Layout Study and Application of Mobile App Recommendation Approach Based On Spark Streaming Framework

NASA Astrophysics Data System (ADS)

Wang, H. T.; Chen, T. T.; Yan, C.; Pan, H.

2018-05-01

For App recommended areas of mobile phone software, made while using conduct App application recommended combined weighted Slope One algorithm collaborative filtering algorithm items based on further improvement of the traditional collaborative filtering algorithm in cold start, data matrix sparseness and other issues, will recommend Spark stasis parallel algorithm platform, the introduction of real-time streaming streaming real-time computing framework to improve real-time software applications recommended.

Enhancing collaborative filtering by user interest expansion via personalized ranking.

PubMed

Liu, Qi; Chen, Enhong; Xiong, Hui; Ding, Chris H Q; Chen, Jian

2012-02-01

Recommender systems suggest a few items from many possible choices to the users by understanding their past behaviors. In these systems, the user behaviors are influenced by the hidden interests of the users. Learning to leverage the information about user interests is often critical for making better recommendations. However, existing collaborative-filtering-based recommender systems are usually focused on exploiting the information about the user's interaction with the systems; the information about latent user interests is largely underexplored. To that end, inspired by the topic models, in this paper, we propose a novel collaborative-filtering-based recommender system by user interest expansion via personalized ranking, named iExpand. The goal is to build an item-oriented model-based collaborative-filtering framework. The iExpand method introduces a three-layer, user-interests-item, representation scheme, which leads to more accurate ranking recommendation results with less computation cost and helps the understanding of the interactions among users, items, and user interests. Moreover, iExpand strategically deals with many issues that exist in traditional collaborative-filtering approaches, such as the overspecialization problem and the cold-start problem. Finally, we evaluate iExpand on three benchmark data sets, and experimental results show that iExpand can lead to better ranking performance than state-of-the-art methods with a significant margin.

Coarse cluster enhancing collaborative recommendation for social network systems

NASA Astrophysics Data System (ADS)

Zhao, Yao-Dong; Cai, Shi-Min; Tang, Ming; Shang, Min-Sheng

2017-10-01

Traditional collaborative filtering based recommender systems for social network systems bring very high demands on time complexity due to computing similarities of all pairs of users via resource usages and annotation actions, which thus strongly suppresses recommending speed. In this paper, to overcome this drawback, we propose a novel approach, namely coarse cluster that partitions similar users and associated items at a high speed to enhance user-based collaborative filtering, and then develop a fast collaborative user model for the social tagging systems. The experimental results based on Delicious dataset show that the proposed model is able to dramatically reduce the processing time cost greater than 90 % and relatively improve the accuracy in comparison with the ordinary user-based collaborative filtering, and is robust for the initial parameter. Most importantly, the proposed model can be conveniently extended by introducing more users' information (e.g., profiles) and practically applied for the large-scale social network systems to enhance the recommending speed without accuracy loss.

Collaborative filtering for brain-computer interaction using transfer learning and active class selection.

PubMed

Wu, Dongrui; Lance, Brent J; Parsons, Thomas D

2013-01-01

Brain-computer interaction (BCI) and physiological computing are terms that refer to using processed neural or physiological signals to influence human interaction with computers, environment, and each other. A major challenge in developing these systems arises from the large individual differences typically seen in the neural/physiological responses. As a result, many researchers use individually-trained recognition algorithms to process this data. In order to minimize time, cost, and barriers to use, there is a need to minimize the amount of individual training data required, or equivalently, to increase the recognition accuracy without increasing the number of user-specific training samples. One promising method for achieving this is collaborative filtering, which combines training data from the individual subject with additional training data from other, similar subjects. This paper describes a successful application of a collaborative filtering approach intended for a BCI system. This approach is based on transfer learning (TL), active class selection (ACS), and a mean squared difference user-similarity heuristic. The resulting BCI system uses neural and physiological signals for automatic task difficulty recognition. TL improves the learning performance by combining a small number of user-specific training samples with a large number of auxiliary training samples from other similar subjects. ACS optimally selects the classes to generate user-specific training samples. Experimental results on 18 subjects, using both k nearest neighbors and support vector machine classifiers, demonstrate that the proposed approach can significantly reduce the number of user-specific training data samples. This collaborative filtering approach will also be generalizable to handling individual differences in many other applications that involve human neural or physiological data, such as affective computing.

Collaborative Filtering for Brain-Computer Interaction Using Transfer Learning and Active Class Selection

PubMed Central

Wu, Dongrui; Lance, Brent J.; Parsons, Thomas D.

2013-01-01

Brain-computer interaction (BCI) and physiological computing are terms that refer to using processed neural or physiological signals to influence human interaction with computers, environment, and each other. A major challenge in developing these systems arises from the large individual differences typically seen in the neural/physiological responses. As a result, many researchers use individually-trained recognition algorithms to process this data. In order to minimize time, cost, and barriers to use, there is a need to minimize the amount of individual training data required, or equivalently, to increase the recognition accuracy without increasing the number of user-specific training samples. One promising method for achieving this is collaborative filtering, which combines training data from the individual subject with additional training data from other, similar subjects. This paper describes a successful application of a collaborative filtering approach intended for a BCI system. This approach is based on transfer learning (TL), active class selection (ACS), and a mean squared difference user-similarity heuristic. The resulting BCI system uses neural and physiological signals for automatic task difficulty recognition. TL improves the learning performance by combining a small number of user-specific training samples with a large number of auxiliary training samples from other similar subjects. ACS optimally selects the classes to generate user-specific training samples. Experimental results on 18 subjects, using both nearest neighbors and support vector machine classifiers, demonstrate that the proposed approach can significantly reduce the number of user-specific training data samples. This collaborative filtering approach will also be generalizable to handling individual differences in many other applications that involve human neural or physiological data, such as affective computing. PMID:23437188

COMPUTING THERAPY FOR PRECISION MEDICINE: COLLABORATIVE FILTERING INTEGRATES AND PREDICTS MULTI-ENTITY INTERACTIONS.

PubMed

Regenbogen, Sam; Wilkins, Angela D; Lichtarge, Olivier

2016-01-01

Biomedicine produces copious information it cannot fully exploit. Specifically, there is considerable need to integrate knowledge from disparate studies to discover connections across domains. Here, we used a Collaborative Filtering approach, inspired by online recommendation algorithms, in which non-negative matrix factorization (NMF) predicts interactions among chemicals, genes, and diseases only from pairwise information about their interactions. Our approach, applied to matrices derived from the Comparative Toxicogenomics Database, successfully recovered Chemical-Disease, Chemical-Gene, and Disease-Gene networks in 10-fold cross-validation experiments. Additionally, we could predict each of these interaction matrices from the other two. Integrating all three CTD interaction matrices with NMF led to good predictions of STRING, an independent, external network of protein-protein interactions. Finally, this approach could integrate the CTD and STRING interaction data to improve Chemical-Gene cross-validation performance significantly, and, in a time-stamped study, it predicted information added to CTD after a given date, using only data prior to that date. We conclude that collaborative filtering can integrate information across multiple types of biological entities, and that as a first step towards precision medicine it can compute drug repurposing hypotheses.

COMPUTING THERAPY FOR PRECISION MEDICINE: COLLABORATIVE FILTERING INTEGRATES AND PREDICTS MULTI-ENTITY INTERACTIONS

PubMed Central

REGENBOGEN, SAM; WILKINS, ANGELA D.; LICHTARGE, OLIVIER

2015-01-01

Biomedicine produces copious information it cannot fully exploit. Specifically, there is considerable need to integrate knowledge from disparate studies to discover connections across domains. Here, we used a Collaborative Filtering approach, inspired by online recommendation algorithms, in which non-negative matrix factorization (NMF) predicts interactions among chemicals, genes, and diseases only from pairwise information about their interactions. Our approach, applied to matrices derived from the Comparative Toxicogenomics Database, successfully recovered Chemical-Disease, Chemical-Gene, and Disease-Gene networks in 10-fold cross-validation experiments. Additionally, we could predict each of these interaction matrices from the other two. Integrating all three CTD interaction matrices with NMF led to good predictions of STRING, an independent, external network of protein-protein interactions. Finally, this approach could integrate the CTD and STRING interaction data to improve Chemical-Gene cross-validation performance significantly, and, in a time-stamped study, it predicted information added to CTD after a given date, using only data prior to that date. We conclude that collaborative filtering can integrate information across multiple types of biological entities, and that as a first step towards precision medicine it can compute drug repurposing hypotheses. PMID:26776170

Importance of Personalized Health-Care Models: A Case Study in Activity Recognition.

PubMed

Zdravevski, Eftim; Lameski, Petre; Trajkovik, Vladimir; Pombo, Nuno; Garcia, Nuno

2018-01-01

Novel information and communication technologies create possibilities to change the future of health care. Ambient Assisted Living (AAL) is seen as a promising supplement of the current care models. The main goal of AAL solutions is to apply ambient intelligence technologies to enable elderly people to continue to live in their preferred environments. Applying trained models from health data is challenging because the personalized environments could differ significantly than the ones which provided training data. This paper investigates the effects on activity recognition accuracy using single accelerometer of personalized models compared to models built on general population. In addition, we propose a collaborative filtering based approach which provides balance between fully personalized models and generic models. The results show that the accuracy could be improved to 95% with fully personalized models, and up to 91.6% with collaborative filtering based models, which is significantly better than common models that exhibit accuracy of 85.1%. The collaborative filtering approach seems to provide highly personalized models with substantial accuracy, while overcoming the cold start problem that is common for fully personalized models.

Geometric Models for Collaborative Search and Filtering

ERIC Educational Resources Information Center

Bitton, Ephrat

2011-01-01

This dissertation explores the use of geometric and graphical models for a variety of information search and filtering applications. These models serve to provide an intuitive understanding of the problem domains and as well as computational efficiencies to our solution approaches. We begin by considering a search and rescue scenario where both…

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

Information filtering based on transferring similarity.

PubMed

Sun, Duo; Zhou, Tao; Liu, Jian-Guo; Liu, Run-Ran; Jia, Chun-Xiao; Wang, Bing-Hong

2009-07-01

In this Brief Report, we propose an index of user similarity, namely, the transferring similarity, which involves all high-order similarities between users. Accordingly, we design a modified collaborative filtering algorithm, which provides remarkably higher accurate predictions than the standard collaborative filtering. More interestingly, we find that the algorithmic performance will approach its optimal value when the parameter, contained in the definition of transferring similarity, gets close to its critical value, before which the series expansion of transferring similarity is convergent and after which it is divergent. Our study is complementary to the one reported in [E. A. Leicht, P. Holme, and M. E. J. Newman, Phys. Rev. E 73, 026120 (2006)], and is relevant to the missing link prediction problem.

Leveraging Collaborative Filtering to Accelerate Rare Disease Diagnosis

PubMed Central

Shen, Feichen; Liu, Sijia; Wang, Yanshan; Wang, Liwei; Afzal, Naveed; Liu, Hongfang

2017-01-01

In the USA, rare diseases are defined as those affecting fewer than 200,000 patients at any given time. Patients with rare diseases are frequently misdiagnosed or undiagnosed which may due to the lack of knowledge and experience of care providers. We hypothesize that patients’ phenotypic information available in electronic medical records (EMR) can be leveraged to accelerate disease diagnosis based on the intuition that providers need to document associated phenotypic information to support the diagnosis decision, especially for rare diseases. In this study, we proposed a collaborative filtering system enriched with natural language processing and semantic techniques to assist rare disease diagnosis based on phenotypic characterization. Specifically, we leveraged four similarity measurements with two neighborhood algorithms on 2010-2015 Mayo Clinic unstructured large patient cohort and evaluated different approaches. Preliminary results demonstrated that the use of collaborative filtering with phenotypic information is able to stratify patients with relatively similar rare diseases. PMID:29854225

Leveraging Collaborative Filtering to Accelerate Rare Disease Diagnosis.

PubMed

Shen, Feichen; Liu, Sijia; Wang, Yanshan; Wang, Liwei; Afzal, Naveed; Liu, Hongfang

2017-01-01

In the USA, rare diseases are defined as those affecting fewer than 200,000 patients at any given time. Patients with rare diseases are frequently misdiagnosed or undiagnosed which may due to the lack of knowledge and experience of care providers. We hypothesize that patients' phenotypic information available in electronic medical records (EMR) can be leveraged to accelerate disease diagnosis based on the intuition that providers need to document associated phenotypic information to support the diagnosis decision, especially for rare diseases. In this study, we proposed a collaborative filtering system enriched with natural language processing and semantic techniques to assist rare disease diagnosis based on phenotypic characterization. Specifically, we leveraged four similarity measurements with two neighborhood algorithms on 2010-2015 Mayo Clinic unstructured large patient cohort and evaluated different approaches. Preliminary results demonstrated that the use of collaborative filtering with phenotypic information is able to stratify patients with relatively similar rare diseases.

A content-boosted collaborative filtering algorithm for personalized training in interpretation of radiological imaging.

PubMed

Lin, Hongli; Yang, Xuedong; Wang, Weisheng

2014-08-01

Devising a method that can select cases based on the performance levels of trainees and the characteristics of cases is essential for developing a personalized training program in radiology education. In this paper, we propose a novel hybrid prediction algorithm called content-boosted collaborative filtering (CBCF) to predict the difficulty level of each case for each trainee. The CBCF utilizes a content-based filtering (CBF) method to enhance existing trainee-case ratings data and then provides final predictions through a collaborative filtering (CF) algorithm. The CBCF algorithm incorporates the advantages of both CBF and CF, while not inheriting the disadvantages of either. The CBCF method is compared with the pure CBF and pure CF approaches using three datasets. The experimental data are then evaluated in terms of the MAE metric. Our experimental results show that the CBCF outperforms the pure CBF and CF methods by 13.33 and 12.17 %, respectively, in terms of prediction precision. This also suggests that the CBCF can be used in the development of personalized training systems in radiology education.

Comparing Information Access Approaches.

ERIC Educational Resources Information Center

Chalmers, Matthew

1999-01-01

Presents a broad view of information access, drawing from philosophy and semiology in constructing a framework for comparative discussion that is used to examine the information representations that underlie four approaches to information access--information retrieval, workflow, collaborative filtering, and the path model. Contains 32 references.…

A PageRank-based reputation model for personalised manufacturing service recommendation

NASA Astrophysics Data System (ADS)

Zhang, W. Y.; Zhang, S.; Guo, S. S.

2017-05-01

The number of manufacturing services for cross-enterprise business collaborations is increasing rapidly because of the explosive growth of Web service technologies. This trend demands intelligent and robust models to address information overload in order to enable efficient discovery of manufacturing services. In this paper, we present a personalised manufacturing service recommendation approach, which combines a PageRank-based reputation model and a collaborative filtering technique in a unified framework for recommending the right manufacturing services to an active service user for supply chain deployment. The novel aspect of this research is adapting the PageRank algorithm to a network of service-oriented multi-echelon supply chain in order to determine both user reputation and service reputation. In addition, it explores the use of these methods in alleviating data sparsity and cold start problems that hinder traditional collaborative filtering techniques. A case study is conducted to validate the practicality and effectiveness of the proposed approach in recommending the right manufacturing services to active service users.

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.

Rotational response of suspended particles to turbulent flow: laboratory and numerical synthesis

NASA Astrophysics Data System (ADS)

Variano, Evan; Zhao, Lihao; Byron, Margaret; Bellani, Gabriele; Tao, Yiheng; Andersson, Helge

2014-11-01

Using laboratory and DNS measurements, we consider how aspherical and inertial particles suspended in a turbulent flow act to ``filter'' the fluid-phase vorticity. We use three approaches to predict the magnitude and structure of this filter. The first approach is based on Buckingham's Pi theorem, which shows a clear result for the relationship between filter strength and particle aspect ratio. Results are less clear for the dependence of filter strength on Stokes number; we briefly discuss some issues in the proper definition of Stokes number for use in this context. The second approach to predicting filter strength is based on a consideration of vorticity and enstrophy spectra in the fluid phase. This method has a useful feature: it can be used to predict the filter a priori, without need for measurements as input. We compare the results of this approach to measurements as a method of validation. The third and final approach to predicting filter strength is from the consideration of torques experienced by particles, and how the ``angular slip'' or ``spin slip'' evolves in an unsteady flow. We show results from our DNS that indicate different flow conditions in which the spin slip is more or less important in setting the particle rotation dynamics. Collaboration made possible by the Peder Sather Center.

Advances in Collaborative Filtering

NASA Astrophysics Data System (ADS)

Koren, Yehuda; Bell, Robert

The collaborative filtering (CF) approach to recommenders has recently enjoyed much interest and progress. The fact that it played a central role within the recently completed Netflix competition has contributed to its popularity. This chapter surveys the recent progress in the field. Matrix factorization techniques, which became a first choice for implementing CF, are described together with recent innovations. We also describe several extensions that bring competitive accuracy into neighborhood methods, which used to dominate the field. The chapter demonstrates how to utilize temporal models and implicit feedback to extend models accuracy. In passing, we include detailed descriptions of some the central methods developed for tackling the challenge of the Netflix Prize competition.

Collaborative filtering to improve navigation of large radiology knowledge resources.

PubMed

Kahn, Charles E

2005-06-01

Collaborative filtering is a knowledge-discovery technique that can help guide readers to items of potential interest based on the experience of prior users. This study sought to determine the impact of collaborative filtering on navigation of a large, Web-based radiology knowledge resource. Collaborative filtering was applied to a collection of 1,168 radiology hypertext documents available via the Internet. An item-based collaborative filtering algorithm identified each document's six most closely related documents based on 248,304 page views in an 18-day period. Documents were amended to include links to their related documents, and use was analyzed over the next 5 days. The mean number of documents viewed per visit increased from 1.57 to 1.74 (P < 0.0001). Collaborative filtering can increase a radiology information resource's utilization and can improve its usefulness and ease of navigation. The technique holds promise for improving navigation of large Internet-based radiology knowledge resources.

Cohort Selection and Management Application Leveraging Standards-based Semantic Interoperability and a Groovy DSL

PubMed Central

Bucur, Anca; van Leeuwen, Jasper; Chen, Njin-Zu; Claerhout, Brecht; de Schepper, Kristof; Perez-Rey, David; Paraiso-Medina, Sergio; Alonso-Calvo, Raul; Mehta, Keyur; Krykwinski, Cyril

2016-01-01

This paper describes a new Cohort Selection application implemented to support streamlining the definition phase of multi-centric clinical research in oncology. Our approach aims at both ease of use and precision in defining the selection filters expressing the characteristics of the desired population. The application leverages our standards-based Semantic Interoperability Solution and a Groovy DSL to provide high expressiveness in the definition of filters and flexibility in their composition into complex selection graphs including splits and merges. Widely-adopted ontologies such as SNOMED-CT are used to represent the semantics of the data and to express concepts in the application filters, facilitating data sharing and collaboration on joint research questions in large communities of clinical users. The application supports patient data exploration and efficient collaboration in multi-site, heterogeneous and distributed data environments. PMID:27570644

A comparative study: classification vs. user-based collaborative filtering for clinical prediction.

PubMed

Hao, Fang; Blair, Rachael Hageman

2016-12-08

Recommender systems have shown tremendous value for the prediction of personalized item recommendations for individuals in a variety of settings (e.g., marketing, e-commerce, etc.). User-based collaborative filtering is a popular recommender system, which leverages an individuals' prior satisfaction with items, as well as the satisfaction of individuals that are "similar". Recently, there have been applications of collaborative filtering based recommender systems for clinical risk prediction. In these applications, individuals represent patients, and items represent clinical data, which includes an outcome. Application of recommender systems to a problem of this type requires the recasting a supervised learning problem as unsupervised. The rationale is that patients with similar clinical features carry a similar disease risk. As the "Big Data" era progresses, it is likely that approaches of this type will be reached for as biomedical data continues to grow in both size and complexity (e.g., electronic health records). In the present study, we set out to understand and assess the performance of recommender systems in a controlled yet realistic setting. User-based collaborative filtering recommender systems are compared to logistic regression and random forests with different types of imputation and varying amounts of missingness on four different publicly available medical data sets: National Health and Nutrition Examination Survey (NHANES, 2011-2012 on Obesity), Study to Understand Prognoses Preferences Outcomes and Risks of Treatment (SUPPORT), chronic kidney disease, and dermatology data. We also examined performance using simulated data with observations that are Missing At Random (MAR) or Missing Completely At Random (MCAR) under various degrees of missingness and levels of class imbalance in the response variable. Our results demonstrate that user-based collaborative filtering is consistently inferior to logistic regression and random forests with different imputations on real and simulated data. The results warrant caution for the collaborative filtering for the purpose of clinical risk prediction when traditional classification is feasible and practical. CF may not be desirable in datasets where classification is an acceptable alternative. We describe some natural applications related to "Big Data" where CF would be preferred and conclude with some insights as to why caution may be warranted in this context.

Social and content aware One-Class recommendation of papers in scientific social networks.

PubMed

Wang, Gang; He, XiRan; Ishuga, Carolyne Isigi

2017-01-01

With the rapid development of information technology, scientific social networks (SSNs) have become the fastest and most convenient way for researchers to communicate with each other. Many published papers are shared via SSNs every day, resulting in the problem of information overload. How to appropriately recommend personalized and highly valuable papers for researchers is becoming more urgent. However, when recommending papers in SSNs, only a small amount of positive instances are available, leaving a vast amount of unlabelled data, in which negative instances and potential unseen positive instances are mixed together, which naturally belongs to One-Class Collaborative Filtering (OCCF) problem. Therefore, considering the extreme data imbalance and data sparsity of this OCCF problem, a hybrid approach of Social and Content aware One-class Recommendation of Papers in SSNs, termed SCORP, is proposed in this study. Unlike previous approaches recommended to address the OCCF problem, social information, which has been proved playing a significant role in performing recommendations in many domains, is applied in both the profiling of content-based filtering and the collaborative filtering to achieve superior recommendations. To verify the effectiveness of the proposed SCORP approach, a real-life dataset from CiteULike was employed. The experimental results demonstrate that the proposed approach is superior to all of the compared approaches, thus providing a more effective method for recommending papers in SSNs.

Social and content aware One-Class recommendation of papers in scientific social networks

PubMed Central

Wang, Gang; He, XiRan

2017-01-01

With the rapid development of information technology, scientific social networks (SSNs) have become the fastest and most convenient way for researchers to communicate with each other. Many published papers are shared via SSNs every day, resulting in the problem of information overload. How to appropriately recommend personalized and highly valuable papers for researchers is becoming more urgent. However, when recommending papers in SSNs, only a small amount of positive instances are available, leaving a vast amount of unlabelled data, in which negative instances and potential unseen positive instances are mixed together, which naturally belongs to One-Class Collaborative Filtering (OCCF) problem. Therefore, considering the extreme data imbalance and data sparsity of this OCCF problem, a hybrid approach of Social and Content aware One-class Recommendation of Papers in SSNs, termed SCORP, is proposed in this study. Unlike previous approaches recommended to address the OCCF problem, social information, which has been proved playing a significant role in performing recommendations in many domains, is applied in both the profiling of content-based filtering and the collaborative filtering to achieve superior recommendations. To verify the effectiveness of the proposed SCORP approach, a real-life dataset from CiteULike was employed. The experimental results demonstrate that the proposed approach is superior to all of the compared approaches, thus providing a more effective method for recommending papers in SSNs. PMID:28771495

Multidimensional student skills with collaborative filtering

NASA Astrophysics Data System (ADS)

Bergner, Yoav; Rayyan, Saif; Seaton, Daniel; Pritchard, David E.

2013-01-01

Despite the fact that a physics course typically culminates in one final grade for the student, many instructors and researchers believe that there are multiple skills that students acquire to achieve mastery. Assessment validation and data analysis in general may thus benefit from extension to multidimensional ability. This paper introduces an approach for model determination and dimensionality analysis using collaborative filtering (CF), which is related to factor analysis and item response theory (IRT). Model selection is guided by machine learning perspectives, seeking to maximize the accuracy in predicting which students will answer which items correctly. We apply the CF to response data for the Mechanics Baseline Test and combine the results with prior analysis using unidimensional IRT.

A Personalized QoS Prediction Approach for CPS Service Recommendation Based on Reputation and Location-Aware Collaborative Filtering.

PubMed

Kuang, Li; Yu, Long; Huang, Lan; Wang, Yin; Ma, Pengju; Li, Chuanbin; Zhu, Yujia

2018-05-14

With the rapid development of cyber-physical systems (CPS), building cyber-physical systems with high quality of service (QoS) has become an urgent requirement in both academia and industry. During the procedure of building Cyber-physical systems, it has been found that a large number of functionally equivalent services exist, so it becomes an urgent task to recommend suitable services from the large number of services available in CPS. However, since it is time-consuming, and even impractical, for a single user to invoke all of the services in CPS to experience their QoS, a robust QoS prediction method is needed to predict unknown QoS values. A commonly used method in QoS prediction is collaborative filtering, however, it is hard to deal with the data sparsity and cold start problem, and meanwhile most of the existing methods ignore the data credibility issue. Thence, in order to solve both of these challenging problems, in this paper, we design a framework of QoS prediction for CPS services, and propose a personalized QoS prediction approach based on reputation and location-aware collaborative filtering. Our approach first calculates the reputation of users by using the Dirichlet probability distribution, so as to identify untrusted users and process their unreliable data, and then it digs out the geographic neighborhood in three levels to improve the similarity calculation of users and services. Finally, the data from geographical neighbors of users and services are fused to predict the unknown QoS values. The experiments using real datasets show that our proposed approach outperforms other existing methods in terms of accuracy, efficiency, and robustness.

A Personalized QoS Prediction Approach for CPS Service Recommendation Based on Reputation and Location-Aware Collaborative Filtering

PubMed Central

Huang, Lan; Wang, Yin; Ma, Pengju; Li, Chuanbin; Zhu, Yujia

2018-01-01

With the rapid development of cyber-physical systems (CPS), building cyber-physical systems with high quality of service (QoS) has become an urgent requirement in both academia and industry. During the procedure of building Cyber-physical systems, it has been found that a large number of functionally equivalent services exist, so it becomes an urgent task to recommend suitable services from the large number of services available in CPS. However, since it is time-consuming, and even impractical, for a single user to invoke all of the services in CPS to experience their QoS, a robust QoS prediction method is needed to predict unknown QoS values. A commonly used method in QoS prediction is collaborative filtering, however, it is hard to deal with the data sparsity and cold start problem, and meanwhile most of the existing methods ignore the data credibility issue. Thence, in order to solve both of these challenging problems, in this paper, we design a framework of QoS prediction for CPS services, and propose a personalized QoS prediction approach based on reputation and location-aware collaborative filtering. Our approach first calculates the reputation of users by using the Dirichlet probability distribution, so as to identify untrusted users and process their unreliable data, and then it digs out the geographic neighborhood in three levels to improve the similarity calculation of users and services. Finally, the data from geographical neighbors of users and services are fused to predict the unknown QoS values. The experiments using real datasets show that our proposed approach outperforms other existing methods in terms of accuracy, efficiency, and robustness. PMID:29757995

Hybrid context aware recommender systems

NASA Astrophysics Data System (ADS)

Jain, Rajshree; Tyagi, Jaya; Singh, Sandeep Kumar; Alam, Taj

2017-10-01

Recommender systems and context awareness is currently a vital field of research. Most hybrid recommendation systems implement content based and collaborative filtering techniques whereas this work combines context and collaborative filtering. The paper presents a hybrid context aware recommender system for books and movies that gives recommendations based on the user context as well as user or item similarity. It also addresses the issue of dimensionality reduction using weighted pre filtering based on dynamically entered user context and preference of context. This unique step helps to reduce the size of dataset for collaborative filtering. Bias subtracted collaborative filtering is used so as to consider the relative rating of a particular user and not the absolute values. Cosine similarity is used as a metric to determine the similarity between users or items. The unknown ratings are calculated and evaluated using MSE (Mean Squared Error) in test and train datasets. The overall process of recommendation has helped to personalize recommendations and give more accurate results with reduced complexity in collaborative filtering.

What Do You Recommend? Implementation and Analyses of Collaborative Information Filtering of Web Resources for Education.

ERIC Educational Resources Information Center

Recker, Mimi M.; Walker, Andrew; Lawless, Kimberly

2003-01-01

Examines results from one pilot study and two empirical studies of a collaborative filtering system applied in higher education settings. Explains the use of collaborative filtering in electronic commerce and suggests it can be adapted to education to help find useful Web resources and to bring people together with similar interests and beliefs.…

A Performance Weighted Collaborative Filtering algorithm for personalized radiology education.

PubMed

Lin, Hongli; Yang, Xuedong; Wang, Weisheng; Luo, Jiawei

2014-10-01

Devising an accurate prediction algorithm that can predict the difficulty level of cases for individuals and then selects suitable cases for them is essential to the development of a personalized training system. In this paper, we propose a novel approach, called Performance Weighted Collaborative Filtering (PWCF), to predict the difficulty level of each case for individuals. The main idea of PWCF is to assign an optimal weight to each rating used for predicting the difficulty level of a target case for a trainee, rather than using an equal weight for all ratings as in traditional collaborative filtering methods. The assigned weight is a function of the performance level of the trainee at which the rating was made. The PWCF method and the traditional method are compared using two datasets. The experimental data are then evaluated by means of the MAE metric. Our experimental results show that PWCF outperforms the traditional methods by 8.12% and 17.05%, respectively, over the two datasets, in terms of prediction precision. This suggests that PWCF is a viable method for the development of personalized training systems in radiology education. Copyright © 2014. Published by Elsevier Inc.

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).…

A highly efficient approach to protein interactome mapping based on collaborative filtering framework.

PubMed

Luo, Xin; You, Zhuhong; Zhou, Mengchu; Li, Shuai; Leung, Hareton; Xia, Yunni; Zhu, Qingsheng

2015-01-09

The comprehensive mapping of protein-protein interactions (PPIs) is highly desired for one to gain deep insights into both fundamental cell biology processes and the pathology of diseases. Finely-set small-scale experiments are not only very expensive but also inefficient to identify numerous interactomes despite their high accuracy. High-throughput screening techniques enable efficient identification of PPIs; yet the desire to further extract useful knowledge from these data leads to the problem of binary interactome mapping. Network topology-based approaches prove to be highly efficient in addressing this problem; however, their performance deteriorates significantly on sparse putative PPI networks. Motivated by the success of collaborative filtering (CF)-based approaches to the problem of personalized-recommendation on large, sparse rating matrices, this work aims at implementing a highly efficient CF-based approach to binary interactome mapping. To achieve this, we first propose a CF framework for it. Under this framework, we model the given data into an interactome weight matrix, where the feature-vectors of involved proteins are extracted. With them, we design the rescaled cosine coefficient to model the inter-neighborhood similarity among involved proteins, for taking the mapping process. Experimental results on three large, sparse datasets demonstrate that the proposed approach outperforms several sophisticated topology-based approaches significantly.

A Highly Efficient Approach to Protein Interactome Mapping Based on Collaborative Filtering Framework

PubMed Central

Luo, Xin; You, Zhuhong; Zhou, Mengchu; Li, Shuai; Leung, Hareton; Xia, Yunni; Zhu, Qingsheng

2015-01-01

The comprehensive mapping of protein-protein interactions (PPIs) is highly desired for one to gain deep insights into both fundamental cell biology processes and the pathology of diseases. Finely-set small-scale experiments are not only very expensive but also inefficient to identify numerous interactomes despite their high accuracy. High-throughput screening techniques enable efficient identification of PPIs; yet the desire to further extract useful knowledge from these data leads to the problem of binary interactome mapping. Network topology-based approaches prove to be highly efficient in addressing this problem; however, their performance deteriorates significantly on sparse putative PPI networks. Motivated by the success of collaborative filtering (CF)-based approaches to the problem of personalized-recommendation on large, sparse rating matrices, this work aims at implementing a highly efficient CF-based approach to binary interactome mapping. To achieve this, we first propose a CF framework for it. Under this framework, we model the given data into an interactome weight matrix, where the feature-vectors of involved proteins are extracted. With them, we design the rescaled cosine coefficient to model the inter-neighborhood similarity among involved proteins, for taking the mapping process. Experimental results on three large, sparse datasets demonstrate that the proposed approach outperforms several sophisticated topology-based approaches significantly. PMID:25572661

A Highly Efficient Approach to Protein Interactome Mapping Based on Collaborative Filtering Framework

NASA Astrophysics Data System (ADS)

Luo, Xin; You, Zhuhong; Zhou, Mengchu; Li, Shuai; Leung, Hareton; Xia, Yunni; Zhu, Qingsheng

2015-01-01

The comprehensive mapping of protein-protein interactions (PPIs) is highly desired for one to gain deep insights into both fundamental cell biology processes and the pathology of diseases. Finely-set small-scale experiments are not only very expensive but also inefficient to identify numerous interactomes despite their high accuracy. High-throughput screening techniques enable efficient identification of PPIs; yet the desire to further extract useful knowledge from these data leads to the problem of binary interactome mapping. Network topology-based approaches prove to be highly efficient in addressing this problem; however, their performance deteriorates significantly on sparse putative PPI networks. Motivated by the success of collaborative filtering (CF)-based approaches to the problem of personalized-recommendation on large, sparse rating matrices, this work aims at implementing a highly efficient CF-based approach to binary interactome mapping. To achieve this, we first propose a CF framework for it. Under this framework, we model the given data into an interactome weight matrix, where the feature-vectors of involved proteins are extracted. With them, we design the rescaled cosine coefficient to model the inter-neighborhood similarity among involved proteins, for taking the mapping process. Experimental results on three large, sparse datasets demonstrate that the proposed approach outperforms several sophisticated topology-based approaches significantly.

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…

Probability-based collaborative filtering model for predicting gene-disease associations.

PubMed

Zeng, Xiangxiang; Ding, Ningxiang; Rodríguez-Patón, Alfonso; Zou, Quan

2017-12-28

Accurately predicting pathogenic human genes has been challenging in recent research. Considering extensive gene-disease data verified by biological experiments, we can apply computational methods to perform accurate predictions with reduced time and expenses. We propose a probability-based collaborative filtering model (PCFM) to predict pathogenic human genes. Several kinds of data sets, containing data of humans and data of other nonhuman species, are integrated in our model. Firstly, on the basis of a typical latent factorization model, we propose model I with an average heterogeneous regularization. Secondly, we develop modified model II with personal heterogeneous regularization to enhance the accuracy of aforementioned models. In this model, vector space similarity or Pearson correlation coefficient metrics and data on related species are also used. We compared the results of PCFM with the results of four state-of-arts approaches. The results show that PCFM performs better than other advanced approaches. PCFM model can be leveraged for predictions of disease genes, especially for new human genes or diseases with no known relationships.

A collaborative filtering recommendation algorithm based on weighted SimRank and social trust

NASA Astrophysics Data System (ADS)

Su, Chang; Zhang, Butao

2017-05-01

Collaborative filtering is one of the most widely used recommendation technologies, but the data sparsity and cold start problem of collaborative filtering algorithms are difficult to solve effectively. In order to alleviate the problem of data sparsity in collaborative filtering algorithm, firstly, a weighted improved SimRank algorithm is proposed to compute the rating similarity between users in rating data set. The improved SimRank can find more nearest neighbors for target users according to the transmissibility of rating similarity. Then, we build trust network and introduce the calculation of trust degree in the trust relationship data set. Finally, we combine rating similarity and trust to build a comprehensive similarity in order to find more appropriate nearest neighbors for target user. Experimental results show that the algorithm proposed in this paper improves the recommendation precision of the Collaborative algorithm effectively.

A collaborative filtering approach for protein-protein docking scoring functions.

PubMed

Bourquard, Thomas; Bernauer, Julie; Azé, Jérôme; Poupon, Anne

2011-04-22

A protein-protein docking procedure traditionally consists in two successive tasks: a search algorithm generates a large number of candidate conformations mimicking the complex existing in vivo between two proteins, and a scoring function is used to rank them in order to extract a native-like one. We have already shown that using Voronoi constructions and a well chosen set of parameters, an accurate scoring function could be designed and optimized. However to be able to perform large-scale in silico exploration of the interactome, a near-native solution has to be found in the ten best-ranked solutions. This cannot yet be guaranteed by any of the existing scoring functions. In this work, we introduce a new procedure for conformation ranking. We previously developed a set of scoring functions where learning was performed using a genetic algorithm. These functions were used to assign a rank to each possible conformation. We now have a refined rank using different classifiers (decision trees, rules and support vector machines) in a collaborative filtering scheme. The scoring function newly obtained is evaluated using 10 fold cross-validation, and compared to the functions obtained using either genetic algorithms or collaborative filtering taken separately. This new approach was successfully applied to the CAPRI scoring ensembles. We show that for 10 targets out of 12, we are able to find a near-native conformation in the 10 best ranked solutions. Moreover, for 6 of them, the near-native conformation selected is of high accuracy. Finally, we show that this function dramatically enriches the 100 best-ranking conformations in near-native structures.

A Collaborative Filtering Approach for Protein-Protein Docking Scoring Functions

PubMed Central

Bourquard, Thomas; Bernauer, Julie; Azé, Jérôme; Poupon, Anne

2011-01-01

A protein-protein docking procedure traditionally consists in two successive tasks: a search algorithm generates a large number of candidate conformations mimicking the complex existing in vivo between two proteins, and a scoring function is used to rank them in order to extract a native-like one. We have already shown that using Voronoi constructions and a well chosen set of parameters, an accurate scoring function could be designed and optimized. However to be able to perform large-scale in silico exploration of the interactome, a near-native solution has to be found in the ten best-ranked solutions. This cannot yet be guaranteed by any of the existing scoring functions. In this work, we introduce a new procedure for conformation ranking. We previously developed a set of scoring functions where learning was performed using a genetic algorithm. These functions were used to assign a rank to each possible conformation. We now have a refined rank using different classifiers (decision trees, rules and support vector machines) in a collaborative filtering scheme. The scoring function newly obtained is evaluated using 10 fold cross-validation, and compared to the functions obtained using either genetic algorithms or collaborative filtering taken separately. This new approach was successfully applied to the CAPRI scoring ensembles. We show that for 10 targets out of 12, we are able to find a near-native conformation in the 10 best ranked solutions. Moreover, for 6 of them, the near-native conformation selected is of high accuracy. Finally, we show that this function dramatically enriches the 100 best-ranking conformations in near-native structures. PMID:21526112

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.

Integrated approach for automatic target recognition using a network of collaborative sensors.

PubMed

Mahalanobis, Abhijit; Van Nevel, Alan

2006-10-01

We introduce what is believed to be a novel concept by which several sensors with automatic target recognition (ATR) capability collaborate to recognize objects. Such an approach would be suitable for netted systems in which the sensors and platforms can coordinate to optimize end-to-end performance. We use correlation filtering techniques to facilitate the development of the concept, although other ATR algorithms may be easily substituted. Essentially, a self-configuring geometry of netted platforms is proposed that positions the sensors optimally with respect to each other, and takes into account the interactions among the sensor, the recognition algorithms, and the classes of the objects to be recognized. We show how such a paradigm optimizes overall performance, and illustrate the collaborative ATR scheme for recognizing targets in synthetic aperture radar imagery by using viewing position as a sensor parameter.

Walking on a user similarity network towards personalized recommendations.

PubMed

Gan, Mingxin

2014-01-01

Personalized recommender systems have been receiving more and more attention in addressing the serious problem of information overload accompanying the rapid evolution of the world-wide-web. Although traditional collaborative filtering approaches based on similarities between users have achieved remarkable success, it has been shown that the existence of popular objects may adversely influence the correct scoring of candidate objects, which lead to unreasonable recommendation results. Meanwhile, recent advances have demonstrated that approaches based on diffusion and random walk processes exhibit superior performance over collaborative filtering methods in both the recommendation accuracy and diversity. Building on these results, we adopt three strategies (power-law adjustment, nearest neighbor, and threshold filtration) to adjust a user similarity network from user similarity scores calculated on historical data, and then propose a random walk with restart model on the constructed network to achieve personalized recommendations. We perform cross-validation experiments on two real data sets (MovieLens and Netflix) and compare the performance of our method against the existing state-of-the-art methods. Results show that our method outperforms existing methods in not only recommendation accuracy and diversity, but also retrieval performance.

A collaborative approach for research paper recommender system.

PubMed

Haruna, Khalid; Akmar Ismail, Maizatul; Damiasih, Damiasih; Sutopo, Joko; Herawan, Tutut

2017-01-01

Research paper recommenders emerged over the last decade to ease finding publications relating to researchers' area of interest. The challenge was not just to provide researchers with very rich publications at any time, any place and in any form but to also offer the right publication to the right researcher in the right way. Several approaches exist in handling paper recommender systems. However, these approaches assumed the availability of the whole contents of the recommending papers to be freely accessible, which is not always true due to factors such as copyright restrictions. This paper presents a collaborative approach for research paper recommender system. By leveraging the advantages of collaborative filtering approach, we utilize the publicly available contextual metadata to infer the hidden associations that exist between research papers in order to personalize recommendations. The novelty of our proposed approach is that it provides personalized recommendations regardless of the research field and regardless of the user's expertise. Using a publicly available dataset, our proposed approach has recorded a significant improvement over other baseline methods in measuring both the overall performance and the ability to return relevant and useful publications at the top of the recommendation list.

A collaborative approach for research paper recommender system

PubMed Central

Akmar Ismail, Maizatul; Damiasih, Damiasih; Sutopo, Joko; Herawan, Tutut

2017-01-01

Research paper recommenders emerged over the last decade to ease finding publications relating to researchers’ area of interest. The challenge was not just to provide researchers with very rich publications at any time, any place and in any form but to also offer the right publication to the right researcher in the right way. Several approaches exist in handling paper recommender systems. However, these approaches assumed the availability of the whole contents of the recommending papers to be freely accessible, which is not always true due to factors such as copyright restrictions. This paper presents a collaborative approach for research paper recommender system. By leveraging the advantages of collaborative filtering approach, we utilize the publicly available contextual metadata to infer the hidden associations that exist between research papers in order to personalize recommendations. The novelty of our proposed approach is that it provides personalized recommendations regardless of the research field and regardless of the user’s expertise. Using a publicly available dataset, our proposed approach has recorded a significant improvement over other baseline methods in measuring both the overall performance and the ability to return relevant and useful publications at the top of the recommendation list. PMID:28981512

Collaborative filtering recommendation model based on fuzzy clustering algorithm

NASA Astrophysics Data System (ADS)

Yang, Ye; Zhang, Yunhua

2018-05-01

As one of the most widely used algorithms in recommender systems, collaborative filtering algorithm faces two serious problems, which are the sparsity of data and poor recommendation effect in big data environment. In traditional clustering analysis, the object is strictly divided into several classes and the boundary of this division is very clear. However, for most objects in real life, there is no strict definition of their forms and attributes of their class. Concerning the problems above, this paper proposes to improve the traditional collaborative filtering model through the hybrid optimization of implicit semantic algorithm and fuzzy clustering algorithm, meanwhile, cooperating with collaborative filtering algorithm. In this paper, the fuzzy clustering algorithm is introduced to fuzzy clustering the information of project attribute, which makes the project belong to different project categories with different membership degrees, and increases the density of data, effectively reduces the sparsity of data, and solves the problem of low accuracy which is resulted from the inaccuracy of similarity calculation. Finally, this paper carries out empirical analysis on the MovieLens dataset, and compares it with the traditional user-based collaborative filtering algorithm. The proposed algorithm has greatly improved the recommendation accuracy.

Filtering data from the collaborative initial glaucoma treatment study for improved identification of glaucoma progression.

PubMed

Schell, Greggory J; Lavieri, Mariel S; Stein, Joshua D; Musch, David C

2013-12-21

Open-angle glaucoma (OAG) is a prevalent, degenerate ocular disease which can lead to blindness without proper clinical management. The tests used to assess disease progression are susceptible to process and measurement noise. The aim of this study was to develop a methodology which accounts for the inherent noise in the data and improve significant disease progression identification. Longitudinal observations from the Collaborative Initial Glaucoma Treatment Study (CIGTS) were used to parameterize and validate a Kalman filter model and logistic regression function. The Kalman filter estimates the true value of biomarkers associated with OAG and forecasts future values of these variables. We develop two logistic regression models via generalized estimating equations (GEE) for calculating the probability of experiencing significant OAG progression: one model based on the raw measurements from CIGTS and another model based on the Kalman filter estimates of the CIGTS data. Receiver operating characteristic (ROC) curves and associated area under the ROC curve (AUC) estimates are calculated using cross-fold validation. The logistic regression model developed using Kalman filter estimates as data input achieves higher sensitivity and specificity than the model developed using raw measurements. The mean AUC for the Kalman filter-based model is 0.961 while the mean AUC for the raw measurements model is 0.889. Hence, using the probability function generated via Kalman filter estimates and GEE for logistic regression, we are able to more accurately classify patients and instances as experiencing significant OAG progression. A Kalman filter approach for estimating the true value of OAG biomarkers resulted in data input which improved the accuracy of a logistic regression classification model compared to a model using raw measurements as input. This methodology accounts for process and measurement noise to enable improved discrimination between progression and nonprogression in chronic diseases.

Improved collaborative filtering recommendation algorithm of similarity measure

NASA Astrophysics Data System (ADS)

Zhang, Baofu; Yuan, Baoping

2017-05-01

The Collaborative filtering recommendation algorithm is one of the most widely used recommendation algorithm in personalized recommender systems. The key is to find the nearest neighbor set of the active user by using similarity measure. However, the methods of traditional similarity measure mainly focus on the similarity of user common rating items, but ignore the relationship between the user common rating items and all items the user rates. And because rating matrix is very sparse, traditional collaborative filtering recommendation algorithm is not high efficiency. In order to obtain better accuracy, based on the consideration of common preference between users, the difference of rating scale and score of common items, this paper presents an improved similarity measure method, and based on this method, a collaborative filtering recommendation algorithm based on similarity improvement is proposed. Experimental results show that the algorithm can effectively improve the quality of recommendation, thus alleviate the impact of data sparseness.

A Hybrid Approach using Collaborative filtering and Content based Filtering for Recommender System

NASA Astrophysics Data System (ADS)

Geetha, G.; Safa, M.; Fancy, C.; Saranya, D.

2018-04-01

In today’s digital world, it has become an irksome task to find the content of one's liking in an endless variety of content that are being consumed like books, videos, articles, movies, etc. On the other hand there has been an emerging growth among the digital content providers who want to engage as many users on their service as possible for the maximum time. This gave birth to the recommender system comes wherein the content providers recommend users the content according to the users’ taste and liking. In this paper we have proposed a movie recommendation system. A movie recommendation is important in our social life due to its features such as suggesting a set of movies to users based on their interest, or the popularities of the movies. In this paper we are proposing a movie recommendation system that has the ability to recommend movies to a new user as well as the other existing users. It mines movie databases to collect all the important information, such as, popularity and attractiveness, which are required for recommendation. We use content-based and collaborative filtering and also hybrid filtering, which is a combination of the results of these two techniques, to construct a system that provides more precise recommendations concerning movies.

Dual linear structured support vector machine tracking method via scale correlation filter

NASA Astrophysics Data System (ADS)

Li, Weisheng; Chen, Yanquan; Xiao, Bin; Feng, Chen

2018-01-01

Adaptive tracking-by-detection methods based on structured support vector machine (SVM) performed well on recent visual tracking benchmarks. However, these methods did not adopt an effective strategy of object scale estimation, which limits the overall tracking performance. We present a tracking method based on a dual linear structured support vector machine (DLSSVM) with a discriminative scale correlation filter. The collaborative tracker comprised of a DLSSVM model and a scale correlation filter obtains good results in tracking target position and scale estimation. The fast Fourier transform is applied for detection. Extensive experiments show that our tracking approach outperforms many popular top-ranking trackers. On a benchmark including 100 challenging video sequences, the average precision of the proposed method is 82.8%.

Efficient OCT Image Enhancement Based on Collaborative Shock Filtering

PubMed Central

2018-01-01

Efficient enhancement of noisy optical coherence tomography (OCT) images is a key task for interpreting them correctly. In this paper, to better enhance details and layered structures of a human retina image, we propose a collaborative shock filtering for OCT image denoising and enhancement. Noisy OCT image is first denoised by a collaborative filtering method with new similarity measure, and then the denoised image is sharpened by a shock-type filtering for edge and detail enhancement. For dim OCT images, in order to improve image contrast for the detection of tiny lesions, a gamma transformation is first used to enhance the images within proper gray levels. The proposed method integrating image smoothing and sharpening simultaneously obtains better visual results in experiments. PMID:29599954

Efficient OCT Image Enhancement Based on Collaborative Shock Filtering.

PubMed

Liu, Guohua; Wang, Ziyu; Mu, Guoying; Li, Peijin

2018-01-01

Efficient enhancement of noisy optical coherence tomography (OCT) images is a key task for interpreting them correctly. In this paper, to better enhance details and layered structures of a human retina image, we propose a collaborative shock filtering for OCT image denoising and enhancement. Noisy OCT image is first denoised by a collaborative filtering method with new similarity measure, and then the denoised image is sharpened by a shock-type filtering for edge and detail enhancement. For dim OCT images, in order to improve image contrast for the detection of tiny lesions, a gamma transformation is first used to enhance the images within proper gray levels. The proposed method integrating image smoothing and sharpening simultaneously obtains better visual results in experiments.

A filter-mediated communication model for design collaboration in building construction.

PubMed

Lee, Jaewook; Jeong, Yongwook; Oh, Minho; Hong, Seung Wan

2014-01-01

Multidisciplinary collaboration is an important aspect of modern engineering activities, arising from the growing complexity of artifacts whose design and construction require knowledge and skills that exceed the capacities of any one professional. However, current collaboration in the architecture, engineering, and construction industries often fails due to lack of shared understanding between different participants and limitations of their supporting tools. To achieve a high level of shared understanding, this study proposes a filter-mediated communication model. In the proposed model, participants retain their own data in the form most appropriate for their needs with domain-specific filters that transform the neutral representations into semantically rich ones, as needed by the participants. Conversely, the filters can translate semantically rich, domain-specific data into a neutral representation that can be accessed by other domain-specific filters. To validate the feasibility of the proposed model, we computationally implement the filter mechanism and apply it to a hypothetical test case. The result acknowledges that the filter mechanism can let the participants know ahead of time what will be the implications of their proposed actions, as seen from other participants' points of view.

Collaborative Information Filtering in Cooperative Communities.

ERIC Educational Resources Information Center

Okamoto, T.; Miyahara, K.

1998-01-01

The purpose of this study was to develop an information filtering system which collects, classifies, selects, and stores various kinds of information found through the Internet. A collaborative form of information gathering was examined and a model was built and implemented in the Internet information space. (AEF)

A CANDLE for a deeper in vivo insight

PubMed Central

Coupé, Pierrick; Munz, Martin; Manjón, Jose V; Ruthazer, Edward S; Louis Collins, D.

2012-01-01

A new Collaborative Approach for eNhanced Denoising under Low-light Excitation (CANDLE) is introduced for the processing of 3D laser scanning multiphoton microscopy images. CANDLE is designed to be robust for low signal-to-noise ratio (SNR) conditions typically encountered when imaging deep in scattering biological specimens. Based on an optimized non-local means filter involving the comparison of filtered patches, CANDLE locally adapts the amount of smoothing in order to deal with the noise inhomogeneity inherent to laser scanning fluorescence microscopy images. An extensive validation on synthetic data, images acquired on microspheres and in vivo images is presented. These experiments show that the CANDLE filter obtained competitive results compared to a state-of-the-art method and a locally adaptive optimized nonlocal means filter, especially under low SNR conditions (PSNR<8dB). Finally, the deeper imaging capabilities enabled by the proposed filter are demonstrated on deep tissue in vivo images of neurons and fine axonal processes in the Xenopus tadpole brain. PMID:22341767

Walking on a User Similarity Network towards Personalized Recommendations

PubMed Central

Gan, Mingxin

2014-01-01

Personalized recommender systems have been receiving more and more attention in addressing the serious problem of information overload accompanying the rapid evolution of the world-wide-web. Although traditional collaborative filtering approaches based on similarities between users have achieved remarkable success, it has been shown that the existence of popular objects may adversely influence the correct scoring of candidate objects, which lead to unreasonable recommendation results. Meanwhile, recent advances have demonstrated that approaches based on diffusion and random walk processes exhibit superior performance over collaborative filtering methods in both the recommendation accuracy and diversity. Building on these results, we adopt three strategies (power-law adjustment, nearest neighbor, and threshold filtration) to adjust a user similarity network from user similarity scores calculated on historical data, and then propose a random walk with restart model on the constructed network to achieve personalized recommendations. We perform cross-validation experiments on two real data sets (MovieLens and Netflix) and compare the performance of our method against the existing state-of-the-art methods. Results show that our method outperforms existing methods in not only recommendation accuracy and diversity, but also retrieval performance. PMID:25489942

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.

A Filter-Mediated Communication Model for Design Collaboration in Building Construction

PubMed Central

Oh, Minho

2014-01-01

Multidisciplinary collaboration is an important aspect of modern engineering activities, arising from the growing complexity of artifacts whose design and construction require knowledge and skills that exceed the capacities of any one professional. However, current collaboration in the architecture, engineering, and construction industries often fails due to lack of shared understanding between different participants and limitations of their supporting tools. To achieve a high level of shared understanding, this study proposes a filter-mediated communication model. In the proposed model, participants retain their own data in the form most appropriate for their needs with domain-specific filters that transform the neutral representations into semantically rich ones, as needed by the participants. Conversely, the filters can translate semantically rich, domain-specific data into a neutral representation that can be accessed by other domain-specific filters. To validate the feasibility of the proposed model, we computationally implement the filter mechanism and apply it to a hypothetical test case. The result acknowledges that the filter mechanism can let the participants know ahead of time what will be the implications of their proposed actions, as seen from other participants' points of view. PMID:25309958

Image denoising by sparse 3-D transform-domain collaborative filtering.

PubMed

Dabov, Kostadin; Foi, Alessandro; Katkovnik, Vladimir; Egiazarian, Karen

2007-08-01

We propose a novel image denoising strategy based on an enhanced sparse representation in transform domain. The enhancement of the sparsity is achieved by grouping similar 2-D image fragments (e.g., blocks) into 3-D data arrays which we call "groups." Collaborative filtering is a special procedure developed to deal with these 3-D groups. We realize it using the three successive steps: 3-D transformation of a group, shrinkage of the transform spectrum, and inverse 3-D transformation. The result is a 3-D estimate that consists of the jointly filtered grouped image blocks. By attenuating the noise, the collaborative filtering reveals even the finest details shared by grouped blocks and, at the same time, it preserves the essential unique features of each individual block. The filtered blocks are then returned to their original positions. Because these blocks are overlapping, for each pixel, we obtain many different estimates which need to be combined. Aggregation is a particular averaging procedure which is exploited to take advantage of this redundancy. A significant improvement is obtained by a specially developed collaborative Wiener filtering. An algorithm based on this novel denoising strategy and its efficient implementation are presented in full detail; an extension to color-image denoising is also developed. The experimental results demonstrate that this computationally scalable algorithm achieves state-of-the-art denoising performance in terms of both peak signal-to-noise ratio and subjective visual quality.

Collaborative Filtering Based on Sequential Extraction of User-Item Clusters

NASA Astrophysics Data System (ADS)

Honda, Katsuhiro; Notsu, Akira; Ichihashi, Hidetomo

Collaborative filtering is a computational realization of “word-of-mouth” in network community, in which the items prefered by “neighbors” are recommended. This paper proposes a new item-selection model for extracting user-item clusters from rectangular relation matrices, in which mutual relations between users and items are denoted in an alternative process of “liking or not”. A technique for sequential co-cluster extraction from rectangular relational data is given by combining the structural balancing-based user-item clustering method with sequential fuzzy cluster extraction appraoch. Then, the tecunique is applied to the collaborative filtering problem, in which some items may be shared by several user clusters.

A Strategy toward Collaborative Filter Recommended Location Service for Privacy Protection

PubMed Central

Wang, Peng; Yang, Jing; Zhang, Jianpei

2018-01-01

A new collaborative filtered recommendation strategy was proposed for existing privacy and security issues in location services. In this strategy, every user establishes his/her own position profiles according to their daily position data, which is preprocessed using a density clustering method. Then, density prioritization was used to choose similar user groups as service request responders and the neighboring users in the chosen groups recommended appropriate location services using a collaborative filter recommendation algorithm. The two filter algorithms based on position profile similarity and position point similarity measures were designed in the recommendation, respectively. At the same time, the homomorphic encryption method was used to transfer location data for effective protection of privacy and security. A real location dataset was applied to test the proposed strategy and the results showed that the strategy provides better location service and protects users’ privacy. PMID:29751670

A Strategy toward Collaborative Filter Recommended Location Service for Privacy Protection.

PubMed

Wang, Peng; Yang, Jing; Zhang, Jianpei

2018-05-11

A new collaborative filtered recommendation strategy was proposed for existing privacy and security issues in location services. In this strategy, every user establishes his/her own position profiles according to their daily position data, which is preprocessed using a density clustering method. Then, density prioritization was used to choose similar user groups as service request responders and the neighboring users in the chosen groups recommended appropriate location services using a collaborative filter recommendation algorithm. The two filter algorithms based on position profile similarity and position point similarity measures were designed in the recommendation, respectively. At the same time, the homomorphic encryption method was used to transfer location data for effective protection of privacy and security. A real location dataset was applied to test the proposed strategy and the results showed that the strategy provides better location service and protects users' privacy.

Personality Diagnosis for Personalized eHealth Services

NASA Astrophysics Data System (ADS)

Cortellese, Fabio; Nalin, Marco; Morandi, Angelica; Sanna, Alberto; Grasso, Floriana

In this paper we present two different approaches to personality diagnosis, for the provision of innovative personalized services, as used in a case study where diabetic patients were supported in the improvement of physical activity in their daily life. The first approach presented relies on a static clustering of the population, with a specific motivation strategy designed for each cluster. The second approach relies on a dynamic population clustering, making use of recommendation systems and algorithms, like Collaborative Filtering. We discuss pro and cons of each approach and a possible combination of the two, as the most promising solution for this and other personalization services in eHealth.

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

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…

Electrostatic atomization--Experiment, theory and industrial applications

NASA Astrophysics Data System (ADS)

Okuda, H.; Kelly, Arnold J.

1996-05-01

Experimental and theoretical research has been initiated at the Princeton Plasma Physics Laboratory on the electrostatic atomization process in collaboration with Charged Injection Corporation. The goal of this collaboration is to set up a comprehensive research and development program on the electrostatic atomization at the Princeton Plasma Physics Laboratory so that both institutions can benefit from the collaboration. Experimental, theoretical and numerical simulation approaches are used for this purpose. An experiment consisting of a capillary sprayer combined with a quadrupole mass filter and a charge detector was installed at the Electrostatic Atomization Laboratory to study fundamental properties of the charged droplets such as the distribution of charges with respect to the droplet radius. In addition, a numerical simulation model is used to study interaction of beam electrons with atmospheric pressure water vapor, supporting an effort to develop an electrostatic water mist fire-fighting nozzle.

Singular value decomposition for collaborative filtering on a GPU

NASA Astrophysics Data System (ADS)

Kato, Kimikazu; Hosino, Tikara

2010-06-01

A collaborative filtering predicts customers' unknown preferences from known preferences. In a computation of the collaborative filtering, a singular value decomposition (SVD) is needed to reduce the size of a large scale matrix so that the burden for the next phase computation will be decreased. In this application, SVD means a roughly approximated factorization of a given matrix into smaller sized matrices. Webb (a.k.a. Simon Funk) showed an effective algorithm to compute SVD toward a solution of an open competition called "Netflix Prize". The algorithm utilizes an iterative method so that the error of approximation improves in each step of the iteration. We give a GPU version of Webb's algorithm. Our algorithm is implemented in the CUDA and it is shown to be efficient by an experiment.

Ultrasound Image Despeckling Using Stochastic Distance-Based BM3D.

PubMed

Santos, Cid A N; Martins, Diego L N; Mascarenhas, Nelson D A

2017-06-01

Ultrasound image despeckling is an important research field, since it can improve the interpretability of one of the main categories of medical imaging. Many techniques have been tried over the years for ultrasound despeckling, and more recently, a great deal of attention has been focused on patch-based methods, such as non-local means and block-matching collaborative filtering (BM3D). A common idea in these recent methods is the measure of distance between patches, originally proposed as the Euclidean distance, for filtering additive white Gaussian noise. In this paper, we derive new stochastic distances for the Fisher-Tippett distribution, based on well-known statistical divergences, and use them as patch distance measures in a modified version of the BM3D algorithm for despeckling log-compressed ultrasound images. State-of-the-art results in filtering simulated, synthetic, and real ultrasound images confirm the potential of the proposed approach.

Correction of false memory for associated word lists by collaborating groups.

PubMed

Weigold, Arne; Russell, Elizabeth J; Natera, Sara N

2014-01-01

Collaborative inhibition is often observed for both correct and false memories. However, research examining the mechanisms by which collaborative inhibition occurs, such as retrieval disruption, reality monitoring, or group filtering, is lacking. In addition, the creation of the nominal groups (i.e., groups artificially developed by combining individuals' recall) necessary for examining collaborative inhibition do not use statistical best practices. Using the Deese-Roediger-McDermott paradigm, we examined percentages of correct and false memories in individuals, collaborative interactive groups, and correctly created nominal groups, as well as the processes that the collaborative interactive groups used to determine which memories to report. Results showed evidence of the collaborative inhibition effect. In addition, analyses of the collaborative interactive groups' discussions found that these groups wrote down almost all presented words but less than half of nonpresented critical words, after discussing them, with nonpresented critical words being stated to the group with lower confidence and rejected by other group members more often. Overall, our findings indicated support for the group filtering hypothesis.

Detecting Inappropriate Access to Electronic Health Records Using Collaborative Filtering.

PubMed

Menon, Aditya Krishna; Jiang, Xiaoqian; Kim, Jihoon; Vaidya, Jaideep; Ohno-Machado, Lucila

2014-04-01

Many healthcare facilities enforce security on their electronic health records (EHRs) through a corrective mechanism: some staff nominally have almost unrestricted access to the records, but there is a strict ex post facto audit process for inappropriate accesses, i.e., accesses that violate the facility's security and privacy policies. This process is inefficient, as each suspicious access has to be reviewed by a security expert, and is purely retrospective, as it occurs after damage may have been incurred. This motivates automated approaches based on machine learning using historical data. Previous attempts at such a system have successfully applied supervised learning models to this end, such as SVMs and logistic regression. While providing benefits over manual auditing, these approaches ignore the identity of the users and patients involved in a record access. Therefore, they cannot exploit the fact that a patient whose record was previously involved in a violation has an increased risk of being involved in a future violation. Motivated by this, in this paper, we propose a collaborative filtering inspired approach to predicting inappropriate accesses. Our solution integrates both explicit and latent features for staff and patients, the latter acting as a personalized "finger-print" based on historical access patterns. The proposed method, when applied to real EHR access data from two tertiary hospitals and a file-access dataset from Amazon, shows not only significantly improved performance compared to existing methods, but also provides insights as to what indicates an inappropriate access.

Detecting Inappropriate Access to Electronic Health Records Using Collaborative Filtering

PubMed Central

Menon, Aditya Krishna; Jiang, Xiaoqian; Kim, Jihoon; Vaidya, Jaideep; Ohno-Machado, Lucila

2013-01-01

Many healthcare facilities enforce security on their electronic health records (EHRs) through a corrective mechanism: some staff nominally have almost unrestricted access to the records, but there is a strict ex post facto audit process for inappropriate accesses, i.e., accesses that violate the facility’s security and privacy policies. This process is inefficient, as each suspicious access has to be reviewed by a security expert, and is purely retrospective, as it occurs after damage may have been incurred. This motivates automated approaches based on machine learning using historical data. Previous attempts at such a system have successfully applied supervised learning models to this end, such as SVMs and logistic regression. While providing benefits over manual auditing, these approaches ignore the identity of the users and patients involved in a record access. Therefore, they cannot exploit the fact that a patient whose record was previously involved in a violation has an increased risk of being involved in a future violation. Motivated by this, in this paper, we propose a collaborative filtering inspired approach to predicting inappropriate accesses. Our solution integrates both explicit and latent features for staff and patients, the latter acting as a personalized “finger-print” based on historical access patterns. The proposed method, when applied to real EHR access data from two tertiary hospitals and a file-access dataset from Amazon, shows not only significantly improved performance compared to existing methods, but also provides insights as to what indicates an inappropriate access. PMID:24683293

Military Application of Networking by Touch in Collaborative Planning and Tactical Environments

DTIC Science & Technology

2007-09-01

the network. For example, processing, or understanding, four plus pages per second, let alone 1500 pages, far surpasses a normal user’s ability to...discovering rapid, evolutionary approaches for filtering four to 1500 pages per second into knowledgeable forms relevant to the user. Unless we...weapons at the ready when the TL receives a slight vibration in the upper right quadrant of a vest he is wearing. The familiar tactile sensation

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…

Empirical comparison of local structural similarity indices for collaborative-filtering-based recommender systems

NASA Astrophysics Data System (ADS)

Zhang, Qian-Ming; Shang, Ming-Sheng; Zeng, Wei; Chen, Yong; Lü, Linyuan

2010-08-01

Collaborative filtering is one of the most successful recommendation techniques, which can effectively predict the possible future likes of users based on their past preferences. The key problem of this method is how to define the similarity between users. A standard approach is using the correlation between the ratings that two users give to a set of objects, such as Cosine index and Pearson correlation coefficient. However, the costs of computing this kind of indices are relatively high, and thus it is impossible to be applied in the huge-size systems. To solve this problem, in this paper, we introduce six local-structure-based similarity indices and compare their performances with the above two benchmark indices. Experimental results on two data sets demonstrate that the structure-based similarity indices overall outperform the Pearson correlation coefficient. When the data is dense, the structure-based indices can perform competitively good as Cosine index, while with lower computational complexity. Furthermore, when the data is sparse, the structure-based indices give even better results than Cosine index.

The Development of a Microbial Challenge Test with Acholeplasma laidlawii To Rate Mycoplasma-Retentive Filters by Filter Manufacturers.

PubMed

Folmsbee, Martha; Lentine, Kerry Roche; Wright, Christine; Haake, Gerhard; Mcburnie, Leesa; Ashtekar, Dilip; Beck, Brian; Hutchison, Nick; Okhio-Seaman, Laura; Potts, Barbara; Pawar, Vinayak; Windsor, Helena

2014-01-01

Mycoplasma are bacteria that can penetrate 0.2 and 0.22 μm rated sterilizing-grade filters and even some 0.1 μm rated filters. Primary applications for mycoplasma filtration include large scale mammalian and bacterial cell culture media and serum filtration. The Parenteral Drug Association recognized the absence of standard industry test parameters for testing and classifying 0.1 μm rated filters for mycoplasma clearance and formed a task force to formulate consensus test parameters. The task force established some test parameters by common agreement, based upon general industry practices, without the need for additional testing. However, the culture medium and incubation conditions, for generating test mycoplasma cells, varied from filter company to filter company and was recognized as a serious gap by the task force. Standardization of the culture medium and incubation conditions required collaborative testing in both commercial filter company laboratories and in an Independent laboratory (Table I). The use of consensus test parameters will facilitate the ultimate cross-industry goal of standardization of 0.1 μm filter claims for mycoplasma clearance. However, it is still important to recognize filter performance will depend on the actual conditions of use. Therefore end users should consider, using a risk-based approach, whether process-specific evaluation of filter performance may be warranted for their application. Mycoplasma are small bacteria that have the ability to penetrate sterilizing-grade filters. Filtration of large-scale mammalian and bacterial cell culture media is an example of an industry process where effective filtration of mycoplasma is required. The Parenteral Drug Association recognized the absence of industry standard test parameters for evaluating mycoplasma clearance filters by filter manufacturers and formed a task force to formulate such a consensus among manufacturers. The use of standardized test parameters by filter manufacturers, including the preparation of the culture broth, will facilitate the end user's evaluation of the mycoplasma clearance claims provided by filter vendors. However, it is still important to recognize filter performance will depend on the actual conditions of use; therefore end users should consider, using a risk-based approach, whether process-specific evaluation of filter performance may be warranted for their application. © PDA, Inc. 2014.

Improved genome-scale multi-target virtual screening via a novel collaborative filtering approach to cold-start problem

PubMed Central

Lim, Hansaim; Gray, Paul; Xie, Lei; Poleksic, Aleksandar

2016-01-01

Conventional one-drug-one-gene approach has been of limited success in modern drug discovery. Polypharmacology, which focuses on searching for multi-targeted drugs to perturb disease-causing networks instead of designing selective ligands to target individual proteins, has emerged as a new drug discovery paradigm. Although many methods for single-target virtual screening have been developed to improve the efficiency of drug discovery, few of these algorithms are designed for polypharmacology. Here, we present a novel theoretical framework and a corresponding algorithm for genome-scale multi-target virtual screening based on the one-class collaborative filtering technique. Our method overcomes the sparseness of the protein-chemical interaction data by means of interaction matrix weighting and dual regularization from both chemicals and proteins. While the statistical foundation behind our method is general enough to encompass genome-wide drug off-target prediction, the program is specifically tailored to find protein targets for new chemicals with little to no available interaction data. We extensively evaluate our method using a number of the most widely accepted gene-specific and cross-gene family benchmarks and demonstrate that our method outperforms other state-of-the-art algorithms for predicting the interaction of new chemicals with multiple proteins. Thus, the proposed algorithm may provide a powerful tool for multi-target drug design. PMID:27958331

Improved genome-scale multi-target virtual screening via a novel collaborative filtering approach to cold-start problem.

PubMed

Lim, Hansaim; Gray, Paul; Xie, Lei; Poleksic, Aleksandar

2016-12-13

Conventional one-drug-one-gene approach has been of limited success in modern drug discovery. Polypharmacology, which focuses on searching for multi-targeted drugs to perturb disease-causing networks instead of designing selective ligands to target individual proteins, has emerged as a new drug discovery paradigm. Although many methods for single-target virtual screening have been developed to improve the efficiency of drug discovery, few of these algorithms are designed for polypharmacology. Here, we present a novel theoretical framework and a corresponding algorithm for genome-scale multi-target virtual screening based on the one-class collaborative filtering technique. Our method overcomes the sparseness of the protein-chemical interaction data by means of interaction matrix weighting and dual regularization from both chemicals and proteins. While the statistical foundation behind our method is general enough to encompass genome-wide drug off-target prediction, the program is specifically tailored to find protein targets for new chemicals with little to no available interaction data. We extensively evaluate our method using a number of the most widely accepted gene-specific and cross-gene family benchmarks and demonstrate that our method outperforms other state-of-the-art algorithms for predicting the interaction of new chemicals with multiple proteins. Thus, the proposed algorithm may provide a powerful tool for multi-target drug design.

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.

Local gradient Gabor pattern (LGGP) with applications in face recognition, cross-spectral matching, and soft biometrics

NASA Astrophysics Data System (ADS)

Chen, Cunjian; Ross, Arun

2013-05-01

Researchers in face recognition have been using Gabor filters for image representation due to their robustness to complex variations in expression and illumination. Numerous methods have been proposed to model the output of filter responses by employing either local or global descriptors. In this work, we propose a novel but simple approach for encoding Gradient information on Gabor-transformed images to represent the face, which can be used for identity, gender and ethnicity assessment. Extensive experiments on the standard face benchmark FERET (Visible versus Visible), as well as the heterogeneous face dataset HFB (Near-infrared versus Visible), suggest that the matching performance due to the proposed descriptor is comparable against state-of-the-art descriptor-based approaches in face recognition applications. Furthermore, the same feature set is used in the framework of a Collaborative Representation Classification (CRC) scheme for deducing soft biometric traits such as gender and ethnicity from face images in the AR, Morph and CAS-PEAL databases.

Assessing the performance of methodological search filters to improve the efficiency of evidence information retrieval: five literature reviews and a qualitative study.

PubMed

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

2017-11-01

Effective study identification is essential for conducting health research, developing clinical guidance and health policy and supporting health-care decision-making. Methodological search filters (combinations of search terms to capture a specific study design) can assist in searching to achieve this. This project investigated the methods used to assess the performance of methodological search filters, the information that searchers require when choosing search filters and how that information could be better provided. Five literature reviews were undertaken in 2010/11: search filter development and testing; comparison of search filters; decision-making in choosing search filters; diagnostic test accuracy (DTA) study methods; and decision-making in choosing diagnostic tests. We conducted interviews and a questionnaire with experienced searchers to learn what information assists in the choice of search filters and how filters are used. These investigations informed the development of various approaches to gathering and reporting search filter performance data. We acknowledge that there has been a regrettable delay between carrying out the project, including the searches, and the publication of this report, because of serious illness of the principal investigator. The development of filters most frequently involved using a reference standard derived from hand-searching journals. Most filters were validated internally only. Reporting of methods was generally poor. Sensitivity, precision and specificity were the most commonly reported performance measures and were presented in tables. Aspects of DTA study methods are applicable to search filters, particularly in the development of the reference standard. There is limited evidence on how clinicians choose between diagnostic tests. No published literature was found on how searchers select filters. Interviewing and questioning searchers via a questionnaire found that filters were not appropriate for all tasks but were predominantly used to reduce large numbers of retrieved records and to introduce focus. The Inter Technology Appraisal Support Collaboration (InterTASC) Information Specialists' Sub-Group (ISSG) Search Filters Resource was most frequently mentioned by both groups as the resource consulted to select a filter. Randomised controlled trial (RCT) and systematic review filters, in particular the Cochrane RCT and the McMaster Hedges filters, were most frequently mentioned. The majority indicated that they used different filters depending on the requirement for sensitivity or precision. Over half of the respondents used the filters available in databases. Interviewees used various approaches when using and adapting search filters. Respondents suggested that the main factors that would make choosing a filter easier were the availability of critical appraisals and more detailed performance information. Provenance and having the filter available in a central storage location were also important. The questionnaire could have been shorter and could have included more multiple choice questions, and the reviews of filter performance focused on only four study designs. Search filter studies should use a representative reference standard and explicitly report methods and results. Performance measures should be presented systematically and clearly. Searchers find filters useful in certain circumstances but expressed a need for more user-friendly performance information to aid filter choice. We suggest approaches to use, adapt and report search filter performance. Future work could include research around search filters and performance measures for study designs not addressed here, exploration of alternative methods of displaying performance results and numerical synthesis of performance comparison results. The National Institute for Health Research (NIHR) Health Technology Assessment programme and Medical Research Council-NIHR Methodology Research Programme (grant number G0901496).

Assessing the performance of methodological search filters to improve the efficiency of evidence information retrieval: five literature reviews and a qualitative study.

PubMed Central

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

2017-01-01

BACKGROUND Effective study identification is essential for conducting health research, developing clinical guidance and health policy and supporting health-care decision-making. Methodological search filters (combinations of search terms to capture a specific study design) can assist in searching to achieve this. OBJECTIVES This project investigated the methods used to assess the performance of methodological search filters, the information that searchers require when choosing search filters and how that information could be better provided. METHODS Five literature reviews were undertaken in 2010/11: search filter development and testing; comparison of search filters; decision-making in choosing search filters; diagnostic test accuracy (DTA) study methods; and decision-making in choosing diagnostic tests. We conducted interviews and a questionnaire with experienced searchers to learn what information assists in the choice of search filters and how filters are used. These investigations informed the development of various approaches to gathering and reporting search filter performance data. We acknowledge that there has been a regrettable delay between carrying out the project, including the searches, and the publication of this report, because of serious illness of the principal investigator. RESULTS The development of filters most frequently involved using a reference standard derived from hand-searching journals. Most filters were validated internally only. Reporting of methods was generally poor. Sensitivity, precision and specificity were the most commonly reported performance measures and were presented in tables. Aspects of DTA study methods are applicable to search filters, particularly in the development of the reference standard. There is limited evidence on how clinicians choose between diagnostic tests. No published literature was found on how searchers select filters. Interviewing and questioning searchers via a questionnaire found that filters were not appropriate for all tasks but were predominantly used to reduce large numbers of retrieved records and to introduce focus. The Inter Technology Appraisal Support Collaboration (InterTASC) Information Specialists' Sub-Group (ISSG) Search Filters Resource was most frequently mentioned by both groups as the resource consulted to select a filter. Randomised controlled trial (RCT) and systematic review filters, in particular the Cochrane RCT and the McMaster Hedges filters, were most frequently mentioned. The majority indicated that they used different filters depending on the requirement for sensitivity or precision. Over half of the respondents used the filters available in databases. Interviewees used various approaches when using and adapting search filters. Respondents suggested that the main factors that would make choosing a filter easier were the availability of critical appraisals and more detailed performance information. Provenance and having the filter available in a central storage location were also important. LIMITATIONS The questionnaire could have been shorter and could have included more multiple choice questions, and the reviews of filter performance focused on only four study designs. CONCLUSIONS Search filter studies should use a representative reference standard and explicitly report methods and results. Performance measures should be presented systematically and clearly. Searchers find filters useful in certain circumstances but expressed a need for more user-friendly performance information to aid filter choice. We suggest approaches to use, adapt and report search filter performance. Future work could include research around search filters and performance measures for study designs not addressed here, exploration of alternative methods of displaying performance results and numerical synthesis of performance comparison results. FUNDING The National Institute for Health Research (NIHR) Health Technology Assessment programme and Medical Research Council-NIHR Methodology Research Programme (grant number G0901496). PMID:29188764

ITrace: An implicit trust inference method for trust-aware collaborative filtering

NASA Astrophysics Data System (ADS)

He, Xu; Liu, Bin; Chen, Kejia

2018-04-01

The growth of Internet commerce has stimulated the use of collaborative filtering (CF) algorithms as recommender systems. A CF algorithm recommends items of interest to the target user by leveraging the votes given by other similar users. In a standard CF framework, it is assumed that the credibility of every voting user is exactly the same with respect to the target user. This assumption is not satisfied and thus may lead to misleading recommendations in many practical applications. A natural countermeasure is to design a trust-aware CF (TaCF) algorithm, which can take account of the difference in the credibilities of the voting users when performing CF. To this end, this paper presents a trust inference approach, which can predict the implicit trust of the target user on every voting user from a sparse explicit trust matrix. Then an improved CF algorithm termed iTrace is proposed, which takes advantage of both the explicit and the predicted implicit trust to provide recommendations with the CF framework. An empirical evaluation on a public dataset demonstrates that the proposed algorithm provides a significant improvement in recommendation quality in terms of mean absolute error.

Collaborative recall in face-to-face and electronic groups.

PubMed

Ekeocha, Justina Ohaeri; Brennan, Susan E

2008-04-01

When people remember shared experiences, the amount they recall as a collaborating group is less than the amount obtained by pooling their individual memories. We tested the hypothesis that reduced group productivity can be attributed, at least in part, to content filtering, where information is omitted from group products either because individuals fail to retrieve it or choose to withhold it (self-filtering), or because groups reject or fail to incorporate it (group-filtering). Three-person groups viewed a movie clip together and recalled it, first individually, then in face-to-face or electronic groups, and finally individually again. Although both kinds of groups recalled equal amounts, group-filtering occurred more often face-to-face, while self-filtering occurred more often electronically. This suggests that reduced group productivity is due not only to intrapersonal factors stemming from cognitive interference, but also to interpersonal costs of coordinating the group product. Finally, face-to-face group interaction facilitated subsequent individual recall.

Video denoising, deblocking, and enhancement through separable 4-D nonlocal spatiotemporal transforms.

PubMed

Maggioni, Matteo; Boracchi, Giacomo; Foi, Alessandro; Egiazarian, Karen

2012-09-01

We propose a powerful video filtering algorithm that exploits temporal and spatial redundancy characterizing natural video sequences. The algorithm implements the paradigm of nonlocal grouping and collaborative filtering, where a higher dimensional transform-domain representation of the observations is leveraged to enforce sparsity, and thus regularize the data: 3-D spatiotemporal volumes are constructed by tracking blocks along trajectories defined by the motion vectors. Mutually similar volumes are then grouped together by stacking them along an additional fourth dimension, thus producing a 4-D structure, termed group, where different types of data correlation exist along the different dimensions: local correlation along the two dimensions of the blocks, temporal correlation along the motion trajectories, and nonlocal spatial correlation (i.e., self-similarity) along the fourth dimension of the group. Collaborative filtering is then realized by transforming each group through a decorrelating 4-D separable transform and then by shrinkage and inverse transformation. In this way, the collaborative filtering provides estimates for each volume stacked in the group, which are then returned and adaptively aggregated to their original positions in the video. The proposed filtering procedure addresses several video processing applications, such as denoising, deblocking, and enhancement of both grayscale and color data. Experimental results prove the effectiveness of our method in terms of both subjective and objective visual quality, and show that it outperforms the state of the art in video denoising.

Enabling Open Research Data Discovery through a Recommender System

NASA Astrophysics Data System (ADS)

Devaraju, Anusuriya; Jayasinghe, Gaya; Klump, Jens; Hogan, Dominic

2017-04-01

Government agencies, universities, research and nonprofit organizations are increasingly publishing their datasets to promote transparency, induce new research and generate economic value through the development of new products or services. The datasets may be downloaded from various data portals (data repositories) which are general or domain-specific. The Registry of Research Data Repository (re3data.org) lists more than 2500 such data repositories from around the globe. Data portals allow keyword search and faceted navigation to facilitate discovery of research datasets. However, the volume and variety of datasets have made finding relevant datasets more difficult. Common dataset search mechanisms may be time consuming, may produce irrelevant results and are primarily suitable for users who are familiar with the general structure and contents of the respective database. Therefore, we need new approaches to support research data discovery. Recommender systems offer new possibilities for users to find datasets that are relevant to their research interests. This study presents a recommender system developed for the CSIRO Data Access Portal (DAP, http://data.csiro.au). The datasets hosted on the portal are diverse, published by researchers from 13 business units in the organisation. The goal of the study is not to replace the current search mechanisms on the data portal, but rather to extend the data discovery through an exploratory search, in this case by building a recommender system. We adopted a hybrid recommendation approach, comprising content-based filtering and item-item collaborative filtering. The content-based filtering computes similarities between datasets based on metadata such as title, keywords, descriptions, fields of research, location, contributors, etc. The collaborative filtering utilizes user search behaviour and download patterns derived from the server logs to determine similar datasets. Similarities above are then combined with different degrees of importance (weights) to determine the overall data similarity. We determined the similarity weights based on a survey involving 150 users of the portal. The recommender results for a given dataset are accessible programmatically via a RESTful web service. An offline evaluation involving data users demonstrates the ability of the recommender system to discover relevant and 'novel' datasets.

Aggregation Trade Offs in Family Based Recommendations

NASA Astrophysics Data System (ADS)

Berkovsky, Shlomo; Freyne, Jill; Coombe, Mac

Personalized information access tools are frequently based on collaborative filtering recommendation algorithms. Collaborative filtering recommender systems typically suffer from a data sparsity problem, where systems do not have sufficient user data to generate accurate and reliable predictions. Prior research suggested using group-based user data in the collaborative filtering recommendation process to generate group-based predictions and partially resolve the sparsity problem. Although group recommendations are less accurate than personalized recommendations, they are more accurate than general non-personalized recommendations, which are the natural fall back when personalized recommendations cannot be generated. In this work we present initial results of a study that exploits the browsing logs of real families of users gathered in an eHealth portal. The browsing logs allowed us to experimentally compare the accuracy of two group-based recommendation strategies: aggregated group models and aggregated predictions. Our results showed that aggregating individual models into group models resulted in more accurate predictions than aggregating individual predictions into group predictions.

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.

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…

A hybrid algorithm for speckle noise reduction of ultrasound images.

PubMed

Singh, Karamjeet; Ranade, Sukhjeet Kaur; Singh, Chandan

2017-09-01

Medical images are contaminated by multiplicative speckle noise which significantly reduce the contrast of ultrasound images and creates a negative effect on various image interpretation tasks. In this paper, we proposed a hybrid denoising approach which collaborate the both local and nonlocal information in an efficient manner. The proposed hybrid algorithm consist of three stages in which at first stage the use of local statistics in the form of guided filter is used to reduce the effect of speckle noise initially. Then, an improved speckle reducing bilateral filter (SRBF) is developed to further reduce the speckle noise from the medical images. Finally, to reconstruct the diffused edges we have used the efficient post-processing technique which jointly considered the advantages of both bilateral and nonlocal mean (NLM) filter for the attenuation of speckle noise efficiently. The performance of proposed hybrid algorithm is evaluated on synthetic, simulated and real ultrasound images. The experiments conducted on various test images demonstrate that our proposed hybrid approach outperforms the various traditional speckle reduction approaches included recently proposed NLM and optimized Bayesian-based NLM. The results of various quantitative, qualitative measures and by visual inspection of denoise synthetic and real ultrasound images demonstrate that the proposed hybrid algorithm have strong denoising capability and able to preserve the fine image details such as edge of a lesion better than previously developed methods for speckle noise reduction. The denoising and edge preserving capability of hybrid algorithm is far better than existing traditional and recently proposed speckle reduction (SR) filters. The success of proposed algorithm would help in building the lay foundation for inventing the hybrid algorithms for denoising of ultrasound images. Copyright © 2017 Elsevier B.V. All rights reserved.

Comments on "Image denoising by sparse 3-D transform-domain collaborative filtering".

PubMed

Hou, Yingkun; Zhao, Chunxia; Yang, Deyun; Cheng, Yong

2011-01-01

In order to resolve the problem that the denoising performance has a sharp drop when noise standard deviation reaches 40, proposed to replace the wavelet transform by the DCT. In this comment, we argue that this replacement is unnecessary, and that the problem can be solved by adjusting some numerical parameters. We also present this parameter modification approach here. Experimental results demonstrate that the proposed modification achieves better results in terms of both peak signal-to-noise ratio and subjective visual quality than the original method for strong noise.

Graphene-Based Filters and Supercapacitors for Space and Aeronautical Applications

NASA Technical Reports Server (NTRS)

Calle, Carlos I.

2015-01-01

Overview of the capabilities of graphene for selective filters and for energy storage with a general description of the work being done at NASA Kennedy Space Center in collaboration with the University of California Los Angeles for space and aeronautical applications.

A New Adaptive Framework for Collaborative Filtering Prediction

PubMed Central

Almosallam, Ibrahim A.; Shang, Yi

2010-01-01

Collaborative filtering is one of the most successful techniques for recommendation systems and has been used in many commercial services provided by major companies including Amazon, TiVo and Netflix. In this paper we focus on memory-based collaborative filtering (CF). Existing CF techniques work well on dense data but poorly on sparse data. To address this weakness, we propose to use z-scores instead of explicit ratings and introduce a mechanism that adaptively combines global statistics with item-based values based on data density level. We present a new adaptive framework that encapsulates various CF algorithms and the relationships among them. An adaptive CF predictor is developed that can self adapt from user-based to item-based to hybrid methods based on the amount of available ratings. Our experimental results show that the new predictor consistently obtained more accurate predictions than existing CF methods, with the most significant improvement on sparse data sets. When applied to the Netflix Challenge data set, our method performed better than existing CF and singular value decomposition (SVD) methods and achieved 4.67% improvement over Netflix’s system. PMID:21572924

A New Adaptive Framework for Collaborative Filtering Prediction.

PubMed

Almosallam, Ibrahim A; Shang, Yi

2008-06-01

Collaborative filtering is one of the most successful techniques for recommendation systems and has been used in many commercial services provided by major companies including Amazon, TiVo and Netflix. In this paper we focus on memory-based collaborative filtering (CF). Existing CF techniques work well on dense data but poorly on sparse data. To address this weakness, we propose to use z-scores instead of explicit ratings and introduce a mechanism that adaptively combines global statistics with item-based values based on data density level. We present a new adaptive framework that encapsulates various CF algorithms and the relationships among them. An adaptive CF predictor is developed that can self adapt from user-based to item-based to hybrid methods based on the amount of available ratings. Our experimental results show that the new predictor consistently obtained more accurate predictions than existing CF methods, with the most significant improvement on sparse data sets. When applied to the Netflix Challenge data set, our method performed better than existing CF and singular value decomposition (SVD) methods and achieved 4.67% improvement over Netflix's system.

Evaluating Assessment Using N-Dimensional Filtering.

ERIC Educational Resources Information Center

Dron, Jon; Boyne, Chris; Mitchell, Richard

This paper describes the use of the CoFIND (Collaborative Filter in N Dimensions) system to evaluate two assessment styles. CoFIND is a resource database that organizes itself around its users' needs. Learners enter resources, categorize, then rate them using "qualities," aspects of resources which learners find worthwhile, the n…

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.

Hybrid employment recommendation algorithm based on Spark

NASA Astrophysics Data System (ADS)

Li, Zuoquan; Lin, Yubei; Zhang, Xingming

2017-08-01

Aiming at the real-time application of collaborative filtering employment recommendation algorithm (CF), a clustering collaborative filtering recommendation algorithm (CCF) is developed, which applies hierarchical clustering to CF and narrows the query range of neighbour items. In addition, to solve the cold-start problem of content-based recommendation algorithm (CB), a content-based algorithm with users’ information (CBUI) is introduced for job recommendation. Furthermore, a hybrid recommendation algorithm (HRA) which combines CCF and CBUI algorithms is proposed, and implemented on Spark platform. The experimental results show that HRA can overcome the problems of cold start and data sparsity, and achieve good recommendation accuracy and scalability for employment recommendation.

Top 20 Collaborative Internet-Based Science Projects of 1998: Characteristics and Comparisons to Exemplary Science Instruction.

ERIC Educational Resources Information Center

Berg, Craig A.; Jefson, Cristy

This paper utilizes the characteristics of model science instruction to identify exemplary Internet-based science collaborations. The filter for attaining "exemplary" status was based on state and national standards-generating initiatives and the corresponding implications for appropriate student activity in science classrooms. Twenty…

A Cross-Domain Collaborative Filtering Algorithm Based on Feature Construction and Locally Weighted Linear Regression

PubMed Central

Jiang, Feng; Han, Ji-zhong

2018-01-01

Cross-domain collaborative filtering (CDCF) solves the sparsity problem by transferring rating knowledge from auxiliary domains. Obviously, different auxiliary domains have different importance to the target domain. However, previous works cannot evaluate effectively the significance of different auxiliary domains. To overcome this drawback, we propose a cross-domain collaborative filtering algorithm based on Feature Construction and Locally Weighted Linear Regression (FCLWLR). We first construct features in different domains and use these features to represent different auxiliary domains. Thus the weight computation across different domains can be converted as the weight computation across different features. Then we combine the features in the target domain and in the auxiliary domains together and convert the cross-domain recommendation problem into a regression problem. Finally, we employ a Locally Weighted Linear Regression (LWLR) model to solve the regression problem. As LWLR is a nonparametric regression method, it can effectively avoid underfitting or overfitting problem occurring in parametric regression methods. We conduct extensive experiments to show that the proposed FCLWLR algorithm is effective in addressing the data sparsity problem by transferring the useful knowledge from the auxiliary domains, as compared to many state-of-the-art single-domain or cross-domain CF methods. PMID:29623088

A Cross-Domain Collaborative Filtering Algorithm Based on Feature Construction and Locally Weighted Linear Regression.

PubMed

Yu, Xu; Lin, Jun-Yu; Jiang, Feng; Du, Jun-Wei; Han, Ji-Zhong

2018-01-01

Cross-domain collaborative filtering (CDCF) solves the sparsity problem by transferring rating knowledge from auxiliary domains. Obviously, different auxiliary domains have different importance to the target domain. However, previous works cannot evaluate effectively the significance of different auxiliary domains. To overcome this drawback, we propose a cross-domain collaborative filtering algorithm based on Feature Construction and Locally Weighted Linear Regression (FCLWLR). We first construct features in different domains and use these features to represent different auxiliary domains. Thus the weight computation across different domains can be converted as the weight computation across different features. Then we combine the features in the target domain and in the auxiliary domains together and convert the cross-domain recommendation problem into a regression problem. Finally, we employ a Locally Weighted Linear Regression (LWLR) model to solve the regression problem. As LWLR is a nonparametric regression method, it can effectively avoid underfitting or overfitting problem occurring in parametric regression methods. We conduct extensive experiments to show that the proposed FCLWLR algorithm is effective in addressing the data sparsity problem by transferring the useful knowledge from the auxiliary domains, as compared to many state-of-the-art single-domain or cross-domain CF methods.

Recommending personally interested contents by text mining, filtering, and interfaces

DOEpatents

Xu, Songhua

2015-10-27

A personalized content recommendation system includes a client interface device configured to monitor a user's information data stream. A collaborative filter remote from the client interface device generates automated predictions about the interests of the user. A database server stores personal behavioral profiles and user's preferences based on a plurality of monitored past behaviors and an output of the collaborative user personal interest inference engine. A programmed personal content recommendation server filters items in an incoming information stream with the personal behavioral profile and identifies only those items of the incoming information stream that substantially matches the personal behavioral profile. The identified personally relevant content is then recommended to the user following some priority that may consider the similarity between the personal interest matches, the context of the user information consumption behaviors that may be shown by the user's content consumption mode.

Similarity from multi-dimensional scaling: solving the accuracy and diversity dilemma in information filtering.

PubMed

Zeng, Wei; Zeng, An; Liu, Hao; Shang, Ming-Sheng; Zhang, Yi-Cheng

2014-01-01

Recommender systems are designed to assist individual users to navigate through the rapidly growing amount of information. One of the most successful recommendation techniques is the collaborative filtering, which has been extensively investigated and has already found wide applications in e-commerce. One of challenges in this algorithm is how to accurately quantify the similarities of user pairs and item pairs. In this paper, we employ the multidimensional scaling (MDS) method to measure the similarities between nodes in user-item bipartite networks. The MDS method can extract the essential similarity information from the networks by smoothing out noise, which provides a graphical display of the structure of the networks. With the similarity measured from MDS, we find that the item-based collaborative filtering algorithm can outperform the diffusion-based recommendation algorithms. Moreover, we show that this method tends to recommend unpopular items and increase the global diversification of the networks in long term.

The Evaluation of Forms of Assessment Using N-Dimensional Filtering

ERIC Educational Resources Information Center

Dron, Jon; Boyne, Chris; Mitchell, Richard

2004-01-01

This paper describes the use of the CoFIND (Collaborative Filter in N Dimensions) system to evaluate two assessment styles. CoFIND is a resource database which organizes itself around its users' needs. Learners enter resources, categorize, then rate them using "qualities," aspects of resources which learners find worthwhile, the n dimensions of…

Miniaturized High-Temperature Superconducting/Dielectric Multilayer Filters for Satellite Communications

NASA Technical Reports Server (NTRS)

Miranda, Felix A.

1997-01-01

Most communication satellites contain well over a hundred filters in their payload. Current technology in typical satellite multiplexers use dual-mode cavity or dielectric resonator filters that are large (approx. 25 to 125 cu in) and heavy (up to 600 g). As the complexity of future advanced electronic systems for satellite communications increases, even more filters will be needed, requiring filter miniaturization without performance degradation. Such improvements in filter technology will enhance satellite performance. To reduce the size, weight, and cost of the multiplexers without compromising performance, the NASA Lewis Research Center is collaborating with industry to develop a new class of dual-mode multilayer filters consisting of YBa2Cu3O7-delta high-temperature superconducting (HTS) thin films on LaAlO3 substrates.

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.

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.

Nonlinear Attitude Filtering Methods

NASA Technical Reports Server (NTRS)

Markley, F. Landis; Crassidis, John L.; Cheng, Yang

2005-01-01

This paper provides a survey of modern nonlinear filtering methods for attitude estimation. Early applications relied mostly on the extended Kalman filter for attitude estimation. Since these applications, several new approaches have been developed that have proven to be superior to the extended Kalman filter. Several of these approaches maintain the basic structure of the extended Kalman filter, but employ various modifications in order to provide better convergence or improve other performance characteristics. Examples of such approaches include: filter QUEST, extended QUEST, the super-iterated extended Kalman filter, the interlaced extended Kalman filter, and the second-order Kalman filter. Filters that propagate and update a discrete set of sigma points rather than using linearized equations for the mean and covariance are also reviewed. A two-step approach is discussed with a first-step state that linearizes the measurement model and an iterative second step to recover the desired attitude states. These approaches are all based on the Gaussian assumption that the probability density function is adequately specified by its mean and covariance. Other approaches that do not require this assumption are reviewed, including particle filters and a Bayesian filter based on a non-Gaussian, finite-parameter probability density function on SO(3). Finally, the predictive filter, nonlinear observers and adaptive approaches are shown. The strengths and weaknesses of the various approaches are discussed.

An Algorithm Framework for Isolating Anomalous Signals in Electromagnetic Data

NASA Astrophysics Data System (ADS)

Kappler, K. N.; Schneider, D.; Bleier, T.; MacLean, L. S.

2016-12-01

QuakeFinder and its international collaborators have installed and currently maintain an array of 165 three-axis induction magnetometer instrument sites in California, Peru, Taiwan, Greece, Chile and Sumatra. Based on research by Bleier et al. (2009), Fraser-Smith et al. (1990), and Freund (2007), the electromagnetic data from these instruments are being analyzed for pre-earthquake signatures. This analysis consists of both private research by QuakeFinder, and institutional collaborators (PUCP in Peru, NCU in Taiwan, NOA in Greece, LASP at University of Colorado, Stanford, UCLA, NASA-ESI, NASA-AMES and USC-CSEP). QuakeFinder has developed an algorithm framework aimed at isolating anomalous signals (pulses) in the time series. Results are presented from an application of this framework to induction-coil magnetometer data. Our data driven approach starts with sliding windows applied to uniformly resampled array data with a variety of lengths and overlap. Data variance (a proxy for energy) is calculated on each window and a short-term average/ long-term average (STA/LTA) filter is applied to the variance time series. Pulse identification is done by flagging time intervals in the STA/LTA filtered time series which exceed a threshold. Flagged time intervals are subsequently fed into a feature extraction program which computes statistical properties of the resampled data. These features are then filtered using a Principal Component Analysis (PCA) based method to cluster similar pulses. We explore the extent to which this approach categorizes pulses with known sources (e.g. cars, lightning, etc.) and the remaining pulses of unknown origin can be analyzed with respect to their relationship with seismicity. We seek a correlation between these daily pulse-counts (with known sources removed) and subsequent (days to weeks) seismic events greater than M5 within 15km radius. Thus we explore functions which map daily pulse-counts to a time series representing the likelihood of a seismic event occurring at some future time. These "pseudo-probabilities" can in turn be represented as Molchan diagrams. The Molchan curve provides an effective cost function for optimization and allows for a rigorous statistical assessment of the validity of pre-earthquake signals in the electromagnetic data.

Shopping For Danger: E-commerce techniques applied to collaboration in cyber security

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

Bruce, Joseph R.; Fink, Glenn A.

Collaboration among cyber security analysts is essential to a successful protection strategy on the Internet today, but it is uncommonly practiced or encouraged in operating environments. Barriers to productive collaboration often include data sensitivity, time and effort to communicate, institutional policy, and protection of domain knowledge. We propose an ambient collaboration framework, Vulcan, designed to remove the barriers of time and effort and mitigate the others. Vulcan automated data collection, collaborative filtering, and asynchronous dissemination, eliminating the effort implied by explicit collaboration among peers. We instrumented two analytic applications and performed a mock analysis session to build a dataset andmore » test the output of the system.« less

Building and Running a Collaborative Internet Filter Is Akin to a Kansas Barn Raising

ERIC Educational Resources Information Center

Reddick, Thomas

2004-01-01

The Northeast Kansas Library System's filtering project started out as a response to the passage of CIPA, the Children's Internet Protection Act, in January 2001. Originally called "onGuard," it was a service that the Northeast Kansas Library System created for its members. When the Supreme Court ruling did uphold the constitutionality…

Performance study for a set of BLUE based Filters applied to amplitude estimation using as a reference the single photoelectron signal of the ν-Angra Experiment

NASA Astrophysics Data System (ADS)

Souza, D. M.; Costa, I. A.; Nóbrega, R. A.

2017-10-01

This document presents a detailed study of the performance of a set of digital filters whose implementations are based on the best linear unbiased estimator theory interpreted as a constrained optimization problem that could be relaxed depending on the input signal characteristics. This approach has been employed by a number of recent particle physics experiments for measuring the energy of particle events interacting with their detectors. The considered filters have been designed to measure the peak amplitude of signals produced by their detectors based on the digitized version of such signals. This study provides a clear understanding of the characteristics of those filters in the context of particle physics and, additionally, it proposes a phase related constraint based on the second derivative of the Taylor expansion in order to make the estimator less sensitive to phase variation (phase between the analog signal shaping and its sampled version), which is stronger in asynchronous experiments. The asynchronous detector developed by the ν-Angra Collaboration is used as the basis for this work. Nevertheless, the proposed analysis goes beyond, considering a wide range of conditions related to signal parameters such as pedestal, phase, sampling rate, amplitude resolution, noise and pile-up; therefore crossing the bounds of the ν-Angra Experiment to make it interesting and useful for different asynchronous and even synchronous experiments.

Analysis of the Mean Absolute Error (MAE) and the Root Mean Square Error (RMSE) in Assessing Rounding Model

NASA Astrophysics Data System (ADS)

Wang, Weijie; Lu, Yanmin

2018-03-01

Most existing Collaborative Filtering (CF) algorithms predict a rating as the preference of an active user toward a given item, which is always a decimal fraction. Meanwhile, the actual ratings in most data sets are integers. In this paper, we discuss and demonstrate why rounding can bring different influences to these two metrics; prove that rounding is necessary in post-processing of the predicted ratings, eliminate of model prediction bias, improving the accuracy of the prediction. In addition, we also propose two new rounding approaches based on the predicted rating probability distribution, which can be used to round the predicted rating to an optimal integer rating, and get better prediction accuracy compared to the Basic Rounding approach. Extensive experiments on different data sets validate the correctness of our analysis and the effectiveness of our proposed rounding approaches.

Developing Interoperable Air Quality Community Portals

NASA Astrophysics Data System (ADS)

Falke, S. R.; Husar, R. B.; Yang, C. P.; Robinson, E. M.; Fialkowski, W. E.

2009-04-01

Web portals are intended to provide consolidated discovery, filtering and aggregation of content from multiple, distributed web sources targeted at particular user communities. This paper presents a standards-based information architectural approach to developing portals aimed at air quality community collaboration in data access and analysis. An important characteristic of the approach is to advance beyond the present stand-alone design of most portals to achieve interoperability with other portals and information sources. We show how using metadata standards, web services, RSS feeds and other Web 2.0 technologies, such as Yahoo! Pipes and del.icio.us, helps increase interoperability among portals. The approach is illustrated within the context of the GEOSS Architecture Implementation Pilot where an air quality community portal is being developed to provide a user interface between the portals and clearinghouse of the GEOSS Common Infrastructure and the air quality community catalog of metadata and data services.

Fast Markerless Tracking for Augmented Reality in Planar Environment

NASA Astrophysics Data System (ADS)

Basori, Ahmad Hoirul; Afif, Fadhil Noer; Almazyad, Abdulaziz S.; AbuJabal, Hamza Ali S.; Rehman, Amjad; Alkawaz, Mohammed Hazim

2015-12-01

Markerless tracking for augmented reality should not only be accurate but also fast enough to provide a seamless synchronization between real and virtual beings. Current reported methods showed that a vision-based tracking is accurate but requires high computational power. This paper proposes a real-time hybrid-based method for tracking unknown environments in markerless augmented reality. The proposed method provides collaboration of vision-based approach with accelerometers and gyroscopes sensors as camera pose predictor. To align the augmentation relative to camera motion, the tracking method is done by substituting feature-based camera estimation with combination of inertial sensors with complementary filter to provide more dynamic response. The proposed method managed to track unknown environment with faster processing time compared to available feature-based approaches. Moreover, the proposed method can sustain its estimation in a situation where feature-based tracking loses its track. The collaboration of sensor tracking managed to perform the task for about 22.97 FPS, up to five times faster than feature-based tracking method used as comparison. Therefore, the proposed method can be used to track unknown environments without depending on amount of features on scene, while requiring lower computational cost.

Micro-telerobotic applications for microsurgery

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

Ford, W.E.; Morimoto, A.K.; Kozlowski, D.M.

MicroDexterity Systems Inc. and Sandia National Laboratories are collaborating on the design of a six degree-of-freedom surgeon-controlled micropositioner and a six degree-of-freedom surgeon-controlled master for use in microsurgery. A control system will provide the linkage between the force-reflecting master and micropositioner for force scaling, position scaling, and tremor filtering. The technologies developed by this project are expected to enhance the skills of surgeons, improve the success rates for existing microsurgical procedures, make new high-dexterity procedures possible, and ultimately reduce surgical costs by increasing the precision and speed of operations. This paper discusses the motivation, approach, and accomplishments to date.

E-Learning 3.0 = E-Learning 2.0 + Web 3.0?

ERIC Educational Resources Information Center

Hussain, Fehmida

2012-01-01

Web 3.0, termed as the semantic web or the web of data is the transformed version of Web 2.0 with technologies and functionalities such as intelligent collaborative filtering, cloud computing, big data, linked data, openness, interoperability and smart mobility. If Web 2.0 is about social networking and mass collaboration between the creator and…

Sensing in the collaborative Internet of Things.

PubMed

Borges Neto, João B; Silva, Thiago H; Assunção, Renato Martins; Mini, Raquel A F; Loureiro, Antonio A F

2015-03-19

We are entering a new era of computing technology, the era of Internet of Things (IoT). An important element for this popularization is the large use of off-the-shelf sensors. Most of those sensors will be deployed by different owners, generally common users, creating what we call the Collaborative IoT. This collaborative IoT helps to increase considerably the amount and availability of collected data for different purposes, creating new interesting opportunities, but also several challenges. For example, it is very challenging to search for and select a desired sensor or a group of sensors when there is no description about the provided sensed data or when it is imprecise. Given that, in this work we characterize the properties of the sensed data in the Internet of Things, mainly the sensed data contributed by several sources, including sensors from common users. We conclude that, in order to safely use data available in the IoT, we need a filtering process to increase the data reliability. In this direction, we propose a new simple and powerful approach that helps to select reliable sensors. We tested our method for different types of sensed data, and the results reveal the effectiveness in the correct selection of sensor data.

Similarity from Multi-Dimensional Scaling: Solving the Accuracy and Diversity Dilemma in Information Filtering

PubMed Central

Zeng, Wei; Zeng, An; Liu, Hao; Shang, Ming-Sheng; Zhang, Yi-Cheng

2014-01-01

Recommender systems are designed to assist individual users to navigate through the rapidly growing amount of information. One of the most successful recommendation techniques is the collaborative filtering, which has been extensively investigated and has already found wide applications in e-commerce. One of challenges in this algorithm is how to accurately quantify the similarities of user pairs and item pairs. In this paper, we employ the multidimensional scaling (MDS) method to measure the similarities between nodes in user-item bipartite networks. The MDS method can extract the essential similarity information from the networks by smoothing out noise, which provides a graphical display of the structure of the networks. With the similarity measured from MDS, we find that the item-based collaborative filtering algorithm can outperform the diffusion-based recommendation algorithms. Moreover, we show that this method tends to recommend unpopular items and increase the global diversification of the networks in long term. PMID:25343243

A Catchment Systems Engineering (CSE) approach to managing intensively farmed land

NASA Astrophysics Data System (ADS)

Jonczyk, Jennine; Quinn, Paul; Barber, Nicholas; Wilkinson, Mark; ODonnell, Greg

2014-05-01

Rural land management practices can have a significant impact on the hydrological and nutrient dynamics within a catchment which can dramatically alter the way it processes water, exacerbating nutrient losses from the system. A collaborative and holistic approach for managing potential conflicts between land management activity for food production alongside the aspiration to achieve good water quality and the need to make space for water can ensure the long-term sustainability of our agricultural catchments. Catchment System Engineering (CSE) is an interventionist approach to altering the catchment scale runoff regime through the manipulation of hydrological flow pathways throughout the catchment. By targeting hydrological flow pathways at source, such as overland flow, field drain and ditch function, a significant component of the runoff generation can be managed, greatly reducing erosive soil losses. Coupled with management of farm nutrients at source many runoff attenuation features or measures can be co-located to achieve benefits for water quality. Examples of community-led mitigation measures using the CSE approach will be presented from two catchments in Northumberland, Northern England, that demonstrate the generic framework for identification of multipurpose features that slow, store and filter runoff at strategic locations in the landscape. Measures include within-field barriers, edge of field traps and within-field sediment filters and sediment traps which demonstrate how sediment can be trapped locally (including silt and clay fractions) and be recovered for use back on the land. Deliverables from this CSE approach includes the reduction of downstream flood risk and capturing of sediment and associated nutrients. The CSE approach allows for a more natural flood and nutrient management approach which helps to restore vital catchment functions to re-establish a healthy catchment system.

Collaborative study of an enzymatic digestion method for the isolation of light filth from ground beef or hamburger.

PubMed

Alioto, P; Andreas, M

1976-01-01

Collaborative results are presented for a proposed method for light filth extraction from ground beef or hamburger. The method involves enzymatic digestion, wet sieving, and extraction with light mineral oil from 40% isopropanol. Recoveries are good and filter papers are clean. This method has been adopted as official first action.

Multi-Beam Radio Frequency (RF) Aperture Arrays Using Multiplierless Approximate Fast Fourier Transform (FFT)

DTIC Science & Technology

2017-08-01

filtering, correlation and radio- astronomy . In this report approximate transforms that closely follow the DFT have been studied and found. The approximate...communications, data networks, sensor networks, cognitive radio, radar and beamforming, imaging, filtering, correlation and radio- astronomy . FFTs efficiently...public release; distribution is unlimited. 4.3 Digital Hardware and Design Architectures Collaboration for Astronomy Signal Processing and Electronics

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

Enhancement of the Comb Filtering Selectivity Using Iterative Moving Average for Periodic Waveform and Harmonic Elimination

PubMed Central

Wu, Yan; Aarts, Ronald M.

2018-01-01

A recurring problem regarding the use of conventional comb filter approaches for elimination of periodic waveforms is the degree of selectivity achieved by the filtering process. Some applications, such as the gradient artefact correction in EEG recordings during coregistered EEG-fMRI, require a highly selective comb filtering that provides effective attenuation in the stopbands and gain close to unity in the pass-bands. In this paper, we present a novel comb filtering implementation whereby the iterative filtering application of FIR moving average-based approaches is exploited in order to enhance the comb filtering selectivity. Our results indicate that the proposed approach can be used to effectively approximate the FIR moving average filter characteristics to those of an ideal filter. A cascaded implementation using the proposed approach shows to further increase the attenuation in the filter stopbands. Moreover, broadening of the bandwidth of the comb filtering stopbands around −3 dB according to the fundamental frequency of the stopband can be achieved by the novel method, which constitutes an important characteristic to account for broadening of the harmonic gradient artefact spectral lines. In parallel, the proposed filtering implementation can also be used to design a novel notch filtering approach with enhanced selectivity as well. PMID:29599955

Transversity 2005

NASA Astrophysics Data System (ADS)

Barone, Vincenzo; Ratcliffe, Philip G.

Introduction. Purpose and status of the Italian Transversity Project / F. Bradamante -- Opening lecture. Transversity / M. Anselmino -- Experimental lectures. Azimuthal single-spin asymmetries from polarized and unpolarized hydrogen targets at HERMES / G. Schnell (for the HERMES Collaboration). Collins and Sivers asymmetries on the deuteron from COMPASS data / I. Horn (for the COMPASS Collaboration). First measurement of interference fragmentation on a transversely polarized hydrogen target / P. B. van der Nat (for the HERMES Collaboration). Two-hadron asymmetries at the COMPASS experiment / A. Mielech (for the COMPASS Collaboration). Measurements of chiral-odd fragmentation functions at Belle / R. Seidl ... [et al.]. Lambda asymmetries / A. Ferrero (for the COMPASS Collaboration). Transverse spin at PHENIX: results and prospects / C. Aidala (for the PHENIX Collaboration). Transverse spin and RHIC / L. Bland. Studies of transverse spin effects at JLab / H. Avakian ... [et al.] (for the CLAS Collaboration). Neutron transversity at Jefferson Lab / J. P. Chen ... [et al.] (for the Jefferson Lab Hall A Collaboration). PAX: polarized antiproton experiments / M. Contalbrigo. Single and double spin N-N interactions at GSI / M. Maggiora (for the ASSIA Collaboration). Spin filtering in storage rings / N. N. Nikolaev & F. F. Pavlov -- Theory lectures. Single-spin asymmetries and transversity in QCD / S. J. Brodsky. The relativistic hydrogen atom: a theoretical laboratory for structure functions / X. Artru & K. Benhizia. GPD's and SSA's / M. Burkardt. Time reversal odd distribution functions in chiral models / A. Drago. Soffer bound and transverse spin densities from lattice QCD / M. Diehl ... [et al.]. Single-spin asymmetries and Qiu-Sterman effect(s) / A. Bacchetta. Sivers function: SIDIS data, fits and predictions / M. Anselmino ... [et al.]. Twist-3 effects in semi-inclusive deep inelastic scattering / M. Schlegel, K. Goeke & A. Metz. Quark and gluon Sivers functions / I. Schmidt. Sivers effect in semi-inclusive deeply inelastic scattering and Drell-Yan / J. C. Collins ... [et al.]. Helicity formalism and spin asymmetries in hadronic processes / M. Anselmino ... [et al.]. Including Cahn and Sivers effects into event generators / A. Kotzinian. Comparing extractions of Sivers functions / M. Anselmino ... [et al.]. Anomalous Drell-Yan asymmetry from hadronic or QCD vacuum effects / D. Boer. "T-odd" effects in transverse spin and azimuthal asymmetries in SIDIS / L. P. Gamberg & G. R. Goldstein. T-odd effects in unpolarized Drell-Yan scattering / G. R. Goldstein & L. P. Gamberg. Alternative approaches to transversity: how convenient and feasible are they? / M. Radici. Relations between single and double transverse asymmetries / O. V. Teryaev. Cross sections, error bars and event distributions in simulated Drell-Yan azimuthal asymmetry measurements / A. Bianconi. Next-to-leading order QCD corrections for transversely polarized pp and p¯p collisions / A. Mukherjee, M. Stratmann & W. Vogelsang. Double transverse-spin asymmetries in Drell-Yan and J/[symbol] production from proton-antiproton collisions / M. Guzzi ... [et al.]. The quark-quark correlator: theory and phenomenology / E. Di Salvo. Chiral quark model spin filtering mechanism and hyperon polarization / S. M. Troshin & N. E. Tyurin -- Closing lecture. Where we've been ... and where we're going / G. Bunce.

Reference Architecture for MNE 5 Technical System

DTIC Science & Technology

2007-05-30

of being available in most experiments. Core Services A core set of applications whi directories, web portal and collaboration applications etc. A...classifications Messages (xml, JMS, content level…) Meta data filtering, who can initiate services Web browsing Collaboration & messaging Border...Exchange Ref Architecture for MNE5 Tech System.doc 9 of 21 audit logging Person and machine Data lev objects, web services, messages rification el

Video denoising using low rank tensor decomposition

NASA Astrophysics Data System (ADS)

Gui, Lihua; Cui, Gaochao; Zhao, Qibin; Wang, Dongsheng; Cichocki, Andrzej; Cao, Jianting

2017-03-01

Reducing noise in a video sequence is of vital important in many real-world applications. One popular method is block matching collaborative filtering. However, the main drawback of this method is that noise standard deviation for the whole video sequence is known in advance. In this paper, we present a tensor based denoising framework that considers 3D patches instead of 2D patches. By collecting the similar 3D patches non-locally, we employ the low-rank tensor decomposition for collaborative filtering. Since we specify the non-informative prior over the noise precision parameter, the noise variance can be inferred automatically from observed video data. Therefore, our method is more practical, which does not require knowing the noise variance. The experimental on video denoising demonstrates the effectiveness of our proposed method.

Sensing in the Collaborative Internet of Things

PubMed Central

Borges Neto, João B.; Silva, Thiago H.; Assunção, Renato Martins; Mini, Raquel A. F.; Loureiro, Antonio A. F.

2015-01-01

We are entering a new era of computing technology, the era of Internet of Things (IoT). An important element for this popularization is the large use of off-the-shelf sensors. Most of those sensors will be deployed by different owners, generally common users, creating what we call the Collaborative IoT. This collaborative IoT helps to increase considerably the amount and availability of collected data for different purposes, creating new interesting opportunities, but also several challenges. For example, it is very challenging to search for and select a desired sensor or a group of sensors when there is no description about the provided sensed data or when it is imprecise. Given that, in this work we characterize the properties of the sensed data in the Internet of Things, mainly the sensed data contributed by several sources, including sensors from common users. We conclude that, in order to safely use data available in the IoT, we need a filtering process to increase the data reliability. In this direction, we propose a new simple and powerful approach that helps to select reliable sensors. We tested our method for different types of sensed data, and the results reveal the effectiveness in the correct selection of sensor data. PMID:25808766

Time-Domain Filtering for Spatial Large-Eddy Simulation

NASA Technical Reports Server (NTRS)

Pruett, C. David

1997-01-01

An approach to large-eddy simulation (LES) is developed whose subgrid-scale model incorporates filtering in the time domain, in contrast to conventional approaches, which exploit spatial filtering. The method is demonstrated in the simulation of a heated, compressible, axisymmetric jet, and results are compared with those obtained from fully resolved direct numerical simulation. The present approach was, in fact, motivated by the jet-flow problem and the desire to manipulate the flow by localized (point) sources for the purposes of noise suppression. Time-domain filtering appears to be more consistent with the modeling of point sources; moreover, time-domain filtering may resolve some fundamental inconsistencies associated with conventional space-filtered LES approaches.

Detection of abnormal item based on time intervals for recommender systems.

PubMed

Gao, Min; Yuan, Quan; Ling, Bin; Xiong, Qingyu

2014-01-01

With the rapid development of e-business, personalized recommendation has become core competence for enterprises to gain profits and improve customer satisfaction. Although collaborative filtering is the most successful approach for building a recommender system, it suffers from "shilling" attacks. In recent years, the research on shilling attacks has been greatly improved. However, the approaches suffer from serious problem in attack model dependency and high computational cost. To solve the problem, an approach for the detection of abnormal item is proposed in this paper. In the paper, two common features of all attack models are analyzed at first. A revised bottom-up discretized approach is then proposed based on time intervals and the features for the detection. The distributions of ratings in different time intervals are compared to detect anomaly based on the calculation of chi square distribution (χ(2)). We evaluated our approach on four types of items which are defined according to the life cycles of these items. The experimental results show that the proposed approach achieves a high detection rate with low computational cost when the number of attack profiles is more than 15. It improves the efficiency in shilling attacks detection by narrowing down the suspicious users.

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…

Early and Late Retrieval of the ALN Removable Vena Cava Filter: Results from a Multicenter Study

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

Pellerin, O., E-mail: olivier.pellerin@egp.aphp.f; Barral, F. G.; Lions, C.

Retrieval of removable inferior vena cava (IVC) filters in selected patients is widely practiced. The purpose of this multicenter study was to evaluate the feasibility and results of percutaneous removal of the ALN removable filter in a large patient cohort. Between November 2003 and June 2006, 123 consecutive patients were referred for percutaneous extraction of the ALN filter at three centers. The ALN filter is a removable filter that can be implanted through a femoral/jugular vein approach and extracted by the jugular vein approach. Filter removal was attempted after an implantation period of 93 {+-} 15 days (range, 6-722 days)more » through the right internal jugular vein approach using the dedicated extraction kit after control inferior vena cavography. Following filter removal, vena cavograms were obtained in all patients. Successful extraction was achieved in all but one case. Among these successful retrievals, additional manipulation using a femoral approach was needed when the apex of the filter was close to the IVC wall in two patients. No immediate IVC complications were observed according to the postimplantation cavography. Neither technical nor clinical differences between early and late filter retrieval were noticed. Our data confirm the safety of ALN filter retrieval up to 722 days after implantation. In infrequent cases, additional endovenous filter manipulation is needed to facilitate extraction.« less

Fast Collaborative Filtering from Implicit Feedback withProvable Guarantees

DTIC Science & Technology

2016-11-22

n2 = Ω (( ε d̃2sσK(M2) )2) • n3 = Ω ( K2 ( 10 d̃2sσK(M2)5/2 + 2 √ 2 d̃3sσK(M2)3/2 )2 ε2 ) 212 Fast Collaborative Filtering for some constants c1 and c2...drawback of Method of Moments is that it will not work when there are only a few users available such that N < Θ( K2 ). However, modern recommendation systems...2 √ 2 d̃3sσK(M2)3/2 ) 2ε√ N ≤ c1 1 K √ πmax Since πmax ≤ 1, we need N ≥ Ω ( K2 ( 10 d̃2sσK(M2)5/2 + 2 √ 2 d̃3sσK(M2)3/2 )2 ε2 ) . This con- tributes

[Pediatric surgery 2.0].

PubMed

Mesa-Gutiérrez, J C; Bardají, C; Brun, N; Núñez, B; Sánchez, B; Sanvicente, B; Obiols, P; Rigol, S

2012-04-01

New tools from the web are a complete breakthrough in management of information. The aim of this paper is to present different resources in a friendly way, with apps and examples in the different phases of the knowledge management for the paediatric surgeon: search, filter, reception, classification, sharing, collaborative work and publication. We are assisting to a real revolution on how to manage knowledge and information. The main charateristics are: immediateness, social component, growing interaction, and easiness. Every physician has clinical questions and the Internet gives us more and more resources to make searchs easier. Along with them we need electronic resources to filter information of quality and to make easier transfer of knowledge to clinical practice. Cloud computing is on continuous development and makes possible sharing information with differents users and computers. The main feature of the apps from the Intenet is the social component, that makes possible interaction, sharing and collaborative work.

Analysis and hit filtering of a very large library of compounds screened against Mycobacterium tuberculosis.

PubMed

Ekins, Sean; Kaneko, Takushi; Lipinski, Christopher A; Bradford, Justin; Dole, Krishna; Spektor, Anna; Gregory, Kellan; Blondeau, David; Ernst, Sylvia; Yang, Jeremy; Goncharoff, Nicko; Hohman, Moses M; Bunin, Barry A

2010-11-01

There is an urgent need for new drugs against tuberculosis which annually claims 1.7-1.8 million lives. One approach to identify potential leads is to screen in vitro small molecules against Mycobacterium tuberculosis (Mtb). Until recently there was no central repository to collect information on compounds screened. Consequently, it has been difficult to analyze molecular properties of compounds that inhibit the growth of Mtb in vitro. We have collected data from publically available sources on over 300 000 small molecules deposited in the Collaborative Drug Discovery TB Database. A cheminformatics analysis on these compounds indicates that inhibitors of the growth of Mtb have statistically higher mean logP, rule of 5 alerts, while also having lower HBD count, atom count and lower PSA (ChemAxon descriptors), compared to compounds that are classed as inactive. Additionally, Bayesian models for selecting Mtb active compounds were evaluated with over 100 000 compounds and, they demonstrated 10 fold enrichment over random for the top ranked 600 compounds. This represents a promising approach for finding compounds active against Mtb in whole cells screened under the same in vitro conditions. Various sets of Mtb hit molecules were also examined by various filtering rules used widely in the pharmaceutical industry to identify compounds with potentially reactive moieties. We found differences between the number of compounds flagged by these rules in Mtb datasets, malaria hits, FDA approved drugs and antibiotics. Combining these approaches may enable selection of compounds with increased probability of inhibition of whole cell Mtb activity.

Optical filters for UV to near IR space applications

NASA Astrophysics Data System (ADS)

Begou, T.; Krol, H.; Hecquet, Christophe; Bondet, C.; Lumeau, J.; Grèzes-Besset, C.; Lequime, M.

2017-11-01

We present hereafter the results on the fabrication of complex optical filters within the Institut Fresnel in close collaboration with CILAS. Bandpass optical filters dedicated to astronomy and space applications, with central wavelengths ranging from ultraviolet to near infrared, were deposited on both sides of glass substrates with performances in very good congruence with theoretical designs. For these applications, the required functions are particularly complex as they must present a very narrow bandwidth as well as a high level of rejection over a broad spectral range. In addition to those severe optical performances, insensitivity to environmental conditions is necessary. For this purpose, robust solutions with particularly stable performances have to be proposed.

Collaboration spotting for dental science.

PubMed

Leonardi, E; Agocs, A; Fragkiskos, S; Kasfikis, N; Le Goff, J M; Cristalli, M P; Luzzi, V; Polimeni, A

2014-10-06

The goal of the Collaboration Spotting project is to create an automatic system to collect information about publications and patents related to a given technology, to identify the key players involved, and to highlight collaborations and related technologies. The collected information can be visualized in a web browser as interactive graphical maps showing in an intuitive way the players and their collaborations (Sociogram) and the relations among the technologies (Technogram). We propose to use the system to study technologies related to Dental Science. In order to create a Sociogram, we create a logical filter based on a set of keywords related to the technology under study. This filter is used to extract a list of publications from the Web of Science™ database. The list is validated by an expert in the technology and sent to CERN where it is inserted in the Collaboration Spotting database. Here, an automatic software system uses the data to generate the final maps. We studied a set of recent technologies related to bone regeneration procedures of oro--maxillo--facial critical size defects, namely the use of Porous HydroxyApatite (HA) as a bone substitute alone (bone graft) or as a tridimensional support (scaffold) for insemination and differentiation ex--vivo of Mesenchymal Stem Cells. We produced the Sociograms for these technologies and the resulting maps are now accessible on--line. The Collaboration Spotting system allows the automatic creation of interactive maps to show the current and historical state of research on a specific technology. These maps are an ideal tool both for researchers who want to assess the state--of--the--art in a given technology, and for research organizations who want to evaluate their contribution to the technological development in a given field. We demonstrated that the system can be used for Dental Science and produced the maps for an initial set of technologies in this field. We now plan to enlarge the set of mapped technologies in order to make the Collaboration Spotting system a useful reference tool for Dental Science research.

Collaboration Spotting for oral medicine.

PubMed

Leonardi, E; Agocs, A; Fragkiskos, S; Kasfikis, N; Le Goff, J M; Cristalli, M P; Luzzi, V; Polimeni, A

2014-09-01

The goal of the Collaboration Spotting project is to create an automatic system to collect information about publications and patents related to a given technology, to identify the key players involved, and to highlight collaborations and related technologies. The collected information can be visualized in a web browser as interactive graphical maps showing in an intuitive way the players and their collaborations (Sociogram) and the relations among the technologies (Technogram). We propose to use the system to study technologies related to oral medicine. In order to create a sociogram, we create a logical filter based on a set of keywords related to the technology under study. This filter is used to extract a list of publications from the Web of Science™ database. The list is validated by an expert in the technology and sent to CERN where it is inserted in the Collaboration Spotting database. Here, an automatic software system uses the data to generate the final maps. We studied a set of recent technologies related to bone regeneration procedures of oro-maxillo-facial critical size defects, namely the use of porous hydroxyapatite (HA) as a bone substitute alone (bone graft) or as a tridimensional support (scaffold) for insemination and differentiation ex vivo of mesenchymal stem cells. We produced the sociograms for these technologies and the resulting maps are now accessible on-line. The Collaboration Spotting system allows the automatic creation of interactive maps to show the current and historical state of research on a specific technology. These maps are an ideal tool both for researchers who want to assess the state-of-the-art in a given technology, and for research organizations who want to evaluate their contribution to the technological development in a given field. We demonstrated that the system can be used in oral medicine as is produced the maps for an initial set of technologies in this field. We now plan to enlarge the set of mapped technologies in order to make the Collaboration Spotting system a useful reference tool for oral medicine research.

Toward visual user interfaces supporting collaborative multimedia content management

NASA Astrophysics Data System (ADS)

Husein, Fathi; Leissler, Martin; Hemmje, Matthias

2000-12-01

Supporting collaborative multimedia content management activities, as e.g., image and video acquisition, exploration, and access dialogues between naive users and multi media information systems is a non-trivial task. Although a wide variety of experimental and prototypical multimedia storage technologies as well as corresponding indexing and retrieval engines are available, most of them lack appropriate support for collaborative end-user oriented user interface front ends. The development of advanced user adaptable interfaces is necessary for building collaborative multimedia information- space presentations based upon advanced tools for information browsing, searching, filtering, and brokering to be applied on potentially very large and highly dynamic multimedia collections with a large number of users and user groups. Therefore, the development of advanced and at the same time adaptable and collaborative computer graphical information presentation schemes that allow to easily apply adequate visual metaphors for defined target user stereotypes has to become a key focus within ongoing research activities trying to support collaborative information work with multimedia collections.

Performance Evaluation of Axial Flow AG-1 FC and Prototype FM (High Strength) HEPA Filters - 13123

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

Giffin, Paxton K.; Parsons, Michael S.; Wilson, John A.

High efficiency particulate air (HEPA) filters are routinely used in DOE nuclear containment activities. The Nuclear Air Cleaning Handbook (NACH) stipulates that air cleaning devices and equipment used in DOE nuclear applications must meet the American Society of Mechanical Engineers (ASME) Code on Nuclear Air and Gas Treatment (AG-1) standard. This testing activity evaluates two different axial flow HEPA filters, those from AG-1 Sections FC and FM. Section FM is under development and has not yet been added to AG-1 due to a lack of qualification data available for these filters. Section FC filters are axial flow units that utilizemore » a fibrous glass filtering medium. The section FM filters utilize a similar fibrous glass medium, but also have scrim backing. The scrim-backed filters have demonstrated the ability to endure pressure impulses capable of completely destroying FC filters. The testing activities presented herein will examine the total lifetime loading for both FC and FM filters under ambient conditions and at elevated conditions of temperature and relative humidity. Results will include loading curves, penetration curves, and testing condition parameters. These testing activities have been developed through collaborations with representatives from the National Nuclear Security Administration (NNSA), DOE Office of Environmental Management (DOE-EM), New Mexico State University, and Mississippi State University. (authors)« less

Current-State Constrained Filter Bank for Wald Testing of Spacecraft Conjunctions

NASA Technical Reports Server (NTRS)

Carpenter, J. Russell; Markley, F. Landis

2012-01-01

We propose a filter bank consisting of an ordinary current-state extended Kalman filter, and two similar but constrained filters: one is constrained by a null hypothesis that the miss distance between two conjuncting spacecraft is inside their combined hard body radius at the predicted time of closest approach, and one is constrained by an alternative complementary hypothesis. The unconstrained filter is the basis of an initial screening for close approaches of interest. Once the initial screening detects a possibly risky conjunction, the unconstrained filter also governs measurement editing for all three filters, and predicts the time of closest approach. The constrained filters operate only when conjunctions of interest occur. The computed likelihoods of the innovations of the two constrained filters form a ratio for a Wald sequential probability ratio test. The Wald test guides risk mitigation maneuver decisions based on explicit false alarm and missed detection criteria. Since only current-state Kalman filtering is required to compute the innovations for the likelihood ratio, the present approach does not require the mapping of probability density forward to the time of closest approach. Instead, the hard-body constraint manifold is mapped to the filter update time by applying a sigma-point transformation to a projection function. Although many projectors are available, we choose one based on Lambert-style differential correction of the current-state velocity. We have tested our method using a scenario based on the Magnetospheric Multi-Scale mission, scheduled for launch in late 2014. This mission involves formation flight in highly elliptical orbits of four spinning spacecraft equipped with antennas extending 120 meters tip-to-tip. Eccentricities range from 0.82 to 0.91, and close approaches generally occur in the vicinity of perigee, where rapid changes in geometry may occur. Testing the method using two 12,000-case Monte Carlo simulations, we found the method achieved a missed detection rate of 0.1%, and a false alarm rate of 2%.

Correlation Filter Learning Toward Peak Strength for Visual Tracking.

PubMed

Sui, Yao; Wang, Guanghui; Zhang, Li

2018-04-01

This paper presents a novel visual tracking approach to correlation filter learning toward peak strength of correlation response. Previous methods leverage all features of the target and the immediate background to learn a correlation filter. Some features, however, may be distractive to tracking, like those from occlusion and local deformation, resulting in unstable tracking performance. This paper aims at solving this issue and proposes a novel algorithm to learn the correlation filter. The proposed approach, by imposing an elastic net constraint on the filter, can adaptively eliminate those distractive features in the correlation filtering. A new peak strength metric is proposed to measure the discriminative capability of the learned correlation filter. It is demonstrated that the proposed approach effectively strengthens the peak of the correlation response, leading to more discriminative performance than previous methods. Extensive experiments on a challenging visual tracking benchmark demonstrate that the proposed tracker outperforms most state-of-the-art methods.

External Aiding Methods for IMU-Based Navigation

DTIC Science & Technology

2016-11-26

Carlo simulation and particle filtering . This approach allows for the utilization of highly complex systems in a black box configuration with minimal...alternative method, which has the advantage of being less computationally demanding, is to use a Kalman filtering -based approach. The particular...Kalman filtering -based approach used here is known as linear covariance analysis. In linear covariance analysis, the nonlinear systems describing the

Influence of Coliform Source on Evaluation of Membrane Filters

PubMed Central

Brodsky, M. H.; Schiemann, D. A.

1975-01-01

Four brands of membrane filters were examined for total and fecal coliform recovery performance by two experimental approaches. Using diluted EC broth cultures of water samples, Johns-Manville filters were superior to Sartorius filters for fecal coliform but equivalent for total coliform recovery. Using river water samples, Johns-Manville filters were superior to Sartorius filters for total coliform but equivalent for fecal coliform recovery. No differences were observed between Johns-Manville and Millipore or Millipore and Sartorius filters for total or fecal coliform recoveries using either approach, nor was any difference observed between Millipore and Gelman filters for fecal coliform recovery from river water samples. These results indicate that the source of the coliform bacteria has an important influence on the conclusions of membrane filter evaluation studies. PMID:1106318

Multi-Dimensional High Order Essentially Non-Oscillatory Finite Difference Methods in Generalized Coordinates

NASA Technical Reports Server (NTRS)

Shu, Chi-Wang

1998-01-01

This project is about the development of high order, non-oscillatory type schemes for computational fluid dynamics. Algorithm analysis, implementation, and applications are performed. Collaborations with NASA scientists have been carried out to ensure that the research is relevant to NASA objectives. The combination of ENO finite difference method with spectral method in two space dimension is considered, jointly with Cai [3]. The resulting scheme behaves nicely for the two dimensional test problems with or without shocks. Jointly with Cai and Gottlieb, we have also considered one-sided filters for spectral approximations to discontinuous functions [2]. We proved theoretically the existence of filters to recover spectral accuracy up to the discontinuity. We also constructed such filters for practical calculations.

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…

Imagining the Digital Library in a Commercialized Internet.

ERIC Educational Resources Information Center

Heckart, Ronald J.

1999-01-01

Discusses digital library planning in light of Internet commerce and technological innovation in marketing and customer relations that are transforming user expectations about Web sites that offer products and services. Topics include user self-sufficiency; personalized service; artificial intelligence; collaborative filtering; and electronic…

A robust spatial filtering technique for multisource localization and geoacoustic inversion.

PubMed

Stotts, S A

2005-07-01

Geoacoustic inversion and source localization using beamformed data from a ship of opportunity has been demonstrated with a bottom-mounted array. An alternative approach, which lies within a class referred to as spatial filtering, transforms element level data into beam data, applies a bearing filter, and transforms back to element level data prior to performing inversions. Automation of this filtering approach is facilitated for broadband applications by restricting the inverse transform to the degrees of freedom of the array, i.e., the effective number of elements, for frequencies near or below the design frequency. A procedure is described for nonuniformly spaced elements that guarantees filter stability well above the design frequency. Monitoring energy conservation with respect to filter output confirms filter stability. Filter performance with both uniformly spaced and nonuniformly spaced array elements is discussed. Vertical (range and depth) and horizontal (range and bearing) ambiguity surfaces are constructed to examine filter performance. Examples that demonstrate this filtering technique with both synthetic data and real data are presented along with comparisons to inversion results using beamformed data. Examinations of cost functions calculated within a simulated annealing algorithm reveal the efficacy of the approach.

Correction of Bowtie-Filter Normalization and Crescent Artifacts for a Clinical CBCT System.

PubMed

Zhang, Hong; Kong, Vic; Huang, Ke; Jin, Jian-Yue

2017-02-01

To present our experiences in understanding and minimizing bowtie-filter crescent artifacts and bowtie-filter normalization artifacts in a clinical cone beam computed tomography system. Bowtie-filter position and profile variations during gantry rotation were studied. Two previously proposed strategies (A and B) were applied to the clinical cone beam computed tomography system to correct bowtie-filter crescent artifacts. Physical calibration and analytical approaches were used to minimize the norm phantom misalignment and to correct for bowtie-filter normalization artifacts. A combined procedure to reduce bowtie-filter crescent artifacts and bowtie-filter normalization artifacts was proposed and tested on a norm phantom, CatPhan, and a patient and evaluated using standard deviation of Hounsfield unit along a sampling line. The bowtie-filter exhibited not only a translational shift but also an amplitude variation in its projection profile during gantry rotation. Strategy B was better than strategy A slightly in minimizing bowtie-filter crescent artifacts, possibly because it corrected the amplitude variation, suggesting that the amplitude variation plays a role in bowtie-filter crescent artifacts. The physical calibration largely reduced the misalignment-induced bowtie-filter normalization artifacts, and the analytical approach further reduced bowtie-filter normalization artifacts. The combined procedure minimized both bowtie-filter crescent artifacts and bowtie-filter normalization artifacts, with Hounsfield unit standard deviation being 63.2, 45.0, 35.0, and 18.8 Hounsfield unit for the best correction approaches of none, bowtie-filter crescent artifacts, bowtie-filter normalization artifacts, and bowtie-filter normalization artifacts + bowtie-filter crescent artifacts, respectively. The combined procedure also demonstrated reduction of bowtie-filter crescent artifacts and bowtie-filter normalization artifacts in a CatPhan and a patient. We have developed a step-by-step procedure that can be directly used in clinical cone beam computed tomography systems to minimize both bowtie-filter crescent artifacts and bowtie-filter normalization artifacts.

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.

Beyond Information Retrieval: Ways To Provide Content in Context.

ERIC Educational Resources Information Center

Wiley, Deborah Lynne

1998-01-01

Provides an overview of information retrieval from mainframe systems to Web search engines; discusses collaborative filtering, data extraction, data visualization, agent technology, pattern recognition, classification and clustering, and virtual communities. Argues that rather than huge data-storage centers and proprietary software, we need…

An Exploration of Trainer Filtering Approaches

NASA Technical Reports Server (NTRS)

Hester, Patrick; Tolk, Andreas; Gadi, Sandeep; Carver, Quinn; Roland, Philippe

2011-01-01

Simutator operators face a twofold entity management problem during Live-Virtual-Constructive (LVC) training events. They first must filter potentially hundreds of thousands of simulation entities in order 10 determine which elements are necessary for optimal trainee comprehension. Secondarily, they must manage the number of entities entering the simulation from those present in the object model in order to limit the computational burden on the simulation system and prevent unnecessary entities from entering the simulation, This paper focuses on the first filtering stage and describes a novel approach to entity filtering undertaken to maximize trainee awareness and learning. The feasibility of this novel approach is demonstrated on a case study and limitations to the proposed approach and future work are discussed.

Naval Research Laboratory Industrial Chemical Analysis and Respiratory Filter Standards Development

DTIC Science & Technology

2017-09-29

Filter Standards Development September 29, 2017 Approved for public release; distribution is unlimited. Thomas E. suTTo Materials and Systems Branch...LIMITATION OF ABSTRACT Naval Research Laboratory Industrial Chemical Analysis and Respiratory Filter Standards Development Thomas E. Sutto Naval Research...approach, developed by NRL, is tested by examining the filter behavior against a number of chemicals to determine if the NRL approach resulted in the

A Dynamic Recommender System for Improved Web Usage Mining and CRM Using Swarm Intelligence.

PubMed

Alphy, Anna; Prabakaran, S

2015-01-01

In modern days, to enrich e-business, the websites are personalized for each user by understanding their interests and behavior. The main challenges of online usage data are information overload and their dynamic nature. In this paper, to address these issues, a WebBluegillRecom-annealing dynamic recommender system that uses web usage mining techniques in tandem with software agents developed for providing dynamic recommendations to users that can be used for customizing a website is proposed. The proposed WebBluegillRecom-annealing dynamic recommender uses swarm intelligence from the foraging behavior of a bluegill fish. It overcomes the information overload by handling dynamic behaviors of users. Our dynamic recommender system was compared against traditional collaborative filtering systems. The results show that the proposed system has higher precision, coverage, F1 measure, and scalability than the traditional collaborative filtering systems. Moreover, the recommendations given by our system overcome the overspecialization problem by including variety in recommendations.

Can Dissimilar Users Contribute to Accuracy and Diversity of Personalized Recommendation?

NASA Astrophysics Data System (ADS)

Zeng, Wei; Shang, Ming-Sheng; Zhang, Qian-Ming; Lü, Linyuan; Zhou, Tao

Recommender systems are becoming a popular and important set of personalization techniques that assist individual users with navigating through the rapidly growing amount of information. A good recommender system should be able to not only find out the objects preferred by users, but also help users in discovering their personalized tastes. The former corresponds to high accuracy of the recommendation, while the latter to high diversity. A big challenge is to design an algorithm that provides both highly accurate and diverse recommendation. Traditional recommendation algorithms only take into account the contributions of similar users, thus, they tend to recommend popular items for users ignoring the diversity of recommendations. In this paper, we propose a recommendation algorithm by considering both the effects of similar and dissimilar users under the framework of collaborative filtering. Extensive analyses on three datasets, namely MovieLens, Netflix and Amazon, show that our method performs much better than the standard collaborative filtering algorithm for both accuracy and diversity.

Effects of high-order correlations on personalized recommendations for bipartite networks

NASA Astrophysics Data System (ADS)

Liu, Jian-Guo; Zhou, Tao; Che, Hong-An; Wang, Bing-Hong; Zhang, Yi-Cheng

2010-02-01

In this paper, we introduce a modified collaborative filtering (MCF) algorithm, which has remarkably higher accuracy than the standard collaborative filtering. In the MCF, instead of the cosine similarity index, the user-user correlations are obtained by a diffusion process. Furthermore, by considering the second-order correlations, we design an effective algorithm that depresses the influence of mainstream preferences. Simulation results show that the algorithmic accuracy, measured by the average ranking score, is further improved by 20.45% and 33.25% in the optimal cases of MovieLens and Netflix data. More importantly, the optimal value λ depends approximately monotonously on the sparsity of the training set. Given a real system, we could estimate the optimal parameter according to the data sparsity, which makes this algorithm easy to be applied. In addition, two significant criteria of algorithmic performance, diversity and popularity, are also taken into account. Numerical results show that as the sparsity increases, the algorithm considering the second-order correlation can outperform the MCF simultaneously in all three criteria.

Towards collaborative filtering recommender systems for tailored health communications.

PubMed

Marlin, Benjamin M; Adams, Roy J; Sadasivam, Rajani; Houston, Thomas K

2013-01-01

The goal of computer tailored health communications (CTHC) is to promote healthy behaviors by sending messages tailored to individual patients. Current CTHC systems collect baseline patient "profiles" and then use expert-written, rule-based systems to target messages to subsets of patients. Our main interest in this work is the study of collaborative filtering-based CTHC systems that can learn to tailor future message selections to individual patients based explicit feedback about past message selections. This paper reports the results of a study designed to collect explicit feedback (ratings) regarding four aspects of messages from 100 subjects in the smoking cessation support domain. Our results show that most users have positive opinions of most messages and that the ratings for all four aspects of the messages are highly correlated with each other. Finally, we conduct a range of rating prediction experiments comparing several different model variations. Our results show that predicting future ratings based on each user's past ratings contributes the most to predictive accuracy.

Towards Collaborative Filtering Recommender Systems for Tailored Health Communications

PubMed Central

Marlin, Benjamin M.; Adams, Roy J.; Sadasivam, Rajani; Houston, Thomas K.

2013-01-01

The goal of computer tailored health communications (CTHC) is to promote healthy behaviors by sending messages tailored to individual patients. Current CTHC systems collect baseline patient “profiles” and then use expert-written, rule-based systems to target messages to subsets of patients. Our main interest in this work is the study of collaborative filtering-based CTHC systems that can learn to tailor future message selections to individual patients based explicit feedback about past message selections. This paper reports the results of a study designed to collect explicit feedback (ratings) regarding four aspects of messages from 100 subjects in the smoking cessation support domain. Our results show that most users have positive opinions of most messages and that the ratings for all four aspects of the messages are highly correlated with each other. Finally, we conduct a range of rating prediction experiments comparing several different model variations. Our results show that predicting future ratings based on each user’s past ratings contributes the most to predictive accuracy. PMID:24551430

Making sense of sparse rating data in collaborative filtering via topographic organization of user preference patterns.

PubMed

Polcicová, Gabriela; Tino, Peter

2004-01-01

We introduce topographic versions of two latent class models (LCM) for collaborative filtering. Latent classes are topologically organized on a square grid. Topographic organization of latent classes makes orientation in rating/preference patterns captured by the latent classes easier and more systematic. The variation in film rating patterns is modelled by multinomial and binomial distributions with varying independence assumptions. In the first stage of topographic LCM construction, self-organizing maps with neural field organized according to the LCM topology are employed. We apply our system to a large collection of user ratings for films. The system can provide useful visualization plots unveiling user preference patterns buried in the data, without loosing potential to be a good recommender model. It appears that multinomial distribution is most adequate if the model is regularized by tight grid topologies. Since we deal with probabilistic models of the data, we can readily use tools from probability and information theories to interpret and visualize information extracted by our system.

A Dynamic Recommender System for Improved Web Usage Mining and CRM Using Swarm Intelligence

PubMed Central

Alphy, Anna; Prabakaran, S.

2015-01-01

In modern days, to enrich e-business, the websites are personalized for each user by understanding their interests and behavior. The main challenges of online usage data are information overload and their dynamic nature. In this paper, to address these issues, a WebBluegillRecom-annealing dynamic recommender system that uses web usage mining techniques in tandem with software agents developed for providing dynamic recommendations to users that can be used for customizing a website is proposed. The proposed WebBluegillRecom-annealing dynamic recommender uses swarm intelligence from the foraging behavior of a bluegill fish. It overcomes the information overload by handling dynamic behaviors of users. Our dynamic recommender system was compared against traditional collaborative filtering systems. The results show that the proposed system has higher precision, coverage, F1 measure, and scalability than the traditional collaborative filtering systems. Moreover, the recommendations given by our system overcome the overspecialization problem by including variety in recommendations. PMID:26229978

Application of recursive approaches to differential orbit correction of near Earth asteroids

NASA Astrophysics Data System (ADS)

Dmitriev, Vasily; Lupovka, Valery; Gritsevich, Maria

2016-10-01

Comparison of three approaches to the differential orbit correction of celestial bodies was performed: batch least squares fitting, Kalman filter, and recursive least squares filter. The first two techniques are well known and widely used (Montenbruck, O. & Gill, E., 2000). The most attention is paid to the algorithm and details of program realization of recursive least squares filter. The filter's algorithm was derived based on recursive least squares technique that are widely used in data processing applications (Simon, D, 2006). Usage recursive least squares filter, makes possible to process a new set of observational data, without reprocessing data, which has been processed before. Specific feature of such approach is that number of observation in data set may be variable. This feature makes recursive least squares filter more flexible approach compare to batch least squares (process complete set of observations in each iteration) and Kalman filtering (suppose updating state vector on each epoch with measurements).Advantages of proposed approach are demonstrated by processing of real astrometric observations of near Earth asteroids. The case of 2008 TC3 was studied. 2008 TC3 was discovered just before its impact with Earth. There are a many closely spaced observations of 2008 TC3 on the interval between discovering and impact, which creates favorable conditions for usage of recursive approaches. Each of approaches has very similar precision in case of 2008 TC3. At the same time, recursive least squares approaches have much higher performance. Thus, this approach more favorable for orbit fitting of a celestial body, which was detected shortly before the collision or close approach to the Earth.This work was carried out at MIIGAiK and supported by the Russian Science Foundation, Project no. 14-22-00197.References:O. Montenbruck and E. Gill, "Satellite Orbits, Models, Methods and Applications," Springer-Verlag, 2000, pp. 1-369.D. Simon, "Optimal State Estimation: Kalman, H Infinity, and Nonlinear Approaches",1 edition. Hoboken, N.J.: Wiley-Interscience, 2006.

Quantitative filter forensics for indoor particle sampling.

PubMed

Haaland, D; Siegel, J A

2017-03-01

Filter forensics is a promising indoor air investigation technique involving the analysis of dust which has collected on filters in central forced-air heating, ventilation, and air conditioning (HVAC) or portable systems to determine the presence of indoor particle-bound contaminants. In this study, we summarize past filter forensics research to explore what it reveals about the sampling technique and the indoor environment. There are 60 investigations in the literature that have used this sampling technique for a variety of biotic and abiotic contaminants. Many studies identified differences between contaminant concentrations in different buildings using this technique. Based on this literature review, we identified a lack of quantification as a gap in the past literature. Accordingly, we propose an approach to quantitatively link contaminants extracted from HVAC filter dust to time-averaged integrated air concentrations. This quantitative filter forensics approach has great potential to measure indoor air concentrations of a wide variety of particle-bound contaminants. Future studies directly comparing quantitative filter forensics to alternative sampling techniques are required to fully assess this approach, but analysis of past research suggests the enormous possibility of this approach. © 2016 John Wiley & Sons A/S. Published by John Wiley & Sons Ltd.

A Novel Approach to the Design of Passive Filters in Electric Grids

NASA Astrophysics Data System (ADS)

Filho da Costa Castro, José; Lima, Lucas Ramalho; Belchior, Fernando Nunes; Ribeiro, Paulo Fernando

2016-12-01

The design of shunt passive filters has been a topic of constant research since the 70's. Due to the lower cost, passive shunt filters are still considered a preferred option. This paper presents a novel approach for the placement and sizing of passive filters through ranking solutions based on the minimization of the total harmonic distortion (THDV) of the supply system rather than one specific bus, without neglecting the individual harmonic distortions. The developed method was implemented using Matlab/Simulink and applied to a test system. The results shown that is possible to minimize the total voltage harmonic distortion using a system approach during the filter selection. Additionally, since the method is mainly based on a heurist approach, it avoids the complexity associated with of use of advanced mathematical tools such as artificial intelligence techniques. The analyses contemplate a sinusoidal voltage utility and also the condition with background distortion utility.

A new smooth-k space filter approach to calculate halo abundances

NASA Astrophysics Data System (ADS)

Leo, Matteo; Baugh, Carlton M.; Li, Baojiu; Pascoli, Silvia

2018-04-01

We propose a new filter, a smooth-k space filter, to use in the Press-Schechter approach to model the dark matter halo mass function which overcomes shortcomings of other filters. We test this against the mass function measured in N-body simulations. We find that the commonly used sharp-k filter fails to reproduce the behaviour of the halo mass function at low masses measured from simulations of models with a sharp truncation in the linear power spectrum. We show that the predictions with our new filter agree with the simulation results over a wider range of halo masses for both damped and undamped power spectra than is the case with the sharp-k and real-space top-hat filters.

GSEH: A Novel Approach to Select Prostate Cancer-Associated Genes Using Gene Expression Heterogeneity.

PubMed

Kim, Hyunjin; Choi, Sang-Min; Park, Sanghyun

2018-01-01

When a gene shows varying levels of expression among normal people but similar levels in disease patients or shows similar levels of expression among normal people but different levels in disease patients, we can assume that the gene is associated with the disease. By utilizing this gene expression heterogeneity, we can obtain additional information that abets discovery of disease-associated genes. In this study, we used collaborative filtering to calculate the degree of gene expression heterogeneity between classes and then scored the genes on the basis of the degree of gene expression heterogeneity to find "differentially predicted" genes. Through the proposed method, we discovered more prostate cancer-associated genes than 10 comparable methods. The genes prioritized by the proposed method are potentially significant to biological processes of a disease and can provide insight into them.

A 3D ultrasound scanner: real time filtering and rendering algorithms.

PubMed

Cifarelli, D; Ruggiero, C; Brusacà, M; Mazzarella, M

1997-01-01

The work described here has been carried out within a collaborative project between DIST and ESAOTE BIOMEDICA aiming to set up a new ultrasonic scanner performing 3D reconstruction. A system is being set up to process and display 3D ultrasonic data in a fast, economical and user friendly way to help the physician during diagnosis. A comparison is presented among several algorithms for digital filtering, data segmentation and rendering for real time, PC based, three-dimensional reconstruction from B-mode ultrasonic biomedical images. Several algorithms for digital filtering have been compared as relates to processing time and to final image quality. Three-dimensional data segmentation techniques and rendering has been carried out with special reference to user friendly features for foreseeable applications and reconstruction speed.

Optical filter selection for high confidence discrimination of strongly overlapping infrared chemical spectra.

PubMed

Major, Kevin J; Poutous, Menelaos K; Ewing, Kenneth J; Dunnill, Kevin F; Sanghera, Jasbinder S; Aggarwal, Ishwar D

2015-09-01

Optical filter-based chemical sensing techniques provide a new avenue to develop low-cost infrared sensors. These methods utilize multiple infrared optical filters to selectively measure different response functions for various chemicals, dependent on each chemical's infrared absorption. Rather than identifying distinct spectral features, which can then be used to determine the identity of a target chemical, optical filter-based approaches rely on measuring differences in the ensemble response between a given filter set and specific chemicals of interest. Therefore, the results of such methods are highly dependent on the original optical filter choice, which will dictate the selectivity, sensitivity, and stability of any filter-based sensing method. Recently, a method has been developed that utilizes unique detection vector operations defined by optical multifilter responses, to discriminate between volatile chemical vapors. This method, comparative-discrimination spectral detection (CDSD), is a technique which employs broadband optical filters to selectively discriminate between chemicals with highly overlapping infrared absorption spectra. CDSD has been shown to correctly distinguish between similar chemicals in the carbon-hydrogen stretch region of the infrared absorption spectra from 2800-3100 cm(-1). A key challenge to this approach is how to determine which optical filter sets should be utilized to achieve the greatest discrimination between target chemicals. Previous studies used empirical approaches to select the optical filter set; however this is insufficient to determine the optimum selectivity between strongly overlapping chemical spectra. Here we present a numerical approach to systematically study the effects of filter positioning and bandwidth on a number of three-chemical systems. We describe how both the filter properties, as well as the chemicals in each set, affect the CDSD results and subsequent discrimination. These results demonstrate the importance of choosing the proper filter set and chemicals for comparative discrimination, in order to identify the target chemical of interest in the presence of closely matched chemical interferents. These findings are an integral step in the development of experimental prototype sensors, which will utilize CDSD.

Kalman Filtering Approach to Blind Equalization

DTIC Science & Technology

1993-12-01

NAVAL POSTGRADUATE SCHOOL Monterey, California •GR AD13 DTIC 94-07381 AR 0C199 THESIS S 0 LECTE4u KALMAN FILTERING APPROACH TO BLIND EQUALIZATION by...FILTERING APPROACH 5. FUNDING NUMBERS TO BLIND EQUALIZATION S. AUTHOR(S) Mehmet Kutlu 7. PERFORMING ORGANIZATION NAME(S) AND ADDRESS(ES) S...which introduces errors due to intersymbol interference. The solution to this problem is provided by equalizers which use a training sequence to adapt to

A Low Cost Structurally Optimized Design for Diverse Filter Types

PubMed Central

Kazmi, Majida; Aziz, Arshad; Akhtar, Pervez; Ikram, Nassar

2016-01-01

A wide range of image processing applications deploys two dimensional (2D)-filters for performing diversified tasks such as image enhancement, edge detection, noise suppression, multi scale decomposition and compression etc. All of these tasks require multiple type of 2D-filters simultaneously to acquire the desired results. The resource hungry conventional approach is not a viable option for implementing these computationally intensive 2D-filters especially in a resource constraint environment. Thus it calls for optimized solutions. Mostly the optimization of these filters are based on exploiting structural properties. A common shortcoming of all previously reported optimized approaches is their restricted applicability only for a specific filter type. These narrow scoped solutions completely disregard the versatility attribute of advanced image processing applications and in turn offset their effectiveness while implementing a complete application. This paper presents an efficient framework which exploits the structural properties of 2D-filters for effectually reducing its computational cost along with an added advantage of versatility for supporting diverse filter types. A composite symmetric filter structure is introduced which exploits the identities of quadrant and circular T-symmetries in two distinct filter regions simultaneously. These T-symmetries effectually reduce the number of filter coefficients and consequently its multipliers count. The proposed framework at the same time empowers this composite filter structure with additional capabilities of realizing all of its Ψ-symmetry based subtypes and also its special asymmetric filters case. The two-fold optimized framework thus reduces filter computational cost up to 75% as compared to the conventional approach as well as its versatility attribute not only supports diverse filter types but also offers further cost reduction via resource sharing for sequential implementation of diversified image processing applications especially in a constraint environment. PMID:27832133

Aligning Collaborative and Culturally Responsive Evaluation Approaches

ERIC Educational Resources Information Center

Askew, Karyl; Beverly, Monifa Green; Jay, Michelle L.

2012-01-01

The authors, three African-American women trained as collaborative evaluators, offer a comparative analysis of collaborative evaluation (O'Sullivan, 2004) and culturally responsive evaluation approaches (Frierson, Hood, & Hughes, 2002; Kirkhart & Hopson, 2010). Collaborative evaluation techniques immerse evaluators in the cultural milieu…

High-Performance Monitoring Architecture for Large-Scale Distributed Systems Using Event Filtering

NASA Technical Reports Server (NTRS)

Maly, K.

1998-01-01

Monitoring is an essential process to observe and improve the reliability and the performance of large-scale distributed (LSD) systems. In an LSD environment, a large number of events is generated by the system components during its execution or interaction with external objects (e.g. users or processes). Monitoring such events is necessary for observing the run-time behavior of LSD systems and providing status information required for debugging, tuning and managing such applications. However, correlated events are generated concurrently and could be distributed in various locations in the applications environment which complicates the management decisions process and thereby makes monitoring LSD systems an intricate task. We propose a scalable high-performance monitoring architecture for LSD systems to detect and classify interesting local and global events and disseminate the monitoring information to the corresponding end- points management applications such as debugging and reactive control tools to improve the application performance and reliability. A large volume of events may be generated due to the extensive demands of the monitoring applications and the high interaction of LSD systems. The monitoring architecture employs a high-performance event filtering mechanism to efficiently process the large volume of event traffic generated by LSD systems and minimize the intrusiveness of the monitoring process by reducing the event traffic flow in the system and distributing the monitoring computation. Our architecture also supports dynamic and flexible reconfiguration of the monitoring mechanism via its Instrumentation and subscription components. As a case study, we show how our monitoring architecture can be utilized to improve the reliability and the performance of the Interactive Remote Instruction (IRI) system which is a large-scale distributed system for collaborative distance learning. The filtering mechanism represents an Intrinsic component integrated with the monitoring architecture to reduce the volume of event traffic flow in the system, and thereby reduce the intrusiveness of the monitoring process. We are developing an event filtering architecture to efficiently process the large volume of event traffic generated by LSD systems (such as distributed interactive applications). This filtering architecture is used to monitor collaborative distance learning application for obtaining debugging and feedback information. Our architecture supports the dynamic (re)configuration and optimization of event filters in large-scale distributed systems. Our work represents a major contribution by (1) survey and evaluating existing event filtering mechanisms In supporting monitoring LSD systems and (2) devising an integrated scalable high- performance architecture of event filtering that spans several kev application domains, presenting techniques to improve the functionality, performance and scalability. This paper describes the primary characteristics and challenges of developing high-performance event filtering for monitoring LSD systems. We survey existing event filtering mechanisms and explain key characteristics for each technique. In addition, we discuss limitations with existing event filtering mechanisms and outline how our architecture will improve key aspects of event filtering.

An information-theoretic approach to motor action decoding with a reconfigurable parallel architecture.

PubMed

Craciun, Stefan; Brockmeier, Austin J; George, Alan D; Lam, Herman; Príncipe, José C

2011-01-01

Methods for decoding movements from neural spike counts using adaptive filters often rely on minimizing the mean-squared error. However, for non-Gaussian distribution of errors, this approach is not optimal for performance. Therefore, rather than using probabilistic modeling, we propose an alternate non-parametric approach. In order to extract more structure from the input signal (neuronal spike counts) we propose using minimum error entropy (MEE), an information-theoretic approach that minimizes the error entropy as part of an iterative cost function. However, the disadvantage of using MEE as the cost function for adaptive filters is the increase in computational complexity. In this paper we present a comparison between the decoding performance of the analytic Wiener filter and a linear filter trained with MEE, which is then mapped to a parallel architecture in reconfigurable hardware tailored to the computational needs of the MEE filter. We observe considerable speedup from the hardware design. The adaptation of filter weights for the multiple-input, multiple-output linear filters, necessary in motor decoding, is a highly parallelizable algorithm. It can be decomposed into many independent computational blocks with a parallel architecture readily mapped to a field-programmable gate array (FPGA) and scales to large numbers of neurons. By pipelining and parallelizing independent computations in the algorithm, the proposed parallel architecture has sublinear increases in execution time with respect to both window size and filter order.

Collaboration in River Basin Management: The Great Rivers Project

NASA Astrophysics Data System (ADS)

Crowther, S.; Vridhachalam, M.; Tomala-Reyes, A.; Guerra, A.; Chu, H.; Eckman, B.

2008-12-01

The health of the world's freshwater ecosystems is fundamental to the health of people, plants and animals around the world. The sustainable use of the world's freshwater resources is recognized as one of the most urgent challenges facing society today. An estimated 1.3 billion people currently lack access to safe drinking water, an issue the United Nations specifically includes in its recently published Millennium Development Goals. IBM is collaborating with The Nature Conservancy and the Center for Sustainability and the Global Environment (SAGE) at the University of Wisconsin, Madison to build a Modeling Collaboration Framework and Decision Support System (DSS) designed to help policy makers and a variety of stakeholders (farmers, fish and wildlife managers, hydropower operators, et al.) to assess, come to consensus, and act on land use decisions representing effective compromises between human use and ecosystem preservation/restoration efforts. Initially focused on Brazil's Paraguay-Parana, China's Yangtze, and the Mississippi Basin in the US, the DSS integrates data and models from a wide variety of environmental sectors, including water balance, water quality, carbon balance, crop production, hydropower, and biodiversity. In this presentation we focus on the collaboration aspects of the DSS. The DSS is an open environment tool that allows scientists, policy makers, politicians, land owners, and anyone who desires to take ownership of their actions in support of the environment to work together to that end. The DSS supports a range of features that empower such a community to collaboratively work together. Supported collaboration mediums include peer reviews, live chat, static comments, and Web 2.0 functionality such as tagging. In addition, we are building a 3-D virtual world component which will allow users to experience and share system results, first-hand. Models and simulation results may be annotated with free-text comments and tags, whether unique or chosen from a predefined tag taxonomy. These comments and tag clouds may be used by the community to filter results and identify models or simulations of interest, e.g, by region, modeling approach, spatiotemporal resolution, etc. Users may discuss methods or results in real-time with a built-in chat feature. Separate user groups may be defined for logical groups of collaboration partners, e.g., expert modelers, land managers, policy makers, school children, or the general public, to optimize the collaboration signal-to-noise ratio for all.

An annotation system for 3D fluid flow visualization

NASA Technical Reports Server (NTRS)

Loughlin, Maria M.; Hughes, John F.

1995-01-01

Annotation is a key activity of data analysis. However, current systems for data analysis focus almost exclusively on visualization. We propose a system which integrates annotations into a visualization system. Annotations are embedded in 3D data space, using the Post-it metaphor. This embedding allows contextual-based information storage and retrieval, and facilitates information sharing in collaborative environments. We provide a traditional database filter and a Magic Lens filter to create specialized views of the data. The system has been customized for fluid flow applications, with features which allow users to store parameters of visualization tools and sketch 3D volumes.

[Investigation of fast filter of ECG signals with lifting wavelet and smooth filter].

PubMed

Li, Xuefei; Mao, Yuxing; He, Wei; Yang, Fan; Zhou, Liang

2008-02-01

The lifting wavelet is used to decompose the original ECG signals and separate them into the approach signals with low frequency and the detail signals with high frequency, based on frequency characteristic. Parts of the detail signals are ignored according to the frequency characteristic. To avoid the distortion of QRS Complexes, the approach signals are filtered by an adaptive smooth filter with a proper threshold value. Through the inverse transform of the lifting wavelet, the reserved approach signals are reconstructed, and the three primary kinds of noise are limited effectively. In addition, the method is fast and there is no time delay between input and output.

Designing the Undesignable: Social Software and Control

ERIC Educational Resources Information Center

Dron, Jon

2007-01-01

Social software, such as blogs, wikis, tagging systems and collaborative filters, treats the group as a first-class object within the system. Drawing from theories of transactional distance and control, this paper proposes a model of e-learning that extends traditional concepts of learner-teacher-content interactions to include these emergent…

Memory in the Information Age: New Tools for Second Language Acquisition.

ERIC Educational Resources Information Center

Chapin, Alex

2003-01-01

Describes a Middlebury College second language vocabulary learning database that goes well beyond flashcards, because it keeps track of what students learn. Discusses further expansion of the system through collaborative filtering software to establish learner profiles. A learner profile could then be used to create instructional materials just…

Networked Information: Finding What's Out There.

ERIC Educational Resources Information Center

Lynch, Clifford A.

1997-01-01

Clifford A. Lynch, developer of MELVYL and former director of library automation at the University of California, is now executive director for the Coalition for Networked Information (CNI). This interview discusses Lynch's background, MELVYL, the Web and the role of libraries and librarians, community and collaborative filtering, the library of…

Blogging and Internet Filters in Schools

ERIC Educational Resources Information Center

Shearer, Kimberly M.

2010-01-01

Success in today's global market requires students to attain numerous 21st-Century skills, including collaborative and communication skills, and knowledge of how to use technology to both locate and create information. The use of instructional blogging in the classroom is one way to help students develop such skills. The Children's Internet…

On the Recommender System for University Library

ERIC Educational Resources Information Center

Fu, Shunkai; Zhang, Yao; Seinminn

2013-01-01

Libraries are important to universities, and they have two primary features: readers as well as collections are highly professional. In this study, based on the experimental study with five millions of users' borrowing records, our discussion covers: (1) the necessity of recommender system for university libraries; (2) collaborative filtering (CF)…

Semantic Web-Driven LMS Architecture towards a Holistic Learning Process Model Focused on Personalization

ERIC Educational Resources Information Center

Kerkiri, Tania

2010-01-01

A comprehensive presentation is here made on the modular architecture of an e-learning platform with a distinctive emphasis on content personalization, combining advantages from semantic web technology, collaborative filtering and recommendation systems. Modules of this architecture handle information about both the domain-specific didactic…

Personalized Recommendation of Learning Material Using Sequential Pattern Mining and Attribute Based Collaborative Filtering

ERIC Educational Resources Information Center

Salehi, Mojtaba; Nakhai Kamalabadi, Isa; Ghaznavi Ghoushchi, Mohammad Bagher

2014-01-01

Material recommender system is a significant part of e-learning systems for personalization and recommendation of appropriate materials to learners. However, in the existing recommendation algorithms, dynamic interests and multi-preference of learners and multidimensional-attribute of materials are not fully considered simultaneously. Moreover,…

Contexts in a Paper Recommendation System with Collaborative Filtering

ERIC Educational Resources Information Center

Winoto, Pinata; Tang, Tiffany Ya; McCalla, Gordon

2012-01-01

Making personalized paper recommendations to users in an educational domain is not a trivial task of simply matching users' interests with a paper topic. Therefore, we proposed a context-aware multidimensional paper recommendation system that considers additional user and paper features. Earlier experiments on experienced graduate students…

A Flexible Component based Access Control Architecture for OPeNDAP Services

NASA Astrophysics Data System (ADS)

Kershaw, Philip; Ananthakrishnan, Rachana; Cinquini, Luca; Lawrence, Bryan; Pascoe, Stephen; Siebenlist, Frank

2010-05-01

Network data access services such as OPeNDAP enable widespread access to data across user communities. However, without ready means to restrict access to data for such services, data providers and data owners are constrained from making their data more widely available. Even with such capability, the range of different security technologies available can make interoperability between services and user client tools a challenge. OPeNDAP is a key data access service in the infrastructure under development to support the CMIP5 (Couple Model Intercomparison Project Phase 5). The work is being carried out as part of an international collaboration including the US Earth System Grid and Curator projects and the EU funded IS-ENES and Metafor projects. This infrastructure will bring together Petabytes of climate model data and associated metadata from over twenty modelling centres around the world in a federation with a core archive mirrored at three data centres. A security system is needed to meet the requirements of organisations responsible for model data including the ability to restrict data access to registered users, keep them up to date with changes to data and services, audit access and protect finite computing resources. Individual organisations have existing tools and services such as OPeNDAP with which users in the climate research community are already familiar. The security system should overlay access control in a way which maintains the usability and ease of access to these services. The BADC (British Atmospheric Data Centre) has been working in collaboration with the Earth System Grid development team and partner organisations to develop the security architecture. OpenID and MyProxy were selected at an early stage in the ESG project to provide single sign-on capability across the federation of participating organisations. Building on the existing OPeNDAP specification an architecture based on pluggable server side components has been developed at the BADC. These components filter requests to the service they protect and apply the required authentication and authorisation schemes. Filters have been developed for OpenID and SSL client based authentication. The latter enabling access with MyProxy issued credentials. By preserving a clear separation between the security and application functionality, multiple authentication technologies may be supported without the need for modification to the underlying OPeNDAP application. The software has been developed in the Python programming language securing the Python based OPeNDAP implementation, PyDAP. This utilises the Python WSGI (Web Server Gateway Interface) specification to create distinct security filter components. Work is also currently underway to develop a parallel Java based filter implementation to secure the THREDDS Data Server. Whilst the ability to apply this flexible approach to the server side security layer is important, the development of compatible client software is vital to the take up of these services across a wide user base. To date PyDAP and wget based clients have been tested and work is planned to integrate the required security interface into the netCDF API. This forms part of ongoing collaboration with the OPeNDAP user and development community to ensure interoperability.

PBL and beyond: trends in collaborative learning.

PubMed

Pluta, William J; Richards, Boyd F; Mutnick, Andrew

2013-01-01

Building upon the disruption to lecture-based methods triggered by the introduction of problem-based learning, approaches to promote collaborative learning are becoming increasingly diverse, widespread and generally well accepted within medical education. Examples of relatively new, structured collaborative learning methods include team-based learning and just-in-time teaching. Examples of less structured approaches include think-pair share, case discussions, and the flipped classroom. It is now common practice in medical education to employ a range of instructional approaches to support collaborative learning. We believe that the adoption of such approaches is entering a new and challenging era. We define collaborate learning by drawing on the broader literature, including Chi's ICAP framework that emphasizes the importance of sustained, interactive explanation and elaboration by learners. We distinguish collaborate learning from constructive, active, and passive learning and provide preliminary evidence documenting the growth of methods that support collaborative learning. We argue that the rate of adoption of collaborative learning methods will accelerate due to a growing emphasis on the development of team competencies and the increasing availability of digital media. At the same time, the adoption collaborative learning strategies face persistent challenges, stemming from an overdependence on comparative-effectiveness research and a lack of useful guidelines about how best to adapt collaborative learning methods to given learning contexts. The medical education community has struggled to consistently demonstrate superior outcomes when using collaborative learning methods and strategies. Despite this, support for their use will continue to expand. To select approaches with the greatest utility, instructors must carefully align conditions of the learning context with the learning approaches under consideration. Further, it is critical that modifications are made with caution and that instructors verify that modifications do not impede the desired cognitive activities needed to support meaningful collaborative learning.

An extended Kalman filter approach to non-stationary Bayesian estimation of reduced-order vocal fold model parameters.

PubMed

Hadwin, Paul J; Peterson, Sean D

2017-04-01

The Bayesian framework for parameter inference provides a basis from which subject-specific reduced-order vocal fold models can be generated. Previously, it has been shown that a particle filter technique is capable of producing estimates and associated credibility intervals of time-varying reduced-order vocal fold model parameters. However, the particle filter approach is difficult to implement and has a high computational cost, which can be barriers to clinical adoption. This work presents an alternative estimation strategy based upon Kalman filtering aimed at reducing the computational cost of subject-specific model development. The robustness of this approach to Gaussian and non-Gaussian noise is discussed. The extended Kalman filter (EKF) approach is found to perform very well in comparison with the particle filter technique at dramatically lower computational cost. Based upon the test cases explored, the EKF is comparable in terms of accuracy to the particle filter technique when greater than 6000 particles are employed; if less particles are employed, the EKF actually performs better. For comparable levels of accuracy, the solution time is reduced by 2 orders of magnitude when employing the EKF. By virtue of the approximations used in the EKF, however, the credibility intervals tend to be slightly underpredicted.

An approach for fixed coefficient RNS-based FIR filter

NASA Astrophysics Data System (ADS)

Srinivasa Reddy, Kotha; Sahoo, Subhendu Kumar

2017-08-01

In this work, an efficient new modular multiplication method for {2k-1, 2k, 2k+1-1} moduli set is proposed to implement a residue number system (RNS)-based fixed coefficient finite impulse response filter. The new multiplication approach reduces the number of partial products by using pre-loaded product block. The reduction in partial products with the proposed modular multiplication improves the clock frequency and reduces the area and power as compared with the conventional modular multiplication. Further, the present approach eliminates a binary number to residue number converter circuit, which is usually needed at the front end of RNS-based system. In this work, two fixed coefficient filter architectures with the new modular multiplication approach are proposed. The filters are implemented using Verilog hardware description language. The United Microelectronics Corporation 90 nm technology library has been used for synthesis and the results area, power and delay are obtained with the help of Cadence register transfer level compiler. The power delay product (PDP) is also considered for performance comparison among the proposed filters. One of the proposed architecture is found to improve PDP gain by 60.83% as compared with the filter implemented with conventional modular multiplier. The filters functionality is validated with the help of Altera DSP Builder.

Orion Emergency Mask Approach

NASA Technical Reports Server (NTRS)

Tuan, George C.; Graf, John C.

2008-01-01

Emergency mask approach on Orion poses a challenge to the traditional Shuttle or Station approaches. Currently, in the case of a fire or toxic spill event, the crew utilizes open loop oxygen masks that provide the crew with oxygen to breath, but also dumps the exhaled oxygen into the cabin. For Orion, with a small cabin volume, the extra oxygen will exceed the flammability limit within a short period of time, unless a nitrogen purge is also provided. Another approach to a fire or toxic spill event is the use of a filtering emergency masks. These masks utilize some form of chemical beds to scrub the air clean of toxic providing the crew safe breathing air for a period without elevating the oxygen level in the cabin. Using the masks and a form of smoke-eater filter, it may be possible to clean the cabin completely or to a level for safe transition to a space suit to perform a cabin purge. Issues with filters in the past have been the reaction temperature and high breathing resistance. Development in a new form of chemical filters has shown promise to make the filtering approach feasible.

Orion Emergency Mask Approach

NASA Technical Reports Server (NTRS)

Tuan, George C.; Graf, John C.

2009-01-01

Emergency mask approach on Orion poses a challenge to the traditional Shuttle or Station approaches. Currently, in the case of a fire or toxic spill event, the crew utilizes open loop oxygen masks that provide the crew with oxygen to breath, but also dumps the exhaled oxygen into the cabin. For Orion, with a small cabin volume, the extra oxygen will exceed the flammability limit within a short period of time, unless a nitrogen purge is also provided. Another approach to a fire or toxic spill event is the use of a filtering emergency masks. These masks utilize some form of chemical beds to scrub the air clean of toxic providing the crew safe breathing air for a period without elevating the oxygen level in the cabin. Using the masks and a form of smoke-eater filter, it may be possible to clean the cabin completely or to a level for safe transition to a space suit to perform a cabin purge. Issues with filters in the past have been the reaction time, breakthroughs, and high breathing resistance. Development in a new form of chemical filters has shown promise to make the filtering approach feasible.

Novel approach for modifying microporous filters for virus concentration from water.

PubMed Central

Preston, D R; Vasudevan, T V; Bitton, G; Farrah, S R; Morel, J L

1988-01-01

Electronegative microporous filters composed of epoxyfiberglass (Filterite) were treated with cationic polymers to enhance their virus-adsorbing properties. This novel and inexpensive approach to microporous filter modification entails soaking filters in an aqueous solution of a cationic polymer such as polyethyleneimine (PEI) for 2 h at room temperature and then allowing the filters to air dry overnight on absorbent paper towels. PEI-treated filters were evaluated for coliphage (MS2, T2, and phi X174) and enterovirus (poliovirus type 1 and coxsackievirus type B5) adsorption from buffer at pH 3.5 to 9.0 and for indigenous coliphages from unchlorinated secondary effluent at ambient pH. Adsorbed viruses were recovered with 3% beef extract (pH 9). Several other cationic polymers were used to modify epoxyfiberglass filters and were evaluated for their ability to concentrate viruses from water. Zeta potentials of disrupted filter material indicated that electronegative epoxyfiberglass filters were made more electropositive when treated with cationic polymers. In general, epoxyfiberglass filters treated with cationic polymers were found to adsorb a greater percentage of coliphages and enteroviruses than were untreated filters. PMID:2843091

Ultra-accurate collaborative information filtering via directed user similarity

NASA Astrophysics Data System (ADS)

Guo, Q.; Song, W.-J.; Liu, J.-G.

2014-07-01

A key challenge of the collaborative filtering (CF) information filtering is how to obtain the reliable and accurate results with the help of peers' recommendation. Since the similarities from small-degree users to large-degree users would be larger than the ones in opposite direction, the large-degree users' selections are recommended extensively by the traditional second-order CF algorithms. By considering the users' similarity direction and the second-order correlations to depress the influence of mainstream preferences, we present the directed second-order CF (HDCF) algorithm specifically to address the challenge of accuracy and diversity of the CF algorithm. The numerical results for two benchmark data sets, MovieLens and Netflix, show that the accuracy of the new algorithm outperforms the state-of-the-art CF algorithms. Comparing with the CF algorithm based on random walks proposed by Liu et al. (Int. J. Mod. Phys. C, 20 (2009) 285) the average ranking score could reach 0.0767 and 0.0402, which is enhanced by 27.3% and 19.1% for MovieLens and Netflix, respectively. In addition, the diversity, precision and recall are also enhanced greatly. Without relying on any context-specific information, tuning the similarity direction of CF algorithms could obtain accurate and diverse recommendations. This work suggests that the user similarity direction is an important factor to improve the personalized recommendation performance.

Advanced Sine Wave Modulation of Continuous Wave Laser System for Atmospheric CO2 Differential Absorption Measurements

NASA Technical Reports Server (NTRS)

Campbell, Joel F.; Lin, Bing; Nehrir, Amin R.

2014-01-01

NASA Langley Research Center in collaboration with ITT Exelis have been experimenting with Continuous Wave (CW) laser absorption spectrometer (LAS) as a means of performing atmospheric CO2 column measurements from space to support the Active Sensing of CO2 Emissions over Nights, Days, and Seasons (ASCENDS) mission.Because range resolving Intensity Modulated (IM) CW lidar techniques presented here rely on matched filter correlations, autocorrelation properties without side lobes or other artifacts are highly desirable since the autocorrelation function is critical for the measurements of lidar return powers, laser path lengths, and CO2 column amounts. In this paper modulation techniques are investigated that improve autocorrelation properties. The modulation techniques investigated in this paper include sine waves modulated by maximum length (ML) sequences in various hardware configurations. A CW lidar system using sine waves modulated by ML pseudo random noise codes is described, which uses a time shifting approach to separate channels and make multiple, simultaneous online/offline differential absorption measurements. Unlike the pure ML sequence, this technique is useful in hardware that is band pass filtered as the IM sine wave carrier shifts the main power band. Both amplitude and Phase Shift Keying (PSK) modulated IM carriers are investigated that exibit perfect autocorrelation properties down to one cycle per code bit. In addition, a method is presented to bandwidth limit the ML sequence based on a Gaussian filter implemented in terms of Jacobi theta functions that does not seriously degrade the resolution or introduce side lobes as a means of reducing aliasing and IM carrier bandwidth.

Input filter compensation for switching regulators

NASA Technical Reports Server (NTRS)

Lee, F. C.; Kelkar, S. S.

1982-01-01

The problems caused by the interaction between the input filter, output filter, and the control loop are discussed. The input filter design is made more complicated because of the need to avoid performance degradation and also stay within the weight and loss limitations. Conventional input filter design techniques are then dicussed. The concept of pole zero cancellation is reviewed; this concept is the basis for an approach to control the peaking of the output impedance of the input filter and thus mitigate some of the problems caused by the input filter. The proposed approach for control of the peaking of the output impedance of the input filter is to use a feedforward loop working in conjunction with feedback loops, thus forming a total state control scheme. The design of the feedforward loop for a buck regulator is described. A possible implementation of the feedforward loop design is suggested.

Accurate and scalable social recommendation using mixed-membership stochastic block models.

PubMed

Godoy-Lorite, Antonia; Guimerà, Roger; Moore, Cristopher; Sales-Pardo, Marta

2016-12-13

With increasing amounts of information available, modeling and predicting user preferences-for books or articles, for example-are becoming more important. We present a collaborative filtering model, with an associated scalable algorithm, that makes accurate predictions of users' ratings. Like previous approaches, we assume that there are groups of users and of items and that the rating a user gives an item is determined by their respective group memberships. However, we allow each user and each item to belong simultaneously to mixtures of different groups and, unlike many popular approaches such as matrix factorization, we do not assume that users in each group prefer a single group of items. In particular, we do not assume that ratings depend linearly on a measure of similarity, but allow probability distributions of ratings to depend freely on the user's and item's groups. The resulting overlapping groups and predicted ratings can be inferred with an expectation-maximization algorithm whose running time scales linearly with the number of observed ratings. Our approach enables us to predict user preferences in large datasets and is considerably more accurate than the current algorithms for such large datasets.

Accurate and scalable social recommendation using mixed-membership stochastic block models

PubMed Central

Godoy-Lorite, Antonia; Moore, Cristopher

2016-01-01

With increasing amounts of information available, modeling and predicting user preferences—for books or articles, for example—are becoming more important. We present a collaborative filtering model, with an associated scalable algorithm, that makes accurate predictions of users’ ratings. Like previous approaches, we assume that there are groups of users and of items and that the rating a user gives an item is determined by their respective group memberships. However, we allow each user and each item to belong simultaneously to mixtures of different groups and, unlike many popular approaches such as matrix factorization, we do not assume that users in each group prefer a single group of items. In particular, we do not assume that ratings depend linearly on a measure of similarity, but allow probability distributions of ratings to depend freely on the user’s and item’s groups. The resulting overlapping groups and predicted ratings can be inferred with an expectation-maximization algorithm whose running time scales linearly with the number of observed ratings. Our approach enables us to predict user preferences in large datasets and is considerably more accurate than the current algorithms for such large datasets. PMID:27911773

Leveraging tagging and rating for recommendation: RMF meets weighted diffusion on tripartite graphs

NASA Astrophysics Data System (ADS)

Li, Jianguo; Tang, Yong; Chen, Jiemin

2017-10-01

Recommender systems (RSs) have been a widely exploited approach to solving the information overload problem. However, the performance is still limited due to the extreme sparsity of the rating data. With the popularity of Web 2.0, the social tagging system provides more external information to improve recommendation accuracy. Although some existing approaches combine the matrix factorization models with the tag co-occurrence and context of tags, they neglect the issue of tag sparsity that would also result in inaccurate recommendations. Consequently, in this paper, we propose a novel hybrid collaborative filtering model named WUDiff_RMF, which improves regularized matrix factorization (RMF) model by integrating Weighted User-Diffusion-based CF algorithm(WUDiff) that obtains the information of similar users from the weighted tripartite user-item-tag graph. This model aims to capture the degree correlation of the user-item-tag tripartite network to enhance the performance of recommendation. Experiments conducted on four real-world datasets demonstrate that our approach significantly performs better than already widely used methods in the accuracy of recommendation. Moreover, results show that WUDiff_RMF can alleviate the data sparsity, especially in the circumstance that users have made few ratings and few tags.

An Efficient Recommendation Filter Model on Smart Home Big Data Analytics for Enhanced Living Environments.

PubMed

Chen, Hao; Xie, Xiaoyun; Shu, Wanneng; Xiong, Naixue

2016-10-15

With the rapid growth of wireless sensor applications, the user interfaces and configurations of smart homes have become so complicated and inflexible that users usually have to spend a great amount of time studying them and adapting to their expected operation. In order to improve user experience, a weighted hybrid recommender system based on a Kalman Filter model is proposed to predict what users might want to do next, especially when users are located in a smart home with an enhanced living environment. Specifically, a weight hybridization method was introduced, which combines contextual collaborative filter and the contextual content-based recommendations. This method inherits the advantages of the optimum regression and the stability features of the proposed adaptive Kalman Filter model, and it can predict and revise the weight of each system component dynamically. Experimental results show that the hybrid recommender system can optimize the distribution of weights of each component, and achieve more reasonable recall and precision rates.

An Efficient Recommendation Filter Model on Smart Home Big Data Analytics for Enhanced Living Environments

PubMed Central

Chen, Hao; Xie, Xiaoyun; Shu, Wanneng; Xiong, Naixue

2016-01-01

With the rapid growth of wireless sensor applications, the user interfaces and configurations of smart homes have become so complicated and inflexible that users usually have to spend a great amount of time studying them and adapting to their expected operation. In order to improve user experience, a weighted hybrid recommender system based on a Kalman Filter model is proposed to predict what users might want to do next, especially when users are located in a smart home with an enhanced living environment. Specifically, a weight hybridization method was introduced, which combines contextual collaborative filter and the contextual content-based recommendations. This method inherits the advantages of the optimum regression and the stability features of the proposed adaptive Kalman Filter model, and it can predict and revise the weight of each system component dynamically. Experimental results show that the hybrid recommender system can optimize the distribution of weights of each component, and achieve more reasonable recall and precision rates. PMID:27754456

Computational approaches for predicting biomedical research collaborations.

PubMed

Zhang, Qing; Yu, Hong

2014-01-01

Biomedical research is increasingly collaborative, and successful collaborations often produce high impact work. Computational approaches can be developed for automatically predicting biomedical research collaborations. Previous works of collaboration prediction mainly explored the topological structures of research collaboration networks, leaving out rich semantic information from the publications themselves. In this paper, we propose supervised machine learning approaches to predict research collaborations in the biomedical field. We explored both the semantic features extracted from author research interest profile and the author network topological features. We found that the most informative semantic features for author collaborations are related to research interest, including similarity of out-citing citations, similarity of abstracts. Of the four supervised machine learning models (naïve Bayes, naïve Bayes multinomial, SVMs, and logistic regression), the best performing model is logistic regression with an ROC ranging from 0.766 to 0.980 on different datasets. To our knowledge we are the first to study in depth how research interest and productivities can be used for collaboration prediction. Our approach is computationally efficient, scalable and yet simple to implement. The datasets of this study are available at https://github.com/qingzhanggithub/medline-collaboration-datasets.

Fast estimate of Hartley entropy in image sharpening

NASA Astrophysics Data System (ADS)

Krbcová, Zuzana; Kukal, Jaromír.; Svihlik, Jan; Fliegel, Karel

2016-09-01

Two classes of linear IIR filters: Laplacian of Gaussian (LoG) and Difference of Gaussians (DoG) are frequently used as high pass filters for contextual vision and edge detection. They are also used for image sharpening when linearly combined with the original image. Resulting sharpening filters are radially symmetric in spatial and frequency domains. Our approach is based on the radial approximation of unknown optimal filter, which is designed as a weighted sum of Gaussian filters with various radii. The novel filter is designed for MRI image enhancement where the image intensity represents anatomical structure plus additive noise. We prefer the gradient norm of Hartley entropy of whole image intensity as a measure which has to be maximized for the best sharpening. The entropy estimation procedure is as fast as FFT included in the filter but this estimate is a continuous function of enhanced image intensities. Physically motivated heuristic is used for optimum sharpening filter design by its parameter tuning. Our approach is compared with Wiener filter on MRI images.

Advanced Value Chain Collaboration in Ghana's Cocoa Sector: An Entry Point for Integrated Landscape Approaches?

PubMed

Deans, Howard; Ros-Tonen, Mirjam A F; Derkyi, Mercy

2017-04-15

Value chain analyses have focused mainly on collaboration between chain actors, often neglecting collaboration "beyond the chain" with non-chain actors to tackle food security, poverty and sustainability issues in the landscapes in which these value chains are embedded. Comparing conventional and advanced value chain collaborations involving small-scale cocoa farmers in Ghana, this paper analyzes the merits of a more integrated approach toward value chain collaboration. It particularly asks whether advanced value chain collaboration targeting cocoa-producing areas potentially offers an entry point for implementing a landscape approach. The findings detail current chain actors and institutions and show how advanced value chain collaboration has a greater positive impact than conventional value chain collaboration on farmers' social, human and natural capital. The paper concludes that the integrated approach, focus on learning, and stable relationships with small-scale farmers inherent in advanced value chain collaboration makes it both more sustainable and effective at the local level than conventional approaches. However, its scope and the actors' jurisdictional powers and self-organization are too limited to be the sole tool in negotiating land use and trade-offs at the landscape level. To evolve as such would require certification beyond the farm level, partnering with other landscape stakeholders, and brokering by bridging organizations.

Weighted Optimization-Based Distributed Kalman Filter for Nonlinear Target Tracking in Collaborative Sensor Networks.

PubMed

Chen, Jie; Li, Jiahong; Yang, Shuanghua; Deng, Fang

2017-11-01

The identification of the nonlinearity and coupling is crucial in nonlinear target tracking problem in collaborative sensor networks. According to the adaptive Kalman filtering (KF) method, the nonlinearity and coupling can be regarded as the model noise covariance, and estimated by minimizing the innovation or residual errors of the states. However, the method requires large time window of data to achieve reliable covariance measurement, making it impractical for nonlinear systems which are rapidly changing. To deal with the problem, a weighted optimization-based distributed KF algorithm (WODKF) is proposed in this paper. The algorithm enlarges the data size of each sensor by the received measurements and state estimates from its connected sensors instead of the time window. A new cost function is set as the weighted sum of the bias and oscillation of the state to estimate the "best" estimate of the model noise covariance. The bias and oscillation of the state of each sensor are estimated by polynomial fitting a time window of state estimates and measurements of the sensor and its neighbors weighted by the measurement noise covariance. The best estimate of the model noise covariance is computed by minimizing the weighted cost function using the exhaustive method. The sensor selection method is in addition to the algorithm to decrease the computation load of the filter and increase the scalability of the sensor network. The existence, suboptimality and stability analysis of the algorithm are given. The local probability data association method is used in the proposed algorithm for the multitarget tracking case. The algorithm is demonstrated in simulations on tracking examples for a random signal, one nonlinear target, and four nonlinear targets. Results show the feasibility and superiority of WODKF against other filtering algorithms for a large class of systems.

Event-triggered resilient filtering with stochastic uncertainties and successive packet dropouts via variance-constrained approach

NASA Astrophysics Data System (ADS)

Jia, Chaoqing; Hu, Jun; Chen, Dongyan; Liu, Yurong; Alsaadi, Fuad E.

2018-07-01

In this paper, we discuss the event-triggered resilient filtering problem for a class of time-varying systems subject to stochastic uncertainties and successive packet dropouts. The event-triggered mechanism is employed with hope to reduce the communication burden and save network resources. The stochastic uncertainties are considered to describe the modelling errors and the phenomenon of successive packet dropouts is characterized by a random variable obeying the Bernoulli distribution. The aim of the paper is to provide a resilient event-based filtering approach for addressed time-varying systems such that, for all stochastic uncertainties, successive packet dropouts and filter gain perturbation, an optimized upper bound of the filtering error covariance is obtained by designing the filter gain. Finally, simulations are provided to demonstrate the effectiveness of the proposed robust optimal filtering strategy.

Angle-Beam Shear Wave Scattering from Buried Crack-like Defects in Bonded Specimens (Postprint)

DTIC Science & Technology

2017-02-01

wavenumber filtering and spatial windowing is proposed and implemented as an alternative approach to quantify scattering from damage. 15. SUBJECT...TERMS Backscattering . Ultrasonography . Spatial filtering . Ultrasonic scattering . Scattering measurement 16. SECURITY CLASSIFICATION OF: 17...of frequency- wavenumber filtering and spatial windowing is proposed and implemented as an alternative approach to quantify scattering from damage

OPEN RADIATION: a collaborative project for radioactivity measurement in the environment by the public

NASA Astrophysics Data System (ADS)

Bottollier-Depois, Jean-François; Allain, E.; Baumont, G.; Berthelot, N.; Clairand, I.; Couvez, C.; Darley, G.; Henry, B.; Jolivet, T.; Laroche, P.; Lebau-Livé, A.; Lejeune, V.; Miss, J.; Monange, W.; Quéinnec, F.; Richet, Y.; Simon, C.; Trompier, F.; Vayron, F.

2017-09-01

After the Fukushima accident, initiatives emerged from the public to carry out themselves measurements of the radioactivity in the environment with various devices, among which smartphones, and to share data and experiences through collaborative tools and social networks. Such measurements have two major interests, on the one hand, to enable each individual of the public to assess his own risk regarding the radioactivity and, on the other hand, to provide "real time" data from the field at various locations, especially in the early phase of an emergency situation, which could be very useful for the emergency management. The objective of the OPENRADIATION project is to offer to the public the opportunity to be an actor for measurements of the radioactivity in the environment using connected dosimetric applications on smartphones. The challenge is to operate such a system on a sustainable basis in peaceful time and be useful in case of emergency. In "peaceful situation", this project is based on a collaborative approach with the aim to get complementary data to the existing ones, to consolidate the radiation background, to generate alerts in case of problem and to provide education & training and enhanced pedagogical approaches for a clear understanding of measures for the public. In case of emergency situation, data will be available "spontaneously" from the field in "real time" providing an opportunity for the emergency management and the communication with the public. … The practical objective is i) to develop a website centralising data from various systems/dosimeters, providing dose maps with raw and filtered data and creating dedicated areas for specific initiatives and exchanges of data and ii) to develop a data acquisition protocol and a dosimetric application using a connected dosimeter with a bluetooth connection. This project is conducted within a partnership between organisms' representative of the scientific community and associations to create links with the public.

Inside and outside: Teacher-Researcher Collaboration

ERIC Educational Resources Information Center

Herrenkohl, Leslie Rupert; Kawasaki, Keiko; DeWater, Lezlie Salvatore

2010-01-01

In this paper, we discuss our approach to teacher-researcher collaboration and how it is similar and different from other models of teacher collaboration. Our approach to collaboration employed design experimentation (Brown, 1992; Design Based Research Collective, 2003) as a central method since it yields important findings for teachers'…

Modeling Adsorption Based Filters (Bio-remediation of Heavy Metal Contaminated Water)

NASA Astrophysics Data System (ADS)

McCarthy, Chris

I will discuss kinetic models of adsorption, as well as models of filters based on those mechanisms. These mathematical models have been developed in support of our interdisciplinary lab group, which is centered at BMCC/CUNY (City University of New York). Our group conducts research into bio-remediation of heavy metal contaminated water via filtration. The filters are constructed out of biomass, such as spent tea leaves. The spent tea leaves are available in large quantities as a result of the industrial production of tea beverages. The heavy metals bond with the surfaces of the tea leaves (adsorption). The models involve differential equations, stochastic methods, and recursive functions. I will compare the models' predictions to data obtained from computer simulations and experimentally by our lab group. Funding: CUNY Collaborative Incentive Research Grant (Round 12); CUNY Research Scholars Program.

Scalable Open Science Approach for Mutation Calling of Tumor Exomes Using Multiple Genomic Pipelines.

PubMed

Ellrott, Kyle; Bailey, Matthew H; Saksena, Gordon; Covington, Kyle R; Kandoth, Cyriac; Stewart, Chip; Hess, Julian; Ma, Singer; Chiotti, Kami E; McLellan, Michael; Sofia, Heidi J; Hutter, Carolyn; Getz, Gad; Wheeler, David; Ding, Li

2018-03-28

The Cancer Genome Atlas (TCGA) cancer genomics dataset includes over 10,000 tumor-normal exome pairs across 33 different cancer types, in total >400 TB of raw data files requiring analysis. Here we describe the Multi-Center Mutation Calling in Multiple Cancers project, our effort to generate a comprehensive encyclopedia of somatic mutation calls for the TCGA data to enable robust cross-tumor-type analyses. Our approach accounts for variance and batch effects introduced by the rapid advancement of DNA extraction, hybridization-capture, sequencing, and analysis methods over time. We present best practices for applying an ensemble of seven mutation-calling algorithms with scoring and artifact filtering. The dataset created by this analysis includes 3.5 million somatic variants and forms the basis for PanCan Atlas papers. The results have been made available to the research community along with the methods used to generate them. This project is the result of collaboration from a number of institutes and demonstrates how team science drives extremely large genomics projects. Copyright © 2018 The Authors. Published by Elsevier Inc. All rights reserved.

DNA Microarray Data Analysis: A Novel Biclustering Algorithm Approach

NASA Astrophysics Data System (ADS)

Tchagang, Alain B.; Tewfik, Ahmed H.

2006-12-01

Biclustering algorithms refer to a distinct class of clustering algorithms that perform simultaneous row-column clustering. Biclustering problems arise in DNA microarray data analysis, collaborative filtering, market research, information retrieval, text mining, electoral trends, exchange analysis, and so forth. When dealing with DNA microarray experimental data for example, the goal of biclustering algorithms is to find submatrices, that is, subgroups of genes and subgroups of conditions, where the genes exhibit highly correlated activities for every condition. In this study, we develop novel biclustering algorithms using basic linear algebra and arithmetic tools. The proposed biclustering algorithms can be used to search for all biclusters with constant values, biclusters with constant values on rows, biclusters with constant values on columns, and biclusters with coherent values from a set of data in a timely manner and without solving any optimization problem. We also show how one of the proposed biclustering algorithms can be adapted to identify biclusters with coherent evolution. The algorithms developed in this study discover all valid biclusters of each type, while almost all previous biclustering approaches will miss some.

Shilling Attacks Detection in Recommender Systems Based on Target Item Analysis

PubMed Central

Zhou, Wei; Wen, Junhao; Koh, Yun Sing; Xiong, Qingyu; Gao, Min; Dobbie, Gillian; Alam, Shafiq

2015-01-01

Recommender systems are highly vulnerable to shilling attacks, both by individuals and groups. Attackers who introduce biased ratings in order to affect recommendations, have been shown to negatively affect collaborative filtering (CF) algorithms. Previous research focuses only on the differences between genuine profiles and attack profiles, ignoring the group characteristics in attack profiles. In this paper, we study the use of statistical metrics to detect rating patterns of attackers and group characteristics in attack profiles. Another question is that most existing detecting methods are model specific. Two metrics, Rating Deviation from Mean Agreement (RDMA) and Degree of Similarity with Top Neighbors (DegSim), are used for analyzing rating patterns between malicious profiles and genuine profiles in attack models. Building upon this, we also propose and evaluate a detection structure called RD-TIA for detecting shilling attacks in recommender systems using a statistical approach. In order to detect more complicated attack models, we propose a novel metric called DegSim’ based on DegSim. The experimental results show that our detection model based on target item analysis is an effective approach for detecting shilling attacks. PMID:26222882

Estimation of positive semidefinite correlation matrices by using convex quadratic semidefinite programming.

PubMed

Fushiki, Tadayoshi

2009-07-01

The correlation matrix is a fundamental statistic that is used in many fields. For example, GroupLens, a collaborative filtering system, uses the correlation between users for predictive purposes. Since the correlation is a natural similarity measure between users, the correlation matrix may be used in the Gram matrix in kernel methods. However, the estimated correlation matrix sometimes has a serious defect: although the correlation matrix is originally positive semidefinite, the estimated one may not be positive semidefinite when not all ratings are observed. To obtain a positive semidefinite correlation matrix, the nearest correlation matrix problem has recently been studied in the fields of numerical analysis and optimization. However, statistical properties are not explicitly used in such studies. To obtain a positive semidefinite correlation matrix, we assume the approximate model. By using the model, an estimate is obtained as the optimal point of an optimization problem formulated with information on the variances of the estimated correlation coefficients. The problem is solved by a convex quadratic semidefinite program. A penalized likelihood approach is also examined. The MovieLens data set is used to test our approach.

Pixelated filters for spatial imaging

NASA Astrophysics Data System (ADS)

Mathieu, Karine; Lequime, Michel; Lumeau, Julien; Abel-Tiberini, Laetitia; Savin De Larclause, Isabelle; Berthon, Jacques

2015-10-01

Small satellites are often used by spatial agencies to meet scientific spatial mission requirements. Their payloads are composed of various instruments collecting an increasing amount of data, as well as respecting the growing constraints relative to volume and mass; So small-sized integrated camera have taken a favored place among these instruments. To ensure scene specific color information sensing, pixelated filters seem to be more attractive than filter wheels. The work presented here, in collaboration with Institut Fresnel, deals with the manufacturing of this kind of component, based on thin film technologies and photolithography processes. CCD detectors with a pixel pitch about 30 μm were considered. In the configuration where the matrix filters are positioned the closest to the detector, the matrix filters are composed of 2x2 macro pixels (e.g. 4 filters). These 4 filters have a bandwidth about 40 nm and are respectively centered at 550, 700, 770 and 840 nm with a specific rejection rate defined on the visible spectral range [500 - 900 nm]. After an intense design step, 4 thin-film structures have been elaborated with a maximum thickness of 5 μm. A run of tests has allowed us to choose the optimal micro-structuration parameters. The 100x100 matrix filters prototypes have been successfully manufactured with lift-off and ion assisted deposition processes. High spatial and spectral characterization, with a dedicated metrology bench, showed that initial specifications and simulations were globally met. These excellent performances knock down the technological barriers for high-end integrated specific multi spectral imaging.

Standardization of search methods for guideline development: an international survey of evidence-based guideline development groups.

PubMed

Deurenberg, Rikie; Vlayen, Joan; Guillo, Sylvie; Oliver, Thomas K; Fervers, Beatrice; Burgers, Jako

2008-03-01

Effective literature searching is particularly important for clinical practice guideline development. Sophisticated searching and filtering mechanisms are needed to help ensure that all relevant research is reviewed. To assess the methods used for the selection of evidence for guideline development by evidence-based guideline development organizations. A semistructured questionnaire assessing the databases, search filters and evaluation methods used for literature retrieval was distributed to eight major organizations involved in evidence-based guideline development. All of the organizations used search filters as part of guideline development. The medline database was the primary source accessed for literature retrieval. The OVID or SilverPlatter interfaces were used in preference to the freely accessed PubMed interface. The Cochrane Library, embase, cinahl and psycinfo databases were also frequently used by the organizations. All organizations reported the intention to improve and validate their filters for finding literature specifically relevant for guidelines. In the first international survey of its kind, eight major guideline development organizations indicated a strong interest in identifying, improving and standardizing search filters to improve guideline development. It is to be hoped that this will result in the standardization of, and open access to, search filters, an improvement in literature searching outcomes and greater collaboration among guideline development organizations.

Collaborative emitter tracking using Rao-Blackwellized random exchange diffusion particle filtering

NASA Astrophysics Data System (ADS)

Bruno, Marcelo G. S.; Dias, Stiven S.

2014-12-01

We introduce in this paper the fully distributed, random exchange diffusion particle filter (ReDif-PF) to track a moving emitter using multiple received signal strength (RSS) sensors. We consider scenarios with both known and unknown sensor model parameters. In the unknown parameter case, a Rao-Blackwellized (RB) version of the random exchange diffusion particle filter, referred to as the RB ReDif-PF, is introduced. In a simulated scenario with a partially connected network, the proposed ReDif-PF outperformed a PF tracker that assimilates local neighboring measurements only and also outperformed a linearized random exchange distributed extended Kalman filter (ReDif-EKF). Furthermore, the novel ReDif-PF matched the tracking error performance of alternative suboptimal distributed PFs based respectively on iterative Markov chain move steps and selective average gossiping with an inter-node communication cost that is roughly two orders of magnitude lower than the corresponding cost for the Markov chain and selective gossip filters. Compared to a broadcast-based filter which exactly mimics the optimal centralized tracker or its equivalent (exact) consensus-based implementations, ReDif-PF showed a degradation in steady-state error performance. However, compared to the optimal consensus-based trackers, ReDif-PF is better suited for real-time applications since it does not require iterative inter-node communication between measurement arrivals.

Restructuring the Future Classroom--A Global Perspective

ERIC Educational Resources Information Center

Shivakumar, G. S.; Manichander, T.

2013-01-01

The students are the consumers as well as co-creators of knowledge. Information does not flow top-down any more. Networks, peers and students inquisitiveness teach students. Teachers act as filters. Collaboration is the key. In today's world for the netgen, knowingly or unknowingly technology and the free flow of information via internet has made…

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…

Improved Personalized Recommendation Based on Causal Association Rule and Collaborative Filtering

ERIC Educational Resources Information Center

Lei, Wu; Qing, Fang; Zhou, Jin

2016-01-01

There are usually limited user evaluation of resources on a recommender system, which caused an extremely sparse user rating matrix, and this greatly reduce the accuracy of personalized recommendation, especially for new users or new items. This paper presents a recommendation method based on rating prediction using causal association rules.…

USE OF BONE CHAR FOR THE REMOVAL OF ARSENIC AND URANIUM FROM GROUNDWATER AT THE PINE RIDGE RESERVATION

EPA Science Inventory

The student project team will work with faculty advisors at UIUC, advisors at Oglala Lakota College, and with residents of the Pine Ridge Reservation. Through this collaborative effort, we expect to identify filter materials including bone char that will effectively remove ars...

Symposium on Applications and the Internet (SAINT 2003) Proceedings (Orlando, Florida, January 27-31, 2003).

ERIC Educational Resources Information Center

Helal, Sumi, Ed.; Oie, Yuji, Ed.; Chang, Carl, Ed.; Murai, Jun, Ed.

This proceedings from the 2003 Symposium on Applications and the Internet (SAINT) contains papers from sessions on: (1) mobile Internet, including a target-driven cache replacement policy, context-awareness for service discovery, and XML transformation; (2) collaboration technology I, including human-network-based filtering, virtual collaboration…

Career Goal-Based E-Learning Recommendation Using Enhanced Collaborative Filtering and PrefixSpan

ERIC Educational Resources Information Center

Ma, Xueying; Ye, Lu

2018-01-01

This article describes how e-learning recommender systems nowadays have applied different kinds of techniques to recommend personalized learning content for users based on their preference, goals, interests and background information. However, the cold-start problem which exists in traditional recommendation algorithms are still left over in…

Lazy collaborative filtering for data sets with missing values.

PubMed

Ren, Yongli; Li, Gang; Zhang, Jun; Zhou, Wanlei

2013-12-01

As one of the biggest challenges in research on recommender systems, the data sparsity issue is mainly caused by the fact that users tend to rate a small proportion of items from the huge number of available items. This issue becomes even more problematic for the neighborhood-based collaborative filtering (CF) methods, as there are even lower numbers of ratings available in the neighborhood of the query item. In this paper, we aim to address the data sparsity issue in the context of neighborhood-based CF. For a given query (user, item), a set of key ratings is first identified by taking the historical information of both the user and the item into account. Then, an auto-adaptive imputation (AutAI) method is proposed to impute the missing values in the set of key ratings. We present a theoretical analysis to show that the proposed imputation method effectively improves the performance of the conventional neighborhood-based CF methods. The experimental results show that our new method of CF with AutAI outperforms six existing recommendation methods in terms of accuracy.

Comparison of different Kalman filter approaches in deriving time varying connectivity from EEG data.

PubMed

Ghumare, Eshwar; Schrooten, Maarten; Vandenberghe, Rik; Dupont, Patrick

2015-08-01

Kalman filter approaches are widely applied to derive time varying effective connectivity from electroencephalographic (EEG) data. For multi-trial data, a classical Kalman filter (CKF) designed for the estimation of single trial data, can be implemented by trial-averaging the data or by averaging single trial estimates. A general linear Kalman filter (GLKF) provides an extension for multi-trial data. In this work, we studied the performance of the different Kalman filtering approaches for different values of signal-to-noise ratio (SNR), number of trials and number of EEG channels. We used a simulated model from which we calculated scalp recordings. From these recordings, we estimated cortical sources. Multivariate autoregressive model parameters and partial directed coherence was calculated for these estimated sources and compared with the ground-truth. The results showed an overall superior performance of GLKF except for low levels of SNR and number of trials.

Fuzzy Finite-Time Command Filtered Control of Nonlinear Systems With Input Saturation.

PubMed

Yu, Jinpeng; Zhao, Lin; Yu, Haisheng; Lin, Chong; Dong, Wenjie

2017-08-22

This paper considers the fuzzy finite-time tracking control problem for a class of nonlinear systems with input saturation. A novel fuzzy finite-time command filtered backstepping approach is proposed by introducing the fuzzy finite-time command filter, designing the new virtual control signals and the modified error compensation signals. The proposed approach not only holds the advantages of the conventional command-filtered backstepping control, but also guarantees the finite-time convergence. A practical example is included to show the effectiveness of the proposed method.

A Hybrid Probabilistic Model for Unified Collaborative and Content-Based Image Tagging.

PubMed

Zhou, Ning; Cheung, William K; Qiu, Guoping; Xue, Xiangyang

2011-07-01

The increasing availability of large quantities of user contributed images with labels has provided opportunities to develop automatic tools to tag images to facilitate image search and retrieval. In this paper, we present a novel hybrid probabilistic model (HPM) which integrates low-level image features and high-level user provided tags to automatically tag images. For images without any tags, HPM predicts new tags based solely on the low-level image features. For images with user provided tags, HPM jointly exploits both the image features and the tags in a unified probabilistic framework to recommend additional tags to label the images. The HPM framework makes use of the tag-image association matrix (TIAM). However, since the number of images is usually very large and user-provided tags are diverse, TIAM is very sparse, thus making it difficult to reliably estimate tag-to-tag co-occurrence probabilities. We developed a collaborative filtering method based on nonnegative matrix factorization (NMF) for tackling this data sparsity issue. Also, an L1 norm kernel method is used to estimate the correlations between image features and semantic concepts. The effectiveness of the proposed approach has been evaluated using three databases containing 5,000 images with 371 tags, 31,695 images with 5,587 tags, and 269,648 images with 5,018 tags, respectively.

The rules of information aggregation and emergence of collective intelligent behavior.

PubMed

Bettencourt, Luís M A

2009-10-01

Information is a peculiar quantity. Unlike matter and energy, which are conserved by the laws of physics, the aggregation of knowledge from many sources can in fact produce more information (synergy) or less (redundancy) than the sum of its parts. This feature can endow groups with problem-solving strategies that are superior to those possible among noninteracting individuals and, in turn, may provide a selection drive toward collective cooperation and coordination. Here we explore the formal properties of information aggregation as a general principle for explaining features of social organization. We quantify information in terms of the general formalism of information theory, which also prescribes the rules of how different pieces of evidence inform the solution of a given problem. We then show how several canonical examples of collective cognition and coordination can be understood through principles of minimization of uncertainty (maximization of predictability) under information pooling over many individuals. We discuss in some detail how collective coordination in swarms, markets, natural language processing, and collaborative filtering may be guided by the optimal aggregation of information in social collectives. We also identify circumstances when these processes fail, leading, for example, to inefficient markets. The contrast to approaches to understand coordination and collaboration via decision and game theory is also briefly discussed. Copyright © 2009 Cognitive Science Society, Inc.

Collaboration Levels in Asynchronous Discussion Forums: A Social Network Analysis Approach

ERIC Educational Resources Information Center

Luhrs, Cecilia; McAnally-Salas, Lewis

2016-01-01

Computer Supported Collaborative Learning literature relates high levels of collaboration to enhanced learning outcomes. However, an agreement on what is considered a high level of collaboration is unclear, especially if a qualitative approach is taken. This study describes how methods of Social Network Analysis were used to design a collaboration…

Effects of Collaborative Learning Styles on Performance of Students in a Ubiquitous Collaborative Mobile Learning Environment

ERIC Educational Resources Information Center

Fakomogbon, Michael Ayodele; Bolaji, Hameed Olalekan

2017-01-01

Collaborative learning is an approach employed by instructors to facilitate learning and improve learner's performance. Mobile learning can accommodate a variety of learning approaches. This study, therefore, investigated the effects of collaborative learning styles on performance of students in a mobile learning environment. The specific purposes…

Collaborative Distributed Scheduling Approaches for Wireless Sensor Network

PubMed Central

Niu, Jianjun; Deng, Zhidong

2009-01-01

Energy constraints restrict the lifetime of wireless sensor networks (WSNs) with battery-powered nodes, which poses great challenges for their large scale application. In this paper, we propose a family of collaborative distributed scheduling approaches (CDSAs) based on the Markov process to reduce the energy consumption of a WSN. The family of CDSAs comprises of two approaches: a one-step collaborative distributed approach and a two-step collaborative distributed approach. The approaches enable nodes to learn the behavior information of its environment collaboratively and integrate sleep scheduling with transmission scheduling to reduce the energy consumption. We analyze the adaptability and practicality features of the CDSAs. The simulation results show that the two proposed approaches can effectively reduce nodes' energy consumption. Some other characteristics of the CDSAs like buffer occupation and packet delay are also analyzed in this paper. We evaluate CDSAs extensively on a 15-node WSN testbed. The test results show that the CDSAs conserve the energy effectively and are feasible for real WSNs. PMID:22408491

An in-flight investigation of pilot-induced oscillation suppression filters during the fighter approach and landing task

NASA Technical Reports Server (NTRS)

Bailey, R. E.; Smith, R. E.

1982-01-01

An investigation of pilot-induced oscillation suppression (PIOS) filters was performed using the USAF/Flight Dynamics Laboratory variable stability NT-33 aircraft, modified and operated by Calspan. This program examined the effects of PIOS filtering on the longitudinal flying qualities of fighter aircraft during the visual approach and landing task. Forty evaluations were flown to test the effects of different PIOS filters. Although detailed analyses were not undertaken, the results indicate that PIOS filtering can improve the flying qualities of an otherwise unacceptable aircraft configuration (Level 3 flying qualities). However, the ability of the filters to suppress pilot-induced oscillations appears to be dependent upon the aircraft configuration characteristics. Further, the data show that the filters can adversely affect landing flying qualities if improperly designed. The data provide an excellent foundation from which detail analyses can be performed.

Arbitrary-shaped Brillouin microwave photonic filter by manipulating a directly modulated pump.

PubMed

Wei, Wei; Yi, Lilin; Jaouën, Yves; Hu, Weisheng

2017-10-15

We present a cost-effective gigahertz-wide arbitrary-shaped microwave photonic filter based on stimulated Brillouin scattering in fiber using a directly modulated laser (DML). After analyzing the relationship between the spectral power density and the modulation current of the DML, we manage to precisely adjust the optical spectrum of the DML, thereby controlling the Brillouin filter response arbitrarily for the first time, to the best of our knowledge. The filter performance is evaluated by amplifying a 500 Mb/s non-return-to-zero on-off keying signal using a 1 GHz rectangular filter. The comparison between the proposed DML approach and the previous approach adopting a complex IQ modulator shows similar filter flexibility, shape fidelity, and noise performance, proving that the DML-based Brillouin filter technique is a cost-effective and valid solution for microwave photonic applications.

Sequential Probability Ratio Test for Collision Avoidance Maneuver Decisions Based on a Bank of Norm-Inequality-Constrained Epoch-State Filters

NASA Technical Reports Server (NTRS)

Carpenter, J. R.; Markley, F. L.; Alfriend, K. T.; Wright, C.; Arcido, J.

2011-01-01

Sequential probability ratio tests explicitly allow decision makers to incorporate false alarm and missed detection risks, and are potentially less sensitive to modeling errors than a procedure that relies solely on a probability of collision threshold. Recent work on constrained Kalman filtering has suggested an approach to formulating such a test for collision avoidance maneuver decisions: a filter bank with two norm-inequality-constrained epoch-state extended Kalman filters. One filter models 1he null hypothesis 1ha1 the miss distance is inside the combined hard body radius at the predicted time of closest approach, and one filter models the alternative hypothesis. The epoch-state filter developed for this method explicitly accounts for any process noise present in the system. The method appears to work well using a realistic example based on an upcoming highly-elliptical orbit formation flying mission.

NASA Tech Briefs, April 2003

NASA Technical Reports Server (NTRS)

2003-01-01

Topics include: Tool for Bending a Metal Tube Precisely in a Confined Space; Multiple-Use Mechanisms for Attachment to Seat Tracks; Force-Measuring Clamps; Cellular Pressure-Actuated Joint; Block QCA Fault-Tolerant Logic Gates; Hybrid VLSI/QCA Architecture for Computing FFTs; Arrays of Carbon Nanotubes as RF Filters in Waveguides; Carbon Nanotubes as Resonators for RF Spectrum Analyzers; Software for Viewing Landsat Mosaic Images; Updated Integrated Mission Program; Software for Sharing and Management of Information; Optical-Quality Thin Polymer Membranes; Rollable Thin Shell Composite-Material Paraboloidal Mirrors; Folded Resonant Horns for Power Ultrasonic Applications; Touchdown Ball-Bearing System for Magnetic Bearings; Flux-Based Deadbeat Control of Induction-Motor Torque; Block Copolymers as Templates for Arrays of Carbon Nanotubes; Throttling Cryogen Boiloff To Control Cryostat Temperature; Collaborative Software Development Approach Used to Deliver the New Shuttle Telemetry Ground Station; Turbulence in Supercritical O2/H2 and C7H16/N2 Mixing Layers; and Time-Resolved Measurements in Optoelectronic Microbioanal.

Integration of hybrid wireless networks in cloud services oriented enterprise information systems

NASA Astrophysics Data System (ADS)

Li, Shancang; Xu, Lida; Wang, Xinheng; Wang, Jue

2012-05-01

This article presents a hybrid wireless network integration scheme in cloud services-based enterprise information systems (EISs). With the emerging hybrid wireless networks and cloud computing technologies, it is necessary to develop a scheme that can seamlessly integrate these new technologies into existing EISs. By combining the hybrid wireless networks and computing in EIS, a new framework is proposed, which includes frontend layer, middle layer and backend layers connected to IP EISs. Based on a collaborative architecture, cloud services management framework and process diagram are presented. As a key feature, the proposed approach integrates access control functionalities within the hybrid framework that provide users with filtered views on available cloud services based on cloud service access requirements and user security credentials. In future work, we will implement the proposed framework over SwanMesh platform by integrating the UPnP standard into an enterprise information system.

Patient Perspectives on the Learning Health System: The Importance of Trust and Shared Decision Making.

PubMed

Kelley, Maureen; James, Cyan; Alessi Kraft, Stephanie; Korngiebel, Diane; Wijangco, Isabelle; Rosenthal, Emily; Joffe, Steven; Cho, Mildred K; Wilfond, Benjamin; Lee, Sandra Soo-Jin

2015-01-01

We conducted focus groups to assess patient attitudes toward research on medical practices in the context of usual care. We found that patients focus on the implications of this research for their relationship with and trust in their physicians. Patients view research on medical practices as separate from usual care, demanding dissemination of information and in most cases, individual consent. Patients expect information about this research to come through their physician, whom they rely on to identify and filter associated risks. In general, patients support this research, but worry that participation in research involving randomization may undermine individualized care that acknowledges their unique medical histories. These findings suggest the need for public education on variation in practice among physicians and the need for a collaborative approach to the governance of research on medical practices that addresses core values of trust, transparency, and partnership.

“Going Episodic”: Collaborative Inhibition and Facilitation When Long-Married Couples Remember Together

PubMed Central

Harris, Celia B.; Barnier, Amanda J.; Sutton, John; Keil, Paul G.; Dixon, Roger A.

2017-01-01

Two complementary approaches to the study of collaborative remembering have produced contrasting results. In the experimental “collaborative recall” approach within cognitive psychology, collaborative remembering typically results in ‘collaborative inhibition’: laboratory groups recall fewer items than their estimated potential. In the cognitive ageing approach, collaborative remembering with a partner or spouse may provide cuing and support to benefit older adults’ performance on everyday memory tasks. To combine the value of experimental and cognitive ageing approaches, we tested the effects of collaborative remembering in older, long-married couples who recalled a non-personal word list and a personal semantic list of shared trips. We scored amount recalled as well as the kinds of details remembered. We found evidence for collaborative inhibition across both tasks when scored strictly as number of list items recalled. However, we found collaborative facilitation of specific episodic details on the personal semantic list, details which were not strictly required for the completion of the task. In fact, there was a trade-off between recall of specific episodic details and number of trips recalled during collaboration. We discuss these results in terms of the functions of shared remembering and what constitutes memory success, particularly for intimate groups and for older adults. PMID:28071300

An Attitude Filtering and Magnetometer Calibration Approach for Nanosatellites

NASA Astrophysics Data System (ADS)

Söken, Halil Ersin

2018-04-01

We propose an attitude filtering and magnetometer calibration approach for nanosatellites. Measurements from magnetometers, Sun sensor and gyros are used in the filtering algorithm to estimate the attitude of the satellite together with the bias terms for the gyros and magnetometers. In the traditional approach for the attitude filtering, the attitude sensor measurements are used in the filter with a nonlinear vector measurement model. In the proposed algorithm, the TRIAD algorithm is used in conjunction with the unscented Kalman filter (UKF) to form the nontraditional attitude filter. First the vector measurements from the magnetometer and Sun sensor are processed with the TRIAD algorithm to obtain a coarse attitude estimate for the spacecraft. In the second phase the estimated coarse attitude is used as quaternion measurements for the UKF. The UKF estimates the fine attitude, and the gyro and magnetometer biases. We evaluate the algorithm for a hypothetical nanosatellite by numerical simulations. The results show that the attitude of the satellite can be estimated with an accuracy better than 0.5{°} and the computational load decreases more than 25% compared to a traditional UKF algorithm. We discuss the algorithm's performance in case of a time-variance in the magnetometer errors.

Comparison of Cross Flow Filtration Performance for Manganese Oxide/Sludge Mixtures and Monosodium Titanate/Sludge Mixtures

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

Poirier, M.R.

2002-06-07

Personnel performed engineering-scale tests at the Filtration Research Engineering Demonstration (FRED) to determine crossflow filter performance with a 5.6 M sodium solution containing varying concentrations of sludge and sodium permanganate. The work represents another in a series of collaborative efforts between the University of South Carolina and the Savannah River Technology Center in support of the process development efforts for the Savannah River Site. The current tests investigated filter performance with slurry containing simulated Tank 40H Sludge and sodium permanganate at concentrations between 0.070 weight percent and 3.04 weight percent insoluble solids.

Developing a denoising filter for electron microscopy and tomography data in the cloud.

PubMed

Starosolski, Zbigniew; Szczepanski, Marek; Wahle, Manuel; Rusu, Mirabela; Wriggers, Willy

2012-09-01

The low radiation conditions and the predominantly phase-object image formation of cryo-electron microscopy (cryo-EM) result in extremely high noise levels and low contrast in the recorded micrographs. The process of single particle or tomographic 3D reconstruction does not completely eliminate this noise and is even capable of introducing new sources of noise during alignment or when correcting for instrument parameters. The recently developed Digital Paths Supervised Variance (DPSV) denoising filter uses local variance information to control regional noise in a robust and adaptive manner. The performance of the DPSV filter was evaluated in this review qualitatively and quantitatively using simulated and experimental data from cryo-EM and tomography in two and three dimensions. We also assessed the benefit of filtering experimental reconstructions for visualization purposes and for enhancing the accuracy of feature detection. The DPSV filter eliminates high-frequency noise artifacts (density gaps), which would normally preclude the accurate segmentation of tomography reconstructions or the detection of alpha-helices in single-particle reconstructions. This collaborative software development project was carried out entirely by virtual interactions among the authors using publicly available development and file sharing tools.

An Inter-Personal Information Sharing Model Based on Personalized Recommendations

NASA Astrophysics Data System (ADS)

Kamei, Koji; Funakoshi, Kaname; Akahani, Jun-Ichi; Satoh, Tetsuji

In this paper, we propose an inter-personal information sharing model among individuals based on personalized recommendations. In the proposed model, we define an information resource as shared between people when both of them consider it important --- not merely when they both possess it. In other words, the model defines the importance of information resources based on personalized recommendations from identifiable acquaintances. The proposed method is based on a collaborative filtering system that focuses on evaluations from identifiable acquaintances. It utilizes both user evaluations for documents and their contents. In other words, each user profile is represented as a matrix of credibility to the other users' evaluations on each domain of interests. We extended the content-based collaborative filtering method to distinguish other users to whom the documents should be recommended. We also applied a concept-based vector space model to represent the domain of interests instead of the previous method which represented them by a term-based vector space model. We introduce a personalized concept-base compiled from each user's information repository to improve the information retrieval in the user's environment. Furthermore, the concept-spaces change from user to user since they reflect the personalities of the users. Because of different concept-spaces, the similarity between a document and a user's interest varies for each user. As a result, a user receives recommendations from other users who have different view points, achieving inter-personal information sharing based on personalized recommendations. This paper also describes an experimental simulation of our information sharing model. In our laboratory, five participants accumulated a personal repository of e-mails and web pages from which they built their own concept-base. Then we estimated the user profiles according to personalized concept-bases and sets of documents which others evaluated. We simulated inter-personal recommendation based on the user profiles and evaluated the performance of the recommendation method by comparing the recommended documents to the result of the content-based collaborative filtering.

Correlation Filter Synthesis Using Neural Networks.

DTIC Science & Technology

1993-12-01

trained neural networks may be understood as "smart" data interpolators, the stored filter and the filter synthesis approaches have much in common: in...the former new filters are found by searching a data bank consisting of the filters themselves; in the latter filters are formed from a distributed... data bank that contains neural network interaction strengths or weights. 1.2 Key Results and Outputs Excellent computer simulation results were

Large-Scale Off-Target Identification Using Fast and Accurate Dual Regularized One-Class Collaborative Filtering and Its Application to Drug Repurposing.

PubMed

Lim, Hansaim; Poleksic, Aleksandar; Yao, Yuan; Tong, Hanghang; He, Di; Zhuang, Luke; Meng, Patrick; Xie, Lei

2016-10-01

Target-based screening is one of the major approaches in drug discovery. Besides the intended target, unexpected drug off-target interactions often occur, and many of them have not been recognized and characterized. The off-target interactions can be responsible for either therapeutic or side effects. Thus, identifying the genome-wide off-targets of lead compounds or existing drugs will be critical for designing effective and safe drugs, and providing new opportunities for drug repurposing. Although many computational methods have been developed to predict drug-target interactions, they are either less accurate than the one that we are proposing here or computationally too intensive, thereby limiting their capability for large-scale off-target identification. In addition, the performances of most machine learning based algorithms have been mainly evaluated to predict off-target interactions in the same gene family for hundreds of chemicals. It is not clear how these algorithms perform in terms of detecting off-targets across gene families on a proteome scale. Here, we are presenting a fast and accurate off-target prediction method, REMAP, which is based on a dual regularized one-class collaborative filtering algorithm, to explore continuous chemical space, protein space, and their interactome on a large scale. When tested in a reliable, extensive, and cross-gene family benchmark, REMAP outperforms the state-of-the-art methods. Furthermore, REMAP is highly scalable. It can screen a dataset of 200 thousands chemicals against 20 thousands proteins within 2 hours. Using the reconstructed genome-wide target profile as the fingerprint of a chemical compound, we predicted that seven FDA-approved drugs can be repurposed as novel anti-cancer therapies. The anti-cancer activity of six of them is supported by experimental evidences. Thus, REMAP is a valuable addition to the existing in silico toolbox for drug target identification, drug repurposing, phenotypic screening, and side effect prediction. The software and benchmark are available at https://github.com/hansaimlim/REMAP.

Large-Scale Off-Target Identification Using Fast and Accurate Dual Regularized One-Class Collaborative Filtering and Its Application to Drug Repurposing

PubMed Central

Poleksic, Aleksandar; Yao, Yuan; Tong, Hanghang; Meng, Patrick; Xie, Lei

2016-01-01

Target-based screening is one of the major approaches in drug discovery. Besides the intended target, unexpected drug off-target interactions often occur, and many of them have not been recognized and characterized. The off-target interactions can be responsible for either therapeutic or side effects. Thus, identifying the genome-wide off-targets of lead compounds or existing drugs will be critical for designing effective and safe drugs, and providing new opportunities for drug repurposing. Although many computational methods have been developed to predict drug-target interactions, they are either less accurate than the one that we are proposing here or computationally too intensive, thereby limiting their capability for large-scale off-target identification. In addition, the performances of most machine learning based algorithms have been mainly evaluated to predict off-target interactions in the same gene family for hundreds of chemicals. It is not clear how these algorithms perform in terms of detecting off-targets across gene families on a proteome scale. Here, we are presenting a fast and accurate off-target prediction method, REMAP, which is based on a dual regularized one-class collaborative filtering algorithm, to explore continuous chemical space, protein space, and their interactome on a large scale. When tested in a reliable, extensive, and cross-gene family benchmark, REMAP outperforms the state-of-the-art methods. Furthermore, REMAP is highly scalable. It can screen a dataset of 200 thousands chemicals against 20 thousands proteins within 2 hours. Using the reconstructed genome-wide target profile as the fingerprint of a chemical compound, we predicted that seven FDA-approved drugs can be repurposed as novel anti-cancer therapies. The anti-cancer activity of six of them is supported by experimental evidences. Thus, REMAP is a valuable addition to the existing in silico toolbox for drug target identification, drug repurposing, phenotypic screening, and side effect prediction. The software and benchmark are available at https://github.com/hansaimlim/REMAP. PMID:27716836

A simulation test of the effectiveness of several methods for error-checking non-invasive genetic data

USGS Publications Warehouse

Roon, David A.; Waits, L.P.; Kendall, K.C.

2005-01-01

Non-invasive genetic sampling (NGS) is becoming a popular tool for population estimation. However, multiple NGS studies have demonstrated that polymerase chain reaction (PCR) genotyping errors can bias demographic estimates. These errors can be detected by comprehensive data filters such as the multiple-tubes approach, but this approach is expensive and time consuming as it requires three to eight PCR replicates per locus. Thus, researchers have attempted to correct PCR errors in NGS datasets using non-comprehensive error checking methods, but these approaches have not been evaluated for reliability. We simulated NGS studies with and without PCR error and 'filtered' datasets using non-comprehensive approaches derived from published studies and calculated mark-recapture estimates using CAPTURE. In the absence of data-filtering, simulated error resulted in serious inflations in CAPTURE estimates; some estimates exceeded N by ??? 200%. When data filters were used, CAPTURE estimate reliability varied with per-locus error (E??). At E?? = 0.01, CAPTURE estimates from filtered data displayed < 5% deviance from error-free estimates. When E?? was 0.05 or 0.09, some CAPTURE estimates from filtered data displayed biases in excess of 10%. Biases were positive at high sampling intensities; negative biases were observed at low sampling intensities. We caution researchers against using non-comprehensive data filters in NGS studies, unless they can achieve baseline per-locus error rates below 0.05 and, ideally, near 0.01. However, we suggest that data filters can be combined with careful technique and thoughtful NGS study design to yield accurate demographic information. ?? 2005 The Zoological Society of London.

Caught in a net: Retention efficiency of microplankton ≥ 10 and < 50 μm collected on mesh netting

NASA Astrophysics Data System (ADS)

Molina, Vanessa; Robbins-Wamsley, Stephanie H.; Riley, Scott C.; First, Matthew R.; Drake, Lisa A.

2018-03-01

Living organisms ≥ 10 μm and < 50 μm in ballast water discharged from ships are typically collected by filtering samples through a monofilament mesh net with pore openings sized to retain organisms ≥ 10 μm. This (or any) filtering method does not result in perfect size fractionation, and it can induce stress, mortality, and loss of organisms that, in turn, may underestimate the concentration of organisms within samples. To address this loss, the retention efficiency (RE) was determined for six filtration approaches using laboratory cultures of microalgae and ambient marine organisms. The approaches employed a membrane filter or mesh nettings of different compositions (nylon, stainless steel, polyester, and polycarbonate), nominal pore sizes (5, 7, and 10 μm), and filtering sequences (e.g., pre-filtering water through a coarse filter). Additionally, in trials with polycarbonate track etched (PCTE) membrane filters, water was amended with particulate material to increase turbidity. Organisms ≥ 10 μm were counted in the material retained on the filter (the filtrand), the material passing through the filter (the filtrate), and the whole water (i.e., unfiltered water). In addition, variable fluorescence fluorometry was used to gauge the relative photochemical yield of phytoplankton-a proximal measurement of the physiological status of phytoplankton-in the size fractions. Further, the mesh types and filters were examined using scanning electron microscopy, which showed irregular openings. The RE of cultured organisms-calculated as the concentration in the filtrand relative to combined concentration in the filtrand and the filtrate-was high for all filtration approaches when laboratory cultures were assessed (> 93%), but RE ranged from 66 to 98% when mixed assemblages of ambient organisms were evaluated. Although PCTE membrane filters had the highest RE (98%), it was not significantly higher than the efficiencies of the 7-μm polyester, Double 7-μm polyester, and Dual 35-μm and 7-μm polyester approaches, but it was significantly higher than the 5-μm nylon and 5-μm stainless steel techniques. This result suggests that PCTE membrane filters perform comparably to 7-μm polyester meshes, so that any of these approaches could be used for concentrating organisms. However, the potential for handling loss is inherently lower for one rinsing step rather than two. Therefore, it is recommended that, either PCTE filters or 7-μm polyester mesh could be used to concentrate organisms ≥ 10 μm and < 50 μm. In trials conducted using a 10-μm PCTE filters with water amended to increase the particulate concentration, no significant difference in RE of ambient organisms was found compared to unamended water. Finally, photochemical yield did not vary significantly between organisms in the filtrand or filtrate, regardless of the filtration approach used.

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.

Potential and Impact Factors of the Knowledge and Information Awareness Approach for Fostering Net-Based Collaborative Problem-Solving: An Overview

ERIC Educational Resources Information Center

Engelmann, Tanja

2014-01-01

For effective communication and collaboration in learning situations, it is important to know what the collaboration partners know. However, the acquisition of this knowledge is difficult, especially in collaborating groups with spatially distributed members. One solution is the "Knowledge and Information Awareness" approach developed by…

Global HRSC Image Mosaics of Mars: Dodging for High-Pass Filtering, Combined with Low-Pass-Filtered OMEGA Mosaics

NASA Astrophysics Data System (ADS)

McGuire, P. C.; Walter, S. H. G.; van Gasselt, S.; Dumke, A.; Dunker, T.; Gross, C.; Michael, G.; Wendt, L.; Audouard, J.; Ody, A.; Poulet, F.

2014-07-01

We discuss our approach towards automatically mosaicking hundreds of the HRSC panchromatic or RGB images together. Our best results consist of adding a high-pass-filtered HRSC mosaic to a low-pass-filtered OMEGA global mosaic.

Quasi-disjoint pentadiagonal matrix systems for the parallelization of compact finite-difference schemes and filters

NASA Astrophysics Data System (ADS)

Kim, Jae Wook

2013-05-01

This paper proposes a novel systematic approach for the parallelization of pentadiagonal compact finite-difference schemes and filters based on domain decomposition. The proposed approach allows a pentadiagonal banded matrix system to be split into quasi-disjoint subsystems by using a linear-algebraic transformation technique. As a result the inversion of pentadiagonal matrices can be implemented within each subdomain in an independent manner subject to a conventional halo-exchange process. The proposed matrix transformation leads to new subdomain boundary (SB) compact schemes and filters that require three halo terms to exchange with neighboring subdomains. The internode communication overhead in the present approach is equivalent to that of standard explicit schemes and filters based on seven-point discretization stencils. The new SB compact schemes and filters demand additional arithmetic operations compared to the original serial ones. However, it is shown that the additional cost becomes sufficiently low by choosing optimal sizes of their discretization stencils. Compared to earlier published results, the proposed SB compact schemes and filters successfully reduce parallelization artifacts arising from subdomain boundaries to a level sufficiently negligible for sophisticated aeroacoustic simulations without degrading parallel efficiency. The overall performance and parallel efficiency of the proposed approach are demonstrated by stringent benchmark tests.

Collaborate!

ERIC Educational Resources Information Center

Villano, Matt

2007-01-01

This article explores different approaches that facilitate online collaboration. The newest efforts in collaboration revolve around wikis. These websites allow visitors to add, remove, edit, and change content directly online. Another fairly affordable approach involves open source, a programming language that is, in many ways, collaborative…

Precise orbit determination using the batch filter based on particle filtering with genetic resampling approach

NASA Astrophysics Data System (ADS)

Kim, Young-Rok; Park, Eunseo; Choi, Eun-Jung; Park, Sang-Young; Park, Chandeok; Lim, Hyung-Chul

2014-09-01

In this study, genetic resampling (GRS) approach is utilized for precise orbit determination (POD) using the batch filter based on particle filtering (PF). Two genetic operations, which are arithmetic crossover and residual mutation, are used for GRS of the batch filter based on PF (PF batch filter). For POD, Laser-ranging Precise Orbit Determination System (LPODS) and satellite laser ranging (SLR) observations of the CHAMP satellite are used. Monte Carlo trials for POD are performed by one hundred times. The characteristics of the POD results by PF batch filter with GRS are compared with those of a PF batch filter with minimum residual resampling (MRRS). The post-fit residual, 3D error by external orbit comparison, and POD repeatability are analyzed for orbit quality assessments. The POD results are externally checked by NASA JPL’s orbits using totally different software, measurements, and techniques. For post-fit residuals and 3D errors, both MRRS and GRS give accurate estimation results whose mean root mean square (RMS) values are at a level of 5 cm and 10-13 cm, respectively. The mean radial orbit errors of both methods are at a level of 5 cm. For POD repeatability represented as the standard deviations of post-fit residuals and 3D errors by repetitive PODs, however, GRS yields 25% and 13% more robust estimation results than MRRS for post-fit residual and 3D error, respectively. This study shows that PF batch filter with GRS approach using genetic operations is superior to PF batch filter with MRRS in terms of robustness in POD with SLR observations.

The Nuisance of Nuisance Regression: Spectral Misspecification in a Common Approach to Resting-State fMRI Preprocessing Reintroduces Noise and Obscures Functional Connectivity

PubMed Central

Hallquist, Michael N.; Hwang, Kai; Luna, Beatriz

2013-01-01

Recent resting-state functional connectivity fMRI (RS-fcMRI) research has demonstrated that head motion during fMRI acquisition systematically influences connectivity estimates despite bandpass filtering and nuisance regression, which are intended to reduce such nuisance variability. We provide evidence that the effects of head motion and other nuisance signals are poorly controlled when the fMRI time series are bandpass-filtered but the regressors are unfiltered, resulting in the inadvertent reintroduction of nuisance-related variation into frequencies previously suppressed by the bandpass filter, as well as suboptimal correction for noise signals in the frequencies of interest. This is important because many RS-fcMRI studies, including some focusing on motion-related artifacts, have applied this approach. In two cohorts of individuals (n = 117 and 22) who completed resting-state fMRI scans, we found that the bandpass-regress approach consistently overestimated functional connectivity across the brain, typically on the order of r = .10 – .35, relative to a simultaneous bandpass filtering and nuisance regression approach. Inflated correlations under the bandpass-regress approach were associated with head motion and cardiac artifacts. Furthermore, distance-related differences in the association of head motion and connectivity estimates were much weaker for the simultaneous filtering approach. We recommend that future RS-fcMRI studies ensure that the frequencies of nuisance regressors and fMRI data match prior to nuisance regression, and we advocate a simultaneous bandpass filtering and nuisance regression strategy that better controls nuisance-related variability. PMID:23747457

UV holographic filters

NASA Astrophysics Data System (ADS)

Kalyashova, Zoya N.

2017-11-01

A new approach to UV holographic filter's manufacturing, when the filters are the volume reflection holograms, working in UV region in the second Bragg diffraction order, is offered. The method is experimentally realized for wavelength of 266 nm.

A Flexible Electronic Commerce Recommendation System

NASA Astrophysics Data System (ADS)

Gong, Songjie

Recommendation systems have become very popular in E-commerce websites. Many of the largest commerce websites are already using recommender technologies to help their customers find products to purchase. An electronic commerce recommendation system learns from a customer and recommends products that the customer will find most valuable from among the available products. But most recommendation methods are hard-wired into the system and they support only fixed recommendations. This paper presented a framework of flexible electronic commerce recommendation system. The framework is composed by user model interface, recommendation engine, recommendation strategy model, recommendation technology group, user interest model and database interface. In the recommender strategy model, the method can be collaborative filtering, content-based filtering, mining associate rules method, knowledge-based filtering method or the mixed method. The system mapped the implementation and demand through strategy model, and the whole system would be design as standard parts to adapt to the change of the recommendation strategy.

Education and training column: the learning collaborative.

PubMed

MacDonald-Wilson, Kim L; Nemec, Patricia B

2015-03-01

This column describes the key components of a learning collaborative, with examples from the experience of 1 organization. A learning collaborative is a method for management, learning, and improvement of products or processes, and is a useful approach to implementation of a new service design or approach. This description draws from published material on learning collaboratives and the authors' experiences. The learning collaborative approach offers an effective method to improve service provider skills, provide support, and structure environments to result in lasting change for people using behavioral health services. This approach is consistent with psychiatric rehabilitation principles and practices, and serves to increase the overall capacity of the mental health system by structuring a process for discovering and sharing knowledge and expertise across provider agencies. (PsycINFO Database Record (c) 2015 APA, all rights reserved).

Particle filters, a quasi-Monte-Carlo-solution for segmentation of coronaries.

PubMed

Florin, Charles; Paragios, Nikos; Williams, Jim

2005-01-01

In this paper we propose a Particle Filter-based approach for the segmentation of coronary arteries. To this end, successive planes of the vessel are modeled as unknown states of a sequential process. Such states consist of the orientation, position, shape model and appearance (in statistical terms) of the vessel that are recovered in an incremental fashion, using a sequential Bayesian filter (Particle Filter). In order to account for bifurcations and branchings, we consider a Monte Carlo sampling rule that propagates in parallel multiple hypotheses. Promising results on the segmentation of coronary arteries demonstrate the potential of the proposed approach.

Collaborative Negotiations: A Successful Approach for Negotiation Compliance Milestones for the transition of the PFP Hanford Nuclear Reservation

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

HOPKINS, A.M.

The new approach to negotiations was termed collaborative (win-win) rather than positional (win-lose). Collaborative negotiations were conducted to establish milestones for the decommissioning of the Plutonium Finishing Plant, PFP.

Collaborative Evaluation within a Framework of Stakeholder-Oriented Evaluation Approaches

ERIC Educational Resources Information Center

O'Sullivan, Rita G.

2012-01-01

Collaborative Evaluation systematically invites and engages stakeholders in program evaluation planning and implementation. Unlike "distanced" evaluation approaches, which reject stakeholder participation as evaluation team members, Collaborative Evaluation assumes that active, on-going engagement between evaluators and program staff,…

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…

Recommending Learning Activities in Social Network Using Data Mining Algorithms

ERIC Educational Resources Information Center

Mahnane, Lamia

2017-01-01

In this paper, we show how data mining algorithms (e.g. Apriori Algorithm (AP) and Collaborative Filtering (CF)) is useful in New Social Network (NSN-AP-CF). "NSN-AP-CF" processes the clusters based on different learning styles. Next, it analyzes the habits and the interests of the users through mining the frequent episodes by the…

Joint 3-D vessel segmentation and centerline extraction using oblique Hough forests with steerable filters.

PubMed

Schneider, Matthias; Hirsch, Sven; Weber, Bruno; Székely, Gábor; Menze, Bjoern H

2015-01-01

We propose a novel framework for joint 3-D vessel segmentation and centerline extraction. The approach is based on multivariate Hough voting and oblique random forests (RFs) that we learn from noisy annotations. It relies on steerable filters for the efficient computation of local image features at different scales and orientations. We validate both the segmentation performance and the centerline accuracy of our approach both on synthetic vascular data and four 3-D imaging datasets of the rat visual cortex at 700 nm resolution. First, we evaluate the most important structural components of our approach: (1) Orthogonal subspace filtering in comparison to steerable filters that show, qualitatively, similarities to the eigenspace filters learned from local image patches. (2) Standard RF against oblique RF. Second, we compare the overall approach to different state-of-the-art methods for (1) vessel segmentation based on optimally oriented flux (OOF) and the eigenstructure of the Hessian, and (2) centerline extraction based on homotopic skeletonization and geodesic path tracing. Our experiments reveal the benefit of steerable over eigenspace filters as well as the advantage of oblique split directions over univariate orthogonal splits. We further show that the learning-based approach outperforms different state-of-the-art methods and proves highly accurate and robust with regard to both vessel segmentation and centerline extraction in spite of the high level of label noise in the training data. Copyright © 2014 Elsevier B.V. All rights reserved.

Multi-Flight-Phase GPS Navigation Filter Applications to Terrestrial Vehicle Navigation and Positioning

NASA Technical Reports Server (NTRS)

Park, Young W.; Montez, Moises N.

1994-01-01

A candidate onboard space navigation filter demonstrated excellent performance (less than 8 meter level RMS semi-major axis accuracy) in performing orbit determination of a low-Earth orbit Explorer satellite using single-frequency real GPS data. This performance is significantly better than predicted by other simulation studies using dual-frequency GPS data. The study results revealed the significance of two new modeling approaches evaluated in the work. One approach introduces a single-frequency ionospheric correction through pseudo-range and phase range averaging implementation. The other approach demonstrates a precise axis-dependent characterization of dynamic sample space uncertainty to compute a more accurate Kalman filter gain. Additionally, this navigation filter demonstrates a flexibility to accommodate both perturbational dynamic and observational biases required for multi-flight phase and inhomogeneous application environments. This paper reviews the potential application of these methods and the filter structure to terrestrial vehicle and positioning applications. Both the single-frequency ionospheric correction method and the axis-dependent state noise modeling approach offer valuable contributions in cost and accuracy improvements for terrestrial GPS receivers. With a modular design approach to either 'plug-in' or 'unplug' various force models, this multi-flight phase navigation filter design structure also provides a versatile GPS navigation software engine for both atmospheric and exo-atmospheric navigation or positioning use, thereby streamlining the flight phase or application-dependent software requirements. Thus, a standardized GPS navigation software engine that can reduce the development and maintenance cost of commercial GPS receivers is now possible.

Contingency designs for attitude determination of TRMM

NASA Technical Reports Server (NTRS)

Crassidis, John L.; Andrews, Stephen F.; Markley, F. Landis; Ha, Kong

1995-01-01

In this paper, several attitude estimation designs are developed for the Tropical Rainfall Measurement Mission (TRMM) spacecraft. A contingency attitude determination mode is required in the event of a primary sensor failure. The final design utilizes a full sixth-order Kalman filter. However, due to initial software concerns, the need to investigate simpler designs was required. The algorithms presented in this paper can be utilized in place of a full Kalman filter, and require less computational burden. These algorithms are based on filtered deterministic approaches and simplified Kalman filter approaches. Comparative performances of all designs are shown by simulating the TRMM spacecraft in mission mode. Comparisons of the simulation results indicate that comparable accuracy with respect to a full Kalman filter design is possible.

Quantitative evaluation of phase processing approaches in susceptibility weighted imaging

NASA Astrophysics Data System (ADS)

Li, Ningzhi; Wang, Wen-Tung; Sati, Pascal; Pham, Dzung L.; Butman, John A.

2012-03-01

Susceptibility weighted imaging (SWI) takes advantage of the local variation in susceptibility between different tissues to enable highly detailed visualization of the cerebral venous system and sensitive detection of intracranial hemorrhages. Thus, it has been increasingly used in magnetic resonance imaging studies of traumatic brain injury as well as other intracranial pathologies. In SWI, magnitude information is combined with phase information to enhance the susceptibility induced image contrast. Because of global susceptibility variations across the image, the rate of phase accumulation varies widely across the image resulting in phase wrapping artifacts that interfere with the local assessment of phase variation. Homodyne filtering is a common approach to eliminate this global phase variation. However, filter size requires careful selection in order to preserve image contrast and avoid errors resulting from residual phase wraps. An alternative approach is to apply phase unwrapping prior to high pass filtering. A suitable phase unwrapping algorithm guarantees no residual phase wraps but additional computational steps are required. In this work, we quantitatively evaluate these two phase processing approaches on both simulated and real data using different filters and cutoff frequencies. Our analysis leads to an improved understanding of the relationship between phase wraps, susceptibility effects, and acquisition parameters. Although homodyne filtering approaches are faster and more straightforward, phase unwrapping approaches perform more accurately in a wider variety of acquisition scenarios.

Angular velocity estimation from measurement vectors of star tracker.

PubMed

Liu, Hai-bo; Yang, Jun-cai; Yi, Wen-jun; Wang, Jiong-qi; Yang, Jian-kun; Li, Xiu-jian; Tan, Ji-chun

2012-06-01

In most spacecraft, there is a need to know the craft's angular rate. Approaches with least squares and an adaptive Kalman filter are proposed for estimating the angular rate directly from the star tracker measurements. In these approaches, only knowledge of the vector measurements and sampling interval is required. The designed adaptive Kalman filter can filter out noise without information of the dynamic model and inertia dyadic. To verify the proposed estimation approaches, simulations based on the orbit data of the challenging minisatellite payload (CHAMP) satellite and experimental tests with night-sky observation are performed. Both the simulations and experimental testing results have demonstrated that the proposed approach performs well in terms of accuracy, robustness, and performance.

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.

How to achieve a collaborative approach in health promotion: preferences and ideas of users of mental health services.

PubMed

Pals, Regitze Anne Saurbrey; Hempler, Nana Folmann

2018-02-12

Collaborative approaches to consensus building or decision-making are beneficial in health-promoting activities targeting users of mental health services (users). However, little is known about how to achieve a collaborative approach in practice. The purpose of this study was to explore: (1) users' preferences and ideas related to achieving a collaborative approach in health-related communication and (2) perspectives of healthcare and social work professionals and family members on users' ideas and preferences. Data were collected through interactive workshops with users (n = 15), professionals (n = 21) and users' family members (n = 12). Data were analysed using systematic text condensation. Users provided three recommendations for establishing a collaborative approach in communication about health: (1) involving users in deciding the agenda and setting for health-promoting activities; (2) exchanging knowledge between users and professionals about health and values; and (3) exploring users' motivation for change. Users and professionals had diverging perceptions of the value of establishing a collaborative approach. Professionals regarded relationship building and health promotion as separate phenomena, whereas users perceived relationship building as inherently health promoting. Family members of users requested specific guidance and support with regard to clarifying and fulfilling the best possible support role as a family member. The findings suggest that a collaborative approach in health promotion may be difficult to achieve without a focus on professional development for healthcare and social work professionals. © 2018 Nordic College of Caring Science.

Evaluation of an Enhanced Bank of Kalman Filters for In-Flight Aircraft Engine Sensor Fault Diagnostics

NASA Technical Reports Server (NTRS)

Kobayashi, Takahisa; Simon, Donald L.

2004-01-01

In this paper, an approach for in-flight fault detection and isolation (FDI) of aircraft engine sensors based on a bank of Kalman filters is developed. This approach utilizes multiple Kalman filters, each of which is designed based on a specific fault hypothesis. When the propulsion system experiences a fault, only one Kalman filter with the correct hypothesis is able to maintain the nominal estimation performance. Based on this knowledge, the isolation of faults is achieved. Since the propulsion system may experience component and actuator faults as well, a sensor FDI system must be robust in terms of avoiding misclassifications of any anomalies. The proposed approach utilizes a bank of (m+1) Kalman filters where m is the number of sensors being monitored. One Kalman filter is used for the detection of component and actuator faults while each of the other m filters detects a fault in a specific sensor. With this setup, the overall robustness of the sensor FDI system to anomalies is enhanced. Moreover, numerous component fault events can be accounted for by the FDI system. The sensor FDI system is applied to a commercial aircraft engine simulation, and its performance is evaluated at multiple power settings at a cruise operating point using various fault scenarios.

High-performance analysis of filtered semantic graphs

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

Buluc, Aydin; Fox, Armando; Gilbert, John R.

2012-01-01

High performance is a crucial consideration when executing a complex analytic query on a massive semantic graph. In a semantic graph, vertices and edges carry "attributes" of various types. Analytic queries on semantic graphs typically depend on the values of these attributes; thus, the computation must either view the graph through a filter that passes only those individual vertices and edges of interest, or else must first materialize a subgraph or subgraphs consisting of only the vertices and edges of interest. The filtered approach is superior due to its generality, ease of use, and memory efficiency, but may carry amore » performance cost. In the Knowledge Discovery Toolbox (KDT), a Python library for parallel graph computations, the user writes filters in a high-level language, but those filters result in relatively low performance due to the bottleneck of having to call into the Python interpreter for each edge. In this work, we use the Selective Embedded JIT Specialization (SEJITS) approach to automatically translate filters defined by programmers into a lower-level efficiency language, bypassing the upcall into Python. We evaluate our approach by comparing it with the high-performance C++ /MPI Combinatorial BLAS engine, and show that the productivity gained by using a high-level filtering language comes without sacrificing performance.« less

Comment on "A re-assessment of the safety of silver in household water treatment: rapid systematic review of mammalian in vivo genotoxicity studies".

PubMed

Lantagne, Daniele; Rayner, Justine; Mittelman, Anjuliee; Pennell, Kurt

2017-11-13

We wish to thank Fewtrell, Majuru, and Hunter for their article highlighting genotoxic risks associated with the use of particulate silver for primary drinking water treatment. The recent promotion of colloidal silver products for household water treatment in developing countries is problematic due to previously identified concerns regarding manufacturing quality and questionable advertising practices, as well as the low efficiency of silver nanoparticles to treat bacteria, viruses, and protozoa in source waters. However, in the conclusion statement of the manuscript, Fewtrell et al. state, "Before colloidal Ag or AgNP are used in filter matrices for drinking water treatment, consideration needs to be given to how much silver is likely to be released from the matrix during the life of the filter." Unfortunately, it appears Fewtrell et al. were unaware that studies of silver nanoparticle and silver ion elution from ceramic filters manufactured and used in developing countries have already been completed. These existing studies have found that: 1) silver ions, not silver nanoparticles, are eluted from ceramic filters treated with silver nanoparticles or silver nitrate; and, 2) silver ions have not been shown to be genotoxic. Thus, the existing recommendation of applying silver nanoparticles to ceramic filters to prevent biofilm formation within the filter and improve microbiological efficacy should still be adhered to, as there is no identified risk to people who drink water from ceramic filters treated with silver nanoparticles or silver nitrate. We note that efforts should continue to minimize exposure to silver nanoparticles (and silica) to employees in ceramic filter factories in collaboration with the organizations that provide technical assistance to ceramic filter factories.

Hybrid Kalman Filter: A New Approach for Aircraft Engine In-Flight Diagnostics

NASA Technical Reports Server (NTRS)

Kobayashi, Takahisa; Simon, Donald L.

2006-01-01

In this paper, a uniquely structured Kalman filter is developed for its application to in-flight diagnostics of aircraft gas turbine engines. The Kalman filter is a hybrid of a nonlinear on-board engine model (OBEM) and piecewise linear models. The utilization of the nonlinear OBEM allows the reference health baseline of the in-flight diagnostic system to be updated to the degraded health condition of the engines through a relatively simple process. Through this health baseline update, the effectiveness of the in-flight diagnostic algorithm can be maintained as the health of the engine degrades over time. Another significant aspect of the hybrid Kalman filter methodology is its capability to take advantage of conventional linear and nonlinear Kalman filter approaches. Based on the hybrid Kalman filter, an in-flight fault detection system is developed, and its diagnostic capability is evaluated in a simulation environment. Through the evaluation, the suitability of the hybrid Kalman filter technique for aircraft engine in-flight diagnostics is demonstrated.

WikiSensing: An Online Collaborative Approach for Sensor Data Management

PubMed Central

Silva, Dilshan; Ghanem, Moustafa; Guo, Yike

2012-01-01

This paper presents a new methodology for collaborative sensor data management known as WikiSensing. It is a novel approach that incorporates online collaboration with sensor data management. We introduce the work on this research by describing the motivation and challenges of designing and developing an online collaborative sensor data management system. This is followed by a brief survey on popular sensor data management and online collaborative systems. We then present the architecture for WikiSensing highlighting its main components and features. Several example scenarios are described to present the functionality of the system. We evaluate the approach by investigating the performance of aggregate queries and the scalability of the system. PMID:23201997

On selecting satellite conjunction filter parameters

NASA Astrophysics Data System (ADS)

Alfano, Salvatore; Finkleman, David

2014-06-01

This paper extends concepts of signal detection theory to predict the performance of conjunction screening techniques and guiding the selection of keepout and screening thresholds. The most efficient way to identify satellites likely to collide is to employ filters to identify orbiting pairs that should not come close enough over a prescribed time period to be considered hazardous. Such pairings can then be eliminated from further computation to accelerate overall processing time. Approximations inherent in filtering techniques include screening using only unperturbed Newtonian two body astrodynamics and uncertainties in orbit elements. Therefore, every filtering process is vulnerable to including objects that are not threats and excluding some that are threats, Type I and Type II errors. The approach in this paper guides selection of the best operating point for the filters suited to a user's tolerance for false alarms and unwarned threats. We demonstrate the approach using three archetypal filters with an initial three-day span, select filter parameters based on performance, and then test those parameters using eight historical snapshots of the space catalog. This work provides a mechanism for selecting filter parameters but the choices depend on the circumstances.

Mapping an Experiment-Based Assessment of Collaborative Behavior onto Collaborative Problem Solving in PISA 2015: A Cluster Analysis Approach for Collaborator Profiles

ERIC Educational Resources Information Center

Herborn, Katharina; Mustafic, Maida; Greiff, Samuel

2017-01-01

Collaborative problem solving (CPS) assessment is a new academic research field with a number of educational implications. In 2015, the Programme for International Student Assessment (PISA) assessed CPS with a computer-simulated human-agent (H-A) approach that claimed to measure 12 individual CPS skills for the first time. After reviewing the…

A Collaborative Neurodynamic Approach to Multiple-Objective Distributed Optimization.

PubMed

Yang, Shaofu; Liu, Qingshan; Wang, Jun

2018-04-01

This paper is concerned with multiple-objective distributed optimization. Based on objective weighting and decision space decomposition, a collaborative neurodynamic approach to multiobjective distributed optimization is presented. In the approach, a system of collaborative neural networks is developed to search for Pareto optimal solutions, where each neural network is associated with one objective function and given constraints. Sufficient conditions are derived for ascertaining the convergence to a Pareto optimal solution of the collaborative neurodynamic system. In addition, it is proved that each connected subsystem can generate a Pareto optimal solution when the communication topology is disconnected. Then, a switching-topology-based method is proposed to compute multiple Pareto optimal solutions for discretized approximation of Pareto front. Finally, simulation results are discussed to substantiate the performance of the collaborative neurodynamic approach. A portfolio selection application is also given.

A motion-constraint logic for moving-base simulators based on variable filter parameters

NASA Technical Reports Server (NTRS)

Miller, G. K., Jr.

1974-01-01

A motion-constraint logic for moving-base simulators has been developed that is a modification to the linear second-order filters generally employed in conventional constraints. In the modified constraint logic, the filter parameters are not constant but vary with the instantaneous motion-base position to increase the constraint as the system approaches the positional limits. With the modified constraint logic, accelerations larger than originally expected are limited while conventional linear filters would result in automatic shutdown of the motion base. In addition, the modified washout logic has frequency-response characteristics that are an improvement over conventional linear filters with braking for low-frequency pilot inputs. During simulated landing approaches of an externally blown flap short take-off and landing (STOL) transport using decoupled longitudinal controls, the pilots were unable to detect much difference between the modified constraint logic and the logic based on linear filters with braking.

Sequential Probability Ratio Test for Spacecraft Collision Avoidance Maneuver Decisions

NASA Technical Reports Server (NTRS)

Carpenter, J. Russell; Markley, F. Landis

2013-01-01

A document discusses sequential probability ratio tests that explicitly allow decision-makers to incorporate false alarm and missed detection risks, and are potentially less sensitive to modeling errors than a procedure that relies solely on a probability of collision threshold. Recent work on constrained Kalman filtering has suggested an approach to formulating such a test for collision avoidance maneuver decisions: a filter bank with two norm-inequality-constrained epoch-state extended Kalman filters. One filter models the null hypotheses that the miss distance is inside the combined hard body radius at the predicted time of closest approach, and one filter models the alternative hypothesis. The epoch-state filter developed for this method explicitly accounts for any process noise present in the system. The method appears to work well using a realistic example based on an upcoming, highly elliptical orbit formation flying mission.

Design of efficient circularly symmetric two-dimensional variable digital FIR filters.

PubMed

Bindima, Thayyil; Elias, Elizabeth

2016-05-01

Circularly symmetric two-dimensional (2D) finite impulse response (FIR) filters find extensive use in image and medical applications, especially for isotropic filtering. Moreover, the design and implementation of 2D digital filters with variable fractional delay and variable magnitude responses without redesigning the filter has become a crucial topic of interest due to its significance in low-cost applications. Recently the design using fixed word length coefficients has gained importance due to the replacement of multipliers by shifters and adders, which reduces the hardware complexity. Among the various approaches to 2D design, transforming a one-dimensional (1D) filter to 2D by transformation, is reported to be an efficient technique. In this paper, 1D variable digital filters (VDFs) with tunable cut-off frequencies are designed using Farrow structure based interpolation approach, and the sub-filter coefficients in the Farrow structure are made multiplier-less using canonic signed digit (CSD) representation. The resulting performance degradation in the filters is overcome by using artificial bee colony (ABC) optimization. Finally, the optimized 1D VDFs are mapped to 2D using generalized McClellan transformation resulting in low complexity, circularly symmetric 2D VDFs with real-time tunability.

Towards accurate localization: long- and short-term correlation filters for tracking

NASA Astrophysics Data System (ADS)

Li, Minglangjun; Tian, Chunna

2018-04-01

Visual tracking is a challenging problem, especially using a single model. In this paper, we propose a discriminative correlation filter (DCF) based tracking approach that exploits both the long-term and short-term information of the target, named LSTDCF, to improve the tracking performance. In addition to a long-term filter learned through the whole sequence, a short-term filter is trained using only features extracted from most recent frames. The long-term filter tends to capture more semantics of the target as more frames are used for training. However, since the target may undergo large appearance changes, features extracted around the target in non-recent frames prevent the long-term filter from locating the target in the current frame accurately. In contrast, the short-term filter learns more spatial details of the target from recent frames but gets over-fitting easily. Thus the short-term filter is less robust to handle cluttered background and prone to drift. We take the advantage of both filters and fuse their response maps to make the final estimation. We evaluate our approach on a widely-used benchmark with 100 image sequences and achieve state-of-the-art results.

Design of efficient circularly symmetric two-dimensional variable digital FIR filters

PubMed Central

Bindima, Thayyil; Elias, Elizabeth

2016-01-01

Circularly symmetric two-dimensional (2D) finite impulse response (FIR) filters find extensive use in image and medical applications, especially for isotropic filtering. Moreover, the design and implementation of 2D digital filters with variable fractional delay and variable magnitude responses without redesigning the filter has become a crucial topic of interest due to its significance in low-cost applications. Recently the design using fixed word length coefficients has gained importance due to the replacement of multipliers by shifters and adders, which reduces the hardware complexity. Among the various approaches to 2D design, transforming a one-dimensional (1D) filter to 2D by transformation, is reported to be an efficient technique. In this paper, 1D variable digital filters (VDFs) with tunable cut-off frequencies are designed using Farrow structure based interpolation approach, and the sub-filter coefficients in the Farrow structure are made multiplier-less using canonic signed digit (CSD) representation. The resulting performance degradation in the filters is overcome by using artificial bee colony (ABC) optimization. Finally, the optimized 1D VDFs are mapped to 2D using generalized McClellan transformation resulting in low complexity, circularly symmetric 2D VDFs with real-time tunability. PMID:27222739

Absorption/Transmission Measurements of PSAP Particle-Laden Filters from the Biomass Burning Observation Project (BBOP) Field Campaign

PubMed Central

Presser, Cary; Nazarian, Ashot; Conny, Joseph M.; Chand, Duli; Sedlacek, Arthur; Hubbe, John M.

2017-01-01

Absorptivity measurements with a laser-heating approach, referred to as the laser-driven thermal reactor (LDTR), were carried out in the infrared and applied at ambient (laboratory) non-reacting conditions to particle-laden filters from a three-wavelength (visible) particle/soot absorption photometer (PSAP). The particles were obtained during the Biomass Burning Observation Project (BBOP) field campaign. The focus of this study was to determine the particle absorption coefficient from field-campaign filter samples using the LDTR approach, and compare results with other commercially available instrumentation (in this case with the PSAP, which has been compared with numerous other optical techniques). Advantages of the LDTR approach include 1) direct estimation of material absorption from temperature measurements (as opposed to resolving the difference between the measured reflection/scattering and transmission), 2) information on the filter optical properties, and 3) identification of the filter material effects on particle absorption (e.g., leading to particle absorption enhancement or shadowing). For measurements carried out under ambient conditions, the particle absorptivity is obtained with a thermocouple placed flush with the filter back surface and the laser probe beam impinging normal to the filter particle-laden surface. Thus, in principle one can employ a simple experimental arrangement to measure simultaneously both the transmissivity and absorptivity (at different discrete wavelengths) and ascertain the particle absorption coefficient. For this investigation, LDTR measurements were carried out with PSAP filters (pairs with both blank and exposed filters) from eight different days during the campaign, having relatively light but different particle loadings. The observed particles coating the filters were found to be carbonaceous (having broadband absorption characteristics). The LDTR absorption coefficient compared well with results from the PSAP. The analysis was also expanded to account for the filter fiber scattering on particle absorption in assessing particle absorption enhancement and shadowing effects. The results indicated that absorption enhancement effects were significant, and diminished with increased filter particle loading. PMID:28690360

Absorption/Transmission Measurements of PSAP Particle-Laden Filters from the Biomass Burning Observation Project (BBOP) Field Campaign.

PubMed

Presser, Cary; Nazarian, Ashot; Conny, Joseph M; Chand, Duli; Sedlacek, Arthur; Hubbe, John M

2017-01-01

Absorptivity measurements with a laser-heating approach, referred to as the laser-driven thermal reactor (LDTR), were carried out in the infrared and applied at ambient (laboratory) non-reacting conditions to particle-laden filters from a three-wavelength (visible) particle/soot absorption photometer (PSAP). The particles were obtained during the Biomass Burning Observation Project (BBOP) field campaign. The focus of this study was to determine the particle absorption coefficient from field-campaign filter samples using the LDTR approach, and compare results with other commercially available instrumentation (in this case with the PSAP, which has been compared with numerous other optical techniques). Advantages of the LDTR approach include 1) direct estimation of material absorption from temperature measurements (as opposed to resolving the difference between the measured reflection/scattering and transmission), 2) information on the filter optical properties, and 3) identification of the filter material effects on particle absorption (e.g., leading to particle absorption enhancement or shadowing). For measurements carried out under ambient conditions, the particle absorptivity is obtained with a thermocouple placed flush with the filter back surface and the laser probe beam impinging normal to the filter particle-laden surface. Thus, in principle one can employ a simple experimental arrangement to measure simultaneously both the transmissivity and absorptivity (at different discrete wavelengths) and ascertain the particle absorption coefficient. For this investigation, LDTR measurements were carried out with PSAP filters (pairs with both blank and exposed filters) from eight different days during the campaign, having relatively light but different particle loadings. The observed particles coating the filters were found to be carbonaceous (having broadband absorption characteristics). The LDTR absorption coefficient compared well with results from the PSAP. The analysis was also expanded to account for the filter fiber scattering on particle absorption in assessing particle absorption enhancement and shadowing effects. The results indicated that absorption enhancement effects were significant, and diminished with increased filter particle loading.

Image search engine with selective filtering and feature-element-based classification

NASA Astrophysics Data System (ADS)

Li, Qing; Zhang, Yujin; Dai, Shengyang

2001-12-01

With the growth of Internet and storage capability in recent years, image has become a widespread information format in World Wide Web. However, it has become increasingly harder to search for images of interest, and effective image search engine for the WWW needs to be developed. We propose in this paper a selective filtering process and a novel approach for image classification based on feature element in the image search engine we developed for the WWW. First a selective filtering process is embedded in a general web crawler to filter out the meaningless images with GIF format. Two parameters that can be obtained easily are used in the filtering process. Our classification approach first extract feature elements from images instead of feature vectors. Compared with feature vectors, feature elements can better capture visual meanings of the image according to subjective perception of human beings. Different from traditional image classification method, our classification approach based on feature element doesn't calculate the distance between two vectors in the feature space, while trying to find associations between feature element and class attribute of the image. Experiments are presented to show the efficiency of the proposed approach.

A Filtering Approach for Image-Guided Surgery With a Highly Articulated Surgical Snake Robot.

PubMed

Tully, Stephen; Choset, Howie

2016-02-01

The objective of this paper is to introduce a probabilistic filtering approach to estimate the pose and internal shape of a highly flexible surgical snake robot during minimally invasive surgery. Our approach renders a depiction of the robot that is registered to preoperatively reconstructed organ models to produce a 3-D visualization that can be used for surgical feedback. Our filtering method estimates the robot shape using an extended Kalman filter that fuses magnetic tracker data with kinematic models that define the motion of the robot. Using Lie derivative analysis, we show that this estimation problem is observable, and thus, the shape and configuration of the robot can be successfully recovered with a sufficient number of magnetic tracker measurements. We validate this study with benchtop and in-vivo image-guidance experiments in which the surgical robot was driven along the epicardial surface of a porcine heart. This paper introduces a filtering approach for shape estimation that can be used for image guidance during minimally invasive surgery. The methods being introduced in this paper enable informative image guidance for highly articulated surgical robots, which benefits the advancement of robotic surgery.

Carotid Artery Stenting With Proximal Embolic Protection via a Transradial or Transbrachial Approach: Pushing the Boundaries of the Technique While Maintaining Safety and Efficacy.

PubMed

Montorsi, Piero; Galli, Stefano; Ravagnani, Paolo M; Tresoldi, Simone; Teruzzi, Giovanni; Caputi, Luigi; Trabattoni, Daniela; Fabbiocchi, Franco; Calligaris, Giuseppe; Grancini, Luca; Lualdi, Alessandro; de Martini, Stefano; Bartorelli, Antonio L

2016-08-01

To compare the feasibility and safety of proximal cerebral protection to a distal filter during carotid artery stenting (CAS) via a transbrachial (TB) or transradial (TR) approach. Among 856 patients who underwent CAS between January 2007 and July 2015, 214 (25%) patients (mean age 72±8 years; 154 men) had the procedure via a TR (n=154) or TB (n=60) approach with either Mo.MA proximal protection (n=61) or distal filter protection (n=153). The Mo.MA group (mean age 73±7 years; 54 men) had significantly more men and more severe stenosis than the filter group (mean age 71±8 years; 100 men). Stent type and CAS technique were left to operator discretion. Heparin and a dedicated closure device or bivalirudin and manual compression were used in TR and TB accesses, respectively. Technical and procedure success, crossover to femoral artery, 30-day major adverse cardiovascular/cerebrovascular events (MACCE; death, all strokes, and myocardial infarction), vascular complications, and radiation exposure were compared between groups. Crossover to a femoral approach was required in 1/61 (1.6%) Mo.MA patient vs 11/153 (7.1%) filter patients mainly due to technical difficulty in engaging the target vessel. Five Mo.MA patients developed acute intolerance to proximal occlusion; 4 were successfully shifted to filter protection. A TR patient was shifted to filter because the Mo.MA system was too short. CAS was technically successful in the remaining 55 (90%) Mo.MA patients and 142 (93%) filter patients. The MACCE rate was 0% in the Mo.MA patients and 2.8% in the filter group (p=0.18). Radiation exposure was similar between groups. Major vascular complications occurred in 1/61 (1.6%) and in 3/153 (1.96%) patients in the Mo.MA and filter groups (p=0.18), respectively, and were confined to the TB approach in the early part of the learning curve. Chronic radial artery occlusion was detected by Doppler ultrasound in 2/30 (6.6%) Mo.MA patients and in 4/124 (3.2%) filter patients by clinical assessment (p=0.25) at 8.1±7.5-month follow-up. CAS with proximal protection via a TR or TB approach is a feasible, safe, and effective technique with a low rate of vascular complications. © The Author(s) 2016.

Real Time Filtering of Tweets Using Wikipedia Concepts and Google Tri-gram Semantic Relatedness

DTIC Science & Technology

2015-11-20

Real Time Filtering of Tweets Using Wikipedia Concepts and Google Tri-gram Semantic Relatedness Anh Dang1, Raheleh Makki1, Abidalrahman Moh’d1...of a topic that the user is interested in receiving relevant posts in real-time. Our proposed approach extracts Wikipedia concepts for profiles and...group name “DALTREC”. Our proposed approach for this year’s filtering task is based on using Wikipedia and Google Trigram for calculating the semantic

Leveraging Open Standard Interfaces in Accessing and Processing NASA Data Model Outputs

NASA Astrophysics Data System (ADS)

Falke, S. R.; Alameh, N. S.; Hoijarvi, K.; de La Beaujardiere, J.; Bambacus, M. J.

2006-12-01

An objective of NASA's Earth Science Division is to develop advanced information technologies for processing, archiving, accessing, visualizing, and communicating Earth Science data. To this end, NASA and other federal agencies have collaborated with the Open Geospatial Consortium (OGC) to research, develop, and test interoperability specifications within projects and testbeds benefiting the government, industry, and the public. This paper summarizes the results of a recent effort under the auspices of the OGC Web Services testbed phase 4 (OWS-4) to explore standardization approaches for accessing and processing the outputs of NASA models of physical phenomena. Within the OWS-4 context, experiments were designed to leverage the emerging OGC Web Processing Service (WPS) and Web Coverage Service (WCS) specifications to access, filter and manipulate the outputs of the NASA Goddard Earth Observing System (GEOS) and Goddard Chemistry Aerosol Radiation and Transport (GOCART) forecast models. In OWS-4, the intent is to provide the users with more control over the subsets of data that they can extract from the model results as well as over the final portrayal of that data. To meet that goal, experiments have been designed to test the suitability of use of OGC's Web Processing Service (WPS) and Web Coverage Service (WCS) for filtering, processing and portraying the model results (including slices by height or by time), and to identify any enhancements to the specs to meet the desired objectives. This paper summarizes the findings of the experiments highlighting the value of the Web Processing Service in providing standard interfaces for accessing and manipulating model data within spatial and temporal frameworks. The paper also points out the key shortcomings of the WPS especially in terms in comparison with a SOAP/WSDL approach towards solving the same problem.

A Framework for Enhancing Real-time Social Media Data to Improve Disaster Management Process

NASA Astrophysics Data System (ADS)

Attique Shah, Syed; Zafer Şeker, Dursun; Demirel, Hande

2018-05-01

Social Media datasets are playing a vital role to provide information that can support decision making in nearly all domains of technology. It is due to the fact that social media is a quick and economical approach for data collection from public through methods like crowdsourcing. It is already proved by existing research that in case of any disaster (natural or man-made) the information extracted from Social Media sites is very critical to Disaster Management Systems for response and reconstruction. This study comprises of two components, the first part proposes a framework that provides updated and filtered real time input data for the disaster management system through social media and the second part consists of a designed web user API for a structured and defined real time data input process. This study contributes to the discipline of design science for the information systems domain. The aim of this study is to propose a framework that can filter and organize data from the unstructured social media sources through recognized methods and to bring this retrieved data to the same level as that of taken through a structured and predefined mechanism of a web API. Both components are designed to a level such that they can potentially collaborate and produce updated information for a disaster management system to carry out accurate and effective.

A Personalized Electronic Movie Recommendation System Based on Support Vector Machine and Improved Particle Swarm Optimization

PubMed Central

Wang, Xibin; Luo, Fengji; Qian, Ying; Ranzi, Gianluca

2016-01-01

With the rapid development of ICT and Web technologies, a large an amount of information is becoming available and this is producing, in some instances, a condition of information overload. Under these conditions, it is difficult for a person to locate and access useful information for making decisions. To address this problem, there are information filtering systems, such as the personalized recommendation system (PRS) considered in this paper, that assist a person in identifying possible products or services of interest based on his/her preferences. Among available approaches, collaborative Filtering (CF) is one of the most widely used recommendation techniques. However, CF has some limitations, e.g., the relatively simple similarity calculation, cold start problem, etc. In this context, this paper presents a new regression model based on the support vector machine (SVM) classification and an improved PSO (IPSO) for the development of an electronic movie PRS. In its implementation, a SVM classification model is first established to obtain a preliminary movie recommendation list based on which a SVM regression model is applied to predict movies’ ratings. The proposed PRS not only considers the movie’s content information but also integrates the users’ demographic and behavioral information to better capture the users’ interests and preferences. The efficiency of the proposed method is verified by a series of experiments based on the MovieLens benchmark data set. PMID:27898691

A Personalized Electronic Movie Recommendation System Based on Support Vector Machine and Improved Particle Swarm Optimization.

PubMed

Wang, Xibin; Luo, Fengji; Qian, Ying; Ranzi, Gianluca

2016-01-01

With the rapid development of ICT and Web technologies, a large an amount of information is becoming available and this is producing, in some instances, a condition of information overload. Under these conditions, it is difficult for a person to locate and access useful information for making decisions. To address this problem, there are information filtering systems, such as the personalized recommendation system (PRS) considered in this paper, that assist a person in identifying possible products or services of interest based on his/her preferences. Among available approaches, collaborative Filtering (CF) is one of the most widely used recommendation techniques. However, CF has some limitations, e.g., the relatively simple similarity calculation, cold start problem, etc. In this context, this paper presents a new regression model based on the support vector machine (SVM) classification and an improved PSO (IPSO) for the development of an electronic movie PRS. In its implementation, a SVM classification model is first established to obtain a preliminary movie recommendation list based on which a SVM regression model is applied to predict movies' ratings. The proposed PRS not only considers the movie's content information but also integrates the users' demographic and behavioral information to better capture the users' interests and preferences. The efficiency of the proposed method is verified by a series of experiments based on the MovieLens benchmark data set.

An ASIC-chip for stereoscopic depth analysis in video-real-time based on visual cortical cell behavior.

PubMed

Wörgötter, F

1999-10-01

In a stereoscopic system both eyes or cameras have a slightly different view. As a consequence small variations between the projected images exist ("disparities") which are spatially evaluated in order to retrieve depth information. We will show that two related algorithmic versions can be designed which recover disparity. Both approaches are based on the comparison of filter outputs from filtering the left and the right image. The difference of the phase components between left and right filter responses encodes the disparity. One approach uses regular Gabor filters and computes the spatial phase differences in a conventional way as described already in 1988 by Sanger. Novel to this approach, however, is that we formulate it in a way which is fully compatible with neural operations in the visual cortex. The second approach uses the apparently paradoxical similarity between the analysis of visual disparities and the determination of the azimuth of a sound source. Animals determine the direction of the sound from the temporal delay between the left and right ear signals. Similarly, in our second approach we transpose the spatially defined problem of disparity analysis into the temporal domain and utilize two resonators implemented in the form of causal (electronic) filters to determine the disparity as local temporal phase differences between the left and right filter responses. This approach permits video real-time analysis of stereo image sequences (see movies at http://www.neurop.ruhr-uni-bochum.de/Real- Time-Stereo) and a FPGA-based PC-board has been developed which performs stereo-analysis at full PAL resolution in video real-time. An ASIC chip will be available in March 2000.

Signal Processing for Time-Series Functions on a Graph

DTIC Science & Technology

2018-02-01

as filtering to functions supported on graphs. These methods can be applied to scalar functions with a domain that can be described by a fixed...classical signal processing such as filtering to account for the graph domain. This work essentially divides into 2 basic approaches: graph Laplcian...based filtering and weighted adjacency matrix-based filtering . In Shuman et al.,11 and elaborated in Bronstein et al.,13 filtering operators are

Visual Tracking Using 3D Data and Region-Based Active Contours

DTIC Science & Technology

2016-09-28

adaptive control strategies which explicitly take uncertainty into account. Filtering methods ranging from the classical Kalman filters valid for...linear systems to the much more general particle filters also fit into this framework in a very natural manner. In particular, the particle filtering ...the number of samples required for accurate filtering increases with the dimension of the system noise. In our approach, we approximate curve

Collaborative Group Action Research: A Constructivist Approach to Developing an Integrated Curriculum.

ERIC Educational Resources Information Center

Saurino, Penny L.; Saurino, Dan R.

Elementary teachers collaborated on a research project that investigated how a constructivist approach to gifted and talented integrated curriculum strategies and techniques could be developed and implemented. The collaborative group action research cycle involved planning, collecting baseline data, intervening strategies/modifying interventions,…

A filtering approach to edge preserving MAP estimation of images.

PubMed

Humphrey, David; Taubman, David

2011-05-01

The authors present a computationally efficient technique for maximum a posteriori (MAP) estimation of images in the presence of both blur and noise. The image is divided into statistically independent regions. Each region is modelled with a WSS Gaussian prior. Classical Wiener filter theory is used to generate a set of convex sets in the solution space, with the solution to the MAP estimation problem lying at the intersection of these sets. The proposed algorithm uses an underlying segmentation of the image, and a means of determining the segmentation and refining it are described. The algorithm is suitable for a range of image restoration problems, as it provides a computationally efficient means to deal with the shortcomings of Wiener filtering without sacrificing the computational simplicity of the filtering approach. The algorithm is also of interest from a theoretical viewpoint as it provides a continuum of solutions between Wiener filtering and Inverse filtering depending upon the segmentation used. We do not attempt to show here that the proposed method is the best general approach to the image reconstruction problem. However, related work referenced herein shows excellent performance in the specific problem of demosaicing.

Hybrid Discrete Wavelet Transform and Gabor Filter Banks Processing for Features Extraction from Biomedical Images

PubMed Central

Lahmiri, Salim; Boukadoum, Mounir

2013-01-01

A new methodology for automatic feature extraction from biomedical images and subsequent classification is presented. The approach exploits the spatial orientation of high-frequency textural features of the processed image as determined by a two-step process. First, the two-dimensional discrete wavelet transform (DWT) is applied to obtain the HH high-frequency subband image. Then, a Gabor filter bank is applied to the latter at different frequencies and spatial orientations to obtain new Gabor-filtered image whose entropy and uniformity are computed. Finally, the obtained statistics are fed to a support vector machine (SVM) binary classifier. The approach was validated on mammograms, retina, and brain magnetic resonance (MR) images. The obtained classification accuracies show better performance in comparison to common approaches that use only the DWT or Gabor filter banks for feature extraction. PMID:27006906

New estimation architecture for multisensor data fusion

NASA Astrophysics Data System (ADS)

Covino, Joseph M.; Griffiths, Barry E.

1991-07-01

This paper describes a novel method of hierarchical asynchronous distributed filtering called the Net Information Approach (NIA). The NIA is a Kalman-filter-based estimation scheme for spatially distributed sensors which must retain their local optimality yet require a nearly optimal global estimate. The key idea of the NIA is that each local sensor-dedicated filter tells the global filter 'what I've learned since the last local-to-global transmission,' whereas in other estimation architectures the local-to-global transmission consists of 'what I think now.' An algorithm based on this idea has been demonstrated on a small-scale target-tracking problem with many encouraging results. Feasibility of this approach was demonstrated by comparing NIA performance to an optimal centralized Kalman filter (lower bound) via Monte Carlo simulations.

Improved determination of particulate absorption from combined filter pad and PSICAM measurements.

PubMed

Lefering, Ina; Röttgers, Rüdiger; Weeks, Rebecca; Connor, Derek; Utschig, Christian; Heymann, Kerstin; McKee, David

2016-10-31

Filter pad light absorption measurements are subject to two major sources of experimental uncertainty: the so-called pathlength amplification factor, β, and scattering offsets, o, for which previous null-correction approaches are limited by recent observations of non-zero absorption in the near infrared (NIR). A new filter pad absorption correction method is presented here which uses linear regression against point-source integrating cavity absorption meter (PSICAM) absorption data to simultaneously resolve both β and the scattering offset. The PSICAM has previously been shown to provide accurate absorption data, even in highly scattering waters. Comparisons of PSICAM and filter pad particulate absorption data reveal linear relationships that vary on a sample by sample basis. This regression approach provides significantly improved agreement with PSICAM data (3.2% RMS%E) than previously published filter pad absorption corrections. Results show that direct transmittance (T-method) filter pad absorption measurements perform effectively at the same level as more complex geometrical configurations based on integrating cavity measurements (IS-method and QFT-ICAM) because the linear regression correction compensates for the sensitivity to scattering errors in the T-method. This approach produces accurate filter pad particulate absorption data for wavelengths in the blue/UV and in the NIR where sensitivity issues with PSICAM measurements limit performance. The combination of the filter pad absorption and PSICAM is therefore recommended for generating full spectral, best quality particulate absorption data as it enables correction of multiple errors sources across both measurements.

The Cognition and Neuroergonomics (CaN) Collaborative Technology Alliance (CTA): Scientific Vision, Approach, and Translational Paths

DTIC Science & Technology

2012-09-01

The Cognition and Neuroergonomics (CaN) Collaborative Technology Alliance (CTA): Scientific Vision, Approach, and Translational Paths by...The Cognition and Neuroergonomics (CaN) Collaborative Technology Alliance (CTA): Scientific Vision, Approach, and Translational Paths Kelvin S. Oie...REPORT DATE (DD-MM-YYYY) September 2012 2. REPORT TYPE Final 3. DATES COVERED (From - To) 4. TITLE AND SUBTITLE The Cognition and Neuroergonomics

Propagating probability distributions of stand variables using sequential Monte Carlo methods

Treesearch

Jeffrey H. Gove

2009-01-01

A general probabilistic approach to stand yield estimation is developed based on sequential Monte Carlo filters, also known as particle filters. The essential steps in the development of the sampling importance resampling (SIR) particle filter are presented. The SIR filter is then applied to simulated and observed data showing how the 'predictor - corrector'...

Monitoring Collaborative Activities in Computer Supported Collaborative Learning

ERIC Educational Resources Information Center

Persico, Donatella; Pozzi, Francesca; Sarti, Luigi

2010-01-01

Monitoring the learning process in computer supported collaborative learning (CSCL) environments is a key element for supporting the efficacy of tutor actions. This article proposes an approach for analysing learning processes in a CSCL environment to support tutors in their monitoring tasks. The approach entails tracking the interactions within…

The polarised internal target for the PAX experiment

NASA Astrophysics Data System (ADS)

Ciullo, G.; Barion, L.; Barschel, C.; Grigoriev, K.; Lenisa, P.; Nass, A.; Sarkadi, J.; Statera, M.; Steffens, E.; Tagliente, G.

2011-05-01

The PAX (Polarized Antiproton eXperiment) collaboration aims to polarise antiproton beams stored in ring by means of spin-filtering. The experimental setup is based on a polarised internal gas target, surrounded by a detection system for the measurement of spin observables. In this report, we present results from the commission of the PAX target (atomic beam source, openable cell, and polarimeter).

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…

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.

Mutual Comparative Filtering for Change Detection in Videos with Unstable Illumination Conditions

NASA Astrophysics Data System (ADS)

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

2016-06-01

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

Don't forget to look down - collaborative approaches to predator conservation.

PubMed

Redpath, Steve M; Linnell, John D C; Festa-Bianchet, Marco; Boitani, Luigi; Bunnefeld, Nils; Dickman, Amy; Gutiérrez, R J; Irvine, R J; Johansson, Maria; Majić, Aleksandra; McMahon, Barry J; Pooley, Simon; Sandström, Camilla; Sjölander-Lindqvist, Annelie; Skogen, Ketil; Swenson, Jon E; Trouwborst, Arie; Young, Juliette; Milner-Gulland, E J

2017-11-01

Finding effective ways of conserving large carnivores is widely recognised as a priority in conservation. However, there is disagreement about the most effective way to do this, with some favouring top-down 'command and control' approaches and others favouring collaboration. Arguments for coercive top-down approaches have been presented elsewhere; here we present arguments for collaboration. In many parts of the developed world, flexibility of approach is built into the legislation, so that conservation objectives are balanced with other legitimate goals. In the developing world, limited resources, poverty and weak governance mean that collaborative approaches are likely to play a particularly important part in carnivore conservation. In general, coercive policies may lead to the deterioration of political legitimacy and potentially to non-compliance issues such as illegal killing, whereas collaborative approaches may lead to psychological ownership, enhanced trust, learning, and better social outcomes. Sustainable hunting/trapping plays a crucial part in the conservation and management of many large carnivores. There are many different models for how to conserve carnivores effectively across the world, research is now required to reduce uncertainty and examine the effectiveness of these approaches in different contexts. © 2017 Cambridge Philosophical Society.

Time-correlated gust loads using matched filter theory and random process theory - A new way of looking at things

NASA Technical Reports Server (NTRS)

Pototzky, Anthony S.; Zeiler, Thomas A.; Perry, Boyd, III

1989-01-01

This paper describes and illustrates two ways of performing time-correlated gust-load calculations. The first is based on Matched Filter Theory; the second on Random Process Theory. Both approaches yield theoretically identical results and represent novel applications of the theories, are computationally fast, and may be applied to other dynamic-response problems. A theoretical development and example calculations using both Matched Filter Theory and Random Process Theory approaches are presented.

Eulerian Time-Domain Filtering for Spatial LES

NASA Technical Reports Server (NTRS)

Pruett, C. David

1997-01-01

Eulerian time-domain filtering seems to be appropriate for LES (large eddy simulation) of flows whose large coherent structures convect approximately at a common characteristic velocity; e.g., mixing layers, jets, and wakes. For these flows, we develop an approach to LES based on an explicit second-order digital Butterworth filter, which is applied in,the time domain in an Eulerian context. The approach is validated through a priori and a posteriori analyses of the simulated flow of a heated, subsonic, axisymmetric jet.

Time-correlated gust loads using Matched-Filter Theory and Random-Process Theory: A new way of looking at things

NASA Technical Reports Server (NTRS)

Pototzky, Anthony S.; Zeiler, Thomas A.; Perry, Boyd, III

1989-01-01

Two ways of performing time-correlated gust-load calculations are described and illustrated. The first is based on Matched Filter Theory; the second on Random Process Theory. Both approaches yield theoretically identical results and represent novel applications of the theories, are computationally fast, and may be applied to other dynamic-response problems. A theoretical development and example calculations using both Matched Filter Theory and Random Process Theory approaches are presented.

Superresolution restoration of an image sequence: adaptive filtering approach.

PubMed

Elad, M; Feuer, A

1999-01-01

This paper presents a new method based on adaptive filtering theory for superresolution restoration of continuous image sequences. The proposed methodology suggests least squares (LS) estimators which adapt in time, based on adaptive filters, least mean squares (LMS) or recursive least squares (RLS). The adaptation enables the treatment of linear space and time-variant blurring and arbitrary motion, both of them assumed known. The proposed new approach is shown to be of relatively low computational requirements. Simulations demonstrating the superresolution restoration algorithms are presented.

A Multi Directional Perfect Reconstruction Filter Bank Designed with 2-D Eigenfilter Approach: Application to Ultrasound Speckle Reduction.

PubMed

Nagare, Mukund B; Patil, Bhushan D; Holambe, Raghunath S

2017-02-01

B-Mode ultrasound images are degraded by inherent noise called Speckle, which creates a considerable impact on image quality. This noise reduces the accuracy of image analysis and interpretation. Therefore, reduction of speckle noise is an essential task which improves the accuracy of the clinical diagnostics. In this paper, a Multi-directional perfect-reconstruction (PR) filter bank is proposed based on 2-D eigenfilter approach. The proposed method used for the design of two-dimensional (2-D) two-channel linear-phase FIR perfect-reconstruction filter bank. In this method, the fan shaped, diamond shaped and checkerboard shaped filters are designed. The quadratic measure of the error function between the passband and stopband of the filter has been used an objective function. First, the low-pass analysis filter is designed and then the PR condition has been expressed as a set of linear constraints on the corresponding synthesis low-pass filter. Subsequently, the corresponding synthesis filter is designed using the eigenfilter design method with linear constraints. The newly designed 2-D filters are used in translation invariant pyramidal directional filter bank (TIPDFB) for reduction of speckle noise in ultrasound images. The proposed 2-D filters give better symmetry, regularity and frequency selectivity of the filters in comparison to existing design methods. The proposed method is validated on synthetic and real ultrasound data which ensures improvement in the quality of ultrasound images and efficiently suppresses the speckle noise compared to existing methods.

Processing Functional Near Infrared Spectroscopy Signal with a Kalman Filter to Assess Working Memory during Simulated Flight.

PubMed

Durantin, Gautier; Scannella, Sébastien; Gateau, Thibault; Delorme, Arnaud; Dehais, Frédéric

2015-01-01

Working memory (WM) is a key executive function for operating aircraft, especially when pilots have to recall series of air traffic control instructions. There is a need to implement tools to monitor WM as its limitation may jeopardize flight safety. An innovative way to address this issue is to adopt a Neuroergonomics approach that merges knowledge and methods from Human Factors, System Engineering, and Neuroscience. A challenge of great importance for Neuroergonomics is to implement efficient brain imaging techniques to measure the brain at work and to design Brain Computer Interfaces (BCI). We used functional near infrared spectroscopy as it has been already successfully tested to measure WM capacity in complex environment with air traffic controllers (ATC), pilots, or unmanned vehicle operators. However, the extraction of relevant features from the raw signal in ecological environment is still a critical issue due to the complexity of implementing real-time signal processing techniques without a priori knowledge. We proposed to implement the Kalman filtering approach, a signal processing technique that is efficient when the dynamics of the signal can be modeled. We based our approach on the Boynton model of hemodynamic response. We conducted a first experiment with nine participants involving a basic WM task to estimate the noise covariances of the Kalman filter. We then conducted a more ecological experiment in our flight simulator with 18 pilots who interacted with ATC instructions (two levels of difficulty). The data was processed with the same Kalman filter settings implemented in the first experiment. This filter was benchmarked with a classical pass-band IIR filter and a Moving Average Convergence Divergence (MACD) filter. Statistical analysis revealed that the Kalman filter was the most efficient to separate the two levels of load, by increasing the observed effect size in prefrontal areas involved in WM. In addition, the use of a Kalman filter increased the performance of the classification of WM levels based on brain signal. The results suggest that Kalman filter is a suitable approach for real-time improvement of near infrared spectroscopy signal in ecological situations and the development of BCI.

Processing Functional Near Infrared Spectroscopy Signal with a Kalman Filter to Assess Working Memory during Simulated Flight

PubMed Central

Durantin, Gautier; Scannella, Sébastien; Gateau, Thibault; Delorme, Arnaud; Dehais, Frédéric

2016-01-01

Working memory (WM) is a key executive function for operating aircraft, especially when pilots have to recall series of air traffic control instructions. There is a need to implement tools to monitor WM as its limitation may jeopardize flight safety. An innovative way to address this issue is to adopt a Neuroergonomics approach that merges knowledge and methods from Human Factors, System Engineering, and Neuroscience. A challenge of great importance for Neuroergonomics is to implement efficient brain imaging techniques to measure the brain at work and to design Brain Computer Interfaces (BCI). We used functional near infrared spectroscopy as it has been already successfully tested to measure WM capacity in complex environment with air traffic controllers (ATC), pilots, or unmanned vehicle operators. However, the extraction of relevant features from the raw signal in ecological environment is still a critical issue due to the complexity of implementing real-time signal processing techniques without a priori knowledge. We proposed to implement the Kalman filtering approach, a signal processing technique that is efficient when the dynamics of the signal can be modeled. We based our approach on the Boynton model of hemodynamic response. We conducted a first experiment with nine participants involving a basic WM task to estimate the noise covariances of the Kalman filter. We then conducted a more ecological experiment in our flight simulator with 18 pilots who interacted with ATC instructions (two levels of difficulty). The data was processed with the same Kalman filter settings implemented in the first experiment. This filter was benchmarked with a classical pass-band IIR filter and a Moving Average Convergence Divergence (MACD) filter. Statistical analysis revealed that the Kalman filter was the most efficient to separate the two levels of load, by increasing the observed effect size in prefrontal areas involved in WM. In addition, the use of a Kalman filter increased the performance of the classification of WM levels based on brain signal. The results suggest that Kalman filter is a suitable approach for real-time improvement of near infrared spectroscopy signal in ecological situations and the development of BCI. PMID:26834607

Multi-agent cooperation rescue algorithm based on influence degree and state prediction

NASA Astrophysics Data System (ADS)

Zheng, Yanbin; Ma, Guangfu; Wang, Linlin; Xi, Pengxue

2018-04-01

Aiming at the multi-agent cooperative rescue in disaster, a multi-agent cooperative rescue algorithm based on impact degree and state prediction is proposed. Firstly, based on the influence of the information in the scene on the collaborative task, the influence degree function is used to filter the information. Secondly, using the selected information to predict the state of the system and Agent behavior. Finally, according to the result of the forecast, the cooperative behavior of Agent is guided and improved the efficiency of individual collaboration. The simulation results show that this algorithm can effectively solve the cooperative rescue problem of multi-agent and ensure the efficient completion of the task.

Application of a Bank of Kalman Filters for Aircraft Engine Fault Diagnostics

NASA Technical Reports Server (NTRS)

Kobayashi, Takahisa; Simon, Donald L.

2003-01-01

In this paper, a bank of Kalman filters is applied to aircraft gas turbine engine sensor and actuator fault detection and isolation (FDI) in conjunction with the detection of component faults. This approach uses multiple Kalman filters, each of which is designed for detecting a specific sensor or actuator fault. In the event that a fault does occur, all filters except the one using the correct hypothesis will produce large estimation errors, thereby isolating the specific fault. In the meantime, a set of parameters that indicate engine component performance is estimated for the detection of abrupt degradation. The proposed FDI approach is applied to a nonlinear engine simulation at nominal and aged conditions, and the evaluation results for various engine faults at cruise operating conditions are given. The ability of the proposed approach to reliably detect and isolate sensor and actuator faults is demonstrated.

GPS vertical axis performance enhancement for helicopter precision landing approach

NASA Technical Reports Server (NTRS)

Denaro, Robert P.; Beser, Jacques

1986-01-01

Several areas were investigated for improving vertical accuracy for a rotorcraft using the differential Global Positioning System (GPS) during a landing approach. Continuous deltaranging was studied and the potential improvement achieved by estimating acceleration was studied by comparing the performance on a constant acceleration turn and a rough landing profile of several filters: a position-velocity (PV) filter, a position-velocity-constant acceleration (PVAC) filter, and a position-velocity-turning acceleration (PVAT) filter. In overall statistics, the PVAC filter was found to be most efficient with the more complex PVAT performing equally well. Vertical performance was not significantly different among the filters. Satellite selection algorithms based on vertical errors only (vertical dilution of precision or VDOP) and even-weighted cross-track and vertical errors (XVDOP) were tested. The inclusion of an altimeter was studied by modifying the PVAC filter to include a baro bias estimate. Improved vertical accuracy during degraded DOP conditions resulted. Flight test results for raw differential results excluding filter effects indicated that the differential performance significantly improved overall navigation accuracy. A landing glidepath steering algorithm was devised which exploits the flexibility of GPS in determining precise relative position. A method for propagating the steering command over the GPS update interval was implemented.

Accuracy of telemetry signal power loss in a filter as an estimate for telemetry degradation

NASA Technical Reports Server (NTRS)

Koerner, M. A.

1989-01-01

When telemetry data is transmitted through a communication link, some degradation in telemetry performance occurs as a result of the imperfect frequency response of the channel. The term telemetry degradation as used here is the increase in received signal power required to offset this filtering. The usual approach to assessing this degradation is to assume that it is equal to the signal power loss in the filtering, which is easily calculated. However, this approach neglects the effects of the nonlinear phase response of the filter, the effect of any reduction of the receiving system noise due to the filter, and intersymbol interference. Here, an exact calculation of the telemetry degradation, which includes all of the above effects, is compared with the signal power loss calculation for RF filtering of NRZ data on a carrier. The signal power loss calculation is found to be a reasonable approximation when the filter follows the point at which the receiving system noise is introduced, especially if the signal power loss is less than 0.5 dB. The signal power loss approximation is less valid when the receiving system noise is not filtered.

A Comparison of Filter-based Approaches for Model-based Prognostics

NASA Technical Reports Server (NTRS)

Daigle, Matthew John; Saha, Bhaskar; Goebel, Kai

2012-01-01

Model-based prognostics approaches use domain knowledge about a system and its failure modes through the use of physics-based models. Model-based prognosis is generally divided into two sequential problems: a joint state-parameter estimation problem, in which, using the model, the health of a system or component is determined based on the observations; and a prediction problem, in which, using the model, the stateparameter distribution is simulated forward in time to compute end of life and remaining useful life. The first problem is typically solved through the use of a state observer, or filter. The choice of filter depends on the assumptions that may be made about the system, and on the desired algorithm performance. In this paper, we review three separate filters for the solution to the first problem: the Daum filter, an exact nonlinear filter; the unscented Kalman filter, which approximates nonlinearities through the use of a deterministic sampling method known as the unscented transform; and the particle filter, which approximates the state distribution using a finite set of discrete, weighted samples, called particles. Using a centrifugal pump as a case study, we conduct a number of simulation-based experiments investigating the performance of the different algorithms as applied to prognostics.

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.

An adaptive Kalman filter approach for cardiorespiratory signal extraction and fusion of non-contacting sensors

PubMed Central

2014-01-01

Background Extracting cardiorespiratory signals from non-invasive and non-contacting sensor arrangements, i.e. magnetic induction sensors, is a challenging task. The respiratory and cardiac signals are mixed on top of a large and time-varying offset and are likely to be disturbed by measurement noise. Basic filtering techniques fail to extract relevant information for monitoring purposes. Methods We present a real-time filtering system based on an adaptive Kalman filter approach that separates signal offsets, respiratory and heart signals from three different sensor channels. It continuously estimates respiration and heart rates, which are fed back into the system model to enhance performance. Sensor and system noise covariance matrices are automatically adapted to the aimed application, thus improving the signal separation capabilities. We apply the filtering to two different subjects with different heart rates and sensor properties and compare the results to the non-adaptive version of the same Kalman filter. Also, the performance, depending on the initialization of the filters, is analyzed using three different configurations ranging from best to worst case. Results Extracted data are compared with reference heart rates derived from a standard pulse-photoplethysmographic sensor and respiration rates from a flowmeter. In the worst case for one of the subjects the adaptive filter obtains mean errors (standard deviations) of -0.2 min −1 (0.3 min −1) and -0.7 bpm (1.7 bpm) (compared to -0.2 min −1 (0.4 min −1) and 42.0 bpm (6.1 bpm) for the non-adaptive filter) for respiration and heart rate, respectively. In bad conditions the heart rate is only correctly measurable when the Kalman matrices are adapted to the target sensor signals. Also, the reduced mean error between the extracted offset and the raw sensor signal shows that adapting the Kalman filter continuously improves the ability to separate the desired signals from the raw sensor data. The average total computational time needed for the Kalman filters is under 25% of the total signal length rendering it possible to perform the filtering in real-time. Conclusions It is possible to measure in real-time heart and breathing rates using an adaptive Kalman filter approach. Adapting the Kalman filter matrices improves the estimation results and makes the filter universally deployable when measuring cardiorespiratory signals. PMID:24886253

An adaptive Kalman filter approach for cardiorespiratory signal extraction and fusion of non-contacting sensors.

PubMed

Foussier, Jerome; Teichmann, Daniel; Jia, Jing; Misgeld, Berno; Leonhardt, Steffen

2014-05-09

Extracting cardiorespiratory signals from non-invasive and non-contacting sensor arrangements, i.e. magnetic induction sensors, is a challenging task. The respiratory and cardiac signals are mixed on top of a large and time-varying offset and are likely to be disturbed by measurement noise. Basic filtering techniques fail to extract relevant information for monitoring purposes. We present a real-time filtering system based on an adaptive Kalman filter approach that separates signal offsets, respiratory and heart signals from three different sensor channels. It continuously estimates respiration and heart rates, which are fed back into the system model to enhance performance. Sensor and system noise covariance matrices are automatically adapted to the aimed application, thus improving the signal separation capabilities. We apply the filtering to two different subjects with different heart rates and sensor properties and compare the results to the non-adaptive version of the same Kalman filter. Also, the performance, depending on the initialization of the filters, is analyzed using three different configurations ranging from best to worst case. Extracted data are compared with reference heart rates derived from a standard pulse-photoplethysmographic sensor and respiration rates from a flowmeter. In the worst case for one of the subjects the adaptive filter obtains mean errors (standard deviations) of -0.2 min(-1) (0.3 min(-1)) and -0.7 bpm (1.7 bpm) (compared to -0.2 min(-1) (0.4 min(-1)) and 42.0 bpm (6.1 bpm) for the non-adaptive filter) for respiration and heart rate, respectively. In bad conditions the heart rate is only correctly measurable when the Kalman matrices are adapted to the target sensor signals. Also, the reduced mean error between the extracted offset and the raw sensor signal shows that adapting the Kalman filter continuously improves the ability to separate the desired signals from the raw sensor data. The average total computational time needed for the Kalman filters is under 25% of the total signal length rendering it possible to perform the filtering in real-time. It is possible to measure in real-time heart and breathing rates using an adaptive Kalman filter approach. Adapting the Kalman filter matrices improves the estimation results and makes the filter universally deployable when measuring cardiorespiratory signals.

Assessing Collaboration Networks in Educational Research: A Co-Authorship-Based Social Network Analysis Approach

ERIC Educational Resources Information Center

Munoz, David Andres; Queupil, Juan Pablo; Fraser, Pablo

2016-01-01

Purpose: The purpose of this paper is to analyze collaboration networks and their patterns among higher education institutions (HEIs) in Chile and the Latin American region. This will provide evidence to educational managements in order to properly allocate their efforts to improve collaboration. Design/methodology/approach: This quantitative…

Towards an Agile Approach to Adapting Dynamic Collaboration Support to Student Needs

ERIC Educational Resources Information Center

Adamson, David; Dyke, Gregory; Jang, Hyeju; Rosé, Carolyn Penstein

2014-01-01

This paper investigates the use of conversational agents to scaffold on-line collaborative learning discussions through an approach called Academically Productive Talk (APT). In contrast to past work on dynamic support for collaborative learning, where agents were used to elevate conceptual depth by leading students through directed lines of…

Professional Development through Teacher Collaboration: An Approach to Enhance Teaching and Learning in Science and Mathematics in Tanzania

ERIC Educational Resources Information Center

Kafyulilo, Ayoub Cherd

2013-01-01

This study introduces "teachers' collaboration" as an approach to teachers' professional development geared at enhancing science and mathematics teaching in Tanzania secondary schools. Teachers' professional development through teachers' collaboration has been reported to be effective for the improvement of schools' performance and…

How Can Teachers' Entrepreneurial Competences Be Developed? A Collaborative Learning Perspective

ERIC Educational Resources Information Center

Peltonen, Katariina

2015-01-01

Purpose: The purpose of this paper is to explore the role of collaborative learning in the development of teachers' entrepreneurial competences in the school context at primary, secondary and vocational levels of education. Design/methodology/approach: The research is based on an interpretative and collaborative learning approach to teachers'…

Collaboration between team members in inclusive educational settings.

PubMed

Nochajski, Susan M

2002-01-01

SUMMARY The inclusion of students with disabilities into general education settings and programs has necessitated the development of integrated, collaborative service delivery models that are compatible with the goals and purpose of inclusive education. Although there is considerable theoretical literature on collaboration, there is minimal empirical data available on the process or its outcomes. The purpose of this exploratory study was to gain insight on the perspectives of regular and special educators, and occupational, physical, and speech-language therapists towards collaboration. Using a semi-structured interview, participants (n = 51) responded to questions concerning the definition, nature, and extent of collaboration in their school setting. Participants also responded to questions related to the advantages of, barriers towards, and strategies to promote collaboration. Participants typically defined collaboration as not a problem-solving process, but in terms of activities associated with it. Results indicate that participants believed collaboration was mutually beneficial for both students and team members. However, implementing a collaborative approach was problematic. Lack of administrative approval for time for planning meetings was the most frequently cited barrier to collaboration. Although 51.6% of the participants reported time available for collaborative planning by regular and special educators, only 21.5% of the participants reported this time being available for therapists to meet with educators. Education about collaboration, either in professional/preservice education programs or as continuing education, was recommended as a strategy to facilitate a collaborative approach. Although a collaborative approach is being used by therapists and educators more and more frequently, there is a need for research to validate its efficacy.

Comparative Study of Different Methods for Soot Sensing and Filter Monitoring in Diesel Exhausts.

PubMed

Feulner, Markus; Hagen, Gunter; Hottner, Kathrin; Redel, Sabrina; Müller, Andreas; Moos, Ralf

2017-02-18

Due to increasingly tighter emission limits for diesel and gasoline engines, especially concerning particulate matter emissions, particulate filters are becoming indispensable devices for exhaust gas after treatment. Thereby, for an efficient engine and filter control strategy and a cost-efficient filter design, reliable technologies to determine the soot load of the filters and to measure particulate matter concentrations in the exhaust gas during vehicle operation are highly needed. In this study, different approaches for soot sensing are compared. Measurements were conducted on a dynamometer diesel engine test bench with a diesel particulate filter (DPF). The DPF was monitored by a relatively new microwave-based approach. Simultaneously, a resistive type soot sensor and a Pegasor soot sensing device as a reference system measured the soot concentration exhaust upstream of the DPF. By changing engine parameters, different engine out soot emission rates were set. It was found that the microwave-based signal may not only indicate directly the filter loading, but by a time derivative, the engine out soot emission rate can be deduced. Furthermore, by integrating the measured particulate mass in the exhaust, the soot load of the filter can be determined. In summary, all systems coincide well within certain boundaries and the filter itself can act as a soot sensor.

Comparative Study of Different Methods for Soot Sensing and Filter Monitoring in Diesel Exhausts

PubMed Central

Feulner, Markus; Hagen, Gunter; Hottner, Kathrin; Redel, Sabrina; Müller, Andreas; Moos, Ralf

2017-01-01

Due to increasingly tighter emission limits for diesel and gasoline engines, especially concerning particulate matter emissions, particulate filters are becoming indispensable devices for exhaust gas after treatment. Thereby, for an efficient engine and filter control strategy and a cost-efficient filter design, reliable technologies to determine the soot load of the filters and to measure particulate matter concentrations in the exhaust gas during vehicle operation are highly needed. In this study, different approaches for soot sensing are compared. Measurements were conducted on a dynamometer diesel engine test bench with a diesel particulate filter (DPF). The DPF was monitored by a relatively new microwave-based approach. Simultaneously, a resistive type soot sensor and a Pegasor soot sensing device as a reference system measured the soot concentration exhaust upstream of the DPF. By changing engine parameters, different engine out soot emission rates were set. It was found that the microwave-based signal may not only indicate directly the filter loading, but by a time derivative, the engine out soot emission rate can be deduced. Furthermore, by integrating the measured particulate mass in the exhaust, the soot load of the filter can be determined. In summary, all systems coincide well within certain boundaries and the filter itself can act as a soot sensor. PMID:28218700

Fabrication and stabilization of silicon-based photonic crystals with tuned morphology for multi-band optical filtering

NASA Astrophysics Data System (ADS)

Salem, Mohamed Shaker; Abdelaleem, Asmaa Mohamed; El-Gamal, Abear Abdullah; Amin, Mohamed

2017-01-01

One-dimensional silicon-based photonic crystals are formed by the electrochemical anodization of silicon substrates in hydrofluoric acid-based solution using an appropriate current density profile. In order to create a multi-band optical filter, two fabrication approaches are compared and discussed. The first approach utilizes a current profile composed of a linear combination of sinusoidal current waveforms having different frequencies. The individual frequency of the waveform maps to a characteristic stop band in the reflectance spectrum. The stopbands of the optical filter created by the second approach, on the other hand, are controlled by stacking multiple porous silicon rugate multilayers having different fabrication conditions. The morphology of the resulting optical filters is tuned by controlling the electrolyte composition and the type of the silicon substrate. The reduction of sidelobes arising from the interference in the multilayers is observed by applying an index matching current profile to the anodizing current waveform. In order to stabilize the resulting optical filters against natural oxidation, atomic layer deposition of silicon dioxide on the pore wall is employed.

High performance incandescent lighting using a selective emitter and nanophotonic filters

NASA Astrophysics Data System (ADS)

Leroy, Arny; Bhatia, Bikram; Wilke, Kyle; Ilic, Ognjen; Soljačić, Marin; Wang, Evelyn N.

2017-09-01

Previous approaches for improving the efficiency of incandescent light bulbs (ILBs) have relied on tailoring the emitted spectrum using cold-side interference filters that reflect the infrared energy back to the emitter while transmitting the visible light. While this approach has, in theory, potential to surpass light-emitting diodes (LEDs) in terms of luminous efficiency while conserving the excellent color rendering index (CRI) inherent to ILBs, challenges such as low view factor between the emitter and filter, high emitter (>2800 K) and filter temperatures and emitter evaporation have significantly limited the maximum efficiency. In this work, we first analyze the effect of non-idealities in the cold-side filter, the emitter and the view factor on the luminous efficiency. Second, we theoretically and experimentally demonstrate that the loss in efficiency associated with low view factors can be minimized by using a selective emitter (e.g., high emissivity in the visible and low emissivity in the infrared) with a filter. Finally, we discuss the challenges in achieving a high performance and long-lasting incandescent light source including the emitter and filter thermal stability as well as emitter evaporation.

A ubiquitous method for street scale spatial data collection and analysis in challenging urban environments: mapping health risks using spatial video in Haiti

PubMed Central

2013-01-01

Background Fine-scale and longitudinal geospatial analysis of health risks in challenging urban areas is often limited by the lack of other spatial layers even if case data are available. Underlying population counts, residential context, and associated causative factors such as standing water or trash locations are often missing unless collected through logistically difficult, and often expensive, surveys. The lack of spatial context also hinders the interpretation of results and designing intervention strategies structured around analytical insights. This paper offers a ubiquitous spatial data collection approach using a spatial video that can be used to improve analysis and involve participatory collaborations. A case study will be used to illustrate this approach with three health risks mapped at the street scale for a coastal community in Haiti. Methods Spatial video was used to collect street and building scale information, including standing water, trash accumulation, presence of dogs, cohort specific population characteristics, and other cultural phenomena. These data were digitized into Google Earth and then coded and analyzed in a GIS using kernel density and spatial filtering approaches. The concentrations of these risks around area schools which are sometimes sources of diarrheal disease infection because of the high concentration of children and variable sanitary practices will show the utility of the method. In addition schools offer potential locations for cholera education interventions. Results Previously unavailable fine scale health risk data vary in concentration across the town, with some schools being proximate to greater concentrations of the mapped risks. The spatial video is also used to validate coded data and location specific risks within these “hotspots”. Conclusions Spatial video is a tool that can be used in any environment to improve local area health analysis and intervention. The process is rapid and can be repeated in study sites through time to track spatio-temporal dynamics of the communities. Its simplicity should also be used to encourage local participatory collaborations. PMID:23587358

A ubiquitous method for street scale spatial data collection and analysis in challenging urban environments: mapping health risks using spatial video in Haiti.

PubMed

Curtis, Andrew; Blackburn, Jason K; Widmer, Jocelyn M; Morris, J Glenn

2013-04-15

Fine-scale and longitudinal geospatial analysis of health risks in challenging urban areas is often limited by the lack of other spatial layers even if case data are available. Underlying population counts, residential context, and associated causative factors such as standing water or trash locations are often missing unless collected through logistically difficult, and often expensive, surveys. The lack of spatial context also hinders the interpretation of results and designing intervention strategies structured around analytical insights. This paper offers a ubiquitous spatial data collection approach using a spatial video that can be used to improve analysis and involve participatory collaborations. A case study will be used to illustrate this approach with three health risks mapped at the street scale for a coastal community in Haiti. Spatial video was used to collect street and building scale information, including standing water, trash accumulation, presence of dogs, cohort specific population characteristics, and other cultural phenomena. These data were digitized into Google Earth and then coded and analyzed in a GIS using kernel density and spatial filtering approaches. The concentrations of these risks around area schools which are sometimes sources of diarrheal disease infection because of the high concentration of children and variable sanitary practices will show the utility of the method. In addition schools offer potential locations for cholera education interventions. Previously unavailable fine scale health risk data vary in concentration across the town, with some schools being proximate to greater concentrations of the mapped risks. The spatial video is also used to validate coded data and location specific risks within these "hotspots". Spatial video is a tool that can be used in any environment to improve local area health analysis and intervention. The process is rapid and can be repeated in study sites through time to track spatio-temporal dynamics of the communities. Its simplicity should also be used to encourage local participatory collaborations.

A Novel Technique for Inferior Vena Cava Filter Extraction

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

Johnston, Edward William, E-mail: ed.johnston@doctors.org.uk; Rowe, Luke Michael Morgan; Brookes, Jocelyn

Inferior vena cava (IVC) filters are used to protect against pulmonary embolism in high-risk patients. Whilst the insertion of retrievable IVC filters is gaining popularity, a proportion of such devices cannot be removed using standard techniques. We describe a novel approach for IVC filter removal that involves snaring the filter superiorly along with the use of flexible forceps or laser devices to dissect the filter struts from the caval wall. This technique has used to successfully treat three patients without complications in whom standard techniques failed.

Integrating powdered activated carbon into wastewater tertiary filter for micro-pollutant removal.

PubMed

Hu, Jingyi; Aarts, Annelies; Shang, Ran; Heijman, Bas; Rietveld, Luuk

2016-07-15

Integrating powdered activated carbon (PAC) into wastewater tertiary treatment is a promising technology to reduce organic micro-pollutant (OMP) discharge into the receiving waters. To take advantage of the existing tertiary filter, PAC was pre-embedded inside the filter bed acting as a fixed-bed adsorber. The pre-embedding (i.e. immobilization) of PAC was realized by direct dosing a PAC solution on the filter top, which was then promoted to penetrate into the filter media by a down-flow of tap water. In order to examine the effectiveness of this PAC pre-embedded filter towards OMP removal, batch adsorption tests, representing PAC contact reactor (with the same PAC mass-to-treated water volume ratio as in the PAC pre-embedded filter) were performed as references. Moreover, as a conventional dosing option, PAC was dosed continuously with the filter influent (i.e. the wastewater secondary effluent with the investigated OMPs). Comparative results confirmed a higher OMP removal efficiency associated with the PAC pre-embedded filter, as compared to the batch system with a practical PAC residence time. Furthermore, over a filtration period of 10 h (approximating a realistic filtration cycle for tertiary filters), the continuous dosing approach resulted in less OMP removal. Therefore, it was concluded that the pre-embedding approach can be preferentially considered when integrating PAC into the wastewater tertiary treatment for OMP elimination. Copyright © 2016 Elsevier Ltd. All rights reserved.

Impact of backwashing procedures on deep bed filtration productivity in drinking water treatment.

PubMed

Slavik, Irene; Jehmlich, Alexander; Uhl, Wolfgang

2013-10-15

Backwash procedures for deep bed filters were evaluated and compared by means of a new integrated approach based on productivity. For this, different backwash procedures were experimentally evaluated by using a pilot plant for direct filtration. A standard backwash mode as applied in practice served as a reference and effluent turbidity was used as the criterion for filter run termination. The backwash water volumes needed, duration of the filter-to-waste period, time out of operation, total volume discharged and filter run-time were determined and used to calculate average filtration velocity and average productivity. Results for filter run-times, filter backwash volumes, and filter-to-waste volumes showed considerable differences between the backwash procedures. Thus, backwash procedures with additional clear flushing phases were characterised by an increased need for backwash water. However, this additional water consumption could not be compensated by savings during filter ripening. Compared to the reference backwash procedure, filter run-times were longer for both single-media and dual-media filters when air scour and air/water flush were optimised with respect to flow rates and the proportion of air and water. This means that drinking water production time is longer and less water is needed for filter bed cleaning. Also, backwashing with additional clear flushing phases resulted in longer filter run-times before turbidity breakthrough. However, regarding the productivity of the filtration process, it was shown that it was almost the same for all of the backwash procedures investigated in this study. Due to this unexpected finding, the relationships between filter bed cleaning, filter ripening and filtration performance were considered and important conclusions and new approaches for process optimisation and resource savings were derived. Copyright © 2013 Elsevier Ltd. All rights reserved.

The Value of Rotational Venography Versus Anterior–Posterior Venography in 100 Consecutive IVC Filter Retrievals

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

Kiefer, Ryan M., E-mail: rkiefer11@gmail.com; Pandey, Nirnimesh; Trerotola, Scott O.

PurposeAccurately detecting inferior vena cava (IVC) filter complications is important for safe and successful retrieval as tip-embedded filters require removal with non-standard techniques. Venography prior to IVC filter retrieval has traditionally used a single anterior–posterior (AP) projection. This study compares the utility of rotational venography to AP venography prior to IVC filter removal.Materials and MethodsThe rotational venograms from 100 consecutive IVC filter retrievals over a 35-month period were evaluated retrospectively. The AP view of the rotational venogram was examined separately from the full series by a radiologist blinded to alternative imaging and operative findings. The venograms were evaluated for tipmore » embedding, filter fracture, filter thrombus, and IVC thrombus. Statistical analysis was performed.ResultsUsing operative findings and peri-procedural imaging as the reference standard, tip embedding occurred in 59 of the 100 filters (59 %). AP venography was used to correctly identify 31 tip-embedded filters (53 % sensitivity) with two false positives (95 % specificity) for an accuracy of 70 %. Rotational venography was used to correctly identify 58 tip-embedded filters (98 % sensitivity) with one false positive (98 % specificity) for an accuracy of 98 %. A significant difference was found in the sensitivities of the two diagnostic approaches (P < .01). Other findings of thrombus and filter fracture were not significantly different between the two groups.ConclusionRotational venograms allow for more accurate detection of tip-embedded IVC filters compared to AP views alone. As this determines the approach taken, rotational venograms are helpful if obtained prior to IVC filter retrieval.« less

A Proposal of Product Development Collaboration Method Using User Support Information and its Experimental Evaluation

NASA Astrophysics Data System (ADS)

Tanaka, Mitsuru; Kataoka, Masatoshi; Koizumi, Hisao

As the market changes more rapidly and new products continue to get more complex and multifunctional, product development collaboration with competent partners and leading users is getting more important to come up with new products that are successful in the market in a timely manner. ECM (engineering chain management) and SCM (supply chain management) are supply-side approaches toward this collaboration. In this paper, we propose a demand-side approach toward product development collaboration with users based on the information gathered through user support interactions. The approach and methodology proposed here was applied to a real data set, and its effectiveness was verified.

High-speed spectral calibration by complex FIR filter in phase-sensitive optical coherence tomography.

PubMed

Kim, Sangmin; Raphael, Patrick D; Oghalai, John S; Applegate, Brian E

2016-04-01

Swept-laser sources offer a number of advantages for Phase-sensitive Optical Coherence Tomography (PhOCT). However, inter- and intra-sweep variability leads to calibration errors that adversely affect phase sensitivity. While there are several approaches to overcoming this problem, our preferred method is to simply calibrate every sweep of the laser. This approach offers high accuracy and phase stability at the expense of a substantial processing burden. In this approach, the Hilbert phase of the interferogram from a reference interferometer provides the instantaneous wavenumber of the laser, but is computationally expensive. Fortunately, the Hilbert transform may be approximated by a Finite Impulse-Response (FIR) filter. Here we explore the use of several FIR filter based Hilbert transforms for calibration, explicitly considering the impact of filter choice on phase sensitivity and OCT image quality. Our results indicate that the complex FIR filter approach is the most robust and accurate among those considered. It provides similar image quality and slightly better phase sensitivity than the traditional FFT-IFFT based Hilbert transform while consuming fewer resources in an FPGA implementation. We also explored utilizing the Hilbert magnitude of the reference interferogram to calculate an ideal window function for spectral amplitude calibration. The ideal window function is designed to carefully control sidelobes on the axial point spread function. We found that after a simple chromatic correction, calculating the window function using the complex FIR filter and the reference interferometer gave similar results to window functions calculated using a mirror sample and the FFT-IFFT Hilbert transform. Hence, the complex FIR filter can enable accurate and high-speed calibration of the magnitude and phase of spectral interferograms.

High-speed spectral calibration by complex FIR filter in phase-sensitive optical coherence tomography

PubMed Central

Kim, Sangmin; Raphael, Patrick D.; Oghalai, John S.; Applegate, Brian E.

2016-01-01

Swept-laser sources offer a number of advantages for Phase-sensitive Optical Coherence Tomography (PhOCT). However, inter- and intra-sweep variability leads to calibration errors that adversely affect phase sensitivity. While there are several approaches to overcoming this problem, our preferred method is to simply calibrate every sweep of the laser. This approach offers high accuracy and phase stability at the expense of a substantial processing burden. In this approach, the Hilbert phase of the interferogram from a reference interferometer provides the instantaneous wavenumber of the laser, but is computationally expensive. Fortunately, the Hilbert transform may be approximated by a Finite Impulse-Response (FIR) filter. Here we explore the use of several FIR filter based Hilbert transforms for calibration, explicitly considering the impact of filter choice on phase sensitivity and OCT image quality. Our results indicate that the complex FIR filter approach is the most robust and accurate among those considered. It provides similar image quality and slightly better phase sensitivity than the traditional FFT-IFFT based Hilbert transform while consuming fewer resources in an FPGA implementation. We also explored utilizing the Hilbert magnitude of the reference interferogram to calculate an ideal window function for spectral amplitude calibration. The ideal window function is designed to carefully control sidelobes on the axial point spread function. We found that after a simple chromatic correction, calculating the window function using the complex FIR filter and the reference interferometer gave similar results to window functions calculated using a mirror sample and the FFT-IFFT Hilbert transform. Hence, the complex FIR filter can enable accurate and high-speed calibration of the magnitude and phase of spectral interferograms. PMID:27446666

Experiments with explicit filtering for LES using a finite-difference method

NASA Technical Reports Server (NTRS)

Lund, T. S.; Kaltenbach, H. J.

1995-01-01

The equations for large-eddy simulation (LES) are derived formally by applying a spatial filter to the Navier-Stokes equations. The filter width as well as the details of the filter shape are free parameters in LES, and these can be used both to control the effective resolution of the simulation and to establish the relative importance of different portions of the resolved spectrum. An analogous, but less well justified, approach to filtering is more or less universally used in conjunction with LES using finite-difference methods. In this approach, the finite support provided by the computational mesh as well as the wavenumber-dependent truncation errors associated with the finite-difference operators are assumed to define the filter operation. This approach has the advantage that it is also 'automatic' in the sense that no explicit filtering: operations need to be performed. While it is certainly convenient to avoid the explicit filtering operation, there are some practical considerations associated with finite-difference methods that favor the use of an explicit filter. Foremost among these considerations is the issue of truncation error. All finite-difference approximations have an associated truncation error that increases with increasing wavenumber. These errors can be quite severe for the smallest resolved scales, and these errors will interfere with the dynamics of the small eddies if no corrective action is taken. Years of experience at CTR with a second-order finite-difference scheme for high Reynolds number LES has repeatedly indicated that truncation errors must be minimized in order to obtain acceptable simulation results. While the potential advantages of explicit filtering are rather clear, there is a significant cost associated with its implementation. In particular, explicit filtering reduces the effective resolution of the simulation compared with that afforded by the mesh. The resolution requirements for LES are usually set by the need to capture most of the energy-containing eddies, and if explicit filtering is used, the mesh must be enlarged so that these motions are passed by the filter. Given the high cost of explicit filtering, the following interesting question arises. Since the mesh must be expanded in order to perform the explicit filter, might it be better to take advantage of the increased resolution and simply perform an unfiltered simulation on the larger mesh? The cost of the two approaches is roughly the same, but the philosophy is rather different. In the filtered simulation, resolution is sacrificed in order to minimize the various forms of numerical error. In the unfiltered simulation, the errors are left intact, but they are concentrated at very small scales that could be dynamically unimportant from a LES perspective. Very little is known about this tradeoff and the objective of this work is to study this relationship in high Reynolds number channel flow simulations using a second-order finite-difference method.

A comparison of methods for DPLL loop filter design

NASA Technical Reports Server (NTRS)

Aguirre, S.; Hurd, W. J.; Kumar, R.; Statman, J.

1986-01-01

Four design methodologies for loop filters for a class of digital phase-locked loops (DPLLs) are presented. The first design maps an optimum analog filter into the digital domain; the second approach designs a filter that minimizes in discrete time weighted combination of the variance of the phase error due to noise and the sum square of the deterministic phase error component; the third method uses Kalman filter estimation theory to design a filter composed of a least squares fading memory estimator and a predictor. The last design relies on classical theory, including rules for the design of compensators. Linear analysis is used throughout the article to compare different designs, and includes stability, steady state performance and transient behavior of the loops. Design methodology is not critical when the loop update rate can be made high relative to loop bandwidth, as the performance approaches that of continuous time. For low update rates, however, the miminization method is significantly superior to the other methods.

Polymer based resonant waveguide grating photonic filter with on-chip thermal tuning

NASA Astrophysics Data System (ADS)

Chaudhuri, Ritesh Ray; Enemuo, Amarachukwu N.; Song, Youngsik; Seo, Sang-Woo

2018-07-01

In this paper, we present the development of a multilayer polymer resonant waveguide grating (RWG)-based optical filter with an integrated microheater for on-chip thermal spectral tuning. RWG optical filter is fabricated using polymer-based materials. Therefore, its integration can be applied to different material platforms. Typical RWG structure is sensitive to back optical reflection from the structures below. To reduce the effect of back reflection from the metal heater and improve the quality of the integrated RWG filter output, an intermediate absorption layer was implemented utilizing an epoxy based carbon coating. This approach effectively suppresses the background noise in the RWG characteristics. The central wavelength of the reported filter was designed around 1550 nm. Experimentally, wavelength tuning of 21.96 nm was achieved for operating temperature range of 81 °C with approximately 150mW power consumption. Based on the layer-by-layer fabrication approach, the presented thermally tunable RWG filter on a chip has potential for use in low cost hybrid communication systems and spectral sensing applications.

Accurate human limb angle measurement: sensor fusion through Kalman, least mean squares and recursive least-squares adaptive filtering

NASA Astrophysics Data System (ADS)

Olivares, A.; Górriz, J. M.; Ramírez, J.; Olivares, G.

2011-02-01

Inertial sensors are widely used in human body motion monitoring systems since they permit us to determine the position of the subject's limbs. Limb angle measurement is carried out through the integration of the angular velocity measured by a rate sensor and the decomposition of the components of static gravity acceleration measured by an accelerometer. Different factors derived from the sensors' nature, such as the angle random walk and dynamic bias, lead to erroneous measurements. Dynamic bias effects can be reduced through the use of adaptive filtering based on sensor fusion concepts. Most existing published works use a Kalman filtering sensor fusion approach. Our aim is to perform a comparative study among different adaptive filters. Several least mean squares (LMS), recursive least squares (RLS) and Kalman filtering variations are tested for the purpose of finding the best method leading to a more accurate and robust limb angle measurement. A new angle wander compensation sensor fusion approach based on LMS and RLS filters has been developed.

Improving the retrieval rate of inferior vena cava filters with a multidisciplinary team approach

PubMed Central

Inagaki, Elica; Farber, Alik; Eslami, Mohammad H.; Siracuse, Jeffrey J.; Rybin, Denis V.; Sarosiek, Shayna; Sloan, J. Mark; Kalish, Jeffrey

2017-01-01

Objective The option to retrieve inferior vena cava (IVC) filters has resulted in an increase in the utilization of these devices as stopgap measures in patients with relative contraindications to anticoagulation. These retrievable IVC filters, however, are often not retrieved and become permanent. Recent data from our institution confirmed a historically low retrieval rate. Therefore, we hypothesized that the implementation of a new IVC filter retrieval protocol would increase the retrieval rate of appropriate IVC filters at our institution. Methods All consecutive patients who underwent an IVC filter placement at our institution between September 2003 and July 2012 were retrospectively reviewed. In August 2012, a multidisciplinary task force was established, and a new IVC filter retrieval protocol was implemented. Prospective data were collected using a centralized interdepartmental IVC filter registry for all consecutive patients who underwent an IVC filter placement between August 2012 and September 2014. Patients were chronologically categorized into preimplementation (PRE) and postimplementation (POST) groups. Comparisons of outcome measures, including the retrieval rate of IVC filters along with rates of retrieval attempt and technical failure, were made between the two groups. Results In the PRE and POST groups, a total of 720 and 74 retrievable IVC filters were implanted, respectively. In the POST group, 40 of 74 filters (54%) were successfully retrieved compared with 82 of 720 filters (11%) in the PRE group (P < .001). Furthermore, a greater number of IVC filter retrievals were attempted in the POST group than in the PRE group (66% vs 14%; P < .001). No significant difference was observed between the PRE and POST groups for technical failure (17% vs 18%; P = .9). Conclusions The retrieval rate of retrievable IVC filters at our institution was significantly increased with the implementation of a new IVC filter retrieval protocol with a multidisciplinary team approach. This improved retrieval rate is possible with minimal dedication of resources and can potentially lead to a decrease in IVC filter-related complications in the future. PMID:27318045

An Integrated Optimal Estimation Approach to Spitzer Space Telescope Focal Plane Survey

NASA Technical Reports Server (NTRS)

Bayard, David S.; Kang, Bryan H.; Brugarolas, Paul B.; Boussalis, D.

2004-01-01

This paper discusses an accurate and efficient method for focal plane survey that was used for the Spitzer Space Telescope. The approach is based on using a high-order 37-state Instrument Pointing Frame (IPF) Kalman filter that combines both engineering parameters and science parameters into a single filter formulation. In this approach, engineering parameters such as pointing alignments, thermomechanical drift and gyro drifts are estimated along with science parameters such as plate scales and optical distortions. This integrated approach has many advantages compared to estimating the engineering and science parameters separately. The resulting focal plane survey approach is applicable to a diverse range of science instruments such as imaging cameras, spectroscopy slits, and scanning-type arrays alike. The paper will summarize results from applying the IPF Kalman Filter to calibrating the Spitzer Space Telescope focal plane, containing the MIPS, IRAC, and the IRS science Instrument arrays.

Assessment of (Computer-Supported) Collaborative Learning

ERIC Educational Resources Information Center

Strijbos, J. -W.

2011-01-01

Within the (Computer-Supported) Collaborative Learning (CS)CL research community, there has been an extensive dialogue on theories and perspectives on learning from collaboration, approaches to scaffold (script) the collaborative process, and most recently research methodology. In contrast, the issue of assessment of collaborative learning has…

TrapEase inferior vena cava filter placement: use of the subclavian vein.

PubMed

Stone, Patrick A; Aburahma, Ali F; Hass, Stephen M; Hofeldt, Matthew J; Zimmerman, William B; Deel, John T; Deluca, John A

2004-01-01

The purpose of this paper was to evaluate the safety and technical success of TrapEase inferior vena cava filter placement via the subclavian vein. As of yet, no reports in the literature have specifically investigated the use of the subclavian vein as a route for deploying TrapEase vena cava filters. Retrospective chart review was conducted of 135 patients with attempted TrapEase inferior vena cava filter placement over a 2-year period. In a majority of cases, the choice of subclavian vein approach was based primarily on surgeon preference. Other circumstances for subclavian vein deployment included cervical immobilization secondary to trauma, desire for concomitant placement of a subclavian long-term central venous access catheter, and patient body habitus limiting exposure to the internal jugular vein. One hundred and thirty-five filters were placed over this 2-year period. The internal jugular vein approach was used in 56 patients, the femoral vein approach in 39 patients, and the subclavian vein approach in 40 patients. Thirty-nine of the 40 TrapEase filter placements using the subclavian vein were successful. Twenty-six were deployed through the right subclavian vein and 14 through the left subclavian vein. The single failed subclavian deployment was due to the inability to pass the guidewire adequately into the inferior vena cava after successful cannulation of the right subclavian vein. The average deployment time for subclavian vein placement was 26 minutes when TrapEase filter placement was the only procedure performed. No insertional complications were encountered, specifically no pneumothoraces confirmed by chest radiography or fluoroscopy. The subclavian vein provides an alternative site of access for the TrapEase inferior vena cava filter. This route is comparable to other alternative methods evaluated both in average deployment time and complication occurrence. Furthermore, the subclavian vein route is valuable in patients with limited central access and where combined long-term central venous catheter placement using the subclavian vein is desirable.

Dual-filter estimation for rotating-panel sample designs

Treesearch

Francis Roesch

2017-01-01

Dual-filter estimators are described and tested for use in the annual estimation for national forest inventories. The dual-filter approach involves the use of a moving widow estimator in the first pass, which is used as input to Theil’s mixed estimator in the second pass. The moving window and dual-filter estimators are tested along with two other estimators in a...

An Integrated approach to the Space Situational Awareness Problem

DTIC Science & Technology

2016-12-15

data coming from the sensors. We developed particle-based Gaussian Mixture Filters that are immune to the “curse of dimensionality”/ “particle...depletion” problem inherent in particle filtering . This method maps the data assimilation/ filtering problem into an unsupervised learning problem. Results...Gaussian Mixture Filters ; particle depletion; Finite Set Statistics 16. SECURITY CLASSIFICATION OF: 17. LIMITATION OF ABSTRACT UU 18. NUMBER OF PAGES 1

Exploring the Impact of Students' Learning Approach on Collaborative Group Modeling of Blood Circulation

ERIC Educational Resources Information Center

Lee, Shinyoung; Kang, Eunhee; Kim, Heui-Baik

2015-01-01

This study aimed to explore the effect on group dynamics of statements associated with deep learning approaches (DLA) and their contribution to cognitive collaboration and model development during group modeling of blood circulation. A group was selected for an in-depth analysis of collaborative group modeling. This group constructed a model in a…

Effect of Guided Collaboration on General and Special Educators' Perceptions of Collaboration and Student Achievement

ERIC Educational Resources Information Center

Laine, Sandra

2013-01-01

This study investigated the effects of a guided collaboration approach during professional learning community meetings (PLC's) on the perceptions of general and special educators as well as the effect on student performance as measured by benchmark evaluation. A mixed methodology approach was used to collect data through surveys, weekly…

Information filtering via biased random walk on coupled social network.

PubMed

Nie, Da-Cheng; Zhang, Zi-Ke; Dong, Qiang; Sun, Chongjing; Fu, Yan

2014-01-01

The recommender systems have advanced a great deal in the past two decades. However, most researchers focus their attentions on mining the similarities among users or objects in recommender systems and overlook the social influence which plays an important role in users' purchase process. In this paper, we design a biased random walk algorithm on coupled social networks which gives recommendation results based on both social interests and users' preference. Numerical analyses on two real data sets, Epinions and Friendfeed, demonstrate the improvement of recommendation performance by taking social interests into account, and experimental results show that our algorithm can alleviate the user cold-start problem more effectively compared with the mass diffusion and user-based collaborative filtering methods.

Information Filtering via Biased Random Walk on Coupled Social Network

PubMed Central

Dong, Qiang; Fu, Yan

2014-01-01

The recommender systems have advanced a great deal in the past two decades. However, most researchers focus their attentions on mining the similarities among users or objects in recommender systems and overlook the social influence which plays an important role in users' purchase process. In this paper, we design a biased random walk algorithm on coupled social networks which gives recommendation results based on both social interests and users' preference. Numerical analyses on two real data sets, Epinions and Friendfeed, demonstrate the improvement of recommendation performance by taking social interests into account, and experimental results show that our algorithm can alleviate the user cold-start problem more effectively compared with the mass diffusion and user-based collaborative filtering methods. PMID:25147867

Denitrifying woodchip bioreactor and phosphorus filter pairing to minimize pollution swapping

USDA-ARS?s Scientific Manuscript database

Pairing denitrifying woodchip bioreactors and phosphorus-sorbing filters provides a unique, engineered approach for dual nutrient removal from waters impaired with both nitrogen (N) and phosphorus (P). This column study aimed to test placement of two P-filter media (acid mine drainage treatment resi...

Surgical suite environmental control system. [using halothane absorbing filter

NASA Technical Reports Server (NTRS)

Higginbotham, E. J.; Jacobs, M. L.

1974-01-01

Theoretical and experimental work for a systems analysis approach to the problem of surgical suit exhaust systems centered on evaluation of halothane absorbing filters. An activated charcoal-alumina-charcoal combination proved to be the best filter for eliminating halothane through multilayer absorption of gas molecules.

An information theoretic approach of designing sparse kernel adaptive filters.

PubMed

Liu, Weifeng; Park, Il; Principe, José C

2009-12-01

This paper discusses an information theoretic approach of designing sparse kernel adaptive filters. To determine useful data to be learned and remove redundant ones, a subjective information measure called surprise is introduced. Surprise captures the amount of information a datum contains which is transferable to a learning system. Based on this concept, we propose a systematic sparsification scheme, which can drastically reduce the time and space complexity without harming the performance of kernel adaptive filters. Nonlinear regression, short term chaotic time-series prediction, and long term time-series forecasting examples are presented.

Adaptive identification and control of structural dynamics systems using recursive lattice filters

NASA Technical Reports Server (NTRS)

Sundararajan, N.; Montgomery, R. C.; Williams, J. P.

1985-01-01

A new approach for adaptive identification and control of structural dynamic systems by using least squares lattice filters thar are widely used in the signal processing area is presented. Testing procedures for interfacing the lattice filter identification methods and modal control method for stable closed loop adaptive control are presented. The methods are illustrated for a free-free beam and for a complex flexible grid, with the basic control objective being vibration suppression. The approach is validated by using both simulations and experimental facilities available at the Langley Research Center.

An Integrated Approach for Gear Health Prognostics

NASA Technical Reports Server (NTRS)

He, David; Bechhoefer, Eric; Dempsey, Paula; Ma, Jinghua

2012-01-01

In this paper, an integrated approach for gear health prognostics using particle filters is presented. The presented method effectively addresses the issues in applying particle filters to gear health prognostics by integrating several new components into a particle filter: (1) data mining based techniques to effectively define the degradation state transition and measurement functions using a one-dimensional health index obtained by whitening transform; (2) an unbiased l-step ahead RUL estimator updated with measurement errors. The feasibility of the presented prognostics method is validated using data from a spiral bevel gear case study.

Accurate Attitude Estimation Using ARS under Conditions of Vehicle Movement Based on Disturbance Acceleration Adaptive Estimation and Correction

PubMed Central

Xing, Li; Hang, Yijun; Xiong, Zhi; Liu, Jianye; Wan, Zhong

2016-01-01

This paper describes a disturbance acceleration adaptive estimate and correction approach for an attitude reference system (ARS) so as to improve the attitude estimate precision under vehicle movement conditions. The proposed approach depends on a Kalman filter, where the attitude error, the gyroscope zero offset error and the disturbance acceleration error are estimated. By switching the filter decay coefficient of the disturbance acceleration model in different acceleration modes, the disturbance acceleration is adaptively estimated and corrected, and then the attitude estimate precision is improved. The filter was tested in three different disturbance acceleration modes (non-acceleration, vibration-acceleration and sustained-acceleration mode, respectively) by digital simulation. Moreover, the proposed approach was tested in a kinematic vehicle experiment as well. Using the designed simulations and kinematic vehicle experiments, it has been shown that the disturbance acceleration of each mode can be accurately estimated and corrected. Moreover, compared with the complementary filter, the experimental results have explicitly demonstrated the proposed approach further improves the attitude estimate precision under vehicle movement conditions. PMID:27754469

Accurate Attitude Estimation Using ARS under Conditions of Vehicle Movement Based on Disturbance Acceleration Adaptive Estimation and Correction.

PubMed

Xing, Li; Hang, Yijun; Xiong, Zhi; Liu, Jianye; Wan, Zhong

2016-10-16

This paper describes a disturbance acceleration adaptive estimate and correction approach for an attitude reference system (ARS) so as to improve the attitude estimate precision under vehicle movement conditions. The proposed approach depends on a Kalman filter, where the attitude error, the gyroscope zero offset error and the disturbance acceleration error are estimated. By switching the filter decay coefficient of the disturbance acceleration model in different acceleration modes, the disturbance acceleration is adaptively estimated and corrected, and then the attitude estimate precision is improved. The filter was tested in three different disturbance acceleration modes (non-acceleration, vibration-acceleration and sustained-acceleration mode, respectively) by digital simulation. Moreover, the proposed approach was tested in a kinematic vehicle experiment as well. Using the designed simulations and kinematic vehicle experiments, it has been shown that the disturbance acceleration of each mode can be accurately estimated and corrected. Moreover, compared with the complementary filter, the experimental results have explicitly demonstrated the proposed approach further improves the attitude estimate precision under vehicle movement conditions.

Rao-Blackwellization for Adaptive Gaussian Sum Nonlinear Model Propagation

NASA Technical Reports Server (NTRS)

Semper, Sean R.; Crassidis, John L.; George, Jemin; Mukherjee, Siddharth; Singla, Puneet

2015-01-01

When dealing with imperfect data and general models of dynamic systems, the best estimate is always sought in the presence of uncertainty or unknown parameters. In many cases, as the first attempt, the Extended Kalman filter (EKF) provides sufficient solutions to handling issues arising from nonlinear and non-Gaussian estimation problems. But these issues may lead unacceptable performance and even divergence. In order to accurately capture the nonlinearities of most real-world dynamic systems, advanced filtering methods have been created to reduce filter divergence while enhancing performance. Approaches, such as Gaussian sum filtering, grid based Bayesian methods and particle filters are well-known examples of advanced methods used to represent and recursively reproduce an approximation to the state probability density function (pdf). Some of these filtering methods were conceptually developed years before their widespread uses were realized. Advanced nonlinear filtering methods currently benefit from the computing advancements in computational speeds, memory, and parallel processing. Grid based methods, multiple-model approaches and Gaussian sum filtering are numerical solutions that take advantage of different state coordinates or multiple-model methods that reduced the amount of approximations used. Choosing an efficient grid is very difficult for multi-dimensional state spaces, and oftentimes expensive computations must be done at each point. For the original Gaussian sum filter, a weighted sum of Gaussian density functions approximates the pdf but suffers at the update step for the individual component weight selections. In order to improve upon the original Gaussian sum filter, Ref. [2] introduces a weight update approach at the filter propagation stage instead of the measurement update stage. This weight update is performed by minimizing the integral square difference between the true forecast pdf and its Gaussian sum approximation. By adaptively updating each component weight during the nonlinear propagation stage an approximation of the true pdf can be successfully reconstructed. Particle filtering (PF) methods have gained popularity recently for solving nonlinear estimation problems due to their straightforward approach and the processing capabilities mentioned above. The basic concept behind PF is to represent any pdf as a set of random samples. As the number of samples increases, they will theoretically converge to the exact, equivalent representation of the desired pdf. When the estimated qth moment is needed, the samples are used for its construction allowing further analysis of the pdf characteristics. However, filter performance deteriorates as the dimension of the state vector increases. To overcome this problem Ref. [5] applies a marginalization technique for PF methods, decreasing complexity of the system to one linear and another nonlinear state estimation problem. The marginalization theory was originally developed by Rao and Blackwell independently. According to Ref. [6] it improves any given estimator under every convex loss function. The improvement comes from calculating a conditional expected value, often involving integrating out a supportive statistic. In other words, Rao-Blackwellization allows for smaller but separate computations to be carried out while reaching the main objective of the estimator. In the case of improving an estimator's variance, any supporting statistic can be removed and its variance determined. Next, any other information that dependents on the supporting statistic is found along with its respective variance. A new approach is developed here by utilizing the strengths of the adaptive Gaussian sum propagation in Ref. [2] and a marginalization approach used for PF methods found in Ref. [7]. In the following sections a modified filtering approach is presented based on a special state-space model within nonlinear systems to reduce the dimensionality of the optimization problem in Ref. [2]. First, the adaptive Gaussian sum propagation is explained and then the new marginalized adaptive Gaussian sum propagation is derived. Finally, an example simulation is presented.

a Radical Collaborative Approach: Developing a Model for Learning Theory, Human-Based Computation and Participant Motivation in a Rock-Art Heritage Application

NASA Astrophysics Data System (ADS)

Haubt, R.

2016-06-01

This paper explores a Radical Collaborative Approach in the global and centralized Rock-Art Database project to find new ways to look at rock-art by making information more accessible and more visible through public contributions. It looks at rock-art through the Key Performance Indicator (KPI), identified with the latest Australian State of the Environment Reports to help develop a better understanding of rock-art within a broader Cultural and Indigenous Heritage context. Using a practice-led approach the project develops a conceptual collaborative model that is deployed within the RADB Management System. Exploring learning theory, human-based computation and participant motivation the paper develops a procedure for deploying collaborative functions within the interface design of the RADB Management System. The paper presents the results of the collaborative model implementation and discusses considerations for the next iteration of the RADB Universe within an Agile Development Approach.

Involving Users to Improve the Collaborative Logical Framework

PubMed Central

2014-01-01

In order to support collaboration in web-based learning, there is a need for an intelligent support that facilitates its management during the design, development, and analysis of the collaborative learning experience and supports both students and instructors. At aDeNu research group we have proposed the Collaborative Logical Framework (CLF) to create effective scenarios that support learning through interaction, exploration, discussion, and collaborative knowledge construction. This approach draws on artificial intelligence techniques to support and foster an effective involvement of students to collaborate. At the same time, the instructors' workload is reduced as some of their tasks—especially those related to the monitoring of the students behavior—are automated. After introducing the CLF approach, in this paper, we present two formative evaluations with users carried out to improve the design of this collaborative tool and thus enrich the personalized support provided. In the first one, we analyze, following the layered evaluation approach, the results of an observational study with 56 participants. In the second one, we tested the infrastructure to gather emotional data when carrying out another observational study with 17 participants. PMID:24592196

Librarian and Faculty Collaborative Instruction: A Phenomenological Self-Study

ERIC Educational Resources Information Center

Brown, Jennifer Diane; Duke, Thomas Scott

2005-01-01

Several models of librarian and faculty collaboration are found in the professional librarian literature. The literature on collaborative self-study research in university settings suggests collaborative self-study research can improve interdisciplinary and collaborative approaches to teaching and research and facilitate the transfer of knowledge.…

A Novel Modulation Classification Approach Using Gabor Filter Network

PubMed Central

Ghauri, Sajjad Ahmed; Qureshi, Ijaz Mansoor; Cheema, Tanveer Ahmed; Malik, Aqdas Naveed

2014-01-01

A Gabor filter network based approach is used for feature extraction and classification of digital modulated signals by adaptively tuning the parameters of Gabor filter network. Modulation classification of digitally modulated signals is done under the influence of additive white Gaussian noise (AWGN). The modulations considered for the classification purpose are PSK 2 to 64, FSK 2 to 64, and QAM 4 to 64. The Gabor filter network uses the network structure of two layers; the first layer which is input layer constitutes the adaptive feature extraction part and the second layer constitutes the signal classification part. The Gabor atom parameters are tuned using Delta rule and updating of weights of Gabor filter using least mean square (LMS) algorithm. The simulation results show that proposed novel modulation classification algorithm has high classification accuracy at low signal to noise ratio (SNR) on AWGN channel. PMID:25126603

A robust approach to optimal matched filter design in ultrasonic non-destructive evaluation (NDE)

NASA Astrophysics Data System (ADS)

Li, Minghui; Hayward, Gordon

2017-02-01

The matched filter was demonstrated to be a powerful yet efficient technique to enhance defect detection and imaging in ultrasonic non-destructive evaluation (NDE) of coarse grain materials, provided that the filter was properly designed and optimized. In the literature, in order to accurately approximate the defect echoes, the design utilized the real excitation signals, which made it time consuming and less straightforward to implement in practice. In this paper, we present a more robust and flexible approach to optimal matched filter design using the simulated excitation signals, and the control parameters are chosen and optimized based on the real scenario of array transducer, transmitter-receiver system response, and the test sample, as a result, the filter response is optimized and depends on the material characteristics. Experiments on industrial samples are conducted and the results confirm the great benefits of the method.

Improved photo response non-uniformity (PRNU) based source camera identification.

PubMed

Cooper, Alan J

2013-03-10

The concept of using Photo Response Non-Uniformity (PRNU) as a reliable forensic tool to match an image to a source camera is now well established. Traditionally, the PRNU estimation methodologies have centred on a wavelet based de-noising approach. Resultant filtering artefacts in combination with image and JPEG contamination act to reduce the quality of PRNU estimation. In this paper, it is argued that the application calls for a simplified filtering strategy which at its base level may be realised using a combination of adaptive and median filtering applied in the spatial domain. The proposed filtering method is interlinked with a further two stage enhancement strategy where only pixels in the image having high probabilities of significant PRNU bias are retained. This methodology significantly improves the discrimination between matching and non-matching image data sets over that of the common wavelet filtering approach. Copyright © 2012 Elsevier Ireland Ltd. All rights reserved.

An approach for filtering hyperbolically positioned underwater acoustic telemetry data with position precision estimates

USGS Publications Warehouse

Meckley, Trevor D.; Holbrook, Christopher M.; Wagner, C. Michael; Binder, Thomas R.

2014-01-01

The use of position precision estimates that reflect the confidence in the positioning process should be considered prior to the use of biological filters that rely on a priori expectations of the subject’s movement capacities and tendencies. Position confidence goals should be determined based upon the needs of the research questions and analysis requirements versus arbitrary selection, in which filters of previous studies are adopted. Data filtering with this approach ensures that data quality is sufficient for the selected analyses and presents the opportunity to adjust or identify a different analysis in the event that the requisite precision was not attained. Ignoring these steps puts a practitioner at risk of reporting errant findings.

Fast Katz and Commuters: Efficient Estimation of Social Relatedness in Large Networks

NASA Astrophysics Data System (ADS)

Esfandiar, Pooya; Bonchi, Francesco; Gleich, David F.; Greif, Chen; Lakshmanan, Laks V. S.; On, Byung-Won

Motivated by social network data mining problems such as link prediction and collaborative filtering, significant research effort has been devoted to computing topological measures including the Katz score and the commute time. Existing approaches typically approximate all pairwise relationships simultaneously. In this paper, we are interested in computing: the score for a single pair of nodes, and the top-k nodes with the best scores from a given source node. For the pairwise problem, we apply an iterative algorithm that computes upper and lower bounds for the measures we seek. This algorithm exploits a relationship between the Lanczos process and a quadrature rule. For the top-k problem, we propose an algorithm that only accesses a small portion of the graph and is related to techniques used in personalized PageRank computing. To test the scalability and accuracy of our algorithms we experiment with three real-world networks and find that these algorithms run in milliseconds to seconds without any preprocessing.

NASA Tech Briefs, March 2003

NASA Technical Reports Server (NTRS)

2003-01-01

Topics covered include: Tool for Bending a Metal Tube Precisely in a Confined Space; Multiple-Use Mechanisms for Attachment to Seat Tracks; Force-Measuring Clamps; Cellular Pressure-Actuated Joint; Block QCA Fault-Tolerant Logic Gates; Hybrid VLSI/QCA Architecture for Computing FFTs; Arrays of Carbon Nanotubes as RF Filters in Waveguides; Carbon Nanotubes as Resonators for RF Spectrum Analyzers; Software for Viewing Landsat Mosaic Images; Updated Integrated Mission Program; Software for Sharing and Management of Information; Update on Integrated Optical Design Analyzer; Optical-Quality Thin Polymer Membranes; Rollable Thin Shell Composite-Material Paraboloidal Mirrors; Folded Resonant Horns for Power Ultrasonic Applications; Touchdown Ball-Bearing System for Magnetic Bearings; Flux-Based Deadbeat Control of Induction-Motor Torque; Block Copolymers as Templates for Arrays of Carbon Nanotubes; Throttling Cryogen Boiloff To Control Cryostat Temperature; Collaborative Software Development Approach Used to Deliver the New Shuttle Telemetry Ground Station; Turbulence in Supercritical O2/H2 and C7H16/N2 Mixing Layers; and Time-Resolved Measurements in Optoelectronic Microbioanal.

A comprehensive approach to decipher biological computation to achieve next generation high-performance exascale computing.

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

James, Conrad D.; Schiess, Adrian B.; Howell, Jamie

2013-10-01

The human brain (volume=1200cm3) consumes 20W and is capable of performing > 10^16 operations/s. Current supercomputer technology has reached 1015 operations/s, yet it requires 1500m^3 and 3MW, giving the brain a 10^12 advantage in operations/s/W/cm^3. Thus, to reach exascale computation, two achievements are required: 1) improved understanding of computation in biological tissue, and 2) a paradigm shift towards neuromorphic computing where hardware circuits mimic properties of neural tissue. To address 1), we will interrogate corticostriatal networks in mouse brain tissue slices, specifically with regard to their frequency filtering capabilities as a function of input stimulus. To address 2), we willmore » instantiate biological computing characteristics such as multi-bit storage into hardware devices with future computational and memory applications. Resistive memory devices will be modeled, designed, and fabricated in the MESA facility in consultation with our internal and external collaborators.« less

A trust-based recommendation method using network diffusion processes

NASA Astrophysics Data System (ADS)

Chen, Ling-Jiao; Gao, Jian

2018-09-01

A variety of rating-based recommendation methods have been extensively studied including the well-known collaborative filtering approaches and some network diffusion-based methods, however, social trust relations are not sufficiently considered when making recommendations. In this paper, we contribute to the literature by proposing a trust-based recommendation method, named CosRA+T, after integrating the information of trust relations into the resource-redistribution process. Specifically, a tunable parameter is used to scale the resources received by trusted users before the redistribution back to the objects. Interestingly, we find an optimal scaling parameter for the proposed CosRA+T method to achieve its best recommendation accuracy, and the optimal value seems to be universal under several evaluation metrics across different datasets. Moreover, results of extensive experiments on the two real-world rating datasets with trust relations, Epinions and FriendFeed, suggest that CosRA+T has a remarkable improvement in overall accuracy, diversity and novelty. Our work takes a step towards designing better recommendation algorithms by employing multiple resources of social network information.

Fast katz and commuters : efficient estimation of social relatedness in large networks.

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

On, Byung-Won; Lakshmanan, Laks V. S.; Greif, Chen

Motivated by social network data mining problems such as link prediction and collaborative filtering, significant research effort has been devoted to computing topological measures including the Katz score and the commute time. Existing approaches typically approximate all pairwise relationships simultaneously. In this paper, we are interested in computing: the score for a single pair of nodes, and the top-k nodes with the best scores from a given source node. For the pairwise problem, we apply an iterative algorithm that computes upper and lower bounds for the measures we seek. This algorithm exploits a relationship between the Lanczos process and amore » quadrature rule. For the top-k problem, we propose an algorithm that only accesses a small portion of the graph and is related to techniques used in personalized PageRank computing. To test the scalability and accuracy of our algorithms we experiment with three real-world networks and find that these algorithms run in milliseconds to seconds without any preprocessing.« less

Endobronchial Forceps-Assisted and Excimer Laser-Assisted Inferior Vena Cava Filter Removal: The Data, Where We Are, and How It Is Done.

PubMed

Chen, James X; Montgomery, Jennifer; McLennan, Gordon; Stavropoulos, S William

2018-06-01

The recognition of inferior vena cava filter related complications has motivated increased attentiveness in clinical follow-up of patients with inferior vena cava filters and has led to development of multiple approaches for retrieving filters that are challenging or impossible to remove using conventional techniques. Endobronchial forceps and excimer lasers are tools for designed to aid in complex inferior vena cava filter removals. This article discusses endobronchial forceps-assisted and excimer laser-assisted inferior vena cava filter retrievals. Copyright © 2018 Elsevier Inc. All rights reserved.

Frequency domain FIR and IIR adaptive filters

NASA Technical Reports Server (NTRS)

Lynn, D. W.

1990-01-01

A discussion of the LMS adaptive filter relating to its convergence characteristics and the problems associated with disparate eigenvalues is presented. This is used to introduce the concept of proportional convergence. An approach is used to analyze the convergence characteristics of block frequency-domain adaptive filters. This leads to a development showing how the frequency-domain FIR adaptive filter is easily modified to provide proportional convergence. These ideas are extended to a block frequency-domain IIR adaptive filter and the idea of proportional convergence is applied. Experimental results illustrating proportional convergence in both FIR and IIR frequency-domain block adaptive filters is presented.

Optical calculation of correlation filters for a robotic vision system

NASA Technical Reports Server (NTRS)

Knopp, Jerome

1989-01-01

A method is presented for designing optical correlation filters based on measuring three intensity patterns: the Fourier transform of a filter object, a reference wave and the interference pattern produced by the sum of the object transform and the reference. The method can produce a filter that is well matched to both the object, its transforming optical system and the spatial light modulator used in the correlator input plane. A computer simulation was presented to demonstrate the approach for the special case of a conventional binary phase-only filter. The simulation produced a workable filter with a sharp correlation peak.

Case report on the non-operative management of a retrievable inferior vena cava filter perforating the duodenum.

PubMed

Fernandez-Moure, Joseph S; Kim, Keemberly; Zubair, M Haseeb; Rosenberg, Wade R

2017-01-01

Deep vein thrombosis (DVT) continues to be a significant source of morbidity for surgical patients. Placement of a retrievable inferior vena cava (IVC) filter is used when patients have contraindications to anticoagulation or recurrent pulmonary embolism despite therapeutic anticoagulation. Although retrievable IVC filters are often used, they carry a unique set of risks. A 67-year-old man presents to the Emergency Room (ER) following large volume melena and complaining of syncope. One year prior, the patient had been diagnosed with Glioblastoma multiforme, for which he underwent a craniotomy with near-total resection of the mass. He subsequently developed a deep vein thrombosis and underwent placement of a retrievable inferior vena cava (IVC) filter. Computerized tomography (CT) and esophagogastroduodenoscopy showed duodenal perforation by the retrievable IVC filter. The filter was successfully retrieved through an endovascular approach. Retrievable IVC filter placement is the preferred method of pulmonary embolism prevention in patients with significant risk for bleeding. Duodenal perforation by a retrievable IVC filter is a rare and serious complication. It is usually managed surgically, but can also be managed non-operatively. For patients with significant comorbidities or patients who are poor surgical candidates, non-operative management with close monitoring can serve as an initial approach to the patient with a caval enteric perforation secondary to a retrievable IVC filter. Copyright © 2017. Published by Elsevier Ltd.

[Factors affecting biological removal of iron and manganese in groundwater].

PubMed

Xue, Gang; He, Sheng-Bing; Wang, Xin-Ze

2006-01-01

Factors affecting biological process for removing iron and manganese in groundwater were analyzed. When DO and pH in groundwater after aeration were 7.0 - 7.5 mg/L and 6.8 - 7.0 respectively, not only can the activation of Mn2+ oxidizing bacteria be maintained, but also the demand of iron and manganese removal can be satisfied. A novel inoculating approach of grafting mature filter material into filter bed, which is easier to handle than selective culture media, was employed in this research. However, this approach was only suitable to the filter material of high-quality manganese sand with strong Mn2+ adsorption capacity. For the filter material of quartz sand with weak adsorption capacity, only culturing and domesticating Mn2+ oxidizing bacteria by selective culture media can be adopted as inoculation in filter bed. The optimal backwashing rate of biological filter bed filled with manganese sand and quartz sand should be kept at a relatively low level of 6 - 9 L/(m2 x s) and 7 -11 L/( m2 x s), respectively. Then the stability of microbial phase in filter bed was not disturbed, and iron and manganese removal efficiency recovered in less than 5h. Moreover, by using filter material with uniform particle size of 1.0 - 1.2 mm in filter bed, the filtration cycle reached as long as 35 - 38h.

Game Mastering in Collaborative Serious Games: A Novel Approach for Instructor Support in Multiplayer Serious Games

ERIC Educational Resources Information Center

Wendel, Viktor; Krepp, Stefan; Gutjahr, Michael Oliver; Göbel, Stefan; Steinmetz, Ralf

2015-01-01

In collaborative learning scenarios, the role of the instructor is vital. This aspect directly carries over to the concept of collaborative multiplayer Serious Games, where a group of players is learning together using a Serious Game. In this paper, the authors propose a novel approach for integration and support of instructors in collaborative…

Optimal Recursive Digital Filters for Active Bending Stabilization

NASA Technical Reports Server (NTRS)

Orr, Jeb S.

2013-01-01

In the design of flight control systems for large flexible boosters, it is common practice to utilize active feedback control of the first lateral structural bending mode so as to suppress transients and reduce gust loading. Typically, active stabilization or phase stabilization is achieved by carefully shaping the loop transfer function in the frequency domain via the use of compensating filters combined with the frequency response characteristics of the nozzle/actuator system. In this paper we present a new approach for parameterizing and determining optimal low-order recursive linear digital filters so as to satisfy phase shaping constraints for bending and sloshing dynamics while simultaneously maximizing attenuation in other frequency bands of interest, e.g. near higher frequency parasitic structural modes. By parameterizing the filter directly in the z-plane with certain restrictions, the search space of candidate filter designs that satisfy the constraints is restricted to stable, minimum phase recursive low-pass filters with well-conditioned coefficients. Combined with optimal output feedback blending from multiple rate gyros, the present approach enables rapid and robust parametrization of autopilot bending filters to attain flight control performance objectives. Numerical results are presented that illustrate the application of the present technique to the development of rate gyro filters for an exploration-class multi-engined space launch vehicle.

Development of a low-power, low-cost front end electronics module for large scale distributed neutrino detectors

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

James J. Beatty

2008-03-08

A number of concepts have been presented for distributed neutrino detectors formed of large numbers of autonomous detectors. Examples include the Antarctic Ross Ice Shelf Antenna Neutrino Array (ARIANNA) [Barwick 2006], as well as proposed radio extensions to the IceCube detector at South Pole Station such as AURA and IceRay. [Besson 2008]. We have focused on key enabling technical developments required by this class of experiments. The radio Cherenkov signal, generated by the Askaryan mechanism [Askaryan 1962, 1965], is impulsive and coherent up to above 1 GHz. In the frequency domain, the impulsive character of the emission results in simultaneousmore » increase of the power detected in multiple frequency bands. This multiband triggering approach has proven fruitful, especially as anthropogenic interference often results from narrowband communications signals. A typical distributed experiment of this type consists of a station responsible for the readout of a cluster of antennas either near the surface of the ice or deployed in boreholes. Each antenna is instrumented with a broadband low-noise amplifier, followed by an array of filters to facilitate multi-band coincidence trigger schemes at the antenna level. The power in each band is detected at the output of each band filter, using either square-law diode detectors or log-power detectors developed for the cellular telephone market. The use of multiple antennas per station allows a local coincidence among antennas to be used as the next stage of the trigger. Station triggers can then be combined into an array trigger by comparing timestamps of triggers among stations and identifying space-time clusters of station triggers. Data from each station is buffered and can be requested from the individual stations when a multi-station coincidence occurs. This approach has been successfully used in distributed experiments such as the Pierre Auger Observatory. [Abraham et al. 2004] We identified the filters as being especially critical. The frequency range of interest, {approx}200 MHz to {approx}1.2 GHz, is a transitional region where the lumped circuit element approach taken at low frequencies begins to reach limitations due to component tolerances, component losses, and parasitic effects. Active circuits can help to mitigate against these effects at the cost of added power consumption that becomes prohibitive for distributed experiments across the band of interest. At higher frequency microstrip, stripline, and other microwave techniques come to the fore. We have developed designs and design tools for passive filters extending the high frequency techniques to the frequency range of interest. Microstrip and stripline techniques are not usually attractive here because of the large physical dimensions of the resulting circuits, but in this application the tradeoff of size against power consumption favors this choice. These techniques are also intrinsically low-cost, as the filter is built into the circuit boards and the cost of components and their assembly onto the board is avoided. The basic element of the filter tree is an impedance matched wideband diplexer. This consists of a pair of low pass and high pass filters with a shared cutoff frequency and complementary frequency responses. These are designing the lowpass filter as a high order LC filter, which can be implemented as a series of transmission line segments of varying width. This can be transformed in to a CL high pass filter with a complementary frequency response. When the two filters are coupled to a common input, the input impedances of the networks add in parallel to give a constant input impedance as a function of frequency, with power flowing into one leg or the other of the filter pair. These filters can be cascaded to divide the band into the frequency ranges of interest; the broadband impedance matching at the inputs makes coupling of successive stages straightforward. These circuits can be produced in quantity at low cost using standard PCB fabrication techniques. We have determined that to achieve best performance the circuits should be built on a low loss-tangent RF substrate. We are working in cooperation with our colleagues in condensed matter who also have a need for this capability to purchase the equipment for in-house fabrication of prototype quantities of these circuits. We plan to continue the work on these filtersusing internal funds, and produce and characterize the performance of prototypes. We also participated in deployment of a prototype detector station near McMurdo Station, Antarctica in collaboration with colleagues at UCLA and UC-Irvine. The prototype station includes a single-board computer, GPS receiver, ADC board, and Iridium satellite modem powered by an omnidirectional solar array. We operated this station in the austral summer of 2006-2007, and used the Iridium SMS mode to transmit the status of the station until the end of the daylight season.« less

Collaboration, negotiation, and coalescence for interagency-collaborative teams to scale-up evidence-based practice.

PubMed

Aarons, Gregory A; Fettes, Danielle L; Hurlburt, Michael S; Palinkas, Lawrence A; Gunderson, Lara; Willging, Cathleen E; Chaffin, Mark J

2014-01-01

Implementation and scale-up of evidence-based practices (EBPs) is often portrayed as involving multiple stakeholders collaborating harmoniously in the service of a shared vision. In practice, however, collaboration is a more complex process that may involve shared and competing interests and agendas, and negotiation. The present study examined the scale-up of an EBP across an entire service system using the Interagency Collaborative Team approach. Participants were key stakeholders in a large-scale county-wide implementation of an EBP to reduce child neglect, SafeCare. Semistructured interviews and/or focus groups were conducted with 54 individuals representing diverse constituents in the service system, followed by an iterative approach to coding and analysis of transcripts. The study was conceptualized using the Exploration, Preparation, Implementation, and Sustainment framework. Although community stakeholders eventually coalesced around implementation of SafeCare, several challenges affected the implementation process. These challenges included differing organizational cultures, strategies, and approaches to collaboration; competing priorities across levels of leadership; power struggles; and role ambiguity. Each of the factors identified influenced how stakeholders approached the EBP implementation process. System-wide scale-up of EBPs involves multiple stakeholders operating in a nexus of differing agendas, priorities, leadership styles, and negotiation strategies. The term collaboration may oversimplify the multifaceted nature of the scale-up process. Implementation efforts should openly acknowledge and consider this nexus when individual stakeholders and organizations enter into EBP implementation through collaborative processes.

Collaboration, Negotiation, and Coalescence for Interagency-Collaborative Teams to Scale-up Evidence-Based Practice

PubMed Central

Aarons, Gregory A.; Fettes, Danielle; Hurlburt, Michael; Palinkas, Lawrence; Gunderson, Lara; Willging, Cathleen; Chaffin, Mark

2014-01-01

Objective Implementation and scale-up of evidence-based practices (EBPs) is often portrayed as involving multiple stakeholders collaborating harmoniously in the service of a shared vision. In practice, however, collaboration is a more complex process that may involve shared and competing interests and agendas, and negotiation. The present study examined the scale-up of an EBP across an entire service system using the Interagency Collaborative Team (ICT) approach. Methods Participants were key stakeholders in a large-scale county-wide implementation of an EBP to reduce child neglect, SafeCare®. Semi-structured interviews and/or focus groups were conducted with 54 individuals representing diverse constituents in the service system, followed by an iterative approach to coding and analysis of transcripts. The study was conceptualized using the Exploration, Preparation, Implementation, and Sustainment (EPIS) framework. Results Although community stakeholders eventually coalesced around implementation of SafeCare, several challenges affected the implementation process. These challenges included differing organizational cultures, strategies, and approaches to collaboration, competing priorities across levels of leadership, power struggles, and role ambiguity. Each of the factors identified influenced how stakeholders approached the EBP implementation process. Conclusions System wide scale-up of EBPs involves multiple stakeholders operating in a nexus of differing agendas, priorities, leadership styles, and negotiation strategies. The term collaboration may oversimplify the multifaceted nature of the scale-up process. Implementation efforts should openly acknowledge and consider this nexus when individual stakeholders and organizations enter into EBP implementation through collaborative processes. PMID:24611580

Occurrence of eight UV filters in beaches of Gran Canaria (Canary Islands). An approach to environmental risk assessment.

PubMed

Sánchez Rodríguez, A; Rodrigo Sanz, M; Betancort Rodríguez, J R

2015-07-01

Due to the growing concern about human health effects of ultraviolet (UV) radiation, the use of UV filters has increased in recent decades. Unfortunately, some common UV filters are bioaccumulated in aquatic organisms and show a potential for estrogenic activity. The aim of the present study is to determine the presence of some UV filters in the coastal waters of six beaches around Gran Canaria Island as consequence of recreational seaside activities. Eight commonly used UV filters: benzophenone-3 (BP-3), octocrylene (OC), octyl-dimethyl-PABA (OD-PABA), ethylhexyl methoxy cinnamate (EHMC), homosalate (HMS), butyl methoxydibenzoyl methane (BMDBM), 4-methylbenzylidene camphor (4-MBC) and diethylamino hydroxybenzoyl hexyl benzoate (DHHB), were monitored and, with the exception of OD-PABA, all were detected in the samples collected. 99% of the samples showed some UV filters and concentration levels reached up to 3316.7 ng/L for BP-3. Environmental risk assessment (ERA) approach showed risk quotients (RQ) higher than 10, which means that there is a significant potential for adverse effects, for 4-MBC and EHMC for those samples with highest levels of UV filters. Copyright © 2015 Elsevier Ltd. All rights reserved.

Development of an adaptive bilateral filter for evaluating color image difference

NASA Astrophysics Data System (ADS)

Wang, Zhaohui; Hardeberg, Jon Yngve

2012-04-01

Spatial filtering, which aims to mimic the contrast sensitivity function (CSF) of the human visual system (HVS), has previously been combined with color difference formulae for measuring color image reproduction errors. These spatial filters attenuate imperceptible information in images, unfortunately including high frequency edges, which are believed to be crucial in the process of scene analysis by the HVS. The adaptive bilateral filter represents a novel approach, which avoids the undesirable loss of edge information introduced by CSF-based filtering. The bilateral filter employs two Gaussian smoothing filters in different domains, i.e., spatial domain and intensity domain. We propose a method to decide the parameters, which are designed to be adaptive to the corresponding viewing conditions, and the quantity and homogeneity of information contained in an image. Experiments and discussions are given to support the proposal. A series of perceptual experiments were conducted to evaluate the performance of our approach. The experimental sample images were reproduced with variations in six image attributes: lightness, chroma, hue, compression, noise, and sharpness/blurriness. The Pearson's correlation values between the model-predicted image difference and the observed difference were employed to evaluate the performance, and compare it with that of spatial CIELAB and image appearance model.

Adapted all-numerical correlator for face recognition applications

NASA Astrophysics Data System (ADS)

Elbouz, M.; Bouzidi, F.; Alfalou, A.; Brosseau, C.; Leonard, I.; Benkelfat, B.-E.

2013-03-01

In this study, we suggest and validate an all-numerical implementation of a VanderLugt correlator which is optimized for face recognition applications. The main goal of this implementation is to take advantage of the benefits (detection, localization, and identification of a target object within a scene) of correlation methods and exploit the reconfigurability of numerical approaches. This technique requires a numerical implementation of the optical Fourier transform. We pay special attention to adapt the correlation filter to this numerical implementation. One main goal of this work is to reduce the size of the filter in order to decrease the memory space required for real time applications. To fulfil this requirement, we code the reference images with 8 bits and study the effect of this coding on the performances of several composite filters (phase-only filter, binary phase-only filter). The saturation effect has for effect to decrease the performances of the correlator for making a decision when filters contain up to nine references. Further, an optimization is proposed based for an optimized segmented composite filter. Based on this approach, we present tests with different faces demonstrating that the above mentioned saturation effect is significantly reduced while minimizing the size of the learning data base.

Optimal Filter Estimation for Lucas-Kanade Optical Flow

PubMed Central

Sharmin, Nusrat; Brad, Remus

2012-01-01

Optical flow algorithms offer a way to estimate motion from a sequence of images. The computation of optical flow plays a key-role in several computer vision applications, including motion detection and segmentation, frame interpolation, three-dimensional scene reconstruction, robot navigation and video compression. In the case of gradient based optical flow implementation, the pre-filtering step plays a vital role, not only for accurate computation of optical flow, but also for the improvement of performance. Generally, in optical flow computation, filtering is used at the initial level on original input images and afterwards, the images are resized. In this paper, we propose an image filtering approach as a pre-processing step for the Lucas-Kanade pyramidal optical flow algorithm. Based on a study of different types of filtering methods and applied on the Iterative Refined Lucas-Kanade, we have concluded on the best filtering practice. As the Gaussian smoothing filter was selected, an empirical approach for the Gaussian variance estimation was introduced. Tested on the Middlebury image sequences, a correlation between the image intensity value and the standard deviation value of the Gaussian function was established. Finally, we have found that our selection method offers a better performance for the Lucas-Kanade optical flow algorithm.

Collaborative learning: A next step in the training of peer support providers.

PubMed

Cronise, Rita

2016-09-01

This column explores how peer support provider training is enhanced through collaborative learning. Collaborative learning is an approach that draws upon the "real life" experiences of individual learners and encompasses opportunities to explore varying perspectives and collectively construct solutions that enrich the practice of all participants. This description draws upon published articles and examples of collaborative learning in training and communities of practice of peer support providers. Similar to person-centered practices that enhance the recovery experience of individuals receiving services, collaborative learning enhances the experience of peer support providers as they explore relevant "real world" issues, offer unique contributions, and work together toward improving practice. Three examples of collaborative learning approaches are provided that have resulted in successful collaborative learning opportunities for peer support providers. (PsycINFO Database Record (c) 2016 APA, all rights reserved).

An Innovative Strategy for Accurate Thermal Compensation of Gyro Bias in Inertial Units by Exploiting a Novel Augmented Kalman Filter

PubMed Central

Angrisani, Leopoldo; Simone, Domenico De

2018-01-01

This paper presents an innovative model for integrating thermal compensation of gyro bias error into an augmented state Kalman filter. The developed model is applied in the Zero Velocity Update filter for inertial units manufactured by exploiting Micro Electro-Mechanical System (MEMS) gyros. It is used to remove residual bias at startup. It is a more effective alternative to traditional approach that is realized by cascading bias thermal correction by calibration and traditional Kalman filtering for bias tracking. This function is very useful when adopted gyros are manufactured using MEMS technology. These systems have significant limitations in terms of sensitivity to environmental conditions. They are characterized by a strong correlation of the systematic error with temperature variations. The traditional process is divided into two separated algorithms, i.e., calibration and filtering, and this aspect reduces system accuracy, reliability, and maintainability. This paper proposes an innovative Zero Velocity Update filter that just requires raw uncalibrated gyro data as input. It unifies in a single algorithm the two steps from the traditional approach. Therefore, it saves time and economic resources, simplifying the management of thermal correction process. In the paper, traditional and innovative Zero Velocity Update filters are described in detail, as well as the experimental data set used to test both methods. The performance of the two filters is compared both in nominal conditions and in the typical case of a residual initial alignment bias. In this last condition, the innovative solution shows significant improvements with respect to the traditional approach. This is the typical case of an aircraft or a car in parking conditions under solar input. PMID:29735956

An Innovative Strategy for Accurate Thermal Compensation of Gyro Bias in Inertial Units by Exploiting a Novel Augmented Kalman Filter.

PubMed

Fontanella, Rita; Accardo, Domenico; Moriello, Rosario Schiano Lo; Angrisani, Leopoldo; Simone, Domenico De

2018-05-07

This paper presents an innovative model for integrating thermal compensation of gyro bias error into an augmented state Kalman filter. The developed model is applied in the Zero Velocity Update filter for inertial units manufactured by exploiting Micro Electro-Mechanical System (MEMS) gyros. It is used to remove residual bias at startup. It is a more effective alternative to traditional approach that is realized by cascading bias thermal correction by calibration and traditional Kalman filtering for bias tracking. This function is very useful when adopted gyros are manufactured using MEMS technology. These systems have significant limitations in terms of sensitivity to environmental conditions. They are characterized by a strong correlation of the systematic error with temperature variations. The traditional process is divided into two separated algorithms, i.e., calibration and filtering, and this aspect reduces system accuracy, reliability, and maintainability. This paper proposes an innovative Zero Velocity Update filter that just requires raw uncalibrated gyro data as input. It unifies in a single algorithm the two steps from the traditional approach. Therefore, it saves time and economic resources, simplifying the management of thermal correction process. In the paper, traditional and innovative Zero Velocity Update filters are described in detail, as well as the experimental data set used to test both methods. The performance of the two filters is compared both in nominal conditions and in the typical case of a residual initial alignment bias. In this last condition, the innovative solution shows significant improvements with respect to the traditional approach. This is the typical case of an aircraft or a car in parking conditions under solar input.

Approximation of optimal filter for Ornstein-Uhlenbeck process with quantised discrete-time observation

NASA Astrophysics Data System (ADS)

Bania, Piotr; Baranowski, Jerzy

2018-02-01

Quantisation of signals is a ubiquitous property of digital processing. In many cases, it introduces significant difficulties in state estimation and in consequence control. Popular approaches either do not address properly the problem of system disturbances or lead to biased estimates. Our intention was to find a method for state estimation for stochastic systems with quantised and discrete observation, that is free of the mentioned drawbacks. We have formulated a general form of the optimal filter derived by a solution of Fokker-Planck equation. We then propose the approximation method based on Galerkin projections. We illustrate the approach for the Ornstein-Uhlenbeck process, and derive analytic formulae for the approximated optimal filter, also extending the results for the variant with control. Operation is illustrated with numerical experiments and compared with classical discrete-continuous Kalman filter. Results of comparison are substantially in favour of our approach, with over 20 times lower mean squared error. The proposed filter is especially effective for signal amplitudes comparable to the quantisation thresholds. Additionally, it was observed that for high order of approximation, state estimate is very close to the true process value. The results open the possibilities of further analysis, especially for more complex processes.

An Unscented Kalman Filter Approach to the Estimation of Nonlinear Dynamical Systems Models

ERIC Educational Resources Information Center

Chow, Sy-Miin; Ferrer, Emilio; Nesselroade, John R.

2007-01-01

In the past several decades, methodologies used to estimate nonlinear relationships among latent variables have been developed almost exclusively to fit cross-sectional models. We present a relatively new estimation approach, the unscented Kalman filter (UKF), and illustrate its potential as a tool for fitting nonlinear dynamic models in two ways:…

A Kalman-Filter-Based Approach to Combining Independent Earth-Orientation Series

NASA Technical Reports Server (NTRS)

Gross, Richard S.; Eubanks, T. M.; Steppe, J. A.; Freedman, A. P.; Dickey, J. O.; Runge, T. F.

1998-01-01

An approach. based upon the use of a Kalman filter. that is currently employed at the Jet Propulsion Laboratory (JPL) for combining independent measurements of the Earth's orientation, is presented. Since changes in the Earth's orientation can be described is a randomly excited stochastic process, the uncertainty in our knowledge of the Earth's orientation grows rapidly in the absence of measurements. The Kalman-filter methodology allows for an objective accounting of this uncertainty growth, thereby facilitating the intercomparison of measurements taken at different epochs (not necessarily uniformly spaced in time) and with different precision. As an example of this approach to combining Earth-orientation series, a description is given of a combination, SPACE95, that has been generated recently at JPL.

Application of analytical redundancy management to Shuttle crafts. [computerized simulation of microelectronic implementation

NASA Technical Reports Server (NTRS)

Montgomery, R. C.; Tabak, D.

1979-01-01

The study involves the bank of filters approach to analytical redundancy management since this is amenable to microelectronic implementation. Attention is given to a study of the UD factorized filter to determine if it gives more accurate estimates than the standard Kalman filter when data processing word size is reduced. It is reported that, as the word size is reduced, the effect of modeling error dominates the filter performance of the two filters. However, the UD filter is shown to maintain a slight advantage in tracking performance. It is concluded that because of the UD filter's stability in the serial processing mode, it remains the leading candidate for microelectronic implementation.

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.

Radio/FADS/IMU integrated navigation for Mars entry

NASA Astrophysics Data System (ADS)

Jiang, Xiuqiang; Li, Shuang; Huang, Xiangyu

2018-03-01

Supposing future orbiting and landing collaborative exploration mission as the potential project background, this paper addresses the issue of Mars entry integrated navigation using radio beacon, flush air data sensing system (FADS), and inertial measurement unit (IMU). The range and Doppler information sensed from an orbiting radio beacon, the dynamic pressure and heating data sensed from flush air data sensing system, and acceleration and attitude angular rate outputs from an inertial measurement unit are integrated in an unscented Kalman filter to perform state estimation and suppress the system and measurement noise. Computer simulations show that the proposed integrated navigation scheme can enhance the navigation accuracy, which enables precise entry guidance for the given Mars orbiting and landing collaborative exploration mission.

DWI filtering using joint information for DTI and HARDI.

PubMed

Tristán-Vega, Antonio; Aja-Fernández, Santiago

2010-04-01

The filtering of the Diffusion Weighted Images (DWI) prior to the estimation of the diffusion tensor or other fiber Orientation Distribution Functions (ODF) has been proved to be of paramount importance in the recent literature. More precisely, it has been evidenced that the estimation of the diffusion tensor without a previous filtering stage induces errors which cannot be recovered by further regularization of the tensor field. A number of approaches have been intended to overcome this problem, most of them based on the restoration of each DWI gradient image separately. In this paper we propose a methodology to take advantage of the joint information in the DWI volumes, i.e., the sum of the information given by all DWI channels plus the correlations between them. This way, all the gradient images are filtered together exploiting the first and second order information they share. We adapt this methodology to two filters, namely the Linear Minimum Mean Squared Error (LMMSE) and the Unbiased Non-Local Means (UNLM). These new filters are tested over a wide variety of synthetic and real data showing the convenience of the new approach, especially for High Angular Resolution Diffusion Imaging (HARDI). Among the techniques presented, the joint LMMSE is proved a very attractive approach, since it shows an accuracy similar to UNLM (or even better in some situations) with a much lighter computational load. Copyright 2009 Elsevier B.V. All rights reserved.

Do quality improvement collaboratives' educational components match the dominant learning style preferences of the participants?

PubMed

Weggelaar-Jansen, Anne Marie; van Wijngaarden, Jeroen; Slaghuis, Sarah-Sue

2015-06-20

Quality improvement collaboratives are used to improve healthcare by various organizations. Despite their popularity literature shows mixed results on their effectiveness. A quality improvement collaborative can be seen as a temporary learning organization in which knowledge about improvement themes and methods is exchanged. In this research we studied: Does the learning approach of a quality improvement collaborative match the learning styles preferences of the individual participants and how does that affect the learning process of participants? This research used a mixed methods design combining a validated learning style questionnaire with data collected in the tradition of action research methodology to study two Dutch quality improvement collaboratives. The questionnaire is based on the learning style model of Ruijters and Simons, distinguishing five learning style preferences: Acquisition of knowledge, Apperception from others, Discovery of new insights, Exercising in fictitious situations and Participation with others. The most preferred learning styles of the participants were Discovery and Participation. The learning style Acquisition was moderately preferred and Apperception and Exercising were least preferred. The educational components of the quality improvement collaboratives studied (national conferences, half-day learning sessions, faculty site visits and use of an online tool) were predominantly associated with the learning styles Acquisition and Apperception. We observed a decrease in attendance to the learning activities and non-conformance with the standardized set goals and approaches. We conclude that the participants' satisfaction with the offered learning approach changed over time. The lacking match between these learning style preferences and the learning approach in the educational components of the quality improvement collaboratives studied might be the reason why the participants felt they did not gain new insights and therefore ceased their participation in the collaborative. This study provides guidance for future organisers and participants of quality improvement collaboratives about which learning approaches will best suit the participants and enhance improvement work.

Public and Private School Collaborations: Educational Bridges into the 21st Century.

ERIC Educational Resources Information Center

Hanford, Seth; Houck, Jay; Iler, Edith; Morgan, Pam

Public and private school collaboration is one approach to educational reform that may be working in many schools across the country. The Forum for Public and Private Collaboration is committed to publicizing successful collaborative efforts while providing an outlet for educators involved in collaboration to share ideas and receive help. The…

Online Collaboration and Cooperation: The Recurring Importance of Evidence, Rationale and Viability

ERIC Educational Resources Information Center

Hammond, Michael

2017-01-01

This paper investigates collaboration in teaching and learning and draws out implications for the promotion of collaboration within online environments. It is divided into four sections. First the case for collaboration, including specifically cooperative approaches, is explored. This case revolves around the impact of collaboration on the quality…

A Data Mining Approach to Reveal Representative Collaboration Indicators in Open Collaboration Frameworks

ERIC Educational Resources Information Center

Anaya, Antonio R.; Boticario, Jesus G.

2009-01-01

Data mining methods are successful in educational environments to discover new knowledge or learner skills or features. Unfortunately, they have not been used in depth with collaboration. We have developed a scalable data mining method, whose objective is to infer information on the collaboration during the collaboration process in a…

Making the Health Insurance Flexibility and Accountability (HIFA) waiver work through collaborative governance.

PubMed

Zabawa, Barbara J

2003-01-01

This paper argues that collaborative governance should be an essential component in any HIFA waiver proposal, due to the fact that the health care system is moving away from a federal and hierarchical program design and implementation towards a more local, collaborative approach. As several current collaborative projects demonstrate, collaboration may overcome barriers to health expansion program success, such as stakeholder buy-in, notice, and state access to private health coverage information. Furthermore, collaboration within the context of the HIFA waiver process may maximize the strengths of current collaborations, such as providing: (a) access to greater and more stable funding sources; (b) access to a facilitator that can collect and distribute data; and (c) an avenue for accountability. Multiple challenges in ensuring collaborative governance are reviewed. Ms. Zabawa argues that these challenges are not insurmountable if states adopt a truly collaborative approach to designing and implementing programs under the HIFA waiver; there may be hope in expanding and improving health coverage, since collaboration is the most appropriate mechanism to address the complexity of health system reform.

An Impact-Based Filtering Approach for Literature Searches

ERIC Educational Resources Information Center

Vista, Alvin

2013-01-01

This paper aims to present an alternative and simple method to improve the filtering of search results so as to increase the efficiency of literature searches, particularly for individual researchers who have limited logistical resources. The method proposed here is scope restriction using an impact-based filter, made possible by the emergence of…

Nanoimprinted photonic crystal color filters for solar-powered reflective displays.

PubMed

Cho, Eun-Hyoung; Kim, Hae-Sung; Sohn, Jin-Seung; Moon, Chang-Youl; Park, No-Cheol; Park, Young-Pil

2010-12-20

A novel concept for reflective displays that uses two-dimensional photonic crystals with subwavelength gratings is introduced. A solar-powered reflective display with photonic crystal color filters was analyzed by a theoretical approach. We fabricated the photonic crystal color filters on a glass substrate by using low-cost nanoimprint lithography and multi-scan excimer laser annealing to produce RGB color filters through a single patterning process. The theoretical and experimental results show that the color filters have high reflectance and angular tolerance, which was qualitatively confirmed by chromaticity coordination analysis.

Successful Percutaneous Retrieval of an Inferior Vena Cava Filter Migrating to the Right Ventricle in a Bariatric Patient

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

Veerapong, Jula; Wahlgren, Carl Magnus, E-mail: carl.wahlgren@karolinska.s; Jolly, Neeraj

The use of an inferior vena cava filter has an important role in the management of patients who are at high risk for development of pulmonary embolism. Migration is a rare but known complication of inferior vena cava filter placement. We herein describe a case of a prophylactic retrievable vena cava filter migrating to the right ventricle in a bariatric patient. The filter was retrieved percutaneously by transjugular approach and the patient did well postoperatively. A review of the current literature is given.

Divergence Free High Order Filter Methods for Multiscale Non-ideal MHD Flows

NASA Technical Reports Server (NTRS)

Yee, H. C.; Sjoegreen, Bjoern

2003-01-01

Low-dissipative high order filter finite difference methods for long time wave propagation of shock/turbulence/combustion compressible viscous MHD flows has been constructed. Several variants of the filter approach that cater to different flow types are proposed. These filters provide a natural and efficient way for the minimization of the divergence of the magnetic field (Delta . B) numerical error in the sense that no standard divergence cleaning is required. For certain 2-D MHD test problems, divergence free preservation of the magnetic fields of these filter schemes has been achieved.

Method for hyperspectral imagery exploitation and pixel spectral unmixing

NASA Technical Reports Server (NTRS)

Lin, Ching-Fang (Inventor)

2003-01-01

An efficiently hybrid approach to exploit hyperspectral imagery and unmix spectral pixels. This hybrid approach uses a genetic algorithm to solve the abundance vector for the first pixel of a hyperspectral image cube. This abundance vector is used as initial state in a robust filter to derive the abundance estimate for the next pixel. By using Kalman filter, the abundance estimate for a pixel can be obtained in one iteration procedure which is much fast than genetic algorithm. The output of the robust filter is fed to genetic algorithm again to derive accurate abundance estimate for the current pixel. The using of robust filter solution as starting point of the genetic algorithm speeds up the evolution of the genetic algorithm. After obtaining the accurate abundance estimate, the procedure goes to next pixel, and uses the output of genetic algorithm as the previous state estimate to derive abundance estimate for this pixel using robust filter. And again use the genetic algorithm to derive accurate abundance estimate efficiently based on the robust filter solution. This iteration continues until pixels in a hyperspectral image cube end.

A moving hum filter to suppress rotor noise in high-resolution airborne magnetic data

USGS Publications Warehouse

Xia, J.; Doll, W.E.; Miller, R.D.; Gamey, T.J.; Emond, A.M.

2005-01-01

A unique filtering approach is developed to eliminate helicopter rotor noise. It is designed to suppress harmonic noise from a rotor that varies slightly in amplitude, phase, and frequency and that contaminates aero-magnetic data. The filter provides a powerful harmonic noise-suppression tool for data acquired with modern large-dynamic-range recording systems. This three-step approach - polynomial fitting, bandpass filtering, and rotor-noise synthesis - significantly reduces rotor noise without altering the spectra of signals of interest. Two steps before hum filtering - polynomial fitting and bandpass filtering - are critical to accurately model the weak rotor noise. During rotor-noise synthesis, amplitude, phase, and frequency are determined. Data are processed segment by segment so that there is no limit on the length of data. The segment length changes dynamically along a line based on modeling results. Modeling the rotor noise is stable and efficient. Real-world data examples demonstrate that this method can suppress rotor noise by more than 95% when implemented in an aeromagnetic data-processing flow. ?? 2005 Society of Exploration Geophysicists. All rights reserved.

The Chronic Care Model: A Collaborative Approach to Preventing and Treating Asthma in Infants and Young Children

ERIC Educational Resources Information Center

Wessel, Lois; Spain, Jacqueline

2005-01-01

The authors that a collaborative approach between parents and professionals is the best way to care for a young child with asthma. They use Ed Wagner's transdisciplinary 1998 Chronic Care Model as their preferred method for collaboration. More than 5 million children in the U.S. are currently affected by asthma, and a growing body of evidence…

Department of Defense International Space Cooperation Strategy

DTIC Science & Technology

2017-01-01

Secretary of Defense on .January 18. 2017. the unclassified version provides DoD’s approach for invigorating cooperation and collaboration with trusted ...Cooperation Strategy (ISCS) establishes DoD’ s approach for invigorating cooperation and collaboration with trusted allies and partners across the...collaborating with trusted allies and partners to address shared security challenges by leveraging allies’ and partners ’ capabilities to enhance space mission

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.

No case of Guinea worm. Just because governments like the United States and Japan, private organizations, corporations, and particularly the leaders and the villagers and afflicted countries have cooperated.

PubMed

Carter, J

1998-01-01

Collaboration among governments, private organizations, the World Bank, UN agencies, corporations, and the leaders and villagers of afflicted countries is producing substantial progress toward global eradication of many parasitic diseases. For example, there are now less than 100,000 cases of Guinea worm in the world--a 98% reduction. Strategies to prevent villagers from drinking infested water have included drilling deep wells, putting a nontoxic larvicide in the water, and straining the water through cloth filters. Both the larvicide and filters were provided free of charge to the eradication effort by US corporations. Similarly, a pharmaceutical company contributed 21.5 million free doses of mectizanr--a drug that prevents river blindness for a year--in the past year. Another pharmaceutical company donated albendazole for the global elimination of lymphatic filariasis. National pledges to a World Bank trust fund cover the costs of distributing donated medicines to the affected villages. The Common Agenda, a collaboration established between the US and Japan by the author, is an example of the potential of partnerships to create global political stability, correct environmental degradation, and promote the advantages of science and technology.

Estimating Characteristics of a Maneuvering Reentry Vehicle Observed by Multiple Sensors

DTIC Science & Technology

2010-03-01

instead of as one large data set. This method allowed the filter to respond to changing dynamics. Jackson and Farbman’s approach could be of...portion of the entire acceleration was due to drag. Lee and Liu adopted a more hybrid approach , combining a least squares and Kalman filters [9...grows again as the window approaches the end of the available data. Three values for minimum window size, window size, and maximum window size are

Optoelectronic simulation of GaAs solar cells with angularly selective filters

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

Kraus, Tobias, E-mail: tobias.kraus@ise.fraunhofer.de; Höhn, Oliver; Hauser, Hubert

We discuss the influence of angularly selective filters on thin film gallium arsenide solar cells. For this reason, the detailed balance model was refined to fit our needs with respect to Auger recombination, reflection, transmission, and realistic absorption. For calculating real systems, an approach was made to include optical effects of angularly selective filters into electron-hole dynamic equations implemented in PC1D, a one dimensional solar cell calculation tool. With this approach, we find a relative V{sub oc} increase of 5% for an idealized 100 nm GaAs cell, including Auger recombination.

Planning for population viability on Northern Great Plains national grasslands

USGS Publications Warehouse

Samson, F.B.; Knopf, F.L.; McCarthy, C.W.; Noon, B.R.; Ostlie, W.R.; Rinehart, S.M.; Larson, S.; Plumb, G.E.; Schenbeck, G.L.; Svingen, D.N.; Byer, T.W.

2003-01-01

Broad-scale information in concert with conservation of individual species must be used to develop conservation priorities and a more integrated ecosystem protection strategy. In 1999 the United States Forest Service initiated an approach for the 1.2× 106 ha of national grasslands in the Northern Great Plains to fulfill the requirement to maintain viable populations of all native and desirable introduced vertebrate and plant species. The challenge was threefold: 1) develop basic building blocks in the conservation planning approach, 2) apply the approach to national grasslands, and 3) overcome differences that may exist in agency-specific legal and policy requirements. Key assessment components in the approach included a bioregional assessment, coarse-filter analysis, and fine-filter analysis aimed at species considered at-risk. A science team of agency, conservation organization, and university personnel was established to develop the guidelines and standards and other formal procedures for implementation of conservation strategies. Conservation strategies included coarse-filter recommendations to restore the tallgrass, mixed, and shortgrass prairies to conditions that approximate historical ecological processes and landscape patterns, and fine-filter recommendations to address viability needs of individual and multiple species of native animals and plants. Results include a cost-effective approach to conservation planning and recommendations for addressing population viability and biodiversity concerns on national grasslands in the Northern Great Plains.

A Direct and Non-Singular UKF Approach Using Euler Angle Kinematics for Integrated Navigation Systems

PubMed Central

Ran, Changyan; Cheng, Xianghong

2016-01-01

This paper presents a direct and non-singular approach based on an unscented Kalman filter (UKF) for the integration of strapdown inertial navigation systems (SINSs) with the aid of velocity. The state vector includes velocity and Euler angles, and the system model contains Euler angle kinematics equations. The measured velocity in the body frame is used as the filter measurement. The quaternion nonlinear equality constraint is eliminated, and the cross-noise problem is overcome. The filter model is simple and easy to apply without linearization. Data fusion is performed by an UKF, which directly estimates and outputs the navigation information. There is no need to process navigation computation and error correction separately because the navigation computation is completed synchronously during the filter time updating. In addition, the singularities are avoided with the help of the dual-Euler method. The performance of the proposed approach is verified by road test data from a land vehicle equipped with an odometer aided SINS, and a singularity turntable test is conducted using three-axis turntable test data. The results show that the proposed approach can achieve higher navigation accuracy than the commonly-used indirect approach, and the singularities can be efficiently removed as the result of dual-Euler method. PMID:27598169

Improving the retrieval rate of inferior vena cava filters with a multidisciplinary team approach.

PubMed

Inagaki, Elica; Farber, Alik; Eslami, Mohammad H; Siracuse, Jeffrey J; Rybin, Denis V; Sarosiek, Shayna; Sloan, J Mark; Kalish, Jeffrey

2016-07-01

The option to retrieve inferior vena cava (IVC) filters has resulted in an increase in the utilization of these devices as stopgap measures in patients with relative contraindications to anticoagulation. These retrievable IVC filters, however, are often not retrieved and become permanent. Recent data from our institution confirmed a historically low retrieval rate. Therefore, we hypothesized that the implementation of a new IVC filter retrieval protocol would increase the retrieval rate of appropriate IVC filters at our institution. All consecutive patients who underwent an IVC filter placement at our institution between September 2003 and July 2012 were retrospectively reviewed. In August 2012, a multidisciplinary task force was established, and a new IVC filter retrieval protocol was implemented. Prospective data were collected using a centralized interdepartmental IVC filter registry for all consecutive patients who underwent an IVC filter placement between August 2012 and September 2014. Patients were chronologically categorized into preimplementation (PRE) and postimplementation (POST) groups. Comparisons of outcome measures, including the retrieval rate of IVC filters along with rates of retrieval attempt and technical failure, were made between the two groups. In the PRE and POST groups, a total of 720 and 74 retrievable IVC filters were implanted, respectively. In the POST group, 40 of 74 filters (54%) were successfully retrieved compared with 82 of 720 filters (11%) in the PRE group (P < .001). Furthermore, a greater number of IVC filter retrievals were attempted in the POST group than in the PRE group (66% vs 14%; P < .001). No significant difference was observed between the PRE and POST groups for technical failure (17% vs 18%; P = .9). The retrieval rate of retrievable IVC filters at our institution was significantly increased with the implementation of a new IVC filter retrieval protocol with a multidisciplinary team approach. This improved retrieval rate is possible with minimal dedication of resources and can potentially lead to a decrease in IVC filter-related complications in the future. Copyright © 2016 Society for Vascular Surgery. Published by Elsevier Inc. All rights reserved.

Application of optical broadband monitoring to quasi-rugate filters by ion-beam sputtering

NASA Astrophysics Data System (ADS)

Lappschies, Marc; Görtz, Björn; Ristau, Detlev

2006-03-01

Methods for the manufacture of rugate filters by the ion-beam-sputtering process are presented. The first approach gives an example of a digitized version of a continuous-layer notch filter. This method allows the comparison of the basic theory of interference coatings containing thin layers with practical results. For the other methods, a movable zone target is employed to fabricate graded and gradual rugate filters. The examples demonstrate the potential of broadband optical monitoring in conjunction with the ion-beam-sputtering process. First-characterization results indicate that these types of filter may exhibit higher laser-induced damage-threshold values than those of classical filters.

3D Data Denoising via Nonlocal Means Filter by Using Parallel GPU Strategies

PubMed Central

Cuomo, Salvatore; De Michele, Pasquale; Piccialli, Francesco

2014-01-01

Nonlocal Means (NLM) algorithm is widely considered as a state-of-the-art denoising filter in many research fields. Its high computational complexity leads researchers to the development of parallel programming approaches and the use of massively parallel architectures such as the GPUs. In the recent years, the GPU devices had led to achieving reasonable running times by filtering, slice-by-slice, and 3D datasets with a 2D NLM algorithm. In our approach we design and implement a fully 3D NonLocal Means parallel approach, adopting different algorithm mapping strategies on GPU architecture and multi-GPU framework, in order to demonstrate its high applicability and scalability. The experimental results we obtained encourage the usability of our approach in a large spectrum of applicative scenarios such as magnetic resonance imaging (MRI) or video sequence denoising. PMID:25045397

High Order Numerical Methods for LES of Turbulent Flows with Shocks

NASA Technical Reports Server (NTRS)

Kotov, D. V.; Yee, H. C.; Hadjadj, A.; Wray, A.; Sjögreen, B.

2014-01-01

Simulation of turbulent flows with shocks employing explicit subgrid-scale (SGS) filtering may encounter a loss of accuracy in the vicinity of a shock. In this work we perform a comparative study of different approaches to reduce this loss of accuracy within the framework of the dynamic Germano SGS model. One of the possible approaches is to apply Harten's subcell resolution procedure to locate and sharpen the shock, and to use a one-sided test filter at the grid points adjacent to the exact shock location. The other considered approach is local disabling of the SGS terms in the vicinity of the shock location. In this study we use a canonical shock-turbulence interaction problem for comparison of the considered modifications of the SGS filtering procedure. For the considered test case both approaches show a similar improvement in the accuracy near the shock.

An approach to integrating interprofessional education in collaborative mental health care.

PubMed

Curran, Vernon; Heath, Olga; Adey, Tanis; Callahan, Terrance; Craig, David; Hearn, Taryn; White, Hubert; Hollett, Ann

2012-03-01

This article describes an evaluation of a curriculum approach to integrating interprofessional education (IPE) in collaborative mental health practice across the pre- to post-licensure continuum of medical education. A systematic evaluation of IPE activities was conducted, utilizing a combination of evaluation study designs, including: pretest-posttest control group; one-group pre-test-post-test; and one-shot case study. Participant satisfaction, attitudes toward teamwork, and self-reported teamwork abilities were key evaluative outcome measures. IPE in collaborative mental health practice was well received at both the pre- and post-licensure levels. Satisfaction scores were very high, and students, trainees, and practitioners welcomed the opportunity to learn about collaboration in the context of mental health. Medical student satisfaction increased significantly with the introduction of standardized patients (SPs) as an interprofessional learning method. Medical students and faculty reported that experiential learning in practice-based settings is a key component of effective approaches to IPE implementation. At a post-licensure level, practitioners reported significant improvement in attitudes toward interprofessional collaboration in mental health care after participation in IPE. IPE in collaborative mental health is feasible, and mental health settings offer practical and useful learning experiences for students, trainees, and practitioners in interprofessional collaboration.

Radar range data signal enhancement tracker

NASA Technical Reports Server (NTRS)

1975-01-01

The design, fabrication, and performance characteristics are described of two digital data signal enhancement filters which are capable of being inserted between the Space Shuttle Navigation Sensor outputs and the guidance computer. Commonality of interfaces has been stressed so that the filters may be evaluated through operation with simulated sensors or with actual prototype sensor hardware. The filters will provide both a smoothed range and range rate output. Different conceptual approaches are utilized for each filter. The first filter is based on a combination low pass nonrecursive filter and a cascaded simple average smoother for range and range rate, respectively. Filter number two is a tracking filter which is capable of following transient data of the type encountered during burn periods. A test simulator was also designed which generates typical shuttle navigation sensor data.

Building international collaborative capacity: contributions of community psychologists to a European network.

PubMed

García-Ramírez, Manuel; Paloma, Virginia; Suarez-Balcazar, Yolanda; Balcazar, Fabricio

2009-09-01

Europe is in the process of building a more participative, just, and inclusive European Union. The European Social Fund, which is an initiative developed to actively promote multinational partnerships that address pressing social issues, is a good example of the European transformation. This article describes the steps taken to develop and evaluate the activities of an international network promoting collaborative capacity among regional partners involved in the prevention of labor discrimination toward immigrants in three European countries-Spain, Belgium, and Italy. An international team of community psychologists proposed an empowering approach to assess the collaborative capacity of the network. This approach consisted of three steps: (1) establishing a collaborative relationship among partners, (2) building collaborative capacity, and (3) evaluating the collaborative capacity of the network. We conclude with lessons learned from the process and provide recommendations for addressing the challenges inherent in international collaboration processes.

SearchLight: a freely available web-based quantitative spectral analysis tool (Conference Presentation)

NASA Astrophysics Data System (ADS)

Prabhat, Prashant; Peet, Michael; Erdogan, Turan

2016-03-01

In order to design a fluorescence experiment, typically the spectra of a fluorophore and of a filter set are overlaid on a single graph and the spectral overlap is evaluated intuitively. However, in a typical fluorescence imaging system the fluorophores and optical filters are not the only wavelength dependent variables - even the excitation light sources have been changing. For example, LED Light Engines may have a significantly different spectral response compared to the traditional metal-halide lamps. Therefore, for a more accurate assessment of fluorophore-to-filter-set compatibility, all sources of spectral variation should be taken into account simultaneously. Additionally, intuitive or qualitative evaluation of many spectra does not necessarily provide a realistic assessment of the system performance. "SearchLight" is a freely available web-based spectral plotting and analysis tool that can be used to address the need for accurate, quantitative spectral evaluation of fluorescence measurement systems. This tool is available at: http://searchlight.semrock.com/. Based on a detailed mathematical framework [1], SearchLight calculates signal, noise, and signal-to-noise ratio for multiple combinations of fluorophores, filter sets, light sources and detectors. SearchLight allows for qualitative and quantitative evaluation of the compatibility of filter sets with fluorophores, analysis of bleed-through, identification of optimized spectral edge locations for a set of filters under specific experimental conditions, and guidance regarding labeling protocols in multiplexing imaging assays. Entire SearchLight sessions can be shared with colleagues and collaborators and saved for future reference. [1] Anderson, N., Prabhat, P. and Erdogan, T., Spectral Modeling in Fluorescence Microscopy, http://www.semrock.com (2010).

Practical Strategies for Collaboration across Discipline-Based Education Research and the Learning Sciences

PubMed Central

Peffer, Melanie; Renken, Maggie

2016-01-01

Rather than pursue questions related to learning in biology from separate camps, recent calls highlight the necessity of interdisciplinary research agendas. Interdisciplinary collaborations allow for a complicated and expanded approach to questions about learning within specific science domains, such as biology. Despite its benefits, interdisciplinary work inevitably involves challenges. Some such challenges originate from differences in theoretical and methodological approaches across lines of work. Thus, aims at developing successful interdisciplinary research programs raise important considerations regarding methodologies for studying biology learning, strategies for approaching collaborations, and training of early-career scientists. Our goal here is to describe two fields important to understanding learning in biology, discipline-based education research and the learning sciences. We discuss differences between each discipline’s approach to biology education research and the benefits and challenges associated with incorporating these perspectives in a single research program. We then propose strategies for building productive interdisciplinary collaboration. PMID:27881446

Mars Science Laboratory Entry, Descent, and Landing Trajectory and Atmosphere Reconstruction

NASA Technical Reports Server (NTRS)

Karlgaard, Christopher D.; Kutty, Prasad; Schoenenberer, Mark; Shidner, Jeremy D.

2013-01-01

On August 5th 2012, The Mars Science Laboratory entry vehicle successfully entered Mars atmosphere and landed the Curiosity rover on its surface. A Kalman filter approach has been implemented to reconstruct the entry, descent, and landing trajectory based on all available data. The data sources considered in the Kalman filtering approach include the inertial measurement unit accelerations and angular rates, the terrain descent sensor, the measured landing site, orbit determination solutions for the initial conditions, and a new set of instrumentation for planetary entry reconstruction consisting of forebody pressure sensors, known as the Mars Entry Atmospheric Data System. These pressure measurements are unique for planetary entry, descent, and landing reconstruction as they enable a reconstruction of the freestream atmospheric conditions without any prior assumptions being made on the vehicle aerodynamics. Moreover, the processing of these pressure measurements in the Kalman filter approach enables the identification of atmospheric winds, which has not been accomplished in past planetary entry reconstructions. This separation of atmosphere and aerodynamics allows for aerodynamic model reconciliation and uncertainty quantification, which directly impacts future missions. This paper describes the mathematical formulation of the Kalman filtering approach, a summary of data sources and preprocessing activities, and results of the reconstruction.

Toeplitz matrices for LTI systems, an illustration of their application to Wiener filters and estimators

NASA Astrophysics Data System (ADS)

Moir, T. J.

2018-03-01

The Wiener-Kolmogorov theory of filtering has been with us since the first half of the twentieth century. A later matrix-based approach which was more general was derived with the steady-state Kalman filter. This approach uses a novel method of representing causal and uncausal systems in the form of convolution matrices and leads to a Wiener solution which is much easier to calculate than either the Kalman or Wiener approaches. For coloured additive noise, it avoids the use of Diophantine equations. The key idea missing in previous work is the close link between polynomials and Toeplitz matrices which are lower triangular in form. There is already a reasonably sized literature in the mathematics field on such matrices and so the area is ripe for exploration. Although the method does not offer a different or better solution, it shows a completely new way of defining linear time-invariant (LTI) systems which is neither transfer-function nor state-space-based. This is achieved by exploiting the connection between polynomials and Toeplitz matrices. The application here is the Wiener filter but there could well be many more as this is a generic approach.

Optimized method for atmospheric signal reduction in irregular sampled InSAR time series assisted by external atmospheric information

NASA Astrophysics Data System (ADS)

Gong, W.; Meyer, F. J.

2013-12-01

It is well known that spatio-temporal the tropospheric phase signatures complicate the interpretation and detection of smaller magnitude deformation signals or unstudied motion fields. Several advanced time-series InSAR techniques were developed in the last decade that make assumptions about the stochastic properties of the signal components in interferometric phases to reduce atmospheric delay effects on surface deformation estimates. However, their need for large datasets to successfully separate the different phase contributions limits their performance if data is scarce and irregularly sampled. Limited SAR data coverage is true for many areas affected by geophysical deformation. This is either due to their low priority in mission programming, unfavorable ground coverage condition, or turbulent seasonal weather effects. In this paper, we present new adaptive atmospheric phase filtering algorithms that are specifically designed to reconstruct surface deformation signals from atmosphere-affected and irregularly sampled InSAR time series. The filters take advantage of auxiliary atmospheric delay information that is extracted from various sources, e.g. atmospheric weather models. They are embedded into a model-free Persistent Scatterer Interferometry (PSI) approach that was selected to accommodate non-linear deformation patterns that are often observed near volcanoes and earthquake zones. Two types of adaptive phase filters were developed that operate in the time dimension and separate atmosphere from deformation based on their different temporal correlation properties. Both filter types use the fact that atmospheric models can reliably predict the spatial statistics and signal power of atmospheric phase delay fields in order to automatically optimize the filter's shape parameters. In essence, both filter types will attempt to maximize the linear correlation between a-priori and the extracted atmospheric phase information. Topography-related phase components, orbit errors and the master atmospheric delays are first removed in a pre-processing step before the atmospheric filters are applied. The first adaptive filter type is using a filter kernel of Gaussian shape and is adaptively adjusting the width (defined in days) of this filter until the correlation of extracted and modeled atmospheric signal power is maximized. If atmospheric properties vary along the time series, this approach will lead to filter setting that are adapted to best reproduce atmospheric conditions at a certain observation epoch. Despite the superior performance of this first filter design, its Gaussian shape imposes non-physical relative weights onto acquisitions that ignore the known atmospheric noise in the data. Hence, in our second approach we are using atmospheric a-priori information to adaptively define the full shape of the atmospheric filter. For this process, we use a so-called normalized convolution (NC) approach that is often used in image reconstruction. Several NC designs will be presented in this paper and studied for relative performance. A cross-validation of all developed algorithms was done using both synthetic and real data. This validation showed designed filters are outperforming conventional filter methods that particularly useful for regions with limited data coverage or lack of a deformation field prior.

A Collaborative Approach to Community Wildfire Hazard Reduction

Treesearch

Marc Titus; Jennifer Hinderman

2006-01-01

This paper highlights the very successful collaborative approach to community wildfire hazard reduction being used in the 5 county NW Region of the Washington State Department of Natural Resources. NW Region cooperators have created a successful model to help affected communities reduce their risks to wildland fire. Identified high risk communities have been approached...

A Collaborative Approach to Family Literacy Evaluation Strategies.

ERIC Educational Resources Information Center

Landerholm, Elizabeth; Karr, Jo Ann; Mushi, Selina

A collaborative approach to program evaluation combined with the use of a variety of evaluation methods using currently available technology can yield valuable information about the effectiveness of family literacy programs. Such an approach was used for McCosh Even Start, a federally-funded family literacy program located at McCosh School in an…

Ceramic High Efficiency Particulate Air (HEPA) Filter Final Report CRADA No. TC02102.0

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

Mitchell, M.; Morse, T.

This was a collaborative effort between Lawrence Livermore National Security, LLC (formerly The Regents of the University of California)/Lawrence Livermor e National Laboratory (LLNL) and Flanders-Precisionaire (Flanders), to develop ceramic HEP A filters under a Thrust II Initiative for Proliferation Prevention (IPP) project. The research was conducted via the IPP Program at Commonwe alth of Independent States (CIS) Institutes, which are handled under a separate agreement. The institutes (collectively referred to as "CIS Institutes") involved with this project were: Bochvar: Federal State Unitarian Enterprise All-Russia Scientific and Research Institute of Inorganic Materials (FSUE VNIINM); Radium Khlopin: Federal State Unitarian Enterprisemore » NPO Radium Institute named (FSUE NPO Radium Institute); and Bakor: Science and Technology Center Bakor (STC Bakor).« less

Information Sharing Modalities for Mobile Ad-Hoc Networks

NASA Astrophysics Data System (ADS)

de Spindler, Alexandre; Grossniklaus, Michael; Lins, Christoph; Norrie, Moira C.

Current mobile phone technologies have fostered the emergence of a new generation of mobile applications. Such applications allow users to interact and share information opportunistically when their mobile devices are in physical proximity or close to fixed installations. It has been shown how mobile applications such as collaborative filtering and location-based services can take advantage of ad-hoc connectivity to use physical proximity as a filter mechanism inherent to the application logic. We discuss the different modes of information sharing that arise in such settings based on the models of persistence and synchronisation. We present a platform that supports the development of applications that can exploit these modes of ad-hoc information sharing and, by means of an example, show how such an application can be realised based on the supported event model.

Filter-based chemical sensors for hazardous materials

NASA Astrophysics Data System (ADS)

Major, Kevin J.; Ewing, Kenneth J.; Poutous, Menelaos K.; Sanghera, Jasbinder S.; Aggarwal, Ishwar D.

2014-05-01

The development of new techniques for the detection of homemade explosive devices is an area of intense research for the defense community. Such sensors must exhibit high selectivity to detect explosives and/or explosives related materials in a complex environment. Spectroscopic techniques such as FTIR are capable of discriminating between the volatile components of explosives; however, there is a need for less expensive systems for wide-range use in the field. To tackle this challenge we are investigating the use of multiple, overlapping, broad-band infrared (IR) filters to enable discrimination of volatile chemicals associated with an explosive device from potential background interferants with similar chemical signatures. We present an optical approach for the detection of fuel oil (the volatile component in ammonium nitrate-fuel oil explosives) that relies on IR absorption spectroscopy in a laboratory environment. Our proposed system utilizes a three filter set to separate the IR signals from fuel oil and various background interferants in the sample headspace. Filter responses for the chemical spectra are calculated using a Gaussian filter set. We demonstrate that using a specifically chosen filter set enables discrimination of pure fuel oil, hexanes, and acetone, as well as various mixtures of these components. We examine the effects of varying carrier gasses and humidity on the collected spectra and corresponding filter response. We study the filter response on these mixtures over time as well as present a variety of methods for observing the filter response functions to determine the response of this approach to detecting fuel oil in various environments.

Assessing FRET using Spectral Techniques

PubMed Central

Leavesley, Silas J.; Britain, Andrea L.; Cichon, Lauren K.; Nikolaev, Viacheslav O.; Rich, Thomas C.

2015-01-01

Förster resonance energy transfer (FRET) techniques have proven invaluable for probing the complex nature of protein–protein interactions, protein folding, and intracellular signaling events. These techniques have traditionally been implemented with the use of one or more fluorescence band-pass filters, either as fluorescence microscopy filter cubes, or as dichroic mirrors and band-pass filters in flow cytometry. In addition, new approaches for measuring FRET, such as fluorescence lifetime and acceptor photobleaching, have been developed. Hyperspectral techniques for imaging and flow cytometry have also shown to be promising for performing FRET measurements. In this study, we have compared traditional (filter-based) FRET approaches to three spectral-based approaches: the ratio of acceptor-to-donor peak emission, linear spectral unmixing, and linear spectral unmixing with a correction for direct acceptor excitation. All methods are estimates of FRET efficiency, except for one-filter set and three-filter set FRET indices, which are included for consistency with prior literature. In the first part of this study, spectrofluorimetric data were collected from a CFP–Epac–YFP FRET probe that has been used for intracellular cAMP measurements. All comparisons were performed using the same spectrofluorimetric datasets as input data, to provide a relevant comparison. Linear spectral unmixing resulted in measurements with the lowest coefficient of variation (0.10) as well as accurate fits using the Hill equation. FRET efficiency methods produced coefficients of variation of less than 0.20, while FRET indices produced coefficients of variation greater than 8.00. These results demonstrate that spectral FRET measurements provide improved response over standard, filter-based measurements. Using spectral approaches, single-cell measurements were conducted through hyperspectral confocal microscopy, linear unmixing, and cell segmentation with quantitative image analysis. Results from these studies confirmed that spectral imaging is effective for measuring subcellular, time-dependent FRET dynamics and that additional fluorescent signals can be readily separated from FRET signals, enabling multilabel studies of molecular interactions. PMID:23929684

Assessing FRET using spectral techniques.

PubMed

Leavesley, Silas J; Britain, Andrea L; Cichon, Lauren K; Nikolaev, Viacheslav O; Rich, Thomas C

2013-10-01

Förster resonance energy transfer (FRET) techniques have proven invaluable for probing the complex nature of protein-protein interactions, protein folding, and intracellular signaling events. These techniques have traditionally been implemented with the use of one or more fluorescence band-pass filters, either as fluorescence microscopy filter cubes, or as dichroic mirrors and band-pass filters in flow cytometry. In addition, new approaches for measuring FRET, such as fluorescence lifetime and acceptor photobleaching, have been developed. Hyperspectral techniques for imaging and flow cytometry have also shown to be promising for performing FRET measurements. In this study, we have compared traditional (filter-based) FRET approaches to three spectral-based approaches: the ratio of acceptor-to-donor peak emission, linear spectral unmixing, and linear spectral unmixing with a correction for direct acceptor excitation. All methods are estimates of FRET efficiency, except for one-filter set and three-filter set FRET indices, which are included for consistency with prior literature. In the first part of this study, spectrofluorimetric data were collected from a CFP-Epac-YFP FRET probe that has been used for intracellular cAMP measurements. All comparisons were performed using the same spectrofluorimetric datasets as input data, to provide a relevant comparison. Linear spectral unmixing resulted in measurements with the lowest coefficient of variation (0.10) as well as accurate fits using the Hill equation. FRET efficiency methods produced coefficients of variation of less than 0.20, while FRET indices produced coefficients of variation greater than 8.00. These results demonstrate that spectral FRET measurements provide improved response over standard, filter-based measurements. Using spectral approaches, single-cell measurements were conducted through hyperspectral confocal microscopy, linear unmixing, and cell segmentation with quantitative image analysis. Results from these studies confirmed that spectral imaging is effective for measuring subcellular, time-dependent FRET dynamics and that additional fluorescent signals can be readily separated from FRET signals, enabling multilabel studies of molecular interactions. © 2013 International Society for Advancement of Cytometry. Copyright © 2013 International Society for Advancement of Cytometry.

LLSURE: local linear SURE-based edge-preserving image filtering.

PubMed

Qiu, Tianshuang; Wang, Aiqi; Yu, Nannan; Song, Aimin

2013-01-01

In this paper, we propose a novel approach for performing high-quality edge-preserving image filtering. Based on a local linear model and using the principle of Stein's unbiased risk estimate as an estimator for the mean squared error from the noisy image only, we derive a simple explicit image filter which can filter out noise while preserving edges and fine-scale details. Moreover, this filter has a fast and exact linear-time algorithm whose computational complexity is independent of the filtering kernel size; thus, it can be applied to real time image processing tasks. The experimental results demonstrate the effectiveness of the new filter for various computer vision applications, including noise reduction, detail smoothing and enhancement, high dynamic range compression, and flash/no-flash denoising.

Interdisciplinary collaboration in gerontology and geriatrics in Latin America: conceptual approaches and health care teams.

PubMed

Gomez, Fernando; Curcio, Carmen Lucia

2013-01-01

The underlying rationale to support interdisciplinary collaboration in geriatrics and gerontology is based on the complexity of elderly care. The most important characteristic about interdisciplinary health care teams for older people in Latin America is their subjective-basis framework. In other regions, teams are organized according to a theoretical knowledge basis with well-justified priorities, functions, and long-term goals, in Latin America teams are arranged according to subjective interests on solving their problems. Three distinct approaches of interdisciplinary collaboration in gerontology are proposed. The first approach is grounded in the scientific rationalism of European origin. Denominated "logical-rational approach," its core is to identify the significance of knowledge. The second approach is grounded in pragmatism and is more associated with a North American tradition. The core of this approach consists in enhancing the skills and competences of each participant; denominated "logical-instrumental approach." The third approach denominated "logical-subjective approach" has a Latin America origin. Its core consists in taking into account the internal and emotional dimensions of the team. These conceptual frameworks based in geographical contexts will permit establishing the differences and shared characteristics of interdisciplinary collaboration in geriatrics and gerontology to look for operational answers to solve the "complex problems" of older adults.

Simulation for noise cancellation using LMS adaptive filter

NASA Astrophysics Data System (ADS)

Lee, Jia-Haw; Ooi, Lu-Ean; Ko, Ying-Hao; Teoh, Choe-Yung

2017-06-01

In this paper, the fundamental algorithm of noise cancellation, Least Mean Square (LMS) algorithm is studied and enhanced with adaptive filter. The simulation of the noise cancellation using LMS adaptive filter algorithm is developed. The noise corrupted speech signal and the engine noise signal are used as inputs for LMS adaptive filter algorithm. The filtered signal is compared to the original noise-free speech signal in order to highlight the level of attenuation of the noise signal. The result shows that the noise signal is successfully canceled by the developed adaptive filter. The difference of the noise-free speech signal and filtered signal are calculated and the outcome implies that the filtered signal is approaching the noise-free speech signal upon the adaptive filtering. The frequency range of the successfully canceled noise by the LMS adaptive filter algorithm is determined by performing Fast Fourier Transform (FFT) on the signals. The LMS adaptive filter algorithm shows significant noise cancellation at lower frequency range.

Research on Collaborative Technology in Distributed Virtual Reality System

NASA Astrophysics Data System (ADS)

Lei, ZhenJiang; Huang, JiJie; Li, Zhao; Wang, Lei; Cui, JiSheng; Tang, Zhi

2018-01-01

Distributed virtual reality technology applied to the joint training simulation needs the CSCW (Computer Supported Cooperative Work) terminal multicast technology to display and the HLA (high-level architecture) technology to ensure the temporal and spatial consistency of the simulation, in order to achieve collaborative display and collaborative computing. In this paper, the CSCW’s terminal multicast technology has been used to modify and expand the implementation framework of HLA. During the simulation initialization period, this paper has used the HLA statement and object management service interface to establish and manage the CSCW network topology, and used the HLA data filtering mechanism for each federal member to establish the corresponding Mesh tree. During the simulation running period, this paper has added a new thread for the RTI and the CSCW real-time multicast interactive technology into the RTI, so that the RTI can also use the window message mechanism to notify the application update the display screen. Through many applications of submerged simulation training in substation under the operation of large power grid, it is shown that this paper has achieved satisfactory training effect on the collaborative technology used in distributed virtual reality simulation.

An estimator-predictor approach to PLL loop filter design

NASA Technical Reports Server (NTRS)

Statman, J. I.; Hurd, W. J.

1986-01-01

An approach to the design of digital phase locked loops (DPLLs), using estimation theory concepts in the selection of a loop filter, is presented. The key concept is that the DPLL closed-loop transfer function is decomposed into an estimator and a predictor. The estimator provides recursive estimates of phase, frequency, and higher order derivatives, while the predictor compensates for the transport lag inherent in the loop. This decomposition results in a straightforward loop filter design procedure, enabling use of techniques from optimal and sub-optimal estimation theory. A design example for a particular choice of estimator is presented, followed by analysis of the associated bandwidth, gain margin, and steady state errors caused by unmodeled dynamics. This approach is under consideration for the design of the Deep Space Network (DSN) Advanced Receiver Carrier DPLL.

Angular velocity estimation based on star vector with improved current statistical model Kalman filter.

PubMed

Zhang, Hao; Niu, Yanxiong; Lu, Jiazhen; Zhang, He

2016-11-20

Angular velocity information is a requisite for a spacecraft guidance, navigation, and control system. In this paper, an approach for angular velocity estimation based merely on star vector measurement with an improved current statistical model Kalman filter is proposed. High-precision angular velocity estimation can be achieved under dynamic conditions. The amount of calculation is also reduced compared to a Kalman filter. Different trajectories are simulated to test this approach, and experiments with real starry sky observation are implemented for further confirmation. The estimation accuracy is proved to be better than 10^-4  rad/s under various conditions. Both the simulation and the experiment demonstrate that the described approach is effective and shows an excellent performance under both static and dynamic conditions.

76 FR 15974 - Office of Research and Development; Ambient Air Monitoring Reference and Equivalent Methods...

Federal Register 2010, 2011, 2012, 2013, 2014

2011-03-22

... on a particle filter. Because this new measurement approach is being approved for NAAQS compliance... Lead (Pb) on TSP High-Volume Filters.'' A sample of total suspended particulate matter (TSP) is collected on a glass fiber filter, using the sampler and procedure of the EPA Reference Method for the...

Cellular traction force recovery: An optimal filtering approach in two-dimensional Fourier space.

PubMed

Huang, Jianyong; Qin, Lei; Peng, Xiaoling; Zhu, Tao; Xiong, Chunyang; Zhang, Youyi; Fang, Jing

2009-08-21

Quantitative estimation of cellular traction has significant physiological and clinical implications. As an inverse problem, traction force recovery is essentially susceptible to noise in the measured displacement data. For traditional procedure of Fourier transform traction cytometry (FTTC), noise amplification is accompanied in the force reconstruction and small tractions cannot be recovered from the displacement field with low signal-noise ratio (SNR). To improve the FTTC process, we develop an optimal filtering scheme to suppress the noise in the force reconstruction procedure. In the framework of the Wiener filtering theory, four filtering parameters are introduced in two-dimensional Fourier space and their analytical expressions are derived in terms of the minimum-mean-squared-error (MMSE) optimization criterion. The optimal filtering approach is validated with simulations and experimental data associated with the adhesion of single cardiac myocyte to elastic substrate. The results indicate that the proposed method can highly enhance SNR of the recovered forces to reveal tiny tractions in cell-substrate interaction.

On-board orbit determination for low thrust LEO-MEO transfer by Consider Kalman Filtering and multi-constellation GNSS

NASA Astrophysics Data System (ADS)

Menzione, Francesco; Renga, Alfredo; Grassi, Michele

2017-09-01

In the framework of the novel navigation scenario offered by the next generation satellite low thrust autonomous LEO-to-MEO orbit transfer, this study proposes and tests a GNSS based navigation system aimed at providing on-board precise and robust orbit determination strategy to override rising criticalities. The analysis introduces the challenging design issues to simultaneously deal with the variable orbit regime, the electric thrust control and the high orbit GNSS visibility conditions. The Consider Kalman Filtering approach is here proposed as the filtering scheme to process the GNSS raw data provided by a multi-antenna/multi-constellation receiver in presence of uncertain parameters affecting measurements, actuation and spacecraft physical properties. Filter robustness and achievable navigation accuracy are verified using a high fidelity simulation of the low-thrust rising scenario and performance are compared with the one of a standard Extended Kalman Filtering approach to highlight the advantages of the proposed solution. Performance assessment of the developed navigation solution is accomplished for different transfer phases.

Model-Based Engine Control Architecture with an Extended Kalman Filter

NASA Technical Reports Server (NTRS)

Csank, Jeffrey T.; Connolly, Joseph W.

2016-01-01

This paper discusses the design and implementation of an extended Kalman filter (EKF) for model-based engine control (MBEC). Previously proposed MBEC architectures feature an optimal tuner Kalman Filter (OTKF) to produce estimates of both unmeasured engine parameters and estimates for the health of the engine. The success of this approach relies on the accuracy of the linear model and the ability of the optimal tuner to update its tuner estimates based on only a few sensors. Advances in computer processing are making it possible to replace the piece-wise linear model, developed off-line, with an on-board nonlinear model running in real-time. This will reduce the estimation errors associated with the linearization process, and is typically referred to as an extended Kalman filter. The non-linear extended Kalman filter approach is applied to the Commercial Modular Aero-Propulsion System Simulation 40,000 (C-MAPSS40k) and compared to the previously proposed MBEC architecture. The results show that the EKF reduces the estimation error, especially during transient operation.

Model-Based Engine Control Architecture with an Extended Kalman Filter

NASA Technical Reports Server (NTRS)

Csank, Jeffrey T.; Connolly, Joseph W.

2016-01-01

This paper discusses the design and implementation of an extended Kalman filter (EKF) for model-based engine control (MBEC). Previously proposed MBEC architectures feature an optimal tuner Kalman Filter (OTKF) to produce estimates of both unmeasured engine parameters and estimates for the health of the engine. The success of this approach relies on the accuracy of the linear model and the ability of the optimal tuner to update its tuner estimates based on only a few sensors. Advances in computer processing are making it possible to replace the piece-wise linear model, developed off-line, with an on-board nonlinear model running in real-time. This will reduce the estimation errors associated with the linearization process, and is typically referred to as an extended Kalman filter. The nonlinear extended Kalman filter approach is applied to the Commercial Modular Aero-Propulsion System Simulation 40,000 (C-MAPSS40k) and compared to the previously proposed MBEC architecture. The results show that the EKF reduces the estimation error, especially during transient operation.

Nuclear counting filter based on a centered Skellam test and a double exponential smoothing

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

Coulon, Romain; Kondrasovs, Vladimir; Dumazert, Jonathan

2015-07-01

Online nuclear counting represents a challenge due to the stochastic nature of radioactivity. The count data have to be filtered in order to provide a precise and accurate estimation of the count rate, this with a response time compatible with the application in view. An innovative filter is presented in this paper addressing this issue. It is a nonlinear filter based on a Centered Skellam Test (CST) giving a local maximum likelihood estimation of the signal based on a Poisson distribution assumption. This nonlinear approach allows to smooth the counting signal while maintaining a fast response when brutal change activitymore » occur. The filter has been improved by the implementation of a Brown's double Exponential Smoothing (BES). The filter has been validated and compared to other state of the art smoothing filters. The CST-BES filter shows a significant improvement compared to all tested smoothing filters. (authors)« less

Finessing filter scarcity problem in face recognition via multi-fold filter convolution

NASA Astrophysics Data System (ADS)

Low, Cheng-Yaw; Teoh, Andrew Beng-Jin

2017-06-01

The deep convolutional neural networks for face recognition, from DeepFace to the recent FaceNet, demand a sufficiently large volume of filters for feature extraction, in addition to being deep. The shallow filter-bank approaches, e.g., principal component analysis network (PCANet), binarized statistical image features (BSIF), and other analogous variants, endure the filter scarcity problem that not all PCA and ICA filters available are discriminative to abstract noise-free features. This paper extends our previous work on multi-fold filter convolution (ℳ-FFC), where the pre-learned PCA and ICA filter sets are exponentially diversified by ℳ folds to instantiate PCA, ICA, and PCA-ICA offspring. The experimental results unveil that the 2-FFC operation solves the filter scarcity state. The 2-FFC descriptors are also evidenced to be superior to that of PCANet, BSIF, and other face descriptors, in terms of rank-1 identification rate (%).

Keyword extraction, ranking, and organization for the neuroinformatics platform.

PubMed

Usui, S; Palmes, P; Nagata, K; Taniguchi, T; Ueda, N

2007-04-01

Brain-related researches encompass many fields of studies and usually involve worldwide collaborations. Recognizing the value of these international collaborations for efficient use of resources and improving the quality of brain research, the International Neuroinformatics Coordinating Facility (INCF) started to coordinate the effort of establishing neuroinformatics (NI) centers and portal sites among the different participating countries. These NI centers and portal sites will serve as the conduit for the interchange of information and brain-related resources among different countries. In Japan, several NI platforms under the support of NIJC (NI Japan Center) are being developed with one platform called, Visiome, already operating and publicly accessible at "http://www.platform.visiome.org". Each of these platforms requires their own set of keywords that represent important terms covering their respective fields of study. One important function of this predefined keyword list is to help contributors classify the contents of their contributions and group related resources. It is vital, therefore, that this predefined list should be properly chosen to cover the necessary areas. Currently, the process of identifying these appropriate keywords relies on the availability of human experts which does not scale well considering that different areas are rapidly evolving. This problem prompted us to develop a tool to automatically filter the most likely terms preferred by human experts. We tested the effectiveness of the proposed approach using the abstracts of the Vision Research Journal (VR) and Investigative Ophthalmology and Visual Science Journal (IOVS) as source files.

Risks and benefits of prophylactic inferior vena cava filters in patients undergoing bariatric surgery.

PubMed

Birkmeyer, Nancy J; Finks, Jonathan F; English, Wayne J; Carlin, Arthur M; Hawasli, Abdelkader A; Genaw, Jeffrey A; Wood, Michael H; Share, David A; Birkmeyer, John D

2013-04-01

The United States Food and Drug Administration recently issued a warning about adverse events in patients receiving inferior vena cava (IVC) filters. To assess relationships between IVC filter insertion and complications while controlling for differences in baseline patient characteristics and medical venous thromboembolism prophylaxis. Propensity-matched cohort study. The prospective, statewide, clinical registry of the Michigan Bariatric Surgery Collaborative. Bariatric surgery patients (n=35,477) from 32 hospitals during the years 2006 through 2012. Prophylactic IVC filter insertion. Outcomes included the occurrence of complications (pulmonary embolism, deep vein thrombosis, and overall combined rates of complications by severity) within 30 days of bariatric surgery. There were no significant differences in baseline characteristics among the 1,077 patients with IVC filters and in 1,077 matched control patients. Patients receiving IVC filters had higher rates of pulmonary embolism (0.84% vs 0.46%; odds ratio [OR], 2.0; 95% confidence interval [CI], 0.6-6.5; P=0.232), deep vein thrombosis (1.2% vs 0.37%; OR, 3.3; 95% CI, 1.1-10.1; P=0.039), venous thromboembolism (1.9% vs 0.74%; OR, 2.7; 95% CI, 1.1-6.3, P=0.027), serious complications (5.8% vs 3.8%; OR, 1.6; 95% CI, 1.0-2.4; P=0.031), permanently disabling complications (1.2% vs 0.37%; OR, 4.3; 95% CI, 1.2-15.6; P=0.028), and death (0.7% vs 0.09%; OR, 7.0; 95% CI, 0.9-57.3; P=0.068). Of the 7 deaths among patients with IVC filters, 4 were attributable to pulmonary embolism and 2 to IVC thrombosis/occlusion. We have identified no benefits and significant risks to the use of prophylactic IVC filters among bariatric surgery patients and believe that their use should be discouraged. Copyright © 2013 Society of Hospital Medicine.

Collaboration in Global Software Engineering Based on Process Description Integration

NASA Astrophysics Data System (ADS)

Klein, Harald; Rausch, Andreas; Fischer, Edward

Globalization is one of the big trends in software development. Development projects need a variety of different resources with appropriate expert knowledge to be successful. More and more of these resources are nowadays obtained from specialized organizations and countries all over the world, varying in development approaches, processes, and culture. As seen with early outsourcing attempts, collaboration may fail due to these differences. Hence, the major challenge in global software engineering is to streamline collaborating organizations towards a successful conjoint development. Based on typical collaboration scenarios, this paper presents a structured approach to integrate processes in a comprehensible way.

Supporting tactical intelligence using collaborative environments and social networking

NASA Astrophysics Data System (ADS)

Wollocko, Arthur B.; Farry, Michael P.; Stark, Robert F.

2013-05-01

Modern military environments place an increased emphasis on the collection and analysis of intelligence at the tactical level. The deployment of analytical tools at the tactical level helps support the Warfighter's need for rapid collection, analysis, and dissemination of intelligence. However, given the lack of experience and staffing at the tactical level, most of the available intelligence is not exploited. Tactical environments are staffed by a new generation of intelligence analysts who are well-versed in modern collaboration environments and social networking. An opportunity exists to enhance tactical intelligence analysis by exploiting these personnel strengths, but is dependent on appropriately designed information sharing technologies. Existing social information sharing technologies enable users to publish information quickly, but do not unite or organize information in a manner that effectively supports intelligence analysis. In this paper, we present an alternative approach to structuring and supporting tactical intelligence analysis that combines the benefits of existing concepts, and provide detail on a prototype system embodying that approach. Since this approach employs familiar collaboration support concepts from social media, it enables new-generation analysts to identify the decision-relevant data scattered among databases and the mental models of other personnel, increasing the timeliness of collaborative analysis. Also, the approach enables analysts to collaborate visually to associate heterogeneous and uncertain data within the intelligence analysis process, increasing the robustness of collaborative analyses. Utilizing this familiar dynamic collaboration environment, we hope to achieve a significant reduction of time and skill required to glean actionable intelligence in these challenging operational environments.

Semantic Service Design for Collaborative Business Processes in Internetworked Enterprises

NASA Astrophysics Data System (ADS)

Bianchini, Devis; Cappiello, Cinzia; de Antonellis, Valeria; Pernici, Barbara

Modern collaborating enterprises can be seen as borderless organizations whose processes are dynamically transformed and integrated with the ones of their partners (Internetworked Enterprises, IE), thus enabling the design of collaborative business processes. The adoption of Semantic Web and service-oriented technologies for implementing collaboration in such distributed and heterogeneous environments promises significant benefits. IE can model their own processes independently by using the Software as a Service paradigm (SaaS). Each enterprise maintains a catalog of available services and these can be shared across IE and reused to build up complex collaborative processes. Moreover, each enterprise can adopt its own terminology and concepts to describe business processes and component services. This brings requirements to manage semantic heterogeneity in process descriptions which are distributed across different enterprise systems. To enable effective service-based collaboration, IEs have to standardize their process descriptions and model them through component services using the same approach and principles. For enabling collaborative business processes across IE, services should be designed following an homogeneous approach, possibly maintaining a uniform level of granularity. In the paper we propose an ontology-based semantic modeling approach apt to enrich and reconcile semantics of process descriptions to facilitate process knowledge management and to enable semantic service design (by discovery, reuse and integration of process elements/constructs). The approach brings together Semantic Web technologies, techniques in process modeling, ontology building and semantic matching in order to provide a comprehensive semantic modeling framework.

Adaptive Low Dissipative High Order Filter Methods for Multiscale MHD Flows

NASA Technical Reports Server (NTRS)

Yee, H. C.; Sjoegreen, Bjoern

2004-01-01

Adaptive low-dissipative high order filter finite difference methods for long time wave propagation of shock/turbulence/combustion compressible viscous MHD flows has been constructed. Several variants of the filter approach that cater to different flow types are proposed. These filters provide a natural and efficient way for the minimization of the divergence of the magnetic field [divergence of B] numerical error in the sense that no standard divergence cleaning is required. For certain 2-D MHD test problems, divergence free preservation of the magnetic fields of these filter schemes has been achieved.

Competition in collaborative clothing: a qualitative case study of influences on collaborative quality improvement in the ICU.

PubMed

Dainty, Katie N; Scales, Damon C; Sinuff, Tasnim; Zwarenstein, Merrick

2013-04-01

Multiorganisational quality improvement (QI) collaborative networks are promoted for improving quality within healthcare. Recently, several large-scale QI initiatives have been conducted in the intensive care unit (ICU) environment with successful quantitative results. However, the mechanisms through which such networks lead to QI success remain uncertain. We aim to understand ICU staff perspectives on collaborative QI based on involvement in a multiorganisational improvement network and hypothesise about theoretical constructs that might explain the effect of collaboration in such networks. Qualitative study using a modified grounded theory approach. Key informant interviews were conducted with staff from 12 community hospital ICUs that participated in a cluster randomized control trial (RCT) of a QI intervention using a collaborative approach between 2006 and 2008. Data analysis followed the standard procedure for grounded theory using constant comparative methodology. The collaborative network was perceived to promote increased intrateam cooperation over interorganisational cooperation, but friendly competition with other ICUs appeared to be a prominent driver of behaviour change. Bedsides, clinicians reported that belonging to a collaborative network provided recognition for the high-quality patient care that they already provided. However, the existing communication structure was perceived to be ineffective for staff engagement since it was based on a hierarchical approach to knowledge transfer and project awareness. QI collaborative networks may promote behaviour change by improving intrateam communication, fostering competition with other institutions, and increasing recognition for providing high-quality care. Other commonly held assumptions about their potential impact, for instance, increasing interorganisational legitimisation, communication and collaboration, may be less important.

A methodology proposal for collaborative business process elaboration using a model-driven approach

NASA Astrophysics Data System (ADS)

Mu, Wenxin; Bénaben, Frédérick; Pingaud, Hervé

2015-05-01

Business process management (BPM) principles are commonly used to improve processes within an organisation. But they can equally be applied to supporting the design of an Information System (IS). In a collaborative situation involving several partners, this type of BPM approach may be useful to support the design of a Mediation Information System (MIS), which would ensure interoperability between the partners' ISs (which are assumed to be service oriented). To achieve this objective, the first main task is to build a collaborative business process cartography. The aim of this article is to present a method for bringing together collaborative information and elaborating collaborative business processes from the information gathered (by using a collaborative situation framework, an organisational model, an informational model, a functional model and a metamodel and by using model transformation rules).

Interdisciplinary knowledge translation: lessons learned from a mental health: fire service collaboration.

PubMed

Henderson, Joanna L; Mackay, Sherri; Peterson-Badali, Michele

2010-12-01

Collaborative approaches are being increasingly advocated for addressing a variety of health, mental health and social needs for children, youth and families. Factors important for effective knowledge translation of collaborative approaches of service delivery across disciplines, however, have not been rigorously examined. TAPP-C: The Arson Prevention Program for Children is an intervention program for child and adolescent firesetters provided collaboratively by fire service and mental health professionals. The present study examined the adopter, innovation, and dissemination characteristics associated with TAPP-C implementation, protocol adherence and extent of collaboration by 241 community-based fire service professionals from communities across Ontario. Results revealed that dissemination factors are particularly important for understanding program implementation, adherence and cross-discipline collaboration. Moreover, the findings of this study show significant benefits to both within discipline (intra-disciplinary) and across discipline (interdisciplinary) knowledge translation strategies.

Collaborative approaches towards building midwifery capacity in low income countries: a review of experiences.

PubMed

Dawson, Angela; Brodie, Patricia; Copeland, Felicity; Rumsey, Michele; Homer, Caroline

2014-04-01

to explore collaborative approaches undertaken to build midwifery education, regulation and professional association in low income countries and identify evidence of strategies that may be useful to scale-up midwifery to achieve MDG 5. an integrative review involving a mapping exercise and a narrative synthesis of the literature was undertaken. The search included peer reviewed research and discursive literature published between 2002 and 2012. fifteen papers were found that related to this topic: 10 discursive papers and five research studies. Collaborative approaches to build midwifery capacity come mainly from Africa and involve partnerships between low income countries and between low and high income countries. Most collaborations focus on building capacity across more than one area and arose through opportunistic and strategic means. A number of factors were found to be integral to maintaining collaborations including the establishment of clear processes for communication, leadership and appropriate membership, effective management, mutual respect, learning and an understanding of the context. Collaborative action can result in effective clinical and research skill building, the development of tailored education programmes and the establishment of structures and systems to enhance the midwifery workforce and ultimately, improve maternal and child health. between country collaborations are one component to building midwifery workforce capacity in order to improve maternal health outcomes. the findings provide insights into how collaboration can be established and maintained and how the contribution collaboration makes to capacity building can be evaluated. Copyright © 2013 Elsevier Ltd. All rights reserved.

Parameter estimation of a three-axis spacecraft simulator using recursive least-squares approach with tracking differentiator and Extended Kalman Filter

NASA Astrophysics Data System (ADS)

Xu, Zheyao; Qi, Naiming; Chen, Yukun

2015-12-01

Spacecraft simulators are widely used to study the dynamics, guidance, navigation, and control of a spacecraft on the ground. A spacecraft simulator can have three rotational degrees of freedom by using a spherical air-bearing to simulate a frictionless and micro-gravity space environment. The moment of inertia and center of mass are essential for control system design of ground-based three-axis spacecraft simulators. Unfortunately, they cannot be known precisely. This paper presents two approaches, i.e. a recursive least-squares (RLS) approach with tracking differentiator (TD) and Extended Kalman Filter (EKF) method, to estimate inertia parameters. The tracking differentiator (TD) filter the noise coupled with the measured signals and generate derivate of the measured signals. Combination of two TD filters in series obtains the angular accelerations that are required in RLS (TD-TD-RLS). Another method that does not need to estimate the angular accelerations is using the integrated form of dynamics equation. An extended TD (ETD) filter which can also generate the integration of the function of signals is presented for RLS (denoted as ETD-RLS). States and inertia parameters are estimated simultaneously using EKF. The observability is analyzed. All proposed methods are illustrated by simulations and experiments.

High-bandwidth and flexible tracking control for precision motion with application to a piezo nanopositioner.

PubMed

Feng, Zhao; Ling, Jie; Ming, Min; Xiao, Xiao-Hui

2017-08-01

For precision motion, high-bandwidth and flexible tracking are the two important issues for significant performance improvement. Iterative learning control (ILC) is an effective feedforward control method only for systems that operate strictly repetitively. Although projection ILC can track varying references, the performance is still limited by the fixed-bandwidth Q-filter, especially for triangular waves tracking commonly used in a piezo nanopositioner. In this paper, a wavelet transform-based linear time-varying (LTV) Q-filter design for projection ILC is proposed to compensate high-frequency errors and improve the ability to tracking varying references simultaneously. The LVT Q-filter is designed based on the modulus maximum of wavelet detail coefficients calculated by wavelet transform to determine the high-frequency locations of each iteration with the advantages of avoiding cross-terms and segmenting manually. The proposed approach was verified on a piezo nanopositioner. Experimental results indicate that the proposed approach can locate the high-frequency regions accurately and achieve the best performance under varying references compared with traditional frequency-domain and projection ILC with a fixed-bandwidth Q-filter, which validates that through implementing the LTV filter on projection ILC, high-bandwidth and flexible tracking can be achieved simultaneously by the proposed approach.

Optimal filter design with progressive genetic algorithm for local damage detection in rolling bearings

NASA Astrophysics Data System (ADS)

Wodecki, Jacek; Michalak, Anna; Zimroz, Radoslaw

2018-03-01

Harsh industrial conditions present in underground mining cause a lot of difficulties for local damage detection in heavy-duty machinery. For vibration signals one of the most intuitive approaches of obtaining signal with expected properties, such as clearly visible informative features, is prefiltration with appropriately prepared filter. Design of such filter is very broad field of research on its own. In this paper authors propose a novel approach to dedicated optimal filter design using progressive genetic algorithm. Presented method is fully data-driven and requires no prior knowledge of the signal. It has been tested against a set of real and simulated data. Effectiveness of operation has been proven for both healthy and damaged case. Termination criterion for evolution process was developed, and diagnostic decision making feature has been proposed for final result determinance.

H∞ filtering for discrete-time systems subject to stochastic missing measurements: a decomposition approach

NASA Astrophysics Data System (ADS)

Gu, Zhou; Fei, Shumin; Yue, Dong; Tian, Engang

2014-07-01

This paper deals with the problem of H∞ filtering for discrete-time systems with stochastic missing measurements. A new missing measurement model is developed by decomposing the interval of the missing rate into several segments. The probability of the missing rate in each subsegment is governed by its corresponding random variables. We aim to design a linear full-order filter such that the estimation error converges to zero exponentially in the mean square with a less conservatism while the disturbance rejection attenuation is constrained to a given level by means of an H∞ performance index. Based on Lyapunov theory, the reliable filter parameters are characterised in terms of the feasibility of a set of linear matrix inequalities. Finally, a numerical example is provided to demonstrate the effectiveness and applicability of the proposed design approach.

Multisensor fusion for 3D target tracking using track-before-detect particle filter

NASA Astrophysics Data System (ADS)

Moshtagh, Nima; Romberg, Paul M.; Chan, Moses W.

2015-05-01

This work presents a novel fusion mechanism for estimating the three-dimensional trajectory of a moving target using images collected by multiple imaging sensors. The proposed projective particle filter avoids the explicit target detection prior to fusion. In projective particle filter, particles that represent the posterior density (of target state in a high-dimensional space) are projected onto the lower-dimensional observation space. Measurements are generated directly in the observation space (image plane) and a marginal (sensor) likelihood is computed. The particles states and their weights are updated using the joint likelihood computed from all the sensors. The 3D state estimate of target (system track) is then generated from the states of the particles. This approach is similar to track-before-detect particle filters that are known to perform well in tracking dim and stealthy targets in image collections. Our approach extends the track-before-detect approach to 3D tracking using the projective particle filter. The performance of this measurement-level fusion method is compared with that of a track-level fusion algorithm using the projective particle filter. In the track-level fusion algorithm, the 2D sensor tracks are generated separately and transmitted to a fusion center, where they are treated as measurements to the state estimator. The 2D sensor tracks are then fused to reconstruct the system track. A realistic synthetic scenario with a boosting target was generated, and used to study the performance of the fusion mechanisms.

Aerostructural interaction in a collaborative MDO environment

NASA Astrophysics Data System (ADS)

Ciampa, Pier Davide; Nagel, Björn

2014-10-01

The work presents an approach for aircraft design and optimization, developed to account for fluid-structure interactions in MDO applications. The approach makes use of a collaborative distributed design environment, and focuses on the influence of multiple physics based aerostructural models, on the overall aircraft synthesis and optimization. The approach is tested for the design of large transportation aircraft.

Student Perspectives on the Flipped-Classroom Approach and Collaborative Problem-Solving Process

ERIC Educational Resources Information Center

Karabulut-Ilgu, Aliye; Yao, Suhan; Savolainen, Peter; Jahren, Charles

2018-01-01

The flipped-classroom approach has gained increasing popularity and interest in engineering education. The purpose of this study was to investigate (a) student perspectives on the flipped-classroom approach in a transportation-engineering course and (b) how students used the in-class time dedicated to collaborative problem solving. To this end,…

Learned filters for object detection in multi-object visual tracking

NASA Astrophysics Data System (ADS)

Stamatescu, Victor; Wong, Sebastien; McDonnell, Mark D.; Kearney, David

2016-05-01

We investigate the application of learned convolutional filters in multi-object visual tracking. The filters were learned in both a supervised and unsupervised manner from image data using artificial neural networks. This work follows recent results in the field of machine learning that demonstrate the use learned filters for enhanced object detection and classification. Here we employ a track-before-detect approach to multi-object tracking, where tracking guides the detection process. The object detection provides a probabilistic input image calculated by selecting from features obtained using banks of generative or discriminative learned filters. We present a systematic evaluation of these convolutional filters using a real-world data set that examines their performance as generic object detectors.

Are too many inferior vena cava filters used? Controversial evidences in different clinical settings: a narrative review.

PubMed

Dalla Vestra, Michele; Grolla, Elisabetta; Bonanni, Luca; Pesavento, Raffaele

2018-03-01

The use of inferior vena cava filters to prevent pulmonary embolism is increasing mainly because of indications that appear to be unclearly codified and recommended. The evidence supporting this approach is often heterogeneous, and mainly based on observational studies and consensus opinions, while the insertion of an IVC filter exposes patients to the risk of complications and increases health care costs. Thus, several proposed indications for an IVC filter placement remain controversial. We attempt to review the proof on the efficacy and safety of IVC filters in several "special" clinical settings, and assess the robustness of the available evidence for any specific indication to place an IVC filter.

The intractable cigarette ‘filter problem’

PubMed Central

2011-01-01

Background 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. Objective 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. Methods 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. Results 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. Conclusions 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. PMID:21504917

Collaborative Learning and Competence Development in School Health Nursing

ERIC Educational Resources Information Center

Nordentoft, Helle Merete; Wistoft, Karen

2012-01-01

Purpose: The purpose of this paper is to investigate the process and learning outcomes of peer collaboration in a Danish health developmental project in school health nursing. The paper explores how peer collaboration influences the school nurses' collaborative learning and competence development. Design/methodology/approach: The article is based…

Physician Trainee Collaborative Competency after Exposure to Interprofessional Education: A Quasi-Experimental Study

ERIC Educational Resources Information Center

Porter, Lori A.

2016-01-01

Interprofessional education (IPE) for health care students may be one approach to improving health care outcomes by increasing collaboration among health professionals. However, the influence that IPE experiences are having on collaborative competency is unknown. Collaboration competency is crucial for physician trainees because they will practice…

Continuing Challenges and Potential for Collaborative Approaches to Education Reform

ERIC Educational Resources Information Center

Bodilly, Susan J.; Karam, Rita; Orr, Nate

2011-01-01

The Ford Foundation began the Collaborating for Education Reform Initiative (CERI) in 1997-1998 by issuing grants and providing grantees with funds, guidance, and technical assistance to develop collaboratives and carry out activities to improve teaching and learning. CERI's collaborative activities were directed at three possible community…

The Measurement of Collaborative Culture in Secondary Schools: An Informal Subgroup Approach

ERIC Educational Resources Information Center

Meredith, Chloé; Moolenaar, Nienke M.; Struyve, Charlotte; Vandecandelaere, Machteld; Gielen, Sarah; Kyndt, Eva

2017-01-01

Research on teacher collaboration underlines the importance of a collaborative culture for teachers' functioning. However, while scholars usually regard collaborative culture as a school team characteristic, this study argues that subgroups may be more meaningful units of analysis to conceptualize and assess teachers' perceptions of collaborative…

Parenting as a Creative Collaboration: A Transpersonal Approach

ERIC Educational Resources Information Center

Netzer, Dorit; Brady, Mark

2009-01-01

This article discusses the authors' dialogue and collaborative writing regarding their professional views on the subject of parenting. The use of metaphor and analogy for parenting as a collaborative, cocreative relationship is woven throughout with references to the authors' own collaboration, research, and clinical applications in the fields of…

An Invitation to Open Innovation in Malaria Drug Discovery: 47 Quality Starting Points from the TCAMS.

PubMed

Calderón, Félix; Barros, David; Bueno, José María; Coterón, José Miguel; Fernández, Esther; Gamo, Francisco Javier; Lavandera, José Luís; León, María Luisa; Macdonald, Simon J F; Mallo, Araceli; Manzano, Pilar; Porras, Esther; Fiandor, José María; Castro, Julia

2011-10-13

In 2010, GlaxoSmithKline published the structures of 13533 chemical starting points for antimalarial lead identification. By using an agglomerative structural clustering technique followed by computational filters such as antimalarial activity, physicochemical properties, and dissimilarity to known antimalarial structures, we have identified 47 starting points for lead optimization. Their structures are provided. We invite potential collaborators to work with us to discover new clinical candidates.

On Services for Collaborative Project Management

NASA Astrophysics Data System (ADS)

Ollus, Martin; Jansson, Kim; Karvonen, Iris; Uoti, Mikko; Riikonen, Heli

This paper presents an approach for collaborative project management. The focus is on the support of collaboration, communication and trust. Several project management tools exist for monitoring and control the performance of project tasks. However, support of important intangible assets is more difficult to find. In the paper a leadership approach is identified as a management means and the use of new IT technology, especially social media for support of leadership in project management is discussed.

Absorption/transmission measurements of PSAP particle-laden filters from the Biomass Burning Observation Project (BBOP) field campaign

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

Presser, Cary; Nazarian, Ashot; Conny, Joseph M.

Absorptivity measurements with a laser-heating approach, referred to as the laser-driven thermal reactor (LDTR), were carried out in the infrared and applied at ambient (laboratory) nonreacting conditions to particle-laden filters from a three-wavelength (visible) particle/soot absorption photometer (PSAP). Here, the particles were obtained during the Biomass Burning Observation Project (BBOP) field campaign. The focus of this study was to determine the particle absorption coefficient from field-campaign filter samples using the LDTR approach, and compare results with other commercially available instrumentation (in this case with the PSAP, which has been compared with numerous other optical techniques).

Absorption/transmission measurements of PSAP particle-laden filters from the Biomass Burning Observation Project (BBOP) field campaign

DOE PAGES

Presser, Cary; Nazarian, Ashot; Conny, Joseph M.; ...

2016-12-02

Absorptivity measurements with a laser-heating approach, referred to as the laser-driven thermal reactor (LDTR), were carried out in the infrared and applied at ambient (laboratory) nonreacting conditions to particle-laden filters from a three-wavelength (visible) particle/soot absorption photometer (PSAP). Here, the particles were obtained during the Biomass Burning Observation Project (BBOP) field campaign. The focus of this study was to determine the particle absorption coefficient from field-campaign filter samples using the LDTR approach, and compare results with other commercially available instrumentation (in this case with the PSAP, which has been compared with numerous other optical techniques).

Fast analytical spectral filtering methods for magnetic resonance perfusion quantification.

PubMed

Reddy, Kasireddy V; Mitra, Abhishek; Yalavarthy, Phaneendra K

2016-08-01

The deconvolution in the perfusion weighted imaging (PWI) plays an important role in quantifying the MR perfusion parameters. The PWI application to stroke and brain tumor studies has become a standard clinical practice. The standard approach for this deconvolution is oscillatory-limited singular value decomposition (oSVD) and frequency domain deconvolution (FDD). The FDD is widely recognized as the fastest approach currently available for deconvolution of MR perfusion data. In this work, two fast deconvolution methods (namely analytical fourier filtering and analytical showalter spectral filtering) are proposed. Through systematic evaluation, the proposed methods are shown to be computationally efficient and quantitatively accurate compared to FDD and oSVD.

Fetal ECG extraction using independent component analysis by Jade approach

NASA Astrophysics Data System (ADS)

Giraldo-Guzmán, Jader; Contreras-Ortiz, Sonia H.; Lasprilla, Gloria Isabel Bautista; Kotas, Marian

2017-11-01

Fetal ECG monitoring is a useful method to assess the fetus health and detect abnormal conditions. In this paper we propose an approach to extract fetal ECG from abdomen and chest signals using independent component analysis based on the joint approximate diagonalization of eigenmatrices approach. The JADE approach avoids redundancy, what reduces matrix dimension and computational costs. Signals were filtered with a high pass filter to eliminate low frequency noise. Several levels of decomposition were tested until the fetal ECG was recognized in one of the separated sources output. The proposed method shows fast and good performance.

A resolved two-way coupled CFD/6-DOF approach for predicting embolus transport and the embolus-trapping efficiency of IVC filters.

PubMed

Aycock, Kenneth I; Campbell, Robert L; Manning, Keefe B; Craven, Brent A

2017-06-01

Inferior vena cava (IVC) filters are medical devices designed to provide a mechanical barrier to the passage of emboli from the deep veins of the legs to the heart and lungs. Despite decades of development and clinical use, IVC filters still fail to prevent the passage of all hazardous emboli. The objective of this study is to (1) develop a resolved two-way computational model of embolus transport, (2) provide verification and validation evidence for the model, and (3) demonstrate the ability of the model to predict the embolus-trapping efficiency of an IVC filter. Our model couples computational fluid dynamics simulations of blood flow to six-degree-of-freedom simulations of embolus transport and resolves the interactions between rigid, spherical emboli and the blood flow using an immersed boundary method. Following model development and numerical verification and validation of the computational approach against benchmark data from the literature, embolus transport simulations are performed in an idealized IVC geometry. Centered and tilted filter orientations are considered using a nonlinear finite element-based virtual filter placement procedure. A total of 2048 coupled CFD/6-DOF simulations are performed to predict the embolus-trapping statistics of the filter. The simulations predict that the embolus-trapping efficiency of the IVC filter increases with increasing embolus diameter and increasing embolus-to-blood density ratio. Tilted filter placement is found to decrease the embolus-trapping efficiency compared with centered filter placement. Multiple embolus-trapping locations are predicted for the IVC filter, and the trapping locations are predicted to shift upstream and toward the vessel wall with increasing embolus diameter. Simulations of the injection of successive emboli into the IVC are also performed and reveal that the embolus-trapping efficiency decreases with increasing thrombus load in the IVC filter. In future work, the computational tool could be used to investigate IVC filter design improvements, the effect of patient anatomy on embolus transport and IVC filter embolus-trapping efficiency, and, with further development and validation, optimal filter selection and placement on a patient-specific basis.

Constrained Kalman Filtering Via Density Function Truncation for Turbofan Engine Health Estimation

NASA Technical Reports Server (NTRS)

Simon, Dan; Simon, Donald L.

2006-01-01

Kalman filters are often used to estimate the state variables of a dynamic system. However, in the application of Kalman filters some known signal information is often either ignored or dealt with heuristically. For instance, state variable constraints (which may be based on physical considerations) are often neglected because they do not fit easily into the structure of the Kalman filter. This paper develops an analytic method of incorporating state variable inequality constraints in the Kalman filter. The resultant filter truncates the PDF (probability density function) of the Kalman filter estimate at the known constraints and then computes the constrained filter estimate as the mean of the truncated PDF. The incorporation of state variable constraints increases the computational effort of the filter but significantly improves its estimation accuracy. The improvement is demonstrated via simulation results obtained from a turbofan engine model. The turbofan engine model contains 3 state variables, 11 measurements, and 10 component health parameters. It is also shown that the truncated Kalman filter may be a more accurate way of incorporating inequality constraints than other constrained filters (e.g., the projection approach to constrained filtering).

Rapid limit tests for metal impurities in pharmaceutical materials by X-ray fluorescence spectroscopy using wavelet transform filtering.

PubMed

Arzhantsev, Sergey; Li, Xiang; Kauffman, John F

2011-02-01

We introduce a new method for analysis of X-ray fluorescence (XRF) spectra based on continuous wavelet transform filters, and the method is applied to the determination of toxic metals in pharmaceutical materials using hand-held XRF spectrometers. The method uses the continuous wavelet transform to filter the signal and noise components of the spectrum. We present a limit test that compares the wavelet domain signal-to-noise ratios at the energies of the elements of interest to an empirically determined signal-to-noise decision threshold. The limit test is advantageous because it does not require the user to measure calibration samples prior to measurement, though system suitability tests are still recommended. The limit test was evaluated in a collaborative study that involved five different hand-held XRF spectrometers used by multiple analysts in six separate laboratories across the United States. In total, more than 1200 measurements were performed. The detection limits estimated for arsenic, lead, mercury, and chromium were 8, 14, 20, and 150 μg/g, respectively.

A class of systolizable IIR digital filters and its design for proper scaling and minimum output roundoff noise

NASA Technical Reports Server (NTRS)

Lei, Shaw-Min; Yao, Kung

1990-01-01

A class of infinite impulse response (IIR) digital filters with a systolizable structure is proposed and its synthesis is investigated. The systolizable structure consists of pipelineable regular modules with local connections and is suitable for VLSI implementation. It is capable of achieving high performance as well as high throughput. This class of filter structure provides certain degrees of freedom that can be used to obtain some desirable properties for the filter. Techniques of evaluating the internal signal powers and the output roundoff noise of the proposed filter structure are developed. Based upon these techniques, a well-scaled IIR digital filter with minimum output roundoff noise is designed using a local optimization approach. The internal signals of all the modes of this filter are scaled to unity in the l2-norm sense. Compared to the Rao-Kailath (1984) orthogonal digital filter and the Gray-Markel (1973) normalized-lattice digital filter, this filter has better scaling properties and lower output roundoff noise.