existing vision-based methods, especially under the circumstance of occlusions, viewpoint change, complex background, lighting condition change, and so on. This paper explores the properties of the channel state information (CSI) of Wi-Fi signals, and presents a robust indoor daily human activity recognition framework with only one pair of transmission points (TP) and access points (AP). First of all, some indoor human actions are selected as primitive actions forming a training set. Then, an online filtering method is designed to make actions' CSI curves smooth and allow them to contain enough pattern information. Each primitive action pattern can be segmented from the outliers of its multi-input multi-output (MIMO) signals by a proposed segmentation method. Lastly, in online activities recognition, by selecting proper features and Support Vector Machine (SVM) based multi-classification, activities constituted by primitive actions can be recognized insensitive to the locations, orientations, and speeds.

Multi-vehicle detection with identity awareness using cascade Adaboost and Adaptive Kalman filter for driver assistant system.

PubMed

Wang, Baofeng; Qi, Zhiquan; Chen, Sizhong; Liu, Zhaodu; Ma, Guocheng

2017-01-01

Vision-based vehicle detection is an important issue for advanced driver assistance systems. In this paper, we presented an improved multi-vehicle detection and tracking method using cascade Adaboost and Adaptive Kalman filter(AKF) with target identity awareness. A cascade Adaboost classifier using Haar-like features was built for vehicle detection, followed by a more comprehensive verification process which could refine the vehicle hypothesis in terms of both location and dimension. In vehicle tracking, each vehicle was tracked with independent identity by an Adaptive Kalman filter in collaboration with a data association approach. The AKF adaptively adjusted the measurement and process noise covariance through on-line stochastic modelling to compensate the dynamics changes. The data association correctly assigned different detections with tracks using global nearest neighbour(GNN) algorithm while considering the local validation. During tracking, a temporal context based track management was proposed to decide whether to initiate, maintain or terminate the tracks of different objects, thus suppressing the sparse false alarms and compensating the temporary detection failures. Finally, the proposed method was tested on various challenging real roads, and the experimental results showed that the vehicle detection performance was greatly improved with higher accuracy and robustness.

Kalman/Map filtering-aided fast normalized cross correlation-based Wi-Fi fingerprinting location sensing.

PubMed

Sun, Yongliang; Xu, Yubin; Li, Cheng; Ma, Lin

2013-11-13

A Kalman/map filtering (KMF)-aided fast normalized cross correlation (FNCC)-based Wi-Fi fingerprinting location sensing system is proposed in this paper. Compared with conventional neighbor selection algorithms that calculate localization results with received signal strength (RSS) mean samples, the proposed FNCC algorithm makes use of all the on-line RSS samples and reference point RSS variations to achieve higher fingerprinting accuracy. The FNCC computes efficiently while maintaining the same accuracy as the basic normalized cross correlation. Additionally, a KMF is also proposed to process fingerprinting localization results. It employs a new map matching algorithm to nonlinearize the linear location prediction process of Kalman filtering (KF) that takes advantage of spatial proximities of consecutive localization results. With a calibration model integrated into an indoor map, the map matching algorithm corrects unreasonable prediction locations of the KF according to the building interior structure. Thus, more accurate prediction locations are obtained. Using these locations, the KMF considerably improves fingerprinting algorithm performance. Experimental results demonstrate that the FNCC algorithm with reduced computational complexity outperforms other neighbor selection algorithms and the KMF effectively improves location sensing accuracy by using indoor map information and spatial proximities of consecutive localization results.

Kalman/Map Filtering-Aided Fast Normalized Cross Correlation-Based Wi-Fi Fingerprinting Location Sensing

PubMed Central

Sun, Yongliang; Xu, Yubin; Li, Cheng; Ma, Lin

2013-01-01

A Kalman/map filtering (KMF)-aided fast normalized cross correlation (FNCC)-based Wi-Fi fingerprinting location sensing system is proposed in this paper. Compared with conventional neighbor selection algorithms that calculate localization results with received signal strength (RSS) mean samples, the proposed FNCC algorithm makes use of all the on-line RSS samples and reference point RSS variations to achieve higher fingerprinting accuracy. The FNCC computes efficiently while maintaining the same accuracy as the basic normalized cross correlation. Additionally, a KMF is also proposed to process fingerprinting localization results. It employs a new map matching algorithm to nonlinearize the linear location prediction process of Kalman filtering (KF) that takes advantage of spatial proximities of consecutive localization results. With a calibration model integrated into an indoor map, the map matching algorithm corrects unreasonable prediction locations of the KF according to the building interior structure. Thus, more accurate prediction locations are obtained. Using these locations, the KMF considerably improves fingerprinting algorithm performance. Experimental results demonstrate that the FNCC algorithm with reduced computational complexity outperforms other neighbor selection algorithms and the KMF effectively improves location sensing accuracy by using indoor map information and spatial proximities of consecutive localization results. PMID:24233027

Offsetting the Affective Filter: A Classic Grounded Theory Study of Post-Secondary Online Foreign Language Learners

ERIC Educational Resources Information Center

Chametzky, Barry

2013-01-01

With the Internet, foreign language learners can interact more easily with native speakers from other countries than in previous generations. For learners to develop the ability to function in foreign environments, it is vital to understand their experiences in postsecondary online foreign language classes. If educators and educational theorists…

User Centric Job Monitoring - a redesign and novel approach in the STAR experiment

NASA Astrophysics Data System (ADS)

Arkhipkin, D.; Lauret, J.; Zulkarneeva, Y.

2014-06-01

User Centric Monitoring (or UCM) has been a long awaited feature in STAR, whereas programs, workflows and system "events" could be logged, broadcast and later analyzed. UCM allows to collect and filter available job monitoring information from various resources and present it to users in a user-centric view rather than an administrative-centric point of view. The first attempt and implementation of "a" UCM approach was made in STAR 2004 using a log4cxx plug-in back-end and then further evolved with an attempt to push toward a scalable database back-end (2006) and finally using a Web-Service approach (2010, CSW4DB SBIR). The latest showed to be incomplete and not addressing the evolving needs of the experiment where streamlined messages for online (data acquisition) purposes as well as the continuous support for the data mining needs and event analysis need to coexists and unified in a seamless approach. The code also revealed to be hardly maintainable. This paper presents the next evolutionary step of the UCM toolkit, a redesign and redirection of our latest attempt acknowledging and integrating recent technologies and a simpler, maintainable and yet scalable manner. The extended version of the job logging package is built upon three-tier approach based on Task, Job and Event, and features a Web-Service based logging API, a responsive AJAX-powered user interface, and a database back-end relying on MongoDB, which is uniquely suited for STAR needs. In addition, we present details of integration of this logging package with the STAR offline and online software frameworks. Leveraging on the reported experience and work from the ATLAS and CMS experience on using the ESPER engine, we discuss and show how such approach has been implemented in STAR for meta-data event triggering stream processing and filtering. An ESPER based solution seems to fit well into the online data acquisition system where many systems are monitored.

Measuring the Interestingness of Articles in a Limited User Environment

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

Pon, Raymond K.

Search engines, such as Google, assign scores to news articles based on their relevancy to a query. However, not all relevant articles for the query may be interesting to a user. For example, if the article is old or yields little new information, the article would be uninteresting. Relevancy scores do not take into account what makes an article interesting, which varies from user to user. Although methods such as collaborative filtering have been shown to be effective in recommendation systems, in a limited user environment, there are not enough users that would make collaborative filtering effective. A general framework,more » called iScore, is presented for defining and measuring the 'interestingness' of articles, incorporating user-feedback. iScore addresses various aspects of what makes an article interesting, such as topic relevancy, uniqueness, freshness, source reputation, and writing style. It employs various methods to measure these features and uses a classifier operating on these features to recommend articles. The basic iScore configuration is shown to improve recommendation results by as much as 20%. In addition to the basic iScore features, additional features are presented to address the deficiencies of existing feature extractors, such as one that tracks multiple topics, called MTT, and a version of the Rocchio algorithm that learns its parameters online as it processes documents, called eRocchio. The inclusion of both MTT and eRocchio into iScore is shown to improve iScore recommendation results by as much as 3.1% and 5.6%, respectively. Additionally, in TREC11 Adaptive Filter Task, eRocchio is shown to be 10% better than the best filter in the last run of the task. In addition to these two major topic relevancy measures, other features are also introduced that employ language models, phrases, clustering, and changes in topics to improve recommendation results. These additional features are shown to improve recommendation results by iScore by up to 14%. Due to varying reasons that users hold regarding why an article is interesting, an online feature selection method in naive Bayes is also introduced. Online feature selection can improve recommendation results in iScore by up to 18.9%. In summary, iScore in its best configuration can outperform traditional IR techniques by as much as 50.7%. iScore and its components are evaluated in the news recommendation task using three datasets from Yahoo! News, actual users, and Digg. iScore and its components are also evaluated in the TREC Adaptive Filter task using the Reuters RCV1 corpus.« less

RGB-D SLAM Combining Visual Odometry and Extended Information Filter

PubMed Central

Zhang, Heng; Liu, Yanli; Tan, Jindong; Xiong, Naixue

2015-01-01

In this paper, we present a novel RGB-D SLAM system based on visual odometry and an extended information filter, which does not require any other sensors or odometry. In contrast to the graph optimization approaches, this is more suitable for online applications. A visual dead reckoning algorithm based on visual residuals is devised, which is used to estimate motion control input. In addition, we use a novel descriptor called binary robust appearance and normals descriptor (BRAND) to extract features from the RGB-D frame and use them as landmarks. Furthermore, considering both the 3D positions and the BRAND descriptors of the landmarks, our observation model avoids explicit data association between the observations and the map by marginalizing the observation likelihood over all possible associations. Experimental validation is provided, which compares the proposed RGB-D SLAM algorithm with just RGB-D visual odometry and a graph-based RGB-D SLAM algorithm using the publicly-available RGB-D dataset. The results of the experiments demonstrate that our system is quicker than the graph-based RGB-D SLAM algorithm. PMID:26263990

INS/EKF-based stride length, height and direction intent detection for walking assistance robots.

PubMed

Brescianini, Dario; Jung, Jun-Young; Jang, In-Hun; Park, Hyun Sub; Riener, Robert

2011-01-01

We propose an algorithm used to obtain the information on stride length, height difference, and direction based on user's intent during walking. For exoskeleton robots used to assist paraplegic patients' walking, this information is used to generate gait patterns by themselves in on-line. To obtain this information, we attach an inertial measurement unit(IMU) on crutches and apply an extended kalman filter-based error correction method to reduce the phenomena of drift due to bias of the IMU. The proposed method is verifed in real walking scenarios including walking, climbing up-stairs, and changing direction of walking with normal. © 2011 IEEE

A modified NARMAX model-based self-tuner with fault tolerance for unknown nonlinear stochastic hybrid systems with an input-output direct feed-through term.

PubMed

Tsai, Jason S-H; Hsu, Wen-Teng; Lin, Long-Guei; Guo, Shu-Mei; Tann, Joseph W

2014-01-01

A modified nonlinear autoregressive moving average with exogenous inputs (NARMAX) model-based state-space self-tuner with fault tolerance is proposed in this paper for the unknown nonlinear stochastic hybrid system with a direct transmission matrix from input to output. Through the off-line observer/Kalman filter identification method, one has a good initial guess of modified NARMAX model to reduce the on-line system identification process time. Then, based on the modified NARMAX-based system identification, a corresponding adaptive digital control scheme is presented for the unknown continuous-time nonlinear system, with an input-output direct transmission term, which also has measurement and system noises and inaccessible system states. Besides, an effective state space self-turner with fault tolerance scheme is presented for the unknown multivariable stochastic system. A quantitative criterion is suggested by comparing the innovation process error estimated by the Kalman filter estimation algorithm, so that a weighting matrix resetting technique by adjusting and resetting the covariance matrices of parameter estimate obtained by the Kalman filter estimation algorithm is utilized to achieve the parameter estimation for faulty system recovery. Consequently, the proposed method can effectively cope with partially abrupt and/or gradual system faults and input failures by the fault detection. Copyright © 2013 ISA. Published by Elsevier Ltd. All rights reserved.

Information Filtering Based on Users' Negative Opinions

NASA Astrophysics Data System (ADS)

Guo, Qiang; Li, Yang; Liu, Jian-Guo

2013-05-01

The process of heat conduction (HC) has recently found application in the information filtering [Zhang et al., Phys. Rev. Lett.99, 154301 (2007)], which is of high diversity but low accuracy. The classical HC model predicts users' potential interested objects based on their interesting objects regardless to the negative opinions. In terms of the users' rating scores, we present an improved user-based HC (UHC) information model by taking into account users' positive and negative opinions. Firstly, the objects rated by users are divided into positive and negative categories, then the predicted interesting and dislike object lists are generated by the UHC model. Finally, the recommendation lists are constructed by filtering out the dislike objects from the interesting lists. By implementing the new model based on nine similarity measures, the experimental results for MovieLens and Netflix datasets show that the new model considering negative opinions could greatly enhance the accuracy, measured by the average ranking score, from 0.049 to 0.036 for Netflix and from 0.1025 to 0.0570 for Movielens dataset, reduced by 26.53% and 44.39%, respectively. Since users prefer to give positive ratings rather than negative ones, the negative opinions contain much more information than the positive ones, the negative opinions, therefore, are very important for understanding users' online collective behaviors and improving the performance of HC model.

Aircraft engine sensor fault diagnostics using an on-line OBEM update method.

PubMed

Liu, Xiaofeng; Xue, Naiyu; Yuan, Ye

2017-01-01

This paper proposed a method to update the on-line health reference baseline of the On-Board Engine Model (OBEM) to maintain the effectiveness of an in-flight aircraft sensor Fault Detection and Isolation (FDI) system, in which a Hybrid Kalman Filter (HKF) was incorporated. Generated from a rapid in-flight engine degradation, a large health condition mismatch between the engine and the OBEM can corrupt the performance of the FDI. Therefore, it is necessary to update the OBEM online when a rapid degradation occurs, but the FDI system will lose estimation accuracy if the estimation and update are running simultaneously. To solve this problem, the health reference baseline for a nonlinear OBEM was updated using the proposed channel controller method. Simulations based on the turbojet engine Linear-Parameter Varying (LPV) model demonstrated the effectiveness of the proposed FDI system in the presence of substantial degradation, and the channel controller can ensure that the update process finishes without interference from a single sensor fault.

Aircraft engine sensor fault diagnostics using an on-line OBEM update method

PubMed Central

Liu, Xiaofeng; Xue, Naiyu; Yuan, Ye

2017-01-01

This paper proposed a method to update the on-line health reference baseline of the On-Board Engine Model (OBEM) to maintain the effectiveness of an in-flight aircraft sensor Fault Detection and Isolation (FDI) system, in which a Hybrid Kalman Filter (HKF) was incorporated. Generated from a rapid in-flight engine degradation, a large health condition mismatch between the engine and the OBEM can corrupt the performance of the FDI. Therefore, it is necessary to update the OBEM online when a rapid degradation occurs, but the FDI system will lose estimation accuracy if the estimation and update are running simultaneously. To solve this problem, the health reference baseline for a nonlinear OBEM was updated using the proposed channel controller method. Simulations based on the turbojet engine Linear-Parameter Varying (LPV) model demonstrated the effectiveness of the proposed FDI system in the presence of substantial degradation, and the channel controller can ensure that the update process finishes without interference from a single sensor fault. PMID:28182692

Nonlinear predictive control of a boiler-turbine unit: A state-space approach with successive on-line model linearisation and quadratic optimisation.

PubMed

Ławryńczuk, Maciej

2017-03-01

This paper details development of a Model Predictive Control (MPC) algorithm for a boiler-turbine unit, which is a nonlinear multiple-input multiple-output process. The control objective is to follow set-point changes imposed on two state (output) variables and to satisfy constraints imposed on three inputs and one output. In order to obtain a computationally efficient control scheme, the state-space model is successively linearised on-line for the current operating point and used for prediction. In consequence, the future control policy is easily calculated from a quadratic optimisation problem. For state estimation the extended Kalman filter is used. It is demonstrated that the MPC strategy based on constant linear models does not work satisfactorily for the boiler-turbine unit whereas the discussed algorithm with on-line successive model linearisation gives practically the same trajectories as the truly nonlinear MPC controller with nonlinear optimisation repeated at each sampling instant. Copyright © 2017 ISA. Published by Elsevier Ltd. All rights reserved.

Collaborative Filtering for Expansion of Learner's Background Knowledge in Online Language Learning: Does "Top-Down" Processing Improve Vocabulary Proficiency?

ERIC Educational Resources Information Center

Yamada, Masanori; Kitamura, Satoshi; Matsukawa, Hideya; Misono, Tadashi; Kitani, Noriko; Yamauchi, Yuhei

2014-01-01

In recent years, collaborative filtering, a recommendation algorithm that incorporates a user's data such as interest, has received worldwide attention as an advanced learning support system. However, accurate recommendations along with a user's interest cannot be ideal as an effective learning environment. This study aims to develop and…

Removal of correlated noise online for in situ measurements by using multichannel magnetic resonance sounding system

NASA Astrophysics Data System (ADS)

Lin, Tingting; Zhang, Siyuan; Zhang, Yang; Wan, Ling; Lin, Jun

2017-01-01

Compared with the other geophysical approaches, magnetic resonance sounding (MRS) technique is direct and nondestructive in subsurface water exploration. It provides water content distribution and estimates hydrogeological properties. The biggest challenge is that MRS measurement always suffers bad signal-to-noise ratio, and it can be carried out only far from sources of noise. To solve this problem, a series of de-noising methods are developed. However, most of them are post-processing, leading the data quality uncontrolled for in situ measurements. In the present study, a new approach that removal of correlated noise online is found to overcome the restriction. Based on LabVIEW, a method is provided to enable online data quality control by the way of realizing signal acquisition and noise filtering simultaneously. Using one or more reference coils, adaptive noise cancellation based on LabVIEW to eliminate the correlated noise is available for in situ measurements. The approach was examined through numerical simulation and field measurements. The correlated noise is mitigated effectively and the application of MRS measurements is feasible in high-level noise environment. The method shortens the measurement time and improves the measurement efficiency.

Nonlinear dynamic model for visual object tracking on Grassmann manifolds with partial occlusion handling.

PubMed

Khan, Zulfiqar Hasan; Gu, Irene Yu-Hua

2013-12-01

This paper proposes a novel Bayesian online learning and tracking scheme for video objects on Grassmann manifolds. Although manifold visual object tracking is promising, large and fast nonplanar (or out-of-plane) pose changes and long-term partial occlusions of deformable objects in video remain a challenge that limits the tracking performance. The proposed method tackles these problems with the main novelties on: 1) online estimation of object appearances on Grassmann manifolds; 2) optimal criterion-based occlusion handling for online updating of object appearances; 3) a nonlinear dynamic model for both the appearance basis matrix and its velocity; and 4) Bayesian formulations, separately for the tracking process and the online learning process, that are realized by employing two particle filters: one is on the manifold for generating appearance particles and another on the linear space for generating affine box particles. Tracking and online updating are performed in an alternating fashion to mitigate the tracking drift. Experiments using the proposed tracker on videos captured by a single dynamic/static camera have shown robust tracking performance, particularly for scenarios when target objects contain significant nonplanar pose changes and long-term partial occlusions. Comparisons with eight existing state-of-the-art/most relevant manifold/nonmanifold trackers with evaluations have provided further support to the proposed scheme.

Robust Visual Tracking Revisited: From Correlation Filter to Template Matching.

PubMed

Liu, Fanghui; Gong, Chen; Huang, Xiaolin; Zhou, Tao; Yang, Jie; Tao, Dacheng

2018-06-01

In this paper, we propose a novel matching based tracker by investigating the relationship between template matching and the recent popular correlation filter based trackers (CFTs). Compared to the correlation operation in CFTs, a sophisticated similarity metric termed mutual buddies similarity is proposed to exploit the relationship of multiple reciprocal nearest neighbors for target matching. By doing so, our tracker obtains powerful discriminative ability on distinguishing target and background as demonstrated by both empirical and theoretical analyses. Besides, instead of utilizing single template with the improper updating scheme in CFTs, we design a novel online template updating strategy named memory, which aims to select a certain amount of representative and reliable tracking results in history to construct the current stable and expressive template set. This scheme is beneficial for the proposed tracker to comprehensively understand the target appearance variations, recall some stable results. Both qualitative and quantitative evaluations on two benchmarks suggest that the proposed tracking method performs favorably against some recently developed CFTs and other competitive trackers.

A novel cost-effective parallel narrowband ANC system with local secondary-path estimation

NASA Astrophysics Data System (ADS)

Delegà, Riccardo; Bernasconi, Giancarlo; Piroddi, Luigi

2017-08-01

Many noise reduction applications are targeted at multi-tonal disturbances. Active noise control (ANC) solutions for such problems are generally based on the combination of multiple adaptive notch filters. Both the performance and the computational cost are negatively affected by an increase in the number of controlled frequencies. In this work we study a different modeling approach for the secondary path, based on the estimation of various small local models in adjacent frequency subbands, that greatly reduces the impact of reference-filtering operations in the ANC algorithm. Furthermore, in combination with a frequency-specific step size tuning method it provides a balanced attenuation performance over the whole controlled frequency range (and particularly in the high end of the range). Finally, the use of small local models is greatly beneficial for the reactivity of the online secondary path modeling algorithm when the characteristics of the acoustic channels are time-varying. Several simulations are provided to illustrate the positive features of the proposed method compared to other well-known techniques.

VizieR Online Data Catalog: M33 GALEX catalogue of UV point sources (Mudd+, 2015)

NASA Astrophysics Data System (ADS)

Mudd, D.; Stanek, K. Z.

2015-11-01

This catalogue was made using the Ultraviolet Imaging Telescope (UIT), an instrument aboard the Astro-1 Mission. UIT used photographic plates with the B1 and A1 filters roughly corresponding to the FUV and NUV filters of GALEX, having central wavelengths of ~1500 and 2400Å, respectively. It should be noted, however, that the A1 filter is significantly broader than the NUV filter on GALEX, reaching several hundred angstroms to the red end of its GALEX counterpart. The field of view of UIT is also circular but has a smaller radius of 18 arcmin The FWHM of UIT is comparable to that of GALEX, at 4 and 5.2 arcsec in the NUV and FUV filters, respectively. (3 data files).

Online measurement of urea concentration in spent dialysate during hemodialysis.

PubMed

Olesberg, Jonathon T; Arnold, Mark A; Flanigan, Michael J

2004-01-01

We describe online optical measurements of urea in the effluent dialysate line during regular hemodialysis treatment of several patients. Monitoring urea removal can provide valuable information about dialysis efficiency. Spectral measurements were performed with a Fourier-transform infrared spectrometer equipped with a flow-through cell. Spectra were recorded across the 5000-4000 cm(-1) (2.0-2.5 microm) wavelength range at 1-min intervals. Savitzky-Golay filtering was used to remove baseline variations attributable to the temperature dependence of the water absorption spectrum. Urea concentrations were extracted from the filtered spectra by use of partial least-squares regression and the net analyte signal of urea. Urea concentrations predicted by partial least-squares regression matched concentrations obtained from standard chemical assays with a root mean square error of 0.30 mmol/L (0.84 mg/dL urea nitrogen) over an observed concentration range of 0-11 mmol/L. The root mean square error obtained with the net analyte signal of urea was 0.43 mmol/L with a calibration based only on a set of pure-component spectra. The error decreased to 0.23 mmol/L when a slope and offset correction were used. Urea concentrations can be continuously monitored during hemodialysis by near-infrared spectroscopy. Calibrations based on the net analyte signal of urea are particularly appealing because they do not require a training step, as do statistical multivariate calibration procedures such as partial least-squares regression.

An adaptive model for vanadium redox flow battery and its application for online peak power estimation

NASA Astrophysics Data System (ADS)

Wei, Zhongbao; Meng, Shujuan; Tseng, King Jet; Lim, Tuti Mariana; Soong, Boon Hee; Skyllas-Kazacos, Maria

2017-03-01

An accurate battery model is the prerequisite for reliable state estimate of vanadium redox battery (VRB). As the battery model parameters are time varying with operating condition variation and battery aging, the common methods where model parameters are empirical or prescribed offline lacks accuracy and robustness. To address this issue, this paper proposes to use an online adaptive battery model to reproduce the VRB dynamics accurately. The model parameters are online identified with both the recursive least squares (RLS) and the extended Kalman filter (EKF). Performance comparison shows that the RLS is superior with respect to the modeling accuracy, convergence property, and computational complexity. Based on the online identified battery model, an adaptive peak power estimator which incorporates the constraints of voltage limit, SOC limit and design limit of current is proposed to fully exploit the potential of the VRB. Experiments are conducted on a lab-scale VRB system and the proposed peak power estimator is verified with a specifically designed "two-step verification" method. It is shown that different constraints dominate the allowable peak power at different stages of cycling. The influence of prediction time horizon selection on the peak power is also analyzed.

How can we improve online HIV and STD prevention for men who have sex with men? Perspectives of hook-up website owners, website users, and HIV/STD directors.

PubMed

Wohlfeiler, Dan; Hecht, Jennifer; Volk, Jonathan; Fisher Raymond, H; Kennedy, Tom; McFarland, Willi

2013-11-01

Internet-based interventions have potential to reduce HIV and STD transmission among men who meet male sexual partners online. From November 2009 to May 2010 we surveyed dating and hook-up website users (n = 3,050), website owners (n = 18), and health department HIV/STD directors (n = 81) to identify structural and behavioral prevention interventions that could be implemented online and which a majority of website users were willing to use, owners were willing to implement, and HIV/STD directors perceived to be effective. A majority of each of the three stakeholder groups agreed on the following: (1) automated HIV/STD testing reminders, (2) local STD test site directories, (3) links to sex-positive safe sex videos, (4) access to sexual health experts, (5) profile options to include safer sex preference, (6) chat rooms for specific sexual interests, (7) filtering partners by their profile information, and (8) anonymous e-card partner notification for STD exposure. Findings help build consensus about how to prioritize resources for implementing online HIV and STD prevention interventions and highlight differences between stakeholders to guide future discussion about how to advance prevention efforts.

A fiber optic sensor for on-line non-touch monitoring of roll shape

NASA Astrophysics Data System (ADS)

Guo, Yuan; Qu, Weijian; Yuan, Qi

2009-07-01

Basing on the principle of reflective displacement fibre-optic sensor, a high accuracy non-touch on-line optical fibre sensor for detecting roll shape is presented. The principle and composition of the detection system and the operation process are expatiated also. By using a novel probe of three optical fibres in equal transverse space, the effects of fluctuations in the light source, reflective changing of target surface and the intensity losses in the fibre lines are automatically compensated. Meantime, an optical fibre sensor model of correcting static error based on BP artificial neural network (ANN) is set up. Also by using interpolation method and value filtering to process the signals, effectively reduce the influence of random noise and the vibration of the roll bearing. So the accuracy and resolution were enhanced remarkably. Experiment proves that the resolution is 1μm and the precision can reach to 0.1%. So the system reaches to the demand of practical production process.

Optical Fiber On-Line Detection System for Non-Touch Monitoring Roller Shape

NASA Astrophysics Data System (ADS)

Guo, Y.; Wang, Y. T.

2006-10-01

Basing on the principle of reflective displacement fiber-optic sensor, a high accuracy non-touch on-line optical fiber measurement system for roller shape is presented. The principle and composition of the detection system and the operation process are expatiated also. By using a novel probe of three optical fibers in equal transverse space, the effects of fluctuations in the light source, reflective changing of target surface and the intensity losses in the fiber lines are automatically compensated. Meantime, an optical fiber sensor model of correcting static error based on BP artificial neural network (ANN) is set up. Also by using interpolation method and value filtering to process the signals, effectively reduce the influence of random noise and the vibration of the roller bearing. So enhance the accuracy and resolution remarkably. Experiment proves that the accuracy of the system reach to the demand of practical production process, it provides a new method for the high speed, accurate and automatic on line detection of the mill roller shape.

Paleo Data Assimilation of Pseudo-Tree-Ring-Width Chronologies in a Climate Model

NASA Astrophysics Data System (ADS)

Fallah Hassanabadi, B.; Acevedo, W.; Reich, S.; Cubasch, U.

2016-12-01

Using the Time-Averaged Ensemble Kalman Filter (EnKF) and a forward model, we assimilate the pseudo Tree-Ring-Width (TRW) chronologies into an Atmospheric Global Circulation model. This study investigates several aspects of Paleo-Data Assimilation (PDA) within a perfect-model set-up: (i) we test the performance of several forward operators in the framework of a PDA-based climate reconstruction, (ii) compare the PDA-based simulations' skill against the free ensemble runs and (iii) inverstigate the skill of the "online" (with cycling) DA and the "off-line" (no-cycling) DA. In our experiments, the "online" (with cycling) PDA approach did not outperform the "off-line" (no-cycling) one, despite its considerable additional implementation complexity. On the other hand, it was observed that the error reduction achieved by assimilating a particular pseudo-TRW chronology is modulated by the strength of the yearly internal variability of the model at the chronology site. This result might help the dendrochronology community to optimize their sampling efforts.

Online prediction of organileptic data for snack food using color images

NASA Astrophysics Data System (ADS)

Yu, Honglu; MacGregor, John F.

2004-11-01

In this paper, a study for the prediction of organileptic properties of snack food in real-time using RGB color images is presented. The so-called organileptic properties, which are properties based on texture, taste and sight, are generally measured either by human sensory response or by mechanical devices. Neither of these two methods can be used for on-line feedback control in high-speed production. In this situation, a vision-based soft sensor is very attractive. By taking images of the products, the samples remain untouched and the product properties can be predicted in real time from image data. Four types of organileptic properties are considered in this study: blister level, toast points, taste and peak break force. Wavelet transform are applied on the color images and the averaged absolute value for each filtered image is used as texture feature variable. In order to handle the high correlation among the feature variables, Partial Least Squares (PLS) is used to regress the extracted feature variables against the four response variables.

ResistoMap-online visualization of human gut microbiota antibiotic resistome.

PubMed

Yarygin, Konstantin S; Kovarsky, Boris A; Bibikova, Tatyana S; Melnikov, Damir S; Tyakht, Alexander V; Alexeev, Dmitry G

2017-07-15

We created ResistoMap—a Web-based interactive visualization of the presence of genetic determinants conferring resistance to antibiotics, biocides and heavy metals in human gut microbiota. ResistoMap displays the data on more than 1500 published gut metagenomes of world populations including both healthy subjects and patients. Multiparameter display filters allow visual assessment of the associations between the meta-data and proportions of resistome. The geographic map navigation layer allows to state hypotheses regarding the global trends of antibiotic resistance and correlates the gut resistome variations with the national clinical guidelines on antibiotics application. ResistoMap was implemented using AngularJS, CoffeeScript, D3.js and TopoJSON. The tool is publicly available at http://resistomap.rcpcm.org. yarygin@phystech.edu. Supplementary data are available at Bioinformatics online. © The Author(s) 2017. Published by Oxford University Press.

A Robust Adaptive Unscented Kalman Filter for Nonlinear Estimation with Uncertain Noise Covariance.

PubMed

Zheng, Binqi; Fu, Pengcheng; Li, Baoqing; Yuan, Xiaobing

2018-03-07

The Unscented Kalman filter (UKF) may suffer from performance degradation and even divergence while mismatch between the noise distribution assumed as a priori by users and the actual ones in a real nonlinear system. To resolve this problem, this paper proposes a robust adaptive UKF (RAUKF) to improve the accuracy and robustness of state estimation with uncertain noise covariance. More specifically, at each timestep, a standard UKF will be implemented first to obtain the state estimations using the new acquired measurement data. Then an online fault-detection mechanism is adopted to judge if it is necessary to update current noise covariance. If necessary, innovation-based method and residual-based method are used to calculate the estimations of current noise covariance of process and measurement, respectively. By utilizing a weighting factor, the filter will combine the last noise covariance matrices with the estimations as the new noise covariance matrices. Finally, the state estimations will be corrected according to the new noise covariance matrices and previous state estimations. Compared with the standard UKF and other adaptive UKF algorithms, RAUKF converges faster to the actual noise covariance and thus achieves a better performance in terms of robustness, accuracy, and computation for nonlinear estimation with uncertain noise covariance, which is demonstrated by the simulation results.

Multi-vehicle detection with identity awareness using cascade Adaboost and Adaptive Kalman filter for driver assistant system

PubMed Central

Wang, Baofeng; Qi, Zhiquan; Chen, Sizhong; Liu, Zhaodu; Ma, Guocheng

2017-01-01

Vision-based vehicle detection is an important issue for advanced driver assistance systems. In this paper, we presented an improved multi-vehicle detection and tracking method using cascade Adaboost and Adaptive Kalman filter(AKF) with target identity awareness. A cascade Adaboost classifier using Haar-like features was built for vehicle detection, followed by a more comprehensive verification process which could refine the vehicle hypothesis in terms of both location and dimension. In vehicle tracking, each vehicle was tracked with independent identity by an Adaptive Kalman filter in collaboration with a data association approach. The AKF adaptively adjusted the measurement and process noise covariance through on-line stochastic modelling to compensate the dynamics changes. The data association correctly assigned different detections with tracks using global nearest neighbour(GNN) algorithm while considering the local validation. During tracking, a temporal context based track management was proposed to decide whether to initiate, maintain or terminate the tracks of different objects, thus suppressing the sparse false alarms and compensating the temporary detection failures. Finally, the proposed method was tested on various challenging real roads, and the experimental results showed that the vehicle detection performance was greatly improved with higher accuracy and robustness. PMID:28296902

A Robust Adaptive Unscented Kalman Filter for Nonlinear Estimation with Uncertain Noise Covariance

PubMed Central

Zheng, Binqi; Yuan, Xiaobing

2018-01-01

The Unscented Kalman filter (UKF) may suffer from performance degradation and even divergence while mismatch between the noise distribution assumed as a priori by users and the actual ones in a real nonlinear system. To resolve this problem, this paper proposes a robust adaptive UKF (RAUKF) to improve the accuracy and robustness of state estimation with uncertain noise covariance. More specifically, at each timestep, a standard UKF will be implemented first to obtain the state estimations using the new acquired measurement data. Then an online fault-detection mechanism is adopted to judge if it is necessary to update current noise covariance. If necessary, innovation-based method and residual-based method are used to calculate the estimations of current noise covariance of process and measurement, respectively. By utilizing a weighting factor, the filter will combine the last noise covariance matrices with the estimations as the new noise covariance matrices. Finally, the state estimations will be corrected according to the new noise covariance matrices and previous state estimations. Compared with the standard UKF and other adaptive UKF algorithms, RAUKF converges faster to the actual noise covariance and thus achieves a better performance in terms of robustness, accuracy, and computation for nonlinear estimation with uncertain noise covariance, which is demonstrated by the simulation results. PMID:29518960

Implementation of a reduced order Kalman filter to assimilate ocean color data into a coupled physical-biochemical model of the North Aegean Sea.

NASA Astrophysics Data System (ADS)

Kalaroni, Sofia; Tsiaras, Kostas; Economou-Amilli, Athena; Petihakis, George; Politikos, Dimitrios; Triantafyllou, George

2013-04-01

Within the framework of the European project OPEC (Operational Ecology), a data assimilation system was implemented to describe chlorophyll-a concentrations of the North Aegean, as well the impact on the European anchovy (Engraulis encrasicolus) biomass distribution provided by a bioenergetics model, related to the density of three low trophic level functional groups of zooplankton (heterotrophic flagellates, microzooplankton and mesozooplankton). The three-dimensional hydrodynamic-biogeochemical model comprises two on-line coupled sub-models: the Princeton Ocean Model (POM) and the European Regional Seas Ecosystem Model (ERSEM). The assimilation scheme is based on the Singular Evolutive Extended Kalman (SEEK) filter and its variant that uses a fixed correction base (SFEK). For the initialization, SEEK filter uses a reduced order error covariance matrix provided by the dominant Empirical Orthogonal Functions (EOF) of model. The assimilation experiments were performed for year 2003 using SeaWiFS chlorophyll-a data during which the physical model uses the atmospheric forcing obtained from the regional climate model HIRHAM5. The assimilation system is validated by assessing the relevance of the system in fitting the data, the impact of the assimilation on non-observed biochemical parameters and the overall quality of the forecasts.

Filter for on-line air monitor unaffected by radon progeny and method of using same

DOEpatents

Phillips, Terrance D.; Edwards, Howard D.

1999-01-01

An apparatus for testing air having contaminants and radon progeny therein. The apparatus includes a sampling box having an inlet for receiving the air and an outlet for discharging the air. The sampling box includes a filter made of a plate of sintered stainless steel. The filter traps the contaminants, yet allows at least a portion of the radon progeny to pass therethrough. A method of testing air having contaminants and radon progeny therein. The method includes providing a testing apparatus that has a sampling box with an inlet for receiving the air and an outlet for discharging the air, and has a sintered stainless steel filter disposed within said sampling box; drawing air from a source into the sampling box using a vacuum pump; passing the air through the filter; monitoring the contaminants trapped by the filter; and providing an alarm when a selected level of contaminants is reached. The filter traps the contaminants, yet allows at least a portion of the radon progeny to pass therethrough.

VizieR Online Data Catalog: M31 PHAT star clusters ages and masses (Fouesneau+, 2014)

NASA Astrophysics Data System (ADS)

Fouesneau, M.; Johnson, L. C.; Weisz, D. R.; Dalcanton, J. J.; Bell, E. F.; Bianchi, L.; Caldwell, N.; Gouliermis, D. A.; Guhathakurta, P.; Kalirai, J.; Larsen, S. S.; Rix, H.-W.; Seth, A. C.; Skillman, E. D.; Williams, B. F.

2017-07-01

For this paper, we use the list of 601 high-probability cluster candidates from the Johnson et al. (2012, J/ApJ/752/95) Year 1 catalog, which contains integrated photometry through six broadband filters from the UV to the near-infrared: F275W (UV), F336W (U), F475W (g), F814W (I), F110W (J), F160W (H). Clusters were detected by eye, primarily based on the F475W images, and visually classified based on their sizes, shapes, and concentrations as explained in Johnson et al. (2012, J/ApJ/752/95). (1 data file).

VizieR Online Data Catalog: Kepler planetary candidates. V. 3yr Q1-Q12 (Rowe+, 2015)

NASA Astrophysics Data System (ADS)

Rowe, J. F.; Coughlin, J. L.; Antoci, V.; Barclay, T.; Batalha, N. M.; Borucki, W. J.; Burke, C. J.; Bryson, S. T.; Caldwell, D. A.; Campbell, J. R.; Catanzarite, J. H.; Christiansen, J. L.; Cochran, W.; Gilliland, R. L.; Girouard, F. R.; Haas, M. R.; Helminiak, K. G.; Henze, C. E.; Hoffman, K. L.; Howell, S. B.; Huber, D.; Hunter, R. C.; Jang-Condell, H.; Jenkins, J. M.; Klaus, T. C.; Latham, D. W.; Li, J.; Lissauer, J. J.; McCauliff, S. D.; Morris, R. L.; Mullally, F.; Ofir, A.; Quarles, B.; Quintana, E.; Sabale, A.; Seader, S.; Shporer, A.; Smith, J. C.; Steffen, J. H.; Still, M.; Tenenbaum, P.; Thompson, S. E.; Twicken, J. D.; van Laerhoven, C.; Wolfgang, A.; Zamudio, K. A.

2015-04-01

We began with the transit-event candidate list from Tenenbaum et al. (2013ApJS..206....5T) based on a wavelet, adaptive matched filter to search 192313 Kepler targets for periodic drops in flux indicative of a transiting planet. Detections are known as Threshold Crossing Events (TCEs). Tenenbaum et al. utilized three years of Kepler photometric observations (Q1-Q12) -the same data span employed by this study based on SOC 8.3 as part of Data Release 21 (Thompson S. E., Christiansen J. L., Jenkins J. M. et al. Kepler (KSCI-19061-001)). (3 data files).

Efficient FIR Filter Implementations for Multichannel BCIs Using Xilinx System Generator.

PubMed

Ghani, Usman; Wasim, Muhammad; Khan, Umar Shahbaz; Mubasher Saleem, Muhammad; Hassan, Ali; Rashid, Nasir; Islam Tiwana, Mohsin; Hamza, Amir; Kashif, Amir

2018-01-01

Background . Brain computer interface (BCI) is a combination of software and hardware communication protocols that allow brain to control external devices. Main purpose of BCI controlled external devices is to provide communication medium for disabled persons. Now these devices are considered as a new way to rehabilitate patients with impunities. There are certain potentials present in electroencephalogram (EEG) that correspond to specific event. Main issue is to detect such event related potentials online in such a low signal to noise ratio (SNR). In this paper we propose a method that will facilitate the concept of online processing by providing an efficient filtering implementation in a hardware friendly environment by switching to finite impulse response (FIR). Main focus of this research is to minimize latency and computational delay of preprocessing related to any BCI application. Four different finite impulse response (FIR) implementations along with large Laplacian filter are implemented in Xilinx System Generator. Efficiency of 25% is achieved in terms of reduced number of coefficients and multiplications which in turn reduce computational delays accordingly.

Envelope filter sequence to delete blinks and overshoots.

PubMed

Merino, Manuel; Gómez, Isabel María; Molina, Alberto J

2015-05-30

Eye movements have been used in control interfaces and as indicators of somnolence, workload and concentration. Different techniques can be used to detect them: we focus on the electrooculogram (EOG) in which two kinds of interference occur: blinks and overshoots. While they both draw bell-shaped waveforms, blinks are caused by the eyelid, whereas overshoots occur due to target localization error and are placed on saccade. They need to be extracted from the EOG to increase processing effectiveness. This paper describes off- and online processing implementations based on lower envelope for removing bell-shaped noise; they are compared with a 300-ms-median filter. Techniques were analyzed using two kinds of EOG data: those modeled from our own design, and real signals. Using a model signal allowed to compare filtered outputs with ideal data, so that it was possible to quantify processing precision to remove noise caused by blinks, overshoots, and general interferences. We analyzed the ability to delete blinks and overshoots, and waveform preservation. Our technique had a high capacity for reducing interference amplitudes (>97%), even exceeding median filter (MF) results. However, the MF obtained better waveform preservation, with a smaller dependence on fixation width. The proposed technique is better at deleting blinks and overshoots than the MF in model and real EOG signals.

Identification of urinary metabolites of imperatorin with a single run on an LC/Triple TOF system based on multiple mass defect filter data acquisition and multiple data mining techniques.

PubMed

Qiao, Shi; Shi, Xiaowei; Shi, Rui; Liu, Man; Liu, Ting; Zhang, Kerong; Wang, Qiao; Yao, Meicun; Zhang, Lantong

2013-08-01

The detection of drug metabolites, especially for minor metabolites, continues to be a challenge because of the complexity of biological samples. Imperatorin (IMP) is an active natural furocoumarin component originating from many traditional Chinese herbal medicines and is expected to be pursued as a new vasorelaxant agent. In the present study, a generic and efficient approach was developed for the in vivo screening and identification of IMP metabolites using liquid chromatography-Triple TOF mass spectrometry. In this approach, a novel on-line data acquisition method mutiple mass defect filter (MMDF) combined with dynamic background subtraction was developed to trace all probable urinary metabolites of IMP. Comparing with the traditionally intensity-dependent data acquisition method, MMDF method could give the information of low-level metabolites masked by background noise and endogenous components. Thus, the minor metabolites in complex biological matrices could be detected. Then, the sensitive and specific multiple data-mining techniques extracted ion chromatography, mass defect filter, product ion filter, and neutral loss filter were used for the discovery of IMP metabolites. Based on the proposed strategy, 44 phase I and 7 phase II metabolites were identified in rat urine after oral administration of IMP. The results indicated that oxidization was the main metabolic pathway and that different oxidized substituent positions had a significant influence on the fragmentation of the metabolites. Two types of characteristic ions at m/z 203 and 219 can be observed in the MS/MS spectra. This is the first study of IMP metabolism in vivo. The interpretation of the MS/MS spectra of these metabolites and the proposed metabolite pathway provide essential data for further pharmacological studies of other linear-type furocoumarins.

A Novel Online Data-Driven Algorithm for Detecting UAV Navigation Sensor Faults.

PubMed

Sun, Rui; Cheng, Qi; Wang, Guanyu; Ochieng, Washington Yotto

2017-09-29

The use of Unmanned Aerial Vehicles (UAVs) has increased significantly in recent years. On-board integrated navigation sensors are a key component of UAVs' flight control systems and are essential for flight safety. In order to ensure flight safety, timely and effective navigation sensor fault detection capability is required. In this paper, a novel data-driven Adaptive Neuron Fuzzy Inference System (ANFIS)-based approach is presented for the detection of on-board navigation sensor faults in UAVs. Contrary to the classic UAV sensor fault detection algorithms, based on predefined or modelled faults, the proposed algorithm combines an online data training mechanism with the ANFIS-based decision system. The main advantages of this algorithm are that it allows real-time model-free residual analysis from Kalman Filter (KF) estimates and the ANFIS to build a reliable fault detection system. In addition, it allows fast and accurate detection of faults, which makes it suitable for real-time applications. Experimental results have demonstrated the effectiveness of the proposed fault detection method in terms of accuracy and misdetection rate.

VizieR Online Data Catalog: AGNs in submm-selected Lockman Hole galaxies (Serjeant+, 2010)

NASA Astrophysics Data System (ADS)

Serjeant, S.; Negrello, M.; Pearson, C.; Mortier, A.; Austermann, J.; Aretxaga, I.; Clements, D.; Chapman, S.; Dye, S.; Dunlop, J.; Dunne, L.; Farrah, D.; Hughes, D.; Lee, H. M.; Matsuhara, H.; Ibar, E.; Im, M.; Jeong, W.-S.; Kim, S.; Oyabu, S.; Takagi, T.; Wada, T.; Wilson, G.; Vaccari, M.; Yun, M.

2013-11-01

We present a comparison of the SCUBA half degree extragalactic survey (SHADES) at 450μm, 850μm and 1100μm with deep guaranteed time 15μm AKARI FU-HYU survey data and Spitzer guaranteed time data at 3.6-24μm in the Lockman hole east. The AKARI data was analysed using bespoke software based in part on the drizzling and minimum-variance matched filtering developed for SHADES, and was cross-calibrated against ISO fluxes. (2 data files).

Differences in High School and College Students' Basic Knowledge and Perceived Education of Internet Safety: Do High School Students Really Benefit from the Children's Internet Protection Act?

ERIC Educational Resources Information Center

Yan, Zheng

2009-01-01

The Children's Internet Protection Act (CIPA; 2000) requires an Internet filtering and public awareness strategy to protect children under 17 from harmful visual Internet depictions. This study compared high school students who went online with the CIPA restriction and college students who went online without the restriction in order to…

Research on signal processing method for total organic carbon of water quality online monitor

NASA Astrophysics Data System (ADS)

Ma, R.; Xie, Z. X.; Chu, D. Z.; Zhang, S. W.; Cao, X.; Wu, N.

2017-08-01

At present, there is no rapid, stable and effective approach of total organic carbon (TOC) measurement in the Marine environmental online monitoring field. Therefore, this paper proposes an online TOC monitor of chemiluminescence signal processing method. The weak optical signal detected by photomultiplier tube can be enhanced and converted by a series of signal processing module: phase-locked amplifier module, fourth-order band pass filter module and AD conversion module. After a long time of comparison test & measurement, compared with the traditional method, on the premise of sufficient accuracy, this chemiluminescence signal processing method can offer greatly improved measuring speed and high practicability for online monitoring.

VizieR Online Data Catalog: MASCARA-2 b (HD185603) light curves and spectra (Talens+, 2018)

NASA Astrophysics Data System (ADS)

Talens, G. J. J.; Justesen, A. B.; Albrecht, S.; McCormac, J.; van Eylen, V.; Otten, G. P. P. L.; Murgas, F.; Palle, E.; Pollacco, D.; Stuik, R.; Spronck, J. F. P.; Lesage, A.-L.; Grundahl, F.; Fredslund Andersen, M.; Antoci, V.; Snellen, I. A. G.

2018-02-01

Photometric time-series and radial velocities obtained for MASCARA-2 using the MASCARA, NITES, IAC80 and SONG telescopes. The MASCARA photometry covers the period from February 2015 to December 2016 and utilizes no filter. The NITES and IAC80 photometry was obtained during the night of 4 June 2017 utilizing the R filter. The SONG RVs cover the period from May-June 2017. (26 data files).

On the Relationship Between Attention Processing and P300-Based Brain Computer Interface Control in Amyotrophic Lateral Sclerosis

PubMed Central

Riccio, Angela; Schettini, Francesca; Simione, Luca; Pizzimenti, Alessia; Inghilleri, Maurizio; Olivetti-Belardinelli, Marta; Mattia, Donatella; Cincotti, Febo

2018-01-01

Our objective was to investigate the capacity to control a P3-based brain-computer interface (BCI) device for communication and its related (temporal) attention processing in a sample of amyotrophic lateral sclerosis (ALS) patients with respect to healthy subjects. The ultimate goal was to corroborate the role of cognitive mechanisms in event-related potential (ERP)-based BCI control in ALS patients. Furthermore, the possible differences in such attentional mechanisms between the two groups were investigated in order to unveil possible alterations associated with the ALS condition. Thirteen ALS patients and 13 healthy volunteers matched for age and years of education underwent a P3-speller BCI task and a rapid serial visual presentation (RSVP) task. The RSVP task was performed by participants in order to screen their temporal pattern of attentional resource allocation, namely: (i) the temporal attentional filtering capacity (scored as T1%); and (ii) the capability to adequately update the attentive filter in the temporal dynamics of the attentional selection (scored as T2%). For the P3-speller BCI task, the online accuracy and information transfer rate (ITR) were obtained. Centroid Latency and Mean Amplitude of N200 and P300 were also obtained. No significant differences emerged between ALS patients and Controls with regards to online accuracy (p = 0.13). Differently, the performance in controlling the P3-speller expressed as ITR values (calculated offline) were compromised in ALS patients (p < 0.05), with a delay in the latency of P3 when processing BCI stimuli as compared with Control group (p < 0.01). Furthermore, the temporal aspect of attentional filtering which was related to BCI control (r = 0.51; p < 0.05) and to the P3 wave amplitude (r = 0.63; p < 0.05) was also altered in ALS patients (p = 0.01). These findings ground the knowledge required to develop sensible classes of BCI specifically designed by taking into account the influence of the cognitive characteristics of the possible candidates in need of a BCI system for communication. PMID:29892218

IDEOM: an Excel interface for analysis of LC-MS-based metabolomics data.

PubMed

Creek, Darren J; Jankevics, Andris; Burgess, Karl E V; Breitling, Rainer; Barrett, Michael P

2012-04-01

The application of emerging metabolomics technologies to the comprehensive investigation of cellular biochemistry has been limited by bottlenecks in data processing, particularly noise filtering and metabolite identification. IDEOM provides a user-friendly data processing application that automates filtering and identification of metabolite peaks, paying particular attention to common sources of noise and false identifications generated by liquid chromatography-mass spectrometry (LC-MS) platforms. Building on advanced processing tools such as mzMatch and XCMS, it allows users to run a comprehensive pipeline for data analysis and visualization from a graphical user interface within Microsoft Excel, a familiar program for most biological scientists. IDEOM is provided free of charge at http://mzmatch.sourceforge.net/ideom.html, as a macro-enabled spreadsheet (.xlsb). Implementation requires Microsoft Excel (2007 or later). R is also required for full functionality. michael.barrett@glasgow.ac.uk Supplementary data are available at Bioinformatics online.

VizieR Online Data Catalog: KiDS-ESO-DR2 multi-band source catalog (de Jong+, 2015)

NASA Astrophysics Data System (ADS)

de Jong, J. T. A.; Verdoes Kleijn, G. A.; Boxhoorn, D. R.; Buddelmeijer, H.; Capaccioli, M.; Getman, F.; Grado, A.; Helmich, E.; Huang, Z.; Irisarri, N.; Kuijken, K.; La Barbera, F.; McFarland, J. P.; Napolitano, N. R.; Radovich, M.; Sikkema, G.; Valentijn, E. A.; Begeman, K. G.; Brescia, M.; Cavuoti, S.; Choi, A.; Cordes, O.-M.; Covone, G.; Dall'Ora, M.; Hildebrandt, H.; Longo, G.; Nakajima, R.; Paolillo, M.; Puddu, E.; Rifatto, A.; Tortora, C.; van Uitert, E.; Buddendiek, A.; Harnois-Deraps, J.; Erben, T.; Eriksen, M. B.; Heymans, C.; Hoekstra, H.; Joachimi, B.; Kitching, T. D.; Klaes, D.; Koopmans, L. V. E.; Koehlinger, F.; Roy, N.; Sifon, C.; Schneider, P.; Sutherland, W. J.; Viola, M.; Vriend, W.-J.

2016-10-01

KiDS data releases consist of ~1 square degree tiles that have been successfully observed in all four survey filters (u,g,r,i). The second data release (KiDS-ESO-DR2) was available in February 2015 and contains imaging data, masks and single-band source lists for all tiles observed in all four filters for which observations were completed during the second year of regular operations (1 October 2012 to 31 September 2013), a total of 98 tiles. Apart from the data products mentioned above, KiDS-ESO-DR2 also provides a multi-band source catalogue based on the combined set of 148 tiles released in the first two data releases. A complete list of all tiles with data quality parameters can be found on the KiDS website: http://kids.strw.leidenuniv.nl/DR2/ (1 data file).

Design of Instrument Control Software for Solar Vector Magnetograph at Udaipur Solar Observatory

NASA Astrophysics Data System (ADS)

Gosain, Sanjay; Venkatakrishnan, P.; Venugopalan, K.

2004-04-01

A magnetograph is an instrument which makes measurement of solar magnetic field by measuring Zeeman induced polarization in solar spectral lines. In a typical filter based magnetograph there are three main modules namely, polarimeter, narrow-band spectrometer (filter), and imager(CCD camera). For a successful operation of magnetograph it is essential that these modules work in synchronization with each other. Here, we describe the design of instrument control system implemented for the Solar Vector Magnetograph under development at Udaipur Solar Observatory. The control software is written in Visual Basic and exploits the Component Object Model (COM) components for a fast and flexible application development. The user can interact with the instrument modules through a Graphical User Interface (GUI) and can program the sequence of magnetograph operations. The integration of Interactive Data Language (IDL) ActiveX components in the interface provides a powerful tool for online visualization, analysis and processing of images.

An Adaptive Low-Cost GNSS/MEMS-IMU Tightly-Coupled Integration System with Aiding Measurement in a GNSS Signal-Challenged Environment

PubMed Central

Zhou, Qifan; Zhang, Hai; Li, You; Li, Zheng

2015-01-01

The main aim of this paper is to develop a low-cost GNSS/MEMS-IMU tightly-coupled integration system with aiding information that can provide reliable position solutions when the GNSS signal is challenged such that less than four satellites are visible in a harsh environment. To achieve this goal, we introduce an adaptive tightly-coupled integration system with height and heading aiding (ATCA). This approach adopts a novel redundant measurement noise estimation method for an adaptive Kalman filter application and also augments external measurements in the filter to aid the position solutions, as well as uses different filters to deal with various situations. On the one hand, the adaptive Kalman filter makes use of the redundant measurement system’s difference sequence to estimate and tune noise variance instead of employing a traditional innovation sequence to avoid coupling with the state vector error. On the other hand, this method uses the external height and heading angle as auxiliary references and establishes a model for the measurement equation in the filter. In the meantime, it also changes the effective filter online based on the number of tracked satellites. These measures have increasingly enhanced the position constraints and the system observability, improved the computational efficiency and have led to a good result. Both simulated and practical experiments have been carried out, and the results demonstrate that the proposed method is effective at limiting the system errors when there are less than four visible satellites, providing a satisfactory navigation solution. PMID:26393605

An Adaptive Low-Cost GNSS/MEMS-IMU Tightly-Coupled Integration System with Aiding Measurement in a GNSS Signal-Challenged Environment.

PubMed

Zhou, Qifan; Zhang, Hai; Li, You; Li, Zheng

2015-09-18

The main aim of this paper is to develop a low-cost GNSS/MEMS-IMU tightly-coupled integration system with aiding information that can provide reliable position solutions when the GNSS signal is challenged such that less than four satellites are visible in a harsh environment. To achieve this goal, we introduce an adaptive tightly-coupled integration system with height and heading aiding (ATCA). This approach adopts a novel redundant measurement noise estimation method for an adaptive Kalman filter application and also augments external measurements in the filter to aid the position solutions, as well as uses different filters to deal with various situations. On the one hand, the adaptive Kalman filter makes use of the redundant measurement system's difference sequence to estimate and tune noise variance instead of employing a traditional innovation sequence to avoid coupling with the state vector error. On the other hand, this method uses the external height and heading angle as auxiliary references and establishes a model for the measurement equation in the filter. In the meantime, it also changes the effective filter online based on the number of tracked satellites. These measures have increasingly enhanced the position constraints and the system observability, improved the computational efficiency and have led to a good result. Both simulated and practical experiments have been carried out, and the results demonstrate that the proposed method is effective at limiting the system errors when there are less than four visible satellites, providing a satisfactory navigation solution.

Physical and Chemical Aerosol Properties At An Urban and A Rural Site During An Episode of Strong Photochemical Activity During Escompte

NASA Astrophysics Data System (ADS)

van Dingenen, R.; Putaud, J. P.; dell'Acqua, A.; Martins-Dos Santos, S.; Viidanoja, J.; Raes, F.

During the ESCOMPTE campaign (10 June to 14 July, 2001), JRC mobile laboratories for aerosol physical and chemical measurements were deployed at two ground-based sites: Vallon Dol, located at the Northern edge of the Marseille agglomeration and Vi- non, a rural site about 80 km North-East of Marseille. Both sites were equipped with on-line instrumentation for number size distributions in the diameter size range 6nm to 10µm (10 minute time resolution), equivalent black carbon (15 minute time reso- lution), major anions and cations (15 minute time resolution). Time-integrated filter sampling at each site with a time resolution of 6-12 hours was performed with 2 sets of virtual impactors, separating the fine and coarse aerosol fraction. One set, loaded with quartz filters, was analyzed off-line using the `evolved gas analysis` technique for organic and elemental carbon. The second set, loaded with paper filters, was analyzed for dust (by ashing) and ionic composition. On top of the common instrumentation, the urban site was additionally performing on-line PM10 measurements (TEOM with sample equilibration system, 10 minute time resolution) and, during intensive obser- vation periods (IOP), size-segregated sampling with a 8 stage low-pressure Berner im- pactor (6-12 hours time resolution). In this presentation we will focus on data obtained during the second IOP (20-6 to 26-6). During this episode, the sea-breeze transported Marseille pollution plume was clearly observed at the Vinon rural site. Comparison of the aerosol properties at both sites will allow to evaluate the processes that contribute to the (trans)formation of particulate matter in the particular conditions of a marine air mass, mixed with local pollution and undergoing strong photochemical processes during in-land transport.

A handheld laser-induced fluorescence detector for multiple applications.

PubMed

Fang, Xiao-Xia; Li, Han-Yang; Fang, Pan; Pan, Jian-Zhang; Fang, Qun

2016-04-01

In this paper, we present a compact handheld laser-induced fluorescence (LIF) detector based on a 450 nm laser diode and quasi-confocal optical configuration with a total size of 9.1 × 6.2 × 4.1 cm(3). Since there are few reports on the use of 450 nm laser diode in LIF detection, especially in miniaturized LIF detector, we systematically investigated various optical arrangements suitable for the requirements of 450 nm laser diode and system miniaturization, including focusing lens, filter combination, and pinhole, as well as Raman effect of water at 450 nm excitation wavelength. As the result, the handheld LIF detector integrates the light source (450 nm laser diode), optical circuit module (including a 450 nm band-pass filter, a dichroic mirror, a collimating lens, a 525 nm band-pass filter, and a 1.0mm aperture), optical detector (miniaturized photomultiplier tube), as well as electronic module (including signal recording, processing and displaying units). This detector is capable of working independently with a cost of ca. $2000 for the whole instrument. The detection limit of the instrument for sodium fluorescein solution is 0.42 nM (S/N=3). The broad applicability of the present system was demonstrated in capillary electrophoresis separation of fluorescein isothiocyanate (FITC) labeled amino acids and in flow cytometry of tumor cells as an on-line LIF detector, as well as in droplet array chip analysis as a LIF scanner. We expect such a compact LIF detector could be applied in flow analysis systems as an on-line detector, and in field analysis and biosensor analysis as a portable universal LIF detector. Copyright © 2015 Elsevier B.V. All rights reserved.

Towards On-Line Services Based on a Holistic Analysis of Human Activities

NASA Technical Reports Server (NTRS)

Clancey, William J.

2004-01-01

Very often computer scientists view computerization of services in terms of the logistics of human-machine interaction, including establishing a contract, accessing records, and of course designing an interface. But this analysis often moves too quickly to tactical details, failing to frame the entire service in human terms, and not recognizing the mutual learning required to define and relate goals, constraints, and the personalized value of available services. In particular, on-line services that "computerize communication" can be improved by constructing an activity model of what the person is trying to do, not just filtering, comparing, and selling piece-meal services. For example, from the customer s perspective the task of an on-line travel service is not merely to establish confirmed reservations, but to have a complete travel plan, usually integrating many days of transportation, lodging, and recreation into a happy experience. The task of the travel agent is not merely "ticketing", but helping the customer understand what they want and providing services that will connect everything together in an enjoyable way.

GRBOX: A New Online Catalog of GRBs

NASA Astrophysics Data System (ADS)

Perley, Daniel; Kemper, Yvonne

2008-05-01

With Swift detecting close to 100 new gamma-ray bursts each year, cataloging past bursts is more important than ever. To this end, we present a new online database (GRBOX: Gamma Ray Burst Online IndeX) indexing properties of every well-localized GRB since 1997. Unlike existing resources, the information in the catalog is presented to the user in the form of a sortable, filterable table and derives information for each burst from published catalogs as well as the GCN circulars and refereed publications. Short text summaries and links to other resources are also provided for every event.

Attitude determination using an adaptive multiple model filtering Scheme

NASA Technical Reports Server (NTRS)

Lam, Quang; Ray, Surendra N.

1995-01-01

Attitude determination has been considered as a permanent topic of active research and perhaps remaining as a forever-lasting interest for spacecraft system designers. Its role is to provide a reference for controls such as pointing the directional antennas or solar panels, stabilizing the spacecraft or maneuvering the spacecraft to a new orbit. Least Square Estimation (LSE) technique was utilized to provide attitude determination for the Nimbus 6 and G. Despite its poor performance (estimation accuracy consideration), LSE was considered as an effective and practical approach to meet the urgent need and requirement back in the 70's. One reason for this poor performance associated with the LSE scheme is the lack of dynamic filtering or 'compensation'. In other words, the scheme is based totally on the measurements and no attempts were made to model the dynamic equations of motion of the spacecraft. We propose an adaptive filtering approach which employs a bank of Kalman filters to perform robust attitude estimation. The proposed approach, whose architecture is depicted, is essentially based on the latest proof on the interactive multiple model design framework to handle the unknown of the system noise characteristics or statistics. The concept fundamentally employs a bank of Kalman filter or submodel, instead of using fixed values for the system noise statistics for each submodel (per operating condition) as the traditional multiple model approach does, we use an on-line dynamic system noise identifier to 'identify' the system noise level (statistics) and update the filter noise statistics using 'live' information from the sensor model. The advanced noise identifier, whose architecture is also shown, is implemented using an advanced system identifier. To insure the robust performance for the proposed advanced system identifier, it is also further reinforced by a learning system which is implemented (in the outer loop) using neural networks to identify other unknown quantities such as spacecraft dynamics parameters, gyro biases, dynamic disturbances, or environment variations.

Attitude determination using an adaptive multiple model filtering Scheme

NASA Astrophysics Data System (ADS)

Lam, Quang; Ray, Surendra N.

1995-05-01

Attitude determination has been considered as a permanent topic of active research and perhaps remaining as a forever-lasting interest for spacecraft system designers. Its role is to provide a reference for controls such as pointing the directional antennas or solar panels, stabilizing the spacecraft or maneuvering the spacecraft to a new orbit. Least Square Estimation (LSE) technique was utilized to provide attitude determination for the Nimbus 6 and G. Despite its poor performance (estimation accuracy consideration), LSE was considered as an effective and practical approach to meet the urgent need and requirement back in the 70's. One reason for this poor performance associated with the LSE scheme is the lack of dynamic filtering or 'compensation'. In other words, the scheme is based totally on the measurements and no attempts were made to model the dynamic equations of motion of the spacecraft. We propose an adaptive filtering approach which employs a bank of Kalman filters to perform robust attitude estimation. The proposed approach, whose architecture is depicted, is essentially based on the latest proof on the interactive multiple model design framework to handle the unknown of the system noise characteristics or statistics. The concept fundamentally employs a bank of Kalman filter or submodel, instead of using fixed values for the system noise statistics for each submodel (per operating condition) as the traditional multiple model approach does, we use an on-line dynamic system noise identifier to 'identify' the system noise level (statistics) and update the filter noise statistics using 'live' information from the sensor model. The advanced noise identifier, whose architecture is also shown, is implemented using an advanced system identifier. To insure the robust performance for the proposed advanced system identifier, it is also further reinforced by a learning system which is implemented (in the outer loop) using neural networks to identify other unknown quantities such as spacecraft dynamics parameters, gyro biases, dynamic disturbances, or environment variations.

SLIM: an alternative Web interface for MEDLINE/PubMed searches – a preliminary study

PubMed Central

Muin, Michael; Fontelo, Paul; Liu, Fang; Ackerman, Michael

2005-01-01

Background With the rapid growth of medical information and the pervasiveness of the Internet, online search and retrieval systems have become indispensable tools in medicine. The progress of Web technologies can provide expert searching capabilities to non-expert information seekers. The objective of the project is to create an alternative search interface for MEDLINE/PubMed searches using JavaScript slider bars. SLIM, or Slider Interface for MEDLINE/PubMed searches, was developed with PHP and JavaScript. Interactive slider bars in the search form controlled search parameters such as limits, filters and MeSH terminologies. Connections to PubMed were done using the Entrez Programming Utilities (E-Utilities). Custom scripts were created to mimic the automatic term mapping process of Entrez. Page generation times for both local and remote connections were recorded. Results Alpha testing by developers showed SLIM to be functionally stable. Page generation times to simulate loading times were recorded the first week of alpha and beta testing. Average page generation times for the index page, previews and searches were 2.94 milliseconds, 0.63 seconds and 3.84 seconds, respectively. Eighteen physicians from the US, Australia and the Philippines participated in the beta testing and provided feedback through an online survey. Most users found the search interface user-friendly and easy to use. Information on MeSH terms and the ability to instantly hide and display abstracts were identified as distinctive features. Conclusion SLIM can be an interactive time-saving tool for online medical literature research that improves user control and capability to instantly refine and refocus search strategies. With continued development and by integrating search limits, methodology filters, MeSH terms and levels of evidence, SLIM may be useful in the practice of evidence-based medicine. PMID:16321145

SLIM: an alternative Web interface for MEDLINE/PubMed searches - a preliminary study.

PubMed

Muin, Michael; Fontelo, Paul; Liu, Fang; Ackerman, Michael

2005-12-01

With the rapid growth of medical information and the pervasiveness of the Internet, online search and retrieval systems have become indispensable tools in medicine. The progress of Web technologies can provide expert searching capabilities to non-expert information seekers. The objective of the project is to create an alternative search interface for MEDLINE/PubMed searches using JavaScript slider bars. SLIM, or Slider Interface for MEDLINE/PubMed searches, was developed with PHP and JavaScript. Interactive slider bars in the search form controlled search parameters such as limits, filters and MeSH terminologies. Connections to PubMed were done using the Entrez Programming Utilities (E-Utilities). Custom scripts were created to mimic the automatic term mapping process of Entrez. Page generation times for both local and remote connections were recorded. Alpha testing by developers showed SLIM to be functionally stable. Page generation times to simulate loading times were recorded the first week of alpha and beta testing. Average page generation times for the index page, previews and searches were 2.94 milliseconds, 0.63 seconds and 3.84 seconds, respectively. Eighteen physicians from the US, Australia and the Philippines participated in the beta testing and provided feedback through an online survey. Most users found the search interface user-friendly and easy to use. Information on MeSH terms and the ability to instantly hide and display abstracts were identified as distinctive features. SLIM can be an interactive time-saving tool for online medical literature research that improves user control and capability to instantly refine and refocus search strategies. With continued development and by integrating search limits, methodology filters, MeSH terms and levels of evidence, SLIM may be useful in the practice of evidence-based medicine.

Microfluidic electrochemical sensor for on-line monitoring of aerosol oxidative activity.

PubMed

Sameenoi, Yupaporn; Koehler, Kirsten; Shapiro, Jeff; Boonsong, Kanokporn; Sun, Yele; Collett, Jeffrey; Volckens, John; Henry, Charles S

2012-06-27

Particulate matter (PM) air pollution has a significant impact on human morbidity and mortality; however, the mechanisms of PM-induced toxicity are poorly defined. A leading hypothesis states that airborne PM induces harm by generating reactive oxygen species in and around human tissues, leading to oxidative stress. We report here a system employing a microfluidic electrochemical sensor coupled directly to a particle-into-liquid sampler (PILS) system to measure aerosol oxidative activity in an on-line format. The oxidative activity measurement is based on the dithiothreitol (DTT) assay, where, after being oxidized by PM, the remaining reduced DTT is analyzed by the microfluidic sensor. The sensor consists of an array of working, reference, and auxiliary electrodes fabricated in a poly(dimethylsiloxane)-based microfluidic device. Cobalt(II) phthalocyanine-modified carbon paste was used as the working electrode material, allowing selective detection of reduced DTT. The electrochemical sensor was validated off-line against the traditional DTT assay using filter samples taken from urban environments and biomass burning events. After off-line characterization, the sensor was coupled to a PILS to enable on-line sampling/analysis of aerosol oxidative activity. Urban dust and industrial incinerator ash samples were aerosolized in an aerosol chamber and analyzed for their oxidative activity. The on-line sensor reported DTT consumption rates (oxidative activity) in good correlation with aerosol concentration (R(2) from 0.86 to 0.97) with a time resolution of approximately 3 min.

Performance analysis of improved iterated cubature Kalman filter and its application to GNSS/INS.

PubMed

Cui, Bingbo; Chen, Xiyuan; Xu, Yuan; Huang, Haoqian; Liu, Xiao

2017-01-01

In order to improve the accuracy and robustness of GNSS/INS navigation system, an improved iterated cubature Kalman filter (IICKF) is proposed by considering the state-dependent noise and system uncertainty. First, a simplified framework of iterated Gaussian filter is derived by using damped Newton-Raphson algorithm and online noise estimator. Then the effect of state-dependent noise coming from iterated update is analyzed theoretically, and an augmented form of CKF algorithm is applied to improve the estimation accuracy. The performance of IICKF is verified by field test and numerical simulation, and results reveal that, compared with non-iterated filter, iterated filter is less sensitive to the system uncertainty, and IICKF improves the accuracy of yaw, roll and pitch by 48.9%, 73.1% and 83.3%, respectively, compared with traditional iterated KF. Copyright © 2016 ISA. Published by Elsevier Ltd. All rights reserved.

Information Filtering via Heterogeneous Diffusion in Online Bipartite Networks

PubMed Central

Zhang, Fu-Guo; Zeng, An

2015-01-01

The rapid expansion of Internet brings us overwhelming online information, which is impossible for an individual to go through all of it. Therefore, recommender systems were created to help people dig through this abundance of information. In networks composed by users and objects, recommender algorithms based on diffusion have been proven to be one of the best performing methods. Previous works considered the diffusion process from user to object, and from object to user to be equivalent. We show in this work that it is not the case and we improve the quality of the recommendation by taking into account the asymmetrical nature of this process. We apply this idea to modify the state-of-the-art recommendation methods. The simulation results show that the new methods can outperform these existing methods in both recommendation accuracy and diversity. Finally, this modification is checked to be able to improve the recommendation in a realistic case. PMID:26125631

Information Filtering via Heterogeneous Diffusion in Online Bipartite Networks.

PubMed

Zhang, Fu-Guo; Zeng, An

2015-01-01

The rapid expansion of Internet brings us overwhelming online information, which is impossible for an individual to go through all of it. Therefore, recommender systems were created to help people dig through this abundance of information. In networks composed by users and objects, recommender algorithms based on diffusion have been proven to be one of the best performing methods. Previous works considered the diffusion process from user to object, and from object to user to be equivalent. We show in this work that it is not the case and we improve the quality of the recommendation by taking into account the asymmetrical nature of this process. We apply this idea to modify the state-of-the-art recommendation methods. The simulation results show that the new methods can outperform these existing methods in both recommendation accuracy and diversity. Finally, this modification is checked to be able to improve the recommendation in a realistic case.

Search strategies for identifying qualitative studies in CINAHL.

PubMed

Wilczynski, Nancy L; Marks, Susan; Haynes, R Brian

2007-05-01

Nurses, allied health professionals, clinicians, and researchers increasingly use online access to evidence in the course of patient care or when conducting reviews on a particular topic. Qualitative research has an important role in evidence-based health care. Online searching for qualitative studies can be difficult, however, resulting in the need to develop search filters. The objective of this study was to develop optimal search strategies to retrieve qualitative studies in CINAHL for the 2000 publishing year. The authors conducted an analytic survey comparing hand searches of journals with retrievals from CINAHL for candidate search terms and combinations. Combinations of search terms reached peak sensitivities of 98.9% and peak specificities of 99.5%. Combining search terms optimized both sensitivity and specificity at 94.2%. Empirically derived search strategies combining indexing terms and textwords can achieve high sensitivity and high specificity for retrieving qualitative studies from CINAHL.

VizieR Online Data Catalog: IC 361 Vilnius photometry (Zdanavicius+, 2010)

NASA Astrophysics Data System (ADS)

Zdanavicius, J.; Bartasiute, S.; Boyle, R. P.; Vrba, F. J.; Zdanavicius, K.

2015-03-01

CCD observations in seven filters U,P,X,Y,Z,V,S of the Vilnius system plus the filter I of the Cousins system were carried out in December of 1999 with a 2K CCD camera on the 1m telescope of the USNO Flagstaff Station (Arizona), which gives a field of the diameter of 20'. Repeated observations in the Vilnius filters were done with the same telescope and a new 2Kx2K CCD camera in March of 2009. During the latter run we have obtained well-calibrated CCD data only for filters Y, Z, V, S, since observations through the remaining three filters on the succeeding night were curtailed by cirrus clouds. Additional frames in the Vilnius filters U,Y,V were taken for the central part of the field (12'x12') in December of 2008 with a 4K CCD camera on the 1.8m Vatican Advanced Technology Telescope (VATT) on Mt. Graham (Arizona). (1 data file).

LHCb Online event processing and filtering

NASA Astrophysics Data System (ADS)

Alessio, F.; Barandela, C.; Brarda, L.; Frank, M.; Franek, B.; Galli, D.; Gaspar, C.; Herwijnen, E. v.; Jacobsson, R.; Jost, B.; Köstner, S.; Moine, G.; Neufeld, N.; Somogyi, P.; Stoica, R.; Suman, S.

2008-07-01

The first level trigger of LHCb accepts one million events per second. After preprocessing in custom FPGA-based boards these events are distributed to a large farm of PC-servers using a high-speed Gigabit Ethernet network. Synchronisation and event management is achieved by the Timing and Trigger system of LHCb. Due to the complex nature of the selection of B-events, which are the main interest of LHCb, a full event-readout is required. Event processing on the servers is parallelised on an event basis. The reduction factor is typically 1/500. The remaining events are forwarded to a formatting layer, where the raw data files are formed and temporarily stored. A small part of the events is also forwarded to a dedicated farm for calibration and monitoring. The files are subsequently shipped to the CERN Tier0 facility for permanent storage and from there to the various Tier1 sites for reconstruction. In parallel files are used by various monitoring and calibration processes running within the LHCb Online system. The entire data-flow is controlled and configured by means of a SCADA system and several databases. After an overview of the LHCb data acquisition and its design principles this paper will emphasize the LHCb event filter system, which is now implemented using the final hardware and will be ready for data-taking for the LHC startup. Control, configuration and security aspects will also be discussed.

Measuring the Interestingness of Articles in a Limited User Environment

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

Pon, R; Cardenas, A; Buttler, David

Search engines, such as Google, assign scores to news articles based on their relevance to a query. However, not all relevant articles for the query may be interesting to a user. For example, if the article is old or yields little new information, the article would be uninteresting. Relevance scores do not take into account what makes an article interesting, which would vary from user to user. Although methods such as collaborative filtering have been shown to be effective in recommendation systems, in a limited user environment, there are not enough users that would make collaborative filtering effective. A generalmore » framework, called iScore, is presented for defining and measuring the ‘‘interestingness of articles, incorporating user-feedback. iScore addresses the various aspects of what makes an article interesting, such as topic relevance, uniqueness, freshness, source reputation, and writing style. It employs various methods, such as multiple topic tracking, online parameter selection, language models, clustering, sentiment analysis, and phrase extraction to measure these features. Due to varying reasons that users hold about why an article is interesting, an online feature selection method in naι¨ve Bayes is also used to improve recommendation results. iScore can outperform traditional IR techniques by as much as 50.7%. iScore and its components are evaluated in the news recommendation task using three datasets from Yahoo! News, actual users, and Digg.« less

Opinion-enhanced collaborative filtering for recommender systems through sentiment analysis

NASA Astrophysics Data System (ADS)

Wang, Wei; Wang, Hongwei

2015-10-01

The motivation of collaborative filtering (CF) comes from the idea that people often get the best recommendations from someone with similar tastes. With the growing popularity of opinion-rich resources such as online reviews, new opportunities arise as we can identify the preferences from user opinions. The main idea of our approach is to elicit user opinions from online reviews, and map such opinions into preferences that can be understood by CF-based recommender systems. We divide recommender systems into two types depending on the number of product category recommended: the multiple-category recommendation and the single-category recommendation. For the former, sentiment polarity in coarse-grained manner is identified while for the latter fine-grained sentiment analysis is conducted for each product aspect. If the evaluation frequency for an aspect by a user is greater than the average frequency by all users, it indicates that the user is more concerned with that aspect. If a user's rating for an aspect is lower than the average rating by all users, he or she is much pickier than others on that aspect. Through sentiment analysis, we then build an opinion-enhanced user preference model, where the higher the similarity between user opinions the more consistent preferences between users are. Experiment results show that the proposed CF algorithm outperforms baseline methods for product recommendation in terms of accuracy and recall.

A user-friendly, low-cost turbidostat with versatile growth rate estimation based on an extended Kalman filter.

PubMed

Hoffmann, Stefan A; Wohltat, Christian; Müller, Kristian M; Arndt, Katja M

2017-01-01

For various experimental applications, microbial cultures at defined, constant densities are highly advantageous over simple batch cultures. Due to high costs, however, devices for continuous culture at freely defined densities still experience limited use. We have developed a small-scale turbidostat for research purposes, which is manufactured from inexpensive components and 3D printed parts. A high degree of spatial system integration and a graphical user interface provide user-friendly operability. The used optical density feedback control allows for constant continuous culture at a wide range of densities and offers to vary culture volume and dilution rates without additional parametrization. Further, a recursive algorithm for on-line growth rate estimation has been implemented. The employed Kalman filtering approach based on a very general state model retains the flexibility of the used control type and can be easily adapted to other bioreactor designs. Within several minutes it can converge to robust, accurate growth rate estimates. This is particularly useful for directed evolution experiments or studies on metabolic challenges, as it allows direct monitoring of the population fitness.

Remaining dischargeable time prediction for lithium-ion batteries using unscented Kalman filter

NASA Astrophysics Data System (ADS)

Dong, Guangzhong; Wei, Jingwen; Chen, Zonghai; Sun, Han; Yu, Xiaowei

2017-10-01

To overcome the range anxiety, one of the important strategies is to accurately predict the range or dischargeable time of the battery system. To accurately predict the remaining dischargeable time (RDT) of a battery, a RDT prediction framework based on accurate battery modeling and state estimation is presented in this paper. Firstly, a simplified linearized equivalent-circuit-model is developed to simulate the dynamic characteristics of a battery. Then, an online recursive least-square-algorithm method and unscented-Kalman-filter are employed to estimate the system matrices and SOC at every prediction point. Besides, a discrete wavelet transform technique is employed to capture the statistical information of past dynamics of input currents, which are utilized to predict the future battery currents. Finally, the RDT can be predicted based on the battery model, SOC estimation results and predicted future battery currents. The performance of the proposed methodology has been verified by a lithium-ion battery cell. Experimental results indicate that the proposed method can provide an accurate SOC and parameter estimation and the predicted RDT can solve the range anxiety issues.

FIBRE AND INTEGRATED OPTICS. OPTICAL PROCESSING OF INFORMATION: Method for optical data processing based on a two-pulse photon echo

NASA Astrophysics Data System (ADS)

Zakharov, S. M.; Manykin, Eduard A.

1995-02-01

The principles of optical processing based on dynamic spatial—temporal properties of two-pulse photon echo signals are considered. The properties of a resonant medium as an on-line filter of temporal and spatial frequencies are discussed. These properties are due to the sensitivity of such a medium to the Fourier spectrum of the second exiting pulse. Degeneracy of quantum resonant systems, demonstrated by the coherent response dependence on the square of the amplitude of the second pulse, can be used for 'simultaneous' correlation processing of optical 'signals'. Various methods for the processing of the Fourier optical image are discussed.

Reconstructing the calibrated strain signal in the Advanced LIGO detectors

NASA Astrophysics Data System (ADS)

Viets, A. D.; Wade, M.; Urban, A. L.; Kandhasamy, S.; Betzwieser, J.; Brown, Duncan A.; Burguet-Castell, J.; Cahillane, C.; Goetz, E.; Izumi, K.; Karki, S.; Kissel, J. S.; Mendell, G.; Savage, R. L.; Siemens, X.; Tuyenbayev, D.; Weinstein, A. J.

2018-05-01

Advanced LIGO’s raw detector output needs to be calibrated to compute dimensionless strain h(t) . Calibrated strain data is produced in the time domain using both a low-latency, online procedure and a high-latency, offline procedure. The low-latency h(t) data stream is produced in two stages, the first of which is performed on the same computers that operate the detector’s feedback control system. This stage, referred to as the front-end calibration, uses infinite impulse response (IIR) filtering and performs all operations at a 16 384 Hz digital sampling rate. Due to several limitations, this procedure currently introduces certain systematic errors in the calibrated strain data, motivating the second stage of the low-latency procedure, known as the low-latency gstlal calibration pipeline. The gstlal calibration pipeline uses finite impulse response (FIR) filtering to apply corrections to the output of the front-end calibration. It applies time-dependent correction factors to the sensing and actuation components of the calibrated strain to reduce systematic errors. The gstlal calibration pipeline is also used in high latency to recalibrate the data, which is necessary due mainly to online dropouts in the calibrated data and identified improvements to the calibration models or filters.

New quality measure for SNP array based CNV detection.

PubMed

Macé, A; Tuke, M A; Beckmann, J S; Lin, L; Jacquemont, S; Weedon, M N; Reymond, A; Kutalik, Z

2016-11-01

Only a few large systematic studies have evaluated the impact of copy number variants (CNVs) on common diseases. Several million individuals have been genotyped on single nucleotide variation arrays, which could be used for genome-wide CNVs association studies. However, CNV calls remain prone to false positives and only empirical filtering strategies exist in the literature. To overcome this issue, we defined a new quality score (QS) estimating the probability of a CNV called by PennCNV to be confirmed by other software. Out-of-sample comparison showed that the correlation between the consensus CNV status and the QS is twice as high as it is for any previously proposed CNV filters. ROC curves displayed an AUC higher than 0.8 and simulations showed an increase up to 20% in statistical power when using QS in comparison to other filtering strategies. Superior performance was confirmed also for alternative consensus CNV definition and through improving known CNV-trait associations. http://goo.gl/T6yuFM CONTACT: zoltan.kutalik@unil.ch or aurelien@mace@unil.chSupplementary information: Supplementary data are available at Bioinformatics online. © The Author 2016. Published by Oxford University Press. All rights reserved. For Permissions, please e-mail: journals.permissions@oup.com.

GARFIELD-NGS: Genomic vARiants FIltering by dEep Learning moDels in NGS.

PubMed

Ravasio, Viola; Ritelli, Marco; Legati, Andrea; Giacopuzzi, Edoardo

2018-04-14

Exome sequencing approach is extensively used in research and diagnostic laboratories to discover pathological variants and study genetic architecture of human diseases. However, a significant proportion of identified genetic variants are actually false positive calls, and this pose serious challenges for variants interpretation. Here, we propose a new tool named GARFIELD-NGS (Genomic vARiants FIltering by dEep Learning moDels in NGS), which rely on deep learning models to dissect false and true variants in exome sequencing experiments performed with Illumina or ION platforms. GARFIELD-NGS showed strong performances for both SNP and INDEL variants (AUC 0.71 - 0.98) and outperformed established hard filters. The method is robust also at low coverage down to 30X and can be applied on data generated with the recent Illumina two-colour chemistry. GARFIELD-NGS processes standard VCF file and produces a regular VCF output. Thus, it can be easily integrated in existing analysis pipeline, allowing application of different thresholds based on desired level of sensitivity and specificity. GARFIELD-NGS available at https://github.com/gedoardo83/GARFIELD-NGS. edoardo.giacopuzzi@unibs.it. Supplementary data are available at Bioinformatics online.

Adaptive noise correction of dual-energy computed tomography images.

PubMed

Maia, Rafael Simon; Jacob, Christian; Hara, Amy K; Silva, Alvin C; Pavlicek, William; Mitchell, J Ross

2016-04-01

Noise reduction in material density images is a necessary preprocessing step for the correct interpretation of dual-energy computed tomography (DECT) images. In this paper we describe a new method based on a local adaptive processing to reduce noise in DECT images An adaptive neighborhood Wiener (ANW) filter was implemented and customized to use local characteristics of material density images. The ANW filter employs a three-level wavelet approach, combined with the application of an anisotropic diffusion filter. Material density images and virtual monochromatic images are noise corrected with two resulting noise maps. The algorithm was applied and quantitatively evaluated in a set of 36 images. From that set of images, three are shown here, and nine more are shown in the online supplementary material. Processed images had higher signal-to-noise ratio (SNR) and contrast-to-noise ratio (CNR) than the raw material density images. The average improvements in SNR and CNR for the material density images were 56.5 and 54.75%, respectively. We developed a new DECT noise reduction algorithm. We demonstrate throughout a series of quantitative analyses that the algorithm improves the quality of material density images and virtual monochromatic images.

Adaptive filtering in biological signal processing.

PubMed

Iyer, V K; Ploysongsang, Y; Ramamoorthy, P A

1990-01-01

The high dependence of conventional optimal filtering methods on the a priori knowledge of the signal and noise statistics render them ineffective in dealing with signals whose statistics cannot be predetermined accurately. Adaptive filtering methods offer a better alternative, since the a priori knowledge of statistics is less critical, real time processing is possible, and the computations are less expensive for this approach. Adaptive filtering methods compute the filter coefficients "on-line", converging to the optimal values in the least-mean square (LMS) error sense. Adaptive filtering is therefore apt for dealing with the "unknown" statistics situation and has been applied extensively in areas like communication, speech, radar, sonar, seismology, and biological signal processing and analysis for channel equalization, interference and echo canceling, line enhancement, signal detection, system identification, spectral analysis, beamforming, modeling, control, etc. In this review article adaptive filtering in the context of biological signals is reviewed. An intuitive approach to the underlying theory of adaptive filters and its applicability are presented. Applications of the principles in biological signal processing are discussed in a manner that brings out the key ideas involved. Current and potential future directions in adaptive biological signal processing are also discussed.

VizieR Online Data Catalog: V873 Per BVR light curves (Kriwattanawong+, 2015)

NASA Astrophysics Data System (ADS)

Kriwattanawong, W.; Poojon, P.

2015-07-01

BVR photometric observations of V873 Per were carried out on the 0.5-m telescope at Sirindhorn Observatory, Chiang Mai University. The photometric data were taken during four nights (November 27-30, 2011), by using SBIG CCD (Model of ST10-XME), with exposure time of 90s. A total of 855 individual observations were obtained in three filter bands (285 observations per filter band). The IRAF package was used for data reduction and the BVR magnitude measurements. (1 data file).

Online ranking by projecting.

PubMed

Crammer, Koby; Singer, Yoram

2005-01-01

We discuss the problem of ranking instances. In our framework, each instance is associated with a rank or a rating, which is an integer in 1 to k. Our goal is to find a rank-prediction rule that assigns each instance a rank that is as close as possible to the instance's true rank. We discuss a group of closely related online algorithms, analyze their performance in the mistake-bound model, and prove their correctness. We describe two sets of experiments, with synthetic data and with the EachMovie data set for collaborative filtering. In the experiments we performed, our algorithms outperform online algorithms for regression and classification applied to ranking.

Estimating population ecology models for the WWW market: evidence of competitive oligopolies.

PubMed

de Cabo, Ruth Mateos; Gimeno, Ricardo

2013-01-01

This paper proposes adapting a particle filtering algorithm to model online Spanish real estate and job search market segments based on the Lotka-Volterra competition equations. For this purpose the authors use data on Internet information searches from Google Trends to proxy for market share. Market share evolution estimations are coherent with those observed in Google Trends. The results show evidence of low website incompatibility in the markets analyzed. Competitive oligopolies are most common in such low-competition markets, instead of the monopolies predicted by theoretical ecology models under strong competition conditions.

Behavior data of battery and battery pack SOC estimation under different working conditions.

PubMed

Zhang, Xu; Wang, Yujie; Yang, Duo; Chen, Zonghai

2016-12-01

This article provides the dataset of operating conditions of battery behavior. The constant current condition and the dynamic stress test (DST) condition were carried out to analyze the battery discharging and charging features. The datasets were achieved at room temperature, in April, 2016. The shared data contributes to clarify the battery pack state-of-charge (SOC) and the battery inconsistency, which is also shown in the article of "An on-line estimation of battery pack parameters and state-of-charge using dual filters based on pack model" (X. Zhang, Y. Wang, D. Yang, et al., 2016) [1].

Can Patients Comprehend the Educational Materials that Hospitals Provide about Common IR Procedures?

PubMed

Sadigh, Gelareh; Hawkins, C Matthew; O'Keefe, John J; Khan, Ramsha; Duszak, Richard

2015-08-01

To assess the readability of online education materials offered by hospitals describing commonly performed interventional radiology (IR) procedures. Online patient education materials from 402 hospitals selected from the Medicare Hospital Compare database were assessed. The presence of an IR service was determined by representation in the Society of Interventional Radiology physician finder directory. Patient online education materials about (i) uterine artery embolization for fibroid tumors, (ii) liver cancer embolization, (iii) varicose vein treatment, (iv) central venous access, (v) inferior vena cava (IVC) filter placement, (vi) nephrostomy tube insertion, (vii) gastrostomy tube placement, and (viii) vertebral augmentation were targeted and assessed by using six validated readability scoring systems. Of 402 hospitals sampled, 156 (39%) were presumed to offer IR services. Of these, 119 (76%) offered online patient education material for one or more of the eight service lines. The average readability scores corresponding to grade varied between the ninth- and 12th-grade levels. All were higher than the recommended seventh-grade level (P < .05) except for nephrostomy and gastrostomy tube placement. Average Flesch-Kincaid Reading Ease scores ranged from 42 to 69, corresponding with fairly difficult to difficult readability for all service lines except IVC filter and gastrostomy tube placement, which corresponded with standard readability. A majority of hospitals offering IR services provide at least some online patient education material. Most, however, are written significantly above the reading comprehension level of most Americans. More attention to health literacy by hospitals and IR physicians is warranted. Copyright © 2015 SIR. Published by Elsevier Inc. All rights reserved.

Data-driven strategies for robust forecast of continuous glucose monitoring time-series.

PubMed

Fiorini, Samuele; Martini, Chiara; Malpassi, Davide; Cordera, Renzo; Maggi, Davide; Verri, Alessandro; Barla, Annalisa

2017-07-01

Over the past decade, continuous glucose monitoring (CGM) has proven to be a very resourceful tool for diabetes management. To date, CGM devices are employed for both retrospective and online applications. Their use allows to better describe the patients' pathology as well as to achieve a better control of patients' level of glycemia. The analysis of CGM sensor data makes possible to observe a wide range of metrics, such as the glycemic variability during the day or the amount of time spent below or above certain glycemic thresholds. However, due to the high variability of the glycemic signals among sensors and individuals, CGM data analysis is a non-trivial task. Standard signal filtering solutions fall short when an appropriate model personalization is not applied. State-of-the-art data-driven strategies for online CGM forecasting rely upon the use of recursive filters. Each time a new sample is collected, such models need to adjust their parameters in order to predict the next glycemic level. In this paper we aim at demonstrating that the problem of online CGM forecasting can be successfully tackled by personalized machine learning models, that do not need to recursively update their parameters.

Fused smart sensor network for multi-axis forward kinematics estimation in industrial robots.

PubMed

Rodriguez-Donate, Carlos; Osornio-Rios, Roque Alfredo; Rivera-Guillen, Jesus Rooney; Romero-Troncoso, Rene de Jesus

2011-01-01

Flexible manipulator robots have a wide industrial application. Robot performance requires sensing its position and orientation adequately, known as forward kinematics. Commercially available, motion controllers use high-resolution optical encoders to sense the position of each joint which cannot detect some mechanical deformations that decrease the accuracy of the robot position and orientation. To overcome those problems, several sensor fusion methods have been proposed but at expenses of high-computational load, which avoids the online measurement of the joint's angular position and the online forward kinematics estimation. The contribution of this work is to propose a fused smart sensor network to estimate the forward kinematics of an industrial robot. The developed smart processor uses Kalman filters to filter and to fuse the information of the sensor network. Two primary sensors are used: an optical encoder, and a 3-axis accelerometer. In order to obtain the position and orientation of each joint online a field-programmable gate array (FPGA) is used in the hardware implementation taking advantage of the parallel computation capabilities and reconfigurability of this device. With the aim of evaluating the smart sensor network performance, three real-operation-oriented paths are executed and monitored in a 6-degree of freedom robot.

Measurements of Organic Composition of Aerosol and Rainwater Samples Using Offline Aerosol Mass Spectrometry

NASA Astrophysics Data System (ADS)

OBrien, R. E.; Ridley, K. J.; Canagaratna, M. R.; Croteau, P.; Budisulistiorini, S. H.; Cui, T.; Green, H. S.; Surratt, J. D.; Jayne, J. T.; Kroll, J. H.

2016-12-01

A thorough understanding of the sources, evolution, and budgets of atmospheric organic aerosol requires widespread measurements of the amount and chemical composition of atmospheric organic carbon in the condensed phase (within particles and water droplets). Collecting such datasets requires substantial spatial and temporal (long term) coverage, which can be challenging when relying on online measurements by state-of-the-art research-grade instrumentation (such as those used in atmospheric chemistry field studies). Instead, samples are routinely collected using relatively low-cost techniques, such as aerosol filters, for offline analysis of their chemical composition. However, measurements made by online and offline instruments can be fundamentally different, leading to disparities between data from field studies and those from more routine monitoring. To better connect these two approaches, and take advantage of the benefits of each, we have developed a method to introduce collected samples into online aerosol instruments using nebulization. Because nebulizers typically require tens to hundreds of milliliters of solution, limiting this technique to large samples, we developed a new, ultrasonic micro-nebulizer that requires only small volumes (tens of microliters) of sample for chemical analysis. The nebulized (resuspended) sample is then sent into a high-resolution Aerosol Mass Spectrometer (AMS), a widely-used instrument that provides key information on the chemical composition of aerosol particulate matter (elemental ratios, carbon oxidation state, etc.), measurements that are not typically made for collected atmospheric samples. Here, we compare AMS data collected using standard on-line techniques with our offline analysis, demonstrating the utility of this new technique to aerosol filter samples. We then apply this approach to organic aerosol filter samples collected in remote regions, as well as rainwater samples from across the US. This data provides information on the sample composition and changes in key chemical characteristics across locations and seasons.

Gas Path On-line Fault Diagnostics Using a Nonlinear Integrated Model for Gas Turbine Engines

NASA Astrophysics Data System (ADS)

Lu, Feng; Huang, Jin-quan; Ji, Chun-sheng; Zhang, Dong-dong; Jiao, Hua-bin

2014-08-01

Gas turbine engine gas path fault diagnosis is closely related technology that assists operators in managing the engine units. However, the performance gradual degradation is inevitable due to the usage, and it result in the model mismatch and then misdiagnosis by the popular model-based approach. In this paper, an on-line integrated architecture based on nonlinear model is developed for gas turbine engine anomaly detection and fault diagnosis over the course of the engine's life. These two engine models have different performance parameter update rate. One is the nonlinear real-time adaptive performance model with the spherical square-root unscented Kalman filter (SSR-UKF) producing performance estimates, and the other is a nonlinear baseline model for the measurement estimates. The fault detection and diagnosis logic is designed to discriminate sensor fault and component fault. This integration architecture is not only aware of long-term engine health degradation but also effective to detect gas path performance anomaly shifts while the engine continues to degrade. Compared to the existing architecture, the proposed approach has its benefit investigated in the experiment and analysis.

Online anomaly detection in wireless body area networks for reliable healthcare monitoring.

PubMed

Salem, Osman; Liu, Yaning; Mehaoua, Ahmed; Boutaba, Raouf

2014-09-01

In this paper, we propose a lightweight approach for online detection of faulty measurements by analyzing the data collected from medical wireless body area networks. The proposed framework performs sequential data analysis using a smart phone as a base station, and takes into account the constrained resources of the smart phone, such as processing power and storage capacity. The main objective is to raise alarms only when patients enter in an emergency situation, and to discard false alarms triggered by faulty measurements or ill-behaved sensors. The proposed approach is based on the Haar wavelet decomposition, nonseasonal Holt-Winters forecasting, and the Hampel filter for spatial analysis, and on for temporal analysis. Our objective is to reduce false alarms resulting from unreliable measurements and to reduce unnecessary healthcare intervention. We apply our proposed approach on real physiological dataset. Our experimental results prove the effectiveness of our approach in achieving good detection accuracy with a low false alarm rate. The simplicity and the processing speed of our proposed framework make it useful and efficient for real time diagnosis.

A generalized model via random walks for information filtering

NASA Astrophysics Data System (ADS)

Ren, Zhuo-Ming; Kong, Yixiu; Shang, Ming-Sheng; Zhang, Yi-Cheng

2016-08-01

There could exist a simple general mechanism lurking beneath collaborative filtering and interdisciplinary physics approaches which have been successfully applied to online E-commerce platforms. Motivated by this idea, we propose a generalized model employing the dynamics of the random walk in the bipartite networks. Taking into account the degree information, the proposed generalized model could deduce the collaborative filtering, interdisciplinary physics approaches and even the enormous expansion of them. Furthermore, we analyze the generalized model with single and hybrid of degree information on the process of random walk in bipartite networks, and propose a possible strategy by using the hybrid degree information for different popular objects to toward promising precision of the recommendation.

A Review of System Identification Methods Applied to Aircraft

NASA Technical Reports Server (NTRS)

Klein, V.

1983-01-01

Airplane identification, equation error method, maximum likelihood method, parameter estimation in frequency domain, extended Kalman filter, aircraft equations of motion, aerodynamic model equations, criteria for the selection of a parsimonious model, and online aircraft identification are addressed.

Detection, Location and Grasping Objects Using a Stereo Sensor on UAV in Outdoor Environments.

PubMed

Ramon Soria, Pablo; Arrue, Begoña C; Ollero, Anibal

2017-01-07

The article presents a vision system for the autonomous grasping of objects with Unmanned Aerial Vehicles (UAVs) in real time. Giving UAVs the capability to manipulate objects vastly extends their applications, as they are capable of accessing places that are difficult to reach or even unreachable for human beings. This work is focused on the grasping of known objects based on feature models. The system runs in an on-board computer on a UAV equipped with a stereo camera and a robotic arm. The algorithm learns a feature-based model in an offline stage, then it is used online for detection of the targeted object and estimation of its position. This feature-based model was proved to be robust to both occlusions and the presence of outliers. The use of stereo cameras improves the learning stage, providing 3D information and helping to filter features in the online stage. An experimental system was derived using a rotary-wing UAV and a small manipulator for final proof of concept. The robotic arm is designed with three degrees of freedom and is lightweight due to payload limitations of the UAV. The system has been validated with different objects, both indoors and outdoors.

GeneCOST: a novel scoring-based prioritization framework for identifying disease causing genes.

PubMed

Ozer, Bugra; Sağıroğlu, Mahmut; Demirci, Hüseyin

2015-11-15

Due to the big data produced by next-generation sequencing studies, there is an evident need for methods to extract the valuable information gathered from these experiments. In this work, we propose GeneCOST, a novel scoring-based method to evaluate every gene for their disease association. Without any prior filtering and any prior knowledge, we assign a disease likelihood score to each gene in correspondence with their variations. Then, we rank all genes based on frequency, conservation, pedigree and detailed variation information to find out the causative reason of the disease state. We demonstrate the usage of GeneCOST with public and real life Mendelian disease cases including recessive, dominant, compound heterozygous and sporadic models. As a result, we were able to identify causative reason behind the disease state in top rankings of our list, proving that this novel prioritization framework provides a powerful environment for the analysis in genetic disease studies alternative to filtering-based approaches. GeneCOST software is freely available at www.igbam.bilgem.tubitak.gov.tr/en/softwares/genecost-en/index.html. buozer@gmail.com 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.

[Automated detection of estrus and mastitis in dairy cows].

PubMed

de Mol, R M

2001-02-15

The development and test of detection models for oestrus and mastitis in dairy cows is described in a PhD thesis that was defended in Wageningen on June 5, 2000. These models were based on sensors for milk yield, milk temperature, electrical conductivity of milk, and cow activity and concentrate intake, and on combined processing of the sensor data. The models alert farmers to cows that need attention, because of possible oestrus or mastitis. A first detection model for cows, milked twice a day, was based on time series models for the sensor variables. A time series model describes the dependence between successive observations. The parameters of the time series models were fitted on-line for each cow after each milking by means of a Kalman filter, a mathematical method to estimate the state of a system on-line. The Kalman filter gives the best estimate of the current state of a system based on all preceding observations. This model was tested for 2 years on two experimental farms, and under field conditions on four farms over several years. A second detection model, for cow milked in an automatic milking system (AMS), was based on a generalization of the first model. Two data sets (one small, one large) were used for testing. The results for oestrus detection were good for both models. The results for mastitis detection were varying (in some cases good, in other cases moderate). Fuzzy logic was used to classify mastitis and oestrus alerts with both detection models, to reduce the number of false positive alerts. Fuzzy logic makes approximate reasoning possible, where statements can be partly true or false. Input for the fuzzy logic model were alerts from the detection models and additional information. The number of false positive alerts decreased considerably, while the number of detected cases remained at the same level. These models make automated detection possible in practice.

A Novel c-VEP BCI Paradigm for Increasing the Number of Stimulus Targets Based on Grouping Modulation With Different Codes.

PubMed

Wei, Qingguo; Liu, Yonghui; Gao, Xiaorong; Wang, Yijun; Yang, Chen; Lu, Zongwu; Gong, Huayuan

2018-06-01

In an existing brain-computer interface (BCI) based on code modulated visual evoked potentials (c-VEP), a method with which to increase the number of targets without increasing code length has not yet been established. In this paper, a novel c-VEP BCI paradigm, namely, grouping modulation with different codes that have good autocorrelation and crosscorrelation properties, is presented to increase the number of targets and information transfer rate (ITR). All stimulus targets are divided into several groups and each group of targets are modulated by a distinct pseudorandom binary code and its circularly shifting codes. Canonical correlation analysis is applied to each group for yielding a spatial filter and templates for all targets in a group are constructed based on spatially filtered signals. Template matching is applied to each group and the attended target is recognized by finding the maximal correlation coefficients of all groups. Based on the paradigm, a BCI with a total of 48 targets divided into three groups was implemented; 12 and 10 subjects participated in an off-line and a simulated online experiments, respectively. Data analysis of the offline experiment showed that the paradigm can massively increase the number of targets from 16 to 48 at the cost of slight compromise in accuracy (95.49% vs. 92.85%). Results of the simulated online experiment suggested that although the averaged accuracy across subjects of all three groups of targets was lower than that of a single group of targets (91.67% vs. 94.9%), the average ITR of the former was substantially higher than that of the later (181 bits/min vs. 135.6 bit/min) due to the large increase of the number of targets. The proposed paradigm significantly improves the performance of the c-VEP BCI, and thereby facilitates its practical applications such as high-speed spelling.

Why not just Google it? An assessment of information literacy skills in a biomedical science curriculum

PubMed Central

2011-01-01

Background Few issues in higher education are as fundamental as the ability to search for, evaluate, and synthesize information. The need to develop information literacy, the process of finding, retrieving, organizing, and evaluating the ever-expanding collection of online information, has precipitated the need for training in skill-based competencies in higher education, as well as medical and dental education. Methods The current study evaluated the information literacy skills of first-year dental students, consisting of two, consecutive dental student cohorts (n = 160). An assignment designed to evaluate information literacy skills was conducted. In addition, a survey of student online search engine or database preferences was conducted to identify any significant associations. Subsequently, an intervention was developed, based upon the results of the assessment and survey, to address any deficiencies in information literacy. Results Nearly half of students (n = 70/160 or 43%) missed one or more question components that required finding an evidence-based citation. Analysis of the survey revealed a significantly higher percentage of students who provided incorrect responses (n = 53/70 or 75.7%) reported using Google as their preferred online search method (p < 0.01). In contrast, a significantly higher percentage of students who reported using PubMed (n = 39/45 or 86.7%) were able to provide correct responses (p < 0.01). Following a one-hour intervention by a health science librarian, virtually all students were able to find and retrieve evidence-based materials for subsequent coursework. Conclusions This study confirmed that information literacy among this student population was lacking and that integration of modules within the curriculum can help students to filter and establish the quality of online information, a critical component in the training of new health care professionals. Furthermore, incorporation of these modules early in the curriculum may be of significant value to other dental, medical, health care, and professional schools with similar goals of incorporating the evidence base into teaching and learning activities. PMID:21518448

Why not just Google it? An assessment of information literacy skills in a biomedical science curriculum.

PubMed

Kingsley, Karl; Galbraith, Gillian M; Herring, Matthew; Stowers, Eva; Stewart, Tanis; Kingsley, Karla V

2011-04-25

Few issues in higher education are as fundamental as the ability to search for, evaluate, and synthesize information. The need to develop information literacy, the process of finding, retrieving, organizing, and evaluating the ever-expanding collection of online information, has precipitated the need for training in skill-based competencies in higher education, as well as medical and dental education. The current study evaluated the information literacy skills of first-year dental students, consisting of two, consecutive dental student cohorts (n = 160). An assignment designed to evaluate information literacy skills was conducted. In addition, a survey of student online search engine or database preferences was conducted to identify any significant associations. Subsequently, an intervention was developed, based upon the results of the assessment and survey, to address any deficiencies in information literacy. Nearly half of students (n = 70/160 or 43%) missed one or more question components that required finding an evidence-based citation. Analysis of the survey revealed a significantly higher percentage of students who provided incorrect responses (n = 53/70 or 75.7%) reported using Google as their preferred online search method (p < 0.01). In contrast, a significantly higher percentage of students who reported using PubMed (n = 39/45 or 86.7%) were able to provide correct responses (p < 0.01). Following a one-hour intervention by a health science librarian, virtually all students were able to find and retrieve evidence-based materials for subsequent coursework. This study confirmed that information literacy among this student population was lacking and that integration of modules within the curriculum can help students to filter and establish the quality of online information, a critical component in the training of new health care professionals. Furthermore, incorporation of these modules early in the curriculum may be of significant value to other dental, medical, health care, and professional schools with similar goals of incorporating the evidence base into teaching and learning activities.

Comparing the quality of pro- and anti-vaccination online information: a content analysis of vaccination-related webpages.

PubMed

Sak, Gabriele; Diviani, Nicola; Allam, Ahmed; Schulz, Peter J

2016-01-15

The exponential increase in health-related online platforms has made the Internet one of the main sources of health information globally. The quality of health contents disseminated on the Internet has been a central focus for many researchers. To date, however, few comparative content analyses of pro- and anti-vaccination websites have been conducted, and none of them compared the quality of information. The main objective of this study was therefore to bring new evidence on this aspect by comparing the quality of pro- and anti-vaccination online sources. Based on past literature and health information quality evaluation initiatives, a 40-categories assessment tool (Online Vaccination Information Quality Codebook) was developed and used to code a sample of 1093 webpages retrieved via Google and two filtered versions of the same search engine. The categories investigated were grouped into four main quality dimensions: web-related design quality criteria (10 categories), health-specific design quality criteria (3 categories), health related content attributes (12 categories) and vaccination-specific content attributes (15 categories). Data analysis comprised frequency counts, cross tabulations, Pearson's chi-square, and other inferential indicators. The final sample included 514 webpages in favor of vaccination, 471 against, and 108 neutral. Generally, webpages holding a favorable view toward vaccination presented more quality indicators compared to both neutral and anti-vaccination pages. However, some notable exceptions to this rule were observed. In particular, no differences were found between pro- and anti-vaccination webpages as regards vaccination-specific content attributes. Our analyses showed that the overall quality of pro-vaccination webpages is superior to anti-vaccination online sources. The developed coding scheme was proven to be a helpful and reliable tool to judge the quality of vaccination-related webpages. Based on the results, we advance recommendations for online health information providers as well as directions for future research in this field.

Laser system for measuring small changes in plasma tracer concentrations.

PubMed

Klaesner, J W; Pou, N A; Parker, R E; Galloway, R L; Roselli, R J

1996-01-01

The authors developed a laser-diode system that can be used for on-line optical concentration measurements in physiologic systems. Previous optical systems applied to whole blood have been hampered by artifacts introduced by red blood cells (RBCs). The system introduced here uses a commercially available filter cartridge to separate RBCs from plasma before plasma concentration measurements are made at a single wavelength. The filtering characteristics of the Cellco filter cartridge (#4007-10, German-town, MD) were adequate for use in the on-line measurement system. The response time of the filter cartridge was less than 40 seconds, and the sieving characteristics of the filter for macromolecules were excellent, with filtrate-to-plasma albumin ratios of 0.98 +/- 0.11 for studies in sheep and 0.94 +/- 0.15 for studies in dogs. The 635-nm laser diode system developed was shown to be more sensitive than the spectrophotometer used in previous studies (Klaesner et al., Annals of Biomedical Engineering, 1994; 22, 660-73). The new system was used to measure the product of filtration coefficient (Kfc) and reflection coefficient for albumin (delta f) in an isolated canine lung preparation. The delta fKfc values [mL/(cmH2O.min.100 g dry lung weight)] measured with the laser diode system (0.33 +/- 0.22) compared favorably with the delta fKfc obtained using a spectrophotometer (0.27 +/- 0.20) and with the Kfc obtained using the blood-corrected gravimetric method (0.32 +/- 0.23). Thus, this new optical system was shown to accurately measure plasma concentration changes in whole blood for physiologic levels of Kfc. The same system can be used with different optical tracers and different source wavelengths to make optical plasma concentration measurements for other physiologic applications.

LHCb Kalman Filter cross architecture studies

NASA Astrophysics Data System (ADS)

Cámpora Pérez, Daniel Hugo

2017-10-01

The 2020 upgrade of the LHCb detector will vastly increase the rate of collisions the Online system needs to process in software, in order to filter events in real time. 30 million collisions per second will pass through a selection chain, where each step is executed conditional to its prior acceptance. The Kalman Filter is a fit applied to all reconstructed tracks which, due to its time characteristics and early execution in the selection chain, consumes 40% of the whole reconstruction time in the current trigger software. This makes the Kalman Filter a time-critical component as the LHCb trigger evolves into a full software trigger in the Upgrade. I present a new Kalman Filter algorithm for LHCb that can efficiently make use of any kind of SIMD processor, and its design is explained in depth. Performance benchmarks are compared between a variety of hardware architectures, including x86_64 and Power8, and the Intel Xeon Phi accelerator, and the suitability of said architectures to efficiently perform the LHCb Reconstruction process is determined.

A Computational Model for Aperture Control in Reach-to-Grasp Movement Based on Predictive Variability

PubMed Central

Takemura, Naohiro; Fukui, Takao; Inui, Toshio

2015-01-01

In human reach-to-grasp movement, visual occlusion of a target object leads to a larger peak grip aperture compared to conditions where online vision is available. However, no previous computational and neural network models for reach-to-grasp movement explain the mechanism of this effect. We simulated the effect of online vision on the reach-to-grasp movement by proposing a computational control model based on the hypothesis that the grip aperture is controlled to compensate for both motor variability and sensory uncertainty. In this model, the aperture is formed to achieve a target aperture size that is sufficiently large to accommodate the actual target; it also includes a margin to ensure proper grasping despite sensory and motor variability. To this end, the model considers: (i) the variability of the grip aperture, which is predicted by the Kalman filter, and (ii) the uncertainty of the object size, which is affected by visual noise. Using this model, we simulated experiments in which the effect of the duration of visual occlusion was investigated. The simulation replicated the experimental result wherein the peak grip aperture increased when the target object was occluded, especially in the early phase of the movement. Both predicted motor variability and sensory uncertainty play important roles in the online visuomotor process responsible for grip aperture control. PMID:26696874

Convis: A Toolbox to Fit and Simulate Filter-Based Models of Early Visual Processing

PubMed Central

Huth, Jacob; Masquelier, Timothée; Arleo, Angelo

2018-01-01

We developed Convis, a Python simulation toolbox for large scale neural populations which offers arbitrary receptive fields by 3D convolutions executed on a graphics card. The resulting software proves to be flexible and easily extensible in Python, while building on the PyTorch library (The Pytorch Project, 2017), which was previously used successfully in deep learning applications, for just-in-time optimization and compilation of the model onto CPU or GPU architectures. An alternative implementation based on Theano (Theano Development Team, 2016) is also available, although not fully supported. Through automatic differentiation, any parameter of a specified model can be optimized to approach a desired output which is a significant improvement over e.g., Monte Carlo or particle optimizations without gradients. We show that a number of models including even complex non-linearities such as contrast gain control and spiking mechanisms can be implemented easily. We show in this paper that we can in particular recreate the simulation results of a popular retina simulation software VirtualRetina (Wohrer and Kornprobst, 2009), with the added benefit of providing (1) arbitrary linear filters instead of the product of Gaussian and exponential filters and (2) optimization routines utilizing the gradients of the model. We demonstrate the utility of 3d convolution filters with a simple direction selective filter. Also we show that it is possible to optimize the input for a certain goal, rather than the parameters, which can aid the design of experiments as well as closed-loop online stimulus generation. Yet, Convis is more than a retina simulator. For instance it can also predict the response of V1 orientation selective cells. Convis is open source under the GPL-3.0 license and available from https://github.com/jahuth/convis/ with documentation at https://jahuth.github.io/convis/. PMID:29563867

Brian hears: online auditory processing using vectorization over channels.

PubMed

Fontaine, Bertrand; Goodman, Dan F M; Benichoux, Victor; Brette, Romain

2011-01-01

The human cochlea includes about 3000 inner hair cells which filter sounds at frequencies between 20 Hz and 20 kHz. This massively parallel frequency analysis is reflected in models of auditory processing, which are often based on banks of filters. However, existing implementations do not exploit this parallelism. Here we propose algorithms to simulate these models by vectorizing computation over frequency channels, which are implemented in "Brian Hears," a library for the spiking neural network simulator package "Brian." This approach allows us to use high-level programming languages such as Python, because with vectorized operations, the computational cost of interpretation represents a small fraction of the total cost. This makes it possible to define and simulate complex models in a simple way, while all previous implementations were model-specific. In addition, we show that these algorithms can be naturally parallelized using graphics processing units, yielding substantial speed improvements. We demonstrate these algorithms with several state-of-the-art cochlear models, and show that they compare favorably with existing, less flexible, implementations.

Sequential inference as a mode of cognition and its correlates in fronto-parietal and hippocampal brain regions

PubMed Central

Friston, Karl J.; Dolan, Raymond J.

2017-01-01

Normative models of human cognition often appeal to Bayesian filtering, which provides optimal online estimates of unknown or hidden states of the world, based on previous observations. However, in many cases it is necessary to optimise beliefs about sequences of states rather than just the current state. Importantly, Bayesian filtering and sequential inference strategies make different predictions about beliefs and subsequent choices, rendering them behaviourally dissociable. Taking data from a probabilistic reversal task we show that subjects’ choices provide strong evidence that they are representing short sequences of states. Between-subject measures of this implicit sequential inference strategy had a neurobiological underpinning and correlated with grey matter density in prefrontal and parietal cortex, as well as the hippocampus. Our findings provide, to our knowledge, the first evidence for sequential inference in human cognition, and by exploiting between-subject variation in this measure we provide pointers to its neuronal substrates. PMID:28486504

An in silico pipeline to filter the Toxoplasma gondii proteome for proteins that could traffic to the host cell nucleus and influence host cell epigenetic regulation.

PubMed

Syn, Genevieve; Blackwell, Jenefer M; Jamieson, Sarra E; Francis, Richard W

2018-01-01

Toxoplasma gondii uses epigenetic mechanisms to regulate both endogenous and host cell gene expression. To identify genes with putative epigenetic functions, we developed an in silico pipeline to interrogate the T. gondii proteome of 8313 proteins. Step 1 employs PredictNLS and NucPred to identify genes predicted to target eukaryotic nuclei. Step 2 uses GOLink to identify proteins of epigenetic function based on Gene Ontology terms. This resulted in 611 putative nuclear localised proteins with predicted epigenetic functions. Step 3 filtered for secretory proteins using SignalP, SecretomeP, and experimental data. This identified 57 of the 611 putative epigenetic proteins as likely to be secreted. The pipeline is freely available online, uses open access tools and software with user-friendly Perl scripts to automate and manage the results, and is readily adaptable to undertake any such in silico search for genes contributing to particular functions.

VizieR Online Data Catalog: Solar neighborhood XXXVIII. Nearby M dwarf systems (Winters+, 2017)

NASA Astrophysics Data System (ADS)

Winters, J. G.; Sevrinsky, R. A.; Jao, W.-C.; Henry, T. J.; Riedel, A. R.; Subasavage, J. P.; Lurie, J. C.; Ianna, P. A.; Finch, C. T.

2017-07-01

Stars reported here were targeted during the astrometry program because they were likely to be red dwarfs within 25pc with no previous published πtrig. Of the 151 systems, 93 are from previous compendia of proper motion stars, primarily based on work by Luyten (Luyten 1979nlcs.book.....L, 1980nltt.bookQ....L), and 58 are from our SuperCOSMOS-RECONS (SCR) search (Hambly et al. 2004AJ....128..437H; Henry et al. 2004AJ....128.2460H). The systems have μ=118-828mas/yr, with 143 having μ>180mas/yr, the canonical cutoff for Luyten's Two-Tenths (LTT) Catalog. For all but two objects (only one epoch of photometry was available for WT 1637 and LHS 2024 at the time of the paper), at least two epochs of absolute VJRKCIKC photometry on the Johnson-Kron-Cousins system were measured for each parallax field. Two V filters that are photometrically indistinguishable to 7 millimagnitudes (mmag) (Jao et al. 2011AJ....141..117J), one R filter, and one I filter were used for series of observations spanning 2-16years, depending on the star (the central wavelengths for the two VJ filters, the RKC filter, and the IKC filter are 5438Å, 5475Å, 6425Å, and 8075Å, respectively). The 2048*2046 Tektronix CCD camera on the CTIO/SMARTS 0.9m with a pixel (px) scale of 0.401''/px was used for both astrometric and photometric observations. In order to mitigate the effects of image distortion at the edges of the CCD, only the central quarter of the chip was used, resulting in a 6.8*6.8 square field of view. Photometry in the near-infrared JHKs filters has been extracted from the Two Micron All Sky Survey (2MASS; Cutri et al. 2003, Cat. II/246) and is rounded to the nearest hundredth magnitude in Table1. (5 data files).

VizieR Online Data Catalog: GLASS. IX. Structural param. from HFF & GLASS (Morishita+, 2017)

NASA Astrophysics Data System (ADS)

Morishita, T.; Abramson, L. E.; Treu, T.; Vulcani, B.; Schmidt, K. B.; Dressler, A.; Poggianti, B. M.; Malkan, M. A.; Wang, X.; Huang, K.-H.; Trenti, M.; Bradac, M.; Hoag, A.

2017-09-01

We base our analysis on Hubble Frontier Fields (HFF; Lotz+ 2017ApJ...837...97L) imaging and Grism Survey from Space (GLASS; Schmidt+ 2014ApJ...782L..36S; Treu+, 2015, J/ApJ/812/114) HST spectroscopy for the first four HFF clusters with complete data: Abell2744 (z=0.308), MACS0416 (0.396), MACS0717 (0.548), and MACS1149 (0.544). HFF imaging spans ACS F435/606/814W through WFC3IR F105/125/140/160W filters (seven bands). Our final catalog consists of 3948 galaxies, with 2200 in clusters and 1748 in field environments. A total of 298 have ground-based and 168 have GLASS spectroscopic redshifts. (1 data file).

Linear Parameter Varying Control Synthesis for Actuator Failure, Based on Estimated Parameter

NASA Technical Reports Server (NTRS)

Shin, Jong-Yeob; Wu, N. Eva; Belcastro, Christine

2002-01-01

The design of a linear parameter varying (LPV) controller for an aircraft at actuator failure cases is presented. The controller synthesis for actuator failure cases is formulated into linear matrix inequality (LMI) optimizations based on an estimated failure parameter with pre-defined estimation error bounds. The inherent conservatism of an LPV control synthesis methodology is reduced using a scaling factor on the uncertainty block which represents estimated parameter uncertainties. The fault parameter is estimated using the two-stage Kalman filter. The simulation results of the designed LPV controller for a HiMXT (Highly Maneuverable Aircraft Technology) vehicle with the on-line estimator show that the desired performance and robustness objectives are achieved for actuator failure cases.

Neuromusculoskeletal model self-calibration for on-line sequential bayesian moment estimation

NASA Astrophysics Data System (ADS)

Bueno, Diana R.; Montano, L.

2017-04-01

Objective. Neuromusculoskeletal models involve many subject-specific physiological parameters that need to be adjusted to adequately represent muscle properties. Traditionally, neuromusculoskeletal models have been calibrated with a forward-inverse dynamic optimization which is time-consuming and unfeasible for rehabilitation therapy. Non self-calibration algorithms have been applied to these models. To the best of our knowledge, the algorithm proposed in this work is the first on-line calibration algorithm for muscle models that allows a generic model to be adjusted to different subjects in a few steps. Approach. In this paper we propose a reformulation of the traditional muscle models that is able to sequentially estimate the kinetics (net joint moments), and also its full self-calibration (subject-specific internal parameters of the muscle from a set of arbitrary uncalibrated data), based on the unscented Kalman filter. The nonlinearity of the model as well as its calibration problem have obliged us to adopt the sum of Gaussians filter suitable for nonlinear systems. Main results. This sequential Bayesian self-calibration algorithm achieves a complete muscle model calibration using as input only a dataset of uncalibrated sEMG and kinematics data. The approach is validated experimentally using data from the upper limbs of 21 subjects. Significance. The results show the feasibility of neuromusculoskeletal model self-calibration. This study will contribute to a better understanding of the generalization of muscle models for subject-specific rehabilitation therapies. Moreover, this work is very promising for rehabilitation devices such as electromyography-driven exoskeletons or prostheses.

VizieR Online Data Catalog: New OB star candidates in Carina Arm (Mohr-Smith+, 2015)

NASA Astrophysics Data System (ADS)

Mohr-Smith, M.; Drew, J. E.; Barentsen, G.; Wright, N. J.; Napiwotzki, R.; Corradi, R. L. M.; Eisloffel, J.; Groot, P.; Kalari, V.; Parker, Q. A.; Raddi, R.; Sale, S. E.; Unruh, Y. C.; Vink, J. S.; Wesson, R.

2017-08-01

We make use of the photometry from two VPHAS+ fields, numbered 1678 and 1679, that are, respectively, centred on RA=10:18:10.91, DE=-58:03:52.3 (J2000) and on RA=10:25:27.27, DE=-58:03:52.3 (J2000). These were observed in succession in the u, g and r filters on the night of 2012 January 22. The red filter data in Hα, r and i were obtained on 2012 April 29. (2 data files).

Towards large scale multi-target tracking

NASA Astrophysics Data System (ADS)

Vo, Ba-Ngu; Vo, Ba-Tuong; Reuter, Stephan; Lam, Quang; Dietmayer, Klaus

2014-06-01

Multi-target tracking is intrinsically an NP-hard problem and the complexity of multi-target tracking solutions usually do not scale gracefully with problem size. Multi-target tracking for on-line applications involving a large number of targets is extremely challenging. This article demonstrates the capability of the random finite set approach to provide large scale multi-target tracking algorithms. In particular it is shown that an approximate filter known as the labeled multi-Bernoulli filter can simultaneously track one thousand five hundred targets in clutter on a standard laptop computer.

Filtering Fixes

ERIC Educational Resources Information Center

Manzo, Kathleen Kennedy

2009-01-01

Faced with concerns about Internet predators, cyberbullying, students' sharing of inappropriate content on social networks, and the abundance of sexually explicit or violent content online, many school leaders and technology directors are placing tighter restrictions on Web access to shield students from potential harm. Yet in Trussville and other…

Motion artifact detection and correction in functional near-infrared spectroscopy: a new hybrid method based on spline interpolation method and Savitzky-Golay filtering.

PubMed

Jahani, Sahar; Setarehdan, Seyed K; Boas, David A; Yücel, Meryem A

2018-01-01

Motion artifact contamination in near-infrared spectroscopy (NIRS) data has become an important challenge in realizing the full potential of NIRS for real-life applications. Various motion correction algorithms have been used to alleviate the effect of motion artifacts on the estimation of the hemodynamic response function. While smoothing methods, such as wavelet filtering, are excellent in removing motion-induced sharp spikes, the baseline shifts in the signal remain after this type of filtering. Methods, such as spline interpolation, on the other hand, can properly correct baseline shifts; however, they leave residual high-frequency spikes. We propose a hybrid method that takes advantage of different correction algorithms. This method first identifies the baseline shifts and corrects them using a spline interpolation method or targeted principal component analysis. The remaining spikes, on the other hand, are corrected by smoothing methods: Savitzky-Golay (SG) filtering or robust locally weighted regression and smoothing. We have compared our new approach with the existing correction algorithms in terms of hemodynamic response function estimation using the following metrics: mean-squared error, peak-to-peak error ([Formula: see text]), Pearson's correlation ([Formula: see text]), and the area under the receiver operator characteristic curve. We found that spline-SG hybrid method provides reasonable improvements in all these metrics with a relatively short computational time. The dataset and the code used in this study are made available online for the use of all interested researchers.

Measuring the Interestingness of News Articles

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

Pon, R K; Cardenas, A F; Buttler, D J

An explosive growth of online news has taken place. Users are inundated with thousands of news articles, only some of which are interesting. A system to filter out uninteresting articles would aid users that need to read and analyze many articles daily, such as financial analysts and government officials. The most obvious approach for reducing the amount of information overload is to learn keywords of interest for a user (Carreira et al., 2004). Although filtering articles based on keywords removes many irrelevant articles, there are still many uninteresting articles that are highly relevant to keyword searches. A relevant article maymore » not be interesting for various reasons, such as the article's age or if it discusses an event that the user has already read about in other articles. Although it has been shown that collaborative filtering can aid in personalized recommendation systems (Wang et al., 2006), a large number of users is needed. In a limited user environment, such as a small group of analysts monitoring news events, collaborative filtering would be ineffective. The definition of what makes an article interesting--or its 'interestingness'--varies from user to user and is continually evolving, calling for adaptable user personalization. Furthermore, due to the nature of news, most articles are uninteresting since many are similar or report events outside the scope of an individual's concerns. There has been much work in news recommendation systems, but none have yet addressed the question of what makes an article interesting.« less

Structural limitations of learning in a crowd: communication vulnerability and information diffusion in MOOCs.

PubMed

Gillani, Nabeel; Yasseri, Taha; Eynon, Rebecca; Hjorth, Isis

2014-09-23

Massive Open Online Courses (MOOCs) bring together a global crowd of thousands of learners for several weeks or months. In theory, the openness and scale of MOOCs can promote iterative dialogue that facilitates group cognition and knowledge construction. Using data from two successive instances of a popular business strategy MOOC, we filter observed communication patterns to arrive at the "significant" interaction networks between learners and use complex network analysis to explore the vulnerability and information diffusion potential of the discussion forums. We find that different discussion topics and pedagogical practices promote varying levels of 1) "significant" peer-to-peer engagement, 2) participant inclusiveness in dialogue, and ultimately, 3) modularity, which impacts information diffusion to prevent a truly "global" exchange of knowledge and learning. These results indicate the structural limitations of large-scale crowd-based learning and highlight the different ways that learners in MOOCs leverage, and learn within, social contexts. We conclude by exploring how these insights may inspire new developments in online education.

Structural limitations of learning in a crowd: communication vulnerability and information diffusion in MOOCs

PubMed Central

Gillani, Nabeel; Yasseri, Taha; Eynon, Rebecca; Hjorth, Isis

2014-01-01

Massive Open Online Courses (MOOCs) bring together a global crowd of thousands of learners for several weeks or months. In theory, the openness and scale of MOOCs can promote iterative dialogue that facilitates group cognition and knowledge construction. Using data from two successive instances of a popular business strategy MOOC, we filter observed communication patterns to arrive at the “significant” interaction networks between learners and use complex network analysis to explore the vulnerability and information diffusion potential of the discussion forums. We find that different discussion topics and pedagogical practices promote varying levels of 1) “significant” peer-to-peer engagement, 2) participant inclusiveness in dialogue, and ultimately, 3) modularity, which impacts information diffusion to prevent a truly “global” exchange of knowledge and learning. These results indicate the structural limitations of large-scale crowd-based learning and highlight the different ways that learners in MOOCs leverage, and learn within, social contexts. We conclude by exploring how these insights may inspire new developments in online education. PMID:25244925

Learning Collaborative Sparse Representation for Grayscale-Thermal Tracking.

PubMed

Li, Chenglong; Cheng, Hui; Hu, Shiyi; Liu, Xiaobai; Tang, Jin; Lin, Liang

2016-09-27

Integrating multiple different yet complementary feature representations has been proved to be an effective way for boosting tracking performance. This paper investigates how to perform robust object tracking in challenging scenarios by adaptively incorporating information from grayscale and thermal videos, and proposes a novel collaborative algorithm for online tracking. In particular, an adaptive fusion scheme is proposed based on collaborative sparse representation in Bayesian filtering framework. We jointly optimize sparse codes and the reliable weights of different modalities in an online way. In addition, this work contributes a comprehensive video benchmark, which includes 50 grayscale-thermal sequences and their ground truth annotations for tracking purpose. The videos are with high diversity and the annotations were finished by one single person to guarantee consistency. Extensive experiments against other stateof- the-art trackers with both grayscale and grayscale-thermal inputs demonstrate the effectiveness of the proposed tracking approach. Through analyzing quantitative results, we also provide basic insights and potential future research directions in grayscale-thermal tracking.

A video, text, and speech-driven realistic 3-d virtual head for human-machine interface.

PubMed

Yu, Jun; Wang, Zeng-Fu

2015-05-01

A multiple inputs-driven realistic facial animation system based on 3-D virtual head for human-machine interface is proposed. The system can be driven independently by video, text, and speech, thus can interact with humans through diverse interfaces. The combination of parameterized model and muscular model is used to obtain a tradeoff between computational efficiency and high realism of 3-D facial animation. The online appearance model is used to track 3-D facial motion from video in the framework of particle filtering, and multiple measurements, i.e., pixel color value of input image and Gabor wavelet coefficient of illumination ratio image, are infused to reduce the influence of lighting and person dependence for the construction of online appearance model. The tri-phone model is used to reduce the computational consumption of visual co-articulation in speech synchronized viseme synthesis without sacrificing any performance. The objective and subjective experiments show that the system is suitable for human-machine interaction.

Structural limitations of learning in a crowd: communication vulnerability and information diffusion in MOOCs

NASA Astrophysics Data System (ADS)

Gillani, Nabeel; Yasseri, Taha; Eynon, Rebecca; Hjorth, Isis

2014-09-01

Massive Open Online Courses (MOOCs) bring together a global crowd of thousands of learners for several weeks or months. In theory, the openness and scale of MOOCs can promote iterative dialogue that facilitates group cognition and knowledge construction. Using data from two successive instances of a popular business strategy MOOC, we filter observed communication patterns to arrive at the ``significant'' interaction networks between learners and use complex network analysis to explore the vulnerability and information diffusion potential of the discussion forums. We find that different discussion topics and pedagogical practices promote varying levels of 1) ``significant'' peer-to-peer engagement, 2) participant inclusiveness in dialogue, and ultimately, 3) modularity, which impacts information diffusion to prevent a truly ``global'' exchange of knowledge and learning. These results indicate the structural limitations of large-scale crowd-based learning and highlight the different ways that learners in MOOCs leverage, and learn within, social contexts. We conclude by exploring how these insights may inspire new developments in online education.

Online Dectection and Modeling of Safety Boundaries for Aerospace Application Using Bayesian Statistics

NASA Technical Reports Server (NTRS)

He, Yuning

2015-01-01

The behavior of complex aerospace systems is governed by numerous parameters. For safety analysis it is important to understand how the system behaves with respect to these parameter values. In particular, understanding the boundaries between safe and unsafe regions is of major importance. In this paper, we describe a hierarchical Bayesian statistical modeling approach for the online detection and characterization of such boundaries. Our method for classification with active learning uses a particle filter-based model and a boundary-aware metric for best performance. From a library of candidate shapes incorporated with domain expert knowledge, the location and parameters of the boundaries are estimated using advanced Bayesian modeling techniques. The results of our boundary analysis are then provided in a form understandable by the domain expert. We illustrate our approach using a simulation model of a NASA neuro-adaptive flight control system, as well as a system for the detection of separation violations in the terminal airspace.

Adaptive Optimal Control Using Frequency Selective Information of the System Uncertainty With Application to Unmanned Aircraft.

PubMed

Maity, Arnab; Hocht, Leonhard; Heise, Christian; Holzapfel, Florian

2018-01-01

A new efficient adaptive optimal control approach is presented in this paper based on the indirect model reference adaptive control (MRAC) architecture for improvement of adaptation and tracking performance of the uncertain system. The system accounts here for both matched and unmatched unknown uncertainties that can act as plant as well as input effectiveness failures or damages. For adaptation of the unknown parameters of these uncertainties, the frequency selective learning approach is used. Its idea is to compute a filtered expression of the system uncertainty using multiple filters based on online instantaneous information, which is used for augmentation of the update law. It is capable of adjusting a sudden change in system dynamics without depending on high adaptation gains and can satisfy exponential parameter error convergence under certain conditions in the presence of structured matched and unmatched uncertainties as well. Additionally, the controller of the MRAC system is designed using a new optimal control method. This method is a new linear quadratic regulator-based optimal control formulation for both output regulation and command tracking problems. It provides a closed-form control solution. The proposed overall approach is applied in a control of lateral dynamics of an unmanned aircraft problem to show its effectiveness.

Real-Time Decentralized Neural Control via Backstepping for a Robotic Arm Powered by Industrial Servomotors.

PubMed

Vazquez, Luis A; Jurado, Francisco; Castaneda, Carlos E; Santibanez, Victor

2018-02-01

This paper presents a continuous-time decentralized neural control scheme for trajectory tracking of a two degrees of freedom direct drive vertical robotic arm. A decentralized recurrent high-order neural network (RHONN) structure is proposed to identify online, in a series-parallel configuration and using the filtered error learning law, the dynamics of the plant. Based on the RHONN subsystems, a local neural controller is derived via backstepping approach. The effectiveness of the decentralized neural controller is validated on a robotic arm platform, of our own design and unknown parameters, which uses industrial servomotors to drive the joints.

BELINDA: Broadband Emission Lidar with Narrowband Determination of Absorption. A new concept for measuring water vapor and temperature profiles

NASA Technical Reports Server (NTRS)

Theopold, F. A.; Weitkamp, C.; Michaelis, W.

1992-01-01

We present a new concept for differential absorption lidar measurements of water vapor and temperature profiles. The idea is to use one broadband emission laser and a narrowband filter system for separation of the 'online' and 'offline' return signals. It is shown that BELINDA offers improvements as to laser emission shape and stability requirements, background suppression, and last and most important a significant reduction of the influence of Rayleigh scattering. A suitably designed system based on this concept is presented, capable of measuring water vapor or temperature profiles throughout the planetary boundary layer.

Toward a Real-Time Measurement-Based System for Estimation of Helicopter Engine Degradation Due to Compressor Erosion

NASA Technical Reports Server (NTRS)

Litt, Jonathan S.; Simo, Donald L.

2007-01-01

This paper presents a preliminary demonstration of an automated health assessment tool, capable of real-time on-board operation using existing engine control hardware. The tool allows operators to discern how rapidly individual turboshaft engines are degrading. As the compressor erodes, performance is lost, and with it the ability to generate power. Thus, such a tool would provide an instant assessment of the engine s fitness to perform a mission, and would help to pinpoint any abnormal wear or performance anomalies before they became serious, thereby decreasing uncertainty and enabling improved maintenance scheduling. The research described in the paper utilized test stand data from a T700-GE-401 turboshaft engine that underwent sand-ingestion testing to scale a model-based compressor efficiency degradation estimation algorithm. This algorithm was then applied to real-time Health Usage and Monitoring System (HUMS) data from a T700-GE-701C to track compressor efficiency on-line. The approach uses an optimal estimator called a Kalman filter. The filter is designed to estimate the compressor efficiency using only data from the engine s sensors as input.

Calibrating Parameters of Power System Stability Models using Advanced Ensemble Kalman Filter

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

Huang, Renke; Diao, Ruisheng; Li, Yuanyuan

With the ever increasing penetration of renewable energy, smart loads, energy storage, and new market behavior, today’s power grid becomes more dynamic and stochastic, which may invalidate traditional study assumptions and pose great operational challenges. Thus, it is of critical importance to maintain good-quality models for secure and economic planning and real-time operation. Following the 1996 Western Systems Coordinating Council (WSCC) system blackout, North American Electric Reliability Corporation (NERC) and Western Electricity Coordinating Council (WECC) in North America enforced a number of policies and standards to guide the power industry to periodically validate power grid models and calibrate poor parametersmore » with the goal of building sufficient confidence in model quality. The PMU-based approach using online measurements without interfering with the operation of generators provides a low-cost alternative to meet NERC standards. This paper presents an innovative procedure and tool suites to validate and calibrate models based on a trajectory sensitivity analysis method and an advanced ensemble Kalman filter algorithm. The developed prototype demonstrates excellent performance in identifying and calibrating bad parameters of a realistic hydro power plant against multiple system events.« less

FPGA-based Fused Smart Sensor for Real-Time Plant-Transpiration Dynamic Estimation

PubMed Central

Millan-Almaraz, Jesus Roberto; de Jesus Romero-Troncoso, Rene; Guevara-Gonzalez, Ramon Gerardo; Contreras-Medina, Luis Miguel; Carrillo-Serrano, Roberto Valentin; Osornio-Rios, Roque Alfredo; Duarte-Galvan, Carlos; Rios-Alcaraz, Miguel Angel; Torres-Pacheco, Irineo

2010-01-01

Plant transpiration is considered one of the most important physiological functions because it constitutes the plants evolving adaptation to exchange moisture with a dry atmosphere which can dehydrate or eventually kill the plant. Due to the importance of transpiration, accurate measurement methods are required; therefore, a smart sensor that fuses five primary sensors is proposed which can measure air temperature, leaf temperature, air relative humidity, plant out relative humidity and ambient light. A field programmable gate array based unit is used to perform signal processing algorithms as average decimation and infinite impulse response filters to the primary sensor readings in order to reduce the signal noise and improve its quality. Once the primary sensor readings are filtered, transpiration dynamics such as: transpiration, stomatal conductance, leaf-air-temperature-difference and vapor pressure deficit are calculated in real time by the smart sensor. This permits the user to observe different primary and calculated measurements at the same time and the relationship between these which is very useful in precision agriculture in the detection of abnormal conditions. Finally, transpiration related stress conditions can be detected in real time because of the use of online processing and embedded communications capabilities. PMID:22163656

Nondestructive, fast, and cost-effective image processing method for roughness measurement of randomly rough metallic surfaces.

PubMed

Ghodrati, Sajjad; Kandi, Saeideh Gorji; Mohseni, Mohsen

2018-06-01

In recent years, various surface roughness measurement methods have been proposed as alternatives to the commonly used stylus profilometry, which is a low-speed, destructive, expensive but precise method. In this study, a novel method, called "image profilometry," has been introduced for nondestructive, fast, and low-cost surface roughness measurement of randomly rough metallic samples based on image processing and machine vision. The impacts of influential parameters such as image resolution and filtering approach for elimination of the long wavelength surface undulations on the accuracy of the image profilometry results have been comprehensively investigated. Ten surface roughness parameters were measured for the samples using both the stylus and image profilometry. Based on the results, the best image resolution was 800 dpi, and the most practical filtering method was Gaussian convolution+cutoff. In these conditions, the best and worst correlation coefficients (R 2 ) between the stylus and image profilometry results were 0.9892 and 0.9313, respectively. Our results indicated that the image profilometry predicted the stylus profilometry results with high accuracy. Consequently, it could be a viable alternative to the stylus profilometry, particularly in online applications.

Performance Enhancement of Pharmacokinetic Diffuse Fluorescence Tomography by Use of Adaptive Extended Kalman Filtering.

PubMed

Wang, Xin; Wu, Linhui; Yi, Xi; Zhang, Yanqi; Zhang, Limin; Zhao, Huijuan; Gao, Feng

2015-01-01

Due to both the physiological and morphological differences in the vascularization between healthy and diseased tissues, pharmacokinetic diffuse fluorescence tomography (DFT) can provide contrast-enhanced and comprehensive information for tumor diagnosis and staging. In this regime, the extended Kalman filtering (EKF) based method shows numerous advantages including accurate modeling, online estimation of multiparameters, and universal applicability to any optical fluorophore. Nevertheless the performance of the conventional EKF highly hinges on the exact and inaccessible prior knowledge about the initial values. To address the above issues, an adaptive-EKF scheme is proposed based on a two-compartmental model for the enhancement, which utilizes a variable forgetting-factor to compensate the inaccuracy of the initial states and emphasize the effect of the current data. It is demonstrated using two-dimensional simulative investigations on a circular domain that the proposed adaptive-EKF can obtain preferable estimation of the pharmacokinetic-rates to the conventional-EKF and the enhanced-EKF in terms of quantitativeness, noise robustness, and initialization independence. Further three-dimensional numerical experiments on a digital mouse model validate the efficacy of the method as applied in realistic biological systems.

A quick on-line state of health estimation method for Li-ion battery with incremental capacity curves processed by Gaussian filter

NASA Astrophysics Data System (ADS)

Li, Yi; Abdel-Monem, Mohamed; Gopalakrishnan, Rahul; Berecibar, Maitane; Nanini-Maury, Elise; Omar, Noshin; van den Bossche, Peter; Van Mierlo, Joeri

2018-01-01

This paper proposes an advanced state of health (SoH) estimation method for high energy NMC lithium-ion batteries based on the incremental capacity (IC) analysis. IC curves are used due to their ability of detect and quantify battery degradation mechanism. A simple and robust smoothing method is proposed based on Gaussian filter to reduce the noise on IC curves, the signatures associated with battery ageing can therefore be accurately identified. A linear regression relationship is found between the battery capacity with the positions of features of interest (FOIs) on IC curves. Results show that the developed SoH estimation function from one single battery cell is able to evaluate the SoH of other batteries cycled under different cycling depth with less than 2.5% maximum errors, which proves the robustness of the proposed method on SoH estimation. With this technique, partial charging voltage curves can be used for SoH estimation and the testing time can be therefore largely reduced. This method shows great potential to be applied in reality, as it only requires static charging curves and can be easily implemented in battery management system (BMS).

Influence of temperature, filter wettability, and timing of filtration on the removal of WBCs from RBC concentrates.

PubMed

van der Meer, P F; Pietersz, R N; Reesink, H W

2001-04-01

The efficacy of the removal of WBCs from buffy coat-reduced RBC concentrates by filtration is determined by many variables. The aim of this study was to investigate the influence of the filtration temperature, the wettability of the filter material, and the timing of the filtration after collection. The investigation used commercially available filters: 3 dry "online" filters (Cellselect FR, Fresenius Hemocare; BioR-01-max, Fresenius; Leucoflex LCG1, MacoPharma) and one wet "inline" filter (Leucoflex LCR4, MacoPharma) that contained saline-adenine-glucose-mannitol additive solution for RBCs and differed from the online version only in wettability. After buffy coat removal and suspension in saline-adenine-glucose-mannitol, filtrations were performed immediately at room temperature (RT) and after 2 hours' storage of the RBC concentrates at 4 degrees C, while the Leucoflex LCR4 was also tested after 24 hours' storage of the RBC concentrates at 4 degrees C. Sets of 12 pooled experiments were performed to prevent donor-dependent differences. The Cellselect FR gave significantly better WBC removal from RBC concentrates at 4 degrees C than at RT, with residual WBCs of 1.44 +/- 0.58 x 10(6) and 2.78 +/- 1.23 x 10(6), respectively (p<0.001). The BioR-01-max gave no significant difference: 0.62 +/- 0.27 x 10(6) WBCs (at 4 degrees C) versus 0.61 +/- 0.25 x 10(6) WBCs (at RT). Filtration with the Leucoflex LCG1 resulted in 0.06 +/- 0.03 x 10(6) and 0.07 +/- 0.07 x 10(6) WBCs at 4 degrees C and RT, respectively, which is not a significant difference. The Leucoflex LCR4, however, gave 2.08 +/- 0.84 x 10(6) WBCs at RT, 0.52 +/- 0.44 x 10(6) WBCs at 4 degrees C after 2 hours' cooling, and 0.05 +/- 0.10 x 10(6) WBCs at 4 degrees C after 24 hours' cooling (all p<0.001). Temperature, filter wettability, and timing of filtration after collection influence the efficacy of a filter for RBC concentrates. These variables need to be established, validated, and controlled before a filter can be selected for routine use.

High-performance information search filters for acute kidney injury content in PubMed, Ovid Medline and Embase.

PubMed

Hildebrand, Ainslie M; Iansavichus, Arthur V; Haynes, R Brian; Wilczynski, Nancy L; Mehta, Ravindra L; Parikh, Chirag R; Garg, Amit X

2014-04-01

We frequently fail to identify articles relevant to the subject of acute kidney injury (AKI) when searching the large bibliographic databases such as PubMed, Ovid Medline or Embase. To address this issue, we used computer automation to create information search filters to better identify articles relevant to AKI in these databases. We first manually reviewed a sample of 22 992 full-text articles and used prespecified criteria to determine whether each article contained AKI content or not. In the development phase (two-thirds of the sample), we developed and tested the performance of >1.3-million unique filters. Filters with high sensitivity and high specificity for the identification of AKI articles were then retested in the validation phase (remaining third of the sample). We succeeded in developing and validating high-performance AKI search filters for each bibliographic database with sensitivities and specificities in excess of 90%. Filters optimized for sensitivity reached at least 97.2% sensitivity, and filters optimized for specificity reached at least 99.5% specificity. The filters were complex; for example one PubMed filter included >140 terms used in combination, including 'acute kidney injury', 'tubular necrosis', 'azotemia' and 'ischemic injury'. In proof-of-concept searches, physicians found more articles relevant to topics in AKI with the use of the filters. PubMed, Ovid Medline and Embase can be filtered for articles relevant to AKI in a reliable manner. These high-performance information filters are now available online and can be used to better identify AKI content in large bibliographic databases.

Fused Smart Sensor Network for Multi-Axis Forward Kinematics Estimation in Industrial Robots

PubMed Central

Rodriguez-Donate, Carlos; Osornio-Rios, Roque Alfredo; Rivera-Guillen, Jesus Rooney; de Jesus Romero-Troncoso, Rene

2011-01-01

Flexible manipulator robots have a wide industrial application. Robot performance requires sensing its position and orientation adequately, known as forward kinematics. Commercially available, motion controllers use high-resolution optical encoders to sense the position of each joint which cannot detect some mechanical deformations that decrease the accuracy of the robot position and orientation. To overcome those problems, several sensor fusion methods have been proposed but at expenses of high-computational load, which avoids the online measurement of the joint’s angular position and the online forward kinematics estimation. The contribution of this work is to propose a fused smart sensor network to estimate the forward kinematics of an industrial robot. The developed smart processor uses Kalman filters to filter and to fuse the information of the sensor network. Two primary sensors are used: an optical encoder, and a 3-axis accelerometer. In order to obtain the position and orientation of each joint online a field-programmable gate array (FPGA) is used in the hardware implementation taking advantage of the parallel computation capabilities and reconfigurability of this device. With the aim of evaluating the smart sensor network performance, three real-operation-oriented paths are executed and monitored in a 6-degree of freedom robot. PMID:22163850

Improving information filtering via network manipulation

NASA Astrophysics Data System (ADS)

Zhang, Fuguo; Zeng, An

2012-12-01

The recommender system is a very promising way to address the problem of overabundant information for online users. Although the information filtering for the online commercial systems has received much attention recently, almost all of the previous works are dedicated to design new algorithms and consider the user-item bipartite networks as given and constant information. However, many problems for recommender systems such as the cold-start problem (i.e., low recommendation accuracy for the small-degree items) are actually due to the limitation of the underlying user-item bipartite networks. In this letter, we propose a strategy to enhance the performance of the already existing recommendation algorithms by directly manipulating the user-item bipartite networks, namely adding some virtual connections to the networks. Numerical analyses on two benchmark data sets, MovieLens and Netflix, show that our method can remarkably improves the recommendation performance. Specifically, it not only improves the recommendations accuracy (especially for the small-degree items), but also helps the recommender systems generate more diverse and novel recommendations.

Adaptive prognosis of lithium-ion batteries based on the combination of particle filters and radial basis function neural networks

NASA Astrophysics Data System (ADS)

Sbarufatti, Claudio; Corbetta, Matteo; Giglio, Marco; Cadini, Francesco

2017-03-01

Lithium-Ion rechargeable batteries are widespread power sources with applications to consumer electronics, electrical vehicles, unmanned aerial and spatial vehicles, etc. The failure to supply the required power levels may lead to severe safety and economical consequences. Thus, in view of the implementation of adequate maintenance strategies, the development of diagnostic and prognostic tools for monitoring the state of health of the batteries and predicting their remaining useful life is becoming a crucial task. Here, we propose a method for predicting the end of discharge of Li-Ion batteries, which stems from the combination of particle filters with radial basis function neural networks. The major innovation lies in the fact that the radial basis function model is adaptively trained on-line, i.e., its parameters are identified in real time by the particle filter as new observations of the battery terminal voltage become available. By doing so, the prognostic algorithm achieves the flexibility needed to provide sound end-of-discharge time predictions as the charge-discharge cycles progress, even in presence of anomalous behaviors due to failures or unforeseen operating conditions. The method is demonstrated with reference to actual Li-Ion battery discharge data contained in the prognostics data repository of the NASA Ames Research Center database.

The recommender system for virtual items in MMORPGs based on a novel collaborative filtering approach

NASA Astrophysics Data System (ADS)

Li, S. G.; Shi, L.

2014-10-01

The recommendation system for virtual items in massive multiplayer online role-playing games (MMORPGs) has aroused the interest of researchers. Of the many approaches to construct a recommender system, collaborative filtering (CF) has been the most successful one. However, the traditional CFs just lure customers into the purchasing action and overlook customers' satisfaction, moreover, these techniques always suffer from low accuracy under cold-start conditions. Therefore, a novel collaborative filtering (NCF) method is proposed to identify like-minded customers according to the preference similarity coefficient (PSC), which implies correlation between the similarity of customers' characteristics and the similarity of customers' satisfaction level for the product. Furthermore, the analytic hierarchy process (AHP) is used to determine the relative importance of each characteristic of the customer and the improved ant colony optimisation (IACO) is adopted to generate the expression of the PSC. The IACO creates solutions using the Markov random walk model, which can accelerate the convergence of algorithm and prevent prematurity. For a target customer whose neighbours can be found, the NCF can predict his satisfaction level towards the suggested products and recommend the acceptable ones. Under cold-start conditions, the NCF will generate the recommendation list by excluding items that other customers prefer.

eTACTS: a method for dynamically filtering clinical trial search results.

PubMed

Miotto, Riccardo; Jiang, Silis; Weng, Chunhua

2013-12-01

Information overload is a significant problem facing online clinical trial searchers. We present eTACTS, a novel interactive retrieval framework using common eligibility tags to dynamically filter clinical trial search results. eTACTS mines frequent eligibility tags from free-text clinical trial eligibility criteria and uses these tags for trial indexing. After an initial search, eTACTS presents to the user a tag cloud representing the current results. When the user selects a tag, eTACTS retains only those trials containing that tag in their eligibility criteria and generates a new cloud based on tag frequency and co-occurrences in the remaining trials. The user can then select a new tag or unselect a previous tag. The process iterates until a manageable number of trials is returned. We evaluated eTACTS in terms of filtering efficiency, diversity of the search results, and user eligibility to the filtered trials using both qualitative and quantitative methods. eTACTS (1) rapidly reduced search results from over a thousand trials to ten; (2) highlighted trials that are generally not top-ranked by conventional search engines; and (3) retrieved a greater number of suitable trials than existing search engines. eTACTS enables intuitive clinical trial searches by indexing eligibility criteria with effective tags. User evaluation was limited to one case study and a small group of evaluators due to the long duration of the experiment. Although a larger-scale evaluation could be conducted, this feasibility study demonstrated significant advantages of eTACTS over existing clinical trial search engines. A dynamic eligibility tag cloud can potentially enhance state-of-the-art clinical trial search engines by allowing intuitive and efficient filtering of the search result space. Copyright © 2013 The Authors. Published by Elsevier Inc. All rights reserved.

eTACTS: A Method for Dynamically Filtering Clinical Trial Search Results

PubMed Central

Miotto, Riccardo; Jiang, Silis; Weng, Chunhua

2013-01-01

Objective Information overload is a significant problem facing online clinical trial searchers. We present eTACTS, a novel interactive retrieval framework using common eligibility tags to dynamically filter clinical trial search results. Materials and Methods eTACTS mines frequent eligibility tags from free-text clinical trial eligibility criteria and uses these tags for trial indexing. After an initial search, eTACTS presents to the user a tag cloud representing the current results. When the user selects a tag, eTACTS retains only those trials containing that tag in their eligibility criteria and generates a new cloud based on tag frequency and co-occurrences in the remaining trials. The user can then select a new tag or unselect a previous tag. The process iterates until a manageable number of trials is returned. We evaluated eTACTS in terms of filtering efficiency, diversity of the search results, and user eligibility to the filtered trials using both qualitative and quantitative methods. Results eTACTS (1) rapidly reduced search results from over a thousand trials to ten; (2) highlighted trials that are generally not top-ranked by conventional search engines; and (3) retrieved a greater number of suitable trials than existing search engines. Discussion eTACTS enables intuitive clinical trial searches by indexing eligibility criteria with effective tags. User evaluation was limited to one case study and a small group of evaluators due to the long duration of the experiment. Although a larger-scale evaluation could be conducted, this feasibility study demonstrated significant advantages of eTACTS over existing clinical trial search engines. Conclusion A dynamic eligibility tag cloud can potentially enhance state-of-the-art clinical trial search engines by allowing intuitive and efficient filtering of the search result space. PMID:23916863

Probabilistic Multi-Sensor Fusion Based Indoor Positioning System on a Mobile Device

PubMed Central

He, Xiang; Aloi, Daniel N.; Li, Jia

2015-01-01

Nowadays, smart mobile devices include more and more sensors on board, such as motion sensors (accelerometer, gyroscope, magnetometer), wireless signal strength indicators (WiFi, Bluetooth, Zigbee), and visual sensors (LiDAR, camera). People have developed various indoor positioning techniques based on these sensors. In this paper, the probabilistic fusion of multiple sensors is investigated in a hidden Markov model (HMM) framework for mobile-device user-positioning. We propose a graph structure to store the model constructed by multiple sensors during the offline training phase, and a multimodal particle filter to seamlessly fuse the information during the online tracking phase. Based on our algorithm, we develop an indoor positioning system on the iOS platform. The experiments carried out in a typical indoor environment have shown promising results for our proposed algorithm and system design. PMID:26694387

Probabilistic Multi-Sensor Fusion Based Indoor Positioning System on a Mobile Device.

PubMed

He, Xiang; Aloi, Daniel N; Li, Jia

2015-12-14

Nowadays, smart mobile devices include more and more sensors on board, such as motion sensors (accelerometer, gyroscope, magnetometer), wireless signal strength indicators (WiFi, Bluetooth, Zigbee), and visual sensors (LiDAR, camera). People have developed various indoor positioning techniques based on these sensors. In this paper, the probabilistic fusion of multiple sensors is investigated in a hidden Markov model (HMM) framework for mobile-device user-positioning. We propose a graph structure to store the model constructed by multiple sensors during the offline training phase, and a multimodal particle filter to seamlessly fuse the information during the online tracking phase. Based on our algorithm, we develop an indoor positioning system on the iOS platform. The experiments carried out in a typical indoor environment have shown promising results for our proposed algorithm and system design.

The TileCal Online Energy Estimation for the Next LHC Operation Period

NASA Astrophysics Data System (ADS)

Sotto-Maior Peralva, B.; ATLAS Collaboration

2015-05-01

The ATLAS Tile Calorimeter (TileCal) is the detector used in the reconstruction of hadrons, jets and missing transverse energy from the proton-proton collisions at the Large Hadron Collider (LHC). It covers the central part of the ATLAS detector (|η| < 1.6). The energy deposited by the particles is read out by approximately 5,000 cells, with double readout channels. The signal provided by the readout electronics for each channel is digitized at 40 MHz and its amplitude is estimated by an optimal filtering algorithm, which expects a single signal with a well-defined shape. However, the LHC luminosity is expected to increase leading to pile-up that deforms the signal of interest. Due to limited resources, the current hardware setup, which is based on Digital Signal Processors (DSP), does not allow the implementation of sophisticated energy estimation methods that deal with the pile-up. Therefore, the technique to be employed for online energy estimation in TileCal for next LHC operation period must be based on fast filters such as the Optimal Filter (OF) and the Matched Filter (MF). Both the OF and MF methods envisage the use of the background second order statistics in its design, more precisely the covariance matrix. However, the identity matrix has been used to describe this quantity. Although this approximation can be valid for low luminosity LHC, it leads to biased estimators under pile- up conditions. Since most of the TileCal cell present low occupancy, the pile-up, which is often modeled by a non-Gaussian distribution, can be seen as outlier events. Consequently, the classical covariance matrix estimation does not describe correctly the second order statistics of the background for the majority of the events, as this approach is very sensitive to outliers. As a result, the OF (or MF) coefficients are miscalculated leading to a larger variance and biased energy estimator. This work evaluates the usage of a robust covariance estimator, namely the Minimum Covariance Determinant (MCD) algorithm, to be applied in the OF design. The goal of the MCD estimator is to find a number of observations whose classical covariance matrix has the lowest determinant. Hence, this procedure avoids taking into account low likelihood events to describe the background. It is worth mentioning that the background covariance matrix as well as the OF coefficients for each TileCal channel are computed offline and stored for both online and offline use. In order to evaluate the impact of the MCD estimator on the performance of the OF, simulated data sets were used. Different average numbers of interactions per bunch crossing and bunch spacings were tested. The results show that the estimation of the background covariance matrix through MCD improves significantly the final energy resolution with respect to the identity matrix which is currently used. Particularly, for high occupancy cells, the final energy resolution is improved by more than 20%. Moreover, the use of the classical covariance matrix degrades the energy resolution for the majority of TileCal cells.

Management of Fractured Inferior Vena Cava Filters: Outcomes by Fragment Location.

PubMed

Trerotola, Scott O; Stavropoulos, S William

2017-09-01

Purpose To inform the management of fractured inferior vena cava filters on the basis of results from a tertiary referral center specializing in complex filter retrieval. Materials and Methods This study had institutional review board approval and was HIPAA compliant. Retrospective analysis of all patients with fractured filters and/or filter fragments evaluated for removal in a complex filter removal program was performed. Removal was attempted when fragments were intravascular or immediately extravascular by using primarily endobronchial forceps for caval fragments and snares for cardiac and pulmonary fragments. Data collected included success rate and complications of filter and fragment removal, symptoms relating to the filter or fragment, techniques used for removal, and follow-up of retained fragments. Results Sixty-five patients (12 men, 53 women) of a total of 222 patients referred for complex retrieval had fractured filters. Of these patients, two had undergone filter removal elsewhere and had retained fragments. All 63 filters were removed successfully with forceps (n = 61), a cone (n = 1), or a snare (n = 1). There were 116 separate filter fragments; removal was attempted for 78 fragments. Removal was successful for 63 (81%) of 78 fragments and varied by location. All extravascular fragments except one were retained. In all, 63 (54%) of 116 fragments were removed percutaneously, rendering 34 (54%) of 63 patients fragment free. Five minor (7.7% [five of 65]) and four major (6.2% [four of 65]) complications occurred. Conclusion Intravascular filter fragments can be removed safely with success rates that vary according to location. Because extravascular fragments are not readily accessible for removal, many patients are not rendered fragment free. © RSNA, 2017 Online supplemental material is available for this article.

Exposure to Political Diversity Online

ERIC Educational Resources Information Center

Munson, Sean A.

2012-01-01

The Internet gives individuals more choice in political news and information sources and more tools to filter out disagreeable information. Citing the preference described by selective exposure theory--that people see and attend to information that supports their beliefs and avoid counter-attitudinal information--observers warn that people may use…

VizieR Online Data Catalog: GRB 120327A afterglow colour variations (Melandri+, 2017)

NASA Astrophysics Data System (ADS)

Melandri, A.; Covino, S.; Zaninoni, E.; Campana, S.; Bolmer, J.; Cobb, B. E.; Gorosabel, J.; Kim, J.-W.; Kuin, P.; Kuroda, D.; Malesani, D.; Mundell, C. G.; Nappo, F.; Sbarufatti, B.; Smith, R. J.; Steele, I. A.; Topinka, M.; Trotter, A. S.; Virgili, F. J.; Bernardini, M. G.; D'Avanzo, P.; D'Elia, V.; Fugazza, D.; Ghirlanda, G.; Gomboc, A.; Greiner, J.; Guidorzi, C.; Haislip, J. B.; Hanayama, H.; Hanlon, L.; Im, M.; Ivarsen, K. M.; Japelj, J.; Jelinek, M.; Kawai, N.; Kobayashi, S.; Kopac, D.; Lacluyze, A. P.; Martin-Carrillo, A.; Murphy, D.; Reichart, D. E.; Salvaterra, R.; Salafia, O. S.; Tagliaferri, G.; Vergani, S. D.

2017-09-01

Photometric data of GRB120327A are presented. Magnitudes are in the Vega system unless for griz filters that are in the AB system, and they are all not corrected for Galactic absorption. Errors are at 1sigma level. (1 data file).

Gates Speaks to Librarians.

ERIC Educational Resources Information Center

St. Lifer, Evan

1997-01-01

In an interview, Microsoft CEO Bill Gates answers questions about the Gates Library Foundation; Libraries Online; tax-support for libraries; comparisons to Andrew Carnegie; charges of "buying" the library market; Internet filters, policies, and government censorship; the future of the World Wide Web and the role of librarians in its…

Detection, Location and Grasping Objects Using a Stereo Sensor on UAV in Outdoor Environments

PubMed Central

Ramon Soria, Pablo; Arrue, Begoña C.; Ollero, Anibal

2017-01-01

The article presents a vision system for the autonomous grasping of objects with Unmanned Aerial Vehicles (UAVs) in real time. Giving UAVs the capability to manipulate objects vastly extends their applications, as they are capable of accessing places that are difficult to reach or even unreachable for human beings. This work is focused on the grasping of known objects based on feature models. The system runs in an on-board computer on a UAV equipped with a stereo camera and a robotic arm. The algorithm learns a feature-based model in an offline stage, then it is used online for detection of the targeted object and estimation of its position. This feature-based model was proved to be robust to both occlusions and the presence of outliers. The use of stereo cameras improves the learning stage, providing 3D information and helping to filter features in the online stage. An experimental system was derived using a rotary-wing UAV and a small manipulator for final proof of concept. The robotic arm is designed with three degrees of freedom and is lightweight due to payload limitations of the UAV. The system has been validated with different objects, both indoors and outdoors. PMID:28067851

Accounting for phase drifts in SSVEP-based BCIs by means of biphasic stimulation.

PubMed

Wu, Hung-Yi; Lee, Po-Lei; Chang, Hsiang-Chih; Hsieh, Jen-Chuen

2011-05-01

This study proposes a novel biphasic stimulation technique to solve the issue of phase drifts in steady-state visual evoked potential (SSVEPs) in phase-tagged systems. Phase calibration was embedded in stimulus sequences using a biphasic flicker, which is driven by a sequence with alternating reference and phase-shift states. Nine subjects were recruited to participate in off-line and online tests. Signals were bandpass filtered and segmented by trigger signals into reference and phase-shift epochs. Frequency components of SSVEP in the reference and phase-shift epochs were extracted using the Fourier method with a 50% overlapped sliding window. The real and imaginary parts of the SSVEP frequency components were organized into complex vectors in each epoch. Hotelling's t-square test was used to determine the significances of nonzero mean vectors. The rejection of noisy data segments and the validation of gaze detections were made based on p values. The phase difference between the valid mean vectors of reference and phase-shift epochs was used to identify user's gazed targets in this system. Data showed an average information transfer rate of 44.55 and 38.21 bits/min in off-line and online tests, respectively. © 2011 IEEE

VizieR Online Data Catalog: Halpha measurements in Abell 2465 (Wegner+, 2015)

NASA Astrophysics Data System (ADS)

Wegner, G. A.; Chu, D. S.; Hwang, H. S.

2015-07-01

The wavelength of the Hα line at the redshift of Abell 2465 is near 817nm in a clear spectral region between the many telluric emission lines. A custom narrow-band filter for observing Hα was obtained from the Andover Corp. It has a peak transmission at 817.7nm (m817) and a full width at half-maximum (FWHM) of 8.77nm. The wide filter was a Gunn i (ig) filter with nearly the same central wavelength of 820nm and a FWHM of 185nm, and was manufactured by Custom Scientific. Hα observations of Abell 2465 were obtained 2012 September 19-23 using the 2.4m Hiltner telescope at the MDM Observatory on Kitt Peak. The 'Nellie' CCD was used. (1 data file).

VizieR Online Data Catalog: Long-term optical monitoring of E1821+643 (Shapovalova+, 2016)

NASA Astrophysics Data System (ADS)

Shapovalova, A. I.; Popovic, L. C.; Chavushyan, V. H.; Burenkov, A. N.; Ilic, D.; Kollatschny, W.; Kovacevic, A.; Valdes, J. R.; Patino-Alvarez, V.; Leon-Tavares, J.; Torrealba, J.; Zhdanova, V. E.

2016-03-01

The photometry in BVR filters of E1821+643 was performed at the Special Astrophysical Observatory of the Russian Academy of Science (SAO RAS) during the 2003-2014 period (98 nights) with the 1m Zeiss telescope. The photometric system of this instrument resembles those of Johnson in the B and V filters and of Cousins in the R filter. Spectra of E1821+643 (~140 nights) were acquired with two telescopes (6m and 1m) at SAO RAS, Russia (during 1998-2014), one telescope (INAOE's 2.1m) at Guillermo Haro Observatory (GHO) Cananea, Sonora, Mexico (during 1998-2007 and 2013), and two telescopes (3.5m and 2.2m) at Calar Alto Observatory, Spain (during 1990-1994). (4 data files).

VizieR Online Data Catalog: Free-form lensing grid solution for A1689 (Diego+, 2015)

NASA Astrophysics Data System (ADS)

Diego, J. M.; Broadhurst, T.; Benitez, N.; Umetsu, K.; Coe, D.; Sendra, I.; Sereno, M.; Izzo, L.; Covone, G.

2018-01-01

In this paper, we used public imaging data obtained from the ACS (filters: F450W and F814W) and the WFC3 (filter F125W), retrieved from the Mikulski Archive for Space Telescope. The data come from two different programmes. The F814W (ACS) and F125W (WFC3) data were obtained within the HST programme 11718 (PI Blakeslee, Cycle 17), from 2010 May 29 to July 8, while the ACS F475W images were obtained within the programme 9289 (PI Fors, Cycle 11) on 2002 June 16. The total exposure time is 9500, 75172 and 14367s in the F450W, F814W and F125W filters, respectively. (1 data file).

Development of Tremor Suppression Control System Using Adaptive Filter and Its Application to Meal-assist Robot

NASA Astrophysics Data System (ADS)

Yano, Ken'ichi; Ohara, Eiichi; Horihata, Satoshi; Aoki, Takaaki; Nishimoto, Yutaka

A robot that supports independent living by assisting with eating and other activities which use the operator's own hand would be helpful for people suffering from tremors of the hand or any other body part. The proposed system using adaptive filter estimates tremor frequencies with a time-varying property and individual differences online. In this study, the estimated frequency is used to adjusting the tremor suppression filter which insulates the voluntary motion signal from the sensor signal containing tremor components. These system are integrated into the control system of the Meal-Assist Robot. As a result, the developed system makes it possible for the person with a tremor to manipulate the supporting robot without causing operability to deteriorate and without hazards due to improper operation.

R2C: improving ab initio residue contact map prediction using dynamic fusion strategy and Gaussian noise filter.

PubMed

Yang, Jing; Jin, Qi-Yu; Zhang, Biao; Shen, Hong-Bin

2016-08-15

Inter-residue contacts in proteins dictate the topology of protein structures. They are crucial for protein folding and structural stability. Accurate prediction of residue contacts especially for long-range contacts is important to the quality of ab inito structure modeling since they can enforce strong restraints to structure assembly. In this paper, we present a new Residue-Residue Contact predictor called R2C that combines machine learning-based and correlated mutation analysis-based methods, together with a two-dimensional Gaussian noise filter to enhance the long-range residue contact prediction. Our results show that the outputs from the machine learning-based method are concentrated with better performance on short-range contacts; while for correlated mutation analysis-based approach, the predictions are widespread with higher accuracy on long-range contacts. An effective query-driven dynamic fusion strategy proposed here takes full advantages of the two different methods, resulting in an impressive overall accuracy improvement. We also show that the contact map directly from the prediction model contains the interesting Gaussian noise, which has not been discovered before. Different from recent studies that tried to further enhance the quality of contact map by removing its transitive noise, we designed a new two-dimensional Gaussian noise filter, which was especially helpful for reinforcing the long-range residue contact prediction. Tested on recent CASP10/11 datasets, the overall top L/5 accuracy of our final R2C predictor is 17.6%/15.5% higher than the pure machine learning-based method and 7.8%/8.3% higher than the correlated mutation analysis-based approach for the long-range residue contact prediction. http://www.csbio.sjtu.edu.cn/bioinf/R2C/Contact:hbshen@sjtu.edu.cn Supplementary data are available at Bioinformatics online. © The Author 2016. Published by Oxford University Press. All rights reserved. For Permissions, please e-mail: journals.permissions@oup.com.

An automated method of on-line extraction coupled with flow injection and capillary electrophoresis for phytochemical analysis.

PubMed

Chen, Hongli; Ding, Xiuping; Wang, Min; Chen, Xingguo

2010-11-01

In this study, an automated system for phytochemical analysis was successfully fabricated for the first time in our laboratory. The system included on-line decocting, filtering, cooling, sample introducing, separation, and detection, which greatly simplified the sample preparation and shortened the analysis time. Samples from the decoction extract were drawn every 5 min through an on-line filter and a condenser pipe to the sample loop from which 20-μL samples were injected into the running buffer and transported into a split-flow interface coupling the flow injection and capillary electrophoresis systems. The separation of glycyrrhetinic acid (GTA) and glycyrrhizic acid (GA) took less than 5 min by using a 10 mM borate buffer (adjusted pH to 8.8) and +10 kV voltage. Calibration curves showed good linearity with correlation coefficients (R) more than 0.9991. The intra-day repeatabilities (n = 5, expressed as relative standard deviation) of the proposed system, obtained using GTA and GA standards, were 1.1% and 0.8% for migration time and 0.7% and 0.9% for peak area, respectively. The mean recoveries of GTA and GA in the off-line extract of Glycyrrhiza uralensis Fisch root were better than 99.0%. The limits of detection (signal-to-noise ratio = 3) of the proposed method were 6.2 μg/mL and 6.9 μg/mL for GTA and GA, respectively. The dynamic changes of GTA and GA on the decoction time were obtained during the on-line decoction process of Glycyrrhiza uralensis Fisch root.

Does attention play a role in dynamic receptive field adaptation to changing acoustic salience in A1?

PubMed

Fritz, Jonathan B; Elhilali, Mounya; David, Stephen V; Shamma, Shihab A

2007-07-01

Acoustic filter properties of A1 neurons can dynamically adapt to stimulus statistics, classical conditioning, instrumental learning and the changing auditory attentional focus. We have recently developed an experimental paradigm that allows us to view cortical receptive field plasticity on-line as the animal meets different behavioral challenges by attending to salient acoustic cues and changing its cortical filters to enhance performance. We propose that attention is the key trigger that initiates a cascade of events leading to the dynamic receptive field changes that we observe. In our paradigm, ferrets were initially trained, using conditioned avoidance training techniques, to discriminate between background noise stimuli (temporally orthogonal ripple combinations) and foreground tonal target stimuli. They learned to generalize the task for a wide variety of distinct background and foreground target stimuli. We recorded cortical activity in the awake behaving animal and computed on-line spectrotemporal receptive fields (STRFs) of single neurons in A1. We observed clear, predictable task-related changes in STRF shape while the animal performed spectral tasks (including single tone and multi-tone detection, and two-tone discrimination) with different tonal targets. A different set of task-related changes occurred when the animal performed temporal tasks (including gap detection and click-rate discrimination). Distinctive cortical STRF changes may constitute a "task-specific signature". These spectral and temporal changes in cortical filters occur quite rapidly, within 2min of task onset, and fade just as quickly after task completion, or in some cases, persisted for hours. The same cell could multiplex by differentially changing its receptive field in different task conditions. On-line dynamic task-related changes, as well as persistent plastic changes, were observed at a single-unit, multi-unit and population level. Auditory attention is likely to be pivotal in mediating these task-related changes since the magnitude of STRF changes correlated with behavioral performance on tasks with novel targets. Overall, these results suggest the presence of an attention-triggered plasticity algorithm in A1 that can swiftly change STRF shape by transforming receptive fields to enhance figure/ground separation, by using a contrast matched filter to filter out the background, while simultaneously enhancing the salient acoustic target in the foreground. These results favor the view of a nimble, dynamic, attentive and adaptive brain that can quickly reshape its sensory filter properties and sensori-motor links on a moment-to-moment basis, depending upon the current challenges the animal faces. In this review, we summarize our results in the context of a broader survey of the field of auditory attention, and then consider neuronal networks that could give rise to this phenomenon of attention-driven receptive field plasticity in A1.

Fast and accurate phylogeny reconstruction using filtered spaced-word matches

PubMed Central

Sohrabi-Jahromi, Salma; Morgenstern, Burkhard

2017-01-01

Abstract Motivation: Word-based or ‘alignment-free’ algorithms are increasingly used for phylogeny reconstruction and genome comparison, since they are much faster than traditional approaches that are based on full sequence alignments. Existing alignment-free programs, however, are less accurate than alignment-based methods. Results: We propose Filtered Spaced Word Matches (FSWM), a fast alignment-free approach to estimate phylogenetic distances between large genomic sequences. For a pre-defined binary pattern of match and don’t-care positions, FSWM rapidly identifies spaced word-matches between input sequences, i.e. gap-free local alignments with matching nucleotides at the match positions and with mismatches allowed at the don’t-care positions. We then estimate the number of nucleotide substitutions per site by considering the nucleotides aligned at the don’t-care positions of the identified spaced-word matches. To reduce the noise from spurious random matches, we use a filtering procedure where we discard all spaced-word matches for which the overall similarity between the aligned segments is below a threshold. We show that our approach can accurately estimate substitution frequencies even for distantly related sequences that cannot be analyzed with existing alignment-free methods; phylogenetic trees constructed with FSWM distances are of high quality. A program run on a pair of eukaryotic genomes of a few hundred Mb each takes a few minutes. Availability and Implementation: The program source code for FSWM including a documentation, as well as the software that we used to generate artificial genome sequences are freely available at http://fswm.gobics.de/ Contact: chris.leimeister@stud.uni-goettingen.de Supplementary information: Supplementary data are available at Bioinformatics online. PMID:28073754

Fast and accurate phylogeny reconstruction using filtered spaced-word matches.

PubMed

Leimeister, Chris-André; Sohrabi-Jahromi, Salma; Morgenstern, Burkhard

2017-04-01

Word-based or 'alignment-free' algorithms are increasingly used for phylogeny reconstruction and genome comparison, since they are much faster than traditional approaches that are based on full sequence alignments. Existing alignment-free programs, however, are less accurate than alignment-based methods. We propose Filtered Spaced Word Matches (FSWM) , a fast alignment-free approach to estimate phylogenetic distances between large genomic sequences. For a pre-defined binary pattern of match and don't-care positions, FSWM rapidly identifies spaced word-matches between input sequences, i.e. gap-free local alignments with matching nucleotides at the match positions and with mismatches allowed at the don't-care positions. We then estimate the number of nucleotide substitutions per site by considering the nucleotides aligned at the don't-care positions of the identified spaced-word matches. To reduce the noise from spurious random matches, we use a filtering procedure where we discard all spaced-word matches for which the overall similarity between the aligned segments is below a threshold. We show that our approach can accurately estimate substitution frequencies even for distantly related sequences that cannot be analyzed with existing alignment-free methods; phylogenetic trees constructed with FSWM distances are of high quality. A program run on a pair of eukaryotic genomes of a few hundred Mb each takes a few minutes. The program source code for FSWM including a documentation, as well as the software that we used to generate artificial genome sequences are freely available at http://fswm.gobics.de/. chris.leimeister@stud.uni-goettingen.de. Supplementary data are available at Bioinformatics online. © The Author 2017. Published by Oxford University Press.

The Myth of Fragmentation: Assessing Political Information Online

ERIC Educational Resources Information Center

Wichowski, Alexis M.

2010-01-01

Internet technology has provided people with unprecedented abilities to filter the information they encounter, leading many scholars to fear that people will be exposed to less diversity of perspectives and fragment into homogeneous interest groups. Exposure to a wide range of topics and perspectives about political information in particular is…

WaVPeak: picking NMR peaks through wavelet-based smoothing and volume-based filtering.

PubMed

Liu, Zhi; Abbas, Ahmed; Jing, Bing-Yi; Gao, Xin

2012-04-01

Nuclear magnetic resonance (NMR) has been widely used as a powerful tool to determine the 3D structures of proteins in vivo. However, the post-spectra processing stage of NMR structure determination usually involves a tremendous amount of time and expert knowledge, which includes peak picking, chemical shift assignment and structure calculation steps. Detecting accurate peaks from the NMR spectra is a prerequisite for all following steps, and thus remains a key problem in automatic NMR structure determination. We introduce WaVPeak, a fully automatic peak detection method. WaVPeak first smoothes the given NMR spectrum by wavelets. The peaks are then identified as the local maxima. The false positive peaks are filtered out efficiently by considering the volume of the peaks. WaVPeak has two major advantages over the state-of-the-art peak-picking methods. First, through wavelet-based smoothing, WaVPeak does not eliminate any data point in the spectra. Therefore, WaVPeak is able to detect weak peaks that are embedded in the noise level. NMR spectroscopists need the most help isolating these weak peaks. Second, WaVPeak estimates the volume of the peaks to filter the false positives. This is more reliable than intensity-based filters that are widely used in existing methods. We evaluate the performance of WaVPeak on the benchmark set proposed by PICKY (Alipanahi et al., 2009), one of the most accurate methods in the literature. The dataset comprises 32 2D and 3D spectra from eight different proteins. Experimental results demonstrate that WaVPeak achieves an average of 96%, 91%, 88%, 76% and 85% recall on (15)N-HSQC, HNCO, HNCA, HNCACB and CBCA(CO)NH, respectively. When the same number of peaks are considered, WaVPeak significantly outperforms PICKY. WaVPeak is an open source program. The source code and two test spectra of WaVPeak are available at http://faculty.kaust.edu.sa/sites/xingao/Pages/Publications.aspx. The online server is under construction. statliuzhi@xmu.edu.cn; ahmed.abbas@kaust.edu.sa; majing@ust.hk; xin.gao@kaust.edu.sa.

WaVPeak: picking NMR peaks through wavelet-based smoothing and volume-based filtering

PubMed Central

Liu, Zhi; Abbas, Ahmed; Jing, Bing-Yi; Gao, Xin

2012-01-01

Motivation: Nuclear magnetic resonance (NMR) has been widely used as a powerful tool to determine the 3D structures of proteins in vivo. However, the post-spectra processing stage of NMR structure determination usually involves a tremendous amount of time and expert knowledge, which includes peak picking, chemical shift assignment and structure calculation steps. Detecting accurate peaks from the NMR spectra is a prerequisite for all following steps, and thus remains a key problem in automatic NMR structure determination. Results: We introduce WaVPeak, a fully automatic peak detection method. WaVPeak first smoothes the given NMR spectrum by wavelets. The peaks are then identified as the local maxima. The false positive peaks are filtered out efficiently by considering the volume of the peaks. WaVPeak has two major advantages over the state-of-the-art peak-picking methods. First, through wavelet-based smoothing, WaVPeak does not eliminate any data point in the spectra. Therefore, WaVPeak is able to detect weak peaks that are embedded in the noise level. NMR spectroscopists need the most help isolating these weak peaks. Second, WaVPeak estimates the volume of the peaks to filter the false positives. This is more reliable than intensity-based filters that are widely used in existing methods. We evaluate the performance of WaVPeak on the benchmark set proposed by PICKY (Alipanahi et al., 2009), one of the most accurate methods in the literature. The dataset comprises 32 2D and 3D spectra from eight different proteins. Experimental results demonstrate that WaVPeak achieves an average of 96%, 91%, 88%, 76% and 85% recall on 15N-HSQC, HNCO, HNCA, HNCACB and CBCA(CO)NH, respectively. When the same number of peaks are considered, WaVPeak significantly outperforms PICKY. Availability: WaVPeak is an open source program. The source code and two test spectra of WaVPeak are available at http://faculty.kaust.edu.sa/sites/xingao/Pages/Publications.aspx. The online server is under construction. Contact: statliuzhi@xmu.edu.cn; ahmed.abbas@kaust.edu.sa; majing@ust.hk; xin.gao@kaust.edu.sa PMID:22328784

Navigating condom use and HIV status disclosure with partners met online: a qualitative pilot study with gay and bisexual men from Craigslist.org.

PubMed

Grov, Christian; Agyemang, Linda; Ventuneac, Ana; Breslow, Aaron S

2013-02-01

We conducted face-to-face semi-structured interviews with 50 men recruited from the New York City men-seeking-men section of Craigslist.org. Participants discussed their favorite venues for meeting sex partners (n = 28 said the Internet), and we focused on these men's responses to probes regarding decisions around condom use and HIV status disclosure with online partners. A majority indicated they set a priori rules for themselves to always use condoms, and they cited the Internet as their favorite venue in part because it helped them sort for like-minded partners. Participants indicated that having in-person conversations around condom use and HIV was often difficult, and that the Internet was a convenient medium to facilitate the process. Notable differences were observed in how HIV-positive and HIV-negative men navigated serostatus disclosure-HIV-negative men were less subtle in starting the conversation. Finally, participants described a common narrative around distrust with online partners, which is one reason why they consistently use condoms. These data suggest that features which allow men to easily indicate, and filter for, condom use preferences should be built into (or maintained on) profile-based sexual networking sites and sexual bulletin board sites.

PROMO – Real-time Prospective Motion Correction in MRI using Image-based Tracking

PubMed Central

White, Nathan; Roddey, Cooper; Shankaranarayanan, Ajit; Han, Eric; Rettmann, Dan; Santos, Juan; Kuperman, Josh; Dale, Anders

2010-01-01

Artifacts caused by patient motion during scanning remain a serious problem in most MRI applications. The prospective motion correction technique attempts to address this problem at its source by keeping the measurement coordinate system fixed with respect to the patient throughout the entire scan process. In this study, a new image-based approach for prospective motion correction is described, which utilizes three orthogonal 2D spiral navigator acquisitions (SP-Navs) along with a flexible image-based tracking method based on the Extended Kalman Filter (EKF) algorithm for online motion measurement. The SP-Nav/EKF framework offers the advantages of image-domain tracking within patient-specific regions-of-interest and reduced sensitivity to off-resonance-induced corruption of rigid-body motion estimates. The performance of the method was tested using offline computer simulations and online in vivo head motion experiments. In vivo validation results covering a broad range of staged head motions indicate a steady-state error of the SP-Nav/EKF motion estimates of less than 10 % of the motion magnitude, even for large compound motions that included rotations over 15 degrees. A preliminary in vivo application in 3D inversion recovery spoiled gradient echo (IR-SPGR) and 3D fast spin echo (FSE) sequences demonstrates the effectiveness of the SP-Nav/EKF framework for correcting 3D rigid-body head motion artifacts prospectively in high-resolution 3D MRI scans. PMID:20027635

Use of Semantic Technology to Create Curated Data Albums

NASA Technical Reports Server (NTRS)

Ramachandran, Rahul; Kulkarni, Ajinkya; Li, Xiang; Sainju, Roshan; Bakare, Rohan; Basyal, Sabin

2014-01-01

One of the continuing challenges in any Earth science investigation is the discovery and access of useful science content from the increasingly large volumes of Earth science data and related information available online. Current Earth science data systems are designed with the assumption that researchers access data primarily by instrument or geophysical parameter. Those who know exactly the data sets they need can obtain the specific files using these systems. However, in cases where researchers are interested in studying an event of research interest, they must manually assemble a variety of relevant data sets by searching the different distributed data systems. Consequently, there is a need to design and build specialized search and discover tools in Earth science that can filter through large volumes of distributed online data and information and only aggregate the relevant resources needed to support climatology and case studies. This paper presents a specialized search and discovery tool that automatically creates curated Data Albums. The tool was designed to enable key elements of the search process such as dynamic interaction and sense-making. The tool supports dynamic interaction via different modes of interactivity and visual presentation of information. The compilation of information and data into a Data Album is analogous to a shoebox within the sense-making framework. This tool automates most of the tedious information/data gathering tasks for researchers. Data curation by the tool is achieved via an ontology-based, relevancy ranking algorithm that filters out nonrelevant information and data. The curation enables better search results as compared to the simple keyword searches provided by existing data systems in Earth science.

Use of Semantic Technology to Create Curated Data Albums

NASA Technical Reports Server (NTRS)

Ramachandran, Rahul; Kulkarni, Ajinkya; Li, Xiang; Sainju, Roshan; Bakare, Rohan; Basyal, Sabin; Fox, Peter (Editor); Norack, Tom (Editor)

2014-01-01

One of the continuing challenges in any Earth science investigation is the discovery and access of useful science content from the increasingly large volumes of Earth science data and related information available online. Current Earth science data systems are designed with the assumption that researchers access data primarily by instrument or geophysical parameter. Those who know exactly the data sets they need can obtain the specific files using these systems. However, in cases where researchers are interested in studying an event of research interest, they must manually assemble a variety of relevant data sets by searching the different distributed data systems. Consequently, there is a need to design and build specialized search and discovery tools in Earth science that can filter through large volumes of distributed online data and information and only aggregate the relevant resources needed to support climatology and case studies. This paper presents a specialized search and discovery tool that automatically creates curated Data Albums. The tool was designed to enable key elements of the search process such as dynamic interaction and sense-making. The tool supports dynamic interaction via different modes of interactivity and visual presentation of information. The compilation of information and data into a Data Album is analogous to a shoebox within the sense-making framework. This tool automates most of the tedious information/data gathering tasks for researchers. Data curation by the tool is achieved via an ontology-based, relevancy ranking algorithm that filters out non-relevant information and data. The curation enables better search results as compared to the simple keyword searches provided by existing data systems in Earth science.

A Data Filter for Identifying Steady-State Operating Points in Engine Flight Data for Condition Monitoring Applications

NASA Technical Reports Server (NTRS)

Simon, Donald L.; Litt, Jonathan S.

2010-01-01

This paper presents an algorithm that automatically identifies and extracts steady-state engine operating points from engine flight data. It calculates the mean and standard deviation of select parameters contained in the incoming flight data stream. If the standard deviation of the data falls below defined constraints, the engine is assumed to be at a steady-state operating point, and the mean measurement data at that point are archived for subsequent condition monitoring purposes. The fundamental design of the steady-state data filter is completely generic and applicable for any dynamic system. Additional domain-specific logic constraints are applied to reduce data outliers and variance within the collected steady-state data. The filter is designed for on-line real-time processing of streaming data as opposed to post-processing of the data in batch mode. Results of applying the steady-state data filter to recorded helicopter engine flight data are shown, demonstrating its utility for engine condition monitoring applications.

Machine health prognostics using the Bayesian-inference-based probabilistic indication and high-order particle filtering framework

NASA Astrophysics Data System (ADS)

Yu, Jianbo

2015-12-01

Prognostics is much efficient to achieve zero-downtime performance, maximum productivity and proactive maintenance of machines. Prognostics intends to assess and predict the time evolution of machine health degradation so that machine failures can be predicted and prevented. A novel prognostics system is developed based on the data-model-fusion scheme using the Bayesian inference-based self-organizing map (SOM) and an integration of logistic regression (LR) and high-order particle filtering (HOPF). In this prognostics system, a baseline SOM is constructed to model the data distribution space of healthy machine under an assumption that predictable fault patterns are not available. Bayesian inference-based probability (BIP) derived from the baseline SOM is developed as a quantification indication of machine health degradation. BIP is capable of offering failure probability for the monitored machine, which has intuitionist explanation related to health degradation state. Based on those historic BIPs, the constructed LR and its modeling noise constitute a high-order Markov process (HOMP) to describe machine health propagation. HOPF is used to solve the HOMP estimation to predict the evolution of the machine health in the form of a probability density function (PDF). An on-line model update scheme is developed to adapt the Markov process changes to machine health dynamics quickly. The experimental results on a bearing test-bed illustrate the potential applications of the proposed system as an effective and simple tool for machine health prognostics.

Desorption atmospheric pressure photoionization high-resolution mass spectrometry: a complementary approach for the chemical analysis of atmospheric aerosols.

PubMed

Parshintsev, Jevgeni; Vaikkinen, Anu; Lipponen, Katriina; Vrkoslav, Vladimir; Cvačka, Josef; Kostiainen, Risto; Kotiaho, Tapio; Hartonen, Kari; Riekkola, Marja-Liisa; Kauppila, Tiina J

2015-07-15

On-line chemical characterization methods of atmospheric aerosols are essential to increase our understanding of physicochemical processes in the atmosphere, and to study biosphere-atmosphere interactions. Several techniques, including aerosol mass spectrometry, are nowadays available, but they all suffer from some disadvantages. In this research, desorption atmospheric pressure photoionization high-resolution (Orbitrap) mass spectrometry (DAPPI-HRMS) is introduced as a complementary technique for the fast analysis of aerosol chemical composition without the need for sample preparation. Atmospheric aerosols from city air were collected on a filter, desorbed in a DAPPI source with a hot stream of toluene and nitrogen, and ionized using a vacuum ultraviolet lamp at atmospheric pressure. To study the applicability of the technique for ambient aerosol analysis, several samples were collected onto filters and analyzed, with the focus being on selected organic acids. To compare the DAPPI-HRMS data with results obtained by an established method, each filter sample was divided into two equal parts, and the second half of the filter was extracted and analyzed by liquid chromatography/mass spectrometry (LC/MS). The DAPPI results agreed with the measured aerosol particle number. In addition to the targeted acids, the LC/MS and DAPPI-HRMS methods were found to detect different compounds, thus providing complementary information about the aerosol samples. DAPPI-HRMS showed several important oxidation products of terpenes, and numerous compounds were tentatively identified. Thanks to the soft ionization, high mass resolution, fast analysis, simplicity and on-line applicability, the proposed methodology has high potential in the field of atmospheric research. Copyright © 2015 John Wiley & Sons, Ltd.

Numerical image manipulation and display in solar astronomy

NASA Technical Reports Server (NTRS)

Levine, R. H.; Flagg, J. C.

1977-01-01

The paper describes the system configuration and data manipulation capabilities of a solar image display system which allows interactive analysis of visual images and on-line manipulation of digital data. Image processing features include smoothing or filtering of images stored in the display, contrast enhancement, and blinking or flickering images. A computer with a core memory of 28,672 words provides the capacity to perform complex calculations based on stored images, including computing histograms, selecting subsets of images for further analysis, combining portions of images to produce images with physical meaning, and constructing mathematical models of features in an image. Some of the processing modes are illustrated by some image sequences from solar observations.

Optical ammonia gas sensor based on a porous silicon rugate filter coated with polymer-supported dye.

PubMed

Shang, Yunling; Wang, Xiaobo; Xu, Erchao; Tong, Changlun; Wu, Jianmin

2011-01-24

An ammonia gas sensor chip was prepared by coating an electrochemically-etched porous Si rugate filter with a chitosan film that is crosslinked by glycidoxypropyltrimethoxysilane (GPTMS). The bromothylmol blue (BTB), a pH indicator, was loaded in the film as ammonia-sensing molecules. White light reflected from the porous Si has a narrow bandwidth spectrum with a peak at 610 nm. Monitoring reflective optical intensity at the peak position allows for direct, real-time observation of changes in the concentration of ammonia gas in air samples. The reflective optical intensity decreased linearly with increasing concentrations of ammonia gas over the range of 0-100 ppm. The lowest detection limit was 0.5 ppm for ammonia gas. At optimum conditions, the full response time of the ammonia gas sensor was less than 15s. The sensor chip also exhibited a good long-term stability over 1 year. Therefore, the simple sensor design has potential application in miniaturized optical measurement for online ammonia gas detection. Copyright © 2010 Elsevier B.V. All rights reserved.

All-fiber wavelength swept ring laser based on Fabry-Perot filter for optical frequency domain imaging

PubMed Central

Jun, Changsu; Villiger, Martin; Oh, Wang-Yuhl; Bouma, Brett E.

2014-01-01

Innovations in laser engineering have yielded several novel configurations for high repetition rate, broad sweep range, and long coherence length wavelength swept lasers. Although these lasers have enabled high performance frequency-domain optical coherence tomography, they are typically complicated and costly and many require access to proprietary materials or devices. Here, we demonstrate a simplified ring resonator configuration that is straightforward to construct from readily available materials at a low total cost. It was enabled by an insight regarding the significance of isolation against bidirectional operation and by configuring the sweep range of the intracavity filter to exceed its free spectral range. The design can easily be optimized to meet a range of operating specifications while yielding robust and stable performance. As an example, we demonstrate 240 kHz operation with 125 nm sweep range and >70 mW of average output power and demonstrate high quality frequency domain OCT imaging. The complete component list and directions for assembly of the laser are posted on-line at www.octresearch.org. PMID:25401614

Brian Hears: Online Auditory Processing Using Vectorization Over Channels

PubMed Central

Fontaine, Bertrand; Goodman, Dan F. M.; Benichoux, Victor; Brette, Romain

2011-01-01

The human cochlea includes about 3000 inner hair cells which filter sounds at frequencies between 20 Hz and 20 kHz. This massively parallel frequency analysis is reflected in models of auditory processing, which are often based on banks of filters. However, existing implementations do not exploit this parallelism. Here we propose algorithms to simulate these models by vectorizing computation over frequency channels, which are implemented in “Brian Hears,” a library for the spiking neural network simulator package “Brian.” This approach allows us to use high-level programming languages such as Python, because with vectorized operations, the computational cost of interpretation represents a small fraction of the total cost. This makes it possible to define and simulate complex models in a simple way, while all previous implementations were model-specific. In addition, we show that these algorithms can be naturally parallelized using graphics processing units, yielding substantial speed improvements. We demonstrate these algorithms with several state-of-the-art cochlear models, and show that they compare favorably with existing, less flexible, implementations. PMID:21811453

VizieR Online Data Catalog: NGC 362 variable V light curves (Rozyczka+, 2016)

NASA Astrophysics Data System (ADS)

Rozyczka, M.; Thompson, I. B.; Narloch, W.; Pych, W.; Schwarzenberg, A.

2018-04-01

Our paper is based on images acquired mainly with the 1.0-m Swope telescope and the 2048x3150 SITe3 camera. The field of view was 14.8x22.8 arcmin2 at a scale of 0.435 arcsec/pixel. Observations were obtained on 205 nights from July 7, 1997 to October 23, 2009. The same set of filters was used for all observations. A total of 3785 V-band images and 1123 B-band images were selected for analysis. The seeing ranged from 1.14" to 4.3" and 1.28" to 4.07" for V and B, respectively, with median values of 1.67" and 1.8". We also used 73 V-frames acquired in 2015 on Swope at the same resolution as before and with the same set of filters, but with the new E2V camera, and 270 frames acquired between 2001 and 2007 on the 2.5m du Pont telescope with a field of view 8.84x8.84arcmin2 at a resolution of 0.259arcsec/pixel. (5 data files).

A square root ensemble Kalman filter application to a motor-imagery brain-computer interface.

PubMed

Kamrunnahar, M; Schiff, S J

2011-01-01

We here investigated a non-linear ensemble Kalman filter (SPKF) application to a motor imagery brain computer interface (BCI). A square root central difference Kalman filter (SR-CDKF) was used as an approach for brain state estimation in motor imagery task performance, using scalp electroencephalography (EEG) signals. Healthy human subjects imagined left vs. right hand movements and tongue vs. bilateral toe movements while scalp EEG signals were recorded. Offline data analysis was conducted for training the model as well as for decoding the imagery movements. Preliminary results indicate the feasibility of this approach with a decoding accuracy of 78%-90% for the hand movements and 70%-90% for the tongue-toes movements. Ongoing research includes online BCI applications of this approach as well as combined state and parameter estimation using this algorithm with different system dynamic models.

VizieR Online Data Catalog: Cheshire Cat galaxies: redshifts and magnitudes (Irwin+, 2015)

NASA Astrophysics Data System (ADS)

Irwin, J. A.; Dupke, R.; Carrasco, E. R.; Maksym, W. P.; Johnson, L.; White, R. E., III

2017-09-01

The optical observations (imaging and spectroscopy) were performed with the Gemini Multi-Object Spectrograph (hereafter GMOS; Hook et al. 2004PASP..116..425H) at the Gemini North Telescope in Hawaii, in queue mode, as part of the program GN-2011A-Q-25. The direct images were recorded through the r' and i' filters during the night of 2011 January 4, in dark time, with seeing median values of 0.8" and 0.9" for the r' and i' filters, respectively. The night was not photometric. Three 300 s exposures (binned by two in both axes, with pixel scale of 0.146") were observed in each filter. Offsets between exposures were used to take into account the gaps between the CCDs (37 un-binned pixels) and for cosmic ray removal. (1 data file).

Determination of As(III) and total inorganic As in water samples using an on-line solid phase extraction and flow injection hydride generation atomic absorption spectrometry.

PubMed

Sigrist, Mirna; Albertengo, Antonela; Beldoménico, Horacio; Tudino, Mabel

2011-04-15

A simple and robust on-line sequential injection system based on solid phase extraction (SPE) coupled to a flow injection hydride generation atomic absorption spectrometer (FI-HGAAS) with a heated quartz tube atomizer (QTA) was developed and optimized for the determination of As(III) in groundwater without any kind of sample pretreatment. The method was based on the selective retention of inorganic As(V) that was carried out by passing the filtered original sample through a cartridge containing a chloride-form strong anion exchanger. Thus the most toxic form, inorganic As(III), was determined fast and directly by AsH(3) generation using 3.5 mol L(-1) HCl as carrier solution and 0.35% (m/v) NaBH(4) in 0.025% NaOH as the reductant. Since the uptake of As(V) should be interfered by several anions of natural occurrence in waters, the effect of Cl(-), SO(4)(2-), NO(3)(-), HPO(4)(2-), HCO(3)(-) on retention was evaluated and discussed. The total soluble inorganic arsenic concentration was determined on aliquots of filtered samples acidified with concentrated HCl and pre-reduced with 5% KI-5% C(6)H(8)O(6) solution. The concentration of As(V) was calculated by difference between the total soluble inorganic arsenic and As(III) concentrations. Detection limits (LODs) of 0.5 μg L(-1) and 0.6 μg L(-1) for As(III) and inorganic total As, respectively, were obtained for a 500 μL sample volume. The obtained limits of detection allowed testing the water quality according to the national and international regulations. The analytical recovery for water samples spiked with As(III) ranged between 98% and 106%. The sampling throughput for As(III) determination was 60 samplesh(-1). The device for groundwater sampling was especially designed for the authors. Metallic components were avoided and the contact between the sample and the atmospheric oxygen was carried to a minimum. On-field arsenic species separation was performed through the employ of a serial connection of membrane filters and anion-exchange cartridges. Advantages derived from this approach were evaluated. HPLC-ICPMS was employed to study the consistency of the analytical results. Copyright © 2011 Elsevier B.V. All rights reserved.

Tissue artifact removal from respiratory signals based on empirical mode decomposition.

PubMed

Liu, Shaopeng; Gao, Robert X; John, Dinesh; Staudenmayer, John; Freedson, Patty

2013-05-01

On-line measurement of respiration plays an important role in monitoring human physical activities. Such measurement commonly employs sensing belts secured around the rib cage and abdomen of the test object. Affected by the movement of body tissues, respiratory signals typically have a low signal-to-noise ratio. Removing tissue artifacts therefore is critical to ensuring effective respiration analysis. This paper presents a signal decomposition technique for tissue artifact removal from respiratory signals, based on the empirical mode decomposition (EMD). An algorithm based on the mutual information and power criteria was devised to automatically select appropriate intrinsic mode functions for tissue artifact removal and respiratory signal reconstruction. Performance of the EMD-algorithm was evaluated through simulations and real-life experiments (N = 105). Comparison with low-pass filtering that has been conventionally applied confirmed the effectiveness of the technique in tissue artifacts removal.

Model reference adaptive control (MRAC)-based parameter identification applied to surface-mounted permanent magnet synchronous motor

NASA Astrophysics Data System (ADS)

Zhong, Chongquan; Lin, Yaoyao

2017-11-01

In this work, a model reference adaptive control-based estimated algorithm is proposed for online multi-parameter identification of surface-mounted permanent magnet synchronous machines. By taking the dq-axis equations of a practical motor as the reference model and the dq-axis estimation equations as the adjustable model, a standard model-reference-adaptive-system-based estimator was established. Additionally, the Popov hyperstability principle was used in the design of the adaptive law to guarantee accurate convergence. In order to reduce the oscillation of identification result, this work introduces a first-order low-pass digital filter to improve precision regarding the parameter estimation. The proposed scheme was then applied to an SPM synchronous motor control system without any additional circuits and implemented using a DSP TMS320LF2812. For analysis, the experimental results reveal the effectiveness of the proposed method.

Object Tracking and Target Reacquisition Based on 3-D Range Data for Moving Vehicles

PubMed Central

Lee, Jehoon; Lankton, Shawn; Tannenbaum, Allen

2013-01-01

In this paper, we propose an approach for tracking an object of interest based on 3-D range data. We employ particle filtering and active contours to simultaneously estimate the global motion of the object and its local deformations. The proposed algorithm takes advantage of range information to deal with the challenging (but common) situation in which the tracked object disappears from the image domain entirely and reappears later. To cope with this problem, a method based on principle component analysis (PCA) of shape information is proposed. In the proposed method, if the target disappears out of frame, shape similarity energy is used to detect target candidates that match a template shape learned online from previously observed frames. Thus, we require no a priori knowledge of the target’s shape. Experimental results show the practical applicability and robustness of the proposed algorithm in realistic tracking scenarios. PMID:21486717

An Improved Strong Tracking Cubature Kalman Filter for GPS/INS Integrated Navigation Systems.

PubMed

Feng, Kaiqiang; Li, Jie; Zhang, Xi; Zhang, Xiaoming; Shen, Chong; Cao, Huiliang; Yang, Yanyu; Liu, Jun

2018-06-12

The cubature Kalman filter (CKF) is widely used in the application of GPS/INS integrated navigation systems. However, its performance may decline in accuracy and even diverge in the presence of process uncertainties. To solve the problem, a new algorithm named improved strong tracking seventh-degree spherical simplex-radial cubature Kalman filter (IST-7thSSRCKF) is proposed in this paper. In the proposed algorithm, the effect of process uncertainty is mitigated by using the improved strong tracking Kalman filter technique, in which the hypothesis testing method is adopted to identify the process uncertainty and the prior state estimate covariance in the CKF is further modified online according to the change in vehicle dynamics. In addition, a new seventh-degree spherical simplex-radial rule is employed to further improve the estimation accuracy of the strong tracking cubature Kalman filter. In this way, the proposed comprehensive algorithm integrates the advantage of 7thSSRCKF’s high accuracy and strong tracking filter’s strong robustness against process uncertainties. The GPS/INS integrated navigation problem with significant dynamic model errors is utilized to validate the performance of proposed IST-7thSSRCKF. Results demonstrate that the improved strong tracking cubature Kalman filter can achieve higher accuracy than the existing CKF and ST-CKF, and is more robust for the GPS/INS integrated navigation system.

Generation of global VTEC maps from low latency GNSS observations based on B-spline modelling and Kalman filtering

NASA Astrophysics Data System (ADS)

Erdogan, Eren; Dettmering, Denise; Limberger, Marco; Schmidt, Michael; Seitz, Florian; Börger, Klaus; Brandert, Sylvia; Görres, Barbara; Kersten, Wilhelm F.; Bothmer, Volker; Hinrichs, Johannes; Venzmer, Malte

2015-04-01

In May 2014 DGFI-TUM (the former DGFI) and the German Space Situational Awareness Centre (GSSAC) started to develop an OPerational Tool for Ionospheric Mapping And Prediction (OPTIMAP); since November 2014 the Institute of Astrophysics at the University of Göttingen (IAG) joined the group as the third partner. This project aims on the computation and prediction of maps of the vertical total electron content (VTEC) and the electron density distribution of the ionosphere on a global scale from both various space-geodetic observation techniques such as GNSS and satellite altimetry as well as Sun observations. In this contribution we present first results, i.e. a near-real time processing framework for generating VTEC maps by assimilating GNSS (GPS, GLONASS) based ionospheric data into a two-dimensional global B-spline approach. To be more specific, the spatial variations of VTEC are modelled by trigonometric B-spline functions in longitude and by endpoint-interpolating polynomial B-spline functions in latitude, respectively. Since B-spline functions are compactly supported and highly localizing our approach can handle large data gaps appropriately and, thus, provides a better approximation of data with heterogeneous density and quality compared to the commonly used spherical harmonics. The presented method models temporal variations of VTEC inside a Kalman filter. The unknown parameters of the filter state vector are composed of the B-spline coefficients as well as the satellite and receiver DCBs. To approximate the temporal variation of these state vector components as part of the filter the dynamical model has to be set up. The current implementation of the filter allows to select between a random walk process, a Gauss-Markov process and a dynamic process driven by an empirical ionosphere model, e.g. the International Reference Ionosphere (IRI). For running the model ionospheric input data is acquired from terrestrial GNSS networks through online archive systems (such as IGS) with approximately one hour latency. Before feeding the filter with new hourly data, the raw GNSS observations are downloaded and pre-processed via geometry free linear combinations to provide signal delay information including the ionospheric effects and the differential code biases. Next steps will implement further space geodetic techniques and will introduce the Sun observations into the procedure. The final destination is to develop a time dependent model of the electron density based on different geodetic and solar observations.

Automatic identification and removal of ocular artifacts in EEG--improved adaptive predictor filtering for portable applications.

PubMed

Zhao, Qinglin; Hu, Bin; Shi, Yujun; Li, Yang; Moore, Philip; Sun, Minghou; Peng, Hong

2014-06-01

Electroencephalogram (EEG) signals have a long history of use as a noninvasive approach to measure brain function. An essential component in EEG-based applications is the removal of Ocular Artifacts (OA) from the EEG signals. In this paper we propose a hybrid de-noising method combining Discrete Wavelet Transformation (DWT) and an Adaptive Predictor Filter (APF). A particularly novel feature of the proposed method is the use of the APF based on an adaptive autoregressive model for prediction of the waveform of signals in the ocular artifact zones. In our test, based on simulated data, the accuracy of noise removal in the proposed model was significantly increased when compared to existing methods including: Wavelet Packet Transform (WPT) and Independent Component Analysis (ICA), Discrete Wavelet Transform (DWT) and Adaptive Noise Cancellation (ANC). The results demonstrate that the proposed method achieved a lower mean square error and higher correlation between the original and corrected EEG. The proposed method has also been evaluated using data from calibration trials for the Online Predictive Tools for Intervention in Mental Illness (OPTIMI) project. The results of this evaluation indicate an improvement in performance in terms of the recovery of true EEG signals with EEG tracking and computational speed in the analysis. The proposed method is well suited to applications in portable environments where the constraints with respect to acceptable wearable sensor attachments usually dictate single channel devices.

A miniature shoe-mounted orientation determination system for accurate indoor heading and trajectory tracking.

PubMed

Zhang, Shengzhi; Yu, Shuai; Liu, Chaojun; Liu, Sheng

2016-06-01

Tracking the position of pedestrian is urgently demanded when the most commonly used GPS (Global Position System) is unavailable. Benefited from the small size, low-power consumption, and relatively high reliability, micro-electro-mechanical system sensors are well suited for GPS-denied indoor pedestrian heading estimation. In this paper, a real-time miniature orientation determination system (MODS) was developed for indoor heading and trajectory tracking based on a novel dual-linear Kalman filter. The proposed filter precludes the impact of geomagnetic distortions on pitch and roll that the heading is subjected to. A robust calibration approach was designed to improve the accuracy of sensors measurements based on a unified sensor model. Online tests were performed on the MODS with an improved turntable. The results demonstrate that the average RMSE (root-mean-square error) of heading estimation is less than 1°. Indoor heading experiments were carried out with the MODS mounted on the shoe of pedestrian. Besides, we integrated the existing MODS into an indoor pedestrian dead reckoning application as an example of its utility in realistic actions. A human attitude-based walking model was developed to calculate the walking distance. Test results indicate that mean percentage error of indoor trajectory tracking achieves 2% of the total walking distance. This paper provides a feasible alternative for accurate indoor heading and trajectory tracking.

Archive 2.0: What Composition Students and Academic Libraries Can Gain from Digital-Collaborative Pedagogies

ERIC Educational Resources Information Center

Vetter, Matthew A.

2014-01-01

Research across disciplines in recent years has demonstrated a number of gains involved in community engagement and service-learning pedagogies. More recently, these pedagogies are being filtered into digital contexts as instructors begin to realize the opportunities made available by online writing venues. This presentation describes a specific…

SAO/NASA ADS at SAO: ADS Abstract Service

Science.gov Websites

Service provides a gateway to the online Astronomy and Physics literature. You can navigate this content filtering options as well as visualizations. Astronomy and Astrophysics Classic Search, an legacy interface which searches the 2,311,600 records currently in the Astronomy database, including 198,834 abstracts

10 Ways To Take Charge of the Web. Easy Strategies for Internet Smarts.

ERIC Educational Resources Information Center

Wood, Julie M.

2000-01-01

Strategies to help teachers use the Internet effectively include: explore individual interests online; develop acceptable use policies; narrow the playing field; know search engines; use filters; utilize the World Wide Web to lighten the load; teach students to investigate websites effectively; use the Web for professional development; teach…

Stochastic Short-term High-resolution Prediction of Solar Irradiance and Photovoltaic Power Output

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

Melin, Alexander M.; Olama, Mohammed M.; Dong, Jin

The increased penetration of solar photovoltaic (PV) energy sources into electric grids has increased the need for accurate modeling and prediction of solar irradiance and power production. Existing modeling and prediction techniques focus on long-term low-resolution prediction over minutes to years. This paper examines the stochastic modeling and short-term high-resolution prediction of solar irradiance and PV power output. We propose a stochastic state-space model to characterize the behaviors of solar irradiance and PV power output. This prediction model is suitable for the development of optimal power controllers for PV sources. A filter-based expectation-maximization and Kalman filtering mechanism is employed tomore » estimate the parameters and states in the state-space model. The mechanism results in a finite dimensional filter which only uses the first and second order statistics. The structure of the scheme contributes to a direct prediction of the solar irradiance and PV power output without any linearization process or simplifying assumptions of the signal’s model. This enables the system to accurately predict small as well as large fluctuations of the solar signals. The mechanism is recursive allowing the solar irradiance and PV power to be predicted online from measurements. The mechanism is tested using solar irradiance and PV power measurement data collected locally in our lab.« less

A low perfusion rate microreactor for continuous monitoring of enzyme characteristics: application to glucose oxidase

PubMed Central

Venema, K.; van Berkel, W. J. H.; Korf, J.

2007-01-01

This report describes a versatile and robust microreactor for bioactive proteins physically immobilized on a polyether sulfone filter. The potential of the reactor is illustrated with glucose oxidase immobilized on a filter with a cut-off value of 30 kDa. A flow-injection system was used to deliver the reactants and the device was linked on-line to an electrochemical detector. The microreactor was used for on-line preparation of apoglucose oxidase in strong acid and its subsequent reactivation with flavin adenine dinucleotide. In addition we describe a miniaturized version of the microreactor used to assess several characteristics of femtomole to attomole amounts of glucose oxidase. A low negative potential over the electrodes was used when ferrocene was the mediator in combination with horseradish peroxidase, ensuring the absence of oxidation of electro-active compounds in biological fluids. A low backpressure at very low flow rates is an advantage, which increases the sensitivity. A variety of further applications of the microreactor are suggested. Figure Preparation of apoGOx and restoration of enzyme activity using a soluton of FAD PMID:17909761

Charge reconstruction in large-area photomultipliers

NASA Astrophysics Data System (ADS)

Grassi, M.; Montuschi, M.; Baldoncini, M.; Mantovani, F.; Ricci, B.; Andronico, G.; Antonelli, V.; Bellato, M.; Bernieri, E.; Brigatti, A.; Brugnera, R.; Budano, A.; Buscemi, M.; Bussino, S.; Caruso, R.; Chiesa, D.; Corti, D.; Dal Corso, F.; Ding, X. F.; Dusini, S.; Fabbri, A.; Fiorentini, G.; Ford, R.; Formozov, A.; Galet, G.; Garfagnini, A.; Giammarchi, M.; Giaz, A.; Insolia, A.; Isocrate, R.; Lippi, I.; Longhitano, F.; Lo Presti, D.; Lombardi, P.; Marini, F.; Mari, S. M.; Martellini, C.; Meroni, E.; Mezzetto, M.; Miramonti, L.; Monforte, S.; Nastasi, M.; Ortica, F.; Paoloni, A.; Parmeggiano, S.; Pedretti, D.; Pelliccia, N.; Pompilio, R.; Previtali, E.; Ranucci, G.; Re, A. C.; Romani, A.; Saggese, P.; Salamanna, G.; Sawy, F. H.; Settanta, G.; Sisti, M.; Sirignano, C.; Spinetti, M.; Stanco, L.; Strati, V.; Verde, G.; Votano, L.

2018-02-01

Large-area PhotoMultiplier Tubes (PMT) allow to efficiently instrument Liquid Scintillator (LS) neutrino detectors, where large target masses are pivotal to compensate for neutrinos' extremely elusive nature. Depending on the detector light yield, several scintillation photons stemming from the same neutrino interaction are likely to hit a single PMT in a few tens/hundreds of nanoseconds, resulting in several photoelectrons (PEs) to pile-up at the PMT anode. In such scenario, the signal generated by each PE is entangled to the others, and an accurate PMT charge reconstruction becomes challenging. This manuscript describes an experimental method able to address the PMT charge reconstruction in the case of large PE pile-up, providing an unbiased charge estimator at the permille level up to 15 detected PEs. The method is based on a signal filtering technique (Wiener filter) which suppresses the noise due to both PMT and readout electronics, and on a Fourier-based deconvolution able to minimize the influence of signal distortions—such as an overshoot. The analysis of simulated PMT waveforms shows that the slope of a linear regression modeling the relation between reconstructed and true charge values improves from 0.769 ± 0.001 (without deconvolution) to 0.989 ± 0.001 (with deconvolution), where unitary slope implies perfect reconstruction. A C++ implementation of the charge reconstruction algorithm is available online at [1].

Dynamic imaging in electrical impedance tomography of the human chest with online transition matrix identification.

PubMed

Moura, Fernando Silva; Aya, Julio Cesar Ceballos; Fleury, Agenor Toledo; Amato, Marcelo Britto Passos; Lima, Raul Gonzalez

2010-02-01

One of the electrical impedance tomography objectives is to estimate the electrical resistivity distribution in a domain based only on electrical potential measurements at its boundary generated by an imposed electrical current distribution into the boundary. One of the methods used in dynamic estimation is the Kalman filter. In biomedical applications, the random walk model is frequently used as evolution model and, under this conditions, poor tracking ability of the extended Kalman filter (EKF) is achieved. An analytically developed evolution model is not feasible at this moment. The paper investigates the identification of the evolution model in parallel to the EKF and updating the evolution model with certain periodicity. The evolution model transition matrix is identified using the history of the estimated resistivity distribution obtained by a sensitivity matrix based algorithm and a Newton-Raphson algorithm. To numerically identify the linear evolution model, the Ibrahim time-domain method is used. The investigation is performed by numerical simulations of a domain with time-varying resistivity and by experimental data collected from the boundary of a human chest during normal breathing. The obtained dynamic resistivity values lie within the expected values for the tissues of a human chest. The EKF results suggest that the tracking ability is significantly improved with this approach.

Improvement of the tetrachloromercurate absorption technique for measuring low atmospheric SO2 mixing ratios

NASA Astrophysics Data System (ADS)

Jaeschke, W.; Beltz, N.; Haunold, W.; Krischke, U.

1997-07-01

During the Gas-Phase Sulfur Intercomparison Experiment (GASIE) in 1994 an analytical system for measuring sulfur dioxide mixing ratios at low parts per trillion (pptv) levels was employed. It is based on the absorption of SO2 on a tetrachloromercurate(II)-impregnated filter. The subsequent analysis uses a chemiluminescence reaction by treating the resulting disulfitomercurate(II) complex with an acidic cerium sulfate solution. An improved sampling device has been introduced that increases the maximum sampling volume from 200 L to 500 L. It is also possible to determine the blank value accurately for each sample. The absorption efficiency of the sampling system is 98.7±6.4% at a nominal flow rate of 10 L/min. The calculated (3σ) detection limit is 3±1 pptv SO2. The sample solution is stable for up to 30 days, which allows the samples to be safely stored or shipped before analysis. This permits the use of a sensitive, compact, and reliable sampling system in the field with subsequent analysis under optimal conditions in the laboratory. A continuous flow chemiluminescence (CFCL) analyzer for on-line measurements is also presented. The system is based on the same chemical principles as the described filter technique.

Tyre-road grip coefficient assessment - Part II: online estimation using instrumented vehicle, extended Kalman filter, and neural network

NASA Astrophysics Data System (ADS)

Luque, Pablo; Mántaras, Daniel A.; Fidalgo, Eloy; Álvarez, Javier; Riva, Paolo; Girón, Pablo; Compadre, Diego; Ferran, Jordi

2013-12-01

The main objective of this work is to determine the limit of safe driving conditions by identifying the maximal friction coefficient in a real vehicle. The study will focus on finding a method to determine this limit before reaching the skid, which is valuable information in the context of traffic safety. Since it is not possible to measure the friction coefficient directly, it will be estimated using the appropriate tools in order to get the most accurate information. A real vehicle is instrumented to collect information of general kinematics and steering tie-rod forces. A real-time algorithm is developed to estimate forces and aligning torque in the tyres using an extended Kalman filter and neural networks techniques. The methodology is based on determining the aligning torque; this variable allows evaluation of the behaviour of the tyre. It transmits interesting information from the tyre-road contact and can be used to predict the maximal tyre grip and safety margin. The maximal grip coefficient is estimated according to a knowledge base, extracted from computer simulation of a high detailed three-dimensional model, using Adams® software. The proposed methodology is validated and applied to real driving conditions, in which maximal grip and safety margin are properly estimated.

Particle filtering based structural assessment with acoustic emission sensing

NASA Astrophysics Data System (ADS)

Yan, Wuzhao; Abdelrahman, Marwa; Zhang, Bin; Ziehl, Paul

2017-02-01

Nuclear structures are designed to withstand severe loading events under various stresses. Over time, aging of structural systems constructed with concrete and steel will occur. This deterioration may reduce service life of nuclear facilities and/or lead to unnecessary or untimely repairs. Therefore, online monitoring of structures in nuclear power plants and waste storage has drawn significant attention in recent years. Of many existing non-destructive evaluation and structural monitoring approaches, acoustic emission is promising for assessment of structural damage because it is non-intrusive and is sensitive to corrosion and crack growth in reinforced concrete elements. To provide a rapid, actionable, and graphical means for interpretation Intensity Analysis plots have been developed. This approach provides a means for classification of damage. Since the acoustic emission measurement is only an indirect indicator of structural damage, potentially corrupted by non-genuine data, it is more suitable to estimate the states of corrosion and cracking in a Bayesian estimation framework. In this paper, we will utilize the accelerated corrosion data from a specimen at the University of South Carolina to develop a particle filtering-based diagnosis and prognosis algorithm. Promising features of the proposed algorithm are described in terms of corrosion state estimation and prediction of degradation over time to a predefined threshold.

VizieR Online Data Catalog: WASP-22, WASP-41, WASP-42, WASP-55 (Southworth+, 2016)

NASA Astrophysics Data System (ADS)

Southworth, J.; Tregloan-Reed, J.; Andersen, M. I.; Calchi Novati, S.; Ciceri, S.; Colque, J. P.; D'Ago, G.; Dominik, M.; Evans, D. F.; Gu, S.-H.; Herrera-Cordova, A.; Hinse, T. C.; Jorgensen, U. G.; Juncher, D.; Kuffmeier, M.; Mancini, L.; Peixinho, N.; Popovas, A.; Rabus, M.; Skottfelt, J.; Tronsgaard, R.; Unda-Sanzana, E.; Wang, X.-B.; Wertz, O.; Alsubai, K. A.; Andersen, J. M.; Bozza, V.; Bramich, D. M.; Burgdorf, M.; Damerdji, Y.; Diehl, C.; Elyiv, A.; Figuera Jaimes, R.; Haugbolle, T.; Hundertmark, M.; Kains, N.; Kerins, E.; Korhonen, H.; Liebig, C.; Mathiasen, M.; Penny, M. T.; Rahvar, S.; Scarpetta, G.; Schmidt, R. W.; Snodgrass, C.; Starkey, D.; Surdej, J.; Vilela, C.; von Essen, C.; Wang, Y.

2018-05-01

17 light curves of transits of the extrasolar planetary systems WASP-22, WASP-41, WASP-42 and WASP-55 are presented. 13 of the light curves were obtained using the Danish 1.54m telescope at ESO La Silla, Chile, in the Bessell R or Bessell I passbands. The other 4 light curves were obtained using the 84cm telescope at Observatorio Cerro Armazones, Chile, using either an R filter or no filter. The errorbars for each transit have been scaled so the best-fitting model (obtained using the JKTEBOP code) has a reduced chi-squared value of 1.0. (4 data files).

Online approximation of the multichannel Wiener filter with preservation of interaural level difference for binaural hearing-aids.

PubMed

Marques do Carmo, Diego; Costa, Márcio Holsbach

2018-04-01

This work presents an online approximation method for the multichannel Wiener filter (MWF) noise reduction technique with preservation of the noise interaural level difference (ILD) for binaural hearing-aids. The steepest descent method is applied to a previously proposed MWF-ILD cost function to both approximate the optimal linear estimator of the desired speech and keep the subjective perception of the original acoustic scenario. The computational cost of the resulting algorithm is estimated in terms of multiply and accumulate operations, whose number can be controlled by setting the number of iterations at each time frame. Simulation results for the particular case of one speech and one-directional noise source show that the proposed method increases the signal-to-noise ratio SNR of the originally acquired speech by up to 16.9 dB in the assessed scenarios. As compared to the online implementation of the conventional MWF technique, the proposed technique provides a reduction of up to 7 dB in the noise ILD error at the price of a reduction of up 3 dB in the output SNR. Subjective experiments with volunteers complement these objective measures with psychoacoustic results, which corroborate the expected spatial preservation of the original acoustic scenario. The proposed method allows practical online implementation of the MWF-ILD noise reduction technique under constrained computational resources. Predicted SNR improvements from 12 dB to 16.9 dB can be obtained in application-specific integrated circuits for hearing-aids and state-of-the-art digital signal processors. Copyright © 2018 Elsevier Ltd. All rights reserved.

Change Semantic Constrained Online Data Cleaning Method for Real-Time Observational Data Stream

NASA Astrophysics Data System (ADS)

Ding, Yulin; Lin, Hui; Li, Rongrong

2016-06-01

Recent breakthroughs in sensor networks have made it possible to collect and assemble increasing amounts of real-time observational data by observing dynamic phenomena at previously impossible time and space scales. Real-time observational data streams present potentially profound opportunities for real-time applications in disaster mitigation and emergency response, by providing accurate and timeliness estimates of environment's status. However, the data are always subject to inevitable anomalies (including errors and anomalous changes/events) caused by various effects produced by the environment they are monitoring. The "big but dirty" real-time observational data streams can rarely achieve their full potential in the following real-time models or applications due to the low data quality. Therefore, timely and meaningful online data cleaning is a necessary pre-requisite step to ensure the quality, reliability, and timeliness of the real-time observational data. In general, a straightforward streaming data cleaning approach, is to define various types of models/classifiers representing normal behavior of sensor data streams and then declare any deviation from this model as normal or erroneous data. The effectiveness of these models is affected by dynamic changes of deployed environments. Due to the changing nature of the complicated process being observed, real-time observational data is characterized by diversity and dynamic, showing a typical Big (Geo) Data characters. Dynamics and diversity is not only reflected in the data values, but also reflected in the complicated changing patterns of the data distributions. This means the pattern of the real-time observational data distribution is not stationary or static but changing and dynamic. After the data pattern changed, it is necessary to adapt the model over time to cope with the changing patterns of real-time data streams. Otherwise, the model will not fit the following observational data streams, which may led to large estimation error. In order to achieve the best generalization error, it is an important challenge for the data cleaning methodology to be able to characterize the behavior of data stream distributions and adaptively update a model to include new information and remove old information. However, the complicated data changing property invalidates traditional data cleaning methods, which rely on the assumption of a stationary data distribution, and drives the need for more dynamic and adaptive online data cleaning methods. To overcome these shortcomings, this paper presents a change semantics constrained online filtering method for real-time observational data. Based on the principle that the filter parameter should vary in accordance to the data change patterns, this paper embeds semantic description, which quantitatively depicts the change patterns in the data distribution to self-adapt the filter parameter automatically. Real-time observational water level data streams of different precipitation scenarios are selected for testing. Experimental results prove that by means of this method, more accurate and reliable water level information can be available, which is prior to scientific and prompt flood assessment and decision-making.

A Statistical Framework for Protein Quantitation in Bottom-Up MS-Based Proteomics

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

Karpievitch, Yuliya; Stanley, Jeffrey R.; Taverner, Thomas

2009-08-15

Motivation: Quantitative mass spectrometry-based proteomics requires protein-level estimates and associated confidence measures. Challenges include the presence of low quality or incorrectly identified peptides and informative missingness. Furthermore, models are required for rolling peptide-level information up to the protein level. Results: We present a statistical model that carefully accounts for informative missingness in peak intensities and allows unbiased, model-based, protein-level estimation and inference. The model is applicable to both label-based and label-free quantitation experiments. We also provide automated, model-based, algorithms for filtering of proteins and peptides as well as imputation of missing values. Two LC/MS datasets are used to illustrate themore » methods. In simulation studies, our methods are shown to achieve substantially more discoveries than standard alternatives. Availability: The software has been made available in the opensource proteomics platform DAnTE (http://omics.pnl.gov/software/). Contact: adabney@stat.tamu.edu Supplementary information: Supplementary data are available at Bioinformatics online.« less

Fault tolerant control of multivariable processes using auto-tuning PID controller.

PubMed

Yu, Ding-Li; Chang, T K; Yu, Ding-Wen

2005-02-01

Fault tolerant control of dynamic processes is investigated in this paper using an auto-tuning PID controller. A fault tolerant control scheme is proposed composing an auto-tuning PID controller based on an adaptive neural network model. The model is trained online using the extended Kalman filter (EKF) algorithm to learn system post-fault dynamics. Based on this model, the PID controller adjusts its parameters to compensate the effects of the faults, so that the control performance is recovered from degradation. The auto-tuning algorithm for the PID controller is derived with the Lyapunov method and therefore, the model predicted tracking error is guaranteed to converge asymptotically. The method is applied to a simulated two-input two-output continuous stirred tank reactor (CSTR) with various faults, which demonstrate the applicability of the developed scheme to industrial processes.

High-field asymmetric waveform ion mobility spectrometry for mass spectrometry-based proteomics.

PubMed

Swearingen, Kristian E; Moritz, Robert L

2012-10-01

High-field asymmetric waveform ion mobility spectrometry (FAIMS) is an atmospheric pressure ion mobility technique that separates gas-phase ions by their behavior in strong and weak electric fields. FAIMS is easily interfaced with electrospray ionization and has been implemented as an additional separation mode between liquid chromatography (LC) and mass spectrometry (MS) in proteomic studies. FAIMS separation is orthogonal to both LC and MS and is used as a means of on-line fractionation to improve the detection of peptides in complex samples. FAIMS improves dynamic range and concomitantly the detection limits of ions by filtering out chemical noise. FAIMS can also be used to remove interfering ion species and to select peptide charge states optimal for identification by tandem MS. Here, the authors review recent developments in LC-FAIMS-MS and its application to MS-based proteomics.

Derivative spectrum chromatographic method for the determination of trimethoprim in honey samples using an on-line solid-phase extraction technique.

PubMed

Uchiyama, Kazuhisa; Kondo, Mari; Yokochi, Rika; Takeuchi, Yuri; Yamamoto, Atsushi; Inoue, Yoshinori

2011-07-01

A simple, selective and rapid analytical method for determination of trimethoprim (TMP) in honey samples was developed and validated. This method is based on a SPE technique followed by HPLC with photodiode array detection. After dilution and filtration, aliquots of 500 μL honey samples were directly injected to an on-line SPE HPLC system. TMP was extracted on an RP SPE column, and separated on a hydrophilic interaction chromatography column during HPLC analysis. At the first detection step, the noise level of the photodiode array data was reduced with two-dimensional equalizer filtering, and then the smoothed data were subjected to derivative spectrum chromatography. On the second-derivative chromatogram at 254 nm, the limit of detection and the limit of quantification of TMP in a honey sample were 5 and 10 ng/g, respectively. The proposed method showed high accuracy (60-103%) with adequate sensitivity for TMP monitoring in honey samples. Copyright © 2011 WILEY-VCH Verlag GmbH & Co. KGaA, Weinheim.

Target-Pathogen: a structural bioinformatic approach to prioritize drug targets in pathogens

PubMed Central

Sosa, Ezequiel J; Burguener, Germán; Lanzarotti, Esteban; Radusky, Leandro; Pardo, Agustín M; Marti, Marcelo

2018-01-01

Abstract Available genomic data for pathogens has created new opportunities for drug discovery and development to fight them, including new resistant and multiresistant strains. In particular structural data must be integrated with both, gene information and experimental results. In this sense, there is a lack of an online resource that allows genome wide-based data consolidation from diverse sources together with thorough bioinformatic analysis that allows easy filtering and scoring for fast target selection for drug discovery. Here, we present Target-Pathogen database (http://target.sbg.qb.fcen.uba.ar/patho), designed and developed as an online resource that allows the integration and weighting of protein information such as: function, metabolic role, off-targeting, structural properties including druggability, essentiality and omic experiments, to facilitate the identification and prioritization of candidate drug targets in pathogens. We include in the database 10 genomes of some of the most relevant microorganisms for human health (Mycobacterium tuberculosis, Mycobacterium leprae, Klebsiella pneumoniae, Plasmodium vivax, Toxoplasma gondii, Leishmania major, Wolbachia bancrofti, Trypanosoma brucei, Shigella dysenteriae and Schistosoma Smanosoni) and show its applicability. New genomes can be uploaded upon request. PMID:29106651

Model Based Optimal Sensor Network Design for Condition Monitoring in an IGCC Plant

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

Kumar, Rajeeva; Kumar, Aditya; Dai, Dan

2012-12-31

This report summarizes the achievements and final results of this program. The objective of this program is to develop a general model-based sensor network design methodology and tools to address key issues in the design of an optimal sensor network configuration: the type, location and number of sensors used in a network, for online condition monitoring. In particular, the focus in this work is to develop software tools for optimal sensor placement (OSP) and use these tools to design optimal sensor network configuration for online condition monitoring of gasifier refractory wear and radiant syngas cooler (RSC) fouling. The methodology developedmore » will be applicable to sensing system design for online condition monitoring for broad range of applications. The overall approach consists of (i) defining condition monitoring requirement in terms of OSP and mapping these requirements in mathematical terms for OSP algorithm, (ii) analyzing trade-off of alternate OSP algorithms, down selecting the most relevant ones and developing them for IGCC applications (iii) enhancing the gasifier and RSC models as required by OSP algorithms, (iv) applying the developed OSP algorithm to design the optimal sensor network required for the condition monitoring of an IGCC gasifier refractory and RSC fouling. Two key requirements for OSP for condition monitoring are desired precision for the monitoring variables (e.g. refractory wear) and reliability of the proposed sensor network in the presence of expected sensor failures. The OSP problem is naturally posed within a Kalman filtering approach as an integer programming problem where the key requirements of precision and reliability are imposed as constraints. The optimization is performed over the overall network cost. Based on extensive literature survey two formulations were identified as being relevant to OSP for condition monitoring; one based on LMI formulation and the other being standard INLP formulation. Various algorithms to solve these two formulations were developed and validated. For a given OSP problem the computation efficiency largely depends on the “size” of the problem. Initially a simplified 1-D gasifier model assuming axial and azimuthal symmetry was used to test out various OSP algorithms. Finally these algorithms were used to design the optimal sensor network for condition monitoring of IGCC gasifier refractory wear and RSC fouling. The sensors type and locations obtained as solution to the OSP problem were validated using model based sensing approach. The OSP algorithm has been developed in a modular form and has been packaged as a software tool for OSP design where a designer can explore various OSP design algorithm is a user friendly way. The OSP software tool is implemented in Matlab/Simulink© in-house. The tool also uses few optimization routines that are freely available on World Wide Web. In addition a modular Extended Kalman Filter (EKF) block has also been developed in Matlab/Simulink© which can be utilized for model based sensing of important process variables that are not directly measured through combining the online sensors with model based estimation once the hardware sensor and their locations has been finalized. The OSP algorithm details and the results of applying these algorithms to obtain optimal sensor location for condition monitoring of gasifier refractory wear and RSC fouling profile are summarized in this final report.« less

Analyzing the uncertainty of suspended sediment load prediction using sequential data assimilation

NASA Astrophysics Data System (ADS)

Leisenring, Marc; Moradkhani, Hamid

2012-10-01

SummaryA first step in understanding the impacts of sediment and controlling the sources of sediment is to quantify the mass loading. Since mass loading is the product of flow and concentration, the quantification of loads first requires the quantification of runoff volume. Using the National Weather Service's SNOW-17 and the Sacramento Soil Moisture Accounting (SAC-SMA) models, this study employed particle filter based Bayesian data assimilation methods to predict seasonal snow water equivalent (SWE) and runoff within a small watershed in the Lake Tahoe Basin located in California, USA. A procedure was developed to scale the variance multipliers (a.k.a hyperparameters) for model parameters and predictions based on the accuracy of the mean predictions relative to the ensemble spread. In addition, an online bias correction algorithm based on the lagged average bias was implemented to detect and correct for systematic bias in model forecasts prior to updating with the particle filter. Both of these methods significantly improved the performance of the particle filter without requiring excessively wide prediction bounds. The flow ensemble was linked to a non-linear regression model that was used to predict suspended sediment concentrations (SSCs) based on runoff rate and time of year. Runoff volumes and SSC were then combined to produce an ensemble of suspended sediment load estimates. Annual suspended sediment loads for the 5 years of simulation were finally computed along with 95% prediction intervals that account for uncertainty in both the SSC regression model and flow rate estimates. Understanding the uncertainty associated with annual suspended sediment load predictions is critical for making sound watershed management decisions aimed at maintaining the exceptional clarity of Lake Tahoe. The computational methods developed and applied in this research could assist with similar studies where it is important to quantify the predictive uncertainty of pollutant load estimates.

Improving the User Experience of Finding and Visualizing Oceanographic Data

NASA Astrophysics Data System (ADS)

Rauch, S.; Allison, M. D.; Groman, R. C.; Chandler, C. L.; Galvarino, C.; Gegg, S. R.; Kinkade, D.; Shepherd, A.; Wiebe, P. H.; Glover, D. M.

2013-12-01

Searching for and locating data of interest can be a challenge to researchers as increasing volumes of data are made available online through various data centers, repositories, and archives. The Biological and Chemical Oceanography Data Management Office (BCO-DMO) is keenly aware of this challenge and, as a result, has implemented features and technologies aimed at improving data discovery and enhancing the user experience. BCO-DMO was created in 2006 to manage and publish data from research projects funded by the Division of Ocean Sciences (OCE) Biological and Chemical Oceanography Sections and the Division of Polar Programs (PLR) Antarctic Sciences Organisms and Ecosystems Program (ANT) of the US National Science Foundation (NSF). The BCO-DMO text-based and geospatial-based data access systems provide users with tools to search, filter, and visualize data in order to efficiently find data of interest. The geospatial interface, developed using a suite of open-source software (including MapServer [1], OpenLayers [2], ExtJS [3], and MySQL [4]), allows users to search and filter/subset metadata based on program, project, or deployment, or by using a simple word search. The map responds based on user selections, presents options that allow the user to choose specific data parameters (e.g., a species or an individual drifter), and presents further options for visualizing those data on the map or in "quick-view" plots. The data managed and made available by BCO-DMO are very heterogeneous in nature, from in-situ biogeochemical, ecological, and physical data, to controlled laboratory experiments. Due to the heterogeneity of the data types, a 'one size fits all' approach to visualization cannot be applied. Datasets are visualized in a way that will best allow users to assess fitness for purpose. An advanced geospatial interface, which contains a semantically-enabled faceted search [5], is also available. These search facets are highly interactive and responsive, allowing users to construct their own custom searches by applying multiple filters. New filtering and visualization tools are continually being added to the BCO-DMO system as new data types are encountered and as we receive feedback from our data contributors and users. As our system becomes more complex, teaching users about the many interactive features becomes increasingly important. Tutorials and videos are made available online. Recent in-person classroom-style tutorials have proven useful for both demonstrating our system to users and for obtaining feedback to further improve the user experience. References: [1] University of Minnesota. MapServer: Open source web mapping. http://www.mapserver.org [2] OpenLayers: Free Maps for the Web. http://www.openlayers.org [3] Sencha. ExtJS. http://www.sencha.com/products/extjs [4] MySQL. http://www.mysql.com/ [5] Maffei, A. R., Rozell, E. A., West, P., Zednik, S., and Fox, P. A. 2011. Open Standards and Technologies in the S2S Framework. Abstract IN31A-1435 presented at American Geophysical Union 2011 Fall Meeting, San Francisco, CA, 7 December 2011.

Reduced kernel recursive least squares algorithm for aero-engine degradation prediction

NASA Astrophysics Data System (ADS)

Zhou, Haowen; Huang, Jinquan; Lu, Feng

2017-10-01

Kernel adaptive filters (KAFs) generate a linear growing radial basis function (RBF) network with the number of training samples, thereby lacking sparseness. To deal with this drawback, traditional sparsification techniques select a subset of original training data based on a certain criterion to train the network and discard the redundant data directly. Although these methods curb the growth of the network effectively, it should be noted that information conveyed by these redundant samples is omitted, which may lead to accuracy degradation. In this paper, we present a novel online sparsification method which requires much less training time without sacrificing the accuracy performance. Specifically, a reduced kernel recursive least squares (RKRLS) algorithm is developed based on the reduced technique and the linear independency. Unlike conventional methods, our novel methodology employs these redundant data to update the coefficients of the existing network. Due to the effective utilization of the redundant data, the novel algorithm achieves a better accuracy performance, although the network size is significantly reduced. Experiments on time series prediction and online regression demonstrate that RKRLS algorithm requires much less computational consumption and maintains the satisfactory accuracy performance. Finally, we propose an enhanced multi-sensor prognostic model based on RKRLS and Hidden Markov Model (HMM) for remaining useful life (RUL) estimation. A case study in a turbofan degradation dataset is performed to evaluate the performance of the novel prognostic approach.

Joint Data Assimilation and Parameter Calibration in on-line groundwater modelling using Sequential Monte Carlo techniques

NASA Astrophysics Data System (ADS)

Ramgraber, M.; Schirmer, M.

2017-12-01

As computational power grows and wireless sensor networks find their way into common practice, it becomes increasingly feasible to pursue on-line numerical groundwater modelling. The reconciliation of model predictions with sensor measurements often necessitates the application of Sequential Monte Carlo (SMC) techniques, most prominently represented by the Ensemble Kalman Filter. In the pursuit of on-line predictions it seems advantageous to transcend the scope of pure data assimilation and incorporate on-line parameter calibration as well. Unfortunately, the interplay between shifting model parameters and transient states is non-trivial. Several recent publications (e.g. Chopin et al., 2013, Kantas et al., 2015) in the field of statistics discuss potential algorithms addressing this issue. However, most of these are computationally intractable for on-line application. In this study, we investigate to what extent compromises between mathematical rigour and computational restrictions can be made within the framework of on-line numerical modelling of groundwater. Preliminary studies are conducted in a synthetic setting, with the goal of transferring the conclusions drawn into application in a real-world setting. To this end, a wireless sensor network has been established in the valley aquifer around Fehraltorf, characterized by a highly dynamic groundwater system and located about 20 km to the East of Zürich, Switzerland. By providing continuous probabilistic estimates of the state and parameter distribution, a steady base for branched-off predictive scenario modelling could be established, providing water authorities with advanced tools for assessing the impact of groundwater management practices. Chopin, N., Jacob, P.E. and Papaspiliopoulos, O. (2013): SMC2: an efficient algorithm for sequential analysis of state space models. Journal of the Royal Statistical Society: Series B (Statistical Methodology), 75 (3), p. 397-426. Kantas, N., Doucet, A., Singh, S.S., Maciejowski, J., and Chopin, N. (2015): On Particle Methods for Parameter Estimation in State-Space Models. Statistical Science, 30 (3), p. 328.-351.

Comparison of online and offline based merging methods for high resolution rainfall intensities

NASA Astrophysics Data System (ADS)

Shehu, Bora; Haberlandt, Uwe

2016-04-01

Accurate rainfall intensities with high spatial and temporal resolution are crucial for urban flow prediction. Commonly, raw or bias corrected radar fields are used for forecasting, while different merging products are employed for simulation. The merging products are proven to be adequate for rainfall intensities estimation, however their application in forecasting is limited as they are developed for offline mode. This study aims at adapting and refining the offline merging techniques for the online implementation, and at comparing the performance of these methods for high resolution rainfall data. Radar bias correction based on mean fields and quantile mapping are analyzed individually and also are implemented in conditional merging. Special attention is given to the impact of different spatial and temporal filters on the predictive skill of all methods. Raw radar data and kriging interpolation of station data are considered as a reference to check the benefit of the merged products. The methods are applied for several extreme events in the time period 2006-2012 caused by different meteorological conditions, and their performance is evaluated by split sampling. The study area is located within the 112 km radius of Hannover radar in Lower Saxony, Germany and the data set constitutes of 80 recording stations in 5 min time steps. The results of this study reveal how the performance of the methods is affected by the adjustment of radar data, choice of merging method and selected event. Merging techniques can be used to improve the performance of online rainfall estimation, which gives way to the application of merging products in forecasting.

VizieR Online Data Catalog: Antennae galaxies (NGC 4038/4039) revisited (Whitmore+, 2010)

NASA Astrophysics Data System (ADS)

Whitmore, B. C.; Chandar, R.; Schweizer, F.; Rothberg, B.; Leitherer, C.; Rieke, M.; Rieke, G.; Blair, W. P.; Mengel, S.; Alonso-Herrero, A.

2012-06-01

Observations of the main bodies of NGC 4038/39 were made with the Hubble Space Telescope (HST), using the ACS, as part of Program GO-10188. Multi-band photometry was obtained in the following optical broadband filters: F435W (~B), F550M (~V), and F814W (~I). Archival F336W photometry of the Antennae (Program GO-5962) was used to supplement our optical ACS/WFC observations. Infrared observations were made using the Near Infrared Camera and Multi-Object Spectrometer (NICMOS) camera on HST as part of Program GO-10188. Observations were made using the NIC2 camera with the F160W, F187N, and F237M filters, and the NIC3 camera with the F110W, F160W, F164W, F187N, and F222M filters. (10 data files).

Towards a Collaborative Filtering Approach to Medication Reconciliation

PubMed Central

Hasan, Sharique; Duncan, George T.; Neill, Daniel B.; Padman, Rema

2008-01-01

A physician’s prescribing decisions depend on knowledge of the patient’s medication list. This knowledge is often incomplete, and errors or omissions could result in adverse outcomes. To address this problem, the Joint Commission recommends medication reconciliation for creating a more accurate list of a patient’s medications. In this paper, we develop techniques for automatic detection of omissions in medication lists, identifying drugs that the patient may be taking but are not on the patient’s medication list. Our key insight is that this problem is analogous to the collaborative filtering framework increasingly used by online retailers to recommend relevant products to customers. The collaborative filtering approach enables a variety of solution techniques, including nearest neighbor and co-occurrence approaches. We evaluate the effectiveness of these approaches using medication data from a long-term care center in the Eastern US. Preliminary results suggest that this framework may become a valuable tool for medication reconciliation. PMID:18998834

Towards a collaborative filtering approach to medication reconciliation.

PubMed

Hasan, Sharique; Duncan, George T; Neill, Daniel B; Padman, Rema

2008-11-06

A physicians prescribing decisions depend on knowledge of the patients medication list. This knowledge is often incomplete, and errors or omissions could result in adverse outcomes. To address this problem, the Joint Commission recommends medication reconciliation for creating a more accurate list of a patients medications. In this paper, we develop techniques for automatic detection of omissions in medication lists, identifying drugs that the patient may be taking but are not on the patients medication list. Our key insight is that this problem is analogous to the collaborative filtering framework increasingly used by online retailers to recommend relevant products to customers. The collaborative filtering approach enables a variety of solution techniques, including nearest neighbor and co-occurrence approaches. We evaluate the effectiveness of these approaches using medication data from a long-term care center in the Eastern US. Preliminary results suggest that this framework may become a valuable tool for medication reconciliation.

VizieR Online Data Catalog: The rising light curves of Type Ia supernovae (Firth+, 2015)

NASA Astrophysics Data System (ADS)

Firth, R. E.; Sullivan, M.; Gal-Yam, A.; Howell, D. A.; Maguire, K.; Nugent, P.; Piro, A. L.; Baltay, C.; Feindt, U.; Hadjiyksta, E.; McKinnon, R.; Ofek, E.; Rabinowitz, D.; Walker, E. S.

2017-11-01

Our data come from two local, rolling SN surveys. The first is the PTF (Law et al. 2009PASP..121.1395L; Rau et al. 2009PASP..121.1334R), a wide-field survey using the CFH12k camera mounted on the 48 inch Samuel Oschin telescope at Palomar Observatory (the P48 telescope). The survey operated primarily in an R-band filter (hereafter RP48), with occasional runs in a g filter (gP48) around new moon. The second survey is the LSQ (Baltay et al. 2013PASP..125..683B), a Southern Hemisphere variability survey using the 10 deg2 QUEST instrument (Baltay et al. 2007PASP..119.1278B) on the 1.0 m European Southern Observatory Schmidt telescope at La Silla, Chile. LSQ operates with a cadence of between 2 h and 2 d, using a broad gr filter (hereafter grLSQ). (2 data files).

A square root ensemble Kalman filter application to a motor-imagery brain-computer interface

PubMed Central

Kamrunnahar, M.; Schiff, S. J.

2017-01-01

We here investigated a non-linear ensemble Kalman filter (SPKF) application to a motor imagery brain computer interface (BCI). A square root central difference Kalman filter (SR-CDKF) was used as an approach for brain state estimation in motor imagery task performance, using scalp electroencephalography (EEG) signals. Healthy human subjects imagined left vs. right hand movements and tongue vs. bilateral toe movements while scalp EEG signals were recorded. Offline data analysis was conducted for training the model as well as for decoding the imagery movements. Preliminary results indicate the feasibility of this approach with a decoding accuracy of 78%–90% for the hand movements and 70%–90% for the tongue-toes movements. Ongoing research includes online BCI applications of this approach as well as combined state and parameter estimation using this algorithm with different system dynamic models. PMID:22255799

VizieR Online Data Catalog: A survey for Hα pulsar bow shocks (Brownsberger+, 2014)

NASA Astrophysics Data System (ADS)

Brownsberger, S.; Romani, R. W.

2017-06-01

Our pulsar Hα campaign was pursued during a series of WIYN and SOAR observations, allocated through the joint Fermi/NOAO program to study the counterparts of LAT pulsar, blazar, and unidentified sources. The PWN data reported here came from portions of five observing runs, covering both the Northern and Southern hemispheres. For all Hα images we used the WIYN W012 (λ=6566 Å, ΔλFWHM=16 Å) narrow band Hα filter, kindly loaned to SOAR for the two southern campaigns. To help distinguish poorly resolved Hα sources near the pulsars from stars, we also obtained, when possible, matching continuum observations using the W014 (λ=6562 Å, ΔλFWHM=378 Å) wide Hα filter or a Sloan Digital Sky Survey (SDSS) r'(λ=6163 Å, ΔλFWHM=1518 Å) broad band filter. (1 data file).

VizieR Online Data Catalog: Optical spectroscopy toward Orion B fields (Kounkel+, 2017)

NASA Astrophysics Data System (ADS)

Kounkel, M.; Hartmann, L.; Mateo, M.; Bailey, J. I., III

2018-03-01

We observed a total of four fields toward the Orion B with Michigan/Magellan Fiber System (M2FS), a multi-object spectrograph on the Magellan Clay Telescope. These fields included regions toward NGC2023, 2024, 2068, and L1622 (Table 1). Due to their spatial proximity, we consider NGC 2023 and NGC 2024 together in the analysis presented in this paper. All regions were observed with the Hα and LiI filters, simultaneously spanning two orders, covering the spectral range of 6525-6750Å with a spectral resolution R~20000 between 2014 Dec and 2017 Mar. A maximum of 128 sources can be observed in this configuration, with the field of view of 29' in diameter. NGC 2068 has also been re-observed a second time with the Hα and the LiI filters, as well as the MgI filter, which spans the spectral range of 5100-5210Å. (2 data files).

Noise-shaping gradient descent-based online adaptation algorithms for digital calibration of analog circuits.

PubMed

Chakrabartty, Shantanu; Shaga, Ravi K; Aono, Kenji

2013-04-01

Analog circuits that are calibrated using digital-to-analog converters (DACs) use a digital signal processor-based algorithm for real-time adaptation and programming of system parameters. In this paper, we first show that this conventional framework for adaptation yields suboptimal calibration properties because of artifacts introduced by quantization noise. We then propose a novel online stochastic optimization algorithm called noise-shaping or ΣΔ gradient descent, which can shape the quantization noise out of the frequency regions spanning the parameter adaptation trajectories. As a result, the proposed algorithms demonstrate superior parameter search properties compared to floating-point gradient methods and better convergence properties than conventional quantized gradient-methods. In the second part of this paper, we apply the ΣΔ gradient descent algorithm to two examples of real-time digital calibration: 1) balancing and tracking of bias currents, and 2) frequency calibration of a band-pass Gm-C biquad filter biased in weak inversion. For each of these examples, the circuits have been prototyped in a 0.5-μm complementary metal-oxide-semiconductor process, and we demonstrate that the proposed algorithm is able to find the optimal solution even in the presence of spurious local minima, which are introduced by the nonlinear and non-monotonic response of calibration DACs.

Automated system for on-line desorption of dried blood spots applied to LC/MS/MS pharmacokinetic study of flurbiprofen and its metabolite.

PubMed

Déglon, Julien; Thomas, Aurélien; Daali, Youssef; Lauer, Estelle; Samer, Caroline; Desmeules, Jules; Dayer, Pierre; Mangin, Patrice; Staub, Christian

2011-01-25

This paper illustrates the development of an automated system for the on-line bioanalysis of dried blood spots (on-line DBS). In this way, a prototype was designed for integration into a conventional LC/MS/MS, allowing the successive extraction of 30 DBS toward the analytical system without any sample pretreatment. The developed method was assessed for the DBS analysis of flurbiprofen (FLB) and its metabolite 4-hydroxyflurbiprofen (OH-FLB) in human whole blood (i.e. 5 μL). The automated procedure was fully validated based on international criteria and showed good precision, trueness, and linearity over the expected concentration range (from 10 to 1000 ng/mL and 100 to 10,000 ng/mL for OH-FLB and FLB respectively). Furthermore, the prototype showed good results in terms of recovery and carry-over. Stability of both analytes on filter paper was also investigated and the results suggested that DBS could be stored at ambient temperature for over 1 month. The on-line DBS automated system was then successfully applied to a pharmacokinetic study performed on healthy male volunteers after oral administration of a single 50-mg dose of FLB. Additionally, a comparison between finger capillary DBS and classic venous plasma concentrations was investigated. A good correlation was observed, demonstrating the complementarity of both sampling forms. The automated system described in this article represents an efficient tool for the LC/MS/MS analysis of DBS samples in many bioanalytical applications. Copyright © 2010 Elsevier B.V. All rights reserved.

VizieR Online Data Catalog: 3D-HST+CANDELS catalog (Skelton+, 2014)

NASA Astrophysics Data System (ADS)

Skelton, R. E.; Whitaker, K. E.; Momcheva, I. G.; Brammer, G. B.; van Dokkum, P. G.; Labbe, I.; Franx, M.; van der Wel, A.; Bezanson, R.; Da Cunha, E.; Fumagalli, M.; Forster Schreiber, N.; Kriek, M.; Leja, J.; Lundgren, B. F.; Magee, D.; Marchesini, D.; Maseda, M. V.; Nelson, E. J.; Oesch, P.; Pacifici, C.; Patel, S. G.; Price, S.; Rix, H.-W.; Tal, T.; Wake, D. A.; Wuyts, S.

2015-09-01

The majority of HST/WFC3 imaging comes from the 3D-HST and CANDELS surveys which, jointly, have covered ~940arcmin2 in three infrared filters: F125W, F140W, and F160W (HST cycle 11, 17, 18 and 19). See section 2 for further explanations. (1 data file).

Exploring Affiliation Network Models as a Collaborative Filtering Mechanism in E-Learning

ERIC Educational Resources Information Center

Rodriguez, Daniel; Sicilia, Miguel Angel; Sanchez-Alonso, Salvador; Lezcano, Leonardo; Garcia-Barriocanal, Elena

2011-01-01

The online interaction of learners and tutors in activities with concrete objectives provides a valuable source of data that can be analyzed for different purposes. One of these purposes is the use of the information extracted from that interaction to aid tutors and learners in decision making about either the configuration of further learning…

VizieR Online Data Catalog: Long term R and V band monitoring of AGN (Taris+, 2016)

NASA Astrophysics Data System (ADS)

Taris, F.; Andrei, J.; Roland, A.; Klotz, A.; Vachier, S.; Souchay, J.

2015-11-01

The files table5.dat and table6.dat contain the name of the sources, their observation dates, the location of the telescope used, the filter names, the magnitudes and their corresponding uncertainties. The observations have been carried out between 2011 and 2015 with the TAROT Telescopes. (3 data files).

Navigating iScapes: Australian Youth Constructing Identities and Social Relations in a Network Society

ERIC Educational Resources Information Center

Mallan, Kerry; Ashford, Barbara; Singh, Parlo

2010-01-01

This article extends Appadurai's notion of "scapes" to delineate what we see as "iScapes." We contend that iScapes captures the way online technologies shape interactions that invariably filter into offline contexts, giving shape and meaning to human actions and motivations. By drawing on research on high school students'…

VizieR Online Data Catalog: Absolute UV magnitudes of type Ia SNe (Brown+, 2010)

NASA Astrophysics Data System (ADS)

Brown, P. J.; Roming, P. W. A.; Milne, P.; Bufano, F.; Ciardullo, R.; Elias-Rosa, N.; Filippenko, A. V.; Foley, R. J.; Gehrels, N.; Gronwall, C.; Hicken, M.; Holland, S. T.; Hoversten, E. A.; Immler, S.; Kirshner, R. P.; Li, W.; Mazzali, P.; Phillips, M. M.; Pritchard, T.; Still, M.; Turatto, M.; vanden Berk, D.

2012-07-01

This study includes the 14 Swift/UVOT SNe Ia from Brown et al. (2009, Cat. J/AJ/137/4517) and Milne et al. (2010ApJ...721.1627M) with low extinction (E(B-V)<0.5mag) and well-sampled light curves in at least one UV filter. (7 data files).

Mashing up Multiple Web Feeds Using Yahoo! Pipes

ERIC Educational Resources Information Center

Fagan, Jody Condit

2007-01-01

Pipes is an interactive data aggregator and manipulator that lets you mashup your favorite online data sources. Pipes could be used to "combine many feeds into one, then sort, filter and translate to create your ultimate custom feed. In this article, the author describes how to use Yahoo! Pipes. The author shares what she has learned in…

Development of an Eye-Safe Micro-Pulse Differential Absorption Lidar (DIAL) for Carbon Dioxide Profilings

NASA Astrophysics Data System (ADS)

Johnson, W.; Repasky, K. S.; Nehrir, A. R.; Carlsten, J.

2011-12-01

A differential absorption lidar (DIAL) for monitoring carbon dioxide (CO2) is under development at Montana State University using commercially available parts. Two distributed feedback (DFB) lasers, one at the on-line wavelength and one at the off-line wavelength are used to injection seed a fiber amplifier. The DIAL operates in the 1.57 micron carbon dioxide absorption band at an on-line wavelength of 1.5714060 microns. The laser transmitter produces 40 μJ pulses with a pulse duration of 1 μs and a pulse repetition frequency of 20 kHz. The scattered light from the laser transmitter is collected using a 28 cm diameter Schmidt-Cassegrain telescope. The light collected by the telescope is collimated and then filtered using a 0.8 nm FWHM narrowband interference filter. After the optical filter, the light is coupled into a multimode optical fiber with a 1000 μm core diameter. The output from the optical fiber is coupled into a photomultiplier tube (PMT) used to monitor the return signal. The analog output from the PMT is next incident on a discriminator producing TTL logic pulses for photon counting. The output from the PMT and discriminator is monitored using a multichannel scalar card allowing the counting of the TTL pulses as a function of range. Data from the DIAL instrument is collected in the following manner. The fiber amplifier is injection seeded first with the on-line DFB laser. The return signal as a function of range is integrated using the multichannel scalar for a user defined time, typically set at 6 s. The off-line DFB laser is then used to injection seed the fiber amplifier and the process is repeated. This process is repeated for a user defined period. The CO2 concentration as a function of range is calculated using the on-line and off-line return signals with the DIAL equation. A comparison of the CO2 concentration measured using the DIAL instrument at 1.5 km and a Li-Cor LI-820 in situ sensor located at 1.5 km from the DIAL over a 2.5 hour period indicate that the CO2 DIAL has an accuracy of ±20 parts per million (PPM).

LABORATORY EVALUATION OF A MICROFLUIDIC ELECTROCHEMICAL SENSOR FOR AEROSOL OXIDATIVE LOAD.

PubMed

Koehler, Kirsten; Shapiro, Jeffrey; Sameenoi, Yupaporn; Henry, Charles; Volckens, John

2014-05-01

Human exposure to particulate matter (PM) air pollution is associated with human morbidity and mortality. The mechanisms by which PM impacts human health are unresolved, but evidence suggests that PM intake leads to cellular oxidative stress through the generation of reactive oxygen species (ROS). Therefore, reliable tools are needed for estimating the oxidant generating capacity, or oxidative load, of PM at high temporal resolution (minutes to hours). One of the most widely reported methods for assessing PM oxidative load is the dithiothreitol (DTT) assay. The traditional DTT assay utilizes filter-based PM collection in conjunction with chemical analysis to determine the oxidation rate of reduced DTT in solution with PM. However, the traditional DTT assay suffers from poor time resolution, loss of reactive species during sampling, and high limit of detection. Recently, a new DTT assay was developed that couples a Particle-Into-Liquid-Sampler with microfluidic-electrochemical detection. This 'on-line' system allows high temporal resolution monitoring of PM reactivity with improved detection limits. This study reports on a laboratory comparison of the traditional and on-line DTT approaches. An urban dust sample was aerosolized in a laboratory test chamber at three atmospherically-relevant concentrations. The on-line system gave a stronger correlation between DTT consumption rate and PM mass (R 2 = 0.69) than the traditional method (R 2 = 0.40) and increased precision at high temporal resolution, compared to the traditional method.

On-line training of recurrent neural networks with continuous topology adaptation.

PubMed

Obradovic, D

1996-01-01

This paper presents an online procedure for training dynamic neural networks with input-output recurrences whose topology is continuously adjusted to the complexity of the target system dynamics. This is accomplished by changing the number of the elements of the network hidden layer whenever the existing topology cannot capture the dynamics presented by the new data. The training mechanism is based on the suitably altered extended Kalman filter (EKF) algorithm which is simultaneously used for the network parameter adjustment and for its state estimation. The network consists of a single hidden layer with Gaussian radial basis functions (GRBF), and a linear output layer. The choice of the GRBF is induced by the requirements of the online learning. The latter implies the network architecture which permits only local influence of the new data point in order not to forget the previously learned dynamics. The continuous topology adaptation is implemented in our algorithm to avoid memory and computational problems of using a regular grid of GRBF'S which covers the network input space. Furthermore, we show that the resulting parameter increase can be handled "smoothly" without interfering with the already acquired information. If the target system dynamics are changing over time, we show that a suitable forgetting factor can be used to "unlearn" the no longer-relevant dynamics. The quality of the recurrent network training algorithm is demonstrated on the identification of nonlinear dynamic systems.

Size characterization of airborne SiO2 nanoparticles with on-line and off-line measurement techniques: an interlaboratory comparison study

NASA Astrophysics Data System (ADS)

Motzkus, C.; Macé, T.; Gaie-Levrel, F.; Ducourtieux, S.; Delvallee, A.; Dirscherl, K.; Hodoroaba, V.-D.; Popov, I.; Popov, O.; Kuselman, I.; Takahata, K.; Ehara, K.; Ausset, P.; Maillé, M.; Michielsen, N.; Bondiguel, S.; Gensdarmes, F.; Morawska, L.; Johnson, G. R.; Faghihi, E. M.; Kim, C. S.; Kim, Y. H.; Chu, M. C.; Guardado, J. A.; Salas, A.; Capannelli, G.; Costa, C.; Bostrom, T.; Jämting, Å. K.; Lawn, M. A.; Adlem, L.; Vaslin-Reimann, S.

2013-10-01

Results of an interlaboratory comparison on size characterization of SiO2 airborne nanoparticles using on-line and off-line measurement techniques are discussed. This study was performed in the framework of Technical Working Area (TWA) 34—"Properties of Nanoparticle Populations" of the Versailles Project on Advanced Materials and Standards (VAMAS) in the project no. 3 "Techniques for characterizing size distribution of airborne nanoparticles". Two types of nano-aerosols, consisting of (1) one population of nanoparticles with a mean diameter between 30.3 and 39.0 nm and (2) two populations of non-agglomerated nanoparticles with mean diameters between, respectively, 36.2-46.6 nm and 80.2-89.8 nm, were generated for characterization measurements. Scanning mobility particle size spectrometers (SMPS) were used for on-line measurements of size distributions of the produced nano-aerosols. Transmission electron microscopy, scanning electron microscopy, and atomic force microscopy were used as off-line measurement techniques for nanoparticles characterization. Samples were deposited on appropriate supports such as grids, filters, and mica plates by electrostatic precipitation and a filtration technique using SMPS controlled generation upstream. The results of the main size distribution parameters (mean and mode diameters), obtained from several laboratories, were compared based on metrological approaches including metrological traceability, calibration, and evaluation of the measurement uncertainty. Internationally harmonized measurement procedures for airborne SiO2 nanoparticles characterization are proposed.

Analysis of Anions in Ambient Aerosols by Microchip Capillary Electrophoresis

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

Liu, Yan; MacDonald, David A.; Yu, Xiao-Ying

2006-10-01

We describe a microchip capillary electrophoresis method for the analysis of nitrate and sulfate in ambient aerosols. Investigating the chemical composition of ambient aerosol particles is essential for understanding their sources and effects. Significant progress has been made towards developing mass spectrometry-based instrumentation for rapid qualitative analysis of aerosols. Alternative methods for rapid quantification of selected high abundance compounds are needed to augment the capacity for widespread routine analysis. Such methods could provide much higher temporal and spatial resolution than can be achieved currently. Inorganic anions comprise a large percentage of particulate mass with nitrate and sulfate among the mostmore » abundant species. While ion chromatography has proven very useful for analyzing extracts of time-integrated ambient aerosol samples collected on filters and for semi-continuous, on-line particle composition measurements, there is a growing need for development of new compact, inexpensive approaches to routine on-line aerosol ion analysis for deployment in spatially dense, atmospheric measurement networks. Microchip capillary electrophoresis provides the necessary speed and portability to address this need. In this report, on-column contact conductivity detection is used with hydrodynamic injection to create a simple microchip instrument for analysis of nitrate and sulfate. On-column contact conductivity detection was achieved using a Pd decoupler placed upstream from the working electrodes. Microchips containing two Au or Pd working electrodes showed a good linear range (5-500 µM) and low limits-of-detection for sulfate and nitrate with Au providing the lowest detection limits (1 µM) for both ions. The completed microchip system was used to analyze ambient aerosol filter samples. Nitrate and sulfate concentrations measured by the microchip matched the concentrations measured by ion chromatography.« less

Calculations to Support On-line Neutron Spectrum Adjustment by Measurements with Miniature Fission Chambers in the JSI TRIGA Reactor

NASA Astrophysics Data System (ADS)

Kaiba, Tanja; Radulović, Vladimir; Žerovnik, Gašper; Snoj, Luka; Fourmentel, Damien; Barbot, LoÏc; Destouches, Christophe AE(; )

2018-01-01

Preliminary calculations were performed with the aim to establish optimal experimental conditions for the measurement campaign within the collaboration between the Jožef Stefan Institute (JSI) and Commissariat à l'Énergie Atomique et aux Énergies Alternatives (CEA Cadarache). The goal of the project is to additionally characterize the neutron spectruminside the JSI TRIGA reactor core with focus on the measurement epi-thermal and fast part of the spectrum. Measurements will be performed with fission chambers containing different fissile materials (235U, 237Np and 242Pu) covered with thermal neutron filters (Cd and Gd). The changes in the detected signal and neutron flux spectrum with and without transmission filter were studied. Additional effort was put into evaluation of the effect of the filter geometry (e.g. opening on the top end of the filter) on the detector signal. After the analysis of the scoping calculations it was concluded to position the experiment in the outside core ring inside one of the empty fuel element positions.

VizieR Online Data Catalog: Photometry of multiple stars at NAOR&ASV in 2015 (Cvetkovic+, 2017)

NASA Astrophysics Data System (ADS)

Cvetkovic, Z.; Pavlovic, R.; Boeva, S.

2018-05-01

This is the ninth series of CCD observations of double and multiple stars, obtained at the Bulgarian National Astronomical Observatory at Rozhen (NAOR) over five nights. As previously, the CCD camera VersArray 1300B was used, which was attached to the 2 m telescope. For each double or multiple star, five CCD frames in the Johnson B filter and five frames in the Johnson V filter were taken, which enabled us to determine the magnitude difference for these filters. In 2015 at the Astronomical Station at Vidojevica (ASV), over a total of 23 nights, observations were carried out by using the 60 cm telescope with a Cassegrain optical system. This is the fourth observational series at ASV since the work started there in 2011. In the observations we used the Apogee Alta U42 CCD camera whose characteristics can be found in the paper by Cvetkovic et al. (2016, J/AJ/151/58). Every pair was observed five times in the Cousins/Bessel B filter and five times in the Cousins/Bessel V one. (3 data files).

VizieR Online Data Catalog: BVRI photometry of luminous stars in M31 and M33 (Martin+, 2017)

NASA Astrophysics Data System (ADS)

Martin, J. C.; Humphreys, R. M.

2017-11-01

Images were obtained of M31 and M33 using an Apogee U42 CCD Camera with a back-illuminated E2v CCD42-40 chip on the F/13 20-inch (0.51m) telescope at the University of Illinois Springfield Henry R. Barber Research Observatory near Pleasant Plains, IL. The images are 19.4*19.4 arc minutes squares with a pixel scale of 0.57 arcseconds per pixel. Images were exposed in four high-throughput broad-band filters manufactured by Astrodon: Johnson B and V, and Cousins R and I. The B-filter does not have the red-leak present in most Astrodon B-filers manufactured prior to 2013. Images were taken in V at every epoch except one in 2012. In 2012, many fields were also imaged in R. Imaging in the B-filter started in 2013, and imaging in the I-filter started in 2015. Table 1 gives a record of the 199 images of M31 and 77 images of M33. (5 data files).

Broadband network on-line data acquisition system with web based interface for control and basic analysis

NASA Astrophysics Data System (ADS)

Polkowski, Marcin; Grad, Marek

2016-04-01

Passive seismic experiment "13BB Star" is operated since mid 2013 in northern Poland and consists of 13 broadband seismic stations. One of the elements of this experiment is dedicated on-line data acquisition system comprised of both client (station) side and server side modules with web based interface that allows monitoring of network status and provides tools for preliminary data analysis. Station side is controlled by ARM Linux board that is programmed to maintain 3G/EDGE internet connection, receive data from digitizer, send data do central server among with additional auxiliary parameters like temperatures, voltages and electric current measurements. Station side is controlled by set of easy to install PHP scripts. Data is transmitted securely over SSH protocol to central server. Central server is a dedicated Linux based machine. Its duty is receiving and processing all data from all stations including auxiliary parameters. Server side software is written in PHP and Python. Additionally, it allows remote station configuration and provides web based interface for user friendly interaction. All collected data can be displayed for each day and station. It also allows manual creation of event oriented plots with different filtering abilities and provides numerous status and statistic information. Our solution is very flexible and easy to modify. In this presentation we would like to share our solution and experience. National Science Centre Poland provided financial support for this work via NCN grant DEC-2011/02/A/ST10/00284.

Remote Water Temperature Measurements Based on Brillouin Scattering with a Frequency Doubled Pulsed Yb:doped Fiber Amplifier

PubMed Central

Schorstein, Kai; Popescu, Alexandru; Göbel, Marco; Walther, Thomas

2008-01-01

Temperature profiles of the ocean are of interest for weather forecasts, climate studies and oceanography in general. Currently, mostly in situ techniques such as fixed buoys or bathythermographs deliver oceanic temperature profiles. A LIDAR method based on Brillouin scattering is an attractive alternative for remote sensing of such water temperature profiles. It makes it possible to deliver cost-effective on-line data covering an extended region of the ocean. The temperature measurement is based on spontaneous Brillouin scattering in water. In this contribution, we present the first water temperature measurements using a Yb:doped pulsed fiber amplifier. The fiber amplifier is a custom designed device which can be operated in a vibrational environment while emitting narrow bandwidth laser pulses. The device shows promising performance and demonstrates the feasibility of this approach. Furthermore, the current status of the receiver is briefly discussed; it is based on an excited state Faraday anomalous dispersion optical filter. PMID:27873842

Intercomparison of near real time monitors of PM2.5 nitrate and sulfate at the U.S. Environmental Protection Agency Atlanta Supersite

NASA Astrophysics Data System (ADS)

Weber, R.; Orsini, D.; Duan, Y.; Baumann, K.; Kiang, C. S.; Chameides, W.; Lee, Y. N.; Brechtel, F.; Klotz, P.; Jongejan, P.; ten Brink, H.; Slanina, J.; Boring, C. B.; Genfa, Z.; Dasgupta, P.; Hering, S.; Stolzenburg, M.; Dutcher, D. D.; Edgerton, E.; Hartsell, B.; Solomon, P.; Tanner, R.

2003-04-01

Five new instruments for semicontinuous measurements of fine particle (PM2.5) nitrate and sulfate were deployed in the Atlanta Supersite Experiment during an intensive study in August 1999. The instruments measured bulk aerosol chemical composition at rates ranging from every 5 min to once per hour. The techniques included a filter sampling system with automated water extraction and online ion chromatographic (IC) analysis, two systems that directly collected particles into water for IC analysis, and two techniques that converted aerosol nitrate or sulfate either catalytically or by flash vaporization to gaseous products that were measured with gas analyzers. During the one-month study, 15-min integrated nitrate concentrations were low, ranging from about 0.1 to 3.5 μg m-3 with a mean value of 0.5 μg m-3. Ten-minute integrated sulfate concentrations varied between 0.3 and 40 μg m-3 with a mean of 14 μg m-3. By the end of the one-month study most instruments were in close agreement, with r-squared values between instrument pairs typically ranging from 0.7 to 0.94. Based on comparison between individual semicontinuous devices and 24-hour integrated filter measurements, most instruments were within 20-30% for nitrate (˜0.1-0.2 μg m-3) and 10-15% for sulfate (1-2 μg m-3). Within 95% confidence intervals, linear regression fits suggest that no biases existed between the semicontinuous techniques and the 24-hour integrated filter measurements of nitrate and sulfate;, however, for nitrate, the semicontinuous intercomparisons showed significantly less variability than intercomparisons amongst the 24-hour integrated filters.

MIR-ATR sensor for process monitoring

NASA Astrophysics Data System (ADS)

Geörg, Daniel; Schalk, Robert; Methner, Frank-Jürgen; Beuermann, Thomas

2015-06-01

A mid-infrared attenuated total reflectance (MIR-ATR) sensor has been developed for chemical reaction monitoring. The optical setup of the compact and low-priced sensor consists of an IR emitter as light source, a zinc selenide (ZnSe) ATR prism as boundary to the process, and four thermopile detectors, each equipped with an optical bandpass filter. The practical applicability was tested during esterification of ethanol and formic acid to ethyl formate and water as a model reaction with subsequent distillation. For reference analysis, a Fourier transform mid-infrared (FT-MIR) spectrometer with diamond ATR module was applied. On-line measurements using the MIR-ATR sensor and the FT-MIR spectrometer were performed in a bypass loop. The sensor was calibrated by multiple linear regression in order to link the measured absorbance in the four optical channels to the analyte concentrations. The analytical potential of the MIR-ATR sensor was demonstrated by simultaneous real-time monitoring of all four chemical substances involved in the esterification and distillation process. The temporal courses of the sensor signals are in accordance with the concentration values achieved by the commercial FT-MIR spectrometer. The standard error of prediction for ethanol, formic acid, ethyl formate, and water were 0.38 mol L  -  1, 0.48 mol L  -  1, 0.38 mol L  -  1, and 1.12 mol L  -  1, respectively. A procedure based on MIR spectra is presented to simulate the response characteristics of the sensor if the transmission ranges of the filters are varied. Using this tool analyte specific bandpass filters for a particular chemical reaction can be identified. By exchanging the optical filters, the sensor can be adapted to a wide range of processes in the chemical, pharmaceutical, and beverage industries.

Water risk assessment in China based on the improved Water Risk Filter

NASA Astrophysics Data System (ADS)

Hong, G.; Yaqin, Q.; Qiong, L.; Cunwen, N.; Na, W.; Jiajia, L.; Jongde, G.; Na, Z.; Xiangyi, D.

2014-09-01

Finding an effective way to deal with the water crisis and the relationship between water and development is a major issue for all levels of government and different economic sectors across the world. Scientific understanding of water risk is the basis for achieving a scientific relationship between water and development, and water risk assessment is currently an important research focus. To effectively deal with the global water crisis, the World Wide Fund for Nature and German Investment and Development Company Limited proposed the concept of water risk and released an online Water Risk Filter in March 2012, which has been applied to at least 85 countries. To comprehensively and accurately reflect the situation of water risk in China, this study adjusts the water risk assessment indicators in the Water Risk Filter, taking the actual situation in China and the difficulty of obtaining the information about the indicators into account, and proposes an index system for water risk evaluation for China which consists of physical risk, regulatory risk and reputational risk. The improved Water Risk Filter is further used to assess the sources and causes of the water risks in 10 first-class and seven second-class water resource areas (WRAs). The results indicate that the water risk for the whole country is generally medium and low, while those for different regions in the country vary greatly, and those for southern regions are generally lower than those for northern regions. Government regulatory and policy implementation as well as media supervision in northern regions should be strengthened to reduce the water risk. The research results may provide decision support and references for both governments and industrial enterprises in identifying water risks, formulating prevention and control policies, and improving water resources management in China.

VizieR Online Data Catalog: SG1120-1202 members HST imaging & 24um fluxes (Monroe+, 2017)

NASA Astrophysics Data System (ADS)

Monroe, J. T.; Tran, K.-V. H.; Gonzalez, A. H.

2017-09-01

We employ HST imaging of an ~8'x12' mosaic across three filters: F390W (WFC3/UVIS), F606W (ACS/WFC), and F814W (ACS/WFC) for a total of 44 pointings (combined primary and parallels) during cycles 14 (GO 10499) and 19 (GO 12470). We use the Spitzer MIPS 24um fluxes from Saintonge+ (2008ApJ...685L.113S) and Tran+ (2009ApJ...705..809T). The 24um observations were retrieved from the Spitzer archive. For details on spectroscopy from multi-band ground-based observations using Magellan (in 2006), MMT, and VLT/VIMOS (in 2003), we refer the reader to Tran+ (2009ApJ...705..809T). (1 data file).

Emergency sacrificial sealing method in filters, equipment, or systems

DOEpatents

Brown, Erik P

2014-09-30

A system seals a filter or equipment component to a base and will continue to seal the filter or equipment component to the base in the event of hot air or fire. The system includes a first sealing material between the filter or equipment component and the base; and a second sealing material between the filter or equipment component and the base and proximate the first sealing material. The first sealing material and the second seal material are positioned relative to each other and relative to the filter or equipment component and the base to seal the filter or equipment component to the base and upon the event of fire the second sealing material will be activated and expand to continue to seal the filter or equipment component to the base in the event of hot air or fire.

A microprocessor based anti-aliasing filter for a PCM system

NASA Technical Reports Server (NTRS)

Morrow, D. C.; Sandlin, D. R.

1984-01-01

Described is the design and evaluation of a microprocessor based digital filter. The filter was made to investigate the feasibility of a digital replacement for the analog pre-sampling filters used in telemetry systems at the NASA Ames-Dryden Flight Research Facility (DFRF). The digital filter will utilize an Intel 2920 Analog Signal Processor (ASP) chip. Testing includes measurements of: (1) the filter frequency response and, (2) the filter signal resolution. The evaluation of the digital filter was made on the basis of circuit size, projected environmental stability and filter resolution. The 2920 based digital filter was found to meet or exceed the pre-sampling filter specifications for limited signal resolution applications.

Filter replacement lifetime prediction

DOEpatents

Hamann, Hendrik F.; Klein, Levente I.; Manzer, Dennis G.; Marianno, Fernando J.

2017-10-25

Methods and systems for predicting a filter lifetime include building a filter effectiveness history based on contaminant sensor information associated with a filter; determining a rate of filter consumption with a processor based on the filter effectiveness history; and determining a remaining filter lifetime based on the determined rate of filter consumption. Methods and systems for increasing filter economy include measuring contaminants in an internal and an external environment; determining a cost of a corrosion rate increase if unfiltered external air intake is increased for cooling; determining a cost of increased air pressure to filter external air; and if the cost of filtering external air exceeds the cost of the corrosion rate increase, increasing an intake of unfiltered external air.

A Study of the Effects of Electrode Number and Decoding Algorithm on Online EEG-Based BCI Behavioral Performance

PubMed Central

Meng, Jianjun; Edelman, Bradley J.; Olsoe, Jaron; Jacobs, Gabriel; Zhang, Shuying; Beyko, Angeliki; He, Bin

2018-01-01

Motor imagery–based brain–computer interface (BCI) using electroencephalography (EEG) has demonstrated promising applications by directly decoding users' movement related mental intention. The selection of control signals, e.g., the channel configuration and decoding algorithm, plays a vital role in the online performance and progressing of BCI control. While several offline analyses report the effect of these factors on BCI accuracy for a single session—performance increases asymptotically by increasing the number of channels, saturates, and then decreases—no online study, to the best of our knowledge, has yet been performed to compare for a single session or across training. The purpose of the current study is to assess, in a group of forty-five subjects, the effect of channel number and decoding method on the progression of BCI performance across multiple training sessions and the corresponding neurophysiological changes. The 45 subjects were divided into three groups using Laplacian Filtering (LAP/S) with nine channels, Common Spatial Pattern (CSP/L) with 40 channels and CSP (CSP/S) with nine channels for online decoding. At the first training session, subjects using CSP/L displayed no significant difference compared to CSP/S but a higher average BCI performance over those using LAP/S. Despite the average performance when using the LAP/S method was initially lower, but LAP/S displayed improvement over first three sessions, whereas the other two groups did not. Additionally, analysis of the recorded EEG during BCI control indicates that the LAP/S produces control signals that are more strongly correlated with the target location and a higher R-square value was shown at the fifth session. In the present study, we found that subjects' average online BCI performance using a large EEG montage does not show significantly better performance after the first session than a smaller montage comprised of a common subset of these electrodes. The LAP/S method with a small EEG montage allowed the subjects to improve their skills across sessions, but no improvement was shown for the CSP method. PMID:29681792

A Study of the Effects of Electrode Number and Decoding Algorithm on Online EEG-Based BCI Behavioral Performance.

PubMed

Meng, Jianjun; Edelman, Bradley J; Olsoe, Jaron; Jacobs, Gabriel; Zhang, Shuying; Beyko, Angeliki; He, Bin

2018-01-01

Motor imagery-based brain-computer interface (BCI) using electroencephalography (EEG) has demonstrated promising applications by directly decoding users' movement related mental intention. The selection of control signals, e.g., the channel configuration and decoding algorithm, plays a vital role in the online performance and progressing of BCI control. While several offline analyses report the effect of these factors on BCI accuracy for a single session-performance increases asymptotically by increasing the number of channels, saturates, and then decreases-no online study, to the best of our knowledge, has yet been performed to compare for a single session or across training. The purpose of the current study is to assess, in a group of forty-five subjects, the effect of channel number and decoding method on the progression of BCI performance across multiple training sessions and the corresponding neurophysiological changes. The 45 subjects were divided into three groups using Laplacian Filtering (LAP/S) with nine channels, Common Spatial Pattern (CSP/L) with 40 channels and CSP (CSP/S) with nine channels for online decoding. At the first training session, subjects using CSP/L displayed no significant difference compared to CSP/S but a higher average BCI performance over those using LAP/S. Despite the average performance when using the LAP/S method was initially lower, but LAP/S displayed improvement over first three sessions, whereas the other two groups did not. Additionally, analysis of the recorded EEG during BCI control indicates that the LAP/S produces control signals that are more strongly correlated with the target location and a higher R-square value was shown at the fifth session. In the present study, we found that subjects' average online BCI performance using a large EEG montage does not show significantly better performance after the first session than a smaller montage comprised of a common subset of these electrodes. The LAP/S method with a small EEG montage allowed the subjects to improve their skills across sessions, but no improvement was shown for the CSP method.

What do web-use skill differences imply for online health information searches?

PubMed

Feufel, Markus A; Stahl, S Frederica

2012-06-13

Online health information is of variable and often low scientific quality. In particular, elderly less-educated populations are said to struggle in accessing quality online information (digital divide). Little is known about (1) how their online behavior differs from that of younger, more-educated, and more-frequent Web users, and (2) how the older population may be supported in accessing good-quality online health information. To specify the digital divide between skilled and less-skilled Web users, we assessed qualitative differences in technical skills, cognitive strategies, and attitudes toward online health information. Based on these findings, we identified educational and technological interventions to help Web users find and access good-quality online health information. We asked 22 native German-speaking adults to search for health information online. The skilled cohort consisted of 10 participants who were younger than 30 years of age, had a higher level of education, and were more experienced using the Web than 12 participants in the less-skilled cohort, who were at least 50 years of age. We observed online health information searches to specify differences in technical skills and analyzed concurrent verbal protocols to identify health information seekers' cognitive strategies and attitudes. Our main findings relate to (1) attitudes: health information seekers in both cohorts doubted the quality of information retrieved online; among poorly skilled seekers, this was mainly because they doubted their skills to navigate vast amounts of information; once a website was accessed, quality concerns disappeared in both cohorts, (2) technical skills: skilled Web users effectively filtered information according to search intentions and data sources; less-skilled users were easily distracted by unrelated information, and (3) cognitive strategies: skilled Web users searched to inform themselves; less-skilled users searched to confirm their health-related opinions such as "vaccinations are harmful." Independent of Web-use skills, most participants stopped a search once they had found the first piece of evidence satisfying search intentions, rather than according to quality criteria. Findings related to Web-use skills differences suggest two classes of interventions to facilitate access to good-quality online health information. Challenges related to findings (1) and (2) should be remedied by improving people's basic Web-use skills. In particular, Web users should be taught how to avoid information overload by generating specific search terms and to avoid low-quality information by requesting results from trusted websites only. Problems related to finding (3) may be remedied by visually labeling search engine results according to quality criteria.

VizieR Online Data Catalog: HST BVI catalogue of star clusters in 5 HCGs (Fedotov+, 2015)

NASA Astrophysics Data System (ADS)

Fedotov, K.; Gallagher, S. C.; Durrell, P. R.; Bastian, N.; Konstantopoulos, I. S.; Charlton, J.; Johnson, K. E.; Chandar, R.

2015-11-01

The data for this project were obtained with the Hubble Space Telescope (HST) Advanced Camera for Surveys (ACS) and Wide Field Camera 3 (WFC3). These observations are part of two programmes: ID 10787 (PI J. Charlton) and ID 11502 (PI K. Noll). The observations were carried out in the F435W (F438W for WFC3), F606W, and F814W filters, which are similar to the Johnson BVI bands. Hereafter, we refer to the HST filters as B435, B438, V606, and I814, although we did not make transformations to the Johnson-Cousins system. (2 data files).

A New Statistics-Based Online Baseline Restorer for a High Count-Rate Fully Digital System.

PubMed

Li, Hongdi; Wang, Chao; Baghaei, Hossain; Zhang, Yuxuan; Ramirez, Rocio; Liu, Shitao; An, Shaohui; Wong, Wai-Hoi

2010-04-01

The goal of this work is to develop a novel, accurate, real-time digital baseline restorer using online statistical processing for a high count-rate digital system such as positron emission tomography (PET). In high count-rate nuclear instrumentation applications, analog signals are DC-coupled for better performance. However, the detectors, pre-amplifiers and other front-end electronics would cause a signal baseline drift in a DC-coupling system, which will degrade the performance of energy resolution and positioning accuracy. Event pileups normally exist in a high-count rate system and the baseline drift will create errors in the event pileup-correction. Hence, a baseline restorer (BLR) is required in a high count-rate system to remove the DC drift ahead of the pileup correction. Many methods have been reported for BLR from classic analog methods to digital filter solutions. However a single channel BLR with analog method can only work under 500 kcps count-rate, and normally an analog front-end application-specific integrated circuits (ASIC) is required for the application involved hundreds BLR such as a PET camera. We have developed a simple statistics-based online baseline restorer (SOBLR) for a high count-rate fully digital system. In this method, we acquire additional samples, excluding the real gamma pulses, from the existing free-running ADC in the digital system, and perform online statistical processing to generate a baseline value. This baseline value will be subtracted from the digitized waveform to retrieve its original pulse with zero-baseline drift. This method can self-track the baseline without a micro-controller involved. The circuit consists of two digital counter/timers, one comparator, one register and one subtraction unit. Simulation shows a single channel works at 30 Mcps count-rate with pileup condition. 336 baseline restorer circuits have been implemented into 12 field-programmable-gate-arrays (FPGA) for our new fully digital PET system.

Introducing passive acoustic filter in acoustic based condition monitoring: Motor bike piston-bore fault identification

NASA Astrophysics Data System (ADS)

Jena, D. P.; Panigrahi, S. N.

2016-03-01

Requirement of designing a sophisticated digital band-pass filter in acoustic based condition monitoring has been eliminated by introducing a passive acoustic filter in the present work. So far, no one has attempted to explore the possibility of implementing passive acoustic filters in acoustic based condition monitoring as a pre-conditioner. In order to enhance the acoustic based condition monitoring, a passive acoustic band-pass filter has been designed and deployed. Towards achieving an efficient band-pass acoustic filter, a generalized design methodology has been proposed to design and optimize the desired acoustic filter using multiple filter components in series. An appropriate objective function has been identified for genetic algorithm (GA) based optimization technique with multiple design constraints. In addition, the sturdiness of the proposed method has been demonstrated in designing a band-pass filter by using an n-branch Quincke tube, a high pass filter and multiple Helmholtz resonators. The performance of the designed acoustic band-pass filter has been shown by investigating the piston-bore defect of a motor-bike using engine noise signature. On the introducing a passive acoustic filter in acoustic based condition monitoring reveals the enhancement in machine learning based fault identification practice significantly. This is also a first attempt of its own kind.

Time-based Reconstruction of Free-streaming Data in CBM

NASA Astrophysics Data System (ADS)

Akishina, Valentina; Kisel, Ivan; Vassiliev, Iouri; Zyzak, Maksym

2018-02-01

Traditional latency-limited trigger architectures typical for conventional experiments are inapplicable for the CBM experiment. Instead, CBM will ship and collect time-stamped data into a readout buffer in a form of a time-slice of a certain length and deliver it to a large computer farm, where online event reconstruction and selection will be performed. Grouping measurements into physical collisions must be performed in software and requires reconstruction not only in space, but also in time, the so-called 4-dimensional track reconstruction and event building. The tracks, reconstructed with 4D Cellular Automaton track finder, are combined into event-corresponding clusters according to the estimated time in the target position and the errors, obtained with the Kalman Filter method. The reconstructed events are given as inputs to the KF Particle Finder package for short-lived particle reconstruction. The results of time-based reconstruction of simulated collisions in CBM are presented and discussed in details.

Image based book cover recognition and retrieval

NASA Astrophysics Data System (ADS)

Sukhadan, Kalyani; Vijayarajan, V.; Krishnamoorthi, A.; Bessie Amali, D. Geraldine

2017-11-01

In this we are developing a graphical user interface using MATLAB for the users to check the information related to books in real time. We are taking the photos of the book cover using GUI, then by using MSER algorithm it will automatically detect all the features from the input image, after this it will filter bifurcate non-text features which will be based on morphological difference between text and non-text regions. We implemented a text character alignment algorithm which will improve the accuracy of the original text detection. We will also have a look upon the built in MATLAB OCR recognition algorithm and an open source OCR which is commonly used to perform better detection results, post detection algorithm is implemented and natural language processing to perform word correction and false detection inhibition. Finally, the detection result will be linked to internet to perform online matching. More than 86% accuracy can be obtained by this algorithm.

A Self-Alignment Algorithm for SINS Based on Gravitational Apparent Motion and Sensor Data Denoising

PubMed Central

Liu, Yiting; Xu, Xiaosu; Liu, Xixiang; Yao, Yiqing; Wu, Liang; Sun, Jin

2015-01-01

Initial alignment is always a key topic and difficult to achieve in an inertial navigation system (INS). In this paper a novel self-initial alignment algorithm is proposed using gravitational apparent motion vectors at three different moments and vector-operation. Simulation and analysis showed that this method easily suffers from the random noise contained in accelerometer measurements which are used to construct apparent motion directly. Aiming to resolve this problem, an online sensor data denoising method based on a Kalman filter is proposed and a novel reconstruction method for apparent motion is designed to avoid the collinearity among vectors participating in the alignment solution. Simulation, turntable tests and vehicle tests indicate that the proposed alignment algorithm can fulfill initial alignment of strapdown INS (SINS) under both static and swinging conditions. The accuracy can either reach or approach the theoretical values determined by sensor precision under static or swinging conditions. PMID:25923932

High Field Asymmetric Waveform Ion Mobility Spectrometry (FAIMS) for Mass Spectrometry-Based Proteomics

PubMed Central

Swearingen, Kristian E.; Moritz, Robert L.

2013-01-01

SUMMARY High field asymmetric waveform ion mobility spectrometry (FAIMS) is an atmospheric pressure ion mobility technique that separates gas-phase ions by their behavior in strong and weak electric fields. FAIMS is easily interfaced with electrospray ionization and has been implemented as an additional separation mode between liquid chromatography (LC) and mass spectrometry (MS) in proteomic studies. FAIMS separation is orthogonal to both LC and MS and is used as a means of on-line fractionation to improve detection of peptides in complex samples. FAIMS improves dynamic range and concomitantly the detection limits of ions by filtering out chemical noise. FAIMS can also be used to remove interfering ion species and to select peptide charge states optimal for identification by tandem MS. Here, we review recent developments in LC-FAIMS-MS and its application to MS-based proteomics. PMID:23194268

Adaptive filtering with the self-organizing map: a performance comparison.

PubMed

Barreto, Guilherme A; Souza, Luís Gustavo M

2006-01-01

In this paper we provide an in-depth evaluation of the SOM as a feasible tool for nonlinear adaptive filtering. A comprehensive survey of existing SOM-based and related architectures for learning input-output mappings is carried out and the application of these architectures to nonlinear adaptive filtering is formulated. Then, we introduce two simple procedures for building RBF-based nonlinear filters using the Vector-Quantized Temporal Associative Memory (VQTAM), a recently proposed method for learning dynamical input-output mappings using the SOM. The aforementioned SOM-based adaptive filters are compared with standard FIR/LMS and FIR/LMS-Newton linear transversal filters, as well as with powerful MLP-based filters in nonlinear channel equalization and inverse modeling tasks. The obtained results in both tasks indicate that SOM-based filters can consistently outperform powerful MLP-based ones.

Modeling a multivariable reactor and on-line model predictive control.

PubMed

Yu, D W; Yu, D L

2005-10-01

A nonlinear first principle model is developed for a laboratory-scaled multivariable chemical reactor rig in this paper and the on-line model predictive control (MPC) is implemented to the rig. The reactor has three variables-temperature, pH, and dissolved oxygen with nonlinear dynamics-and is therefore used as a pilot system for the biochemical industry. A nonlinear discrete-time model is derived for each of the three output variables and their model parameters are estimated from the real data using an adaptive optimization method. The developed model is used in a nonlinear MPC scheme. An accurate multistep-ahead prediction is obtained for MPC, where the extended Kalman filter is used to estimate system unknown states. The on-line control is implemented and a satisfactory tracking performance is achieved. The MPC is compared with three decentralized PID controllers and the advantage of the nonlinear MPC over the PID is clearly shown.

Estimation of Hidden State Variables of the Intracranial System Using Constrained Nonlinear Kalman Filters

PubMed Central

Nenov, Valeriy; Bergsneider, Marvin; Glenn, Thomas C.; Vespa, Paul; Martin, Neil

2007-01-01

Impeded by the rigid skull, assessment of physiological variables of the intracranial system is difficult. A hidden state estimation approach is used in the present work to facilitate the estimation of unobserved variables from available clinical measurements including intracranial pressure (ICP) and cerebral blood flow velocity (CBFV). The estimation algorithm is based on a modified nonlinear intracranial mathematical model, whose parameters are first identified in an offline stage using a nonlinear optimization paradigm. Following the offline stage, an online filtering process is performed using a nonlinear Kalman filter (KF)-like state estimator that is equipped with a new way of deriving the Kalman gain satisfying the physiological constraints on the state variables. The proposed method is then validated by comparing different state estimation methods and input/output (I/O) configurations using simulated data. It is also applied to a set of CBFV, ICP and arterial blood pressure (ABP) signal segments from brain injury patients. The results indicated that the proposed constrained nonlinear KF achieved the best performance among the evaluated state estimators and that the state estimator combined with the I/O configuration that has ICP as the measured output can potentially be used to estimate CBFV continuously. Finally, the state estimator combined with the I/O configuration that has both ICP and CBFV as outputs can potentially estimate the lumped cerebral arterial radii, which are not measurable in a typical clinical environment. PMID:17281533

Design and validation of a real-time spiking-neural-network decoder for brain-machine interfaces.

PubMed

Dethier, Julie; Nuyujukian, Paul; Ryu, Stephen I; Shenoy, Krishna V; Boahen, Kwabena

2013-06-01

Cortically-controlled motor prostheses aim to restore functions lost to neurological disease and injury. Several proof of concept demonstrations have shown encouraging results, but barriers to clinical translation still remain. In particular, intracortical prostheses must satisfy stringent power dissipation constraints so as not to damage cortex. One possible solution is to use ultra-low power neuromorphic chips to decode neural signals for these intracortical implants. The first step is to explore in simulation the feasibility of translating decoding algorithms for brain-machine interface (BMI) applications into spiking neural networks (SNNs). Here we demonstrate the validity of the approach by implementing an existing Kalman-filter-based decoder in a simulated SNN using the Neural Engineering Framework (NEF), a general method for mapping control algorithms onto SNNs. To measure this system's robustness and generalization, we tested it online in closed-loop BMI experiments with two rhesus monkeys. Across both monkeys, a Kalman filter implemented using a 2000-neuron SNN has comparable performance to that of a Kalman filter implemented using standard floating point techniques. These results demonstrate the tractability of SNN implementations of statistical signal processing algorithms on different monkeys and for several tasks, suggesting that a SNN decoder, implemented on a neuromorphic chip, may be a feasible computational platform for low-power fully-implanted prostheses. The validation of this closed-loop decoder system and the demonstration of its robustness and generalization hold promise for SNN implementations on an ultra-low power neuromorphic chip using the NEF.

An offline approach for output-only Bayesian identification of stochastic nonlinear systems using unscented Kalman filtering

NASA Astrophysics Data System (ADS)

Erazo, Kalil; Nagarajaiah, Satish

2017-06-01

In this paper an offline approach for output-only Bayesian identification of stochastic nonlinear systems is presented. The approach is based on a re-parameterization of the joint posterior distribution of the parameters that define a postulated state-space stochastic model class. In the re-parameterization the state predictive distribution is included, marginalized, and estimated recursively in a state estimation step using an unscented Kalman filter, bypassing state augmentation as required by existing online methods. In applications expectations of functions of the parameters are of interest, which requires the evaluation of potentially high-dimensional integrals; Markov chain Monte Carlo is adopted to sample the posterior distribution and estimate the expectations. The proposed approach is suitable for nonlinear systems subjected to non-stationary inputs whose realization is unknown, and that are modeled as stochastic processes. Numerical verification and experimental validation examples illustrate the effectiveness and advantages of the approach, including: (i) an increased numerical stability with respect to augmented-state unscented Kalman filtering, avoiding divergence of the estimates when the forcing input is unmeasured; (ii) the ability to handle arbitrary prior and posterior distributions. The experimental validation of the approach is conducted using data from a large-scale structure tested on a shake table. It is shown that the approach is robust to inherent modeling errors in the description of the system and forcing input, providing accurate prediction of the dynamic response when the excitation history is unknown.

A multi-mode real-time terrain parameter estimation method for wheeled motion control of mobile robots

NASA Astrophysics Data System (ADS)

Li, Yuankai; Ding, Liang; Zheng, Zhizhong; Yang, Qizhi; Zhao, Xingang; Liu, Guangjun

2018-05-01

For motion control of wheeled planetary rovers traversing on deformable terrain, real-time terrain parameter estimation is critical in modeling the wheel-terrain interaction and compensating the effect of wheel slipping. A multi-mode real-time estimation method is proposed in this paper to achieve accurate terrain parameter estimation. The proposed method is composed of an inner layer for real-time filtering and an outer layer for online update. In the inner layer, sinkage exponent and internal frictional angle, which have higher sensitivity than that of the other terrain parameters to wheel-terrain interaction forces, are estimated in real time by using an adaptive robust extended Kalman filter (AREKF), whereas the other parameters are fixed with nominal values. The inner layer result can help synthesize the current wheel-terrain contact forces with adequate precision, but has limited prediction capability for time-variable wheel slipping. To improve estimation accuracy of the result from the inner layer, an outer layer based on recursive Gauss-Newton (RGN) algorithm is introduced to refine the result of real-time filtering according to the innovation contained in the history data. With the two-layer structure, the proposed method can work in three fundamental estimation modes: EKF, REKF and RGN, making the method applicable for flat, rough and non-uniform terrains. Simulations have demonstrated the effectiveness of the proposed method under three terrain types, showing the advantages of introducing the two-layer structure.

The importance of hydraulic architecture to the distribution patterns of trees in a central Amazonian forest.

PubMed

Cosme, Luiza H M; Schietti, Juliana; Costa, Flávia R C; Oliveira, Rafael S

2017-07-01

Species distributions and assemblage composition may be the result of trait selection through environmental filters. Here, we ask whether filtering of species at the local scale could be attributed to their hydraulic architectural traits, revealing the basis of hydrological microhabitat partitioning in a Central Amazonian forest. We analyzed the hydraulic characteristics at tissue (anatomical traits, wood specific gravity (WSG)), organ (leaf area, specific leaf area (SLA), leaf area : sapwood area ratio) and whole-plant (height) levels for 28 pairs of congeneric species from 14 genera restricted to either valleys or plateaus of a terra-firme forest in Central Amazonia. On plateaus, species had higher WSG, but lower mean vessel area, mean vessel hydraulic diameter, sapwood area and SLA than in valleys; traits commonly associated with hydraulic safety. Mean vessel hydraulic diameter and mean vessel area increased with height for both habitats, but leaf area and leaf area : sapwood area ratio investments with tree height declined in valley vs plateau species. [Correction added after online publication 29 March 2017: the preceding sentence has been reworded.] Two strategies for either efficiency or safety were detected, based on vessel size or allocation to sapwood. In conclusion, contrasting hydrological conditions act as environmental filters, generating differences in species composition at the local scale. This has important implications for the prediction of species distributions under future climate change scenarios. © 2017 The Authors. New Phytologist © 2017 New Phytologist Trust.

VizieR Online Data Catalog: X-ray emission from the Sombrero galaxy (NGC 4594) (Li+, 2007)

NASA Astrophysics Data System (ADS)

Li, Z.; Wang, Q. D.; Hameed, S.

2007-09-01

The Chandra ACIS-S observation of Sombrero (Obs. ID. 1586) was taken on 2001 May 31 with an exposure of 18.8ks. The XMM-Newton EPIC observation of Sombrero (Obs. ID 0084030101) was taken on 2001 December 28, with the thin filter and with a total exposure of 43ks. (3 data files).

VizieR Online Data Catalog: Spatial deconvolution code (Quintero Noda+, 2015)

NASA Astrophysics Data System (ADS)

Quintero Noda, C.; Asensio Ramos, A.; Orozco Suarez, D.; Ruiz Cobo, B.

2015-05-01

This deconvolution method follows the scheme presented in Ruiz Cobo & Asensio Ramos (2013A&A...549L...4R) The Stokes parameters are projected onto a few spectral eigenvectors and the ensuing maps of coefficients are deconvolved using a standard Lucy-Richardson algorithm. This introduces a stabilization because the PCA filtering reduces the amount of noise. (1 data file).

Using an Interactive Online Quiz to Recalibrate College Students' Attitudes and Behavioral Intentions about Phishing

ERIC Educational Resources Information Center

Perrault, Evan K.

2018-01-01

Despite improved spam filtering technology, phishing continues to be a prevalent threat for college students. The current study found that approximately 4-in-10 of the students surveyed (N = 462) indicate they do not know what phishing is and the threat it poses. Students also report initially overestimating their confidence to successfully…

VizieR Online Data Catalog: PSR J1023+0038 & XSS J12270-4859 VRi polarimetry (Baglio+, 2016)

NASA Astrophysics Data System (ADS)

Baglio, M. C.; D'Avanzo, P.; Campana, S.; Coti Zelati, F.; Covino, S.; Russell, D. M.

2016-05-01

The systems PSR J1023+0038 and XSS J12270-4859 were observed in quiescence on 8 and 9 February 2015 (respectively) with the ESO New Technology Telescope (NTT) located at La Silla (Chile), equipped with the EFOSC2 camera in polarimetric mode, using the B, V, R, i filters. (6 data files).

Overview and benchmark analysis of fuel cell parameters estimation for energy management purposes

NASA Astrophysics Data System (ADS)

Kandidayeni, M.; Macias, A.; Amamou, A. A.; Boulon, L.; Kelouwani, S.; Chaoui, H.

2018-03-01

Proton exchange membrane fuel cells (PEMFCs) have become the center of attention for energy conversion in many areas such as automotive industry, where they confront a high dynamic behavior resulting in their characteristics variation. In order to ensure appropriate modeling of PEMFCs, accurate parameters estimation is in demand. However, parameter estimation of PEMFC models is highly challenging due to their multivariate, nonlinear, and complex essence. This paper comprehensively reviews PEMFC models parameters estimation methods with a specific view to online identification algorithms, which are considered as the basis of global energy management strategy design, to estimate the linear and nonlinear parameters of a PEMFC model in real time. In this respect, different PEMFC models with different categories and purposes are discussed first. Subsequently, a thorough investigation of PEMFC parameter estimation methods in the literature is conducted in terms of applicability. Three potential algorithms for online applications, Recursive Least Square (RLS), Kalman filter, and extended Kalman filter (EKF), which has escaped the attention in previous works, have been then utilized to identify the parameters of two well-known semi-empirical models in the literature, Squadrito et al. and Amphlett et al. Ultimately, the achieved results and future challenges are discussed.

Novel Formulation of Adaptive MPC as EKF Using ANN Model: Multiproduct Semibatch Polymerization Reactor Case Study.

PubMed

Kamesh, Reddi; Rani, Kalipatnapu Yamuna

2017-12-01

In this paper, a novel formulation for nonlinear model predictive control (MPC) has been proposed incorporating the extended Kalman filter (EKF) control concept using a purely data-driven artificial neural network (ANN) model based on measurements for supervisory control. The proposed scheme consists of two modules focusing on online parameter estimation based on past measurements and control estimation over control horizon based on minimizing the deviation of model output predictions from set points along the prediction horizon. An industrial case study for temperature control of a multiproduct semibatch polymerization reactor posed as a challenge problem has been considered as a test bed to apply the proposed ANN-EKFMPC strategy at supervisory level as a cascade control configuration along with proportional integral controller [ANN-EKFMPC with PI (ANN-EKFMPC-PI)]. The proposed approach is formulated incorporating all aspects of MPC including move suppression factor for control effort minimization and constraint-handling capability including terminal constraints. The nominal stability analysis and offset-free tracking capabilities of the proposed controller are proved. Its performance is evaluated by comparison with a standard MPC-based cascade control approach using the same adaptive ANN model. The ANN-EKFMPC-PI control configuration has shown better controller performance in terms of temperature tracking, smoother input profiles, as well as constraint-handling ability compared with the ANN-MPC with PI approach for two products in summer and winter. The proposed scheme is found to be versatile although it is based on a purely data-driven model with online parameter estimation.

Intelligent voltage control strategy for three-phase UPS inverters with output LC filter

NASA Astrophysics Data System (ADS)

Jung, J. W.; Leu, V. Q.; Dang, D. Q.; Do, T. D.; Mwasilu, F.; Choi, H. H.

2015-08-01

This paper presents a supervisory fuzzy neural network control (SFNNC) method for a three-phase inverter of uninterruptible power supplies (UPSs). The proposed voltage controller is comprised of a fuzzy neural network control (FNNC) term and a supervisory control term. The FNNC term is deliberately employed to estimate the uncertain terms, and the supervisory control term is designed based on the sliding mode technique to stabilise the system dynamic errors. To improve the learning capability, the FNNC term incorporates an online parameter training methodology, using the gradient descent method and Lyapunov stability theory. Besides, a linear load current observer that estimates the load currents is used to exclude the load current sensors. The proposed SFNN controller and the observer are robust to the filter inductance variations, and their stability analyses are described in detail. The experimental results obtained on a prototype UPS test bed with a TMS320F28335 DSP are presented to validate the feasibility of the proposed scheme. Verification results demonstrate that the proposed control strategy can achieve smaller steady-state error and lower total harmonic distortion when subjected to nonlinear or unbalanced loads compared to the conventional sliding mode control method.

Hybrid estimation of complex systems.

PubMed

Hofbaur, Michael W; Williams, Brian C

2004-10-01

Modern automated systems evolve both continuously and discretely, and hence require estimation techniques that go well beyond the capability of a typical Kalman Filter. Multiple model (MM) estimation schemes track these system evolutions by applying a bank of filters, one for each discrete system mode. Modern systems, however, are often composed of many interconnected components that exhibit rich behaviors, due to complex, system-wide interactions. Modeling these systems leads to complex stochastic hybrid models that capture the large number of operational and failure modes. This large number of modes makes a typical MM estimation approach infeasible for online estimation. This paper analyzes the shortcomings of MM estimation, and then introduces an alternative hybrid estimation scheme that can efficiently estimate complex systems with large number of modes. It utilizes search techniques from the toolkit of model-based reasoning in order to focus the estimation on the set of most likely modes, without missing symptoms that might be hidden amongst the system noise. In addition, we present a novel approach to hybrid estimation in the presence of unknown behavioral modes. This leads to an overall hybrid estimation scheme for complex systems that robustly copes with unforeseen situations in a degraded, but fail-safe manner.

Cross-Dependency Inference in Multi-Layered Networks: A Collaborative Filtering Perspective.

PubMed

Chen, Chen; Tong, Hanghang; Xie, Lei; Ying, Lei; He, Qing

2017-08-01

The increasingly connected world has catalyzed the fusion of networks from different domains, which facilitates the emergence of a new network model-multi-layered networks. Examples of such kind of network systems include critical infrastructure networks, biological systems, organization-level collaborations, cross-platform e-commerce, and so forth. One crucial structure that distances multi-layered network from other network models is its cross-layer dependency, which describes the associations between the nodes from different layers. Needless to say, the cross-layer dependency in the network plays an essential role in many data mining applications like system robustness analysis and complex network control. However, it remains a daunting task to know the exact dependency relationships due to noise, limited accessibility, and so forth. In this article, we tackle the cross-layer dependency inference problem by modeling it as a collective collaborative filtering problem. Based on this idea, we propose an effective algorithm Fascinate that can reveal unobserved dependencies with linear complexity. Moreover, we derive Fascinate-ZERO, an online variant of Fascinate that can respond to a newly added node timely by checking its neighborhood dependencies. We perform extensive evaluations on real datasets to substantiate the superiority of our proposed approaches.

Long-term recording and automatic analysis of cough using filtered acoustic signals and movements on static charge sensitive bed.

PubMed

Salmi, T; Sovijärvi, A R; Brander, P; Piirilä, P

1988-11-01

Reliable long-term assessment of cough is necessary in many clinical and scientific settings. A new method for long-term recording and automatic analysis of cough is presented. The method is based on simultaneous recording of two independent signals: high-pass filtered cough sounds and cough-induced fast movements of the body. The acoustic signals are recorded with a dynamic microphone in the acoustic focus of a glass fiber paraboloid mirror. Body movements are recorded with a static charge-sensitive bed located under an ordinary plastic foam mattress. The patient can be studied lying or sitting with no transducers or electrodes attached. A microcomputer is used for sampling of signals, detection of cough, statistical analyses, and on-line printing of results. The method was validated in seven adult patients with a total of 809 spontaneous cough events, using clinical observation as a reference. The sensitivity of the method to detect cough was 99.0 percent, and the positive predictivity was 98.1 percent. The system ignored speaking and snoring. The method provides a convenient means of reliable long-term follow-up of cough in clinical work and research.

HealthTrust: a social network approach for retrieving online health videos.

PubMed

Fernandez-Luque, Luis; Karlsen, Randi; Melton, Genevieve B

2012-01-31

Social media are becoming mainstream in the health domain. Despite the large volume of accurate and trustworthy health information available on social media platforms, finding good-quality health information can be difficult. Misleading health information can often be popular (eg, antivaccination videos) and therefore highly rated by general search engines. We believe that community wisdom about the quality of health information can be harnessed to help create tools for retrieving good-quality social media content. To explore approaches for extracting metrics about authoritativeness in online health communities and how these metrics positively correlate with the quality of the content. We designed a metric, called HealthTrust, that estimates the trustworthiness of social media content (eg, blog posts or videos) in a health community. The HealthTrust metric calculates reputation in an online health community based on link analysis. We used the metric to retrieve YouTube videos and channels about diabetes. In two different experiments, health consumers provided 427 ratings of 17 videos and professionals gave 162 ratings of 23 videos. In addition, two professionals reviewed 30 diabetes channels. HealthTrust may be used for retrieving online videos on diabetes, since it performed better than YouTube Search in most cases. Overall, of 20 potential channels, HealthTrust's filtering allowed only 3 bad channels (15%) versus 8 (40%) on the YouTube list. Misleading and graphic videos (eg, featuring amputations) were more commonly found by YouTube Search than by searches based on HealthTrust. However, some videos from trusted sources had low HealthTrust scores, mostly from general health content providers, and therefore not highly connected in the diabetes community. When comparing video ratings from our reviewers, we found that HealthTrust achieved a positive and statistically significant correlation with professionals (Pearson r₁₀ = .65, P = .02) and a trend toward significance with health consumers (r₇ = .65, P = .06) with videos on hemoglobinA(1c), but it did not perform as well with diabetic foot videos. The trust-based metric HealthTrust showed promising results when used to retrieve diabetes content from YouTube. Our research indicates that social network analysis may be used to identify trustworthy social media in health communities.

Adaptive noise Wiener filter for scanning electron microscope imaging system.

PubMed

Sim, K S; Teh, V; Nia, M E

2016-01-01

Noise on scanning electron microscope (SEM) images is studied. Gaussian noise is the most common type of noise in SEM image. We developed a new noise reduction filter based on the Wiener filter. We compared the performance of this new filter namely adaptive noise Wiener (ANW) filter, with four common existing filters as well as average filter, median filter, Gaussian smoothing filter and the Wiener filter. Based on the experiments results the proposed new filter has better performance on different noise variance comparing to the other existing noise removal filters in the experiments. © Wiley Periodicals, Inc.

Laboratory evaluation of an optimised internet-based speech-in-noise test for occupational high-frequency hearing loss screening: Occupational Earcheck.

PubMed

Sheikh Rashid, Marya; Leensen, Monique C J; de Laat, Jan A P M; Dreschler, Wouter A

2017-11-01

The "Occupational Earcheck" (OEC) is a Dutch online self-screening speech-in-noise test developed for the detection of occupational high-frequency hearing loss (HFHL). This study evaluates an optimised version of the test and determines the most appropriate masking noise. The original OEC was improved by homogenisation of the speech material, and shortening the test. A laboratory-based cross-sectional study was performed in which the optimised OEC in five alternative masking noise conditions was evaluated. The study was conducted on 18 normal-hearing (NH) adults, and 15 middle-aged listeners with HFHL. The OEC in a low-pass (LP) filtered stationary background noise (test version LP 3: with a cut-off frequency of 1.6 kHz, and a noise floor of -12 dB) was the most accurate version tested. The test showed a reasonable sensitivity (93%), and specificity (94%) and test reliability (intra-class correlation coefficient: 0.84, mean within-subject standard deviation: 1.5 dB SNR, slope of psychometric function: 13.1%/dB SNR). The improved OEC, with homogenous word material in a LP filtered noise, appears to be suitable for the discrimination between younger NH listeners and older listeners with HFHL. The appropriateness of the OEC for screening purposes in an occupational setting will be studied further.

A hybrid personalized data recommendation approach for geoscience data sharing

NASA Astrophysics Data System (ADS)

WANG, M.; Wang, J.

2016-12-01

Recommender systems are effective tools helping Internet users overcome information overloading. The two most widely used recommendation algorithms are collaborating filtering (CF) and content-based filtering (CBF). A number of recommender systems based on those two algorithms were developed for multimedia, online sells, and other domains. Each of the two algorithms has its advantages and shortcomings. Hybrid approaches that combine these two algorithms are better choices in many cases. In geoscience data sharing domain, where the items (datasets) are more informative (in space and time) and domain-specific, no recommender system is specialized for data users. This paper reports a dynamic weighted hybrid recommendation algorithm that combines CF and CBF for geoscience data sharing portal. We first derive users' ratings on items with their historical visiting time by Jenks Natural Break. In the CBF part, we incorporate the space, time, and subject information of geoscience datasets to compute item similarity. Predicted ratings were computed with k-NN method separately using CBF and CF, and then combined with weights. With training dataset we attempted to find the best model describing ideal weights and users' co-rating numbers. A logarithmic function was confirmed to be the best model. The model was then used to tune the weights of CF and CBF on user-item basis with test dataset. Evaluation results show that the dynamic weighted approach outperforms either solo CF or CBF approach in terms of Precision and Recall.

Emergency sacrificial sealing method in filters, equipment, or systems

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

Brown, Erik P.

A system seals a filter or equipment component to abase and will continue to seal the filter or equipment component to the base in the event of hot air or fire. The system includes a first sealing material between the filter or equipment component and the base; and a second sealing material between the filter or equipment component and the base and proximate the first sealing material. The first sealing material and the second seal material are positioned relative to each other and relative to the filter or equipment component and the base to seal the filter or equipment componentmore » to the base and upon the event of fire the second sealing material will be activated and expand to continue to seal the filter or equipment component to the base in the event of hot air or fire.« less

Integrating the ECG power-line interference removal methods with rule-based system.

PubMed

Kumaravel, N; Senthil, A; Sridhar, K S; Nithiyanandam, N

1995-01-01

The power-line frequency interference in electrocardiographic signals is eliminated to enhance the signal characteristics for diagnosis. The power-line frequency normally varies +/- 1.5 Hz from its standard value of 50 Hz. In the present work, the performances of the linear FIR filter, Wave digital filter (WDF) and adaptive filter for the power-line frequency variations from 48.5 to 51.5 Hz in steps of 0.5 Hz are studied. The advantage of the LMS adaptive filter in the removal of power-line frequency interference even if the frequency of interference varies by +/- 1.5 Hz from its normal value of 50 Hz over other fixed frequency filters is very well justified. A novel method of integrating rule-based system approach with linear FIR filter and also with Wave digital filter are proposed. The performances of Rule-based FIR filter and Rule-based Wave digital filter are compared with the LMS adaptive filter.

Spatio-temporal filtering techniques for the detection of disaster-related communication.

PubMed

Fitzhugh, Sean M; Ben Gibson, C; Spiro, Emma S; Butts, Carter T

2016-09-01

Individuals predominantly exchange information with one another through informal, interpersonal channels. During disasters and other disrupted settings, information spread through informal channels regularly outpaces official information provided by public officials and the press. Social scientists have long examined this kind of informal communication in the rumoring literature, but studying rumoring in disrupted settings has posed numerous methodological challenges. Measuring features of informal communication-timing, content, location-with any degree of precision has historically been extremely challenging in small studies and infeasible at large scales. We address this challenge by using online, informal communication from a popular microblogging website and for which we have precise spatial and temporal metadata. While the online environment provides a new means for observing rumoring, the abundance of data poses challenges for parsing hazard-related rumoring from countless other topics in numerous streams of communication. Rumoring about disaster events is typically temporally and spatially constrained to places where that event is salient. Accordingly, we use spatio and temporal subsampling to increase the resolution of our detection techniques. By filtering out data from known sources of error (per rumor theories), we greatly enhance the signal of disaster-related rumoring activity. We use these spatio-temporal filtering techniques to detect rumoring during a variety of disaster events, from high-casualty events in major population centers to minimally destructive events in remote areas. We consistently find three phases of response: anticipatory excitation where warnings and alerts are issued ahead of an event, primary excitation in and around the impacted area, and secondary excitation which frequently brings a convergence of attention from distant locales onto locations impacted by the event. Our results demonstrate the promise of spatio-temporal filtering techniques for "tuning" measurement of hazard-related rumoring to enable observation of rumoring at scales that have long been infeasible. Copyright © 2016 Elsevier Inc. All rights reserved.

Infrared dim moving target tracking via sparsity-based discriminative classifier and convolutional network

NASA Astrophysics Data System (ADS)

Qian, Kun; Zhou, Huixin; Wang, Bingjian; Song, Shangzhen; Zhao, Dong

2017-11-01

Infrared dim and small target tracking is a great challenging task. The main challenge for target tracking is to account for appearance change of an object, which submerges in the cluttered background. An efficient appearance model that exploits both the global template and local representation over infrared image sequences is constructed for dim moving target tracking. A Sparsity-based Discriminative Classifier (SDC) and a Convolutional Network-based Generative Model (CNGM) are combined with a prior model. In the SDC model, a sparse representation-based algorithm is adopted to calculate the confidence value that assigns more weights to target templates than negative background templates. In the CNGM model, simple cell feature maps are obtained by calculating the convolution between target templates and fixed filters, which are extracted from the target region at the first frame. These maps measure similarities between each filter and local intensity patterns across the target template, therefore encoding its local structural information. Then, all the maps form a representation, preserving the inner geometric layout of a candidate template. Furthermore, the fixed target template set is processed via an efficient prior model. The same operation is applied to candidate templates in the CNGM model. The online update scheme not only accounts for appearance variations but also alleviates the migration problem. At last, collaborative confidence values of particles are utilized to generate particles' importance weights. Experiments on various infrared sequences have validated the tracking capability of the presented algorithm. Experimental results show that this algorithm runs in real-time and provides a higher accuracy than state of the art algorithms.

a Study of the Interferences with the On-Line Radioiodine Measurement Under Nuclear Accident Conditions

NASA Astrophysics Data System (ADS)

Tseng, Tung-Tse

In this research the interferences with the on -line detection of radioiodines, under nuclear accident conditions, were studied. The special tool employed for this research is the developed on-line radioiodine monitor (the Penn State Radioiodine Monitor), which is capable of detecting low levels of radioiodine on-line in air containing orders of magnitude higher levels of radioactive noble gases. Most of the data reported in this thesis were collected during a series of experiments called "Source -Term Experiment Program (STEP)." The experiments were conducted at the Argonne National Laboratory's TREAT reactor located at the Idaho National Engineering Laboratory (INEL). In these tests, fission products were released from the Light Water Reactor (LWR) test fuels as a result of simulating a reactor accident. The Penn State Monitor was then used to sample the fission products accumulated in a large container which simulated the reactor containment building. The test results proved that the Penn State Monitor was not affected significantly by the passage of large amounts of noble gases through the system. Also, it confirmed the predicted results that the operation of conventional on-line radioiodine detectors would, under nuclear accident conditions, be seriously impaired by the passage of high concentrations of radioactive noble gases through such systems. This work also demonstrated that under conditions of high noble gas concentrations and low radioiodine concentrations, the formation of noble-gas-decayed alkali metals can seriously interfere with the on-line detection of radioiodine, especially during the 24 hours immediately after the accident. The decayed alkali metal particulates were also found to be much more penetrating than the ordinary type of particulates, since a large fraction (15%) of the particulates were found to penetrate through the commonly used High Efficiency Particulate Air (HEPA) filter (rated >99.97% for 0.3 (mu)m particulate). Also, a significant fraction ((TURN)40%) of these particles became deposited on silver zeolite iodine filters inside the counting chamber. Finally, the Penn State Monitor proved itself to be a powerful research tool for the on-line source term studies since it can easily produce near noble-gas-free spectra during the real time studies occurring under simulated nuclear accident conditions.

Garbage in, Garbage Out: Data Collection, Quality Assessment and Reporting Standards for Social Media Data Use in Health Research, Infodemiology and Digital Disease Detection.

PubMed

Kim, Yoonsang; Huang, Jidong; Emery, Sherry

2016-02-26

Social media have transformed the communications landscape. People increasingly obtain news and health information online and via social media. Social media platforms also serve as novel sources of rich observational data for health research (including infodemiology, infoveillance, and digital disease detection detection). While the number of studies using social data is growing rapidly, very few of these studies transparently outline their methods for collecting, filtering, and reporting those data. Keywords and search filters applied to social data form the lens through which researchers may observe what and how people communicate about a given topic. Without a properly focused lens, research conclusions may be biased or misleading. Standards of reporting data sources and quality are needed so that data scientists and consumers of social media research can evaluate and compare methods and findings across studies. We aimed to develop and apply a framework of social media data collection and quality assessment and to propose a reporting standard, which researchers and reviewers may use to evaluate and compare the quality of social data across studies. We propose a conceptual framework consisting of three major steps in collecting social media data: develop, apply, and validate search filters. This framework is based on two criteria: retrieval precision (how much of retrieved data is relevant) and retrieval recall (how much of the relevant data is retrieved). We then discuss two conditions that estimation of retrieval precision and recall rely on--accurate human coding and full data collection--and how to calculate these statistics in cases that deviate from the two ideal conditions. We then apply the framework on a real-world example using approximately 4 million tobacco-related tweets collected from the Twitter firehose. We developed and applied a search filter to retrieve e-cigarette-related tweets from the archive based on three keyword categories: devices, brands, and behavior. The search filter retrieved 82,205 e-cigarette-related tweets from the archive and was validated. Retrieval precision was calculated above 95% in all cases. Retrieval recall was 86% assuming ideal conditions (no human coding errors and full data collection), 75% when unretrieved messages could not be archived, 86% assuming no false negative errors by coders, and 93% allowing both false negative and false positive errors by human coders. This paper sets forth a conceptual framework for the filtering and quality evaluation of social data that addresses several common challenges and moves toward establishing a standard of reporting social data. Researchers should clearly delineate data sources, how data were accessed and collected, and the search filter building process and how retrieval precision and recall were calculated. The proposed framework can be adapted to other public social media platforms.

Soft sensor based composition estimation and controller design for an ideal reactive distillation column.

PubMed

Vijaya Raghavan, S R; Radhakrishnan, T K; Srinivasan, K

2011-01-01

In this research work, the authors have presented the design and implementation of a recurrent neural network (RNN) based inferential state estimation scheme for an ideal reactive distillation column. Decentralized PI controllers are designed and implemented. The reactive distillation process is controlled by controlling the composition which has been estimated from the available temperature measurements using a type of RNN called Time Delayed Neural Network (TDNN). The performance of the RNN based state estimation scheme under both open loop and closed loop have been compared with a standard Extended Kalman filter (EKF) and a Feed forward Neural Network (FNN). The online training/correction has been done for both RNN and FNN schemes for every ten minutes whenever new un-trained measurements are available from a conventional composition analyzer. The performance of RNN shows better state estimation capability as compared to other state estimation schemes in terms of qualitative and quantitative performance indices. Copyright © 2010 ISA. Published by Elsevier Ltd. All rights reserved.

Mining adverse drug reactions from online healthcare forums using hidden Markov model.

PubMed

Sampathkumar, Hariprasad; Chen, Xue-wen; Luo, Bo

2014-10-23

Adverse Drug Reactions are one of the leading causes of injury or death among patients undergoing medical treatments. Not all Adverse Drug Reactions are identified before a drug is made available in the market. Current post-marketing drug surveillance methods, which are based purely on voluntary spontaneous reports, are unable to provide the early indications necessary to prevent the occurrence of such injuries or fatalities. The objective of this research is to extract reports of adverse drug side-effects from messages in online healthcare forums and use them as early indicators to assist in post-marketing drug surveillance. We treat the task of extracting adverse side-effects of drugs from healthcare forum messages as a sequence labeling problem and present a Hidden Markov Model(HMM) based Text Mining system that can be used to classify a message as containing drug side-effect information and then extract the adverse side-effect mentions from it. A manually annotated dataset from http://www.medications.com is used in the training and validation of the HMM based Text Mining system. A 10-fold cross-validation on the manually annotated dataset yielded on average an F-Score of 0.76 from the HMM Classifier, in comparison to 0.575 from the Baseline classifier. Without the Plain Text Filter component as a part of the Text Processing module, the F-Score of the HMM Classifier was reduced to 0.378 on average, while absence of the HTML Filter component was found to have no impact. Reducing the Drug names dictionary size by half, on average reduced the F-Score of the HMM Classifier to 0.359, while a similar reduction to the side-effects dictionary yielded an F-Score of 0.651 on average. Adverse side-effects mined from http://www.medications.com and http://www.steadyhealth.com were found to match the Adverse Drug Reactions on the Drug Package Labels of several drugs. In addition, some novel adverse side-effects, which can be potential Adverse Drug Reactions, were also identified. The results from the HMM based Text Miner are encouraging to pursue further enhancements to this approach. The mined novel side-effects can act as early indicators for health authorities to help focus their efforts in post-marketing drug surveillance.

Online damage inspection of optics for ATP system

NASA Astrophysics Data System (ADS)

Chen, Jing; Jiang, Yu; Mao, Yao; Gan, Xun; Liu, Qiong

2016-09-01

In the Electro-Optical acquisition-tracking-pointing system (ATP), the optical components will be damaged with the several influencing factors. In this situation, the rate will increase sharply when the arrival of damage to some extent. As the complex processing techniques and long processing cycle of optical components, the damage will cause the great increase of the system development cost and cycle. Therefore, it is significant to detect the laser-induced damage in the ATP system. At present, the major research on the on-line damage detection technology of optical components is for the large optical system in the international. The relevant detection systems have complicated structures and many of components, and require enough installation space reserved, which do not apply for ATP system. To solve the problem mentioned before, This paper use a method based on machine vision to detect the damage on-line for the present ATP system. To start with, CCD and PC are used for image acquisition. Secondly, smoothing filters are used to restrain false damage points produced by noise. Then, with the shape feature included in the damage image, the OTSU Method which can define the best segmentation threshold automatically is used to achieve the goal to locate the damage regions. At last, we can supply some opinions for the lifetime of the optical components by analyzing the damage data, such as damage area, damage position. The method has the characteristics of few-detectors and simple-structures which can be installed without any changes of the original light path. With the method, experimental results show that it is stable and effective to achieve the goal of detecting the damage of optical components on-line in the ATP system.

artdaq: DAQ software development made simple

NASA Astrophysics Data System (ADS)

Biery, Kurt; Flumerfelt, Eric; Freeman, John; Ketchum, Wesley; Lukhanin, Gennadiy; Rechenmacher, Ron

2017-10-01

For a few years now, the artdaq data acquisition software toolkit has provided numerous experiments with ready-to-use components which allow for rapid development and deployment of DAQ systems. Developed within the Fermilab Scientific Computing Division, artdaq provides data transfer, event building, run control, and event analysis functionality. This latter feature includes built-in support for the art event analysis framework, allowing experiments to run art modules for real-time filtering, compression, disk writing and online monitoring. As art, also developed at Fermilab, is also used for offline analysis, a major advantage of artdaq is that it allows developers to easily switch between developing online and offline software. artdaq continues to be improved. Support for an alternate mode of running whereby data from some subdetector components are only streamed if requested has been added; this option will reduce unnecessary DAQ throughput. Real-time reporting of DAQ metrics has been implemented, along with the flexibility to choose the format through which experiments receive the reports; these formats include the Ganglia, Graphite and syslog software packages, along with flat ASCII files. Additionally, work has been performed investigating more flexible modes of online monitoring, including the capability to run multiple online monitoring processes on different hosts, each running its own set of art modules. Finally, a web-based GUI interface through which users can configure details of their DAQ system has been implemented, increasing the ease of use of the system. Already successfully deployed on the LArlAT, DarkSide-50, DUNE 35ton and Mu2e experiments, artdaq will be employed for SBND and is a strong candidate for use on ICARUS and protoDUNE. With each experiment comes new ideas for how artdaq can be made more flexible and powerful. The above improvements will be described, along with potential ideas for the future.

Infrared machine vision system for the automatic detection of olive fruit quality.

PubMed

Guzmán, Elena; Baeten, Vincent; Pierna, Juan Antonio Fernández; García-Mesa, José A

2013-11-15

External quality is an important factor in the extraction of olive oil and the marketing of olive fruits. The appearance and presence of external damage are factors that influence the quality of the oil extracted and the perception of consumers, determining the level of acceptance prior to purchase in the case of table olives. The aim of this paper is to report on artificial vision techniques developed for the online estimation of olive quality and to assess the effectiveness of these techniques in evaluating quality based on detecting external defects. This method of classifying olives according to the presence of defects is based on an infrared (IR) vision system. Images of defects were acquired using a digital monochrome camera with band-pass filters on near-infrared (NIR). The original images were processed using segmentation algorithms, edge detection and pixel value intensity to classify the whole fruit. The detection of the defect involved a pixel classification procedure based on nonparametric models of the healthy and defective areas of olives. Classification tests were performed on olives to assess the effectiveness of the proposed method. This research showed that the IR vision system is a useful technology for the automatic assessment of olives that has the potential for use in offline inspection and for online sorting for defects and the presence of surface damage, easily distinguishing those that do not meet minimum quality requirements. Crown Copyright © 2013 Published by Elsevier B.V. All rights reserved.

Improving liquid chromatography-tandem mass spectrometry determinations by modifying noise frequency spectrum between two consecutive wavelet-based low-pass filtering procedures.

PubMed

Chen, Hsiao-Ping; Liao, Hui-Ju; Huang, Chih-Min; Wang, Shau-Chun; Yu, Sung-Nien

2010-04-23

This paper employs one chemometric technique to modify the noise spectrum of liquid chromatography-tandem mass spectrometry (LC-MS/MS) chromatogram between two consecutive wavelet-based low-pass filter procedures to improve the peak signal-to-noise (S/N) ratio enhancement. Although similar techniques of using other sets of low-pass procedures such as matched filters have been published, the procedures developed in this work are able to avoid peak broadening disadvantages inherent in matched filters. In addition, unlike Fourier transform-based low-pass filters, wavelet-based filters efficiently reject noises in the chromatograms directly in the time domain without distorting the original signals. In this work, the low-pass filtering procedures sequentially convolve the original chromatograms against each set of low pass filters to result in approximation coefficients, representing the low-frequency wavelets, of the first five resolution levels. The tedious trials of setting threshold values to properly shrink each wavelet are therefore no longer required. This noise modification technique is to multiply one wavelet-based low-pass filtered LC-MS/MS chromatogram with another artificial chromatogram added with thermal noises prior to the other wavelet-based low-pass filter. Because low-pass filter cannot eliminate frequency components below its cut-off frequency, more efficient peak S/N ratio improvement cannot be accomplished using consecutive low-pass filter procedures to process LC-MS/MS chromatograms. In contrast, when the low-pass filtered LC-MS/MS chromatogram is conditioned with the multiplication alteration prior to the other low-pass filter, much better ratio improvement is achieved. The noise frequency spectrum of low-pass filtered chromatogram, which originally contains frequency components below the filter cut-off frequency, is altered to span a broader range with multiplication operation. When the frequency range of this modified noise spectrum shifts toward the high frequency regimes, the other low-pass filter is able to provide better filtering efficiency to obtain higher peak S/N ratios. Real LC-MS/MS chromatograms, of which typically less than 6-fold peak S/N ratio improvement achieved with two consecutive wavelet-based low-pass filters remains the same S/N ratio improvement using one-step wavelet-based low-pass filter, are improved to accomplish much better ratio enhancement 25-folds to 40-folds typically when the noise frequency spectrum is modified between two low-pass filters. The linear standard curves using the filtered LC-MS/MS signals are validated. The filtered LC-MS/MS signals are also reproducible. The more accurate determinations of very low concentration samples (S/N ratio about 7-9) are obtained using the filtered signals than the determinations using the original signals. Copyright 2010 Elsevier B.V. All rights reserved.

Silicon cross-connect filters using microring resonator coupled multimode-interference-based waveguide crossings.

PubMed

Xu, Fang; Poon, Andrew W

2008-06-09

We report silicon cross-connect filters using microring resonator coupled multimode-interference (MMI) based waveguide crossings. Our experiments reveal that the MMI-based cross-connect filters impose lower crosstalk at the crossing than the conventional cross-connect filters using plain crossings, while offering a nearly symmetric resonance line shape in the drop-port transmission. As a proof-of-concept for cross-connection applications, we demonstrate on a silicon-on-insulator substrate (i) a 4-channel 1 x 4 linear-cascaded MMI-based cross-connect filter, and (ii) a 2-channel 2 x 2 array-cascaded MMI-based cross-connect filter.

Target-Pathogen: a structural bioinformatic approach to prioritize drug targets in pathogens.

PubMed

Sosa, Ezequiel J; Burguener, Germán; Lanzarotti, Esteban; Defelipe, Lucas; Radusky, Leandro; Pardo, Agustín M; Marti, Marcelo; Turjanski, Adrián G; Fernández Do Porto, Darío

2018-01-04

Available genomic data for pathogens has created new opportunities for drug discovery and development to fight them, including new resistant and multiresistant strains. In particular structural data must be integrated with both, gene information and experimental results. In this sense, there is a lack of an online resource that allows genome wide-based data consolidation from diverse sources together with thorough bioinformatic analysis that allows easy filtering and scoring for fast target selection for drug discovery. Here, we present Target-Pathogen database (http://target.sbg.qb.fcen.uba.ar/patho), designed and developed as an online resource that allows the integration and weighting of protein information such as: function, metabolic role, off-targeting, structural properties including druggability, essentiality and omic experiments, to facilitate the identification and prioritization of candidate drug targets in pathogens. We include in the database 10 genomes of some of the most relevant microorganisms for human health (Mycobacterium tuberculosis, Mycobacterium leprae, Klebsiella pneumoniae, Plasmodium vivax, Toxoplasma gondii, Leishmania major, Wolbachia bancrofti, Trypanosoma brucei, Shigella dysenteriae and Schistosoma Smanosoni) and show its applicability. New genomes can be uploaded upon request. © The Author(s) 2017. Published by Oxford University Press on behalf of Nucleic Acids Research.

Online estimation of room reverberation time

NASA Astrophysics Data System (ADS)

Ratnam, Rama; Jones, Douglas L.; Wheeler, Bruce C.; Feng, Albert S.

2003-04-01

The reverberation time (RT) is an important parameter for characterizing the quality of an auditory space. Sounds in reverberant environments are subject to coloration. This affects speech intelligibility and sound localization. State-of-the-art signal processing algorithms for hearing aids are expected to have the ability to evaluate the characteristics of the listening environment and turn on an appropriate processing strategy accordingly. Thus, a method for the characterization of room RT based on passively received microphone signals represents an important enabling technology. Current RT estimators, such as Schroeder's method or regression, depend on a controlled sound source, and thus cannot produce an online, blind RT estimate. Here, we describe a method for estimating RT without prior knowledge of sound sources or room geometry. The diffusive tail of reverberation was modeled as an exponentially damped Gaussian white noise process. The time constant of the decay, which provided a measure of the RT, was estimated using a maximum-likelihood procedure. The estimates were obtained continuously, and an order-statistics filter was used to extract the most likely RT from the accumulated estimates. The procedure was illustrated for connected speech. Results obtained for simulated and real room data are in good agreement with the real RT values.

Adaptive learning in complex reproducing kernel Hilbert spaces employing Wirtinger's subgradients.

PubMed

Bouboulis, Pantelis; Slavakis, Konstantinos; Theodoridis, Sergios

2012-03-01

This paper presents a wide framework for non-linear online supervised learning tasks in the context of complex valued signal processing. The (complex) input data are mapped into a complex reproducing kernel Hilbert space (RKHS), where the learning phase is taking place. Both pure complex kernels and real kernels (via the complexification trick) can be employed. Moreover, any convex, continuous and not necessarily differentiable function can be used to measure the loss between the output of the specific system and the desired response. The only requirement is the subgradient of the adopted loss function to be available in an analytic form. In order to derive analytically the subgradients, the principles of the (recently developed) Wirtinger's calculus in complex RKHS are exploited. Furthermore, both linear and widely linear (in RKHS) estimation filters are considered. To cope with the problem of increasing memory requirements, which is present in almost all online schemes in RKHS, the sparsification scheme, based on projection onto closed balls, has been adopted. We demonstrate the effectiveness of the proposed framework in a non-linear channel identification task, a non-linear channel equalization problem and a quadrature phase shift keying equalization scheme, using both circular and non circular synthetic signal sources.

Cascaded deep decision networks for classification of endoscopic images

NASA Astrophysics Data System (ADS)

Murthy, Venkatesh N.; Singh, Vivek; Sun, Shanhui; Bhattacharya, Subhabrata; Chen, Terrence; Comaniciu, Dorin

2017-02-01

Both traditional and wireless capsule endoscopes can generate tens of thousands of images for each patient. It is desirable to have the majority of irrelevant images filtered out by automatic algorithms during an offline review process or to have automatic indication for highly suspicious areas during an online guidance. This also applies to the newly invented endomicroscopy, where online indication of tumor classification plays a significant role. Image classification is a standard pattern recognition problem and is well studied in the literature. However, performance on the challenging endoscopic images still has room for improvement. In this paper, we present a novel Cascaded Deep Decision Network (CDDN) to improve image classification performance over standard Deep neural network based methods. During the learning phase, CDDN automatically builds a network which discards samples that are classified with high confidence scores by a previously trained network and concentrates only on the challenging samples which would be handled by the subsequent expert shallow networks. We validate CDDN using two different types of endoscopic imaging, which includes a polyp classification dataset and a tumor classification dataset. From both datasets we show that CDDN can outperform other methods by about 10%. In addition, CDDN can also be applied to other image classification problems.

On-line preconcentration/determination of lead in Ilex paraguariensis samples (mate tea) using polyurethane foam as filter and USN-ICP-OES.

PubMed

Marchisio, P F; Sales, A; Cerutti, S; Marchevski, E; Martinez, L D

2005-09-30

The present paper proposes an on-line preconcentration procedure for lead determination in Ilex paraguariensis (St. Hilaire) samples by ultrasonic nebulization associated to inductively coupled plasma optical emission spectrometry (USN-ICP-OES). It is based on the precipitation of lead(II) ion on a minicolumn packed with polyurethane foam using 2-(5-bromo-2-pyridilazo)-5-diethylaminophenol (5-Br-PADAP) as precipitating reagent. The collected analyte precipitate was quantitatively eluted from the minicolumn with 20% (v/v) nitric acid. An enhancement factor of 225-fold was obtained (15 for USN and 15 for preconcentration). The detection limit (DL) value for the preconcentration of 10.0 ml of sample was 40.0 ng/l. The relative standard deviation (R.S.D.) was 3.0% for a Pb concentration of 1 microg/l, calculated from the peak heights obtained. The calibration graph using the preconcentration system for lead was linear with a correlation coefficient of 0.9997, at levels near the detection limits up to at least 100 microg/l. The preconcentration procedure was successfully applied to the determination of lead in mate tea samples.

Solution to Detect, Classify, and Report Illicit Online Marketing and Sales of Controlled Substances via Twitter: Using Machine Learning and Web Forensics to Combat Digital Opioid Access

PubMed Central

Klugman, Josh; Kuzmenko, Ella; Gupta, Rashmi

2018-01-01

Background On December 6 and 7, 2017, the US Department of Health and Human Services (HHS) hosted its first Code-a-Thon event aimed at leveraging technology and data-driven solutions to help combat the opioid epidemic. The authors—an interdisciplinary team from academia, the private sector, and the US Centers for Disease Control and Prevention—participated in the Code-a-Thon as part of the prevention track. Objective The aim of this study was to develop and deploy a methodology using machine learning to accurately detect the marketing and sale of opioids by illicit online sellers via Twitter as part of participation at the HHS Opioid Code-a-Thon event. Methods Tweets were collected from the Twitter public application programming interface stream filtered for common prescription opioid keywords in conjunction with participation in the Code-a-Thon from November 15, 2017 to December 5, 2017. An unsupervised machine learning–based approach was developed and used during the Code-a-Thon competition (24 hours) to obtain a summary of the content of the tweets to isolate those clusters associated with illegal online marketing and sale using a biterm topic model (BTM). After isolating relevant tweets, hyperlinks associated with these tweets were reviewed to assess the characteristics of illegal online sellers. Results We collected and analyzed 213,041 tweets over the course of the Code-a-Thon containing keywords codeine, percocet, vicodin, oxycontin, oxycodone, fentanyl, and hydrocodone. Using BTM, 0.32% (692/213,041) tweets were identified as being associated with illegal online marketing and sale of prescription opioids. After removing duplicates and dead links, we identified 34 unique “live” tweets, with 44% (15/34) directing consumers to illicit online pharmacies, 32% (11/34) linked to individual drug sellers, and 21% (7/34) used by marketing affiliates. In addition to offering the “no prescription” sale of opioids, many of these vendors also sold other controlled substances and illicit drugs. Conclusions The results of this study are in line with prior studies that have identified social media platforms, including Twitter, as a potential conduit for supply and sale of illicit opioids. To translate these results into action, authors also developed a prototype wireframe for the purposes of detecting, classifying, and reporting illicit online pharmacy tweets selling controlled substances illegally to the US Food and Drug Administration and the US Drug Enforcement Agency. Further development of solutions based on these methods has the potential to proactively alert regulators and law enforcement agencies of illegal opioid sales, while also making the online environment safer for the public. PMID:29613851

Nonlinear calibration for petroleum water content measurement using PSO

NASA Astrophysics Data System (ADS)

Li, Mingbao; Zhang, Jiawei

2008-10-01

A new algorithmic for strapdown inertial navigation system (SINS) state estimation based on neural networks is introduced. In training strategy, the error vector and its delay are introduced. This error vector is made of the position and velocity difference between the estimations of system and the outputs of GPS. After state prediction and state update, the states of the system are estimated. After off-line training, the network can approach the status switching of SINS and after on-line training, the state estimate precision can be improved further by reducing network output errors. Then the network convergence is discussed. In the end, several simulations with different noise are given. The results show that the neural network state estimator has lower noise sensitivity and better noise immunity than Kalman filter.

In-flight estimation of center of gravity position using all-accelerometers.

PubMed

Al-Rawashdeh, Yazan Mohammad; Elshafei, Moustafa; Al-Malki, Mohammad Fahad

2014-09-19

Changing the position of the Center of Gravity (CoG) for an aerial vehicle is a challenging part in navigation, and control of such vehicles. In this paper, an all-accelerometers-based inertial measurement unit is presented, with a proposed method for on-line estimation of the position of the CoG. The accelerometers' readings are used to find and correct the vehicle's angular velocity and acceleration using an Extended Kalman Filter. Next, the accelerometers' readings along with the estimated angular velocity and acceleration are used in an identification scheme to estimate the position of the CoG and the vehicle's linear acceleration. The estimated position of the CoG and motion measurements can then be used to update the control rules to achieve better trim conditions for the air vehicle.

Determination of papaverine and cocaine by use of a precipitation system coupled on-line to an atomic absorption spectrometer.

PubMed

Eisman, M; Gallego, M; Varcárcel, M

1994-02-01

A continuous-precipitation flame-atomization atomic absorption spectrometric method for the determination of papaverine and cocaine hydrochlorides is proposed. The method is based on the precipitation of reineckates by injection of Reinecke's salt into a carrier containing the alkaloids and their subsequent retention on a stainless steel filter. In this way, papaverine and cocaine hydrochlorides can be determine over the ranges 5-85 and 50-850 micrograms ml-1 with a relative standard deviation of 1.3 and 3.2%, respectively, and a sampling frequency of 150 h-1. The proposed method is more sensitive and selective for papaverine than it is for cocaine and can be applied to the determination of papaverine HCl in pharmaceutical preparations.

VizieR Online Data Catalog: Seven sdB eclipsing binaries data (Pulley+, 2018)

NASA Astrophysics Data System (ADS)

Pulley, D.; Faillace, G.; Smith, D.; Watkins, A.; von Harrach, S.

2017-11-01

File tablea1 contains a list of the observatories and equipment used by this group to gather the data. File tablea2 contains all the observations made by this group It contains timing filter and observatory data. File tablea3 contains a list of the systems studied in this paper with reference stars used in the analysis. (4 data files).

Improvement of mouse controlling in Essential tremor by a tremor filter: A case report.

PubMed

López-Blanco, Roberto; Méndez-Guerrero, Antonio; Velasco, Miguel A

2018-07-15

The interaction with electronic devices is crucial in our technological society. Hand kinetic tremor complicates mouse driving in Essential tremor patients. To solve this issue some technological solutions are available and accessible online. We present a 71-year-old patient with prominent mouse controlling tremor who improved with one of these systems. Copyright © 2018 Elsevier B.V. All rights reserved.

VizieR Online Data Catalog: NGC 752 Vilnius photometry (Bartasiute+, 2011)

NASA Astrophysics Data System (ADS)

Bartasiute, S.; Janusz, R.; Boyle, R. P.; Philip, A. G. D.

2015-03-01

The multicolor observations in eight intermediate-band filters of the Stromvil (Stromgren + Vilnius) system were obtained during two separate runs, on the nights 2007 November 2-8 (run "SQ") and six nights between 2008 October 28 and November 5 (run "SU"), using a 4K CCD camera on the 1.8m VATT telescope on Mt. Graham, Arizona. (2 data files).

VizieR Online Data Catalog: HST observations of star clusters in NGC 3256 (Mulia+, 2016)

NASA Astrophysics Data System (ADS)

Mulia, A. J.; Chandar, R.; Whitmore, B. C.

2016-09-01

Our observations come from the ACS on Hubble Space Telescope (HST). NGC 3256 was observed using the filters F555W (~V in the Johnson-Cousins system; exposed for 2552s), FR656N (Hα; 2552s), and F330W (~U; 11358s) as part of the program GO-9735 (PI: Whitmore). The V and U band images were taken in 2003 November using the Wide Field Camera (WFC) and High Resolution Camera (HRC), respectively. The Hα observations were taken in 2004 March. WFC observations using F435W (~B) and F814W (~I) filters were taken in 2005 November as part of program GO-10592 (PI: Evans) for 1320 and 760s, respectively. (1 data file).

VizieR Online Data Catalog: Astro-photometric catalog of the core of NGC 5139 (Bellini+, 2017)

NASA Astrophysics Data System (ADS)

Bellini, A.; Anderson, J.; Bedin, L. R.; King, I. R.; van der Marel, R. P.; Piotto, G.; Cool, A.

2018-01-01

The core of ω Cen (NGC 5139) has been observed through many of the WFC3 filters since 2009 for calibration purposes, and new observations continue to be scheduled. Table 1 summarizes the massive archive of data, organized in a camera/filter fashion. We downloaded from the archive a total of 655 exposures (~205Ks) taken through 26 HST different bands: 18 for WFC3/UVIS (385 exposures) and 8 for WFC3/IR (270 exposures); spanning 2009 July to 2013 March. This data set can be accessed at MAST via this link: http://archive.stsci.edu/doi/resolve/resolve.html?doi=10.17909/T9WG6S (5 data files).

VizieR Online Data Catalog: Speckle interferometry at SOAR in 2015 (Tokovinin+, 2016)

NASA Astrophysics Data System (ADS)

Tokovinin, A.; Mason, B. D.; Hartkopf, W. I.; Mendez, R. A.; Horch, E. P.

2018-01-01

The observations reported here were obtained with the high-resolution camera (HRCam)-a fast imager designed to work at the 4.1m SOAR telescope. For practical reasons, the camera was mounted on the SOAR Adaptive Module (SAM). We mostly used the Stromgren y filter (543/22nm) and the near-infrared I filter (788/132nm). The observing time for this program was allocated through NOAO (three nights, programs 15A-0097 and 15B-0009, PI A.T.) and by the Chilean National Time Allocation Committee (three nights in 2015B, program CN2015B-6, PI R.A.M.). All observations were made by A.T. Table2 lists 1303 measures of 924 resolved binary stars and subsystems, including 27 newly resolved pairs. Table3 contains the data on 360 unresolved stars, some of which are listed as binaries in the WDS or resolved here in other filters. Table4 lists 27 newly resolved pairs. (5 data files).

Potential of silver nanoparticle-coated polyurethane foam as an antibacterial water filter.

PubMed

Jain, Prashant; Pradeep, T

2005-04-05

Silver nanoparticles can be coated on common polyurethane (PU) foams by overnight exposure of the foams to nanoparticle solutions. Repeated washing and air-drying yields uniformly coated PU foam, which can be used as a drinking water filter where bacterial contamination of the surface water is a health risk. Nanoparticles are stable on the foam and are not washed away by water. Morphology of the foam was retained after coating. The nanoparticle binding is due to its interaction with the nitrogen atom of the PU. Online tests were conducted with a prototypical water filter. At a flow rate of 0.5 L/min, in which contact time was of the order of a second, the output count of Escherichia coli was nil when the input water had a bacterial load of 10(5) colony-forming units (CFU) per mL. Combined with the low cost and effectiveness in its applications, the technology may have large implications to developing countries. Copyright (c) 2005 Wiley Periodicals, Inc.

Effect of the time window on the heat-conduction information filtering model

NASA Astrophysics Data System (ADS)

Guo, Qiang; Song, Wen-Jun; Hou, Lei; Zhang, Yi-Lu; Liu, Jian-Guo

2014-05-01

Recommendation systems have been proposed to filter out the potential tastes and preferences of the normal users online, however, the physics of the time window effect on the performance is missing, which is critical for saving the memory and decreasing the computation complexity. In this paper, by gradually expanding the time window, we investigate the impact of the time window on the heat-conduction information filtering model with ten similarity measures. The experimental results on the benchmark dataset Netflix indicate that by only using approximately 11.11% recent rating records, the accuracy could be improved by an average of 33.16% and the diversity could be improved by 30.62%. In addition, the recommendation performance on the dataset MovieLens could be preserved by only considering approximately 10.91% recent records. Under the circumstance of improving the recommendation performance, our discoveries possess significant practical value by largely reducing the computational time and shortening the data storage space.

Unbalance vibration suppression for AMBs system using adaptive notch filter

NASA Astrophysics Data System (ADS)

Chen, Qi; Liu, Gang; Han, Bangcheng

2017-09-01

The unbalance of rotor levitated by active magnetic bearings (AMBs) will cause synchronous vibration which greatly degrade the performance at high speeds in the rotating machinery. To suppress the unbalance vibration without angular velocity information, a novel modified adaptive notch filter (ANF) with phase shift in the AMBs system is presented in this study. Firstly, a 4-degree-of-freedom (DOF) radial unbalanced AMB rotor system is described and analyzed, and the solution of rotor vibration displacement is compared with the experimental data to verify the preciseness of the dynamic model. Then the principle and structure of the proposed notch filter used for the frequency estimation and online identification of synchronous component are presented. As well, the convergence property of the algorithm is investigated. In addition, the stability analysis of the closed-loop AMB system with the proposed ANF is conducted. Simulation and experiments on an AMB driveline system demonstrate the effectiveness and the adaptive characteristics of the proposed ANF on the elimination of synchronous controlled current in a widely operating speed range.

Real-Time Noise Reduction for Mossbauer Spectroscopy through Online Implementation of a Modified Kalman Filter

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

Abrecht, David G.; Schwantes, Jon M.; Kukkadapu, Ravi K.

2015-02-01

Spectrum-processing software that incorporates a gaussian smoothing kernel within the statistics of first-order Kalman filtration has been developed to provide cross-channel spectral noise reduction for increased real-time signal-to-noise ratios for Mossbauer spectroscopy. The filter was optimized for the breadth of the gaussian using the Mossbauer spectrum of natural iron foil, and comparisons between the peak broadening, signal-to-noise ratios, and shifts in the calculated hyperfine parameters are presented. The results of optimization give a maximum improvement in the signal-to-noise ratio of 51.1% over the unfiltered spectrum at a gaussian breadth of 27 channels, or 2.5% of the total spectrum width. Themore » full-width half-maximum of the spectrum peaks showed an increase of 19.6% at this optimum point, indicating a relatively weak increase in the peak broadening relative to the signal enhancement, leading to an overall increase in the observable signal. Calculations of the hyperfine parameters showed no statistically significant deviations were introduced from the application of the filter, confirming the utility of this filter for spectroscopy applications.« less

On sequential data assimilation for scalar macroscopic traffic flow models

NASA Astrophysics Data System (ADS)

Blandin, Sébastien; Couque, Adrien; Bayen, Alexandre; Work, Daniel

2012-09-01

We consider the problem of sequential data assimilation for transportation networks using optimal filtering with a scalar macroscopic traffic flow model. Properties of the distribution of the uncertainty on the true state related to the specific nonlinearity and non-differentiability inherent to macroscopic traffic flow models are investigated, derived analytically and analyzed. We show that nonlinear dynamics, by creating discontinuities in the traffic state, affect the performances of classical filters and in particular that the distribution of the uncertainty on the traffic state at shock waves is a mixture distribution. The non-differentiability of traffic dynamics around stationary shock waves is also proved and the resulting optimality loss of the estimates is quantified numerically. The properties of the estimates are explicitly studied for the Godunov scheme (and thus the Cell-Transmission Model), leading to specific conclusions about their use in the context of filtering, which is a significant contribution of this article. Analytical proofs and numerical tests are introduced to support the results presented. A Java implementation of the classical filters used in this work is available on-line at http://traffic.berkeley.edu for facilitating further efforts on this topic and fostering reproducible research.

An Interval Type-2 Neural Fuzzy System for Online System Identification and Feature Elimination.

PubMed

Lin, Chin-Teng; Pal, Nikhil R; Wu, Shang-Lin; Liu, Yu-Ting; Lin, Yang-Yin

2015-07-01

We propose an integrated mechanism for discarding derogatory features and extraction of fuzzy rules based on an interval type-2 neural fuzzy system (NFS)-in fact, it is a more general scheme that can discard bad features, irrelevant antecedent clauses, and even irrelevant rules. High-dimensional input variable and a large number of rules not only enhance the computational complexity of NFSs but also reduce their interpretability. Therefore, a mechanism for simultaneous extraction of fuzzy rules and reducing the impact of (or eliminating) the inferior features is necessary. The proposed approach, namely an interval type-2 Neural Fuzzy System for online System Identification and Feature Elimination (IT2NFS-SIFE), uses type-2 fuzzy sets to model uncertainties associated with information and data in designing the knowledge base. The consequent part of the IT2NFS-SIFE is of Takagi-Sugeno-Kang type with interval weights. The IT2NFS-SIFE possesses a self-evolving property that can automatically generate fuzzy rules. The poor features can be discarded through the concept of a membership modulator. The antecedent and modulator weights are learned using a gradient descent algorithm. The consequent part weights are tuned via the rule-ordered Kalman filter algorithm to enhance learning effectiveness. Simulation results show that IT2NFS-SIFE not only simplifies the system architecture by eliminating derogatory/irrelevant antecedent clauses, rules, and features but also maintains excellent performance.

Research Blogging: Indexing and Registering the Change in Science 2.0

PubMed Central

Fausto, Sibele; Machado, Fabio A.; Bento, Luiz Fernando J.; Iamarino, Atila; Nahas, Tatiana R.; Munger, David S.

2012-01-01

Increasing public interest in science information in a digital and 2.0 science era promotes a dramatically, rapid and deep change in science itself. The emergence and expansion of new technologies and internet-based tools is leading to new means to improve scientific methodology and communication, assessment, promotion and certification. It allows methods of acquisition, manipulation and storage, generating vast quantities of data that can further facilitate the research process. It also improves access to scientific results through information sharing and discussion. Content previously restricted only to specialists is now available to a wider audience. This context requires new management systems to make scientific knowledge more accessible and useable, including new measures to evaluate the reach of scientific information. The new science and research quality measures are strongly related to the new online technologies and services based in social media. Tools such as blogs, social bookmarks and online reference managers, Twitter and others offer alternative, transparent and more comprehensive information about the active interest, usage and reach of scientific publications. Another of these new filters is the Research Blogging platform, which was created in 2007 and now has over 1,230 active blogs, with over 26,960 entries posted about peer-reviewed research on subjects ranging from Anthropology to Zoology. This study takes a closer look at RB, in order to get insights into its contribution to the rapidly changing landscape of scientific communication. PMID:23251358

"I Always Vet Things": Navigating Privacy and the Presentation of Self on Health Discussion Boards Among Individuals with Long-Term Conditions.

PubMed

Brady, Ellen; Segar, Julia; Sanders, Caroline

2016-10-13

The ethics of research into online communities is a long-debated issue, with many researchers arguing that open-access discussion groups are publically accessible data and do not require informed consent from participants for their use for research purposes. However, it has been suggested that there is a discrepancy between the perceived and actual privacy of user-generated online content by community members. There has been very little research regarding how privacy is experienced and enacted online. The objective of this study is to address this gap by qualitatively exploring the expectations of privacy on Internet forums among individuals with long-term conditions. Semistructured interviews were conducted with 20 participants with myalgic encephalomyelitis/chronic fatigue syndrome (ME/CFS) and 21 participants with type 1 and 2 diabetes mellitus, and were analyzed using thematic analysis. Participants were recruited via online and offline routes, namely forums, email lists, newsletters, and face-to-face support groups. The findings indicate that privacy online is a nebulous concept. Rather than individuals drawing a clear-cut distinction between what they would and would not be comfortable sharing online, it was evident that these situations were contextually dependent and related to a number of unique and individual factors. Interviewees were seen to carefully manage how they presented themselves on forums, filtering and selecting the information that they shared about themselves in order to develop and maintain a particular online persona, while maintaining and preserving an acceptable level of privacy.

Overview of a FPGA-based nuclear instrumentation dedicated to primary activity measurements.

PubMed

Bobin, C; Bouchard, J; Pierre, S; Thiam, C

2012-09-01

In National Metrology Institutes like LNE-LNHB, renewal and improvement of the instrumentation is an important task. Nowadays, the current trend is to adopt digital boards, which present numerous advantages over the standard electronics. The feasibility of an on-line fulfillment of nuclear-instrumentation functionalities using a commercial FPGA-based (Field-Programmable Gate Array) board has been validated in the case of TDCR primary measurements (Triple to Double Coincidence Ratio method based on liquid scintillation). The new applications presented in this paper have been included to allow either an on-line processing of the information or a raw-data acquisition for an off-line treatment. Developed as a complementary tool for TDCR counting, a time-to-digital converter specifically designed for this technique has been added. In addition, the description is given of a spectrometry channel based on the connection between conventional shaping amplifiers and the analog-to-digital converter (ADC) input available on the same digital board. First results are presented in the case of α- and γ-counting related to, respectively, the defined solid angle and well-type NaI(Tl) primary activity techniques. The combination of two different channels (liquid scintillation and γ-spectrometry) implementing the live-time anticoincidence processing is also described for the application of the 4πβ-γ coincidence method. The need for an optimized coupling between the analog chain and the ADC stage is emphasized. The straight processing of the signals delivered by the preamplifier connected to a HPGe detector is also presented along with the first development of digital filtering. Copyright © 2012 Elsevier Ltd. All rights reserved.

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

PubMed

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

2017-12-01

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

Accurate Heart Rate Monitoring During Physical Exercises Using PPG.

PubMed

Temko, Andriy

2017-09-01

The challenging task of heart rate (HR) estimation from the photoplethysmographic (PPG) signal, during intensive physical exercises, is tackled in this paper. The study presents a detailed analysis of a novel algorithm (WFPV) that exploits a Wiener filter to attenuate the motion artifacts, a phase vocoder to refine the HR estimate and user-adaptive post-processing to track the subject physiology. Additionally, an offline version of the HR estimation algorithm that uses Viterbi decoding is designed for scenarios that do not require online HR monitoring (WFPV+VD). The performance of the HR estimation systems is rigorously compared with existing algorithms on the publically available database of 23 PPG recordings. On the whole dataset of 23 PPG recordings, the algorithms result in average absolute errors of 1.97 and 1.37 BPM in the online and offline modes, respectively. On the test dataset of 10 PPG recordings which were most corrupted with motion artifacts, WFPV has an error of 2.95 BPM on its own and 2.32 BPM in an ensemble with two existing algorithms. The error rate is significantly reduced when compared with the state-of-the art PPG-based HR estimation methods. The proposed system is shown to be accurate in the presence of strong motion artifacts and in contrast to existing alternatives has very few free parameters to tune. The algorithm has a low computational cost and can be used for fitness tracking and health monitoring in wearable devices. The MATLAB implementation of the algorithm is provided online.

On-line monitoring of methanol and methyl formate in the exhaust gas of an industrial formaldehyde production plant by a mid-IR gas sensor based on tunable Fabry-Pérot filter technology.

PubMed

Genner, Andreas; Gasser, Christoph; Moser, Harald; Ofner, Johannes; Schreiber, Josef; Lendl, Bernhard

2017-01-01

On-line monitoring of key chemicals in an industrial production plant ensures economic operation, guarantees the desired product quality, and provides additional in-depth information on the involved chemical processes. For that purpose, rapid, rugged, and flexible measurement systems at reasonable cost are required. Here, we present the application of a flexible mid-IR filtometer for industrial gas sensing. The developed prototype consists of a modulated thermal infrared source, a temperature-controlled gas cell for absorption measurement and an integrated device consisting of a Fabry-Pérot interferometer and a pyroelectric mid-IR detector. The prototype was calibrated in the research laboratory at TU Wien for measuring methanol and methyl formate in the concentration ranges from 660 to 4390 and 747 to 4610 ppmV. Subsequently, the prototype was transferred and installed at the project partner Metadynea Austria GmbH and linked to their Process Control System via a dedicated micro-controller and used for on-line monitoring of the process off-gas. Up to five process streams were sequentially monitored in a fully automated manner. The obtained readings for methanol and methyl formate concentrations provided useful information on the efficiency and correct functioning of the process plant. Of special interest for industry is the now added capability to monitor the start-up phase and process irregularities with high time resolution (5 s).

Design of a composite filter realizable on practical spatial light modulators

NASA Technical Reports Server (NTRS)

Rajan, P. K.; Ramakrishnan, Ramachandran

1994-01-01

Hybrid optical correlator systems use two spatial light modulators (SLM's), one at the input plane and the other at the filter plane. Currently available SLM's such as the deformable mirror device (DMD) and liquid crystal television (LCTV) SLM's exhibit arbitrarily constrained operating characteristics. The pattern recognition filters designed with the assumption that the SLM's have ideal operating characteristic may not behave as expected when implemented on the DMD or LCTV SLM's. Therefore it is necessary to incorporate the SLM constraints in the design of the filters. In this report, an iterative method is developed for the design of an unconstrained minimum average correlation energy (MACE) filter. Then using this algorithm a new approach for the design of a SLM constrained distortion invariant filter in the presence of input SLM is developed. Two different optimization algorithms are used to maximize the objective function during filter synthesis, one based on the simplex method and the other based on the Hooke and Jeeves method. Also, the simulated annealing based filter design algorithm proposed by Khan and Rajan is refined and improved. The performance of the filter is evaluated in terms of its recognition/discrimination capabilities using computer simulations and the results are compared with a simulated annealing optimization based MACE filter. The filters are designed for different LCTV SLM's operating characteristics and the correlation responses are compared. The distortion tolerance and the false class image discrimination qualities of the filter are comparable to those of the simulated annealing based filter but the new filter design takes about 1/6 of the computer time taken by the simulated annealing filter design.

Tunable Microwave Filter Design Using Thin-Film Ferroelectric Varactors

NASA Astrophysics Data System (ADS)

Haridasan, Vrinda

Military, space, and consumer-based communication markets alike are moving towards multi-functional, multi-mode, and portable transceiver units. Ferroelectric-based tunable filter designs in RF front-ends are a relatively new area of research that provides a potential solution to support wideband and compact transceiver units. This work presents design methodologies developed to optimize a tunable filter design for system-level integration, and to improve the performance of a ferroelectric-based tunable bandpass filter. An investigative approach to find the origins of high insertion loss exhibited by these filters is also undertaken. A system-aware design guideline and figure of merit for ferroelectric-based tunable band- pass filters is developed. The guideline does not constrain the filter bandwidth as long as it falls within the range of the analog bandwidth of a system's analog to digital converter. A figure of merit (FOM) that optimizes filter design for a specific application is presented. It considers the worst-case filter performance parameters and a tuning sensitivity term that captures the relation between frequency tunability and the underlying material tunability. A non-tunable parasitic fringe capacitance associated with ferroelectric-based planar capacitors is confirmed by simulated and measured results. The fringe capacitance is an appreciable proportion of the tunable capacitance at frequencies of X-band and higher. As ferroelectric-based tunable capac- itors form tunable resonators in the filter design, a proportionally higher fringe capacitance reduces the capacitance tunability which in turn reduces the frequency tunability of the filter. Methods to reduce the fringe capacitance can thus increase frequency tunability or indirectly reduce the filter insertion-loss by trading off the increased tunability achieved to lower loss. A new two-pole tunable filter topology with high frequency tunability (> 30%), steep filter skirts, wide stopband rejection, and constant bandwidth is designed, simulated, fabricated and measured. The filters are fabricated using barium strontium titanate (BST) varactors. Electromagnetic simulations and measured results of the tunable two-pole ferroelectric filter are analyzed to explore the origins of high insertion loss in ferroelectric filters. The results indicate that the high-permittivity of the BST (a ferroelectric) not only makes the filters tunable and compact, but also increases the conductive loss of the ferroelectric-based tunable resonators which translates into high insertion loss in ferroelectric filters.

Development, characterization and first deployment of an improved online reactive oxygen species analyzer

NASA Astrophysics Data System (ADS)

Zhou, Jun; Bruns, Emily A.; Zotter, Peter; Stefenelli, Giulia; Prévôt, André S. H.; Baltensperger, Urs; El-Haddad, Imad; Dommen, Josef

2018-01-01

Inhalation of atmospheric particles is linked to human diseases. Reactive oxygen species (ROS) present in these atmospheric aerosols may play an important role. However, the ROS content in aerosols and their formation pathways are still largely unknown. Here, we have developed an online and offline ROS analyzer using a 2',7'-dichlorofluorescin (DCFH) based assay. The ROS analyzer was calibrated with H2O2 and its sensitivity was characterized using a suite of model organic compounds. The instrument detection limit determined as 3 times the noise is 1.3 nmol L-1 for offline analysis and 2 nmol m-3 of sampled air when the instrument is operated online at a fluorescence response time of approximately 8 min, while the offline method detection limit is 18 nmol L-1. Potential interferences from gas-phase O3 and NO2 as well as matrix effects of particulate SO42- and NO3- were tested, but not observed. Fe3+ had no influence on the ROS signal, while soluble Fe2+ reduced it if present at high concentrations in the extracts. Both online and offline methods were applied to identify the ROS content of different aerosol types, i.e., ambient aerosols as well as fresh and aged aerosols from wood combustion emissions. The stability of the ROS was assessed by comparing the ROS concentration measured by the same instrumentation online in situ with offline measurements. We also analyzed the evolution of ROS in specific samples by conducting the analysis after storage times of up to 4 months. The ROS were observed to decay with increasing storage duration. From their decay behavior, ROS in secondary organic aerosol (SOA) can be separated into short- and long-lived fractions. The half-life of the short-lived fraction was 1.7 ± 0.4 h, while the half-life of the long-lived fraction could not be determined with our uncertainties. All these measurements showed consistently that on average 60 ± 20 % of the ROS were very reactive and disappeared during the filter storage time. This demonstrates the importance of a fast online measurement of ROS.

High-speed tunable microwave photonic notch filter based on phase modulator incorporated Lyot filter.

PubMed

Ge, Jia; Feng, Hanlin; Scott, Guy; Fok, Mable P

2015-01-01

A high-speed tunable microwave photonic notch filter with ultrahigh rejection ratio is presented, which is achieved by semiconductor optical amplifier (SOA)-based single-sideband modulation and optical spectral filtering with a phase modulator-incorporated Lyot (PM-Lyot) filter. By varying the birefringence of the phase modulator through electro-optic effect, electrically tuning of the microwave photonic notch filter is experimentally achieved at tens of gigahertz speed. The use of SOA-polarizer based single-sideband modulation scheme provides good sideband suppression over a wide frequency range, resulting in an ultrahigh rejection ratio of the microwave photonic notch filter. Stable filter spectrum with bandstop rejection ratio over 60 dB is observed over a frequency tuning range from 1.8 to 10 GHz. Compare with standard interferometric notch filter, narrower bandwidth and sharper notch profile are achieved with the unique PM-Lyot filter, resulting in better filter selectivity. Moreover, bandwidth tuning is also achieved through polarization adjustment inside the PM-Lyot filter, that the 10-dB filter bandwidth is tuned from 0.81 to 1.85 GHz.

Entropy-guided switching trimmed mean deviation-boosted anisotropic diffusion filter

NASA Astrophysics Data System (ADS)

Nnolim, Uche A.

2016-07-01

An effective anisotropic diffusion (AD) mean filter variant is proposed for filtering of salt-and-pepper impulse noise. The implemented filter is robust to impulse noise ranging from low to high density levels. The algorithm involves a switching scheme in addition to utilizing the unsymmetric trimmed mean/median deviation to filter image noise while greatly preserving image edges, regardless of impulse noise density (ND). It operates with threshold parameters selected manually or adaptively estimated from the image statistics. It is further combined with the partial differential equations (PDE)-based AD for edge preservation at high NDs to enhance the properties of the trimmed mean filter. Based on experimental results, the proposed filter easily and consistently outperforms the median filter and its other variants ranging from simple to complex filter structures, especially the known PDE-based variants. In addition, the switching scheme and threshold calculation enables the filter to avoid smoothing an uncorrupted image, and filtering is activated only when impulse noise is present. Ultimately, the particular properties of the filter make its combination with the AD algorithm a unique and powerful edge-preservation smoothing filter at high-impulse NDs.

Minimotif Miner 3.0: database expansion and significantly improved reduction of false-positive predictions from consensus sequences.

PubMed

Mi, Tian; Merlin, Jerlin Camilus; Deverasetty, Sandeep; Gryk, Michael R; Bill, Travis J; Brooks, Andrew W; Lee, Logan Y; Rathnayake, Viraj; Ross, Christian A; Sargeant, David P; Strong, Christy L; Watts, Paula; Rajasekaran, Sanguthevar; Schiller, Martin R

2012-01-01

Minimotif Miner (MnM available at http://minimotifminer.org or http://mnm.engr.uconn.edu) is an online database for identifying new minimotifs in protein queries. Minimotifs are short contiguous peptide sequences that have a known function in at least one protein. Here we report the third release of the MnM database which has now grown 60-fold to approximately 300,000 minimotifs. Since short minimotifs are by their nature not very complex we also summarize a new set of false-positive filters and linear regression scoring that vastly enhance minimotif prediction accuracy on a test data set. This online database can be used to predict new functions in proteins and causes of disease.

The intractable cigarette 'filter problem'.

PubMed

Harris, Bradford

2011-05-01

When lung cancer fears emerged in the 1950s, cigarette companies initiated a shift in cigarette design from unfiltered to filtered cigarettes. Both the ineffectiveness of cigarette filters and the tobacco industry's misleading marketing of the benefits of filtered cigarettes have been well documented. However, during the 1950s and 1960s, American cigarette companies spent millions of dollars to solve what the industry identified as the 'filter problem'. These extensive filter research and development efforts suggest a phase of genuine optimism among cigarette designers that cigarette filters could be engineered to mitigate the health hazards of smoking. This paper explores the early history of cigarette filter research and development in order to elucidate why and when seemingly sincere filter engineering efforts devolved into manipulations in cigarette design to sustain cigarette marketing and mitigate consumers' concerns about the health consequences of smoking. Relevant word and phrase searches were conducted in the Legacy Tobacco Documents Library online database, Google Patents, and media and medical databases including ProQuest, JSTOR, Medline and PubMed. 13 tobacco industry documents were identified that track prominent developments involved in what the industry referred to as the 'filter problem'. These reveal a period of intense focus on the 'filter problem' that persisted from the mid-1950s to the mid-1960s, featuring collaborations between cigarette producers and large American chemical and textile companies to develop effective filters. In addition, the documents reveal how cigarette filter researchers' growing scientific knowledge of smoke chemistry led to increasing recognition that filters were unlikely to offer significant health protection. One of the primary concerns of cigarette producers was to design cigarette filters that could be economically incorporated into the massive scale of cigarette production. The synthetic plastic cellulose acetate became the fundamental cigarette filter material. By the mid-1960s, the meaning of the phrase 'filter problem' changed, such that the effort to develop effective filters became a campaign to market cigarette designs that would sustain the myth of cigarette filter efficacy. This study indicates that cigarette designers at Philip Morris, British-American Tobacco, Lorillard and other companies believed for a time that they might be able to reduce some of the most dangerous substances in mainstream smoke through advanced engineering of filter tips. In their attempts to accomplish this, they developed the now ubiquitous cellulose acetate cigarette filter. By the mid-1960s cigarette designers realised that the intractability of the 'filter problem' derived from a simple fact: that which is harmful in mainstream smoke and that which provides the smoker with 'satisfaction' are essentially one and the same. Only in the wake of this realisation did the agenda of cigarette designers appear to transition away from mitigating the health hazards of smoking and towards the perpetuation of the notion that cigarette filters are effective in reducing these hazards. Filters became a marketing tool, designed to keep and recruit smokers as consumers of these hazardous products.

Coarse Alignment Technology on Moving base for SINS Based on the Improved Quaternion Filter Algorithm.

PubMed

Zhang, Tao; Zhu, Yongyun; Zhou, Feng; Yan, Yaxiong; Tong, Jinwu

2017-06-17

Initial alignment of the strapdown inertial navigation system (SINS) is intended to determine the initial attitude matrix in a short time with certain accuracy. The alignment accuracy of the quaternion filter algorithm is remarkable, but the convergence rate is slow. To solve this problem, this paper proposes an improved quaternion filter algorithm for faster initial alignment based on the error model of the quaternion filter algorithm. The improved quaternion filter algorithm constructs the K matrix based on the principle of optimal quaternion algorithm, and rebuilds the measurement model by containing acceleration and velocity errors to make the convergence rate faster. A doppler velocity log (DVL) provides the reference velocity for the improved quaternion filter alignment algorithm. In order to demonstrate the performance of the improved quaternion filter algorithm in the field, a turntable experiment and a vehicle test are carried out. The results of the experiments show that the convergence rate of the proposed improved quaternion filter is faster than that of the tradition quaternion filter algorithm. In addition, the improved quaternion filter algorithm also demonstrates advantages in terms of correctness, effectiveness, and practicability.

Transistor-based filter for inhibiting load noise from entering a power supply

DOEpatents

Taubman, Matthew S

2013-07-02

A transistor-based filter for inhibiting load noise from entering a power supply is disclosed. The filter includes a first transistor having an emitter coupled to a power supply, a collector coupled to a load, and a base. The filter also includes a first capacitor coupled between the base of the first transistor and a ground terminal. The filter further includes an impedance coupled between the base and a node between the collector and the load, or a second transistor and second capacitor. The impedance can be a resistor or an inductor.

Transistor-based filter for inhibiting load noise from entering a power supply

DOEpatents

Taubman, Matthew S

2015-02-24

A transistor-based filter for inhibiting load noise from entering a power supply is disclosed. The filter includes a first transistor having an emitter coupled to a power supply, a collector coupled to a load, and a base. The filter also includes a first capacitor coupled between the base of the first transistor and a ground terminal The filter further includes an impedance coupled between the base and a node between the collector and the load, or a second transistor and second capacitor. The impedance can be a resistor or an inductor.

Measuring Learner's Performance in E-Learning Recommender Systems

ERIC Educational Resources Information Center

Ghauth, Khairil Imran; Abdullah, Nor Aniza

2010-01-01

A recommender system is a piece of software that helps users to identify the most interesting and relevant learning items from a large number of items. Recommender systems may be based on collaborative filtering (by user ratings), content-based filtering (by keywords), and hybrid filtering (by both collaborative and content-based filtering).…

Personalising e-learning modules: targeting Rasmussen levels using XML.

PubMed

Renard, J M; Leroy, S; Camus, H; Picavet, M; Beuscart, R

2003-01-01

The development of Internet technologies has made it possible to increase the number and the diversity of on-line resources for teachers and students. Initiatives like the French-speaking Virtual Medical University Project (UMVF) try to organise the access to these resources. But both teachers and students are working on a partly redundant subset of knowledge. From the analysis of some French courses we propose a model for knowledge organisation derived from Rasmussen's stepladder. In the context of decision-making Rasmussen has identified skill-based, rule-based and knowledge-based levels for the mental process. In the medical context of problem-solving, we apply these three levels to the definition of three students levels: beginners, intermediate-level learners, experts. Based on our model, we build a representation of the hierarchical structure of data using XML language. We use XSLT Transformation Language in order to filter relevant data according to student level and to propose an appropriate display on students' terminal. The model and the XML implementation we define help to design tools for building personalised e-learning modules.

Automatic detection of omissions in medication lists

PubMed Central

Duncan, George T; Neill, Daniel B; Padman, Rema

2011-01-01

Objective Evidence suggests that the medication lists of patients are often incomplete and could negatively affect patient outcomes. In this article, the authors propose the application of collaborative filtering methods to the medication reconciliation task. Given a current medication list for a patient, the authors employ collaborative filtering approaches to predict drugs the patient could be taking but are missing from their observed list. Design The collaborative filtering approach presented in this paper emerges from the insight that an omission in a medication list is analogous to an item a consumer might purchase from a product list. Online retailers use collaborative filtering to recommend relevant products using retrospective purchase data. In this article, the authors argue that patient information in electronic medical records, combined with artificial intelligence methods, can enhance medication reconciliation. The authors formulate the detection of omissions in medication lists as a collaborative filtering problem. Detection of omissions is accomplished using several machine-learning approaches. The effectiveness of these approaches is evaluated using medication data from three long-term care centers. The authors also propose several decision-theoretic extensions to the methodology for incorporating medical knowledge into recommendations. Results Results show that collaborative filtering identifies the missing drug in the top-10 list about 40–50% of the time and the therapeutic class of the missing drug 50%–65% of the time at the three clinics in this study. Conclusion Results suggest that collaborative filtering can be a valuable tool for reconciling medication lists, complementing currently recommended process-driven approaches. However, a one-size-fits-all approach is not optimal, and consideration should be given to context (eg, types of patients and drug regimens) and consequence (eg, the impact of omission on outcomes). PMID:21447497

Automatic detection of omissions in medication lists.

PubMed

Hasan, Sharique; Duncan, George T; Neill, Daniel B; Padman, Rema

2011-01-01

Evidence suggests that the medication lists of patients are often incomplete and could negatively affect patient outcomes. In this article, the authors propose the application of collaborative filtering methods to the medication reconciliation task. Given a current medication list for a patient, the authors employ collaborative filtering approaches to predict drugs the patient could be taking but are missing from their observed list. The collaborative filtering approach presented in this paper emerges from the insight that an omission in a medication list is analogous to an item a consumer might purchase from a product list. Online retailers use collaborative filtering to recommend relevant products using retrospective purchase data. In this article, the authors argue that patient information in electronic medical records, combined with artificial intelligence methods, can enhance medication reconciliation. The authors formulate the detection of omissions in medication lists as a collaborative filtering problem. Detection of omissions is accomplished using several machine-learning approaches. The effectiveness of these approaches is evaluated using medication data from three long-term care centers. The authors also propose several decision-theoretic extensions to the methodology for incorporating medical knowledge into recommendations. Results show that collaborative filtering identifies the missing drug in the top-10 list about 40-50% of the time and the therapeutic class of the missing drug 50%-65% of the time at the three clinics in this study. Results suggest that collaborative filtering can be a valuable tool for reconciling medication lists, complementing currently recommended process-driven approaches. However, a one-size-fits-all approach is not optimal, and consideration should be given to context (eg, types of patients and drug regimens) and consequence (eg, the impact of omission on outcomes).

Design of order statistics filters using feedforward neural networks

NASA Astrophysics Data System (ADS)

Maslennikova, Yu. S.; Bochkarev, V. V.

2016-08-01

In recent years significant progress have been made in the development of nonlinear data processing techniques. Such techniques are widely used in digital data filtering and image enhancement. Many of the most effective nonlinear filters based on order statistics. The widely used median filter is the best known order statistic filter. Generalized form of these filters could be presented based on Lloyd's statistics. Filters based on order statistics have excellent robustness properties in the presence of impulsive noise. In this paper, we present special approach for synthesis of order statistics filters using artificial neural networks. Optimal Lloyd's statistics are used for selecting of initial weights for the neural network. Adaptive properties of neural networks provide opportunities to optimize order statistics filters for data with asymmetric distribution function. Different examples demonstrate the properties and performance of presented approach.

From continuing education to personal digital assistants: what do physical therapists need to support evidence-based practice in stroke management?

PubMed

Salbach, Nancy M; Veinot, Paula; Jaglal, Susan B; Bayley, Mark; Rolfe, Danielle

2011-08-01

Understanding how to structure educational interventions and resources to facilitate physical therapists' application of the research literature is required. The objective of this study was to explore physical therapists' preferences for strategies to facilitate their access to, evaluation and implementation of the stroke research literature in clinical practice. In-depth, qualitative telephone interviews were conducted with 23 physical therapists who treat people with stroke in Ontario, Canada and who had participated in a previous survey on evidence-based practice. Data were analysed using a constant comparative approach to identify emergent themes. Participants preferred online access to research summaries or systematic reviews to save time to filter and critique research articles. To enable access in the workplace, an acceptable computer-to-staff ratio, permission to access web sites and protected work time were suggested. Participants considered personal digital assistants as excellent tools for quick access to online resources but were unsure of their advantage over a desktop computer. Therapists favoured use of non-technical language, glossaries of research terms and quality ratings of studies to ease understanding and appraisal. Teleconferencing or videoconferencing overcame geographical but not scheduling barriers to accessing education. To achieve behaviour change in clinical practice, therapists preferred multiple interactive, face-to-face education sessions in a group format, with opportunities for case-based learning and practice of new skills. Physical therapists prefer technology-assisted access to resources and education and favour attending multiple interactive, expert-facilitated education sessions incorporating opportunities for case-based learning and practice of new skills to change behaviour related to evidence-based practice. © 2010 Blackwell Publishing Ltd.