Attitude Determination Using a MEMS-Based Flight Information Measurement Unit

PubMed Central

Ma, Der-Ming; Shiau, Jaw-Kuen; Wang, I.-Chiang; Lin, Yu-Heng

2012-01-01

Obtaining precise attitude information is essential for aircraft navigation and control. This paper presents the results of the attitude determination using an in-house designed low-cost MEMS-based flight information measurement unit. This study proposes a quaternion-based extended Kalman filter to integrate the traditional quaternion and gravitational force decomposition methods for attitude determination algorithm. The proposed extended Kalman filter utilizes the evolution of the four elements in the quaternion method for attitude determination as the dynamic model, with the four elements as the states of the filter. The attitude angles obtained from the gravity computations and from the electronic magnetic sensors are regarded as the measurement of the filter. The immeasurable gravity accelerations are deduced from the outputs of the three axes accelerometers, the relative accelerations, and the accelerations due to body rotation. The constraint of the four elements of the quaternion method is treated as a perfect measurement and is integrated into the filter computation. Approximations of the time-varying noise variances of the measured signals are discussed and presented with details through Taylor series expansions. The algorithm is intuitive, easy to implement, and reliable for long-term high dynamic maneuvers. Moreover, a set of flight test data is utilized to demonstrate the success and practicality of the proposed algorithm and the filter design. PMID:22368455

Attitude determination using a MEMS-based flight information measurement unit.

PubMed

Ma, Der-Ming; Shiau, Jaw-Kuen; Wang, I-Chiang; Lin, Yu-Heng

2012-01-01

Obtaining precise attitude information is essential for aircraft navigation and control. This paper presents the results of the attitude determination using an in-house designed low-cost MEMS-based flight information measurement unit. This study proposes a quaternion-based extended Kalman filter to integrate the traditional quaternion and gravitational force decomposition methods for attitude determination algorithm. The proposed extended Kalman filter utilizes the evolution of the four elements in the quaternion method for attitude determination as the dynamic model, with the four elements as the states of the filter. The attitude angles obtained from the gravity computations and from the electronic magnetic sensors are regarded as the measurement of the filter. The immeasurable gravity accelerations are deduced from the outputs of the three axes accelerometers, the relative accelerations, and the accelerations due to body rotation. The constraint of the four elements of the quaternion method is treated as a perfect measurement and is integrated into the filter computation. Approximations of the time-varying noise variances of the measured signals are discussed and presented with details through Taylor series expansions. The algorithm is intuitive, easy to implement, and reliable for long-term high dynamic maneuvers. Moreover, a set of flight test data is utilized to demonstrate the success and practicality of the proposed algorithm and the filter design.

Filtering observations without the initial guess

NASA Astrophysics Data System (ADS)

Chin, T. M.; Abbondanza, C.; Gross, R. S.; Heflin, M. B.; Parker, J. W.; Soja, B.; Wu, X.

2017-12-01

Noisy geophysical observations sampled irregularly over space and time are often numerically "analyzed" or "filtered" before scientific usage. The standard analysis and filtering techniques based on the Bayesian principle requires "a priori" joint distribution of all the geophysical parameters of interest. However, such prior distributions are seldom known fully in practice, and best-guess mean values (e.g., "climatology" or "background" data if available) accompanied by some arbitrarily set covariance values are often used in lieu. It is therefore desirable to be able to exploit efficient (time sequential) Bayesian algorithms like the Kalman filter while not forced to provide a prior distribution (i.e., initial mean and covariance). An example of this is the estimation of the terrestrial reference frame (TRF) where requirement for numerical precision is such that any use of a priori constraints on the observation data needs to be minimized. We will present the Information Filter algorithm, a variant of the Kalman filter that does not require an initial distribution, and apply the algorithm (and an accompanying smoothing algorithm) to the TRF estimation problem. We show that the information filter allows temporal propagation of partial information on the distribution (marginal distribution of a transformed version of the state vector), instead of the full distribution (mean and covariance) required by the standard Kalman filter. The information filter appears to be a natural choice for the task of filtering observational data in general cases where prior assumption on the initial estimate is not available and/or desirable. For application to data assimilation problems, reduced-order approximations of both the information filter and square-root information filter (SRIF) have been published, and the former has previously been applied to a regional configuration of the HYCOM ocean general circulation model. Such approximation approaches are also briefed in the presentation.

Determination of tailored filter sets to create rayfiles including spatial and angular resolved spectral information.

PubMed

Rotscholl, Ingo; Trampert, Klaus; Krüger, Udo; Perner, Martin; Schmidt, Franz; Neumann, Cornelius

2015-11-16

To simulate and optimize optical designs regarding perceived color and homogeneity in commercial ray tracing software, realistic light source models are needed. Spectral rayfiles provide angular and spatial varying spectral information. We propose a spectral reconstruction method with a minimum of time consuming goniophotometric near field measurements with optical filters for the purpose of creating spectral rayfiles. Our discussion focuses on the selection of the ideal optical filter combination for any arbitrary spectrum out of a given filter set by considering measurement uncertainties with Monte Carlo simulations. We minimize the simulation time by a preselection of all filter combinations, which bases on factorial design.

Personalized Recommender System for Digital Libraries

ERIC Educational Resources Information Center

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

2014-01-01

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

More than a filter: Feature-based attention regulates the distribution of visual working memory resources.

PubMed

Dube, Blaire; Emrich, Stephen M; Al-Aidroos, Naseem

2017-10-01

Across 2 experiments we revisited the filter account of how feature-based attention regulates visual working memory (VWM). Originally drawing from discrete-capacity ("slot") models, the filter account proposes that attention operates like the "bouncer in the brain," preventing distracting information from being encoded so that VWM resources are reserved for relevant information. Given recent challenges to the assumptions of discrete-capacity models, we investigated whether feature-based attention plays a broader role in regulating memory. Both experiments used partial report tasks in which participants memorized the colors of circle and square stimuli, and we provided a feature-based goal by manipulating the likelihood that 1 shape would be probed over the other across a range of probabilities. By decomposing participants' responses using mixture and variable-precision models, we estimated the contributions of guesses, nontarget responses, and imprecise memory representations to their errors. Consistent with the filter account, participants were less likely to guess when the probed memory item matched the feature-based goal. Interestingly, this effect varied with goal strength, even across high probabilities where goal-matching information should always be prioritized, demonstrating strategic control over filter strength. Beyond this effect of attention on which stimuli were encoded, we also observed effects on how they were encoded: Estimates of both memory precision and nontarget errors varied continuously with feature-based attention. The results offer support for an extension to the filter account, where feature-based attention dynamically regulates the distribution of resources within working memory so that the most relevant items are encoded with the greatest precision. (PsycINFO Database Record (c) 2017 APA, all rights reserved).

Detail-enhanced multimodality medical image fusion based on gradient minimization smoothing filter and shearing filter.

PubMed

Liu, Xingbin; Mei, Wenbo; Du, Huiqian

2018-02-13

In this paper, a detail-enhanced multimodality medical image fusion algorithm is proposed by using proposed multi-scale joint decomposition framework (MJDF) and shearing filter (SF). The MJDF constructed with gradient minimization smoothing filter (GMSF) and Gaussian low-pass filter (GLF) is used to decompose source images into low-pass layers, edge layers, and detail layers at multiple scales. In order to highlight the detail information in the fused image, the edge layer and the detail layer in each scale are weighted combined into a detail-enhanced layer. As directional filter is effective in capturing salient information, so SF is applied to the detail-enhanced layer to extract geometrical features and obtain directional coefficients. Visual saliency map-based fusion rule is designed for fusing low-pass layers, and the sum of standard deviation is used as activity level measurement for directional coefficients fusion. The final fusion result is obtained by synthesizing the fused low-pass layers and directional coefficients. Experimental results show that the proposed method with shift-invariance, directional selectivity, and detail-enhanced property is efficient in preserving and enhancing detail information of multimodality medical images. Graphical abstract The detailed implementation of the proposed medical image fusion algorithm.

A tool for filtering information in complex systems

PubMed Central

Tumminello, M.; Aste, T.; Di Matteo, T.; Mantegna, R. N.

2005-01-01

We introduce a technique to filter out complex data sets by extracting a subgraph of representative links. Such a filtering can be tuned up to any desired level by controlling the genus of the resulting graph. We show that this technique is especially suitable for correlation-based graphs, giving filtered graphs that preserve the hierarchical organization of the minimum spanning tree but containing a larger amount of information in their internal structure. In particular in the case of planar filtered graphs (genus equal to 0), triangular loops and four-element cliques are formed. The application of this filtering procedure to 100 stocks in the U.S. equity markets shows that such loops and cliques have important and significant relationships with the market structure and properties. PMID:16027373

A tool for filtering information in complex systems.

PubMed

Tumminello, M; Aste, T; Di Matteo, T; Mantegna, R N

2005-07-26

We introduce a technique to filter out complex data sets by extracting a subgraph of representative links. Such a filtering can be tuned up to any desired level by controlling the genus of the resulting graph. We show that this technique is especially suitable for correlation-based graphs, giving filtered graphs that preserve the hierarchical organization of the minimum spanning tree but containing a larger amount of information in their internal structure. In particular in the case of planar filtered graphs (genus equal to 0), triangular loops and four-element cliques are formed. The application of this filtering procedure to 100 stocks in the U.S. equity markets shows that such loops and cliques have important and significant relationships with the market structure and properties.

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.

Evaluation of deconvolution modelling applied to numerical combustion

NASA Astrophysics Data System (ADS)

Mehl, Cédric; Idier, Jérôme; Fiorina, Benoît

2018-01-01

A possible modelling approach in the large eddy simulation (LES) of reactive flows is to deconvolve resolved scalars. Indeed, by inverting the LES filter, scalars such as mass fractions are reconstructed. This information can be used to close budget terms of filtered species balance equations, such as the filtered reaction rate. Being ill-posed in the mathematical sense, the problem is very sensitive to any numerical perturbation. The objective of the present study is to assess the ability of this kind of methodology to capture the chemical structure of premixed flames. For that purpose, three deconvolution methods are tested on a one-dimensional filtered laminar premixed flame configuration: the approximate deconvolution method based on Van Cittert iterative deconvolution, a Taylor decomposition-based method, and the regularised deconvolution method based on the minimisation of a quadratic criterion. These methods are then extended to the reconstruction of subgrid scale profiles. Two methodologies are proposed: the first one relies on subgrid scale interpolation of deconvolved profiles and the second uses parametric functions to describe small scales. Conducted tests analyse the ability of the method to capture the chemical filtered flame structure and front propagation speed. Results show that the deconvolution model should include information about small scales in order to regularise the filter inversion. a priori and a posteriori tests showed that the filtered flame propagation speed and structure cannot be captured if the filter size is too large.

Improved hybrid information filtering based on limited time window

NASA Astrophysics Data System (ADS)

Song, Wen-Jun; Guo, Qiang; Liu, Jian-Guo

2014-12-01

Adopting the entire collecting information of users, the hybrid information filtering of heat conduction and mass diffusion (HHM) (Zhou et al., 2010) was successfully proposed to solve the apparent diversity-accuracy dilemma. Since the recent behaviors are more effective to capture the users' potential interests, we present an improved hybrid information filtering of adopting the partial recent information. We expand the time window to generate a series of training sets, each of which is treated as known information to predict the future links proven by the testing set. The experimental results on one benchmark dataset Netflix indicate that by only using approximately 31% recent rating records, the accuracy could be improved by an average of 4.22% and the diversity could be improved by 13.74%. In addition, the performance on the dataset MovieLens could be preserved by considering approximately 60% recent records. Furthermore, we find that the improved algorithm is effective to solve the cold-start problem. This work could improve the information filtering performance and shorten the computational time.

Detection of circuit-board components with an adaptive multiclass correlation filter

NASA Astrophysics Data System (ADS)

Diaz-Ramirez, Victor H.; Kober, Vitaly

2008-08-01

A new method for reliable detection of circuit-board components is proposed. The method is based on an adaptive multiclass composite correlation filter. The filter is designed with the help of an iterative algorithm using complex synthetic discriminant functions. The impulse response of the filter contains information needed to localize and classify geometrically distorted circuit-board components belonging to different classes. Computer simulation results obtained with the proposed method are provided and compared with those of known multiclass correlation based techniques in terms of performance criteria for recognition and classification of objects.

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.

Absorption Filter Based Optical Diagnostics in High Speed Flows

NASA Technical Reports Server (NTRS)

Samimy, Mo; Elliott, Gregory; Arnette, Stephen

1996-01-01

Two major regimes where laser light scattered by molecules or particles in a flow contains significant information about the flow are Mie scattering and Rayleigh scattering. Mie scattering is used to obtain only velocity information, while Rayleigh scattering can be used to measure both the velocity and the thermodynamic properties of the flow. Now, recently introduced (1990, 1991) absorption filter based diagnostic techniques have started a new era in flow visualization, simultaneous velocity and thermodynamic measurements, and planar velocity measurements. Using a filtered planar velocimetry (FPV) technique, we have modified the optically thick iodine filter profile of Miles, et al., and used it in the pressure-broaden regime which accommodates measurements in a wide range of velocity applications. Measuring velocity and thermodynamic properties simultaneously, using absorption filtered based Rayleigh scattering, involves not only the measurement of the Doppler shift, but also the spectral profile of the Rayleigh scattering signal. Using multiple observation angles, simultaneous measurement of one component velocity and thermodynamic properties in a supersonic jet were measured. Presently, the technique is being extended for simultaneous measurements of all three components of velocity and thermodynamic properties.

Scattering property based contextual PolSAR speckle filter

NASA Astrophysics Data System (ADS)

Mullissa, Adugna G.; Tolpekin, Valentyn; Stein, Alfred

2017-12-01

Reliability of the scattering model based polarimetric SAR (PolSAR) speckle filter depends upon the accurate decomposition and classification of the scattering mechanisms. This paper presents an improved scattering property based contextual speckle filter based upon an iterative classification of the scattering mechanisms. It applies a Cloude-Pottier eigenvalue-eigenvector decomposition and a fuzzy H/α classification to determine the scattering mechanisms on a pre-estimate of the coherency matrix. The H/α classification identifies pixels with homogeneous scattering properties. A coarse pixel selection rule groups pixels that are either single bounce, double bounce or volume scatterers. A fine pixel selection rule is applied to pixels within each canonical scattering mechanism. We filter the PolSAR data and depending on the type of image scene (urban or rural) use either the coarse or fine pixel selection rule. Iterative refinement of the Wishart H/α classification reduces the speckle in the PolSAR data. Effectiveness of this new filter is demonstrated by using both simulated and real PolSAR data. It is compared with the refined Lee filter, the scattering model based filter and the non-local means filter. The study concludes that the proposed filter compares favorably with other polarimetric speckle filters in preserving polarimetric information, point scatterers and subtle features in PolSAR data.

A tool for filtering information in complex systems

NASA Astrophysics Data System (ADS)

Tumminello, M.; Aste, T.; Di Matteo, T.; Mantegna, R. N.

2005-07-01

We introduce a technique to filter out complex data sets by extracting a subgraph of representative links. Such a filtering can be tuned up to any desired level by controlling the genus of the resulting graph. We show that this technique is especially suitable for correlation-based graphs, giving filtered graphs that preserve the hierarchical organization of the minimum spanning tree but containing a larger amount of information in their internal structure. In particular in the case of planar filtered graphs (genus equal to 0), triangular loops and four-element cliques are formed. The application of this filtering procedure to 100 stocks in the U.S. equity markets shows that such loops and cliques have important and significant relationships with the market structure and properties. This paper was submitted directly (Track II) to the PNAS office.Abbreviations: MST, minimum spanning tree; PMFG, Planar Maximally Filtered Graph; r-clique, clique of r elements.

Information theoretic methods for image processing algorithm optimization

NASA Astrophysics Data System (ADS)

Prokushkin, Sergey F.; Galil, Erez

2015-01-01

Modern image processing pipelines (e.g., those used in digital cameras) are full of advanced, highly adaptive filters that often have a large number of tunable parameters (sometimes > 100). This makes the calibration procedure for these filters very complex, and the optimal results barely achievable in the manual calibration; thus an automated approach is a must. We will discuss an information theory based metric for evaluation of algorithm adaptive characteristics ("adaptivity criterion") using noise reduction algorithms as an example. The method allows finding an "orthogonal decomposition" of the filter parameter space into the "filter adaptivity" and "filter strength" directions. This metric can be used as a cost function in automatic filter optimization. Since it is a measure of a physical "information restoration" rather than perceived image quality, it helps to reduce the set of the filter parameters to a smaller subset that is easier for a human operator to tune and achieve a better subjective image quality. With appropriate adjustments, the criterion can be used for assessment of the whole imaging system (sensor plus post-processing).

Hybrid Filtering in Semantic Query Processing

ERIC Educational Resources Information Center

Jeong, Hanjo

2011-01-01

This dissertation presents a hybrid filtering method and a case-based reasoning framework for enhancing the effectiveness of Web search. Web search may not reflect user needs, intent, context, and preferences, because today's keyword-based search is lacking semantic information to capture the user's context and intent in posing the search query.…

An Adaptive Altitude Information Fusion Method for Autonomous Landing Processes of Small Unmanned Aerial Rotorcraft

PubMed Central

Lei, Xusheng; Li, Jingjing

2012-01-01

This paper presents an adaptive information fusion method to improve the accuracy and reliability of the altitude measurement information for small unmanned aerial rotorcraft during the landing process. Focusing on the low measurement performance of sensors mounted on small unmanned aerial rotorcraft, a wavelet filter is applied as a pre-filter to attenuate the high frequency noises in the sensor output. Furthermore, to improve altitude information, an adaptive extended Kalman filter based on a maximum a posteriori criterion is proposed to estimate measurement noise covariance matrix in real time. Finally, the effectiveness of the proposed method is proved by static tests, hovering flight and autonomous landing flight tests. PMID:23201993

[Study on Application of NIR Spectral Information Screening in Identification of Maca Origin].

PubMed

Wang, Yuan-zhong; Zhao, Yan-li; Zhang, Ji; Jin, Hang

2016-02-01

Medicinal and edible plant Maca is rich in various nutrients and owns great medicinal value. Based on near infrared diffuse reflectance spectra, 139 Maca samples collected from Peru and Yunnan were used to identify their geographical origins. Multiplication signal correction (MSC) coupled with second derivative (SD) and Norris derivative filter (ND) was employed in spectral pretreatment. Spectrum range (7,500-4,061 cm⁻¹) was chosen by spectrum standard deviation. Combined with principal component analysis-mahalanobis distance (PCA-MD), the appropriate number of principal components was selected as 5. Based on the spectrum range and the number of principal components selected, two abnormal samples were eliminated by modular group iterative singular sample diagnosis method. Then, four methods were used to filter spectral variable information, competitive adaptive reweighted sampling (CARS), monte carlo-uninformative variable elimination (MC-UVE), genetic algorithm (GA) and subwindow permutation analysis (SPA). The spectral variable information filtered was evaluated by model population analysis (MPA). The results showed that RMSECV(SPA) > RMSECV(CARS) > RMSECV(MC-UVE) > RMSECV(GA), were 2. 14, 2. 05, 2. 02, and 1. 98, and the spectral variables were 250, 240, 250 and 70, respectively. According to the spectral variable filtered, partial least squares discriminant analysis (PLS-DA) was used to build the model, with random selection of 97 samples as training set, and the other 40 samples as validation set. The results showed that, R²: GA > MC-UVE > CARS > SPA, RMSEC and RMSEP: GA < MC-UVE < CARS

Color Restoration of RGBN Multispectral Filter Array Sensor Images Based on Spectral Decomposition.

PubMed

Park, Chulhee; Kang, Moon Gi

2016-05-18

A multispectral filter array (MSFA) image sensor with red, green, blue and near-infrared (NIR) filters is useful for various imaging applications with the advantages that it obtains color information and NIR information simultaneously. Because the MSFA image sensor needs to acquire invisible band information, it is necessary to remove the IR cut-offfilter (IRCF). However, without the IRCF, the color of the image is desaturated by the interference of the additional NIR component of each RGB color channel. To overcome color degradation, a signal processing approach is required to restore natural color by removing the unwanted NIR contribution to the RGB color channels while the additional NIR information remains in the N channel. Thus, in this paper, we propose a color restoration method for an imaging system based on the MSFA image sensor with RGBN filters. To remove the unnecessary NIR component in each RGB color channel, spectral estimation and spectral decomposition are performed based on the spectral characteristics of the MSFA sensor. The proposed color restoration method estimates the spectral intensity in NIR band and recovers hue and color saturation by decomposing the visible band component and the NIR band component in each RGB color channel. The experimental results show that the proposed method effectively restores natural color and minimizes angular errors.

Color Restoration of RGBN Multispectral Filter Array Sensor Images Based on Spectral Decomposition

PubMed Central

Park, Chulhee; Kang, Moon Gi

2016-01-01

A multispectral filter array (MSFA) image sensor with red, green, blue and near-infrared (NIR) filters is useful for various imaging applications with the advantages that it obtains color information and NIR information simultaneously. Because the MSFA image sensor needs to acquire invisible band information, it is necessary to remove the IR cut-offfilter (IRCF). However, without the IRCF, the color of the image is desaturated by the interference of the additional NIR component of each RGB color channel. To overcome color degradation, a signal processing approach is required to restore natural color by removing the unwanted NIR contribution to the RGB color channels while the additional NIR information remains in the N channel. Thus, in this paper, we propose a color restoration method for an imaging system based on the MSFA image sensor with RGBN filters. To remove the unnecessary NIR component in each RGB color channel, spectral estimation and spectral decomposition are performed based on the spectral characteristics of the MSFA sensor. The proposed color restoration method estimates the spectral intensity in NIR band and recovers hue and color saturation by decomposing the visible band component and the NIR band component in each RGB color channel. The experimental results show that the proposed method effectively restores natural color and minimizes angular errors. PMID:27213381

The Amateur Scientist: Simple Optical Experiments in Which Spatial Filtering Removes the "Noise" from Pictures.

ERIC Educational Resources Information Center

Walker, Jearl

1982-01-01

Spatial filtering, based on diffraction/interference of light waves, is a technique by which unwanted information in a picture ("noise") can be separated from wanted information. A series of experiments is described in which students can create a system that functions as an optical computer to create clearer pictures. (Author/JN)

Performance Enhancement of a USV INS/CNS/DVL Integration Navigation System Based on an Adaptive Information Sharing Factor Federated Filter

PubMed Central

Wang, Qiuying; Cui, Xufei; Li, Yibing; Ye, Fang

2017-01-01

To improve the ability of autonomous navigation for Unmanned Surface Vehicles (USVs), multi-sensor integrated navigation based on Inertial Navigation System (INS), Celestial Navigation System (CNS) and Doppler Velocity Log (DVL) is proposed. The CNS position and the DVL velocity are introduced as the reference information to correct the INS divergence error. The autonomy of the integrated system based on INS/CNS/DVL is much better compared with the integration based on INS/GNSS alone. However, the accuracy of DVL velocity and CNS position are decreased by the measurement noise of DVL and bad weather, respectively. Hence, the INS divergence error cannot be estimated and corrected by the reference information. To resolve the problem, the Adaptive Information Sharing Factor Federated Filter (AISFF) is introduced to fuse data. The information sharing factor of the Federated Filter is adaptively adjusted to maintaining multiple component solutions usable as back-ups, which can improve the reliability of overall system. The effectiveness of this approach is demonstrated by simulation and experiment, the results show that for the INS/CNS/DVL integrated system, when the DVL velocity accuracy is decreased and the CNS cannot work under bad weather conditions, the INS/CNS/DVL integrated system can operate stably based on the AISFF method. PMID:28165369

Performance Enhancement of a USV INS/CNS/DVL Integration Navigation System Based on an Adaptive Information Sharing Factor Federated Filter.

PubMed

Wang, Qiuying; Cui, Xufei; Li, Yibing; Ye, Fang

2017-02-03

To improve the ability of autonomous navigation for Unmanned Surface Vehicles (USVs), multi-sensor integrated navigation based on Inertial Navigation System (INS), Celestial Navigation System (CNS) and Doppler Velocity Log (DVL) is proposed. The CNS position and the DVL velocity are introduced as the reference information to correct the INS divergence error. The autonomy of the integrated system based on INS/CNS/DVL is much better compared with the integration based on INS/GNSS alone. However, the accuracy of DVL velocity and CNS position are decreased by the measurement noise of DVL and bad weather, respectively. Hence, the INS divergence error cannot be estimated and corrected by the reference information. To resolve the problem, the Adaptive Information Sharing Factor Federated Filter (AISFF) is introduced to fuse data. The information sharing factor of the Federated Filter is adaptively adjusted to maintaining multiple component solutions usable as back-ups, which can improve the reliability of overall system. The effectiveness of this approach is demonstrated by simulation and experiment, the results show that for the INS/CNS/DVL integrated system, when the DVL velocity accuracy is decreased and the CNS cannot work under bad weather conditions, the INS/CNS/DVL integrated system can operate stably based on the AISFF method.

Evidence-Based Evaluation of Inferior Vena Cava Filter Complications Based on Filter Type

PubMed Central

Deso, Steven E.; Idakoji, Ibrahim A.; Kuo, William T.

2016-01-01

Many inferior vena cava (IVC) filter types, along with their specific risks and complications, are not recognized. The purpose of this study was to evaluate the various FDA-approved IVC filter types to determine device-specific risks, as a way to help identify patients who may benefit from ongoing follow-up versus prompt filter retrieval. An evidence-based electronic search (FDA Premarket Notification, MEDLINE, FDA MAUDE) was performed to identify all IVC filter types and device-specific complications from 1980 to 2014. Twenty-three IVC filter types (14 retrievable, 9 permanent) were identified. The devices were categorized as follows: conical (n = 14), conical with umbrella (n = 1), conical with cylindrical element (n = 2), biconical with cylindrical element (n = 2), helical (n = 1), spiral (n = 1), and complex (n = 1). Purely conical filters were associated with the highest reported risks of penetration (90–100%). Filters with cylindrical or umbrella elements were associated with the highest reported risk of IVC thrombosis (30–50%). Conical Bard filters were associated with the highest reported risks of fracture (40%). The various FDA-approved IVC filter types were evaluated for device-specific complications based on best current evidence. This information can be used to guide and optimize clinical management in patients with indwelling IVC filters. PMID:27247477

Multimodal Medical Image Fusion by Adaptive Manifold Filter.

PubMed

Geng, Peng; Liu, Shuaiqi; Zhuang, Shanna

2015-01-01

Medical image fusion plays an important role in diagnosis and treatment of diseases such as image-guided radiotherapy and surgery. The modified local contrast information is proposed to fuse multimodal medical images. Firstly, the adaptive manifold filter is introduced into filtering source images as the low-frequency part in the modified local contrast. Secondly, the modified spatial frequency of the source images is adopted as the high-frequency part in the modified local contrast. Finally, the pixel with larger modified local contrast is selected into the fused image. The presented scheme outperforms the guided filter method in spatial domain, the dual-tree complex wavelet transform-based method, nonsubsampled contourlet transform-based method, and four classic fusion methods in terms of visual quality. Furthermore, the mutual information values by the presented method are averagely 55%, 41%, and 62% higher than the three methods and those values of edge based similarity measure by the presented method are averagely 13%, 33%, and 14% higher than the three methods for the six pairs of source images.

A method of incident angle estimation for high resolution spectral recovery in filter-array-based spectrometers

NASA Astrophysics Data System (ADS)

Kim, Cheolsun; Lee, Woong-Bi; Ju, Gun Wu; Cho, Jeonghoon; Kim, Seongmin; Oh, Jinkyung; Lim, Dongsung; Lee, Yong Tak; Lee, Heung-No

2017-02-01

In recent years, there has been an increasing interest in miniature spectrometers for research and development. Especially, filter-array-based spectrometers have advantages of low cost and portability, and can be applied in various fields such as biology, chemistry and food industry. Miniaturization in optical filters causes degradation of spectral resolution due to limitations on spectral responses and the number of filters. Nowadays, many studies have been reported that the filter-array-based spectrometers have achieved resolution improvements by using digital signal processing (DSP) techniques. The performance of the DSP-based spectral recovery highly depends on the prior information of transmission functions (TFs) of the filters. The TFs vary with respect to an incident angle of light onto the filter-array. Conventionally, it is assumed that the incident angle of light on the filters is fixed and the TFs are known to the DSP. However, the incident angle is inconstant according to various environments and applications, and thus TFs also vary, which leads to performance degradation of spectral recovery. In this paper, we propose a method of incident angle estimation (IAE) for high resolution spectral recovery in the filter-array-based spectrometers. By exploiting sparse signal reconstruction of the L1- norm minimization, IAE estimates an incident angle among all possible incident angles which minimizes the error of the reconstructed signal. Based on IAE, DSP effectively provides a high resolution spectral recovery in the filter-array-based spectrometers.

A Scalable Monitoring for the CMS Filter Farm Based on Elasticsearch

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

Andre, J.M.; et al.

2015-12-23

A flexible monitoring system has been designed for the CMS File-based Filter Farm making use of modern data mining and analytics components. All the metadata and monitoring information concerning data flow and execution of the HLT are generated locally in the form of small documents using the JSON encoding. These documents are indexed into a hierarchy of elasticsearch (es) clusters along with process and system log information. Elasticsearch is a search server based on Apache Lucene. It provides a distributed, multitenant-capable search and aggregation engine. Since es is schema-free, any new information can be added seamlessly and the unstructured informationmore » can be queried in non-predetermined ways. The leaf es clusters consist of the very same nodes that form the Filter Farm thus providing natural horizontal scaling. A separate central” es cluster is used to collect and index aggregated information. The fine-grained information, all the way to individual processes, remains available in the leaf clusters. The central es cluster provides quasi-real-time high-level monitoring information to any kind of client. Historical data can be retrieved to analyse past problems or correlate them with external information. We discuss the design and performance of this system in the context of the CMS DAQ commissioning for LHC Run 2.« less

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.

The Joint Adaptive Kalman Filter (JAKF) for Vehicle Motion State Estimation.

PubMed

Gao, Siwei; Liu, Yanheng; Wang, Jian; Deng, Weiwen; Oh, Heekuck

2016-07-16

This paper proposes a multi-sensory Joint Adaptive Kalman Filter (JAKF) through extending innovation-based adaptive estimation (IAE) to estimate the motion state of the moving vehicles ahead. JAKF views Lidar and Radar data as the source of the local filters, which aims to adaptively adjust the measurement noise variance-covariance (V-C) matrix 'R' and the system noise V-C matrix 'Q'. Then, the global filter uses R to calculate the information allocation factor 'β' for data fusion. Finally, the global filter completes optimal data fusion and feeds back to the local filters to improve the measurement accuracy of the local filters. Extensive simulation and experimental results show that the JAKF has better adaptive ability and fault tolerance. JAKF enables one to bridge the gap of the accuracy difference of various sensors to improve the integral filtering effectivity. If any sensor breaks down, the filtered results of JAKF still can maintain a stable convergence rate. Moreover, the JAKF outperforms the conventional Kalman filter (CKF) and the innovation-based adaptive Kalman filter (IAKF) with respect to the accuracy of displacement, velocity, and acceleration, respectively.

Autonomous Navigation for Autonomous Underwater Vehicles Based on Information Filters and Active Sensing

PubMed Central

He, Bo; Zhang, Hongjin; Li, Chao; Zhang, Shujing; Liang, Yan; Yan, Tianhong

2011-01-01

This paper addresses an autonomous navigation method for the autonomous underwater vehicle (AUV) C-Ranger applying information-filter-based simultaneous localization and mapping (SLAM), and its sea trial experiments in Tuandao Bay (Shangdong Province, P.R. China). Weak links in the information matrix in an extended information filter (EIF) can be pruned to achieve an efficient approach-sparse EIF algorithm (SEIF-SLAM). All the basic update formulae can be implemented in constant time irrespective of the size of the map; hence the computational complexity is significantly reduced. The mechanical scanning imaging sonar is chosen as the active sensing device for the underwater vehicle, and a compensation method based on feedback of the AUV pose is presented to overcome distortion of the acoustic images due to the vehicle motion. In order to verify the feasibility of the navigation methods proposed for the C-Ranger, a sea trial was conducted in Tuandao Bay. Experimental results and analysis show that the proposed navigation approach based on SEIF-SLAM improves the accuracy of the navigation compared with conventional method; moreover the algorithm has a low computational cost when compared with EKF-SLAM. PMID:22346682

Autonomous navigation for autonomous underwater vehicles based on information filters and active sensing.

PubMed

He, Bo; Zhang, Hongjin; Li, Chao; Zhang, Shujing; Liang, Yan; Yan, Tianhong

2011-01-01

This paper addresses an autonomous navigation method for the autonomous underwater vehicle (AUV) C-Ranger applying information-filter-based simultaneous localization and mapping (SLAM), and its sea trial experiments in Tuandao Bay (Shangdong Province, P.R. China). Weak links in the information matrix in an extended information filter (EIF) can be pruned to achieve an efficient approach-sparse EIF algorithm (SEIF-SLAM). All the basic update formulae can be implemented in constant time irrespective of the size of the map; hence the computational complexity is significantly reduced. The mechanical scanning imaging sonar is chosen as the active sensing device for the underwater vehicle, and a compensation method based on feedback of the AUV pose is presented to overcome distortion of the acoustic images due to the vehicle motion. In order to verify the feasibility of the navigation methods proposed for the C-Ranger, a sea trial was conducted in Tuandao Bay. Experimental results and analysis show that the proposed navigation approach based on SEIF-SLAM improves the accuracy of the navigation compared with conventional method; moreover the algorithm has a low computational cost when compared with EKF-SLAM.

The new approach for infrared target tracking based on the particle filter algorithm

NASA Astrophysics Data System (ADS)

Sun, Hang; Han, Hong-xia

2011-08-01

Target tracking on the complex background in the infrared image sequence is hot research field. It provides the important basis in some fields such as video monitoring, precision, and video compression human-computer interaction. As a typical algorithms in the target tracking framework based on filtering and data connection, the particle filter with non-parameter estimation characteristic have ability to deal with nonlinear and non-Gaussian problems so it were widely used. There are various forms of density in the particle filter algorithm to make it valid when target occlusion occurred or recover tracking back from failure in track procedure, but in order to capture the change of the state space, it need a certain amount of particles to ensure samples is enough, and this number will increase in accompany with dimension and increase exponentially, this led to the increased amount of calculation is presented. In this paper particle filter algorithm and the Mean shift will be combined. Aiming at deficiencies of the classic mean shift Tracking algorithm easily trapped into local minima and Unable to get global optimal under the complex background. From these two perspectives that "adaptive multiple information fusion" and "with particle filter framework combining", we expand the classic Mean Shift tracking framework .Based on the previous perspective, we proposed an improved Mean Shift infrared target tracking algorithm based on multiple information fusion. In the analysis of the infrared characteristics of target basis, Algorithm firstly extracted target gray and edge character and Proposed to guide the above two characteristics by the moving of the target information thus we can get new sports guide grayscale characteristics and motion guide border feature. Then proposes a new adaptive fusion mechanism, used these two new information adaptive to integrate into the Mean Shift tracking framework. Finally we designed a kind of automatic target model updating strategy to further improve tracking performance. Experimental results show that this algorithm can compensate shortcoming of the particle filter has too much computation, and can effectively overcome the fault that mean shift is easy to fall into local extreme value instead of global maximum value .Last because of the gray and fusion target motion information, this approach also inhibit interference from the background, ultimately improve the stability and the real-time of the target track.

Deep learning architecture for iris recognition based on optimal Gabor filters and deep belief network

NASA Astrophysics Data System (ADS)

He, Fei; Han, Ye; Wang, Han; Ji, Jinchao; Liu, Yuanning; Ma, Zhiqiang

2017-03-01

Gabor filters are widely utilized to detect iris texture information in several state-of-the-art iris recognition systems. However, the proper Gabor kernels and the generative pattern of iris Gabor features need to be predetermined in application. The traditional empirical Gabor filters and shallow iris encoding ways are incapable of dealing with such complex variations in iris imaging including illumination, aging, deformation, and device variations. Thereby, an adaptive Gabor filter selection strategy and deep learning architecture are presented. We first employ particle swarm optimization approach and its binary version to define a set of data-driven Gabor kernels for fitting the most informative filtering bands, and then capture complex pattern from the optimal Gabor filtered coefficients by a trained deep belief network. A succession of comparative experiments validate that our optimal Gabor filters may produce more distinctive Gabor coefficients and our iris deep representations be more robust and stable than traditional iris Gabor codes. Furthermore, the depth and scales of the deep learning architecture are also discussed.

Artifact removal from EEG signals using adaptive filters in cascade

NASA Astrophysics Data System (ADS)

Garcés Correa, A.; Laciar, E.; Patiño, H. D.; Valentinuzzi, M. E.

2007-11-01

Artifacts in EEG (electroencephalogram) records are caused by various factors, like line interference, EOG (electro-oculogram) and ECG (electrocardiogram). These noise sources increase the difficulty in analyzing the EEG and to obtaining clinical information. For this reason, it is necessary to design specific filters to decrease such artifacts in EEG records. In this paper, a cascade of three adaptive filters based on a least mean squares (LMS) algorithm is proposed. The first one eliminates line interference, the second adaptive filter removes the ECG artifacts and the last one cancels EOG spikes. Each stage uses a finite impulse response (FIR) filter, which adjusts its coefficients to produce an output similar to the artifacts present in the EEG. The proposed cascade adaptive filter was tested in five real EEG records acquired in polysomnographic studies. In all cases, line-frequency, ECG and EOG artifacts were attenuated. It is concluded that the proposed filter reduces the common artifacts present in EEG signals without removing significant information embedded in these records.

Explicit Filtering Based Low-Dose Differential Phase Reconstruction Algorithm with the Grating Interferometry.

PubMed

Jiang, Xiaolei; Zhang, Li; Zhang, Ran; Yin, Hongxia; Wang, Zhenchang

2015-01-01

X-ray grating interferometry offers a novel framework for the study of weakly absorbing samples. Three kinds of information, that is, the attenuation, differential phase contrast (DPC), and dark-field images, can be obtained after a single scanning, providing additional and complementary information to the conventional attenuation image. Phase shifts of X-rays are measured by the DPC method; hence, DPC-CT reconstructs refraction indexes rather than attenuation coefficients. In this work, we propose an explicit filtering based low-dose differential phase reconstruction algorithm, which enables reconstruction from reduced scanning without artifacts. The algorithm adopts a differential algebraic reconstruction technique (DART) with the explicit filtering based sparse regularization rather than the commonly used total variation (TV) method. Both the numerical simulation and the biological sample experiment demonstrate the feasibility of the proposed algorithm.

Explicit Filtering Based Low-Dose Differential Phase Reconstruction Algorithm with the Grating Interferometry

PubMed Central

Zhang, Li; Zhang, Ran; Yin, Hongxia; Wang, Zhenchang

2015-01-01

X-ray grating interferometry offers a novel framework for the study of weakly absorbing samples. Three kinds of information, that is, the attenuation, differential phase contrast (DPC), and dark-field images, can be obtained after a single scanning, providing additional and complementary information to the conventional attenuation image. Phase shifts of X-rays are measured by the DPC method; hence, DPC-CT reconstructs refraction indexes rather than attenuation coefficients. In this work, we propose an explicit filtering based low-dose differential phase reconstruction algorithm, which enables reconstruction from reduced scanning without artifacts. The algorithm adopts a differential algebraic reconstruction technique (DART) with the explicit filtering based sparse regularization rather than the commonly used total variation (TV) method. Both the numerical simulation and the biological sample experiment demonstrate the feasibility of the proposed algorithm. PMID:26089971

Information-Based Analysis of Data Assimilation (Invited)

NASA Astrophysics Data System (ADS)

Nearing, G. S.; Gupta, H. V.; Crow, W. T.; Gong, W.

2013-12-01

Data assimilation is defined as the Bayesian conditioning of uncertain model simulations on observations for the purpose of reducing uncertainty about model states. Practical data assimilation methods make the application of Bayes' law tractable either by employing assumptions about the prior, posterior and likelihood distributions (e.g., the Kalman family of filters) or by using resampling methods (e.g., bootstrap filter). We propose to quantify the efficiency of these approximations in an OSSE setting using information theory and, in an OSSE or real-world validation setting, to measure the amount - and more importantly, the quality - of information extracted from observations during data assimilation. To analyze DA assumptions, uncertainty is quantified as the Shannon-type entropy of a discretized probability distribution. The maximum amount of information that can be extracted from observations about model states is the mutual information between states and observations, which is equal to the reduction in entropy in our estimate of the state due to Bayesian filtering. The difference between this potential and the actual reduction in entropy due to Kalman (or other type of) filtering measures the inefficiency of the filter assumptions. Residual uncertainty in DA posterior state estimates can be attributed to three sources: (i) non-injectivity of the observation operator, (ii) noise in the observations, and (iii) filter approximations. The contribution of each of these sources is measurable in an OSSE setting. The amount of information extracted from observations by data assimilation (or system identification, including parameter estimation) can also be measured by Shannon's theory. Since practical filters are approximations of Bayes' law, it is important to know whether the information that is extracted form observations by a filter is reliable. We define information as either good or bad, and propose to measure these two types of information using partial Kullback-Leibler divergences. Defined this way, good and bad information sum to total information. This segregation of information into good and bad components requires a validation target distribution; in a DA OSSE setting, this can be the true Bayesian posterior, but in a real-world setting the validation target might be determined by a set of in situ observations.

75 FR 6389 - Notice of a Regional Project Waiver of Section 1605 (Buy American) of the American Recovery and...

Federal Register 2010, 2011, 2012, 2013, 2014

2010-02-09

... existing belt filter press for sludge generated at the plant. Based upon information submitted by the City... dewatering unit to replace the existing belt filter press. The City is requesting a waiver from the Buy... filter press, (2) centrifuge system; (3) screw press and (4) rotary press. Of the four technologies, it...

Frequency-Domain Characterization of Optic Flow and Vision-Based Ocellar Sensing for Rotational Motion

DTIC Science & Technology

2017-04-01

complementary fusion: Fourth-order Butterworth filter was used to high -pass ocelli and low-pass optic flow. The normalized cutoff frequency had to be kept...information introduced by luminance change. The high - frequency cutoff was added to reject the flickering noise for indoor usage. The filtered signals from the...function of the low- pass filter is to attenuate high - frequency noise. The final band-pass filter transfer function is in Eq. 2. (()

Least squares restoration of multi-channel images

NASA Technical Reports Server (NTRS)

Chin, Roland T.; Galatsanos, Nikolas P.

1989-01-01

In this paper, a least squares filter for the restoration of multichannel imagery is presented. The restoration filter is based on a linear, space-invariant imaging model and makes use of an iterative matrix inversion algorithm. The restoration utilizes both within-channel (spatial) and cross-channel information as constraints. Experiments using color images (three-channel imagery with red, green, and blue components) were performed to evaluate the filter's performance and to compare it with other monochrome and multichannel filters.

Online particle detection with Neural Networks based on topological calorimetry information

NASA Astrophysics Data System (ADS)

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

2012-06-01

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

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.

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.

Generic Information Can Retrieve Known Biological Associations: Implications for Biomedical Knowledge Discovery

PubMed Central

van Haagen, Herman H. H. B. M.; 't Hoen, Peter A. C.; Mons, Barend; Schultes, Erik A.

2013-01-01

Motivation Weighted semantic networks built from text-mined literature can be used to retrieve known protein-protein or gene-disease associations, and have been shown to anticipate associations years before they are explicitly stated in the literature. Our text-mining system recognizes over 640,000 biomedical concepts: some are specific (i.e., names of genes or proteins) others generic (e.g., ‘Homo sapiens’). Generic concepts may play important roles in automated information retrieval, extraction, and inference but may also result in concept overload and confound retrieval and reasoning with low-relevance or even spurious links. Here, we attempted to optimize the retrieval performance for protein-protein interactions (PPI) by filtering generic concepts (node filtering) or links to generic concepts (edge filtering) from a weighted semantic network. First, we defined metrics based on network properties that quantify the specificity of concepts. Then using these metrics, we systematically filtered generic information from the network while monitoring retrieval performance of known protein-protein interactions. We also systematically filtered specific information from the network (inverse filtering), and assessed the retrieval performance of networks composed of generic information alone. Results Filtering generic or specific information induced a two-phase response in retrieval performance: initially the effects of filtering were minimal but beyond a critical threshold network performance suddenly drops. Contrary to expectations, networks composed exclusively of generic information demonstrated retrieval performance comparable to unfiltered networks that also contain specific concepts. Furthermore, an analysis using individual generic concepts demonstrated that they can effectively support the retrieval of known protein-protein interactions. For instance the concept “binding” is indicative for PPI retrieval and the concept “mutation abnormality” is indicative for gene-disease associations. Conclusion Generic concepts are important for information retrieval and cannot be removed from semantic networks without negative impact on retrieval performance. PMID:24260124

Resolution improvement in positron emission tomography using anatomical Magnetic Resonance Imaging.

PubMed

Chu, Yong; Su, Min-Ying; Mandelkern, Mark; Nalcioglu, Orhan

2006-08-01

An ideal imaging system should provide information with high-sensitivity, high spatial, and temporal resolution. Unfortunately, it is not possible to satisfy all of these desired features in a single modality. In this paper, we discuss methods to improve the spatial resolution in positron emission imaging (PET) using a priori information from Magnetic Resonance Imaging (MRI). Our approach uses an image restoration algorithm based on the maximization of mutual information (MMI), which has found significant success for optimizing multimodal image registration. The MMI criterion is used to estimate the parameters in the Sharpness-Constrained Wiener filter. The generated filter is then applied to restore PET images of a realistic digital brain phantom. The resulting restored images show improved resolution and better signal-to-noise ratio compared to the interpolated PET images. We conclude that a Sharpness-Constrained Wiener filter having parameters optimized from a MMI criterion may be useful for restoring spatial resolution in PET based on a priori information from correlated MRI.

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.

An improved conscan algorithm based on a Kalman filter

NASA Technical Reports Server (NTRS)

Eldred, D. B.

1994-01-01

Conscan is commonly used by DSN antennas to allow adaptive tracking of a target whose position is not precisely known. This article describes an algorithm that is based on a Kalman filter and is proposed to replace the existing fast Fourier transform based (FFT-based) algorithm for conscan. Advantages of this algorithm include better pointing accuracy, continuous update information, and accommodation of missing data. Additionally, a strategy for adaptive selection of the conscan radius is proposed. The performance of the algorithm is illustrated through computer simulations and compared to the FFT algorithm. The results show that the Kalman filter algorithm is consistently superior.

Event-triggered distributed filtering over sensor networks with deception attacks and partial measurements

NASA Astrophysics Data System (ADS)

Bu, Xianye; Dong, Hongli; Han, Fei; Li, Gongfa

2018-07-01

This paper is concerned with the distributed filtering problem for a class of time-varying systems subject to deception attacks and event-triggering protocols. Due to the bandwidth limitation, an event-triggered communication strategy is adopted to alleviate the data transmission pressure in the algorithm implementation process. The partial nodes-based filtering problem is considered, where only a partial of nodes can measure the information of the plant. Meanwhile, the measurement information possibly suffers the deception attacks in the transmission process. Sufficient conditions can be established such that the error dynamics satisfies the prescribed average ? performance constraints. The parameters of designed filters can be calculated by solving a series of recursive linear matrix inequalities. A simulation example is presented to demonstrate the effectiveness of the proposed filtering method in this paper.

Computational multispectral video imaging [Invited].

PubMed

Wang, Peng; Menon, Rajesh

2018-01-01

Multispectral imagers reveal information unperceivable to humans and conventional cameras. Here, we demonstrate a compact single-shot multispectral video-imaging camera by placing a micro-structured diffractive filter in close proximity to the image sensor. The diffractive filter converts spectral information to a spatial code on the sensor pixels. Following a calibration step, this code can be inverted via regularization-based linear algebra to compute the multispectral image. We experimentally demonstrated spectral resolution of 9.6 nm within the visible band (430-718 nm). We further show that the spatial resolution is enhanced by over 30% compared with the case without the diffractive filter. We also demonstrate Vis-IR imaging with the same sensor. Because no absorptive color filters are utilized, sensitivity is preserved as well. Finally, the diffractive filters can be easily manufactured using optical lithography and replication techniques.

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.

Despeckling Polsar Images Based on Relative Total Variation Model

NASA Astrophysics Data System (ADS)

Jiang, C.; He, X. F.; Yang, L. J.; Jiang, J.; Wang, D. Y.; Yuan, Y.

2018-04-01

Relatively total variation (RTV) algorithm, which can effectively decompose structure information and texture in image, is employed in extracting main structures of the image. However, applying the RTV directly to polarimetric SAR (PolSAR) image filtering will not preserve polarimetric information. A new RTV approach based on the complex Wishart distribution is proposed considering the polarimetric properties of PolSAR. The proposed polarization RTV (PolRTV) algorithm can be used for PolSAR image filtering. The L-band Airborne SAR (AIRSAR) San Francisco data is used to demonstrate the effectiveness of the proposed algorithm in speckle suppression, structural information preservation, and polarimetric property preservation.

A Map/INS/Wi-Fi Integrated System for Indoor Location-Based Service Applications

PubMed Central

Yu, Chunyang; Lan, Haiyu; Gu, Fuqiang; Yu, Fei; El-Sheimy, Naser

2017-01-01

In this research, a new Map/INS/Wi-Fi integrated system for indoor location-based service (LBS) applications based on a cascaded Particle/Kalman filter framework structure is proposed. Two-dimension indoor map information, together with measurements from an inertial measurement unit (IMU) and Received Signal Strength Indicator (RSSI) value, are integrated for estimating positioning information. The main challenge of this research is how to make effective use of various measurements that complement each other in order to obtain an accurate, continuous, and low-cost position solution without increasing the computational burden of the system. Therefore, to eliminate the cumulative drift caused by low-cost IMU sensor errors, the ubiquitous Wi-Fi signal and non-holonomic constraints are rationally used to correct the IMU-derived navigation solution through the extended Kalman Filter (EKF). Moreover, the map-aiding method and map-matching method are innovatively combined to constrain the primary Wi-Fi/IMU-derived position through an Auxiliary Value Particle Filter (AVPF). Different sources of information are incorporated through a cascaded structure EKF/AVPF filter algorithm. Indoor tests show that the proposed method can effectively reduce the accumulation of positioning errors of a stand-alone Inertial Navigation System (INS), and provide a stable, continuous and reliable indoor location service. PMID:28574471

A Map/INS/Wi-Fi Integrated System for Indoor Location-Based Service Applications.

PubMed

Yu, Chunyang; Lan, Haiyu; Gu, Fuqiang; Yu, Fei; El-Sheimy, Naser

2017-06-02

In this research, a new Map/INS/Wi-Fi integrated system for indoor location-based service (LBS) applications based on a cascaded Particle/Kalman filter framework structure is proposed. Two-dimension indoor map information, together with measurements from an inertial measurement unit (IMU) and Received Signal Strength Indicator (RSSI) value, are integrated for estimating positioning information. The main challenge of this research is how to make effective use of various measurements that complement each other in order to obtain an accurate, continuous, and low-cost position solution without increasing the computational burden of the system. Therefore, to eliminate the cumulative drift caused by low-cost IMU sensor errors, the ubiquitous Wi-Fi signal and non-holonomic constraints are rationally used to correct the IMU-derived navigation solution through the extended Kalman Filter (EKF). Moreover, the map-aiding method and map-matching method are innovatively combined to constrain the primary Wi-Fi/IMU-derived position through an Auxiliary Value Particle Filter (AVPF). Different sources of information are incorporated through a cascaded structure EKF/AVPF filter algorithm. Indoor tests show that the proposed method can effectively reduce the accumulation of positioning errors of a stand-alone Inertial Navigation System (INS), and provide a stable, continuous and reliable indoor location service.

An innovative information fusion method with adaptive Kalman filter for integrated INS/GPS navigation of autonomous vehicles

NASA Astrophysics Data System (ADS)

Liu, Yahui; Fan, Xiaoqian; Lv, Chen; Wu, Jian; Li, Liang; Ding, Dawei

2018-02-01

Information fusion method of INS/GPS navigation system based on filtering technology is a research focus at present. In order to improve the precision of navigation information, a navigation technology based on Adaptive Kalman Filter with attenuation factor is proposed to restrain noise in this paper. The algorithm continuously updates the measurement noise variance and processes noise variance of the system by collecting the estimated and measured values, and this method can suppress white noise. Because a measured value closer to the current time would more accurately reflect the characteristics of the noise, an attenuation factor is introduced to increase the weight of the current value, in order to deal with the noise variance caused by environment disturbance. To validate the effectiveness of the proposed algorithm, a series of road tests are carried out in urban environment. The GPS and IMU data of the experiments were collected and processed by dSPACE and MATLAB/Simulink. Based on the test results, the accuracy of the proposed algorithm is 20% higher than that of a traditional Adaptive Kalman Filter. It also shows that the precision of the integrated navigation can be improved due to the reduction of the influence of environment noise.

Model-based approach to partial tracking for musical transcription

NASA Astrophysics Data System (ADS)

Sterian, Andrew; Wakefield, Gregory H.

1998-10-01

We present a new method for musical partial tracking in the context of musical transcription using a time-frequency Kalman filter structure. The filter is based upon a model for the evolution of a partial behavior across a wide range of pitch from four brass instruments. Statistics are computed independently for the partial attributes of frequency and log-power first differences. We present observed power spectral density shapes, total powers, and histograms, as well as least-squares approximations to these. We demonstrate that a Kalman filter tracker using this partial model is capable of tracking partials in music. We discuss how the filter structure naturally provides quality-of-fit information about the data for use in further processing and how this information can be used to perform partial track initiation and termination within a common framework. We propose that a model-based approach to partial tracking is preferable to existing approaches which generally use heuristic rules or birth/death notions over a small time neighborhood. The advantages include better performance in the presence of cluttered data and simplified tracking over missed observations.

Noise Reduction in Breath Sound Files Using Wavelet Transform Based Filter

NASA Astrophysics Data System (ADS)

Syahputra, M. F.; Situmeang, S. I. G.; Rahmat, R. F.; Budiarto, R.

2017-04-01

The development of science and technology in the field of healthcare increasingly provides convenience in diagnosing respiratory system problem. Recording the breath sounds is one example of these developments. Breath sounds are recorded using a digital stethoscope, and then stored in a file with sound format. This breath sounds will be analyzed by health practitioners to diagnose the symptoms of disease or illness. However, the breath sounds is not free from interference signals. Therefore, noise filter or signal interference reduction system is required so that breath sounds component which contains information signal can be clarified. In this study, we designed a filter called a wavelet transform based filter. The filter that is designed in this study is using Daubechies wavelet with four wavelet transform coefficients. Based on the testing of the ten types of breath sounds data, the data is obtained in the largest SNRdB bronchial for 74.3685 decibels.

Multiscale morphological filtering for analysis of noisy and complex images

NASA Astrophysics Data System (ADS)

Kher, A.; Mitra, S.

Images acquired with passive sensing techniques suffer from illumination variations and poor local contrasts that create major difficulties in interpretation and identification tasks. On the other hand, images acquired with active sensing techniques based on monochromatic illumination are degraded with speckle noise. Mathematical morphology offers elegant techniques to handle a wide range of image degradation problems. Unlike linear filters, morphological filters do not blur the edges and hence maintain higher image resolution. Their rich mathematical framework facilitates the design and analysis of these filters as well as their hardware implementation. Morphological filters are easier to implement and are more cost effective and efficient than several conventional linear filters. Morphological filters to remove speckle noise while maintaining high resolution and preserving thin image regions that are particularly vulnerable to speckle noise were developed and applied to SAR imagery. These filters used combination of linear (one-dimensional) structuring elements in different (typically four) orientations. Although this approach preserves more details than the simple morphological filters using two-dimensional structuring elements, the limited orientations of one-dimensional elements approximate the fine details of the region boundaries. A more robust filter designed recently overcomes the limitation of the fixed orientations. This filter uses a combination of concave and convex structuring elements. Morphological operators are also useful in extracting features from visible and infrared imagery. A multiresolution image pyramid obtained with successive filtering and a subsampling process aids in the removal of the illumination variations and enhances local contrasts. A morphology-based interpolation scheme was also introduced to reduce intensity discontinuities created in any morphological filtering task. The generality of morphological filtering techniques in extracting information from a wide variety of images obtained with active and passive sensing techniques is discussed. Such techniques are particularly useful in obtaining more information from fusion of complex images by different sensors such as SAR, visible, and infrared.

Multiscale Morphological Filtering for Analysis of Noisy and Complex Images

NASA Technical Reports Server (NTRS)

Kher, A.; Mitra, S.

1993-01-01

Images acquired with passive sensing techniques suffer from illumination variations and poor local contrasts that create major difficulties in interpretation and identification tasks. On the other hand, images acquired with active sensing techniques based on monochromatic illumination are degraded with speckle noise. Mathematical morphology offers elegant techniques to handle a wide range of image degradation problems. Unlike linear filters, morphological filters do not blur the edges and hence maintain higher image resolution. Their rich mathematical framework facilitates the design and analysis of these filters as well as their hardware implementation. Morphological filters are easier to implement and are more cost effective and efficient than several conventional linear filters. Morphological filters to remove speckle noise while maintaining high resolution and preserving thin image regions that are particularly vulnerable to speckle noise were developed and applied to SAR imagery. These filters used combination of linear (one-dimensional) structuring elements in different (typically four) orientations. Although this approach preserves more details than the simple morphological filters using two-dimensional structuring elements, the limited orientations of one-dimensional elements approximate the fine details of the region boundaries. A more robust filter designed recently overcomes the limitation of the fixed orientations. This filter uses a combination of concave and convex structuring elements. Morphological operators are also useful in extracting features from visible and infrared imagery. A multiresolution image pyramid obtained with successive filtering and a subsampling process aids in the removal of the illumination variations and enhances local contrasts. A morphology-based interpolation scheme was also introduced to reduce intensity discontinuities created in any morphological filtering task. The generality of morphological filtering techniques in extracting information from a wide variety of images obtained with active and passive sensing techniques is discussed. Such techniques are particularly useful in obtaining more information from fusion of complex images by different sensors such as SAR, visible, and infrared.

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.

UltiMatch-NL: A Web Service Matchmaker Based on Multiple Semantic Filters

PubMed Central

Mohebbi, Keyvan; Ibrahim, Suhaimi; Zamani, Mazdak; Khezrian, Mojtaba

2014-01-01

In this paper, a Semantic Web service matchmaker called UltiMatch-NL is presented. UltiMatch-NL applies two filters namely Signature-based and Description-based on different abstraction levels of a service profile to achieve more accurate results. More specifically, the proposed filters rely on semantic knowledge to extract the similarity between a given pair of service descriptions. Thus it is a further step towards fully automated Web service discovery via making this process more semantic-aware. In addition, a new technique is proposed to weight and combine the results of different filters of UltiMatch-NL, automatically. Moreover, an innovative approach is introduced to predict the relevance of requests and Web services and eliminate the need for setting a threshold value of similarity. In order to evaluate UltiMatch-NL, the repository of OWLS-TC is used. The performance evaluation based on standard measures from the information retrieval field shows that semantic matching of OWL-S services can be significantly improved by incorporating designed matching filters. PMID:25157872

UltiMatch-NL: a Web service matchmaker based on multiple semantic filters.

PubMed

Mohebbi, Keyvan; Ibrahim, Suhaimi; Zamani, Mazdak; Khezrian, Mojtaba

2014-01-01

In this paper, a Semantic Web service matchmaker called UltiMatch-NL is presented. UltiMatch-NL applies two filters namely Signature-based and Description-based on different abstraction levels of a service profile to achieve more accurate results. More specifically, the proposed filters rely on semantic knowledge to extract the similarity between a given pair of service descriptions. Thus it is a further step towards fully automated Web service discovery via making this process more semantic-aware. In addition, a new technique is proposed to weight and combine the results of different filters of UltiMatch-NL, automatically. Moreover, an innovative approach is introduced to predict the relevance of requests and Web services and eliminate the need for setting a threshold value of similarity. In order to evaluate UltiMatch-NL, the repository of OWLS-TC is used. The performance evaluation based on standard measures from the information retrieval field shows that semantic matching of OWL-S services can be significantly improved by incorporating designed matching filters.

System and Method for Providing a Real Time Audible Message to a Pilot

NASA Technical Reports Server (NTRS)

Johnson, Walter W. (Inventor); Lachter, Joel B. (Inventor); Koteskey, Robert W. (Inventor); Battiste, Vernol (Inventor)

2016-01-01

A system and method for providing information to a crew of the aircraft while in-flight. The system includes a module having: a receiver for receiving a message while in-flight; a filter having a set of screening parameters and operative to filter the message based on the set of screening parameters; and a converter for converting the message into an audible message. The message includes a pilot report having at least one of weather information, separation information, congestion information, flight deviation information and destination information. The message is sent to the aircraft by another aircraft or an air traffic controller.

Computing local edge probability in natural scenes from a population of oriented simple cells

PubMed Central

Ramachandra, Chaithanya A.; Mel, Bartlett W.

2013-01-01

A key computation in visual cortex is the extraction of object contours, where the first stage of processing is commonly attributed to V1 simple cells. The standard model of a simple cell—an oriented linear filter followed by a divisive normalization—fits a wide variety of physiological data, but is a poor performing local edge detector when applied to natural images. The brain's ability to finely discriminate edges from nonedges therefore likely depends on information encoded by local simple cell populations. To gain insight into the corresponding decoding problem, we used Bayes's rule to calculate edge probability at a given location/orientation in an image based on a surrounding filter population. Beginning with a set of ∼ 100 filters, we culled out a subset that were maximally informative about edges, and minimally correlated to allow factorization of the joint on- and off-edge likelihood functions. Key features of our approach include a new, efficient method for ground-truth edge labeling, an emphasis on achieving filter independence, including a focus on filters in the region orthogonal rather than tangential to an edge, and the use of a customized parametric model to represent the individual filter likelihood functions. The resulting population-based edge detector has zero parameters, calculates edge probability based on a sum of surrounding filter influences, is much more sharply tuned than the underlying linear filters, and effectively captures fine-scale edge structure in natural scenes. Our findings predict nonmonotonic interactions between cells in visual cortex, wherein a cell may for certain stimuli excite and for other stimuli inhibit the same neighboring cell, depending on the two cells' relative offsets in position and orientation, and their relative activation levels. PMID:24381295

Information filtering based on corrected redundancy-eliminating mass diffusion.

PubMed

Zhu, Xuzhen; Yang, Yujie; Chen, Guilin; Medo, Matus; Tian, Hui; Cai, Shi-Min

2017-01-01

Methods used in information filtering and recommendation often rely on quantifying the similarity between objects or users. The used similarity metrics often suffer from similarity redundancies arising from correlations between objects' attributes. Based on an unweighted undirected object-user bipartite network, we propose a Corrected Redundancy-Eliminating similarity index (CRE) which is based on a spreading process on the network. Extensive experiments on three benchmark data sets-Movilens, Netflix and Amazon-show that when used in recommendation, the CRE yields significant improvements in terms of recommendation accuracy and diversity. A detailed analysis is presented to unveil the origins of the observed differences between the CRE and mainstream similarity indices.

Off-axis digital holographic camera for quantitative phase microscopy.

PubMed

Monemhaghdoust, Zahra; Montfort, Frédéric; Emery, Yves; Depeursinge, Christian; Moser, Christophe

2014-06-01

We propose and experimentally demonstrate a digital holographic camera which can be attached to the camera port of a conventional microscope for obtaining digital holograms in a self-reference configuration, under short coherence illumination and in a single shot. A thick holographic grating filters the beam containing the sample information in two dimensions through diffraction. The filtered beam creates the reference arm of the interferometer. The spatial filtering method, based on the high angular selectivity of the thick grating, reduces the alignment sensitivity to angular displacements compared with pinhole based Fourier filtering. The addition of a thin holographic grating alters the coherence plane tilt introduced by the thick grating so as to create high-visibility interference over the entire field of view. The acquired full-field off-axis holograms are processed to retrieve the amplitude and phase information of the sample. The system produces phase images of cheek cells qualitatively similar to phase images extracted with a standard commercial DHM.

A Data Quality Filter for PMU Measurements: Description, Experience, and Examples

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

Follum, James D.; Amidan, Brett G.

Networks of phasor measurement units (PMUs) continue to grow, and along with them, the amount of data available for analysis. With so much data, it is impractical to identify and remove poor quality data manually. The data quality filter described in this paper was developed for use with the Data Integrity and Situation Awareness Tool (DISAT), which analyzes PMU data to identify anomalous system behavior. The filter operates based only on the information included in the data files, without supervisory control and data acquisition (SCADA) data, state estimator values, or system topology information. Measurements are compared to preselected thresholds tomore » determine if they are reliable. Along with the filter's description, examples of data quality issues from application of the filter to nine months of archived PMU data are provided. The paper is intended to aid the reader in recognizing and properly addressing data quality issues in PMU data.« less

Comparison of complementary and Kalman filter based data fusion for attitude heading reference system

NASA Astrophysics Data System (ADS)

Islam, Tariqul; Islam, Md. Saiful; Shajid-Ul-Mahmud, Md.; Hossam-E-Haider, Md

2017-12-01

An Attitude Heading Reference System (AHRS) provides 3D orientation of an aircraft (roll, pitch, and yaw) with instantaneous position and also heading information. For implementation of a low cost AHRS system Micro-electrical-Mechanical system (MEMS) based sensors are used such as accelerometer, gyroscope, and magnetometer. Accelerometers suffer from errors caused by external accelerations that sums to gravity and make accelerometers based rotation inaccurate. Gyroscopes can remove such errors but create drifting problems. So for getting the precise data additionally two very common and well known filters Complementary and Kalman are introduced to the system. In this paper a comparison of system performance using these two filters is shown separately so that one would be able to select filter with better performance for his/her system.

Weighted hybrid technique for recommender system

NASA Astrophysics Data System (ADS)

Suriati, S.; Dwiastuti, Meisyarah; Tulus, T.

2017-12-01

Recommender system becomes very popular and has important role in an information system or webpages nowadays. A recommender system tries to make a prediction of which item a user may like based on his activity on the system. There are some familiar techniques to build a recommender system, such as content-based filtering and collaborative filtering. Content-based filtering does not involve opinions from human to make the prediction, while collaborative filtering does, so collaborative filtering can predict more accurately. However, collaborative filtering cannot give prediction to items which have never been rated by any user. In order to cover the drawbacks of each approach with the advantages of other approach, both approaches can be combined with an approach known as hybrid technique. Hybrid technique used in this work is weighted technique in which the prediction score is combination linear of scores gained by techniques that are combined.The purpose of this work is to show how an approach of weighted hybrid technique combining content-based filtering and item-based collaborative filtering can work in a movie recommender system and to show the performance comparison when both approachare combined and when each approach works alone. There are three experiments done in this work, combining both techniques with different parameters. The result shows that the weighted hybrid technique that is done in this work does not really boost the performance up, but it helps to give prediction score for unrated movies that are impossible to be recommended by only using collaborative filtering.

Identifying city PV roof resource based on Gabor filter

NASA Astrophysics Data System (ADS)

Ruhang, Xu; Zhilin, Liu; Yong, Huang; Xiaoyu, Zhang

2017-06-01

To identify a city’s PV roof resources, the area and ownership distribution of residential buildings in an urban district should be assessed. To achieve this assessment, remote sensing data analysing is a promising approach. Urban building roof area estimation is a major topic for remote sensing image information extraction. There are normally three ways to solve this problem. The first way is pixel-based analysis, which is based on mathematical morphology or statistical methods; the second way is object-based analysis, which is able to combine semantic information and expert knowledge; the third way is signal-processing view method. This paper presented a Gabor filter based method. This result shows that the method is fast and with proper accuracy.

DOA-informed source extraction in the presence of competing talkers and background noise

NASA Astrophysics Data System (ADS)

Taseska, Maja; Habets, Emanuël A. P.

2017-12-01

A desired speech signal in hands-free communication systems is often degraded by noise and interfering speech. Even though the number and locations of the interferers are often unknown in practice, it is justified to assume in certain applications that the direction-of-arrival (DOA) of the desired source is approximately known. Using the known DOA, fixed spatial filters such as the delay-and-sum beamformer can be steered to extract the desired source. However, it is well-known that fixed data-independent spatial filters do not provide sufficient reduction of directional interferers. Instead, the DOA information can be used to estimate the statistics of the desired and the undesired signals and to compute optimal data-dependent spatial filters. One way the DOA is exploited for optimal spatial filtering in the literature, is by designing DOA-based narrowband detectors to determine whether a desired or an undesired signal is dominant at each time-frequency (TF) bin. Subsequently, the statistics of the desired and the undesired signals can be estimated during the TF bins where the respective signal is dominant. In a similar manner, a Gaussian signal model-based detector which does not incorporate DOA information has been used in scenarios where the undesired signal consists of stationary background noise. However, when the undesired signal is non-stationary, resulting for example from interfering speakers, such a Gaussian signal model-based detector is unable to robustly distinguish desired from undesired speech. To this end, we propose a DOA model-based detector to determine the dominant source at each TF bin and estimate the desired and undesired signal statistics. We demonstrate that data-dependent spatial filters that use the statistics estimated by the proposed framework achieve very good undesired signal reduction, even when using only three microphones.

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.

Gravity effects on information filtering and network evolving.

PubMed

Liu, Jin-Hu; Zhang, Zi-Ke; Chen, Lingjiao; Liu, Chuang; Yang, Chengcheng; Wang, Xueqi

2014-01-01

In this paper, based on the gravity principle of classical physics, we propose a tunable gravity-based model, which considers tag usage pattern to weigh both the mass and distance of network nodes. We then apply this model in solving the problems of information filtering and network evolving. Experimental results on two real-world data sets, Del.icio.us and MovieLens, show that it can not only enhance the algorithmic performance, but can also better characterize the properties of real networks. This work may shed some light on the in-depth understanding of the effect of gravity model.

Gravity Effects on Information Filtering and Network Evolving

PubMed Central

Liu, Jin-Hu; Zhang, Zi-Ke; Chen, Lingjiao; Liu, Chuang; Yang, Chengcheng; Wang, Xueqi

2014-01-01

In this paper, based on the gravity principle of classical physics, we propose a tunable gravity-based model, which considers tag usage pattern to weigh both the mass and distance of network nodes. We then apply this model in solving the problems of information filtering and network evolving. Experimental results on two real-world data sets, Del.icio.us and MovieLens, show that it can not only enhance the algorithmic performance, but can also better characterize the properties of real networks. This work may shed some light on the in-depth understanding of the effect of gravity model. PMID:24622162

Neural networks for data compression and invariant image recognition

NASA Technical Reports Server (NTRS)

Gardner, Sheldon

1989-01-01

An approach to invariant image recognition (I2R), based upon a model of biological vision in the mammalian visual system (MVS), is described. The complete I2R model incorporates several biologically inspired features: exponential mapping of retinal images, Gabor spatial filtering, and a neural network associative memory. In the I2R model, exponentially mapped retinal images are filtered by a hierarchical set of Gabor spatial filters (GSF) which provide compression of the information contained within a pixel-based image. A neural network associative memory (AM) is used to process the GSF coded images. We describe a 1-D shape function method for coding of scale and rotationally invariant shape information. This method reduces image shape information to a periodic waveform suitable for coding as an input vector to a neural network AM. The shape function method is suitable for near term applications on conventional computing architectures equipped with VLSI FFT chips to provide a rapid image search capability.

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

Automatic Classification Using Supervised Learning in a Medical Document Filtering Application.

ERIC Educational Resources Information Center

Mostafa, J.; Lam, W.

2000-01-01

Presents a multilevel model of the information filtering process that permits document classification. Evaluates a document classification approach based on a supervised learning algorithm, measures the accuracy of the algorithm in a neural network that was trained to classify medical documents on cell biology, and discusses filtering…

Identifying Optimal Measurement Subspace for the Ensemble Kalman Filter

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

Zhou, Ning; Huang, Zhenyu; Welch, Greg

2012-05-24

To reduce the computational load of the ensemble Kalman filter while maintaining its efficacy, an optimization algorithm based on the generalized eigenvalue decomposition method is proposed for identifying the most informative measurement subspace. When the number of measurements is large, the proposed algorithm can be used to make an effective tradeoff between computational complexity and estimation accuracy. This algorithm also can be extended to other Kalman filters for measurement subspace selection.

An Indoor Slam Method Based on Kinect and Multi-Feature Extended Information Filter

NASA Astrophysics Data System (ADS)

Chang, M.; Kang, Z.

2017-09-01

Based on the frame of ORB-SLAM in this paper the transformation parameters between adjacent Kinect image frames are computed using ORB keypoints, from which priori information matrix and information vector are calculated. The motion update of multi-feature extended information filter is then realized. According to the point cloud data formed by depth image, ICP algorithm was used to extract the point features of the point cloud data in the scene and built an observation model while calculating a-posteriori information matrix and information vector, and weakening the influences caused by the error accumulation in the positioning process. Furthermore, this paper applied ORB-SLAM frame to realize autonomous positioning in real time in interior unknown environment. In the end, Lidar was used to get data in the scene in order to estimate positioning accuracy put forward in this paper.

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…

Change Detection via Selective Guided Contrasting Filters

NASA Astrophysics Data System (ADS)

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

2017-05-01

Change detection scheme based on guided contrasting was previously proposed. Guided contrasting filter takes two images (test and sample) as input and forms the output as filtered version of test image. Such filter preserves the similar details and smooths the non-similar details of test image with respect to sample image. Due to this the difference between test image and its filtered version (difference map) could be a basis for robust change detection. Guided contrasting is performed in two steps: at the first step some smoothing operator (SO) is applied for elimination of test image details; at the second step all matched details are restored with local contrast proportional to the value of some local similarity coefficient (LSC). The guided contrasting filter was proposed based on local average smoothing as SO and local linear correlation as LSC. In this paper we propose and implement new set of selective guided contrasting filters based on different combinations of various SO and thresholded LSC. Linear average and Gaussian smoothing, nonlinear median filtering, morphological opening and closing are considered as SO. Local linear correlation coefficient, morphological correlation coefficient (MCC), mutual information, mean square MCC and geometrical correlation coefficients are applied as LSC. Thresholding of LSC allows operating with non-normalized LSC and enhancing the selective properties of guided contrasting filters: details are either totally recovered or not recovered at all after the smoothing. These different guided contrasting filters are tested as a part of previously proposed change detection pipeline, which contains following stages: guided contrasting filtering on image pyramid, calculation of difference map, binarization, extraction of change proposals and testing change proposals using local MCC. Experiments on real and simulated image bases demonstrate the applicability of all proposed selective guided contrasting filters. All implemented filters provide the robustness relative to weak geometrical discrepancy of compared images. Selective guided contrasting based on morphological opening/closing and thresholded morphological correlation demonstrates the best change detection result.

Rule-based fuzzy vector median filters for 3D phase contrast MRI segmentation

NASA Astrophysics Data System (ADS)

Sundareswaran, Kartik S.; Frakes, David H.; Yoganathan, Ajit P.

2008-02-01

Recent technological advances have contributed to the advent of phase contrast magnetic resonance imaging (PCMRI) as standard practice in clinical environments. In particular, decreased scan times have made using the modality more feasible. PCMRI is now a common tool for flow quantification, and for more complex vector field analyses that target the early detection of problematic flow conditions. Segmentation is one component of this type of application that can impact the accuracy of the final product dramatically. Vascular segmentation, in general, is a long-standing problem that has received significant attention. Segmentation in the context of PCMRI data, however, has been explored less and can benefit from object-based image processing techniques that incorporate fluids specific information. Here we present a fuzzy rule-based adaptive vector median filtering (FAVMF) algorithm that in combination with active contour modeling facilitates high-quality PCMRI segmentation while mitigating the effects of noise. The FAVMF technique was tested on 111 synthetically generated PC MRI slices and on 15 patients with congenital heart disease. The results were compared to other multi-dimensional filters namely the adaptive vector median filter, the adaptive vector directional filter, and the scalar low pass filter commonly used in PC MRI applications. FAVMF significantly outperformed the standard filtering methods (p < 0.0001). Two conclusions can be drawn from these results: a) Filtering should be performed after vessel segmentation of PC MRI; b) Vector based filtering methods should be used instead of scalar techniques.

The Power Plant Operating Data Based on Real-time Digital Filtration Technology

NASA Astrophysics Data System (ADS)

Zhao, Ning; Chen, Ya-mi; Wang, Hui-jie

2018-03-01

Real-time monitoring of the data of the thermal power plant was the basis of accurate analyzing thermal economy and accurate reconstruction of the operating state. Due to noise interference was inevitable; we need real-time monitoring data filtering to get accurate information of the units and equipment operating data of the thermal power plant. Real-time filtering algorithm couldn’t be used to correct the current data with future data. Compared with traditional filtering algorithm, there were a lot of constraints. First-order lag filtering method and weighted recursive average filtering method could be used for real-time filtering. This paper analyzes the characteristics of the two filtering methods and applications for real-time processing of the positive spin simulation data, and the thermal power plant operating data. The analysis was revealed that the weighted recursive average filtering method applied to the simulation and real-time plant data filtering achieved very good results.

Infrared image background modeling based on improved Susan filtering

NASA Astrophysics Data System (ADS)

Yuehua, Xia

2018-02-01

When SUSAN filter is used to model the infrared image, the Gaussian filter lacks the ability of direction filtering. After filtering, the edge information of the image cannot be preserved well, so that there are a lot of edge singular points in the difference graph, increase the difficulties of target detection. To solve the above problems, the anisotropy algorithm is introduced in this paper, and the anisotropic Gauss filter is used instead of the Gauss filter in the SUSAN filter operator. Firstly, using anisotropic gradient operator to calculate a point of image's horizontal and vertical gradient, to determine the long axis direction of the filter; Secondly, use the local area of the point and the neighborhood smoothness to calculate the filter length and short axis variance; And then calculate the first-order norm of the difference between the local area of the point's gray-scale and mean, to determine the threshold of the SUSAN filter; Finally, the built SUSAN filter is used to convolution the image to obtain the background image, at the same time, the difference between the background image and the original image is obtained. The experimental results show that the background modeling effect of infrared image is evaluated by Mean Squared Error (MSE), Structural Similarity (SSIM) and local Signal-to-noise Ratio Gain (GSNR). Compared with the traditional filtering algorithm, the improved SUSAN filter has achieved better background modeling effect, which can effectively preserve the edge information in the image, and the dim small target is effectively enhanced in the difference graph, which greatly reduces the false alarm rate of the image.

Information filtering based on corrected redundancy-eliminating mass diffusion

PubMed Central

Zhu, Xuzhen; Yang, Yujie; Chen, Guilin; Medo, Matus; Tian, Hui

2017-01-01

Methods used in information filtering and recommendation often rely on quantifying the similarity between objects or users. The used similarity metrics often suffer from similarity redundancies arising from correlations between objects’ attributes. Based on an unweighted undirected object-user bipartite network, we propose a Corrected Redundancy-Eliminating similarity index (CRE) which is based on a spreading process on the network. Extensive experiments on three benchmark data sets—Movilens, Netflix and Amazon—show that when used in recommendation, the CRE yields significant improvements in terms of recommendation accuracy and diversity. A detailed analysis is presented to unveil the origins of the observed differences between the CRE and mainstream similarity indices. PMID:28749976

An improved discriminative filter bank selection approach for motor imagery EEG signal classification using mutual information.

PubMed

Kumar, Shiu; Sharma, Alok; Tsunoda, Tatsuhiko

2017-12-28

Common spatial pattern (CSP) has been an effective technique for feature extraction in electroencephalography (EEG) based brain computer interfaces (BCIs). However, motor imagery EEG signal feature extraction using CSP generally depends on the selection of the frequency bands to a great extent. In this study, we propose a mutual information based frequency band selection approach. The idea of the proposed method is to utilize the information from all the available channels for effectively selecting the most discriminative filter banks. CSP features are extracted from multiple overlapping sub-bands. An additional sub-band has been introduced that cover the wide frequency band (7-30 Hz) and two different types of features are extracted using CSP and common spatio-spectral pattern techniques, respectively. Mutual information is then computed from the extracted features of each of these bands and the top filter banks are selected for further processing. Linear discriminant analysis is applied to the features extracted from each of the filter banks. The scores are fused together, and classification is done using support vector machine. The proposed method is evaluated using BCI Competition III dataset IVa, BCI Competition IV dataset I and BCI Competition IV dataset IIb, and it outperformed all other competing methods achieving the lowest misclassification rate and the highest kappa coefficient on all three datasets. Introducing a wide sub-band and using mutual information for selecting the most discriminative sub-bands, the proposed method shows improvement in motor imagery EEG signal classification.

Improving the Response of Accelerometers for Automotive Applications by Using LMS Adaptive Filters: Part II

PubMed Central

Hernandez, Wilmar; de Vicente, Jesús; Sergiyenko, Oleg Y.; Fernández, Eduardo

2010-01-01

In this paper, the fast least-mean-squares (LMS) algorithm was used to both eliminate noise corrupting the important information coming from a piezoresisitive accelerometer for automotive applications, and improve the convergence rate of the filtering process based on the conventional LMS algorithm. The response of the accelerometer under test was corrupted by process and measurement noise, and the signal processing stage was carried out by using both conventional filtering, which was already shown in a previous paper, and optimal adaptive filtering. The adaptive filtering process relied on the LMS adaptive filtering family, which has shown to have very good convergence and robustness properties, and here a comparative analysis between the results of the application of the conventional LMS algorithm and the fast LMS algorithm to solve a real-life filtering problem was carried out. In short, in this paper the piezoresistive accelerometer was tested for a multi-frequency acceleration excitation. Due to the kind of test conducted in this paper, the use of conventional filtering was discarded and the choice of one adaptive filter over the other was based on the signal-to-noise ratio improvement and the convergence rate. PMID:22315579

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.

Maximising information recovery from rank-order codes

NASA Astrophysics Data System (ADS)

Sen, B.; Furber, S.

2007-04-01

The central nervous system encodes information in sequences of asynchronously generated voltage spikes, but the precise details of this encoding are not well understood. Thorpe proposed rank-order codes as an explanation of the observed speed of information processing in the human visual system. The work described in this paper is inspired by the performance of SpikeNET, a biologically inspired neural architecture using rank-order codes for information processing, and is based on the retinal model developed by VanRullen and Thorpe. This model mimics retinal information processing by passing an input image through a bank of Difference of Gaussian (DoG) filters and then encoding the resulting coefficients in rank-order. To test the effectiveness of this encoding in capturing the information content of an image, the rank-order representation is decoded to reconstruct an image that can be compared with the original. The reconstruction uses a look-up table to infer the filter coefficients from their rank in the encoded image. Since the DoG filters are approximately orthogonal functions, they are treated as their own inverses in the reconstruction process. We obtained a quantitative measure of the perceptually important information retained in the reconstructed image relative to the original using a slightly modified version of an objective metric proposed by Petrovic. It is observed that around 75% of the perceptually important information is retained in the reconstruction. In the present work we reconstruct the input using a pseudo-inverse of the DoG filter-bank with the aim of improving the reconstruction and thereby extracting more information from the rank-order encoded stimulus. We observe that there is an increase of 10 - 15% in the information retrieved from a reconstructed stimulus as a result of inverting the filter-bank.

EEG based zero-phase phase-locking value (PLV) and effects of spatial filtering during actual movement.

PubMed

Jian, Wenjuan; Chen, Minyou; McFarland, Dennis J

2017-04-01

Phase-locking value (PLV) is a well-known feature in sensorimotor rhythm (SMR) based BCI. Zero-phase PLV has not been explored because it is generally regarded as the result of volume conduction. Because spatial filters are often used to enhance the amplitude (square root of band power (BP)) feature and attenuate volume conduction, they are frequently applied as pre-processing methods when computing PLV. However, the effects of spatial filtering on PLV are ambiguous. Therefore, this article aims to explore whether zero-phase PLV is meaningful and how this is influenced by spatial filtering. Based on archival EEG data of left and right hand movement tasks for 32 subjects, we compared BP and PLV feature using data with and without pre-processing by a large Laplacian. Results showed that using ear-referenced data, zero-phase PLV provided unique information independent of BP for task prediction which was not explained by volume conduction and was significantly decreased when a large Laplacian was applied. In other words, the large Laplacian eliminated the useful information in zero-phase PLV for task prediction suggesting that it contains effects of both amplitude and phase. Therefore, zero-phase PLV may have functional significance beyond volume conduction. The interpretation of spatial filtering may be complicated by effects of phase. Copyright © 2017 Elsevier Inc. All rights reserved.

Bilateral filtering using the full noise covariance matrix applied to x-ray phase-contrast computed tomography.

PubMed

Allner, S; Koehler, T; Fehringer, A; Birnbacher, L; Willner, M; Pfeiffer, F; Noël, P B

2016-05-21

The purpose of this work is to develop an image-based de-noising algorithm that exploits complementary information and noise statistics from multi-modal images, as they emerge in x-ray tomography techniques, for instance grating-based phase-contrast CT and spectral CT. Among the noise reduction methods, image-based de-noising is one popular approach and the so-called bilateral filter is a well known algorithm for edge-preserving filtering. We developed a generalization of the bilateral filter for the case where the imaging system provides two or more perfectly aligned images. The proposed generalization is statistically motivated and takes the full second order noise statistics of these images into account. In particular, it includes a noise correlation between the images and spatial noise correlation within the same image. The novel generalized three-dimensional bilateral filter is applied to the attenuation and phase images created with filtered backprojection reconstructions from grating-based phase-contrast tomography. In comparison to established bilateral filters, we obtain improved noise reduction and at the same time a better preservation of edges in the images on the examples of a simulated soft-tissue phantom, a human cerebellum and a human artery sample. The applied full noise covariance is determined via cross-correlation of the image noise. The filter results yield an improved feature recovery based on enhanced noise suppression and edge preservation as shown here on the example of attenuation and phase images captured with grating-based phase-contrast computed tomography. This is supported by quantitative image analysis. Without being bound to phase-contrast imaging, this generalized filter is applicable to any kind of noise-afflicted image data with or without noise correlation. Therefore, it can be utilized in various imaging applications and fields.

Information Filtering Based on Users' Negative Opinions

NASA Astrophysics Data System (ADS)

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

2013-05-01

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

AgBufferBuilder: A geographic information system (GIS) tool for precision design and performance assessment of filter strips

Treesearch

M. G. Dosskey; S. Neelakantan; T. G. Mueller; T. Kellerman; M. J. Helmers; E. Rienzi

2015-01-01

Spatially nonuniform runoif reduces the water qua1iry perfortnance of constant- width filter strips. A geographic inlormation system (Gls)-based tool was developed and tested that ernploys terrain analysis to account lor spatially nonuniform runoffand produce more ellbctive filter strip designs.The computer program,AgBufTerBuilder, runs with ATcGIS versions 10.0 and 10...

Hypersonic entry vehicle state estimation using nonlinearity-based adaptive cubature Kalman filters

NASA Astrophysics Data System (ADS)

Sun, Tao; Xin, Ming

2017-05-01

Guidance, navigation, and control of a hypersonic vehicle landing on the Mars rely on precise state feedback information, which is obtained from state estimation. The high uncertainty and nonlinearity of the entry dynamics make the estimation a very challenging problem. In this paper, a new adaptive cubature Kalman filter is proposed for state trajectory estimation of a hypersonic entry vehicle. This new adaptive estimation strategy is based on the measure of nonlinearity of the stochastic system. According to the severity of nonlinearity along the trajectory, the high degree cubature rule or the conventional third degree cubature rule is adaptively used in the cubature Kalman filter. This strategy has the benefit of attaining higher estimation accuracy only when necessary without causing excessive computation load. The simulation results demonstrate that the proposed adaptive filter exhibits better performance than the conventional third-degree cubature Kalman filter while maintaining the same performance as the uniform high degree cubature Kalman filter but with lower computation complexity.

Multiscale infrared and visible image fusion using gradient domain guided image filtering

NASA Astrophysics Data System (ADS)

Zhu, Jin; Jin, Weiqi; Li, Li; Han, Zhenghao; Wang, Xia

2018-03-01

For better surveillance with infrared and visible imaging, a novel hybrid multiscale decomposition fusion method using gradient domain guided image filtering (HMSD-GDGF) is proposed in this study. In this method, hybrid multiscale decomposition with guided image filtering and gradient domain guided image filtering of source images are first applied before the weight maps of each scale are obtained using a saliency detection technology and filtering means with three different fusion rules at different scales. The three types of fusion rules are for small-scale detail level, large-scale detail level, and base level. Finally, the target becomes more salient and can be more easily detected in the fusion result, with the detail information of the scene being fully displayed. After analyzing the experimental comparisons with state-of-the-art fusion methods, the HMSD-GDGF method has obvious advantages in fidelity of salient information (including structural similarity, brightness, and contrast), preservation of edge features, and human visual perception. Therefore, visual effects can be improved by using the proposed HMSD-GDGF method.

Independent Component Analysis of Textures

NASA Technical Reports Server (NTRS)

Manduchi, Roberto; Portilla, Javier

2000-01-01

A common method for texture representation is to use the marginal probability densities over the outputs of a set of multi-orientation, multi-scale filters as a description of the texture. We propose a technique, based on Independent Components Analysis, for choosing the set of filters that yield the most informative marginals, meaning that the product over the marginals most closely approximates the joint probability density function of the filter outputs. The algorithm is implemented using a steerable filter space. Experiments involving both texture classification and synthesis show that compared to Principal Components Analysis, ICA provides superior performance for modeling of natural and synthetic textures.

Sinogram noise reduction for low-dose CT by statistics-based nonlinear filters

NASA Astrophysics Data System (ADS)

Wang, Jing; Lu, Hongbing; Li, Tianfang; Liang, Zhengrong

2005-04-01

Low-dose CT (computed tomography) sinogram data have been shown to be signal-dependent with an analytical relationship between the sample mean and sample variance. Spatially-invariant low-pass linear filters, such as the Butterworth and Hanning filters, could not adequately handle the data noise and statistics-based nonlinear filters may be an alternative choice, in addition to other choices of minimizing cost functions on the noisy data. Anisotropic diffusion filter and nonlinear Gaussian filters chain (NLGC) are two well-known classes of nonlinear filters based on local statistics for the purpose of edge-preserving noise reduction. These two filters can utilize the noise properties of the low-dose CT sinogram for adaptive noise reduction, but can not incorporate signal correlative information for an optimal regularized solution. Our previously-developed Karhunen-Loeve (KL) domain PWLS (penalized weighted least square) minimization considers the signal correlation via the KL strategy and seeks the PWLS cost function minimization for an optimal regularized solution for each KL component, i.e., adaptive to the KL components. This work compared the nonlinear filters with the KL-PWLS framework for low-dose CT application. Furthermore, we investigated the nonlinear filters for post KL-PWLS noise treatment in the sinogram space, where the filters were applied after ramp operation on the KL-PWLS treated sinogram data prior to backprojection operation (for image reconstruction). By both computer simulation and experimental low-dose CT data, the nonlinear filters could not outperform the KL-PWLS framework. The gain of post KL-PWLS edge-preserving noise filtering in the sinogram space is not significant, even the noise has been modulated by the ramp operation.

78 FR 44957 - Agency Information Collection Activities: BioWatch Filter Holder Log, Filter Holder Log DHS Form...

Federal Register 2010, 2011, 2012, 2013, 2014

2013-07-25

... DEPARTMENT OF HOMELAND SECURITY Agency Information Collection Activities: BioWatch Filter Holder Log, Filter Holder Log DHS Form 9500 AGENCY: Office of Health Affairs, DHS. ACTION: 60-Day Notice and....: Daniel Yereb, [email protected] 703- 647-8052. SUPPLEMENTARY INFORMATION: Following collection, the filter...

Visual Information Processing Based on Spatial Filters Constrained by Biological Data.

DTIC Science & Technology

1978-12-01

was provided by Pantie and Sekuler ( 19681. They found that the detection (if gratings was affected most by adapting isee Section 6.1. 11 to square...evidence for certain eye scans being directed by spatial information in filtered images is given. Eye scan paths of a portrait of a young girl I Figure 08...multistable objects to more complex objects such as the man- girl figure of Fisher 119681, decision boundaries that are a natural concomitant to any pattern

Build Trust Index for Volunteered Geographic Information: A Case Study of Safecast

NASA Astrophysics Data System (ADS)

Xin, Y.; Cervone, G.

2017-12-01

Volunteered Geographic Information (VGI), defined as geographic information contributed voluntarily by amateurs, have grown exponentially nowadays with the aid of ubiquitous GPS-enabled telecommunication technologies. VGI projects, like Wikimapia, OpenStreetMap, Flickr and Safecast have shown remarkable success on leveraging citizen science to increase our knowledge about the geographic world. However, in spite of its growing popularity, VGI is still facing the most challenging problem of ensuring data quality. In this study, we proposed a methodology to filter outliers in Safecast measurements through cross-reference among volunteers. Based on the outliers filtered, a trust index is generated for each volunteer. The results are validated using official radiation measurements surveyed by Department of Energy. The validation shows that removing the outliers filtered by our methodology, Safecast measurements yield a better correlation with official measurements.

Multimodal medical image fusion by combining gradient minimization smoothing filter and non-subsampled directional filter bank

NASA Astrophysics Data System (ADS)

Zhang, Cheng; Wenbo, Mei; Huiqian, Du; Zexian, Wang

2018-04-01

A new algorithm was proposed for medical images fusion in this paper, which combined gradient minimization smoothing filter (GMSF) with non-sampled directional filter bank (NSDFB). In order to preserve more detail information, a multi scale edge preserving decomposition framework (MEDF) was used to decompose an image into a base image and a series of detail images. For the fusion of base images, the local Gaussian membership function is applied to construct the fusion weighted factor. For the fusion of detail images, NSDFB was applied to decompose each detail image into multiple directional sub-images that are fused by pulse coupled neural network (PCNN) respectively. The experimental results demonstrate that the proposed algorithm is superior to the compared algorithms in both visual effect and objective assessment.

The Coming of Knowledge-Based Business.

ERIC Educational Resources Information Center

Davis, Stan; Botkin, Jim

1994-01-01

Economic growth will come from knowledge-based businesses whose "smart" products filter and interpret information. Businesses will come to think of themselves as educators and their customers as learners. (SK)

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).

Restoration of distorted depth maps calculated from stereo sequences

NASA Technical Reports Server (NTRS)

Damour, Kevin; Kaufman, Howard

1991-01-01

A model-based Kalman estimator is developed for spatial-temporal filtering of noise and other degradations in velocity and depth maps derived from image sequences or cinema. As an illustration of the proposed procedures, edge information from image sequences of rigid objects is used in the processing of the velocity maps by selecting from a series of models for directional adaptive filtering. Adaptive filtering then allows for noise reduction while preserving sharpness in the velocity maps. Results from several synthetic and real image sequences are given.

Information filtering on coupled social networks.

PubMed

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

2014-01-01

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

Methods in quantitative image analysis.

PubMed

Oberholzer, M; Ostreicher, M; Christen, H; Brühlmann, M

1996-05-01

The main steps of image analysis are image capturing, image storage (compression), correcting imaging defects (e.g. non-uniform illumination, electronic-noise, glare effect), image enhancement, segmentation of objects in the image and image measurements. Digitisation is made by a camera. The most modern types include a frame-grabber, converting the analog-to-digital signal into digital (numerical) information. The numerical information consists of the grey values describing the brightness of every point within the image, named a pixel. The information is stored in bits. Eight bits are summarised in one byte. Therefore, grey values can have a value between 0 and 256 (2(8)). The human eye seems to be quite content with a display of 5-bit images (corresponding to 64 different grey values). In a digitised image, the pixel grey values can vary within regions that are uniform in the original scene: the image is noisy. The noise is mainly manifested in the background of the image. For an optimal discrimination between different objects or features in an image, uniformity of illumination in the whole image is required. These defects can be minimised by shading correction [subtraction of a background (white) image from the original image, pixel per pixel, or division of the original image by the background image]. The brightness of an image represented by its grey values can be analysed for every single pixel or for a group of pixels. The most frequently used pixel-based image descriptors are optical density, integrated optical density, the histogram of the grey values, mean grey value and entropy. The distribution of the grey values existing within an image is one of the most important characteristics of the image. However, the histogram gives no information about the texture of the image. The simplest way to improve the contrast of an image is to expand the brightness scale by spreading the histogram out to the full available range. Rules for transforming the grey value histogram of an existing image (input image) into a new grey value histogram (output image) are most quickly handled by a look-up table (LUT). The histogram of an image can be influenced by gain, offset and gamma of the camera. Gain defines the voltage range, offset defines the reference voltage and gamma the slope of the regression line between the light intensity and the voltage of the camera. A very important descriptor of neighbourhood relations in an image is the co-occurrence matrix. The distance between the pixels (original pixel and its neighbouring pixel) can influence the various parameters calculated from the co-occurrence matrix. The main goals of image enhancement are elimination of surface roughness in an image (smoothing), correction of defects (e.g. noise), extraction of edges, identification of points, strengthening texture elements and improving contrast. In enhancement, two types of operations can be distinguished: pixel-based (point operations) and neighbourhood-based (matrix operations). The most important pixel-based operations are linear stretching of grey values, application of pre-stored LUTs and histogram equalisation. The neighbourhood-based operations work with so-called filters. These are organising elements with an original or initial point in their centre. Filters can be used to accentuate or to suppress specific structures within the image. Filters can work either in the spatial or in the frequency domain. The method used for analysing alterations of grey value intensities in the frequency domain is the Hartley transform. Filter operations in the spatial domain can be based on averaging or ranking the grey values occurring in the organising element. The most important filters, which are usually applied, are the Gaussian filter and the Laplace filter (both averaging filters), and the median filter, the top hat filter and the range operator (all ranking filters). Segmentation of objects is traditionally based on threshold grey values. (AB

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…

Visual information processing II; Proceedings of the Meeting, Orlando, FL, Apr. 14-16, 1993

NASA Technical Reports Server (NTRS)

Huck, Friedrich O. (Editor); Juday, Richard D. (Editor)

1993-01-01

Various papers on visual information processing are presented. Individual topics addressed include: aliasing as noise, satellite image processing using a hammering neural network, edge-detetion method using visual perception, adaptive vector median filters, design of a reading test for low-vision image warping, spatial transformation architectures, automatic image-enhancement method, redundancy reduction in image coding, lossless gray-scale image compression by predictive GDF, information efficiency in visual communication, optimizing JPEG quantization matrices for different applications, use of forward error correction to maintain image fidelity, effect of peanoscanning on image compression. Also discussed are: computer vision for autonomous robotics in space, optical processor for zero-crossing edge detection, fractal-based image edge detection, simulation of the neon spreading effect by bandpass filtering, wavelet transform (WT) on parallel SIMD architectures, nonseparable 2D wavelet image representation, adaptive image halftoning based on WT, wavelet analysis of global warming, use of the WT for signal detection, perfect reconstruction two-channel rational filter banks, N-wavelet coding for pattern classification, simulation of image of natural objects, number-theoretic coding for iconic systems.

A New Polar Transfer Alignment Algorithm with the Aid of a Star Sensor and Based on an Adaptive Unscented Kalman Filter.

PubMed

Cheng, Jianhua; Wang, Tongda; Wang, Lu; Wang, Zhenmin

2017-10-23

Because of the harsh polar environment, the master strapdown inertial navigation system (SINS) has low accuracy and the system model information becomes abnormal. In this case, existing polar transfer alignment (TA) algorithms which use the measurement information provided by master SINS would lose their effectiveness. In this paper, a new polar TA algorithm with the aid of a star sensor and based on an adaptive unscented Kalman filter (AUKF) is proposed to deal with the problems. Since the measurement information provided by master SINS is inaccurate, the accurate information provided by the star sensor is chosen as the measurement. With the compensation of lever-arm effect and the model of star sensor, the nonlinear navigation equations are derived. Combined with the attitude matching method, the filter models for polar TA are designed. An AUKF is introduced to solve the abnormal information of system model. Then, the AUKF is used to estimate the states of TA. Results have demonstrated that the performance of the new polar TA algorithm is better than the state-of-the-art polar TA algorithms. Therefore, the new polar TA algorithm proposed in this paper is effectively to ensure and improve the accuracy of TA in the harsh polar environment.

A New Polar Transfer Alignment Algorithm with the Aid of a Star Sensor and Based on an Adaptive Unscented Kalman Filter

PubMed Central

Cheng, Jianhua; Wang, Tongda; Wang, Lu; Wang, Zhenmin

2017-01-01

Because of the harsh polar environment, the master strapdown inertial navigation system (SINS) has low accuracy and the system model information becomes abnormal. In this case, existing polar transfer alignment (TA) algorithms which use the measurement information provided by master SINS would lose their effectiveness. In this paper, a new polar TA algorithm with the aid of a star sensor and based on an adaptive unscented Kalman filter (AUKF) is proposed to deal with the problems. Since the measurement information provided by master SINS is inaccurate, the accurate information provided by the star sensor is chosen as the measurement. With the compensation of lever-arm effect and the model of star sensor, the nonlinear navigation equations are derived. Combined with the attitude matching method, the filter models for polar TA are designed. An AUKF is introduced to solve the abnormal information of system model. Then, the AUKF is used to estimate the states of TA. Results have demonstrated that the performance of the new polar TA algorithm is better than the state-of-the-art polar TA algorithms. Therefore, the new polar TA algorithm proposed in this paper is effectively to ensure and improve the accuracy of TA in the harsh polar environment. PMID:29065521

Fuzzy Adaptive Cubature Kalman Filter for Integrated Navigation Systems.

PubMed

Tseng, Chien-Hao; Lin, Sheng-Fuu; Jwo, Dah-Jing

2016-07-26

This paper presents a sensor fusion method based on the combination of cubature Kalman filter (CKF) and fuzzy logic adaptive system (FLAS) for the integrated navigation systems, such as the GPS/INS (Global Positioning System/inertial navigation system) integration. The third-degree spherical-radial cubature rule applied in the CKF has been employed to avoid the numerically instability in the system model. In processing navigation integration, the performance of nonlinear filter based estimation of the position and velocity states may severely degrade caused by modeling errors due to dynamics uncertainties of the vehicle. In order to resolve the shortcoming for selecting the process noise covariance through personal experience or numerical simulation, a scheme called the fuzzy adaptive cubature Kalman filter (FACKF) is presented by introducing the FLAS to adjust the weighting factor of the process noise covariance matrix. The FLAS is incorporated into the CKF framework as a mechanism for timely implementing the tuning of process noise covariance matrix based on the information of degree of divergence (DOD) parameter. The proposed FACKF algorithm shows promising accuracy improvement as compared to the extended Kalman filter (EKF), unscented Kalman filter (UKF), and CKF approaches.

Fuzzy Adaptive Cubature Kalman Filter for Integrated Navigation Systems

PubMed Central

Tseng, Chien-Hao; Lin, Sheng-Fuu; Jwo, Dah-Jing

2016-01-01

This paper presents a sensor fusion method based on the combination of cubature Kalman filter (CKF) and fuzzy logic adaptive system (FLAS) for the integrated navigation systems, such as the GPS/INS (Global Positioning System/inertial navigation system) integration. The third-degree spherical-radial cubature rule applied in the CKF has been employed to avoid the numerically instability in the system model. In processing navigation integration, the performance of nonlinear filter based estimation of the position and velocity states may severely degrade caused by modeling errors due to dynamics uncertainties of the vehicle. In order to resolve the shortcoming for selecting the process noise covariance through personal experience or numerical simulation, a scheme called the fuzzy adaptive cubature Kalman filter (FACKF) is presented by introducing the FLAS to adjust the weighting factor of the process noise covariance matrix. The FLAS is incorporated into the CKF framework as a mechanism for timely implementing the tuning of process noise covariance matrix based on the information of degree of divergence (DOD) parameter. The proposed FACKF algorithm shows promising accuracy improvement as compared to the extended Kalman filter (EKF), unscented Kalman filter (UKF), and CKF approaches. PMID:27472336

Multispectral Detection with Metal-Dielectric Filters: An Investigation in Several Wavelength Bands with Temporal Coupled-Mode Theory

NASA Astrophysics Data System (ADS)

Lesmanne, Emeline; Espiau de Lamaestre, Roch; Boutami, Salim; Durantin, Cédric; Dussopt, Laurent; Badano, Giacomo

2016-09-01

Multispectral infrared (IR) detection is of great interest to enhance our ability to gather information from a scene. Filtering is a low-cost alternative to the complex multispectral device architectures to which the IR community has devoted much attention. Multilayer dielectric filters are standard in industry, but they require changing the thickness of at least one layer to tune the wavelength. Here, we pursue an approach based on apertures in a metallic layer of fixed thickness, in which the filtered wavelengths are selected by varying the aperture geometry. In particular, we study filters made of at least one sheet of resonating apertures in metal embedded in dielectrics. We will discuss two interesting problems that arise when one attempts to design such filters. First, metallic absorption must be taken into account. Second, the form and size of the pattern is limited by lithography. We will present some design examples and an attempt at explaining the filtering behavior based on the temporal coupled mode theory. That theory models the filter as a resonator interacting with the environment via loss channels. The transmission is solely determined by the loss rates associated with those channels. This model allows us to give a general picture of the filtering performance and compare their characteristics at different wavelength bands.

QPA-CLIPS: A language and representation for process control

NASA Technical Reports Server (NTRS)

Freund, Thomas G.

1994-01-01

QPA-CLIPS is an extension of CLIPS oriented towards process control applications. Its constructs define a dependency network of process actions driven by sensor information. The language consists of three basic constructs: TASK, SENSOR, and FILTER. TASK's define the dependency network describing alternative state transitions for a process. SENSOR's and FILTER's define sensor information sources used to activate state transitions within the network. Deftemplate's define these constructs and their run-time environment is an interpreter knowledge base, performing pattern matching on sensor information and so activating TASK's in the dependency network. The pattern matching technique is based on the repeatable occurrence of a sensor data pattern. QPA-CIPS has been successfully tested on a SPARCStation providing supervisory control to an Allen-Bradley PLC 5 controller driving molding equipment.

Aircraft Turbofan Engine Health Estimation Using Constrained Kalman Filtering

NASA Technical Reports Server (NTRS)

Simon, Dan; Simon, Donald L.

2003-01-01

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 is a combination of a standard Kalman filter and a quadratic programming problem. The incorporation of state variable constraints increases the computational effort of the filter but significantly improves its estimation accuracy. The improvement is proven theoretically and shown via simulation results obtained from application to a turbofan engine model. This model contains 16 state variables, 12 measurements, and 8 component health parameters. It is shown that the new algorithms provide improved performance in this example over unconstrained Kalman filtering.

Developing topic-specific search filters for PubMed with click-through data.

PubMed

Li, J; Lu, Z

2013-01-01

Search filters have been developed and demonstrated for better information access to the immense and ever-growing body of publications in the biomedical domain. However, to date the number of filters remains quite limited because the current filter development methods require significant human efforts in manual document review and filter term selection. In this regard, we aim to investigate automatic methods for generating search filters. We present an automated method to develop topic-specific filters on the basis of users' search logs in PubMed. Specifically, for a given topic, we first detect its relevant user queries and then include their corresponding clicked articles to serve as the topic-relevant document set accordingly. Next, we statistically identify informative terms that best represent the topic-relevant document set using a background set composed of topic irrelevant articles. Lastly, the selected representative terms are combined with Boolean operators and evaluated on benchmark datasets to derive the final filter with the best performance. We applied our method to develop filters for four clinical topics: nephrology, diabetes, pregnancy, and depression. For the nephrology filter, our method obtained performance comparable to the state of the art (sensitivity of 91.3%, specificity of 98.7%, precision of 94.6%, and accuracy of 97.2%). Similarly, high-performing results (over 90% in all measures) were obtained for the other three search filters. Based on PubMed click-through data, we successfully developed a high-performance method for generating topic-specific search filters that is significantly more efficient than existing manual methods. All data sets (topic-relevant and irrelevant document sets) used in this study and a demonstration system are publicly available at http://www.ncbi.nlm.nih.gov/CBBresearch/Lu/downloads/CQ_filter/

Developing Topic-Specific Search Filters for PubMed with Click-Through Data

PubMed Central

Li, Jiao; Lu, Zhiyong

2013-01-01

Summary Objectives Search filters have been developed and demonstrated for better information access to the immense and ever-growing body of publications in the biomedical domain. However, to date the number of filters remains quite limited because the current filter development methods require significant human efforts in manual document review and filter term selection. In this regard, we aim to investigate automatic methods for generating search filters. Methods We present an automated method to develop topic-specific filters on the basis of users’ search logs in PubMed. Specifically, for a given topic, we first detect its relevant user queries and then include their corresponding clicked articles to serve as the topic-relevant document set accordingly. Next, we statistically identify informative terms that best represent the topic-relevant document set using a background set composed of topic irrelevant articles. Lastly, the selected representative terms are combined with Boolean operators and evaluated on benchmark datasets to derive the final filter with the best performance. Results We applied our method to develop filters for four clinical topics: nephrology, diabetes, pregnancy, and depression. For the nephrology filter, our method obtained performance comparable to the state of the art (sensitivity of 91.3%, specificity of 98.7%, precision of 94.6%, and accuracy of 97.2%). Similarly, high-performing results (over 90% in all measures) were obtained for the other three search filters. Conclusion Based on PubMed click-through data, we successfully developed a high-performance method for generating topic-specific search filters that is significantly more efficient than existing manual methods. All data sets (topic-relevant and irrelevant document sets) used in this study and a demonstration system are publicly available at http://www.ncbi.nlm.nih.gov/CBBresearch/Lu/downloads/CQ_filter/ PMID:23666447

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

PubMed Central

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

2017-01-01

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

Kalman Filter Constraint Tuning for Turbofan Engine Health Estimation

NASA Technical Reports Server (NTRS)

Simon, Dan; Simon, Donald L.

2005-01-01

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 are often neglected because they do not fit easily into the structure of the Kalman filter. Recently published work has shown a new method for incorporating state variable inequality constraints in the Kalman filter, which has been shown to generally improve the filter s estimation accuracy. However, the incorporation of inequality constraints poses some risk to the estimation accuracy as the Kalman filter is theoretically optimal. This paper proposes a way to tune the filter constraints so that the state estimates follow the unconstrained (theoretically optimal) filter when the confidence in the unconstrained filter is high. When confidence in the unconstrained filter is not so high, then we use our heuristic knowledge to constrain the state estimates. The confidence measure is based on the agreement of measurement residuals with their theoretical values. The algorithm is demonstrated on a linearized simulation of a turbofan engine to estimate engine health.

Iterative filtering decomposition based on local spectral evolution kernel

PubMed Central

Wang, Yang; Wei, Guo-Wei; Yang, Siyang

2011-01-01

The synthesizing information, achieving understanding, and deriving insight from increasingly massive, time-varying, noisy and possibly conflicting data sets are some of most challenging tasks in the present information age. Traditional technologies, such as Fourier transform and wavelet multi-resolution analysis, are inadequate to handle all of the above-mentioned tasks. The empirical model decomposition (EMD) has emerged as a new powerful tool for resolving many challenging problems in data processing and analysis. Recently, an iterative filtering decomposition (IFD) has been introduced to address the stability and efficiency problems of the EMD. Another data analysis technique is the local spectral evolution kernel (LSEK), which provides a near prefect low pass filter with desirable time-frequency localizations. The present work utilizes the LSEK to further stabilize the IFD, and offers an efficient, flexible and robust scheme for information extraction, complexity reduction, and signal and image understanding. The performance of the present LSEK based IFD is intensively validated over a wide range of data processing tasks, including mode decomposition, analysis of time-varying data, information extraction from nonlinear dynamic systems, etc. The utility, robustness and usefulness of the proposed LESK based IFD are demonstrated via a large number of applications, such as the analysis of stock market data, the decomposition of ocean wave magnitudes, the understanding of physiologic signals and information recovery from noisy images. The performance of the proposed method is compared with that of existing methods in the literature. Our results indicate that the LSEK based IFD improves both the efficiency and the stability of conventional EMD algorithms. PMID:22350559

Classification of Hyperspectral Data Based on Guided Filtering and Random Forest

NASA Astrophysics Data System (ADS)

Ma, H.; Feng, W.; Cao, X.; Wang, L.

2017-09-01

Hyperspectral images usually consist of more than one hundred spectral bands, which have potentials to provide rich spatial and spectral information. However, the application of hyperspectral data is still challengeable due to "the curse of dimensionality". In this context, many techniques, which aim to make full use of both the spatial and spectral information, are investigated. In order to preserve the geometrical information, meanwhile, with less spectral bands, we propose a novel method, which combines principal components analysis (PCA), guided image filtering and the random forest classifier (RF). In detail, PCA is firstly employed to reduce the dimension of spectral bands. Secondly, the guided image filtering technique is introduced to smooth land object, meanwhile preserving the edge of objects. Finally, the features are fed into RF classifier. To illustrate the effectiveness of the method, we carry out experiments over the popular Indian Pines data set, which is collected by Airborne Visible/Infrared Imaging Spectrometer (AVIRIS) sensor. By comparing the proposed method with the method of only using PCA or guided image filter, we find that effect of the proposed method is better.

Optimization of internet content filtering-Combined with KNN and OCAT algorithms

NASA Astrophysics Data System (ADS)

Guo, Tianze; Wu, Lingjing; Liu, Jiaming

2018-04-01

The face of the status quo that rampant illegal content in the Internet, the result of traditional way to filter information, keyword recognition and manual screening, is getting worse. Based on this, this paper uses OCAT algorithm nested by KNN classification algorithm to construct a corpus training library that can dynamically learn and update, which can be improved on the filter corpus for constantly updated illegal content of the network, including text and pictures, and thus can better filter and investigate illegal content and its source. After that, the research direction will focus on the simplified updating of recognition and comparison algorithms and the optimization of the corpus learning ability in order to improve the efficiency of filtering, save time and resources.

Control of coherent information via on-chip photonic-phononic emitter-receivers.

PubMed

Shin, Heedeuk; Cox, Jonathan A; Jarecki, Robert; Starbuck, Andrew; Wang, Zheng; Rakich, Peter T

2015-03-05

Rapid progress in integrated photonics has fostered numerous chip-scale sensing, computing and signal processing technologies. However, many crucial filtering and signal delay operations are difficult to perform with all-optical devices. Unlike photons propagating at luminal speeds, GHz-acoustic phonons moving at slower velocities allow information to be stored, filtered and delayed over comparatively smaller length-scales with remarkable fidelity. Hence, controllable and efficient coupling between coherent photons and phonons enables new signal processing technologies that greatly enhance the performance and potential impact of integrated photonics. Here we demonstrate a mechanism for coherent information processing based on travelling-wave photon-phonon transduction, which achieves a phonon emit-and-receive process between distinct nanophotonic waveguides. Using this device, physics--which supports GHz frequencies--we create wavelength-insensitive radiofrequency photonic filters with frequency selectivity, narrow-linewidth and high power-handling in silicon. More generally, this emit-receive concept is the impetus for enabling new signal processing schemes.

Filling schemes at submicron scale: Development of submicron sized plasmonic colour filters

PubMed Central

Rajasekharan, Ranjith; Balaur, Eugeniu; Minovich, Alexander; Collins, Sean; James, Timothy D.; Djalalian-Assl, Amir; Ganesan, Kumaravelu; Tomljenovic-Hanic, Snjezana; Kandasamy, Sasikaran; Skafidas, Efstratios; Neshev, Dragomir N.; Mulvaney, Paul; Roberts, Ann; Prawer, Steven

2014-01-01

The pixel size imposes a fundamental limit on the amount of information that can be displayed or recorded on a sensor. Thus, there is strong motivation to reduce the pixel size down to the nanometre scale. Nanometre colour pixels cannot be fabricated by simply downscaling current pixels due to colour cross talk and diffraction caused by dyes or pigments used as colour filters. Colour filters based on plasmonic effects can overcome these difficulties. Although different plasmonic colour filters have been demonstrated at the micron scale, there have been no attempts so far to reduce the filter size to the submicron scale. Here, we present for the first time a submicron plasmonic colour filter design together with a new challenge - pixel boundary errors at the submicron scale. We present simple but powerful filling schemes to produce submicron colour filters, which are free from pixel boundary errors and colour cross- talk, are polarization independent and angle insensitive, and based on LCD compatible aluminium technology. These results lay the basis for the development of submicron pixels in displays, RGB-spatial light modulators, liquid crystal over silicon, Google glasses and pico-projectors. PMID:25242695

Filling schemes at submicron scale: development of submicron sized plasmonic colour filters.

PubMed

Rajasekharan, Ranjith; Balaur, Eugeniu; Minovich, Alexander; Collins, Sean; James, Timothy D; Djalalian-Assl, Amir; Ganesan, Kumaravelu; Tomljenovic-Hanic, Snjezana; Kandasamy, Sasikaran; Skafidas, Efstratios; Neshev, Dragomir N; Mulvaney, Paul; Roberts, Ann; Prawer, Steven

2014-09-22

The pixel size imposes a fundamental limit on the amount of information that can be displayed or recorded on a sensor. Thus, there is strong motivation to reduce the pixel size down to the nanometre scale. Nanometre colour pixels cannot be fabricated by simply downscaling current pixels due to colour cross talk and diffraction caused by dyes or pigments used as colour filters. Colour filters based on plasmonic effects can overcome these difficulties. Although different plasmonic colour filters have been demonstrated at the micron scale, there have been no attempts so far to reduce the filter size to the submicron scale. Here, we present for the first time a submicron plasmonic colour filter design together with a new challenge - pixel boundary errors at the submicron scale. We present simple but powerful filling schemes to produce submicron colour filters, which are free from pixel boundary errors and colour cross- talk, are polarization independent and angle insensitive, and based on LCD compatible aluminium technology. These results lay the basis for the development of submicron pixels in displays, RGB-spatial light modulators, liquid crystal over silicon, Google glasses and pico-projectors.

[Continuum based fast Fourier transform processing of infrared spectrum].

PubMed

Liu, Qing-Jie; Lin, Qi-Zhong; Wang, Qin-Jun; Li, Hui; Li, Shuai

2009-12-01

To recognize ground objects with infrared spectrum, high frequency noise removing is one of the most important phases in spectrum feature analysis and extraction. A new method for infrared spectrum preprocessing was given combining spectrum continuum processing and Fast Fourier Transform (CFFT). Continuum was firstly removed from the noise polluted infrared spectrum to standardize hyper-spectra. Then the spectrum was transformed into frequency domain (FD) with fast Fourier transform (FFT), separating noise information from target information After noise eliminating from useful information with a low-pass filter, the filtered FD spectrum was transformed into time domain (TD) with fast Fourier inverse transform. Finally the continuum was recovered to the spectrum, and the filtered infrared spectrum was achieved. Experiment was performed for chlorite spectrum in USGS polluted with two kinds of simulated white noise to validate the filtering ability of CFFT by contrast with cubic function of five point (CFFP) in time domain and traditional FFT in frequency domain. A circle of CFFP has limited filtering effect, so it should work much with more circles and consume more time to achieve better filtering result. As for conventional FFT, Gibbs phenomenon has great effect on preprocessing result at edge bands because of special character of rock or mineral spectra, while works well at middle bands. Mean squared error of CFFT is 0. 000 012 336 with cut-off frequency of 150, while that of FFT and CFFP is 0. 000 061 074 with cut-off frequency of 150 and 0.000 022 963 with 150 working circles respectively. Besides the filtering result of CFFT can be improved by adjusting the filter cut-off frequency, and has little effect on working time. The CFFT method overcomes the Gibbs problem of FFT in spectrum filtering, and can be more convenient, dependable, and effective than traditional TD filter methods.

Delineation and geometric modeling of road networks

NASA Astrophysics Data System (ADS)

Poullis, Charalambos; You, Suya

In this work we present a novel vision-based system for automatic detection and extraction of complex road networks from various sensor resources such as aerial photographs, satellite images, and LiDAR. Uniquely, the proposed system is an integrated solution that merges the power of perceptual grouping theory (Gabor filtering, tensor voting) and optimized segmentation techniques (global optimization using graph-cuts) into a unified framework to address the challenging problems of geospatial feature detection and classification. Firstly, the local precision of the Gabor filters is combined with the global context of the tensor voting to produce accurate classification of the geospatial features. In addition, the tensorial representation used for the encoding of the data eliminates the need for any thresholds, therefore removing any data dependencies. Secondly, a novel orientation-based segmentation is presented which incorporates the classification of the perceptual grouping, and results in segmentations with better defined boundaries and continuous linear segments. Finally, a set of gaussian-based filters are applied to automatically extract centerline information (magnitude, width and orientation). This information is then used for creating road segments and transforming them to their polygonal representations.

GOES Sounder Instrument - NOAA Satellite Information System (NOAASIS);

Science.gov Websites

ground-based, balloon system. The Sounder has 4 sets of detectors (visible, long wave IR, medium wave IR , short wave IR). The incoming radiation passes through a set of filters before reaching the detectors concentric rings, one for each IR detector group. The outer ring contains 7 long wave filters, the middle

A ROle-Oriented Filtering (ROOF) approach for collaborative recommendation

NASA Astrophysics Data System (ADS)

Ghani, Imran; Jeong, Seung Ryul

2016-09-01

In collaborative filtering (CF) recommender systems, existing techniques frequently focus on determining similarities among users' historical interests. This generally refers to situations in which each user normally plays a single role and his/her taste remains consistent over the long term. However, we note that existing techniques have not been significantly employed in a role-oriented context. This is especially so in situations where users may change their roles over time or play multiple roles simultaneously, while still expecting to access relevant information resources accordingly. Such systems include enterprise architecture management systems, e-commerce sites or journal management systems. In scenarios involving existing techniques, each user needs to build up very different profiles (preferences and interests) based on multiple roles which change over time. Should this not occur to a satisfactory degree, their previous information will either be lost or not utilised at all. To limit the occurrence of such issues, we propose a ROle-Oriented Filtering (ROOF) approach focusing on the manner in which multiple user profiles are obtained and maintained over time. We conducted a number of experiments using an enterprise architecture management scenario. In so doing, we observed that the ROOF approach performs better in comparison with other existing collaborative filtering-based techniques.

Anomaly and signature filtering improve classifier performance for detection of suspicious access to EHRs.

PubMed

Kim, Jihoon; Grillo, Janice M; Boxwala, Aziz A; Jiang, Xiaoqian; Mandelbaum, Rose B; Patel, Bhakti A; Mikels, Debra; Vinterbo, Staal A; Ohno-Machado, Lucila

2011-01-01

Our objective is to facilitate semi-automated detection of suspicious access to EHRs. Previously we have shown that a machine learning method can play a role in identifying potentially inappropriate access to EHRs. However, the problem of sampling informative instances to build a classifier still remained. We developed an integrated filtering method leveraging both anomaly detection based on symbolic clustering and signature detection, a rule-based technique. We applied the integrated filtering to 25.5 million access records in an intervention arm, and compared this with 8.6 million access records in a control arm where no filtering was applied. On the training set with cross-validation, the AUC was 0.960 in the control arm and 0.998 in the intervention arm. The difference in false negative rates on the independent test set was significant, P=1.6×10(-6). Our study suggests that utilization of integrated filtering strategies to facilitate the construction of classifiers can be helpful.

Anomaly and Signature Filtering Improve Classifier Performance For Detection Of Suspicious Access To EHRs

PubMed Central

Kim, Jihoon; Grillo, Janice M; Boxwala, Aziz A; Jiang, Xiaoqian; Mandelbaum, Rose B; Patel, Bhakti A; Mikels, Debra; Vinterbo, Staal A; Ohno-Machado, Lucila

2011-01-01

Our objective is to facilitate semi-automated detection of suspicious access to EHRs. Previously we have shown that a machine learning method can play a role in identifying potentially inappropriate access to EHRs. However, the problem of sampling informative instances to build a classifier still remained. We developed an integrated filtering method leveraging both anomaly detection based on symbolic clustering and signature detection, a rule-based technique. We applied the integrated filtering to 25.5 million access records in an intervention arm, and compared this with 8.6 million access records in a control arm where no filtering was applied. On the training set with cross-validation, the AUC was 0.960 in the control arm and 0.998 in the intervention arm. The difference in false negative rates on the independent test set was significant, P=1.6×10−6. Our study suggests that utilization of integrated filtering strategies to facilitate the construction of classifiers can be helpful. PMID:22195129

New distributed fusion filtering algorithm based on covariances over sensor networks with random packet dropouts

NASA Astrophysics Data System (ADS)

Caballero-Águila, R.; Hermoso-Carazo, A.; Linares-Pérez, J.

2017-07-01

This paper studies the distributed fusion estimation problem from multisensor measured outputs perturbed by correlated noises and uncertainties modelled by random parameter matrices. Each sensor transmits its outputs to a local processor over a packet-erasure channel and, consequently, random losses may occur during transmission. Different white sequences of Bernoulli variables are introduced to model the transmission losses. For the estimation, each lost output is replaced by its estimator based on the information received previously, and only the covariances of the processes involved are used, without requiring the signal evolution model. First, a recursive algorithm for the local least-squares filters is derived by using an innovation approach. Then, the cross-correlation matrices between any two local filters is obtained. Finally, the distributed fusion filter weighted by matrices is obtained from the local filters by applying the least-squares criterion. The performance of the estimators and the influence of both sensor uncertainties and transmission losses on the estimation accuracy are analysed in a numerical example.

Information Filtering on Coupled Social Networks

PubMed Central

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

2014-01-01

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

Private Yet Abuse Resistant Open Publishing

NASA Astrophysics Data System (ADS)

Danezis, George; Laurie, Ben

We present the problem of abusive, off-topic or repetitive postings on open publishing websites, and the difficulties associated with filtering them out. We propose a scheme that extracts enough information to allow for filtering, based on users being embedded in a social network. Our system maintains the privacy of the poster, and does not require full identification to work well. We present a concrete realization using constructions based on discrete logarithms, and a sketch of how our scheme could be implemented in a centralized fashion.

Remote sensing fusion based on guided image filtering

NASA Astrophysics Data System (ADS)

Zhao, Wenfei; Dai, Qinling; Wang, Leiguang

2015-12-01

In this paper, we propose a novel remote sensing fusion approach based on guided image filtering. The fused images can well preserve the spectral features of the original multispectral (MS) images, meanwhile, enhance the spatial details information. Four quality assessment indexes are also introduced to evaluate the fusion effect when compared with other fusion methods. Experiments carried out on Gaofen-2, QuickBird, WorldView-2 and Landsat-8 images. And the results show an excellent performance of the proposed method.

Development of a high-performance noise-reduction filter for tomographic reconstruction

NASA Astrophysics Data System (ADS)

Kao, Chien-Min; Pan, Xiaochuan

2001-07-01

We propose a new noise-reduction method for tomographic reconstruction. The method incorporates a priori information on the source image for allowing the derivation of the energy spectrum of its ideal sinogram. In combination with the energy spectrum of the Poisson noise in the measured sinogram, we are able to derive a Wiener-like filter for effective suppression of the sinogram noise. The filtered backprojection (FBP) algorithm, with a ramp filter, is then applied to the filtered sinogram to produce tomographic images. The resulting filter has a closed-form expression in the frequency space and contains a single user-adjustable regularization parameter. The proposed method is hence simple to implement and easy to use. In contrast to the ad hoc apodizing windows, such as Hanning and Butterworth filters, that are commonly used in the conventional FBP reconstruction, the proposed filter is theoretically more rigorous as it is derived by basing upon an optimization criterion, subject to a known class of source image intensity distributions.

Efficient data assimilation algorithm for bathymetry application

NASA Astrophysics Data System (ADS)

Ghorbanidehno, H.; Lee, J. H.; Farthing, M.; Hesser, T.; Kitanidis, P. K.; Darve, E. F.

2017-12-01

Information on the evolving state of the nearshore zone bathymetry is crucial to shoreline management, recreational safety, and naval operations. The high cost and complex logistics of using ship-based surveys for bathymetry estimation have encouraged the use of remote sensing techniques. Data assimilation methods combine the remote sensing data and nearshore hydrodynamic models to estimate the unknown bathymetry and the corresponding uncertainties. In particular, several recent efforts have combined Kalman Filter-based techniques such as ensembled-based Kalman filters with indirect video-based observations to address the bathymetry inversion problem. However, these methods often suffer from ensemble collapse and uncertainty underestimation. Here, the Compressed State Kalman Filter (CSKF) method is used to estimate the bathymetry based on observed wave celerity. In order to demonstrate the accuracy and robustness of the CSKF method, we consider twin tests with synthetic observations of wave celerity, while the bathymetry profiles are chosen based on surveys taken by the U.S. Army Corps of Engineer Field Research Facility (FRF) in Duck, NC. The first test case is a bathymetry estimation problem for a spatially smooth and temporally constant bathymetry profile. The second test case is a bathymetry estimation problem for a temporally evolving bathymetry from a smooth to a non-smooth profile. For both problems, we compare the results of CSKF with those obtained by the local ensemble transform Kalman filter (LETKF), which is a popular ensemble-based Kalman filter method.

An RC active filter design handbook

NASA Technical Reports Server (NTRS)

Deboo, G. J.

1977-01-01

The design of filters is described. Emphasis is placed on simplified procedures that can be used by the reader who has minimum knowledge about circuit design and little acquaintance with filter theory. The handbook has three main parts. The first part is a review of some information that is essential for work with filters. The second part includes design information for specific types of filter circuitry and describes simple procedures for obtaining the component values for a filter that will have a desired set of characteristics. Pertinent information relating to actual performance is given. The third part (appendix) is a review of certain topics in filter theory and is intended to provide some basic understanding of how filters are designed.

Information overload in healthcare: too much of a good thing?

PubMed

Klerings, Irma; Weinhandl, Alexandra S; Thaler, Kylie J

2015-01-01

The rapidly growing production of healthcare information - both scientific and popular - increasingly leads to a situation of information overload affecting all actors of the healthcare system and threatening to impede the adoption of evidence-based practice. In preparation for the 2015 Cochrane Colloquium in Vienna, we discuss the issues faced by three major actors of this system: patients, healthcare practitioners, and systematic reviewers. We analyze their situation through the concept of "filter failure", positing that the main problem is not that there is "too much information", but that the traditional means of managing and evaluating information are ill-suited to the realities of the digital age. Some of the major instances of filter failure are inadequate information retrieval systems for point-of-care settings, the problem of identifying all relevant evidence in an exceedingly diverse landscape of information resources, and the very basic lack of health information literacy, concerning not only the general public. Finally, we give an overview of proposed solutions to the problem of information overload. These new or adapted filtering systems include adapting review literature to the specific needs of practitioners or patients, technological improvements to information systems, strengthening the roles of intermediaries, as well as improving health literacy. Copyright © 2015. Published by Elsevier GmbH.

Iterated unscented Kalman filter for phase unwrapping of interferometric fringes.

PubMed

Xie, Xianming

2016-08-22

A fresh phase unwrapping algorithm based on iterated unscented Kalman filter is proposed to estimate unambiguous unwrapped phase of interferometric fringes. This method is the result of combining an iterated unscented Kalman filter with a robust phase gradient estimator based on amended matrix pencil model, and an efficient quality-guided strategy based on heap sort. The iterated unscented Kalman filter that is one of the most robust methods under the Bayesian theorem frame in non-linear signal processing so far, is applied to perform simultaneously noise suppression and phase unwrapping of interferometric fringes for the first time, which can simplify the complexity and the difficulty of pre-filtering procedure followed by phase unwrapping procedure, and even can remove the pre-filtering procedure. The robust phase gradient estimator is used to efficiently and accurately obtain phase gradient information from interferometric fringes, which is needed for the iterated unscented Kalman filtering phase unwrapping model. The efficient quality-guided strategy is able to ensure that the proposed method fast unwraps wrapped pixels along the path from the high-quality area to the low-quality area of wrapped phase images, which can greatly improve the efficiency of phase unwrapping. Results obtained from synthetic data and real data show that the proposed method can obtain better solutions with an acceptable time consumption, with respect to some of the most used algorithms.

Spectral information enhancement using wavelet-based iterative filtering for in vivo gamma spectrometry.

PubMed

Paul, Sabyasachi; Sarkar, P K

2013-04-01

Use of wavelet transformation in stationary signal processing has been demonstrated for denoising the measured spectra and characterisation of radionuclides in the in vivo monitoring analysis, where difficulties arise due to very low activity level to be estimated in biological systems. The large statistical fluctuations often make the identification of characteristic gammas from radionuclides highly uncertain, particularly when interferences from progenies are also present. A new wavelet-based noise filtering methodology has been developed for better detection of gamma peaks in noisy data. This sequential, iterative filtering method uses the wavelet multi-resolution approach for noise rejection and an inverse transform after soft 'thresholding' over the generated coefficients. Analyses of in vivo monitoring data of (235)U and (238)U were carried out using this method without disturbing the peak position and amplitude while achieving a 3-fold improvement in the signal-to-noise ratio, compared with the original measured spectrum. When compared with other data-filtering techniques, the wavelet-based method shows the best results.

Introduction to the JASIST Special Topic Issue on Web Retrieval and Mining: A Machine Learning Perspective.

ERIC Educational Resources Information Center

Chen, Hsinchun

2003-01-01

Discusses information retrieval techniques used on the World Wide Web. Topics include machine learning in information extraction; relevance feedback; information filtering and recommendation; text classification and text clustering; Web mining, based on data mining techniques; hyperlink structure; and Web size. (LRW)

Comparative Study of Speckle Filtering Methods in PolSAR Radar Images

NASA Astrophysics Data System (ADS)

Boutarfa, S.; Bouchemakh, L.; Smara, Y.

2015-04-01

Images acquired by polarimetric SAR (PolSAR) radar systems are characterized by the presence of a noise called speckle. This noise has a multiplicative nature, corrupts both the amplitude and phase images, which complicates data interpretation, degrades segmentation performance and reduces the detectability of targets. Hence, the need to preprocess the images by adapted filtering methods before analysis.In this paper, we present a comparative study of implemented methods for reducing speckle in PolSAR images. These developed filters are: refined Lee filter based on the estimation of the minimum mean square error MMSE, improved Sigma filter with detection of strong scatterers based on the calculation of the coherency matrix to detect the different scatterers in order to preserve the polarization signature and maintain structures that are necessary for image interpretation, filtering by stationary wavelet transform SWT using multi-scale edge detection and the technique for improving the wavelet coefficients called SSC (sum of squared coefficients), and Turbo filter which is a combination between two complementary filters the refined Lee filter and the wavelet transform SWT. One filter can boost up the results of the other.The originality of our work is based on the application of these methods to several types of images: amplitude, intensity and complex, from a satellite or an airborne radar, and on the optimization of wavelet filtering by adding a parameter in the calculation of the threshold. This parameter will control the filtering effect and get a good compromise between smoothing homogeneous areas and preserving linear structures.The methods are applied to the fully polarimetric RADARSAT-2 images (HH, HV, VH, VV) acquired on Algiers, Algeria, in C-band and to the three polarimetric E-SAR images (HH, HV, VV) acquired on Oberpfaffenhofen area located in Munich, Germany, in P-band.To evaluate the performance of each filter, we used the following criteria: smoothing homogeneous areas, preserving edges and polarimetric information.Experimental results are included to illustrate the different implemented methods.

Scalable Node Monitoring

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

Drotar, Alexander P.; Quinn, Erin E.; Sutherland, Landon D.

2012-07-30

Project description is: (1) Build a high performance computer; and (2) Create a tool to monitor node applications in Component Based Tool Framework (CBTF) using code from Lightweight Data Metric Service (LDMS). The importance of this project is that: (1) there is a need a scalable, parallel tool to monitor nodes on clusters; and (2) New LDMS plugins need to be able to be easily added to tool. CBTF stands for Component Based Tool Framework. It's scalable and adjusts to different topologies automatically. It uses MRNet (Multicast/Reduction Network) mechanism for information transport. CBTF is flexible and general enough to bemore » used for any tool that needs to do a task on many nodes. Its components are reusable and 'EASILY' added to a new tool. There are three levels of CBTF: (1) frontend node - interacts with users; (2) filter nodes - filters or concatenates information from backend nodes; and (3) backend nodes - where the actual work of the tool is done. LDMS stands for lightweight data metric servies. It's a tool used for monitoring nodes. Ltool is the name of the tool we derived from LDMS. It's dynamically linked and includes the following components: Vmstat, Meminfo, Procinterrupts and more. It works by: Ltool command is run on the frontend node; Ltool collects information from the backend nodes; backend nodes send information to the filter nodes; and filter nodes concatenate information and send to a database on the front end node. Ltool is a useful tool when it comes to monitoring nodes on a cluster because the overhead involved with running the tool is not particularly high and it will automatically scale to any size cluster.« less

Advances in optical information processing IV; Proceedings of the Meeting, Orlando, FL, Apr. 18-20, 1990

NASA Astrophysics Data System (ADS)

Pape, Dennis R.

1990-09-01

The present conference discusses topics in optical image processing, optical signal processing, acoustooptic spectrum analyzer systems and components, and optical computing. Attention is given to tradeoffs in nonlinearly recorded matched filters, miniature spatial light modulators, detection and classification using higher-order statistics of optical matched filters, rapid traversal of an image data base using binary synthetic discriminant filters, wideband signal processing for emitter location, an acoustooptic processor for autonomous SAR guidance, and sampling of Fresnel transforms. Also discussed are an acoustooptic RF signal-acquisition system, scanning acoustooptic spectrum analyzers, the effects of aberrations on acoustooptic systems, fast optical digital arithmetic processors, information utilization in analog and digital processing, optical processors for smart structures, and a self-organizing neural network for unsupervised learning.

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

Event-triggered Kalman-consensus filter for two-target tracking sensor networks.

PubMed

Su, Housheng; Li, Zhenghao; Ye, Yanyan

2017-11-01

This paper is concerned with the problem of event-triggered Kalman-consensus filter for two-target tracking sensor networks. According to the event-triggered protocol and the mean-square analysis, a suboptimal Kalman gain matrix is derived and a suboptimal event-triggered distributed filter is obtained. Based on the Kalman-consensus filter protocol, all sensors which only depend on its neighbors' information can track their corresponding targets. Furthermore, utilizing Lyapunov method and matrix theory, some sufficient conditions are presented for ensuring the stability of the system. Finally, a simulation example is presented to verify the effectiveness of the proposed event-triggered protocol. Copyright © 2017 ISA. Published by Elsevier Ltd. All rights reserved.

An Approach to Improve the Quality of Infrared Images of Vein-Patterns

PubMed Central

Lin, Chih-Lung

2011-01-01

This study develops an approach to improve the quality of infrared (IR) images of vein-patterns, which usually have noise, low contrast, low brightness and small objects of interest, thus requiring preprocessing to improve their quality. The main characteristics of the proposed approach are that no prior knowledge about the IR image is necessary and no parameters must be preset. Two main goals are sought: impulse noise reduction and adaptive contrast enhancement technologies. In our study, a fast median-based filter (FMBF) is developed as a noise reduction method. It is based on an IR imaging mechanism to detect the noisy pixels and on a modified median-based filter to remove the noisy pixels in IR images. FMBF has the advantage of a low computation load. In addition, FMBF can retain reasonably good edges and texture information when the size of the filter window increases. The most important advantage is that the peak signal-to-noise ratio (PSNR) caused by FMBF is higher than the PSNR caused by the median filter. A hybrid cumulative histogram equalization (HCHE) is proposed for adaptive contrast enhancement. HCHE can automatically generate a hybrid cumulative histogram (HCH) based on two different pieces of information about the image histogram. HCHE can improve the enhancement effect on hot objects rather than background. The experimental results are addressed and demonstrate that the proposed approach is feasible for use as an effective and adaptive process for enhancing the quality of IR vein-pattern images. PMID:22247674

An approach to improve the quality of infrared images of vein-patterns.

PubMed

Lin, Chih-Lung

2011-01-01

This study develops an approach to improve the quality of infrared (IR) images of vein-patterns, which usually have noise, low contrast, low brightness and small objects of interest, thus requiring preprocessing to improve their quality. The main characteristics of the proposed approach are that no prior knowledge about the IR image is necessary and no parameters must be preset. Two main goals are sought: impulse noise reduction and adaptive contrast enhancement technologies. In our study, a fast median-based filter (FMBF) is developed as a noise reduction method. It is based on an IR imaging mechanism to detect the noisy pixels and on a modified median-based filter to remove the noisy pixels in IR images. FMBF has the advantage of a low computation load. In addition, FMBF can retain reasonably good edges and texture information when the size of the filter window increases. The most important advantage is that the peak signal-to-noise ratio (PSNR) caused by FMBF is higher than the PSNR caused by the median filter. A hybrid cumulative histogram equalization (HCHE) is proposed for adaptive contrast enhancement. HCHE can automatically generate a hybrid cumulative histogram (HCH) based on two different pieces of information about the image histogram. HCHE can improve the enhancement effect on hot objects rather than background. The experimental results are addressed and demonstrate that the proposed approach is feasible for use as an effective and adaptive process for enhancing the quality of IR vein-pattern images.

An adaptive deep-coupled GNSS/INS navigation system with hybrid pre-filter processing

NASA Astrophysics Data System (ADS)

Wu, Mouyan; Ding, Jicheng; Zhao, Lin; Kang, Yingyao; Luo, Zhibin

2018-02-01

The deep-coupling of a global navigation satellite system (GNSS) with an inertial navigation system (INS) can provide accurate and reliable navigation information. There are several kinds of deeply-coupled structures. These can be divided mainly into coherent and non-coherent pre-filter based structures, which have their own strong advantages and disadvantages, especially in accuracy and robustness. In this paper, the existing pre-filters of the deeply-coupled structures are analyzed and modified to improve them firstly. Then, an adaptive GNSS/INS deeply-coupled algorithm with hybrid pre-filters processing is proposed to combine the advantages of coherent and non-coherent structures. An adaptive hysteresis controller is designed to implement the hybrid pre-filters processing strategy. The simulation and vehicle test results show that the adaptive deeply-coupled algorithm with hybrid pre-filters processing can effectively improve navigation accuracy and robustness, especially in a GNSS-challenged environment.

Worst-error analysis of batch filter and sequential filter in navigation problems. [in spacecraft trajectory estimation

NASA Technical Reports Server (NTRS)

Nishimura, T.

1975-01-01

This paper proposes a worst-error analysis for dealing with problems of estimation of spacecraft trajectories in deep space missions. Navigation filters in use assume either constant or stochastic (Markov) models for their estimated parameters. When the actual behavior of these parameters does not follow the pattern of the assumed model, the filters sometimes result in very poor performance. To prepare for such pathological cases, the worst errors of both batch and sequential filters are investigated based on the incremental sensitivity studies of these filters. By finding critical switching instances of non-gravitational accelerations, intensive tracking can be carried out around those instances. Also the worst errors in the target plane provide a measure in assignment of the propellant budget for trajectory corrections. Thus the worst-error study presents useful information as well as practical criteria in establishing the maneuver and tracking strategy of spacecraft's missions.

Research on a Lamb Wave and Particle Filter-Based On-Line Crack Propagation Prognosis Method.

PubMed

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

2016-03-03

Prognostics and health management techniques have drawn widespread attention due to their ability to facilitate maintenance activities based on need. On-line prognosis of fatigue crack propagation can offer information for optimizing operation and maintenance strategies in real-time. This paper proposes a Lamb wave-particle filter (LW-PF)-based method for on-line prognosis of fatigue crack propagation which takes advantages of the possibility of on-line monitoring to evaluate the actual crack length and uses a particle filter to deal with the crack evolution and monitoring uncertainties. The piezoelectric transducers (PZTs)-based active Lamb wave method is adopted for on-line crack monitoring. The state space model relating to crack propagation is established by the data-driven and finite element methods. Fatigue experiments performed on hole-edge crack specimens have validated the advantages of the proposed method.

Collaborative filtering on a family of biological targets.

PubMed

Erhan, Dumitru; L'heureux, Pierre-Jean; Yue, Shi Yi; Bengio, Yoshua

2006-01-01

Building a QSAR model of a new biological target for which few screening data are available is a statistical challenge. However, the new target may be part of a bigger family, for which we have more screening data. Collaborative filtering or, more generally, multi-task learning, is a machine learning approach that improves the generalization performance of an algorithm by using information from related tasks as an inductive bias. We use collaborative filtering techniques for building predictive models that link multiple targets to multiple examples. The more commonalities between the targets, the better the multi-target model that can be built. We show an example of a multi-target neural network that can use family information to produce a predictive model of an undersampled target. We evaluate JRank, a kernel-based method designed for collaborative filtering. We show their performance on compound prioritization for an HTS campaign and the underlying shared representation between targets. JRank outperformed the neural network both in the single- and multi-target models.

Neuro-inspired smart image sensor: analog Hmax implementation

NASA Astrophysics Data System (ADS)

Paindavoine, Michel; Dubois, Jérôme; Musa, Purnawarman

2015-03-01

Neuro-Inspired Vision approach, based on models from biology, allows to reduce the computational complexity. One of these models - The Hmax model - shows that the recognition of an object in the visual cortex mobilizes V1, V2 and V4 areas. From the computational point of view, V1 corresponds to the area of the directional filters (for example Sobel filters, Gabor filters or wavelet filters). This information is then processed in the area V2 in order to obtain local maxima. This new information is then sent to an artificial neural network. This neural processing module corresponds to area V4 of the visual cortex and is intended to categorize objects present in the scene. In order to realize autonomous vision systems (consumption of a few milliwatts) with such treatments inside, we studied and realized in 0.35μm CMOS technology prototypes of two image sensors in order to achieve the V1 and V2 processing of Hmax model.

SU-F-I-10: Spatially Local Statistics for Adaptive Image Filtering

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

Iliopoulos, AS; Sun, X; Floros, D

Purpose: To facilitate adaptive image filtering operations, addressing spatial variations in both noise and signal. Such issues are prevalent in cone-beam projections, where physical effects such as X-ray scattering result in spatially variant noise, violating common assumptions of homogeneous noise and challenging conventional filtering approaches to signal extraction and noise suppression. Methods: We present a computational mechanism for probing into and quantifying the spatial variance of noise throughout an image. The mechanism builds a pyramid of local statistics at multiple spatial scales; local statistical information at each scale includes (weighted) mean, median, standard deviation, median absolute deviation, as well asmore » histogram or dynamic range after local mean/median shifting. Based on inter-scale differences of local statistics, the spatial scope of distinguishable noise variation is detected in a semi- or un-supervised manner. Additionally, we propose and demonstrate the incorporation of such information in globally parametrized (i.e., non-adaptive) filters, effectively transforming the latter into spatially adaptive filters. The multi-scale mechanism is materialized by efficient algorithms and implemented in parallel CPU/GPU architectures. Results: We demonstrate the impact of local statistics for adaptive image processing and analysis using cone-beam projections of a Catphan phantom, fitted within an annulus to increase X-ray scattering. The effective spatial scope of local statistics calculations is shown to vary throughout the image domain, necessitating multi-scale noise and signal structure analysis. Filtering results with and without spatial filter adaptation are compared visually, illustrating improvements in imaging signal extraction and noise suppression, and in preserving information in low-contrast regions. Conclusion: Local image statistics can be incorporated in filtering operations to equip them with spatial adaptivity to spatial signal/noise variations. An efficient multi-scale computational mechanism is developed to curtail processing latency. Spatially adaptive filtering may impact subsequent processing tasks such as reconstruction and numerical gradient computations for deformable registration. NIH Grant No. R01-184173.« less

Cross-media color reproduction using the frequency-based spatial gamut mapping algorithm based on human color vision

NASA Astrophysics Data System (ADS)

Wu, Guangyuan; Niu, Shijun; Li, Xiaozhou; Hu, Guichun

2018-04-01

Due to the increasing globalization of printing industry, remoting proofing will become the inevitable development trend. Cross-media color reproduction will occur in different color gamuts using remote proofing technologies, which usually leads to the problem of incompatible color gamut. In this paper, to achieve equivalent color reproduction between a monitor and a printer, a frequency-based spatial gamut mapping algorithm is proposed for decreasing the loss of visual color information. The design of algorithm is based on the contrast sensitivity functions (CSF), which exploited CSF spatial filter to preserve luminance of the high spatial frequencies and chrominance of the low frequencies. First we show a general framework for how to apply CSF spatial filter in retention of relevant visual information. Then we compare the proposed framework with HPMINDE, CUSP, Bala's algorithm. The psychophysical experimental results indicated the good performance of the proposed algorithm.

The influence of filtering and downsampling on the estimation of transfer entropy

PubMed Central

Florin, Esther; von Papen, Michael; Timmermann, Lars

2017-01-01

Transfer entropy (TE) provides a generalized and model-free framework to study Wiener-Granger causality between brain regions. Because of its nonparametric character, TE can infer directed information flow also from nonlinear systems. Despite its increasing number of applications in neuroscience, not much is known regarding the influence of common electrophysiological preprocessing on its estimation. We test the influence of filtering and downsampling on a recently proposed nearest neighborhood based TE estimator. Different filter settings and downsampling factors were tested in a simulation framework using a model with a linear coupling function and two nonlinear models with sigmoid and logistic coupling functions. For nonlinear coupling and progressively lower low-pass filter cut-off frequencies up to 72% false negative direct connections and up to 26% false positive connections were identified. In contrast, for the linear model, a monotonic increase was only observed for missed indirect connections (up to 86%). High-pass filtering (1 Hz, 2 Hz) had no impact on TE estimation. After low-pass filtering interaction delays were significantly underestimated. Downsampling the data by a factor greater than the assumed interaction delay erased most of the transmitted information and thus led to a very high percentage (67–100%) of false negative direct connections. Low-pass filtering increases the number of missed connections depending on the filters cut-off frequency. Downsampling should only be done if the sampling factor is smaller than the smallest assumed interaction delay of the analyzed network. PMID:29149201

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.

Reducing uncertainties associated with filter-based optical measurements of light absorbing carbon particles with chemical information

NASA Astrophysics Data System (ADS)

Engström, J. E.; Leck, C.

2011-08-01

The presented filter-based optical method for determination of soot (light absorbing carbon or Black Carbon, BC) can be implemented in the field under primitive conditions and at low cost. This enables researchers with small economical means to perform monitoring at remote locations, especially in the Asia where it is much needed. One concern when applying filter-based optical measurements of BC is that they suffer from systematic errors due to the light scattering of non-absorbing particles co-deposited on the filter, such as inorganic salts and mineral dust. In addition to an optical correction of the non-absorbing material this study provides a protocol for correction of light scattering based on the chemical quantification of the material, which is a novelty. A newly designed photometer was implemented to measure light transmission on particle accumulating filters, which includes an additional sensor recording backscattered light. The choice of polycarbonate membrane filters avoided high chemical blank values and reduced errors associated with length of the light path through the filter. Two protocols for corrections were applied to aerosol samples collected at the Maldives Climate Observatory Hanimaadhoo during episodes with either continentally influenced air from the Indian/Arabian subcontinents (winter season) or pristine air from the Southern Indian Ocean (summer monsoon). The two ways of correction (optical and chemical) lowered the particle light absorption of BC by 63 to 61 %, respectively, for data from the Arabian Sea sourced group, resulting in median BC absorption coefficients of 4.2 and 3.5 Mm-1. Corresponding values for the South Indian Ocean data were 69 and 97 % (0.38 and 0.02 Mm-1). A comparison with other studies in the area indicated an overestimation of their BC levels, by up to two orders of magnitude. This raises the necessity for chemical correction protocols on optical filter-based determinations of BC, before even the sign on the radiative forcing based on their effects can be assessed.

Filtering genetic variants and placing informative priors based on putative biological function.

PubMed

Friedrichs, Stefanie; Malzahn, Dörthe; Pugh, Elizabeth W; Almeida, Marcio; Liu, Xiao Qing; Bailey, Julia N

2016-02-03

High-density genetic marker data, especially sequence data, imply an immense multiple testing burden. This can be ameliorated by filtering genetic variants, exploiting or accounting for correlations between variants, jointly testing variants, and by incorporating informative priors. Priors can be based on biological knowledge or predicted variant function, or even be used to integrate gene expression or other omics data. Based on Genetic Analysis Workshop (GAW) 19 data, this article discusses diversity and usefulness of functional variant scores provided, for example, by PolyPhen2, SIFT, or RegulomeDB annotations. Incorporating functional scores into variant filters or weights and adjusting the significance level for correlations between variants yielded significant associations with blood pressure traits in a large family study of Mexican Americans (GAW19 data set). Marker rs218966 in gene PHF14 and rs9836027 in MAP4 significantly associated with hypertension; additionally, rare variants in SNUPN significantly associated with systolic blood pressure. Variant weights strongly influenced the power of kernel methods and burden tests. Apart from variant weights in test statistics, prior weights may also be used when combining test statistics or to informatively weight p values while controlling false discovery rate (FDR). Indeed, power improved when gene expression data for FDR-controlled informative weighting of association test p values of genes was used. Finally, approaches exploiting variant correlations included identity-by-descent mapping and the optimal strategy for joint testing rare and common variants, which was observed to depend on linkage disequilibrium structure.

Chaos-based wireless communication resisting multipath effects.

PubMed

Yao, Jun-Liang; Li, Chen; Ren, Hai-Peng; Grebogi, Celso

2017-09-01

In additive white Gaussian noise channel, chaos has been shown to be the optimal coherent communication waveform in the sense of using a very simple matched filter to maximize the signal-to-noise ratio. Recently, Lyapunov exponent spectrum of the chaotic signals after being transmitted through a wireless channel has been shown to be unaltered, paving the way for wireless communication using chaos. In wireless communication systems, inter-symbol interference caused by multipath propagation is one of the main obstacles to achieve high bit transmission rate and low bit-error rate (BER). How to resist the multipath effect is a fundamental problem in a chaos-based wireless communication system (CWCS). In this paper, a CWCS is built to transmit chaotic signals generated by a hybrid dynamical system and then to filter the received signals by using the corresponding matched filter to decrease the noise effect and to detect the binary information. We find that the multipath effect can be effectively resisted by regrouping the return map of the received signal and by setting the corresponding threshold based on the available information. We show that the optimal threshold is a function of the channel parameters and of the information symbols. Practically, the channel parameters are time-variant, and the future information symbols are unavailable. In this case, a suboptimal threshold is proposed, and the BER using the suboptimal threshold is derived analytically. Simulation results show that the CWCS achieves a remarkable competitive performance even under inaccurate channel parameters.

Chaos-based wireless communication resisting multipath effects

NASA Astrophysics Data System (ADS)

Yao, Jun-Liang; Li, Chen; Ren, Hai-Peng; Grebogi, Celso

2017-09-01

In additive white Gaussian noise channel, chaos has been shown to be the optimal coherent communication waveform in the sense of using a very simple matched filter to maximize the signal-to-noise ratio. Recently, Lyapunov exponent spectrum of the chaotic signals after being transmitted through a wireless channel has been shown to be unaltered, paving the way for wireless communication using chaos. In wireless communication systems, inter-symbol interference caused by multipath propagation is one of the main obstacles to achieve high bit transmission rate and low bit-error rate (BER). How to resist the multipath effect is a fundamental problem in a chaos-based wireless communication system (CWCS). In this paper, a CWCS is built to transmit chaotic signals generated by a hybrid dynamical system and then to filter the received signals by using the corresponding matched filter to decrease the noise effect and to detect the binary information. We find that the multipath effect can be effectively resisted by regrouping the return map of the received signal and by setting the corresponding threshold based on the available information. We show that the optimal threshold is a function of the channel parameters and of the information symbols. Practically, the channel parameters are time-variant, and the future information symbols are unavailable. In this case, a suboptimal threshold is proposed, and the BER using the suboptimal threshold is derived analytically. Simulation results show that the CWCS achieves a remarkable competitive performance even under inaccurate channel parameters.

Multirate and event-driven Kalman filters for helicopter flight

NASA Technical Reports Server (NTRS)

Sridhar, Banavar; Smith, Phillip; Suorsa, Raymond E.; Hussien, Bassam

1993-01-01

A vision-based obstacle detection system that provides information about objects as a function of azimuth and elevation is discussed. The range map is computed using a sequence of images from a passive sensor, and an extended Kalman filter is used to estimate range to obstacles. The magnitude of the optical flow that provides measurements for each Kalman filter varies significantly over the image depending on the helicopter motion and object location. In a standard Kalman filter, the measurement update takes place at fixed intervals. It may be necessary to use a different measurement update rate in different parts of the image in order to maintain the same signal to noise ratio in the optical flow calculations. A range estimation scheme that accepts the measurement only under certain conditions is presented. The estimation results from the standard Kalman filter are compared with results from a multirate Kalman filter and an event-driven Kalman filter for a sequence of helicopter flight images.

Oil Spill AISA+ Hyperspectral Data Detection Based on Different Sea Surface Glint Suppression Methods

NASA Astrophysics Data System (ADS)

Yang, J.; Ren, G.; Ma, Y.; Dong, L.; Wan, J.

2018-04-01

The marine oil spill is a sudden event, and the airborne hyperspectral means to detect the oil spill is an important part of the rapid response. Sun glint, the specular reflection of sun light from water surface to sensor, is inevitable due to the limitation of observation geometry, which makes so much bright glint in image that it is difficult to extract oil spill feature information from the remote sensing data. This paper takes AISA+ airborne hyperspectral oil spill image as data source, using multi-scale wavelet transform, enhanced Lee filter, enhanced Frost filter and mean filter method for sea surface glint suppression of images. And then the classical SVM method is used for the oil spill information detection, and oil spill information distribution map obtained by human-computer interactive interpretation is used to verify the accuracy of oil spill detection. The results show that the above methods can effectively suppress the sea surface glints and improve the accuracy of oil spill detection. The enhanced Lee filter method has the highest detection accuracy of 88.28 %, which is 12.2 % higher than that of the original image.

Comparison of the bacterial removal performance of silver nanoparticles and a polymer based quaternary amine functiaonalized silsesquioxane coated point-of-use ceramic water filters.

PubMed

Zhang, Hongyin; Oyanedel-Craver, Vinka

2013-09-15

This study compares the disinfection performance of ceramic water filters impregnated with two antibacterial compounds: silver nanoparticles and a polymer based quaternary amine functiaonalized silsesquioxane (poly(trihydroxysilyl) propyldimethyloctadecyl ammonium chloride (TPA)). This study evaluated these compounds using ceramic disks manufactures with clay obtained from a ceramic filter factory located in San Mateo Ixtatan, Guatemala. Instead of using full size ceramic water filters, manufactured 6.5 cm diameter ceramic water filter disks were used. Results showed that TPA can achieve a log bacterial reduction value of 10 while silver nanoparticles reached up to 2 log reduction using a initial concentration of bacteria of 10(10)-10(11)CFU/ml. Similarly, bacterial transport demonstrated that ceramic filter disks painted with TPA achieved a bacterial log reduction value of 6.24, which is about 2 log higher than the values obtained for disks painted with silver nanoparticles (bacterial log reduction value: 4.42). The release of both disinfectants from the ceramic materials to the treated water was determined measuring the effluent concentrations in each test performed. Regarding TPA, about 3% of the total mass applied to the ceramic disks was released in the effluent over 300 min, which is slightly lower than the release percentage for silver nanoparticles (4%). This study showed that TPA provides a comparable disinfection performance than silver nanoparticles in ceramic water filter. Another advantage of using TPA is the cost as the price of TPA is considerable lower than silver nanoparticles. In spite of the use of TPA in several medical related products, there is only partial information regarding the health risk associated with the ingestion of this compound. Additional long-term toxicological information for TPA should be evaluated before its future application in ceramic water filters. Copyright © 2013 Elsevier B.V. All rights reserved.

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.

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.

ScatterBlogs2: real-time monitoring of microblog messages through user-guided filtering.

PubMed

Bosch, Harald; Thom, Dennis; Heimerl, Florian; Püttmann, Edwin; Koch, Steffen; Krüger, Robert; Wörner, Michael; Ertl, Thomas

2013-12-01

The number of microblog posts published daily has reached a level that hampers the effective retrieval of relevant messages, and the amount of information conveyed through services such as Twitter is still increasing. Analysts require new methods for monitoring their topic of interest, dealing with the data volume and its dynamic nature. It is of particular importance to provide situational awareness for decision making in time-critical tasks. Current tools for monitoring microblogs typically filter messages based on user-defined keyword queries and metadata restrictions. Used on their own, such methods can have drawbacks with respect to filter accuracy and adaptability to changes in trends and topic structure. We suggest ScatterBlogs2, a new approach to let analysts build task-tailored message filters in an interactive and visual manner based on recorded messages of well-understood previous events. These message filters include supervised classification and query creation backed by the statistical distribution of terms and their co-occurrences. The created filter methods can be orchestrated and adapted afterwards for interactive, visual real-time monitoring and analysis of microblog feeds. We demonstrate the feasibility of our approach for analyzing the Twitter stream in emergency management scenarios.

Quantum neural network-based EEG filtering for a brain-computer interface.

PubMed

Gandhi, Vaibhav; Prasad, Girijesh; Coyle, Damien; Behera, Laxmidhar; McGinnity, Thomas Martin

2014-02-01

A novel neural information processing architecture inspired by quantum mechanics and incorporating the well-known Schrodinger wave equation is proposed in this paper. The proposed architecture referred to as recurrent quantum neural network (RQNN) can characterize a nonstationary stochastic signal as time-varying wave packets. A robust unsupervised learning algorithm enables the RQNN to effectively capture the statistical behavior of the input signal and facilitates the estimation of signal embedded in noise with unknown characteristics. The results from a number of benchmark tests show that simple signals such as dc, staircase dc, and sinusoidal signals embedded within high noise can be accurately filtered and particle swarm optimization can be employed to select model parameters. The RQNN filtering procedure is applied in a two-class motor imagery-based brain-computer interface where the objective was to filter electroencephalogram (EEG) signals before feature extraction and classification to increase signal separability. A two-step inner-outer fivefold cross-validation approach is utilized to select the algorithm parameters subject-specifically for nine subjects. It is shown that the subject-specific RQNN EEG filtering significantly improves brain-computer interface performance compared to using only the raw EEG or Savitzky-Golay filtered EEG across multiple sessions.

Training of Attentional Filtering, but Not of Memory Storage, Enhances Working Memory Efficiency by Strengthening the Neuronal Gatekeeper Network.

PubMed

Schmicker, Marlen; Schwefel, Melanie; Vellage, Anne-Katrin; Müller, Notger G

2016-04-01

Memory training (MT) in older adults with memory deficits often leads to frustration and, therefore, is usually not recommended. Here, we pursued an alternative approach and looked for transfer effects of 1-week attentional filter training (FT) on working memory performance and its neuronal correlates in young healthy humans. The FT effects were compared with pure MT, which lacked the necessity to filter out irrelevant information. Before and after training, all participants performed an fMRI experiment that included a combined task in which stimuli had to be both filtered based on color and stored in memory. We found that training induced processing changes by biasing either filtering or storage. FT induced larger transfer effects on the untrained cognitive function than MT. FT increased neuronal activity in frontal parts of the neuronal gatekeeper network, which is proposed to hinder irrelevant information from being unnecessarily stored in memory. MT decreased neuronal activity in the BG part of the gatekeeper network but enhanced activity in the parietal storage node. We take these findings as evidence that FT renders working memory more efficient by strengthening the BG-prefrontal gatekeeper network. MT, on the other hand, simply stimulates storage of any kind of information. These findings illustrate a tight connection between working memory and attention, and they may open up new avenues for ameliorating memory deficits in patients with cognitive impairments.

Face recognition algorithm based on Gabor wavelet and locality preserving projections

NASA Astrophysics Data System (ADS)

Liu, Xiaojie; Shen, Lin; Fan, Honghui

2017-07-01

In order to solve the effects of illumination changes and differences of personal features on the face recognition rate, this paper presents a new face recognition algorithm based on Gabor wavelet and Locality Preserving Projections (LPP). The problem of the Gabor filter banks with high dimensions was solved effectively, and also the shortcoming of the LPP on the light illumination changes was overcome. Firstly, the features of global image information were achieved, which used the good spatial locality and orientation selectivity of Gabor wavelet filters. Then the dimensions were reduced by utilizing the LPP, which well-preserved the local information of the image. The experimental results shown that this algorithm can effectively extract the features relating to facial expressions, attitude and other information. Besides, it can reduce influence of the illumination changes and the differences in personal features effectively, which improves the face recognition rate to 99.2%.

76 FR 42130 - Agency Information Collection Activities: BioWatch Filter Holder Log

Federal Register 2010, 2011, 2012, 2013, 2014

2011-07-18

... DEPARTMENT OF HOMELAND SECURITY Agency Information Collection Activities: BioWatch Filter Holder...) assigned responsibility for installing and removing filters from aerosol collection devices and transportation to local laboratories for sample analysis. A standard filter log form is completed for each sample...

76 FR 24504 - Agency Information Collection Activities: BioWatch Filter Holder Log

Federal Register 2010, 2011, 2012, 2013, 2014

2011-05-02

... DEPARTMENT OF HOMELAND SECURITY Agency Information Collection Activities: BioWatch Filter Holder...) assigned responsibility for installing and removing filters from aerosol collection devices and transportation to local laboratories for sample analysis. A standard filter log form is completed for each sample...

Leukocyte-reduced blood components: patient benefits and practical applications.

PubMed

Higgins, V L

1996-05-01

To review the various types of filters used for red blood cell and platelet transfusions and to explain the trend in the use of leukocyte removal filters, practical information about their use, considerations in the selection of a filtration method, and cost-effectiveness issues. Published articles, books, and the author's experience. Leukocyte removal filters are used to reduce complications associated with transfused white blood cells that are contained in units of red blood cells and platelets. These complications include nonhemolytic febrile transfusion reactions (NHFTRs), alloimmunization and refractoriness to platelet transfusion, transfusion-transmitted cytomegalovirus (CMV), and immunomodulation. Leukocyte removal filters may be used at the bedside, in a hospital blood bank, or in a blood collection center. Factors that affect the flow rate of these filters include the variations in the blood component, the equipment used, and filter priming. Studies on the cost-effectiveness of using leukocyte-reduced blood components demonstrate savings based on the reduction of NHFTRs, reduction in the number of blood components used, and the use of filtered blood components as the equivalent of CMV seronegative-screened products. The use of leukocyte-reduced blood components significantly diminishes or prevents many of the adverse transfusion reactions associated with donor white blood cells. Leukocyte removal filters are cost-effective, and filters should be selected based on their ability to consistently achieve low leukocyte residual levels as well as their ease of use. Physicians may order leukocyte-reduced blood components for specific patients, or the components may be used because of an established institutional transfusion policy. Nurses often participate in deciding on a filtration method, primarily based on ease of use. Understanding the considerations in selecting a filtration method will help nurses make appropriate decisions to ensure quality patient care.

Multiple estimation channel decoupling and optimization method based on inverse system

NASA Astrophysics Data System (ADS)

Wu, Peng; Mu, Rongjun; Zhang, Xin; Deng, Yanpeng

2018-03-01

This paper addressed the intelligent autonomous navigation request of intelligent deformation missile, based on the intelligent deformation missile dynamics and kinematics modeling, navigation subsystem solution method and error modeling, and then focuses on the corresponding data fusion and decision fusion technology, decouples the sensitive channel of the filter input through the inverse system of design dynamics to reduce the influence of sudden change of the measurement information on the filter input. Then carrying out a series of simulation experiments, which verified the feasibility of the inverse system decoupling algorithm effectiveness.

Multiple feature extraction by using simultaneous wavelet transforms

NASA Astrophysics Data System (ADS)

Mazzaferri, Javier; Ledesma, Silvia; Iemmi, Claudio

2003-07-01

We propose here a method to optically perform multiple feature extraction by using wavelet transforms. The method is based on obtaining the optical correlation by means of a Vander Lugt architecture, where the scene and the filter are displayed on spatial light modulators (SLMs). Multiple phase filters containing the information about the features that we are interested in extracting are designed and then displayed on an SLM working in phase mostly mode. We have designed filters to simultaneously detect edges and corners or different characteristic frequencies contained in the input scene. Simulated and experimental results are shown.

Evaluating the Informative Quality of Documents in SGML Format from Judgements by Means of Fuzzy Linguistic Techniques Based on Computing with Words.

ERIC Educational Resources Information Center

Herrera-Viedma, Enrique; Peis, Eduardo

2003-01-01

Presents a fuzzy evaluation method of SGML documents based on computing with words. Topics include filtering the amount of information available on the Web to assist users in their search processes; document type definitions; linguistic modeling; user-system interaction; and use with XML and other markup languages. (Author/LRW)

A Novel Robust H∞ Filter Based on Krein Space Theory in the SINS/CNS Attitude Reference System.

PubMed

Yu, Fei; Lv, Chongyang; Dong, Qianhui

2016-03-18

Owing to their numerous merits, such as compact, autonomous and independence, the strapdown inertial navigation system (SINS) and celestial navigation system (CNS) can be used in marine applications. What is more, due to the complementary navigation information obtained from two different kinds of sensors, the accuracy of the SINS/CNS integrated navigation system can be enhanced availably. Thus, the SINS/CNS system is widely used in the marine navigation field. However, the CNS is easily interfered with by the surroundings, which will lead to the output being discontinuous. Thus, the uncertainty problem caused by the lost measurement will reduce the system accuracy. In this paper, a robust H∞ filter based on the Krein space theory is proposed. The Krein space theory is introduced firstly, and then, the linear state and observation models of the SINS/CNS integrated navigation system are established reasonably. By taking the uncertainty problem into account, in this paper, a new robust H∞ filter is proposed to improve the robustness of the integrated system. At last, this new robust filter based on the Krein space theory is estimated by numerical simulations and actual experiments. Additionally, the simulation and experiment results and analysis show that the attitude errors can be reduced by utilizing the proposed robust filter effectively when the measurements are missing discontinuous. Compared to the traditional Kalman filter (KF) method, the accuracy of the SINS/CNS integrated system is improved, verifying the robustness and the availability of the proposed robust H∞ filter.

a Threshold-Free Filtering Algorithm for Airborne LIDAR Point Clouds Based on Expectation-Maximization

NASA Astrophysics Data System (ADS)

Hui, Z.; Cheng, P.; Ziggah, Y. Y.; Nie, Y.

2018-04-01

Filtering is a key step for most applications of airborne LiDAR point clouds. Although lots of filtering algorithms have been put forward in recent years, most of them suffer from parameters setting or thresholds adjusting, which will be time-consuming and reduce the degree of automation of the algorithm. To overcome this problem, this paper proposed a threshold-free filtering algorithm based on expectation-maximization. The proposed algorithm is developed based on an assumption that point clouds are seen as a mixture of Gaussian models. The separation of ground points and non-ground points from point clouds can be replaced as a separation of a mixed Gaussian model. Expectation-maximization (EM) is applied for realizing the separation. EM is used to calculate maximum likelihood estimates of the mixture parameters. Using the estimated parameters, the likelihoods of each point belonging to ground or object can be computed. After several iterations, point clouds can be labelled as the component with a larger likelihood. Furthermore, intensity information was also utilized to optimize the filtering results acquired using the EM method. The proposed algorithm was tested using two different datasets used in practice. Experimental results showed that the proposed method can filter non-ground points effectively. To quantitatively evaluate the proposed method, this paper adopted the dataset provided by the ISPRS for the test. The proposed algorithm can obtain a 4.48 % total error which is much lower than most of the eight classical filtering algorithms reported by the ISPRS.

Control of coherent information via on-chip photonic–phononic emitter–receivers

DOE PAGES

Shin, Heedeuk; Cox, Jonathan A.; Jarecki, Robert; ...

2015-03-05

We report that rapid progress in integrated photonics has fostered numerous chip-scale sensing, computing and signal processing technologies. However, many crucial filtering and signal delay operations are difficult to perform with all-optical devices. Unlike photons propagating at luminal speeds, GHz-acoustic phonons moving at slower velocities allow information to be stored, filtered and delayed over comparatively smaller length-scales with remarkable fidelity. Hence, controllable and efficient coupling between coherent photons and phonons enables new signal processing technologies that greatly enhance the performance and potential impact of integrated photonics. Here we demonstrate a mechanism for coherent information processing based on travelling-wave photon–phonon transduction,more » which achieves a phonon emit-and-receive process between distinct nanophotonic waveguides. Using this device, physics—which supports GHz frequencies—we create wavelength-insensitive radiofrequency photonic filters with frequency selectivity, narrow-linewidth and high power-handling in silicon. More generally, this emit-receive concept is the impetus for enabling new signal processing schemes.« less

Control of coherent information via on-chip photonic–phononic emitter–receivers

PubMed Central

Shin, Heedeuk; Cox, Jonathan A.; Jarecki, Robert; Starbuck, Andrew; Wang, Zheng; Rakich, Peter T.

2015-01-01

Rapid progress in integrated photonics has fostered numerous chip-scale sensing, computing and signal processing technologies. However, many crucial filtering and signal delay operations are difficult to perform with all-optical devices. Unlike photons propagating at luminal speeds, GHz-acoustic phonons moving at slower velocities allow information to be stored, filtered and delayed over comparatively smaller length-scales with remarkable fidelity. Hence, controllable and efficient coupling between coherent photons and phonons enables new signal processing technologies that greatly enhance the performance and potential impact of integrated photonics. Here we demonstrate a mechanism for coherent information processing based on travelling-wave photon–phonon transduction, which achieves a phonon emit-and-receive process between distinct nanophotonic waveguides. Using this device, physics—which supports GHz frequencies—we create wavelength-insensitive radiofrequency photonic filters with frequency selectivity, narrow-linewidth and high power-handling in silicon. More generally, this emit-receive concept is the impetus for enabling new signal processing schemes. PMID:25740405

Characterization Of Improved Binary Phase-Only Filters In A Real-Time Coherent Optical Correlation System

NASA Astrophysics Data System (ADS)

Flannery, D.; Keller, P.; Cartwright, S.; Loomis, J.

1987-06-01

Attractive correlation system performance potential is possible using magneto-optic spatial light modulators (SLM) to implement binary phase-only reference filters at high rates, provided the correlation performance of such reduced-information-content filters is adequate for the application. In the case studied here, the desired filter impulse response is a rectangular shape, which cannot be achieved with the usual binary phase-only filter formulation. The correlation application problem is described and techniques for synthesizing improved filter impulse response are considered. A compromise solution involves the cascading of a fixed amplitude-only weighting mask with the binary phase-only SLM. Based on simulations presented, this approach provides improved impulse responses and good correlation performance, while retaining the critical feature of real-time variations of the size, shape, and orientation of the rectangle by electronic programming of the phase pattern in the SLM. Simulations indicate that, for at least one very challenging input scene clutter situation, these filters provide higher correlation signal-to-noise than does "ideal" correlation, i.e. using a perfect rectangle filter response.

Use of phase-locking value in sensorimotor rhythm-based brain-computer interface: zero-phase coupling and effects of spatial filters.

PubMed

Jian, Wenjuan; Chen, Minyou; McFarland, Dennis J

2017-11-01

Phase-locking value (PLV) is a potentially useful feature in sensorimotor rhythm-based brain-computer interface (BCI). However, volume conduction may cause spurious zero-phase coupling between two EEG signals and it is not clear whether PLV effects are independent of spectral amplitude. Volume conduction might be reduced by spatial filtering, but it is uncertain what impact this might have on PLV. Therefore, the goal of this study was to explore whether zero-phase PLV is meaningful and how it is affected by spatial filtering. Both amplitude and PLV feature were extracted in the frequency band of 10-15 Hz by classical methods using archival EEG data of 18 subjects trained on a two-target BCI task. The results show that with right ear-referenced data, there is meaningful long-range zero-phase synchronization likely involving the primary motor area and the supplementary motor area that cannot be explained by volume conduction. Another novel finding is that the large Laplacian spatial filter enhances the amplitude feature but eliminates most of the phase information seen in ear-referenced data. A bipolar channel using phase-coupled areas also includes both phase and amplitude information and has a significant practical advantage since fewer channels required.

Real-Time Diagnosis of Faults Using a Bank of Kalman Filters

NASA Technical Reports Server (NTRS)

Kobayashi, Takahisa; Simon, Donald L.

2006-01-01

A new robust method of automated real-time diagnosis of faults in an aircraft engine or a similar complex system involves the use of a bank of Kalman filters. In order to be highly reliable, a diagnostic system must be designed to account for the numerous failure conditions that an aircraft engine may encounter in operation. The method achieves this objective though the utilization of multiple Kalman filters, each of which is uniquely designed based on a specific failure hypothesis. A fault-detection-and-isolation (FDI) system, developed based on this method, is able to isolate faults in sensors and actuators while detecting component faults (abrupt degradation in engine component performance). By affording a capability for real-time identification of minor faults before they grow into major ones, the method promises to enhance safety and reduce operating costs. The robustness of this method is further enhanced by incorporating information regarding the aging condition of an engine. In general, real-time fault diagnostic methods use the nominal performance of a "healthy" new engine as a reference condition in the diagnostic process. Such an approach does not account for gradual changes in performance associated with aging of an otherwise healthy engine. By incorporating information on gradual, aging-related changes, the new method makes it possible to retain at least some of the sensitivity and accuracy needed to detect incipient faults while preventing false alarms that could result from erroneous interpretation of symptoms of aging as symptoms of failures. The figure schematically depicts an FDI system according to the new method. The FDI system is integrated with an engine, from which it accepts two sets of input signals: sensor readings and actuator commands. Two main parts of the FDI system are a bank of Kalman filters and a subsystem that implements FDI decision rules. Each Kalman filter is designed to detect a specific sensor or actuator fault. When a sensor or actuator fault occurs, large estimation errors are generated by all filters except the one using the correct hypothesis. By monitoring the residual output of each filter, the specific fault that has occurred can be detected and isolated on the basis of the decision rules. A set of parameters that indicate the performance of the engine components is estimated by the "correct" Kalman filter for use in detecting component faults. To reduce the loss of diagnostic accuracy and sensitivity in the face of aging, the FDI system accepts information from a steady-state-condition-monitoring system. This information is used to update the Kalman filters and a data bank of trim values representative of the current aging condition.

A Novel Segmentation Approach Combining Region- and Edge-Based Information for Ultrasound Images

PubMed Central

Luo, Yaozhong; Liu, Longzhong; Li, Xuelong

2017-01-01

Ultrasound imaging has become one of the most popular medical imaging modalities with numerous diagnostic applications. However, ultrasound (US) image segmentation, which is the essential process for further analysis, is a challenging task due to the poor image quality. In this paper, we propose a new segmentation scheme to combine both region- and edge-based information into the robust graph-based (RGB) segmentation method. The only interaction required is to select two diagonal points to determine a region of interest (ROI) on the original image. The ROI image is smoothed by a bilateral filter and then contrast-enhanced by histogram equalization. Then, the enhanced image is filtered by pyramid mean shift to improve homogeneity. With the optimization of particle swarm optimization (PSO) algorithm, the RGB segmentation method is performed to segment the filtered image. The segmentation results of our method have been compared with the corresponding results obtained by three existing approaches, and four metrics have been used to measure the segmentation performance. The experimental results show that the method achieves the best overall performance and gets the lowest ARE (10.77%), the second highest TPVF (85.34%), and the second lowest FPVF (4.48%). PMID:28536703

Gyro Drift Correction for An Indirect Kalman Filter Based Sensor Fusion Driver.

PubMed

Lee, Chan-Gun; Dao, Nhu-Ngoc; Jang, Seonmin; Kim, Deokhwan; Kim, Yonghun; Cho, Sungrae

2016-06-11

Sensor fusion techniques have made a significant contribution to the success of the recently emerging mobile applications era because a variety of mobile applications operate based on multi-sensing information from the surrounding environment, such as navigation systems, fitness trackers, interactive virtual reality games, etc. For these applications, the accuracy of sensing information plays an important role to improve the user experience (UX) quality, especially with gyroscopes and accelerometers. Therefore, in this paper, we proposed a novel mechanism to resolve the gyro drift problem, which negatively affects the accuracy of orientation computations in the indirect Kalman filter based sensor fusion. Our mechanism focuses on addressing the issues of external feedback loops and non-gyro error elements contained in the state vectors of an indirect Kalman filter. Moreover, the mechanism is implemented in the device-driver layer, providing lower process latency and transparency capabilities for the upper applications. These advances are relevant to millions of legacy applications since utilizing our mechanism does not require the existing applications to be re-programmed. The experimental results show that the root mean square errors (RMSE) before and after applying our mechanism are significantly reduced from 6.3 × 10(-1) to 5.3 × 10(-7), respectively.

Minimum entropy deconvolution optimized sinusoidal synthesis and its application to vibration based fault detection

NASA Astrophysics Data System (ADS)

Li, Gang; Zhao, Qing

2017-03-01

In this paper, a minimum entropy deconvolution based sinusoidal synthesis (MEDSS) filter is proposed to improve the fault detection performance of the regular sinusoidal synthesis (SS) method. The SS filter is an efficient linear predictor that exploits the frequency properties during model construction. The phase information of the harmonic components is not used in the regular SS filter. However, the phase relationships are important in differentiating noise from characteristic impulsive fault signatures. Therefore, in this work, the minimum entropy deconvolution (MED) technique is used to optimize the SS filter during the model construction process. A time-weighted-error Kalman filter is used to estimate the MEDSS model parameters adaptively. Three simulation examples and a practical application case study are provided to illustrate the effectiveness of the proposed method. The regular SS method and the autoregressive MED (ARMED) method are also implemented for comparison. The MEDSS model has demonstrated superior performance compared to the regular SS method and it also shows comparable or better performance with much less computational intensity than the ARMED method.

A proven knowledge-based approach to prioritizing process information

NASA Technical Reports Server (NTRS)

Corsberg, Daniel R.

1991-01-01

Many space-related processes are highly complex systems subject to sudden, major transients. In any complex process control system, a critical aspect is rapid analysis of the changing process information. During a disturbance, this task can overwhelm humans as well as computers. Humans deal with this by applying heuristics in determining significant information. A simple, knowledge-based approach to prioritizing information is described. The approach models those heuristics that humans would use in similar circumstances. The approach described has received two patents and was implemented in the Alarm Filtering System (AFS) at the Idaho National Engineering Laboratory (INEL). AFS was first developed for application in a nuclear reactor control room. It has since been used in chemical processing applications, where it has had a significant impact on control room environments. The approach uses knowledge-based heuristics to analyze data from process instrumentation and respond to that data according to knowledge encapsulated in objects and rules. While AFS cannot perform the complete diagnosis and control task, it has proven to be extremely effective at filtering and prioritizing information. AFS was used for over two years as a first level of analysis for human diagnosticians. Given the approach's proven track record in a wide variety of practical applications, it should be useful in both ground- and space-based systems.

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.

A Novel Multi-Sensor Environmental Perception Method Using Low-Rank Representation and a Particle Filter for Vehicle Reversing Safety

PubMed Central

Zhang, Zutao; Li, Yanjun; Wang, Fubing; Meng, Guanjun; Salman, Waleed; Saleem, Layth; Zhang, Xiaoliang; Wang, Chunbai; Hu, Guangdi; Liu, Yugang

2016-01-01

Environmental perception and information processing are two key steps of active safety for vehicle reversing. Single-sensor environmental perception cannot meet the need for vehicle reversing safety due to its low reliability. In this paper, we present a novel multi-sensor environmental perception method using low-rank representation and a particle filter for vehicle reversing safety. The proposed system consists of four main steps, namely multi-sensor environmental perception, information fusion, target recognition and tracking using low-rank representation and a particle filter, and vehicle reversing speed control modules. First of all, the multi-sensor environmental perception module, based on a binocular-camera system and ultrasonic range finders, obtains the distance data for obstacles behind the vehicle when the vehicle is reversing. Secondly, the information fusion algorithm using an adaptive Kalman filter is used to process the data obtained with the multi-sensor environmental perception module, which greatly improves the robustness of the sensors. Then the framework of a particle filter and low-rank representation is used to track the main obstacles. The low-rank representation is used to optimize an objective particle template that has the smallest L-1 norm. Finally, the electronic throttle opening and automatic braking is under control of the proposed vehicle reversing control strategy prior to any potential collisions, making the reversing control safer and more reliable. The final system simulation and practical testing results demonstrate the validity of the proposed multi-sensor environmental perception method using low-rank representation and a particle filter for vehicle reversing safety. PMID:27294931

A Novel Multi-Sensor Environmental Perception Method Using Low-Rank Representation and a Particle Filter for Vehicle Reversing Safety.

PubMed

Zhang, Zutao; Li, Yanjun; Wang, Fubing; Meng, Guanjun; Salman, Waleed; Saleem, Layth; Zhang, Xiaoliang; Wang, Chunbai; Hu, Guangdi; Liu, Yugang

2016-06-09

Environmental perception and information processing are two key steps of active safety for vehicle reversing. Single-sensor environmental perception cannot meet the need for vehicle reversing safety due to its low reliability. In this paper, we present a novel multi-sensor environmental perception method using low-rank representation and a particle filter for vehicle reversing safety. The proposed system consists of four main steps, namely multi-sensor environmental perception, information fusion, target recognition and tracking using low-rank representation and a particle filter, and vehicle reversing speed control modules. First of all, the multi-sensor environmental perception module, based on a binocular-camera system and ultrasonic range finders, obtains the distance data for obstacles behind the vehicle when the vehicle is reversing. Secondly, the information fusion algorithm using an adaptive Kalman filter is used to process the data obtained with the multi-sensor environmental perception module, which greatly improves the robustness of the sensors. Then the framework of a particle filter and low-rank representation is used to track the main obstacles. The low-rank representation is used to optimize an objective particle template that has the smallest L-1 norm. Finally, the electronic throttle opening and automatic braking is under control of the proposed vehicle reversing control strategy prior to any potential collisions, making the reversing control safer and more reliable. The final system simulation and practical testing results demonstrate the validity of the proposed multi-sensor environmental perception method using low-rank representation and a particle filter for vehicle reversing safety.

Novel joint TOA/RSSI-based WCE location tracking method without prior knowledge of biological human body tissues.

PubMed

Ito, Takahiro; Anzai, Daisuke; Jianqing Wang

2014-01-01

This paper proposes a novel joint time of arrival (TOA)/received signal strength indicator (RSSI)-based wireless capsule endoscope (WCE) location tracking method without prior knowledge of biological human tissues. Generally, TOA-based localization can achieve much higher localization accuracy than other radio frequency-based localization techniques, whereas wireless signals transmitted from a WCE pass through various kinds of human body tissues, as a result, the propagation velocity inside a human body should be different from one in free space. Because the variation of propagation velocity is mainly affected by the relative permittivity of human body tissues, instead of pre-measurement for the relative permittivity in advance, we simultaneously estimate not only the WCE location but also the relative permittivity information. For this purpose, this paper first derives the relative permittivity estimation model with measured RSSI information. Then, we pay attention to a particle filter algorithm with the TOA-based localization and the RSSI-based relative permittivity estimation. Our computer simulation results demonstrates that the proposed tracking methods with the particle filter can accomplish an excellent localization accuracy of around 2 mm without prior information of the relative permittivity of the human body tissues.

Fiber optic sensor for continuous health monitoring in CFRP composite materials

NASA Astrophysics Data System (ADS)

Rippert, Laurent; Papy, Jean-Michel; Wevers, Martine; Van Huffel, Sabine

2002-07-01

An intensity modulated sensor, based on the microbending concept, has been incorporated in laminates produced from a C/epoxy prepreg. Pencil lead break tests (Hsu-Neilsen sources) and tensile tests have been performed on this material. In this research study, fibre optic sensors will be proven to offer an alternative for the robust piezoelectric transducers used for Acoustic Emission (AE) monitoring. The main emphasis has been put on the use of advanced signal processing techniques based on time-frequency analysis. The signal Short Time Fourier Transform (STFT) has been computed and several robust noise reduction algorithms, such as Wiener adaptive filtering, improved spectral subtraction filtering, and Singular Value Decomposition (SVD) -based filtering, have been applied. An energy and frequency -based detection criterion is put forward to detect transient signals that can be correlated with Modal Acoustic Emission (MAE) results and thus damage in the composite material. There is a strong indication that time-frequency analysis and the Hankel Total Least Squares (HTLS) method can also be used for damage characterization. This study shows that the signal from a quite simple microbend optical sensor contains information on the elastic energy released whenever damage is being introduced in the host material by mechanical loading. Robust algorithms can be used to retrieve and analyze this information.

Fast multiview three-dimensional reconstruction method using cost volume filtering

NASA Astrophysics Data System (ADS)

Lee, Seung Joo; Park, Min Ki; Jang, In Yeop; Lee, Kwan H.

2014-03-01

As the number of customers who want to record three-dimensional (3-D) information using a mobile electronic device increases, it becomes more and more important to develop a method which quickly reconstructs a 3-D model from multiview images. A fast multiview-based 3-D reconstruction method is presented, which is suitable for the mobile environment by constructing a cost volume of the 3-D height field. This method consists of two steps: the construction of a reliable base surface and the recovery of shape details. In each step, the cost volume is constructed using photoconsistency and then it is filtered according to the multiscale. The multiscale-based cost volume filtering allows the 3-D reconstruction to maintain the overall shape and to preserve the shape details. We demonstrate the strength of the proposed method in terms of computation time, accuracy, and unconstrained acquisition environment.

A fusion algorithm for infrared and visible based on guided filtering and phase congruency in NSST domain

NASA Astrophysics Data System (ADS)

Liu, Zhanwen; Feng, Yan; Chen, Hang; Jiao, Licheng

2017-10-01

A novel and effective image fusion method is proposed for creating a highly informative and smooth surface of fused image through merging visible and infrared images. Firstly, a two-scale non-subsampled shearlet transform (NSST) is employed to decompose the visible and infrared images into detail layers and one base layer. Then, phase congruency is adopted to extract the saliency maps from the detail layers and a guided filtering is proposed to compute the filtering output of base layer and saliency maps. Next, a novel weighted average technique is used to make full use of scene consistency for fusion and obtaining coefficients map. Finally the fusion image was acquired by taking inverse NSST of the fused coefficients map. Experiments show that the proposed approach can achieve better performance than other methods in terms of subjective visual effect and objective assessment.

Dim target detection method based on salient graph fusion

NASA Astrophysics Data System (ADS)

Hu, Ruo-lan; Shen, Yi-yan; Jiang, Jun

2018-02-01

Dim target detection is one key problem in digital image processing field. With development of multi-spectrum imaging sensor, it becomes a trend to improve the performance of dim target detection by fusing the information from different spectral images. In this paper, one dim target detection method based on salient graph fusion was proposed. In the method, Gabor filter with multi-direction and contrast filter with multi-scale were combined to construct salient graph from digital image. And then, the maximum salience fusion strategy was designed to fuse the salient graph from different spectral images. Top-hat filter was used to detect dim target from the fusion salient graph. Experimental results show that proposal method improved the probability of target detection and reduced the probability of false alarm on clutter background images.

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.

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.

Remote pedestrians detection at night time in FIR Image using contrast filtering and locally projected region based CNN

NASA Astrophysics Data System (ADS)

Kim, Taehwan; Kim, Sungho

2017-02-01

This paper presents a novel method to detect the remote pedestrians. After producing the human temperature based brightness enhancement image using the temperature data input, we generates the regions of interest (ROIs) by the multiscale contrast filtering based approach including the biased hysteresis threshold and clustering, remote pedestrian's height, pixel area and central position information. Afterwards, we conduct local vertical and horizontal projection based ROI refinement and weak aspect ratio based ROI limitation to solve the problem of region expansion in the contrast filtering stage. Finally, we detect the remote pedestrians by validating the final ROIs using transfer learning with convolutional neural network (CNN) feature, following non-maximal suppression (NMS) with strong aspect ratio limitation to improve the detection performance. In the experimental results, we confirmed that the proposed contrast filtering and locally projected region based CNN (CFLP-CNN) outperforms the baseline method by 8% in term of logaveraged miss rate. Also, the proposed method is more effective than the baseline approach and the proposed method provides the better regions that are suitably adjusted to the shape and appearance of remote pedestrians, which makes it detect the pedestrian that didn't find in the baseline approach and are able to help detect pedestrians by splitting the people group into a person.

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.

High-Precision Attitude Estimation Method of Star Sensors and Gyro Based on Complementary Filter and Unscented Kalman Filter

NASA Astrophysics Data System (ADS)

Guo, C.; Tong, X.; Liu, S.; Liu, S.; Lu, X.; Chen, P.; Jin, Y.; Xie, H.

2017-07-01

Determining the attitude of satellite at the time of imaging then establishing the mathematical relationship between image points and ground points is essential in high-resolution remote sensing image mapping. Star tracker is insensitive to the high frequency attitude variation due to the measure noise and satellite jitter, but the low frequency attitude motion can be determined with high accuracy. Gyro, as a short-term reference to the satellite's attitude, is sensitive to high frequency attitude change, but due to the existence of gyro drift and integral error, the attitude determination error increases with time. Based on the opposite noise frequency characteristics of two kinds of attitude sensors, this paper proposes an on-orbit attitude estimation method of star sensors and gyro based on Complementary Filter (CF) and Unscented Kalman Filter (UKF). In this study, the principle and implementation of the proposed method are described. First, gyro attitude quaternions are acquired based on the attitude kinematics equation. An attitude information fusion method is then introduced, which applies high-pass filtering and low-pass filtering to the gyro and star tracker, respectively. Second, the attitude fusion data based on CF are introduced as the observed values of UKF system in the process of measurement updating. The accuracy and effectiveness of the method are validated based on the simulated sensors attitude data. The obtained results indicate that the proposed method can suppress the gyro drift and measure noise of attitude sensors, improving the accuracy of the attitude determination significantly, comparing with the simulated on-orbit attitude and the attitude estimation results of the UKF defined by the same simulation parameters.

Operator Performance Measures for Assessing Voice Communication Effectiveness

DTIC Science & Technology

1989-07-01

performance and work- load assessment techniques have been based.I Broadbent (1958) described a limited capacity filter model of human information...INFORMATION PROCESSING 20 3.1.1. Auditory Attention 20 3.1.2. Auditory Memory 24 3.2. MODELS OF INFORMATION PROCESSING 24 3.2.1. Capacity Theories 25...Learning 0 Attention * Language Specialization • Decision Making• Problem Solving Auditory Information Processing Models of Processing Ooemtor

Signal Conditioning for the Kalman Filter: Application to Satellite Attitude Estimation with Magnetometer and Sun Sensors

PubMed Central

Esteban, Segundo; Girón-Sierra, Jose M.; Polo, Óscar R.; Angulo, Manuel

2016-01-01

Most satellites use an on-board attitude estimation system, based on available sensors. In the case of low-cost satellites, which are of increasing interest, it is usual to use magnetometers and Sun sensors. A Kalman filter is commonly recommended for the estimation, to simultaneously exploit the information from sensors and from a mathematical model of the satellite motion. It would be also convenient to adhere to a quaternion representation. This article focuses on some problems linked to this context. The state of the system should be represented in observable form. Singularities due to alignment of measured vectors cause estimation problems. Accommodation of the Kalman filter originates convergence difficulties. The article includes a new proposal that solves these problems, not needing changes in the Kalman filter algorithm. In addition, the article includes assessment of different errors, initialization values for the Kalman filter; and considers the influence of the magnetic dipole moment perturbation, showing how to handle it as part of the Kalman filter framework. PMID:27809250

Signal Conditioning for the Kalman Filter: Application to Satellite Attitude Estimation with Magnetometer and Sun Sensors.

PubMed

Esteban, Segundo; Girón-Sierra, Jose M; Polo, Óscar R; Angulo, Manuel

2016-10-31

Most satellites use an on-board attitude estimation system, based on available sensors. In the case of low-cost satellites, which are of increasing interest, it is usual to use magnetometers and Sun sensors. A Kalman filter is commonly recommended for the estimation, to simultaneously exploit the information from sensors and from a mathematical model of the satellite motion. It would be also convenient to adhere to a quaternion representation. This article focuses on some problems linked to this context. The state of the system should be represented in observable form. Singularities due to alignment of measured vectors cause estimation problems. Accommodation of the Kalman filter originates convergence difficulties. The article includes a new proposal that solves these problems, not needing changes in the Kalman filter algorithm. In addition, the article includes assessment of different errors, initialization values for the Kalman filter; and considers the influence of the magnetic dipole moment perturbation, showing how to handle it as part of the Kalman filter framework.

Designing a composite correlation filter based on iterative optimization of training images for distortion invariant face recognition

NASA Astrophysics Data System (ADS)

Wang, Q.; Elbouz, M.; Alfalou, A.; Brosseau, C.

2017-06-01

We present a novel method to optimize the discrimination ability and noise robustness of composite filters. This method is based on the iterative preprocessing of training images which can extract boundary and detailed feature information of authentic training faces, thereby improving the peak-to-correlation energy (PCE) ratio of authentic faces and to be immune to intra-class variance and noise interference. By adding the training images directly, one can obtain a composite template with high discrimination ability and robustness for face recognition task. The proposed composite correlation filter does not involve any complicated mathematical analysis and computation which are often required in the design of correlation algorithms. Simulation tests have been conducted to check the effectiveness and feasibility of our proposal. Moreover, to assess robustness of composite filters using receiver operating characteristic (ROC) curves, we devise a new method to count the true positive and false positive rates for which the difference between PCE and threshold is involved.

Adaptive Wiener filter super-resolution of color filter array images.

PubMed

Karch, Barry K; Hardie, Russell C

2013-08-12

Digital color cameras using a single detector array with a Bayer color filter array (CFA) require interpolation or demosaicing to estimate missing color information and provide full-color images. However, demosaicing does not specifically address fundamental undersampling and aliasing inherent in typical camera designs. Fast non-uniform interpolation based super-resolution (SR) is an attractive approach to reduce or eliminate aliasing and its relatively low computational load is amenable to real-time applications. The adaptive Wiener filter (AWF) SR algorithm was initially developed for grayscale imaging and has not previously been applied to color SR demosaicing. Here, we develop a novel fast SR method for CFA cameras that is based on the AWF SR algorithm and uses global channel-to-channel statistical models. We apply this new method as a stand-alone algorithm and also as an initialization image for a variational SR algorithm. This paper presents the theoretical development of the color AWF SR approach and applies it in performance comparisons to other SR techniques for both simulated and real data.

A comparative study of sensor fault diagnosis methods based on observer for ECAS system

NASA Astrophysics Data System (ADS)

Xu, Xing; Wang, Wei; Zou, Nannan; Chen, Long; Cui, Xiaoli

2017-03-01

The performance and practicality of electronically controlled air suspension (ECAS) system are highly dependent on the state information supplied by kinds of sensors, but faults of sensors occur frequently. Based on a non-linearized 3-DOF 1/4 vehicle model, different methods of fault detection and isolation (FDI) are used to diagnose the sensor faults for ECAS system. The considered approaches include an extended Kalman filter (EKF) with concise algorithm, a strong tracking filter (STF) with robust tracking ability, and the cubature Kalman filter (CKF) with numerical precision. We propose three filters of EKF, STF, and CKF to design a state observer of ECAS system under typical sensor faults and noise. Results show that three approaches can successfully detect and isolate faults respectively despite of the existence of environmental noise, FDI time delay and fault sensitivity of different algorithms are different, meanwhile, compared with EKF and STF, CKF method has best performing FDI of sensor faults for ECAS system.

Social Collaborative Filtering by Trust.

PubMed

Yang, Bo; Lei, Yu; Liu, Jiming; Li, Wenjie

2017-08-01

Recommender systems are used to accurately and actively provide users with potentially interesting information or services. Collaborative filtering is a widely adopted approach to recommendation, but sparse data and cold-start users are often barriers to providing high quality recommendations. To address such issues, we propose a novel method that works to improve the performance of collaborative filtering recommendations by integrating sparse rating data given by users and sparse social trust network among these same users. This is a model-based method that adopts matrix factorization technique that maps users into low-dimensional latent feature spaces in terms of their trust relationship, and aims to more accurately reflect the users reciprocal influence on the formation of their own opinions and to learn better preferential patterns of users for high-quality recommendations. We use four large-scale datasets to show that the proposed method performs much better, especially for cold start users, than state-of-the-art recommendation algorithms for social collaborative filtering based on trust.

Tracking Architecture Based on Dual-Filter with State Feedback and Its Application in Ultra-Tight GPS/INS Integration

PubMed Central

Zhang, Xi; Miao, Lingjuan; Shao, Haijun

2016-01-01

If a Kalman Filter (KF) is applied to Global Positioning System (GPS) baseband signal preprocessing, the estimates of signal phase and frequency can have low variance, even in highly dynamic situations. This paper presents a novel preprocessing scheme based on a dual-filter structure. Compared with the traditional model utilizing a single KF, this structure avoids carrier tracking being subjected to code tracking errors. Meanwhile, as the loop filters are completely removed, state feedback values are adopted to generate local carrier and code. Although local carrier frequency has a wide fluctuation, the accuracy of Doppler shift estimation is improved. In the ultra-tight GPS/Inertial Navigation System (INS) integration, the carrier frequency derived from the external navigation information is not viewed as the local carrier frequency directly. That facilitates retaining the design principle of state feedback. However, under harsh conditions, the GPS outputs may still bear large errors which can destroy the estimation of INS errors. Thus, an innovative integrated navigation filter is constructed by modeling the non-negligible errors in the estimated Doppler shifts, to ensure INS is properly calibrated. Finally, field test and semi-physical simulation based on telemetered missile trajectory validate the effectiveness of methods proposed in this paper. PMID:27144570

Tracking Architecture Based on Dual-Filter with State Feedback and Its Application in Ultra-Tight GPS/INS Integration.

PubMed

Zhang, Xi; Miao, Lingjuan; Shao, Haijun

2016-05-02

If a Kalman Filter (KF) is applied to Global Positioning System (GPS) baseband signal preprocessing, the estimates of signal phase and frequency can have low variance, even in highly dynamic situations. This paper presents a novel preprocessing scheme based on a dual-filter structure. Compared with the traditional model utilizing a single KF, this structure avoids carrier tracking being subjected to code tracking errors. Meanwhile, as the loop filters are completely removed, state feedback values are adopted to generate local carrier and code. Although local carrier frequency has a wide fluctuation, the accuracy of Doppler shift estimation is improved. In the ultra-tight GPS/Inertial Navigation System (INS) integration, the carrier frequency derived from the external navigation information is not viewed as the local carrier frequency directly. That facilitates retaining the design principle of state feedback. However, under harsh conditions, the GPS outputs may still bear large errors which can destroy the estimation of INS errors. Thus, an innovative integrated navigation filter is constructed by modeling the non-negligible errors in the estimated Doppler shifts, to ensure INS is properly calibrated. Finally, field test and semi-physical simulation based on telemetered missile trajectory validate the effectiveness of methods proposed in this paper.

Ranking streamflow model performance based on Information theory metrics

NASA Astrophysics Data System (ADS)

Martinez, Gonzalo; Pachepsky, Yakov; Pan, Feng; Wagener, Thorsten; Nicholson, Thomas

2016-04-01

The accuracy-based model performance metrics not necessarily reflect the qualitative correspondence between simulated and measured streamflow time series. The objective of this work was to use the information theory-based metrics to see whether they can be used as complementary tool for hydrologic model evaluation and selection. We simulated 10-year streamflow time series in five watersheds located in Texas, North Carolina, Mississippi, and West Virginia. Eight model of different complexity were applied. The information-theory based metrics were obtained after representing the time series as strings of symbols where different symbols corresponded to different quantiles of the probability distribution of streamflow. The symbol alphabet was used. Three metrics were computed for those strings - mean information gain that measures the randomness of the signal, effective measure complexity that characterizes predictability and fluctuation complexity that characterizes the presence of a pattern in the signal. The observed streamflow time series has smaller information content and larger complexity metrics than the precipitation time series. Watersheds served as information filters and and streamflow time series were less random and more complex than the ones of precipitation. This is reflected the fact that the watershed acts as the information filter in the hydrologic conversion process from precipitation to streamflow. The Nash Sutcliffe efficiency metric increased as the complexity of models increased, but in many cases several model had this efficiency values not statistically significant from each other. In such cases, ranking models by the closeness of the information-theory based parameters in simulated and measured streamflow time series can provide an additional criterion for the evaluation of hydrologic model performance.

Application of filtering techniques in preprocessing magnetic data

NASA Astrophysics Data System (ADS)

Liu, Haijun; Yi, Yongping; Yang, Hongxia; Hu, Guochuang; Liu, Guoming

2010-08-01

High precision magnetic exploration is a popular geophysical technique for its simplicity and its effectiveness. The explanation in high precision magnetic exploration is always a difficulty because of the existence of noise and disturbance factors, so it is necessary to find an effective preprocessing method to get rid of the affection of interference factors before further processing. The common way to do this work is by filtering. There are many kinds of filtering methods. In this paper we introduced in detail three popular kinds of filtering techniques including regularized filtering technique, sliding averages filtering technique, compensation smoothing filtering technique. Then we designed the work flow of filtering program based on these techniques and realized it with the help of DELPHI. To check it we applied it to preprocess magnetic data of a certain place in China. Comparing the initial contour map with the filtered contour map, we can see clearly the perfect effect our program. The contour map processed by our program is very smooth and the high frequency parts of data are disappeared. After filtering, we separated useful signals and noisy signals, minor anomaly and major anomaly, local anomaly and regional anomaly. It made us easily to focus on the useful information. Our program can be used to preprocess magnetic data. The results showed the effectiveness of our program.

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.

Maximally Informative Statistics for Localization and Mapping

NASA Technical Reports Server (NTRS)

Deans, Matthew C.

2001-01-01

This paper presents an algorithm for localization and mapping for a mobile robot using monocular vision and odometry as its means of sensing. The approach uses the Variable State Dimension filtering (VSDF) framework to combine aspects of Extended Kalman filtering and nonlinear batch optimization. This paper describes two primary improvements to the VSDF. The first is to use an interpolation scheme based on Gaussian quadrature to linearize measurements rather than relying on analytic Jacobians. The second is to replace the inverse covariance matrix in the VSDF with its Cholesky factor to improve the computational complexity. Results of applying the filter to the problem of localization and mapping with omnidirectional vision are presented.

A Novel Robust H∞ Filter Based on Krein Space Theory in the SINS/CNS Attitude Reference System

PubMed Central

Yu, Fei; Lv, Chongyang; Dong, Qianhui

2016-01-01

Owing to their numerous merits, such as compact, autonomous and independence, the strapdown inertial navigation system (SINS) and celestial navigation system (CNS) can be used in marine applications. What is more, due to the complementary navigation information obtained from two different kinds of sensors, the accuracy of the SINS/CNS integrated navigation system can be enhanced availably. Thus, the SINS/CNS system is widely used in the marine navigation field. However, the CNS is easily interfered with by the surroundings, which will lead to the output being discontinuous. Thus, the uncertainty problem caused by the lost measurement will reduce the system accuracy. In this paper, a robust H∞ filter based on the Krein space theory is proposed. The Krein space theory is introduced firstly, and then, the linear state and observation models of the SINS/CNS integrated navigation system are established reasonably. By taking the uncertainty problem into account, in this paper, a new robust H∞ filter is proposed to improve the robustness of the integrated system. At last, this new robust filter based on the Krein space theory is estimated by numerical simulations and actual experiments. Additionally, the simulation and experiment results and analysis show that the attitude errors can be reduced by utilizing the proposed robust filter effectively when the measurements are missing discontinuous. Compared to the traditional Kalman filter (KF) method, the accuracy of the SINS/CNS integrated system is improved, verifying the robustness and the availability of the proposed robust H∞ filter. PMID:26999153

An Extension to the Kalman Filter for an Improved Detection of Unknown Behavior

NASA Technical Reports Server (NTRS)

Benazera, Emmanuel; Narasimhan, Sriram

2005-01-01

The use of Kalman filter (KF) interferes with fault detection algorithms based on the residual between estimated and measured variables, since the measured values are used to update the estimates. This feedback results in the estimates being pulled closer to the measured values, influencing the residuals in the process. Here we present a fault detection scheme for systems that are being tracked by a KF. Our approach combines an open-loop prediction over an adaptive window and an information-based measure of the deviation of the Kalman estimate from the prediction to improve fault detection.

Speckle noise reduction in ultrasound images using a discrete wavelet transform-based image fusion technique.

PubMed

Choi, Hyun Ho; Lee, Ju Hwan; Kim, Sung Min; Park, Sung Yun

2015-01-01

Here, the speckle noise in ultrasonic images is removed using an image fusion-based denoising method. To optimize the denoising performance, each discrete wavelet transform (DWT) and filtering technique was analyzed and compared. In addition, the performances were compared in order to derive the optimal input conditions. To evaluate the speckle noise removal performance, an image fusion algorithm was applied to the ultrasound images, and comparatively analyzed with the original image without the algorithm. As a result, applying DWT and filtering techniques caused information loss and noise characteristics, and did not represent the most significant noise reduction performance. Conversely, an image fusion method applying SRAD-original conditions preserved the key information in the original image, and the speckle noise was removed. Based on such characteristics, the input conditions of SRAD-original had the best denoising performance with the ultrasound images. From this study, the best denoising technique proposed based on the results was confirmed to have a high potential for clinical application.

Optical implementation of the synthetic discriminant function

NASA Astrophysics Data System (ADS)

Butler, S.; Riggins, J.

1984-10-01

Much attention is focused on the use of coherent optical pattern recognition (OPR) using matched spatial filters for robotics and intelligent systems. The OPR problem consists of three aspects -- information input, information processing, and information output. This paper discusses the information processing aspect which consists of choosing a filter to provide robust correlation with high efficiency. The filter should ideally be invariant to image shift, rotation and scale, provide a reasonable signal-to-noise (S/N) ratio and allow high throughput efficiency. The physical implementation of a spatial matched filter involves many choices. These include the use of conventional holograms or computer-generated holograms (CGH) and utilizing absorption or phase materials. Conventional holograms inherently modify the reference image by non-uniform emphasis of spatial frequencies. Proper use of film nonlinearity provides improved filter performance by emphasizing frequency ranges crucial to target discrimination. In the case of a CGH, the emphasis of the reference magnitude and phase can be controlled independently of the continuous tone or binary writing processes. This paper describes computer simulation and optical implementation of a geometrical shape and a Synthetic Discriminant Function (SDF) matched filter. The authors chose the binary Allebach-Keegan (AK) CGH algorithm to produce actual filters. The performances of these filters were measured to verify the simulation results. This paper provides a brief summary of the matched filter theory, the SDF, CGH algorithms, Phase-Only-Filtering, simulation procedures, and results.

Psychometric functions for informational masking

NASA Astrophysics Data System (ADS)

Lutfi, Robert A.; Kistler, Doris J.; Callahan, Michael R.; Wightman, Frederic L.

2003-04-01

The method of constant stimuli was used to obtain complete psychometric functions (PFs) from 44 normal-hearing listeners in conditions known to produce varying amounts of informational masking. The task was to detect a pure-tone signal in the presence of a broadband noise and in the presence of multitone maskers with frequencies and amplitudes that varied at random from one presentation to the next. Relative to the broadband noise condition, significant reductions were observed in both the slope and the upper asymptote of the PF for multitone maskers producing large amounts of informational masking. Slope was affected more for some listeners while asymptote was affected more for others. Mean slopes and asymptotes varied nonmonotonically with the number of masker components in much the same manner as mean thresholds. The results are consistent with a model that assumes trial-by-trial judgments are based on a weighted sum of dB levels at the output of independent auditory filters. For many listeners, however, the weights appear to reflect how often a nonsignal auditory filter is mistaken for the signal filter. For these listeners adaptive procedures may produce a significant bias in the estimates of threshold for conditions of informational masking. [Work supported by NIDCD.

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.

Simple and Efficient Single Photon Filter for a Rb-based Quantum Memory

NASA Astrophysics Data System (ADS)

Stack, Daniel; Li, Xiao; Quraishi, Qudsia

2015-05-01

Distribution of entangled quantum states over significant distances is important to the development of future quantum technologies such as long-distance cryptography, networks of atomic clocks, distributed quantum computing, etc. Long-lived quantum memories and single photons are building blocks for systems capable of realizing such applications. The ability to store and retrieve quantum information while filtering unwanted light signals is critical to the operation of quantum memories based on neutral-atom ensembles. We report on an efficient frequency filter which uses a glass cell filled with 85Rb vapor to attenuate noise photons by an order of magnitude with little loss to the single photons associated with the operation of our cold 87Rb quantum memory. An Ar buffer gas is required to differentiate between signal and noise photons or similar statement. Our simple, passive filter requires no optical pumping or external frequency references and provides an additional 18 dB attenuation of our pump laser for every 1 dB loss of the single photon signal. We observe improved non-classical correlations and our data shows that the addition of a frequency filter increases the non-classical correlations and readout efficiency of our quantum memory by ~ 35%.

Initial Alignment of Large Azimuth Misalignment Angles in SINS Based on Adaptive UPF

PubMed Central

Sun, Jin; Xu, Xiao-Su; Liu, Yi-Ting; Zhang, Tao; Li, Yao

2015-01-01

The case of large azimuth misalignment angles in a strapdown inertial navigation system (SINS) is analyzed, and a method of using the adaptive UPF for the initial alignment is proposed. The filter is based on the idea of a strong tracking filter; through the introduction of the attenuation memory factor to effectively enhance the corrections of the current information residual error on the system, it reduces the influence on the system due to the system simplification, and the uncertainty of noise statistical properties to a certain extent; meanwhile, the UPF particle degradation phenomenon is better overcome. Finally, two kinds of non-linear filters, UPF and adaptive UPF, are adopted in the initial alignment of large azimuth misalignment angles in SINS, and the filtering effects of the two kinds of nonlinear filter on the initial alignment were compared by simulation and turntable experiments. The simulation and turntable experiment results show that the speed and precision of the initial alignment using adaptive UPF for a large azimuth misalignment angle in SINS under the circumstance that the statistical properties of the system noise are certain or not have been improved to some extent. PMID:26334277

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.

A Strapdown Interial Navigation System/Beidou/Doppler Velocity Log Integrated Navigation Algorithm Based on a Cubature Kalman Filter

PubMed Central

Gao, Wei; Zhang, Ya; Wang, Jianguo

2014-01-01

The integrated navigation system with strapdown inertial navigation system (SINS), Beidou (BD) receiver and Doppler velocity log (DVL) can be used in marine applications owing to the fact that the redundant and complementary information from different sensors can markedly improve the system accuracy. However, the existence of multisensor asynchrony will introduce errors into the system. In order to deal with the problem, conventionally the sampling interval is subdivided, which increases the computational complexity. In this paper, an innovative integrated navigation algorithm based on a Cubature Kalman filter (CKF) is proposed correspondingly. A nonlinear system model and observation model for the SINS/BD/DVL integrated system are established to more accurately describe the system. By taking multi-sensor asynchronization into account, a new sampling principle is proposed to make the best use of each sensor's information. Further, CKF is introduced in this new algorithm to enable the improvement of the filtering accuracy. The performance of this new algorithm has been examined through numerical simulations. The results have shown that the positional error can be effectively reduced with the new integrated navigation algorithm. Compared with the traditional algorithm based on EKF, the accuracy of the SINS/BD/DVL integrated navigation system is improved, making the proposed nonlinear integrated navigation algorithm feasible and efficient. PMID:24434842

Usability-driven pruning of large ontologies: the case of SNOMED CT.

PubMed

López-García, Pablo; Boeker, Martin; Illarramendi, Arantza; Schulz, Stefan

2012-06-01

To study ontology modularization techniques when applied to SNOMED CT in a scenario in which no previous corpus of information exists and to examine if frequency-based filtering using MEDLINE can reduce subset size without discarding relevant concepts. Subsets were first extracted using four graph-traversal heuristics and one logic-based technique, and were subsequently filtered with frequency information from MEDLINE. Twenty manually coded discharge summaries from cardiology patients were used as signatures and test sets. The coverage, size, and precision of extracted subsets were measured. Graph-traversal heuristics provided high coverage (71-96% of terms in the test sets of discharge summaries) at the expense of subset size (17-51% of the size of SNOMED CT). Pre-computed subsets and logic-based techniques extracted small subsets (1%), but coverage was limited (24-55%). Filtering reduced the size of large subsets to 10% while still providing 80% coverage. Extracting subsets to annotate discharge summaries is challenging when no previous corpus exists. Ontology modularization provides valuable techniques, but the resulting modules grow as signatures spread across subhierarchies, yielding a very low precision. Graph-traversal strategies and frequency data from an authoritative source can prune large biomedical ontologies and produce useful subsets that still exhibit acceptable coverage. However, a clinical corpus closer to the specific use case is preferred when available.

Indoor Map Aided Wi-Fi Integrated Lbs on Smartphone Platforms

NASA Astrophysics Data System (ADS)

Yu, C.; El-Sheimy, N.

2017-09-01

In this research, an indoor map aided INS/Wi-Fi integrated location based services (LBS) applications is proposed and implemented on smartphone platforms. Indoor map information together with measurements from an inertial measurement unit (IMU) and Received Signal Strength Indicator (RSSI) value from Wi-Fi are collected to obtain an accurate, continuous, and low-cost position solution. The main challenge of this research is to make effective use of various measurements that complement each other without increasing the computational burden of the system. The integrated system in this paper includes three modules: INS, Wi-Fi (if signal available) and indoor maps. A cascade structure Particle/Kalman filter framework is applied to combine the different modules. Firstly, INS position and Wi-Fi fingerprint position integrated through Kalman filter for estimating positioning information. Then, indoor map information is applied to correct the error of INS/Wi-Fi estimated position through particle filter. Indoor tests show that the proposed method can effectively reduce the accumulation positioning errors of stand-alone INS systems, and provide stable, continuous and reliable indoor location service.

Removal of power line interference of space bearing vibration signal based on the morphological filter and blind source separation

NASA Astrophysics Data System (ADS)

Dong, Shaojiang; Sun, Dihua; Xu, Xiangyang; Tang, Baoping

2017-06-01

Aiming at the problem that it is difficult to extract the feature information from the space bearing vibration signal because of different noise, for example the running trend information, high-frequency noise and especially the existence of lot of power line interference (50Hz) and its octave ingredients of the running space simulated equipment in the ground. This article proposed a combination method to eliminate them. Firstly, the EMD is used to remove the running trend item information of the signal, the running trend that affect the signal processing accuracy is eliminated. Then the morphological filter is used to eliminate high-frequency noise. Finally, the components and characteristics of the power line interference are researched, based on the characteristics of the interference, the revised blind source separation model is used to remove the power line interferences. Through analysis of simulation and practical application, results suggest that the proposed method can effectively eliminate those noise.

Image classification using multiscale information fusion based on saliency driven nonlinear diffusion filtering.

PubMed

Hu, Weiming; Hu, Ruiguang; Xie, Nianhua; Ling, Haibin; Maybank, Stephen

2014-04-01

In this paper, we propose saliency driven image multiscale nonlinear diffusion filtering. The resulting scale space in general preserves or even enhances semantically important structures such as edges, lines, or flow-like structures in the foreground, and inhibits and smoothes clutter in the background. The image is classified using multiscale information fusion based on the original image, the image at the final scale at which the diffusion process converges, and the image at a midscale. Our algorithm emphasizes the foreground features, which are important for image classification. The background image regions, whether considered as contexts of the foreground or noise to the foreground, can be globally handled by fusing information from different scales. Experimental tests of the effectiveness of the multiscale space for the image classification are conducted on the following publicly available datasets: 1) the PASCAL 2005 dataset; 2) the Oxford 102 flowers dataset; and 3) the Oxford 17 flowers dataset, with high classification rates.

Representation of Probability Density Functions from Orbit Determination using the Particle Filter

NASA Technical Reports Server (NTRS)

Mashiku, Alinda K.; Garrison, James; Carpenter, J. Russell

2012-01-01

Statistical orbit determination enables us to obtain estimates of the state and the statistical information of its region of uncertainty. In order to obtain an accurate representation of the probability density function (PDF) that incorporates higher order statistical information, we propose the use of nonlinear estimation methods such as the Particle Filter. The Particle Filter (PF) is capable of providing a PDF representation of the state estimates whose accuracy is dependent on the number of particles or samples used. For this method to be applicable to real case scenarios, we need a way of accurately representing the PDF in a compressed manner with little information loss. Hence we propose using the Independent Component Analysis (ICA) as a non-Gaussian dimensional reduction method that is capable of maintaining higher order statistical information obtained using the PF. Methods such as the Principal Component Analysis (PCA) are based on utilizing up to second order statistics, hence will not suffice in maintaining maximum information content. Both the PCA and the ICA are applied to two scenarios that involve a highly eccentric orbit with a lower apriori uncertainty covariance and a less eccentric orbit with a higher a priori uncertainty covariance, to illustrate the capability of the ICA in relation to the PCA.

2007 Chicago Regional Household Travel Inventory | Transportation Secure

Science.gov Websites

period. In addition to traditional survey-based data collection, this study featured a sub-sample of processing and filtering routines. Survey Records Survey records include 460 households. More Information For more information about the survey, see the CMAP Regional Travel Survey Final Report. Transportation

An Improved Iris Recognition Algorithm Based on Hybrid Feature and ELM

NASA Astrophysics Data System (ADS)

Wang, Juan

2018-03-01

The iris image is easily polluted by noise and uneven light. This paper proposed an improved extreme learning machine (ELM) based iris recognition algorithm with hybrid feature. 2D-Gabor filters and GLCM is employed to generate a multi-granularity hybrid feature vector. 2D-Gabor filter and GLCM feature work for capturing low-intermediate frequency and high frequency texture information, respectively. Finally, we utilize extreme learning machine for iris recognition. Experimental results reveal our proposed ELM based multi-granularity iris recognition algorithm (ELM-MGIR) has higher accuracy of 99.86%, and lower EER of 0.12% under the premise of real-time performance. The proposed ELM-MGIR algorithm outperforms other mainstream iris recognition algorithms.

Limited effectiveness of household sand filters for removal of arsenic from well water in North Vietnam.

PubMed

Ilmiawati, Cimi; Thang, Nguyen Dinh; Iida, Machiko; Maeda, Masao; Ohnuma, Shoko; Yajima, Ichiro; Ohgami, Nobutaka; Oshino, Reina; Al Hossain, M M Aeorangajeb; Ninomiya, Hiromasa; Kato, Masashi

2016-12-01

Since well water utilized for domestic purposes in the Red River Delta of North Vietnam has been reported to be polluted by arsenic, barium, iron, and manganese, household sand filters consisting of various components are used. Information regarding the effectiveness of various sand filters for removal of the four toxic elements in well water is limited. In this study, arsenic levels in 13/20 of well water samples and 1/7 of tap water samples exceeded World Health Organization (WHO) health-based guideline value for drinking water. Moreover, 2/20, 6/20, and 4/20 of well water samples had levels exceeding the present and previous guideline levels for barium, iron, and manganese, respectively. Levels of iron and manganese, but not arsenic, in well water treated by sand filters were lower than those in untreated water, although previous studies showed that sand filters removed all of those elements from water. A low ratio of iron/arsenic in well water may not be sufficient for efficient removal of arsenic from household sand filters. The levels of barium in well water treated by sand filters, especially a filter composed of sand and charcoal, were significantly lower than those in untreated water. Thus, we demonstrated characteristics of sand filters in North Vietnam.

Reconstruction of elasticity: a stochastic model-based approach in ultrasound elastography

PubMed Central

2013-01-01

Background The convectional strain-based algorithm has been widely utilized in clinical practice. It can only provide the information of relative information of tissue stiffness. However, the exact information of tissue stiffness should be valuable for clinical diagnosis and treatment. Methods In this study we propose a reconstruction strategy to recover the mechanical properties of the tissue. After the discrepancies between the biomechanical model and data are modeled as the process noise, and the biomechanical model constraint is transformed into a state space representation the reconstruction of elasticity can be accomplished through one filtering identification process, which is to recursively estimate the material properties and kinematic functions from ultrasound data according to the minimum mean square error (MMSE) criteria. In the implementation of this model-based algorithm, the linear isotropic elasticity is adopted as the biomechanical constraint. The estimation of kinematic functions (i.e., the full displacement and velocity field), and the distribution of Young’s modulus are computed simultaneously through an extended Kalman filter (EKF). Results In the following experiments the accuracy and robustness of this filtering framework is first evaluated on synthetic data in controlled conditions, and the performance of this framework is then evaluated in the real data collected from elastography phantom and patients using the ultrasound system. Quantitative analysis verifies that strain fields estimated by our filtering strategy are more closer to the ground truth. The distribution of Young’s modulus is also well estimated. Further, the effects of measurement noise and process noise have been investigated as well. Conclusions The advantage of this model-based algorithm over the conventional strain-based algorithm is its potential of providing the distribution of elasticity under a proper biomechanical model constraint. We address the model-data discrepancy and measurement noise by introducing process noise and measurement noise in our framework, and then the absolute values of Young’s modulus are estimated through the EFK in the MMSE sense. However, the initial conditions, and the mesh strategy will affect the performance, i.e., the convergence rate, and computational cost, etc. PMID:23937814

Reconstruction of elasticity: a stochastic model-based approach in ultrasound elastography.

PubMed

Lu, Minhua; Zhang, Heye; Wang, Jun; Yuan, Jinwei; Hu, Zhenghui; Liu, Huafeng

2013-08-10

The convectional strain-based algorithm has been widely utilized in clinical practice. It can only provide the information of relative information of tissue stiffness. However, the exact information of tissue stiffness should be valuable for clinical diagnosis and treatment. In this study we propose a reconstruction strategy to recover the mechanical properties of the tissue. After the discrepancies between the biomechanical model and data are modeled as the process noise, and the biomechanical model constraint is transformed into a state space representation the reconstruction of elasticity can be accomplished through one filtering identification process, which is to recursively estimate the material properties and kinematic functions from ultrasound data according to the minimum mean square error (MMSE) criteria. In the implementation of this model-based algorithm, the linear isotropic elasticity is adopted as the biomechanical constraint. The estimation of kinematic functions (i.e., the full displacement and velocity field), and the distribution of Young's modulus are computed simultaneously through an extended Kalman filter (EKF). In the following experiments the accuracy and robustness of this filtering framework is first evaluated on synthetic data in controlled conditions, and the performance of this framework is then evaluated in the real data collected from elastography phantom and patients using the ultrasound system. Quantitative analysis verifies that strain fields estimated by our filtering strategy are more closer to the ground truth. The distribution of Young's modulus is also well estimated. Further, the effects of measurement noise and process noise have been investigated as well. The advantage of this model-based algorithm over the conventional strain-based algorithm is its potential of providing the distribution of elasticity under a proper biomechanical model constraint. We address the model-data discrepancy and measurement noise by introducing process noise and measurement noise in our framework, and then the absolute values of Young's modulus are estimated through the EFK in the MMSE sense. However, the initial conditions, and the mesh strategy will affect the performance, i.e., the convergence rate, and computational cost, etc.

Symmetrical and overloaded effect of diffusion in information filtering

NASA Astrophysics Data System (ADS)

Zhu, Xuzhen; Tian, Hui; Chen, Guilin; Cai, Shimin

2017-10-01

In physical dynamics, mass diffusion theory has been applied to design effective information filtering models on bipartite network. In previous works, researchers unilaterally believe objects' similarities are determined by single directional mass diffusion from the collected object to the uncollected, meanwhile, inadvertently ignore adverse influence of diffusion overload. It in some extent veils the essence of diffusion in physical dynamics and hurts the recommendation accuracy and diversity. After delicate investigation, we argue that symmetrical diffusion effectively discloses essence of mass diffusion, and high diffusion overload should be published. Accordingly, in this paper, we propose an symmetrical and overload penalized diffusion based model (SOPD), which shows excellent performances in extensive experiments on benchmark datasets Movielens and Netflix.

Edge Probability and Pixel Relativity-Based Speckle Reducing Anisotropic Diffusion.

PubMed

Mishra, Deepak; Chaudhury, Santanu; Sarkar, Mukul; Soin, Arvinder Singh; Sharma, Vivek

2018-02-01

Anisotropic diffusion filters are one of the best choices for speckle reduction in the ultrasound images. These filters control the diffusion flux flow using local image statistics and provide the desired speckle suppression. However, inefficient use of edge characteristics results in either oversmooth image or an image containing misinterpreted spurious edges. As a result, the diagnostic quality of the images becomes a concern. To alleviate such problems, a novel anisotropic diffusion-based speckle reducing filter is proposed in this paper. A probability density function of the edges along with pixel relativity information is used to control the diffusion flux flow. The probability density function helps in removing the spurious edges and the pixel relativity reduces the oversmoothing effects. Furthermore, the filtering is performed in superpixel domain to reduce the execution time, wherein a minimum of 15% of the total number of image pixels can be used. For performance evaluation, 31 frames of three synthetic images and 40 real ultrasound images are used. In most of the experiments, the proposed filter shows a better performance as compared to the state-of-the-art filters in terms of the speckle region's signal-to-noise ratio and mean square error. It also shows a comparative performance for figure of merit and structural similarity measure index. Furthermore, in the subjective evaluation, performed by the expert radiologists, the proposed filter's outputs are preferred for the improved contrast and sharpness of the object boundaries. Hence, the proposed filtering framework is suitable to reduce the unwanted speckle and improve the quality of the ultrasound images.

A hand tracking algorithm with particle filter and improved GVF snake model

NASA Astrophysics Data System (ADS)

Sun, Yi-qi; Wu, Ai-guo; Dong, Na; Shao, Yi-zhe

2017-07-01

To solve the problem that the accurate information of hand cannot be obtained by particle filter, a hand tracking algorithm based on particle filter combined with skin-color adaptive gradient vector flow (GVF) snake model is proposed. Adaptive GVF and skin color adaptive external guidance force are introduced to the traditional GVF snake model, guiding the curve to quickly converge to the deep concave region of hand contour and obtaining the complex hand contour accurately. This algorithm realizes a real-time correction of the particle filter parameters, avoiding the particle drift phenomenon. Experimental results show that the proposed algorithm can reduce the root mean square error of the hand tracking by 53%, and improve the accuracy of hand tracking in the case of complex and moving background, even with a large range of occlusion.

LROC assessment of non-linear filtering methods in Ga-67 SPECT imaging

NASA Astrophysics Data System (ADS)

De Clercq, Stijn; Staelens, Steven; De Beenhouwer, Jan; D'Asseler, Yves; Lemahieu, Ignace

2006-03-01

In emission tomography, iterative reconstruction is usually followed by a linear smoothing filter to make such images more appropriate for visual inspection and diagnosis by a physician. This will result in a global blurring of the images, smoothing across edges and possibly discarding valuable image information for detection tasks. The purpose of this study is to investigate which possible advantages a non-linear, edge-preserving postfilter could have on lesion detection in Ga-67 SPECT imaging. Image quality can be defined based on the task that has to be performed on the image. This study used LROC observer studies based on a dataset created by CPU-intensive Gate Monte Carlo simulations of a voxelized digital phantom. The filters considered in this study were a linear Gaussian filter, a bilateral filter, the Perona-Malik anisotropic diffusion filter and the Catte filtering scheme. The 3D MCAT software phantom was used to simulate the distribution of Ga-67 citrate in the abdomen. Tumor-present cases had a 1-cm diameter tumor randomly placed near the edges of the anatomical boundaries of the kidneys, bone, liver and spleen. Our data set was generated out of a single noisy background simulation using the bootstrap method, to significantly reduce the simulation time and to allow for a larger observer data set. Lesions were simulated separately and added to the background afterwards. These were then reconstructed with an iterative approach, using a sufficiently large number of MLEM iterations to establish convergence. The output of a numerical observer was used in a simplex optimization method to estimate an optimal set of parameters for each postfilter. No significant improvement was found for using edge-preserving filtering techniques over standard linear Gaussian filtering.

Filtering of the Radon transform to enhance linear signal features via wavelet pyramid decomposition

NASA Astrophysics Data System (ADS)

Meckley, John R.

1995-09-01

The information content in many signal processing applications can be reduced to a set of linear features in a 2D signal transform. Examples include the narrowband lines in a spectrogram, ship wakes in a synthetic aperture radar image, and blood vessels in a medical computer-aided tomography scan. The line integrals that generate the values of the projections of the Radon transform can be characterized as a bank of matched filters for linear features. This localization of energy in the Radon transform for linear features can be exploited to enhance these features and to reduce noise by filtering the Radon transform with a filter explicitly designed to pass only linear features, and then reconstructing a new 2D signal by inverting the new filtered Radon transform (i.e., via filtered backprojection). Previously used methods for filtering the Radon transform include Fourier based filtering (a 2D elliptical Gaussian linear filter) and a nonlinear filter ((Radon xfrm)**y with y >= 2.0). Both of these techniques suffer from the mismatch of the filter response to the true functional form of the Radon transform of a line. The Radon transform of a line is not a point but is a function of the Radon variables (rho, theta) and the total line energy. This mismatch leads to artifacts in the reconstructed image and a reduction in achievable processing gain. The Radon transform for a line is computed as a function of angle and offset (rho, theta) and the line length. The 2D wavelet coefficients are then compared for the Haar wavelets and the Daubechies wavelets. These filter responses are used as frequency filters for the Radon transform. The filtering is performed on the wavelet pyramid decomposition of the Radon transform by detecting the most likely positions of lines in the transform and then by convolving the local area with the appropriate response and zeroing the pyramid coefficients outside of the response area. The response area is defined to contain 95% of the total wavelet coefficient energy. The detection algorithm provides an estimate of the line offset, orientation, and length that is then used to index the appropriate filter shape. Additional wavelet pyramid decomposition is performed in areas of high energy to refine the line position estimate. After filtering, the new Radon transform is generated by inverting the wavelet pyramid. The Radon transform is then inverted by filtered backprojection to produce the final 2D signal estimate with the enhanced linear features. The wavelet-based method is compared to both the Fourier and the nonlinear filtering with examples of sparse and dense shapes in imaging, acoustics and medical tomography with test images of noisy concentric lines, a real spectrogram of a blow fish (a very nonstationary spectrum), and the Shepp Logan Computer Tomography phantom image. Both qualitative and derived quantitative measures demonstrate the improvement of wavelet-based filtering. Additional research is suggested based on these results. Open questions include what level(s) to use for detection and filtering because multiple-level representations exist. The lower levels are smoother at reduced spatial resolution, while the higher levels provide better response to edges. Several examples are discussed based on analytical and phenomenological arguments.

Selected annotated bibliographies for adaptive filtering of digital image data

USGS Publications Warehouse

Mayers, Margaret; Wood, Lynnette

1988-01-01

Digital spatial filtering is an important tool both for enhancing the information content of satellite image data and for implementing cosmetic effects which make the imagery more interpretable and appealing to the eye. Spatial filtering is a context-dependent operation that alters the gray level of a pixel by computing a weighted average formed from the gray level values of other pixels in the immediate vicinity.Traditional spatial filtering involves passing a particular filter or set of filters over an entire image. This assumes that the filter parameter values are appropriate for the entire image, which in turn is based on the assumption that the statistics of the image are constant over the image. However, the statistics of an image may vary widely over the image, requiring an adaptive or "smart" filter whose parameters change as a function of the local statistical properties of the image. Then a pixel would be averaged only with more typical members of the same population. This annotated bibliography cites some of the work done in the area of adaptive filtering. The methods usually fall into two categories, (a) those that segment the image into subregions, each assumed to have stationary statistics, and use a different filter on each subregion, and (b) those that use a two-dimensional "sliding window" to continuously estimate the filter either the spatial or frequency domain, or may utilize both domains. They may be used to deal with images degraded by space variant noise, to suppress undesirable local radiometric statistics while enforcing desirable (user-defined) statistics, to treat problems where space-variant point spread functions are involved, to segment images into regions of constant value for classification, or to "tune" images in order to remove (nonstationary) variations in illumination, noise, contrast, shadows, or haze.Since adpative filtering, like nonadaptive filtering, is used in image processing to accomplish various goals, this bibliography is organized in subsections based on application areas. Contrast enhancement, edge enhancement, noise suppression, and smoothing are typically performed in order imaging process, (for example, degradations due to the optics and electronics of the sensor, or to blurring caused by the intervening atmosphere, uniform motion, or defocused optics). Some of the papers listed may apply to more than one of the above categories; when this happens the paper is listed under the category for which the paper's emphasis is greatest. A list of survey articles is also supplied. These articles are general discussions on adaptive filters and reviews of work done. Finally, a short list of miscellaneous articles are listed which were felt to be sufficiently important to be included, but do not fit into any of the above categories. This bibliography, listing items published from 1970 through 1987, is extensive, but by no means complete. It is intended as a guide for scientists and image analysts, listing references for background information as well as areas of significant development in adaptive filtering.

Electroencephalographic compression based on modulated filter banks and wavelet transform.

PubMed

Bazán-Prieto, Carlos; Cárdenas-Barrera, Julián; Blanco-Velasco, Manuel; Cruz-Roldán, Fernando

2011-01-01

Due to the large volume of information generated in an electroencephalographic (EEG) study, compression is needed for storage, processing or transmission for analysis. In this paper we evaluate and compare two lossy compression techniques applied to EEG signals. It compares the performance of compression schemes with decomposition by filter banks or wavelet Packets transformation, seeking the best value for compression, best quality and more efficient real time implementation. Due to specific properties of EEG signals, we propose a quantization stage adapted to the dynamic range of each band, looking for higher quality. The results show that the compressor with filter bank performs better than transform methods. Quantization adapted to the dynamic range significantly enhances the quality.

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.

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.

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)

Lightweight filter architecture for energy efficient mobile vehicle localization based on a distributed acoustic sensor network.

PubMed

Kim, Keonwook

2013-08-23

The generic properties of an acoustic signal provide numerous benefits for localization by applying energy-based methods over a deployed wireless sensor network (WSN). However, the signal generated by a stationary target utilizes a significant amount of bandwidth and power in the system without providing further position information. For vehicle localization, this paper proposes a novel proximity velocity vector estimator (PVVE) node architecture in order to capture the energy from a moving vehicle and reject the signal from motionless automobiles around the WSN node. A cascade structure between analog envelope detector and digital exponential smoothing filter presents the velocity vector-sensitive output with low analog circuit and digital computation complexity. The optimal parameters in the exponential smoothing filter are obtained by analytical and mathematical methods for maximum variation over the vehicle speed. For stationary targets, the derived simulation based on the acoustic field parameters demonstrates that the system significantly reduces the communication requirements with low complexity and can be expected to extend the operation time considerably.

Kalman-Filter-Based Orientation Determination Using Inertial/Magnetic Sensors: Observability Analysis and Performance Evaluation

PubMed Central

Sabatini, Angelo Maria

2011-01-01

In this paper we present a quaternion-based Extended Kalman Filter (EKF) for estimating the three-dimensional orientation of a rigid body. The EKF exploits the measurements from an Inertial Measurement Unit (IMU) that is integrated with a tri-axial magnetic sensor. Magnetic disturbances and gyro bias errors are modeled and compensated by including them in the filter state vector. We employ the observability rank criterion based on Lie derivatives to verify the conditions under which the nonlinear system that describes the process of motion tracking by the IMU is observable, namely it may provide sufficient information for performing the estimation task with bounded estimation errors. The observability conditions are that the magnetic field, perturbed by first-order Gauss-Markov magnetic variations, and the gravity vector are not collinear and that the IMU is subject to some angular motions. Computer simulations and experimental testing are presented to evaluate the algorithm performance, including when the observability conditions are critical. PMID:22163689

Study on UKF based federal integrated navigation for high dynamic aviation

NASA Astrophysics Data System (ADS)

Zhao, Gang; Shao, Wei; Chen, Kai; Yan, Jie

2011-08-01

High dynamic aircraft is a very attractive new generation vehicles, in which provides near space aviation with large flight envelope both speed and altitude, for example the hypersonic vehicles. The complex flight environments for high dynamic vehicles require high accuracy and stability navigation scheme. Since the conventional Strapdown Inertial Navigation System (SINS) and Global Position System (GPS) federal integrated scheme based on EKF (Extended Kalman Filter) is invalidation in GPS single blackout situation because of high speed flight, a new high precision and stability integrated navigation approach is presented in this paper, in which the SINS, GPS and Celestial Navigation System (CNS) is combined as a federal information fusion configuration based on nonlinear Unscented Kalman Filter (UKF) algorithm. Firstly, the new integrated system state error is modeled. According to this error model, the SINS system is used as the navigation solution mathematic platform. The SINS combine with GPS constitute one error estimation filter subsystem based on UKF to obtain local optimal estimation, and the SINS combine with CNS constitute another error estimation subsystem. A non-reset federated configuration filter based on partial information is proposed to fuse two local optimal estimations to get global optimal error estimation, and the global optimal estimation is used to correct the SINS navigation solution. The χ 2 fault detection method is used to detect the subsystem fault, and the fault subsystem is isolation through fault interval to protect system away from the divergence. The integrated system takes advantages of SINS, GPS and CNS to an immense improvement for high accuracy and reliably high dynamic navigation application. Simulation result shows that federated fusion of using GPS and CNS to revise SINS solution is reasonable and availably with good estimation performance, which are satisfied with the demands of high dynamic flight navigation. The UKF is superior than EKF based integrated scheme, in which has smaller estimation error and quickly convergence rate.

Kalman filter based control for Adaptive Optics

NASA Astrophysics Data System (ADS)

Petit, Cyril; Quiros-Pacheco, Fernando; Conan, Jean-Marc; Kulcsár, Caroline; Raynaud, Henri-François; Fusco, Thierry

2004-12-01

Classical Adaptive Optics suffer from a limitation of the corrected Field Of View. This drawback has lead to the development of MultiConjugated Adaptive Optics. While the first MCAO experimental set-ups are presently under construction, little attention has been paid to the control loop. This is however a key element in the optimization process especially for MCAO systems. Different approaches have been proposed in recent articles for astronomical applications : simple integrator, Optimized Modal Gain Integrator and Kalman filtering. We study here Kalman filtering which seems a very promising solution. Following the work of Brice Leroux, we focus on a frequential characterization of kalman filters, computing a transfer matrix. The result brings much information about their behaviour and allows comparisons with classical controllers. It also appears that straightforward improvements of the system models can lead to static aberrations and vibrations filtering. Simulation results are proposed and analysed thanks to our frequential characterization. Related problems such as model errors, aliasing effect reduction or experimental implementation and testing of Kalman filter control loop on a simplified MCAO experimental set-up could be then discussed.

Text extraction method for historical Tibetan document images based on block projections

NASA Astrophysics Data System (ADS)

Duan, Li-juan; Zhang, Xi-qun; Ma, Long-long; Wu, Jian

2017-11-01

Text extraction is an important initial step in digitizing the historical documents. In this paper, we present a text extraction method for historical Tibetan document images based on block projections. The task of text extraction is considered as text area detection and location problem. The images are divided equally into blocks and the blocks are filtered by the information of the categories of connected components and corner point density. By analyzing the filtered blocks' projections, the approximate text areas can be located, and the text regions are extracted. Experiments on the dataset of historical Tibetan documents demonstrate the effectiveness of the proposed method.

Detection of respiratory viruses on air filters from aircraft.

PubMed

Korves, T M; Johnson, D; Jones, B W; Watson, J; Wolk, D M; Hwang, G M

2011-09-01

To evaluate the feasibility of identifying viruses from aircraft cabin air, we evaluated whether respiratory viruses trapped by commercial aircraft air filters can be extracted and detected using a multiplex PCR, bead-based assay. The ResPlex II assay was first tested for its ability to detect inactivated viruses applied to new filter material; all 18 applications of virus at a high concentration were detected. The ResPlex II assay was then used to test for 18 respiratory viruses on 48 used air filter samples from commercial aircraft. Three samples tested positive for viruses, and three viruses were detected: rhinovirus, influenza A and influenza B. For 33 of 48 samples, internal PCR controls performed suboptimally, suggesting sample matrix effect. In some cases, influenza and rhinovirus RNA can be detected on aircraft air filters, even more than 10 days after the filters were removed from aircraft. With protocol modifications to overcome PCR inhibition, air filter sampling and the ResPlex II assay could be used to characterize viruses in aircraft cabin air. Information about viruses in aircraft could support public health measures to reduce disease transmission within aircraft and between cities. © The MITRE corporation. Letters in Applied Microbiology © 2011 The Society for Applied Microbiology.

PARTICLE FILTERING WITH SEQUENTIAL PARAMETER LEARNING FOR NONLINEAR BOLD fMRI SIGNALS.

PubMed

Xia, Jing; Wang, Michelle Yongmei

Analyzing the blood oxygenation level dependent (BOLD) effect in the functional magnetic resonance imaging (fMRI) is typically based on recent ground-breaking time series analysis techniques. This work represents a significant improvement over existing approaches to system identification using nonlinear hemodynamic models. It is important for three reasons. First, instead of using linearized approximations of the dynamics, we present a nonlinear filtering based on the sequential Monte Carlo method to capture the inherent nonlinearities in the physiological system. Second, we simultaneously estimate the hidden physiological states and the system parameters through particle filtering with sequential parameter learning to fully take advantage of the dynamic information of the BOLD signals. Third, during the unknown static parameter learning, we employ the low-dimensional sufficient statistics for efficiency and avoiding potential degeneration of the parameters. The performance of the proposed method is validated using both the simulated data and real BOLD fMRI data.

Real-time Bayesian anomaly detection in streaming environmental data

NASA Astrophysics Data System (ADS)

Hill, David J.; Minsker, Barbara S.; Amir, Eyal

2009-04-01

With large volumes of data arriving in near real time from environmental sensors, there is a need for automated detection of anomalous data caused by sensor or transmission errors or by infrequent system behaviors. This study develops and evaluates three automated anomaly detection methods using dynamic Bayesian networks (DBNs), which perform fast, incremental evaluation of data as they become available, scale to large quantities of data, and require no a priori information regarding process variables or types of anomalies that may be encountered. This study investigates these methods' abilities to identify anomalies in eight meteorological data streams from Corpus Christi, Texas. The results indicate that DBN-based detectors, using either robust Kalman filtering or Rao-Blackwellized particle filtering, outperform a DBN-based detector using Kalman filtering, with the former having false positive/negative rates of less than 2%. These methods were successful at identifying data anomalies caused by two real events: a sensor failure and a large storm.

Combining Charge Couple Devices and Rate Sensors for the Feedforward Control System of a Charge Coupled Device Tracking Loop.

PubMed

Tang, Tao; Tian, Jing; Zhong, Daijun; Fu, Chengyu

2016-06-25

A rate feed forward control-based sensor fusion is proposed to improve the closed-loop performance for a charge couple device (CCD) tracking loop. The target trajectory is recovered by combining line of sight (LOS) errors from the CCD and the angular rate from a fiber-optic gyroscope (FOG). A Kalman filter based on the Singer acceleration model utilizes the reconstructive target trajectory to estimate the target velocity. Different from classical feed forward control, additive feedback loops are inevitably added to the original control loops due to the fact some closed-loop information is used. The transfer function of the Kalman filter in the frequency domain is built for analyzing the closed loop stability. The bandwidth of the Kalman filter is the major factor affecting the control stability and close-loop performance. Both simulations and experiments are provided to demonstrate the benefits of the proposed algorithm.

A new method for E-government procurement using collaborative filtering and Bayesian approach.

PubMed

Zhang, Shuai; Xi, Chengyu; Wang, Yan; Zhang, Wenyu; Chen, Yanhong

2013-01-01

Nowadays, as the Internet services increase faster than ever before, government systems are reinvented as E-government services. Therefore, government procurement sectors have to face challenges brought by the explosion of service information. This paper presents a novel method for E-government procurement (eGP) to search for the optimal procurement scheme (OPS). Item-based collaborative filtering and Bayesian approach are used to evaluate and select the candidate services to get the top-M recommendations such that the involved computation load can be alleviated. A trapezoidal fuzzy number similarity algorithm is applied to support the item-based collaborative filtering and Bayesian approach, since some of the services' attributes can be hardly expressed as certain and static values but only be easily represented as fuzzy values. A prototype system is built and validated with an illustrative example from eGP to confirm the feasibility of our approach.

A New Method for E-Government Procurement Using Collaborative Filtering and Bayesian Approach

PubMed Central

Wang, Yan

2013-01-01

Nowadays, as the Internet services increase faster than ever before, government systems are reinvented as E-government services. Therefore, government procurement sectors have to face challenges brought by the explosion of service information. This paper presents a novel method for E-government procurement (eGP) to search for the optimal procurement scheme (OPS). Item-based collaborative filtering and Bayesian approach are used to evaluate and select the candidate services to get the top-M recommendations such that the involved computation load can be alleviated. A trapezoidal fuzzy number similarity algorithm is applied to support the item-based collaborative filtering and Bayesian approach, since some of the services' attributes can be hardly expressed as certain and static values but only be easily represented as fuzzy values. A prototype system is built and validated with an illustrative example from eGP to confirm the feasibility of our approach. PMID:24385869

Estimating Power System Dynamic States Using Extended Kalman Filter

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

Huang, Zhenyu; Schneider, Kevin P.; Nieplocha, Jaroslaw

2014-10-31

Abstract—The state estimation tools which are currently deployed in power system control rooms are based on a steady state assumption. As a result, the suite of operational tools that rely on state estimation results as inputs do not have dynamic information available and their accuracy is compromised. This paper investigates the application of Extended Kalman Filtering techniques for estimating dynamic states in the state estimation process. The new formulated “dynamic state estimation” includes true system dynamics reflected in differential equations, not like previously proposed “dynamic state estimation” which only considers the time-variant snapshots based on steady state modeling. This newmore » dynamic state estimation using Extended Kalman Filter has been successfully tested on a multi-machine system. Sensitivity studies with respect to noise levels, sampling rates, model errors, and parameter errors are presented as well to illustrate the robust performance of the developed dynamic state estimation process.« less

Single SOA based simultaneous amplitude regeneration for WDM-PDM RZ-PSK signals.

PubMed

Wu, Wenhan; Yu, Yu; Zou, Bingrong; Yang, Weili; Zhang, Xinliang

2013-03-25

We propose and demonstrate all-optical amplitude regeneration for the wavelength division multiplexing and polarization division multiplexing (WDM-PDM) return-to-zero phase shift keying (RZ-PSK) signals using a single semiconductor optical amplifier (SOA) and subsequent filtering. The regeneration is based on the cross phase modulation (XPM) effect in the saturated SOA and the subsequent narrow filtering. The spectrum of the distorted signal can be broadened due to the phase modulation induced by the synchronous optical clock signal. A narrow band pass filter is utilized to extract part of the broadened spectrum and remove the amplitude noise, while preserving the phase information. The working principle for multi-channel and polarization orthogonality preserving is analyzed. 4-channel dual polarization signals can be simultaneously amplitude regenerated without introducing wavelength and polarization demultiplexing. An average power penalty improvement of 1.75dB can be achieved for the WDM-PDM signals.

Particle Filter-Based Recursive Data Fusion With Sensor Indexing for Large Core Neutron Flux Estimation

NASA Astrophysics Data System (ADS)

Tamboli, Prakash Kumar; Duttagupta, Siddhartha P.; Roy, Kallol

2017-06-01

We introduce a sequential importance sampling particle filter (PF)-based multisensor multivariate nonlinear estimator for estimating the in-core neutron flux distribution for pressurized heavy water reactor core. Many critical applications such as reactor protection and control rely upon neutron flux information, and thus their reliability is of utmost importance. The point kinetic model based on neutron transport conveniently explains the dynamics of nuclear reactor. The neutron flux in the large core loosely coupled reactor is sensed by multiple sensors measuring point fluxes located at various locations inside the reactor core. The flux values are coupled to each other through diffusion equation. The coupling facilitates redundancy in the information. It is shown that multiple independent data about the localized flux can be fused together to enhance the estimation accuracy to a great extent. We also propose the sensor anomaly handling feature in multisensor PF to maintain the estimation process even when the sensor is faulty or generates data anomaly.

Dynamic Bus Travel Time Prediction Models on Road with Multiple Bus Routes

PubMed Central

Bai, Cong; Peng, Zhong-Ren; Lu, Qing-Chang; Sun, Jian

2015-01-01

Accurate and real-time travel time information for buses can help passengers better plan their trips and minimize waiting times. A dynamic travel time prediction model for buses addressing the cases on road with multiple bus routes is proposed in this paper, based on support vector machines (SVMs) and Kalman filtering-based algorithm. In the proposed model, the well-trained SVM model predicts the baseline bus travel times from the historical bus trip data; the Kalman filtering-based dynamic algorithm can adjust bus travel times with the latest bus operation information and the estimated baseline travel times. The performance of the proposed dynamic model is validated with the real-world data on road with multiple bus routes in Shenzhen, China. The results show that the proposed dynamic model is feasible and applicable for bus travel time prediction and has the best prediction performance among all the five models proposed in the study in terms of prediction accuracy on road with multiple bus routes. PMID:26294903

Dynamic Bus Travel Time Prediction Models on Road with Multiple Bus Routes.

PubMed

Bai, Cong; Peng, Zhong-Ren; Lu, Qing-Chang; Sun, Jian

2015-01-01

Accurate and real-time travel time information for buses can help passengers better plan their trips and minimize waiting times. A dynamic travel time prediction model for buses addressing the cases on road with multiple bus routes is proposed in this paper, based on support vector machines (SVMs) and Kalman filtering-based algorithm. In the proposed model, the well-trained SVM model predicts the baseline bus travel times from the historical bus trip data; the Kalman filtering-based dynamic algorithm can adjust bus travel times with the latest bus operation information and the estimated baseline travel times. The performance of the proposed dynamic model is validated with the real-world data on road with multiple bus routes in Shenzhen, China. The results show that the proposed dynamic model is feasible and applicable for bus travel time prediction and has the best prediction performance among all the five models proposed in the study in terms of prediction accuracy on road with multiple bus routes.

Detection and extraction of orientation-and-scale-dependent information from two-dimensional GPR data with tuneable directional wavelet filters

NASA Astrophysics Data System (ADS)

Tzanis, Andreas

2013-02-01

The Ground Probing Radar (GPR) is a valuable tool for near surface geological, geotechnical, engineering, environmental, archaeological and other work. GPR images of the subsurface frequently contain geometric information (constant or variable-dip reflections) from various structures such as bedding, cracks, fractures, etc. Such features are frequently the target of the survey; however, they are usually not good reflectors and they are highly localized in time and in space. Their scale is therefore a factor significantly affecting their detectability. At the same time, the GPR method is very sensitive to broadband noise from buried small objects, electromagnetic anthropogenic activity and systemic factors, which frequently blurs the reflections from such targets. This paper introduces a method to de-noise GPR data and extract geometric information from scale-and-dip dependent structural features, based on one-dimensional B-Spline Wavelets, two-dimensional directional B-Spline Wavelet (BSW) Filters and two-dimensional Gabor Filters. A directional BSW Filter is built by sidewise arranging s identical one-dimensional wavelets of length L, tapering the s-parallel direction (span) with a suitable window function and rotating the resulting matrix to the desired orientation. The length L of the wavelet defines the temporal and spatial scale to be isolated and the span determines the length over which to smooth (spatial resolution). The Gabor Filter is generated by multiplying an elliptical Gaussian by a complex plane wave; at any orientation the temporal or spatial scale(s) to be isolated are determined by the wavelength. λ of the plane wave and the spatial resolution by the spatial aspect ratio γ, which specifies the ellipticity of the support of the Gabor function. At any orientation, both types of filter may be tuned at any frequency or spatial wavenumber by varying the length or the wavelength respectively. The filters can be applied directly to two-dimensional radargrams, in which case they abstract information about given scales at given orientations. Alternatively, they can be rotated to different orientations under adaptive control, so that they remain tuned at a given frequency or wavenumber and the resulting images can be stacked in the LS sense, so as to obtain a complete representation of the input data at a given temporal or spatial scale. In addition to isolating geometrical information for further scrutiny, the proposed filtering methods can be used to enhance the S/N ratio in a manner particularly suitable for GPR data, because the frequency response of the filters mimics the frequency characteristics of the source wavelet. Finally, signal attenuation and temporal localization are closely associated: low attenuation interfaces tend to produce reflections rich in high frequencies and fine-scale localization as a function of time. Conversely, high attenuation interfaces will produce reflections rich in low frequencies and broad localization. Accordingly, the temporal localization characteristics of the filters may be exploited to investigate the characteristics of signal propagation (hence material properties). The method is shown to be very effective in extracting fine to coarse scale information from noisy data and is demonstrated with applications to noisy GPR data from archaeometric and geotechnical surveys.

Face identification with frequency domain matched filtering in mobile environments

NASA Astrophysics Data System (ADS)

Lee, Dong-Su; Woo, Yong-Hyun; Yeom, Seokwon; Kim, Shin-Hwan

2012-06-01

Face identification at a distance is very challenging since captured images are often degraded by blur and noise. Furthermore, the computational resources and memory are often limited in the mobile environments. Thus, it is very challenging to develop a real-time face identification system on the mobile device. This paper discusses face identification based on frequency domain matched filtering in the mobile environments. Face identification is performed by the linear or phase-only matched filter and sequential verification stages. The candidate window regions are decided by the major peaks of the linear or phase-only matched filtering outputs. The sequential stages comprise a skin-color test and an edge mask filtering test, which verify color and shape information of the candidate regions in order to remove false alarms. All algorithms are built on the mobile device using Android platform. The preliminary results show that face identification of East Asian people can be performed successfully in the mobile environments.

Design of adaptive control systems by means of self-adjusting transversal filters

NASA Technical Reports Server (NTRS)

Merhav, S. J.

1986-01-01

The design of closed-loop adaptive control systems based on nonparametric identification was addressed. Implementation is by self-adjusting Least Mean Square (LMS) transversal filters. The design concept is Model Reference Adaptive Control (MRAC). Major issues are to preserve the linearity of the error equations of each LMS filter, and to prevent estimation bias that is due to process or measurement noise, thus providing necessary conditions for the convergence and stability of the control system. The controlled element is assumed to be asymptotically stable and minimum phase. Because of the nonparametric Finite Impulse Response (FIR) estimates provided by the LMS filters, a-priori information on the plant model is needed only in broad terms. Following a survey of control system configurations and filter design considerations, system implementation is shown here in Single Input Single Output (SISO) format which is readily extendable to multivariable forms. In extensive computer simulation studies the controlled element is represented by a second-order system with widely varying damping, natural frequency, and relative degree.

Gabor filter based fingerprint image enhancement

NASA Astrophysics Data System (ADS)

Wang, Jin-Xiang

2013-03-01

Fingerprint recognition technology has become the most reliable biometric technology due to its uniqueness and invariance, which has been most convenient and most reliable technique for personal authentication. The development of Automated Fingerprint Identification System is an urgent need for modern information security. Meanwhile, fingerprint preprocessing algorithm of fingerprint recognition technology has played an important part in Automatic Fingerprint Identification System. This article introduces the general steps in the fingerprint recognition technology, namely the image input, preprocessing, feature recognition, and fingerprint image enhancement. As the key to fingerprint identification technology, fingerprint image enhancement affects the accuracy of the system. It focuses on the characteristics of the fingerprint image, Gabor filters algorithm for fingerprint image enhancement, the theoretical basis of Gabor filters, and demonstration of the filter. The enhancement algorithm for fingerprint image is in the windows XP platform with matlab.65 as a development tool for the demonstration. The result shows that the Gabor filter is effective in fingerprint image enhancement technology.

Network Visualization Project (NVP)

DTIC Science & Technology

2016-07-01

network visualization, network traffic analysis, network forensics 16. SECURITY CLASSIFICATION OF: 17. LIMITATION OF ABSTRACT UU 18. NUMBER OF...shell, is a command-line framework used for network forensic analysis. Dshell processes existing pcap files and filters output information based on

A Comparison of Evaluation Metrics for Biomedical Journals, Articles, and Websites in Terms of Sensitivity to Topic

PubMed Central

Fu, Lawrence D.; Aphinyanaphongs, Yindalon; Wang, Lily; Aliferis, Constantin F.

2011-01-01

Evaluating the biomedical literature and health-related websites for quality are challenging information retrieval tasks. Current commonly used methods include impact factor for journals, PubMed’s clinical query filters and machine learning-based filter models for articles, and PageRank for websites. Previous work has focused on the average performance of these methods without considering the topic, and it is unknown how performance varies for specific topics or focused searches. Clinicians, researchers, and users should be aware when expected performance is not achieved for specific topics. The present work analyzes the behavior of these methods for a variety of topics. Impact factor, clinical query filters, and PageRank vary widely across different topics while a topic-specific impact factor and machine learning-based filter models are more stable. The results demonstrate that a method may perform excellently on average but struggle when used on a number of narrower topics. Topic adjusted metrics and other topic robust methods have an advantage in such situations. Users of traditional topic-sensitive metrics should be aware of their limitations. PMID:21419864

Hybrid registration of PET/CT in thoracic region with pre-filtering PET sinogram

NASA Astrophysics Data System (ADS)

Mokri, S. S.; Saripan, M. I.; Marhaban, M. H.; Nordin, A. J.; Hashim, S.

2015-11-01

The integration of physiological (PET) and anatomical (CT) images in cancer delineation requires an accurate spatial registration technique. Although hybrid PET/CT scanner is used to co-register these images, significant misregistrations exist due to patient and respiratory/cardiac motions. This paper proposes a hybrid feature-intensity based registration technique for hybrid PET/CT scanner. First, simulated PET sinogram was filtered with a 3D hybrid mean-median before reconstructing the image. The features were then derived from the segmented structures (lung, heart and tumor) from both images. The registration was performed based on modified multi-modality demon registration with multiresolution scheme. Apart from visual observations improvements, the proposed registration technique increased the normalized mutual information index (NMI) between the PET/CT images after registration. All nine tested datasets show marked improvements in mutual information (MI) index than free form deformation (FFD) registration technique with the highest MI increase is 25%.

Optical multiple-image authentication based on cascaded phase filtering structure

NASA Astrophysics Data System (ADS)

Wang, Q.; Alfalou, A.; Brosseau, C.

2016-10-01

In this study, we report on the recent developments of optical image authentication algorithms. Compared with conventional optical encryption, optical image authentication achieves more security strength because such methods do not need to recover information of plaintext totally during the decryption period. Several recently proposed authentication systems are briefly introduced. We also propose a novel multiple-image authentication system, where multiple original images are encoded into a photon-limited encoded image by using a triple-plane based phase retrieval algorithm and photon counting imaging (PCI) technique. One can only recover a noise-like image using correct keys. To check authority of multiple images, a nonlinear fractional correlation is employed to recognize the original information hidden in the decrypted results. The proposal can be implemented optically using a cascaded phase filtering configuration. Computer simulation results are presented to evaluate the performance of this proposal and its effectiveness.

Use of laser range finders and range image analysis in automated assembly tasks

NASA Technical Reports Server (NTRS)

Alvertos, Nicolas; Dcunha, Ivan

1990-01-01

A proposition to study the effect of filtering processes on range images and to evaluate the performance of two different laser range mappers is made. Median filtering was utilized to remove noise from the range images. First and second order derivatives are then utilized to locate the similarities and dissimilarities between the processed and the original images. Range depth information is converted into spatial coordinates, and a set of coefficients which describe 3-D objects is generated using the algorithm developed in the second phase of this research. Range images of spheres and cylinders are used for experimental purposes. An algorithm was developed to compare the performance of two different laser range mappers based upon the range depth information of surfaces generated by each of the mappers. Furthermore, an approach based on 2-D analytic geometry is also proposed which serves as a basis for the recognition of regular 3-D geometric objects.

Human attention filters for single colors.

PubMed

Sun, Peng; Chubb, Charles; Wright, Charles E; Sperling, George

2016-10-25

The visual images in the eyes contain much more information than the brain can process. An important selection mechanism is feature-based attention (FBA). FBA is best described by attention filters that specify precisely the extent to which items containing attended features are selectively processed and the extent to which items that do not contain the attended features are attenuated. The centroid-judgment paradigm enables quick, precise measurements of such human perceptual attention filters, analogous to transmission measurements of photographic color filters. Subjects use a mouse to locate the centroid-the center of gravity-of a briefly displayed cloud of dots and receive precise feedback. A subset of dots is distinguished by some characteristic, such as a different color, and subjects judge the centroid of only the distinguished subset (e.g., dots of a particular color). The analysis efficiently determines the precise weight in the judged centroid of dots of every color in the display (i.e., the attention filter for the particular attended color in that context). We report 32 attention filters for single colors. Attention filters that discriminate one saturated hue from among seven other equiluminant distractor hues are extraordinarily selective, achieving attended/unattended weight ratios >20:1. Attention filters for selecting a color that differs in saturation or lightness from distractors are much less selective than attention filters for hue (given equal discriminability of the colors), and their filter selectivities are proportional to the discriminability distance of neighboring colors, whereas in the same range hue attention-filter selectivity is virtually independent of discriminabilty.

A Crime Analysis Decision Support System for Crime Report Classification and Visualization

ERIC Educational Resources Information Center

Ku, Chih-Hao

2012-01-01

Today's Internet-based crime reporting systems make timely and anonymous crime reporting possible. However, these reports also result in a rapidly growing set of unstructured text files. Complicating the problem is that the information has not been filtered or guided in a detective-led interview resulting in much irrelevant information. To…

The Ability to Process Abstract Information.

DTIC Science & Technology

1983-09-01

Responses Associated with Stress . .. 8 2. Filter Theories: A. Broadbent’s filter model . . . . 12 B. Treisaman’s attentuation model . . . 12 3... model has been proposed by Schneider and Shiffrin (1977) and Shiffrin and Schneider (1977). Unlike Broadbent’s filter models Schneider and Shiffrin...allows for processing to take place only on the input "selected". This filter model is shown in Figure 2A. According to this theory, any information

An adaptive demodulation approach for bearing fault detection based on adaptive wavelet filtering and spectral subtraction

NASA Astrophysics Data System (ADS)

Zhang, Yan; Tang, Baoping; Liu, Ziran; Chen, Rengxiang

2016-02-01

Fault diagnosis of rolling element bearings is important for improving mechanical system reliability and performance. Vibration signals contain a wealth of complex information useful for state monitoring and fault diagnosis. However, any fault-related impulses in the original signal are often severely tainted by various noises and the interfering vibrations caused by other machine elements. Narrow-band amplitude demodulation has been an effective technique to detect bearing faults by identifying bearing fault characteristic frequencies. To achieve this, the key step is to remove the corrupting noise and interference, and to enhance the weak signatures of the bearing fault. In this paper, a new method based on adaptive wavelet filtering and spectral subtraction is proposed for fault diagnosis in bearings. First, to eliminate the frequency associated with interfering vibrations, the vibration signal is bandpass filtered with a Morlet wavelet filter whose parameters (i.e. center frequency and bandwidth) are selected in separate steps. An alternative and efficient method of determining the center frequency is proposed that utilizes the statistical information contained in the production functions (PFs). The bandwidth parameter is optimized using a local ‘greedy’ scheme along with Shannon wavelet entropy criterion. Then, to further reduce the residual in-band noise in the filtered signal, a spectral subtraction procedure is elaborated after wavelet filtering. Instead of resorting to a reference signal as in the majority of papers in the literature, the new method estimates the power spectral density of the in-band noise from the associated PF. The effectiveness of the proposed method is validated using simulated data, test rig data, and vibration data recorded from the transmission system of a helicopter. The experimental results and comparisons with other methods indicate that the proposed method is an effective approach to detecting the fault-related impulses hidden in vibration signals and performs well for bearing fault diagnosis.

Optimization of a matched-filter receiver for frequency hopping code acquisition in jamming

NASA Astrophysics Data System (ADS)

Pawlowski, P. R.; Polydoros, A.

A matched-filter receiver for frequency hopping (FH) code acquisition is optimized when either partial-band tone jamming or partial-band Gaussian noise jamming is present. The receiver is matched to a segment of the FH code sequence, sums hard per-channel decisions to form a test, and uses multiple tests to verify acquisition. The length of the matched filter and the number of verification tests are fixed. Optimization is then choosing thresholds to maximize performance based upon the receiver's degree of knowledge about the jammer ('side-information'). Four levels of side-information are considered, ranging from none to complete. The latter level results in a constant-false-alarm-rate (CFAR) design. At each level, performance sensitivity to threshold choice is analyzed. Robust thresholds are chosen to maximize performance as the jammer varies its power distribution, resulting in simple design rules which aid threshold selection. Performance results, which show that optimum distributions for the jammer power over the total FH bandwidth exist, are presented.

MULTISCALE TENSOR ANISOTROPIC FILTERING OF FLUORESCENCE MICROSCOPY FOR DENOISING MICROVASCULATURE.

PubMed

Prasath, V B S; Pelapur, R; Glinskii, O V; Glinsky, V V; Huxley, V H; Palaniappan, K

2015-04-01

Fluorescence microscopy images are contaminated by noise and improving image quality without blurring vascular structures by filtering is an important step in automatic image analysis. The application of interest here is to automatically extract the structural components of the microvascular system with accuracy from images acquired by fluorescence microscopy. A robust denoising process is necessary in order to extract accurate vascular morphology information. For this purpose, we propose a multiscale tensor with anisotropic diffusion model which progressively and adaptively updates the amount of smoothing while preserving vessel boundaries accurately. Based on a coherency enhancing flow with planar confidence measure and fused 3D structure information, our method integrates multiple scales for microvasculature preservation and noise removal membrane structures. Experimental results on simulated synthetic images and epifluorescence images show the advantage of our improvement over other related diffusion filters. We further show that the proposed multiscale integration approach improves denoising accuracy of different tensor diffusion methods to obtain better microvasculature segmentation.

Ion beam-based studies for tribological phenomena

NASA Astrophysics Data System (ADS)

Racolta, P. M.; Popa-Simil, L.; Alexandreanu, B.

1996-06-01

Custom-designed experiments based on the Thin Layer Activation technique (TLA) were completed, providing information on the wear level of some engine components with additional data on transfer and adhesion of material between metallic friction couples using the RBS method. RBS experimental results concerning material transfer for a steel-brass friction couple are presented and discussed in the paper. Also, the types and concentrations of the wear products in used lubricant oils were determined by in-air PIXE. A sequential lubricant filtering-based procedure for determining the dimension distribution of the resulting radioactive wear particles by low level γ-spectrometry is presented. Experimental XRF spectra showing the non-homogeneous distribution of the retained waste particles on the filtering paper are shown.

Kalman Filtering with Inequality Constraints for Turbofan Engine Health Estimation

NASA Technical Reports Server (NTRS)

Simon, Dan; Simon, Donald L.

2003-01-01

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 two analytic methods of incorporating state variable inequality constraints in the Kalman filter. The first method is a general technique of using hard constraints to enforce inequalities on the state variable estimates. The resultant filter is a combination of a standard Kalman filter and a quadratic programming problem. The second method uses soft constraints to estimate state variables that are known to vary slowly with time. (Soft constraints are constraints that are required to be approximately satisfied rather than exactly satisfied.) The incorporation of state variable constraints increases the computational effort of the filter but significantly improves its estimation accuracy. The improvement is proven theoretically and shown via simulation results. The use of the algorithm is demonstrated on a linearized simulation of a turbofan engine to estimate health parameters. The turbofan engine model contains 16 state variables, 12 measurements, and 8 component health parameters. It is shown that the new algorithms provide improved performance in this example over unconstrained Kalman filtering.

Space-Based Astronomy: An Educator Guide with Activities for Science, Mathematics, and Technology Education.

ERIC Educational Resources Information Center

Vogt, Gregory L.

This educator's guide features activities for science, mathematics, and technology education. The activities in this curriculum guide were developed based on the hands-on approach. The guide starts with introductory information and is followed by five units: (1) "The Atmospheric Filter"; (2) "The Electromagnetic Spectrum"; (3)…

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…

Distributed Monte Carlo Information Fusion and Distributed Particle Filtering

DTIC Science & Technology

2014-08-24

Distributed Monte Carlo Information Fusion and Distributed Particle Filtering Isaac L. Manuel and Adrian N. Bishop Australian National University and...2 20 + vit , (21) where vit is Gaussian white noise with a random variance. We initialised the filters with the state xi0 = 0.1 for all i ∈ V . This

RGB-D SLAM Combining Visual Odometry and Extended Information Filter

PubMed Central

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

2015-01-01

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

ARTSN: An Automated Real-Time Spacecraft Navigation System

NASA Technical Reports Server (NTRS)

Burkhart, P. Daniel; Pollmeier, Vincent M.

1996-01-01

As part of the Deep Space Network (DSN) advanced technology program an effort is underway to design a filter to automate the deep space navigation process.The automated real-time spacecraft navigation (ARTSN) filter task is based on a prototype consisting of a FORTRAN77 package operating on an HP-9000/700 workstation running HP-UX 9.05. This will be converted to C, and maintained as the operational version. The processing tasks required are: (1) read a measurement, (2) integrate the spacecraft state to the current measurement time, (3) compute the observable based on the integrated state, and (4) incorporate the measurement information into the state using an extended Kalman filter. This filter processes radiometric data collected by the DSN. The dynamic (force) models currently include point mass gravitational terms for all planets, the Sun and Moon, solar radiation pressure, finite maneuvers, and attitude maintenance activity modeled quadratically. In addition, observable errors due to troposphere are included. Further data types, force and observable models will be ncluded to enhance the accuracy of the models and the capability of the package. The heart of the ARSTSN is a currently available continuous-discrete extended Kalman filter. Simulated data used to test the implementation at various stages of development and the results from processing actual mission data are presented.

Exploring an optimal wavelet-based filter for cryo-ET imaging.

PubMed

Huang, Xinrui; Li, Sha; Gao, Song

2018-02-07

Cryo-electron tomography (cryo-ET) is one of the most advanced technologies for the in situ visualization of molecular machines by producing three-dimensional (3D) biological structures. However, cryo-ET imaging has two serious disadvantages-low dose and low image contrast-which result in high-resolution information being obscured by noise and image quality being degraded, and this causes errors in biological interpretation. The purpose of this research is to explore an optimal wavelet denoising technique to reduce noise in cryo-ET images. We perform tests using simulation data and design a filter using the optimum selected wavelet parameters (three-level decomposition, level-1 zeroed out, subband-dependent threshold, a soft-thresholding and spline-based discrete dyadic wavelet transform (DDWT)), which we call a modified wavelet shrinkage filter; this filter is suitable for noisy cryo-ET data. When testing using real cryo-ET experiment data, higher quality images and more accurate measures of a biological structure can be obtained with the modified wavelet shrinkage filter processing compared with conventional processing. Because the proposed method provides an inherent advantage when dealing with cryo-ET images, it can therefore extend the current state-of-the-art technology in assisting all aspects of cryo-ET studies: visualization, reconstruction, structural analysis, and interpretation.

Design of HTS filter for GSM-R communication system

NASA Astrophysics Data System (ADS)

Cui, Hongyu; Ji, Laiyun

2018-04-01

High-temperature superconducting materials with its excellent performance have increasingly been valued by industries, especially in the field of electronic information. The superconducting material has almost zero surface resistance, and the filter made of it has the characteristics of low insertion loss, high edge steepness and good out-of-band rejection. It has higher selectivity for the desired signal and thus less interference from adjacent channels Signal interference, and noise reduction coefficient can improve the ability to detect weak signals. This design is suitable for high temperature superconducting filter of GSM-R communication system, which can overcome many shortcomings of the traditional GSM-R. The filter is made of DyBCO, a high temperature superconducting thin film material based on magnesium oxide (MgO) substrate with the dielectric constant of 9.7, the center frequency at 887.5MHz, bandwidth of 5MHz.

A selective-update affine projection algorithm with selective input vectors

NASA Astrophysics Data System (ADS)

Kong, NamWoong; Shin, JaeWook; Park, PooGyeon

2011-10-01

This paper proposes an affine projection algorithm (APA) with selective input vectors, which based on the concept of selective-update in order to reduce estimation errors and computations. The algorithm consists of two procedures: input- vector-selection and state-decision. The input-vector-selection procedure determines the number of input vectors by checking with mean square error (MSE) whether the input vectors have enough information for update. The state-decision procedure determines the current state of the adaptive filter by using the state-decision criterion. As the adaptive filter is in transient state, the algorithm updates the filter coefficients with the selected input vectors. On the other hand, as soon as the adaptive filter reaches the steady state, the update procedure is not performed. Through these two procedures, the proposed algorithm achieves small steady-state estimation errors, low computational complexity and low update complexity for colored input signals.

Usability-driven pruning of large ontologies: the case of SNOMED CT

PubMed Central

Boeker, Martin; Illarramendi, Arantza; Schulz, Stefan

2012-01-01

Objectives To study ontology modularization techniques when applied to SNOMED CT in a scenario in which no previous corpus of information exists and to examine if frequency-based filtering using MEDLINE can reduce subset size without discarding relevant concepts. Materials and Methods Subsets were first extracted using four graph-traversal heuristics and one logic-based technique, and were subsequently filtered with frequency information from MEDLINE. Twenty manually coded discharge summaries from cardiology patients were used as signatures and test sets. The coverage, size, and precision of extracted subsets were measured. Results Graph-traversal heuristics provided high coverage (71–96% of terms in the test sets of discharge summaries) at the expense of subset size (17–51% of the size of SNOMED CT). Pre-computed subsets and logic-based techniques extracted small subsets (1%), but coverage was limited (24–55%). Filtering reduced the size of large subsets to 10% while still providing 80% coverage. Discussion Extracting subsets to annotate discharge summaries is challenging when no previous corpus exists. Ontology modularization provides valuable techniques, but the resulting modules grow as signatures spread across subhierarchies, yielding a very low precision. Conclusion Graph-traversal strategies and frequency data from an authoritative source can prune large biomedical ontologies and produce useful subsets that still exhibit acceptable coverage. However, a clinical corpus closer to the specific use case is preferred when available. PMID:22268217

The study of infrared target recognition at sea background based on visual attention computational model

NASA Astrophysics Data System (ADS)

Wang, Deng-wei; Zhang, Tian-xu; Shi, Wen-jun; Wei, Long-sheng; Wang, Xiao-ping; Ao, Guo-qing

2009-07-01

Infrared images at sea background are notorious for the low signal-to-noise ratio, therefore, the target recognition of infrared image through traditional methods is very difficult. In this paper, we present a novel target recognition method based on the integration of visual attention computational model and conventional approach (selective filtering and segmentation). The two distinct techniques for image processing are combined in a manner to utilize the strengths of both. The visual attention algorithm searches the salient regions automatically, and represented them by a set of winner points, at the same time, demonstrated the salient regions in terms of circles centered at these winner points. This provides a priori knowledge for the filtering and segmentation process. Based on the winner point, we construct a rectangular region to facilitate the filtering and segmentation, then the labeling operation will be added selectively by requirement. Making use of the labeled information, from the final segmentation result we obtain the positional information of the interested region, label the centroid on the corresponding original image, and finish the localization for the target. The cost time does not depend on the size of the image but the salient regions, therefore the consumed time is greatly reduced. The method is used in the recognition of several kinds of real infrared images, and the experimental results reveal the effectiveness of the algorithm presented in this paper.

Single- and mixture toxicity of three organic UV-filters, ethylhexyl methoxycinnamate, octocrylene, and avobenzone on Daphnia magna.

PubMed

Park, Chang-Beom; Jang, Jiyi; Kim, Sanghun; Kim, Young Jun

2017-03-01

In freshwater environments, aquatic organisms are generally exposed to mixtures of various chemical substances. In this study, we tested the toxicity of three organic UV-filters (ethylhexyl methoxycinnamate, octocrylene, and avobenzone) to Daphnia magna in order to evaluate the combined toxicity of these substances when in they occur in a mixture. The values of effective concentrations (ECx) for each UV-filter were calculated by concentration-response curves; concentration-combinations of three different UV-filters in a mixture were determined by the fraction of components based on EC 25 values predicted by concentration addition (CA) model. The interaction between the UV-filters were also assessed by model deviation ratio (MDR) using observed and predicted toxicity values obtained from mixture-exposure tests and CA model. The results from this study indicated that observed ECx mix (e.g., EC 10mix , EC 25mix , or EC 50mix ) values obtained from mixture-exposure tests were higher than predicted ECx mix (e.g., EC 10mix , EC 25mix , or EC 50mix ) values calculated by CA model. MDR values were also less than a factor of 1.0 in a mixtures of three different UV-filters. Based on these results, we suggest for the first time a reduction of toxic effects in the mixtures of three UV-filters, caused by antagonistic action of the components. Our findings from this study will provide important information for hazard or risk assessment of organic UV-filters, when they existed together in the aquatic environment. To better understand the mixture toxicity and the interaction of components in a mixture, further studies for various combinations of mixture components are also required. Copyright © 2016 Elsevier Inc. All rights reserved.

DNA accumulation on ventilation system filters in university buildings in Singapore

PubMed Central

Luhung, Irvan; Wu, Yan; Xu, Siyu; Yamamoto, Naomichi; Nazaroff, William W.

2017-01-01

Introduction Biological particles deposit on air handling system filters as they process air. This study reports and interprets abundance and diversity information regarding biomass accumulation on ordinarily used filters acquired from several locations in a university environment. Methods DNA-based analysis was applied both to quantify (via DNA fluorometry and qPCR) and to characterize (via high-throughput sequencing) the microbial material on filters, which mainly processed recirculated indoor air. Results were interpreted in relation to building occupancy and ventilation system operational parameters. Results Based on accumulated biomass, average DNA concentrations per AHU filter surface area across nine indoor locations after twelve weeks of filter use were in the respective ranges 1.1 to 41 ng per cm2 for total DNA, 0.02 to 3.3 ng per cm2 for bacterial DNA and 0.2 to 2.0 ng DNA per cm2 for fungal DNA. The most abundant genera detected on the AHU filter samples were Clostridium, Streptophyta, Bacillus, Acinetobacter and Ktedonobacter for bacteria and Aspergillus, Cladosporium, Nigrospora, Rigidoporus and Lentinus for fungi. Conditional indoor airborne DNA concentrations (median (range)) were estimated to be 13 (2.6–107) pg/m3 for total DNA, 0.4 (0.05–8.4) pg/m3 for bacterial DNA and 2.3 (1.0–5.1) pg/m3 for fungal DNA. Conclusion Conditional airborne concentrations and the relative abundances of selected groups of genera correlate well with occupancy level. Bacterial DNA was found to be more responsive than fungal DNA to differences in occupancy level and indoor environmental conditions. PMID:29023520

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

PubMed

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

2011-01-01

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

A 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.

A Damping Grid Strapdown Inertial Navigation System Based on a Kalman Filter for Ships in Polar Regions.

PubMed

Huang, Weiquan; Fang, Tao; Luo, Li; Zhao, Lin; Che, Fengzhu

2017-07-03

The grid strapdown inertial navigation system (SINS) used in polar navigation also includes three kinds of periodic oscillation errors as common SINS are based on a geographic coordinate system. Aiming ships which have the external information to conduct a system reset regularly, suppressing the Schuler periodic oscillation is an effective way to enhance navigation accuracy. The Kalman filter based on the grid SINS error model which applies to the ship is established in this paper. The errors of grid-level attitude angles can be accurately estimated when the external velocity contains constant error, and then correcting the errors of the grid-level attitude angles through feedback correction can effectively dampen the Schuler periodic oscillation. The simulation results show that with the aid of external reference velocity, the proposed external level damping algorithm based on the Kalman filter can suppress the Schuler periodic oscillation effectively. Compared with the traditional external level damping algorithm based on the damping network, the algorithm proposed in this paper can reduce the overshoot errors when the state of grid SINS is switched from the non-damping state to the damping state, and this effectively improves the navigation accuracy of the system.

Filter design for the detection of compact sources based on the Neyman-Pearson detector

NASA Astrophysics Data System (ADS)

López-Caniego, M.; Herranz, D.; Barreiro, R. B.; Sanz, J. L.

2005-05-01

This paper considers the problem of compact source detection on a Gaussian background. We present a one-dimensional treatment (though a generalization to two or more dimensions is possible). Two relevant aspects of this problem are considered: the design of the detector and the filtering of the data. Our detection scheme is based on local maxima and it takes into account not only the amplitude but also the curvature of the maxima. A Neyman-Pearson test is used to define the region of acceptance, which is given by a sufficient linear detector that is independent of the amplitude distribution of the sources. We study how detection can be enhanced by means of linear filters with a scaling parameter, and compare some filters that have been proposed in the literature [the Mexican hat wavelet, the matched filter (MF) and the scale-adaptive filter (SAF)]. We also introduce a new filter, which depends on two free parameters (the biparametric scale-adaptive filter, BSAF). The value of these two parameters can be determined, given the a priori probability density function of the amplitudes of the sources, such that the filter optimizes the performance of the detector in the sense that it gives the maximum number of real detections once it has fixed the number density of spurious sources. The new filter includes as particular cases the standard MF and the SAF. As a result of its design, the BSAF outperforms these filters. The combination of a detection scheme that includes information on the curvature and a flexible filter that incorporates two free parameters (one of them a scaling parameter) improves significantly the number of detections in some interesting cases. In particular, for the case of weak sources embedded in white noise, the improvement with respect to the standard MF is of the order of 40 per cent. Finally, an estimation of the amplitude of the source (most probable value) is introduced and it is proven that such an estimator is unbiased and has maximum efficiency. We perform numerical simulations to test these theoretical ideas in a practical example and conclude that the results of the simulations agree with the analytical results.

Object-oriented and pixel-based classification approach for land cover using airborne long-wave infrared hyperspectral data

NASA Astrophysics Data System (ADS)

Marwaha, Richa; Kumar, Anil; Kumar, Arumugam Senthil

2015-01-01

Our primary objective was to explore a classification algorithm for thermal hyperspectral data. Minimum noise fraction is applied to thermal hyperspectral data and eight pixel-based classifiers, i.e., constrained energy minimization, matched filter, spectral angle mapper (SAM), adaptive coherence estimator, orthogonal subspace projection, mixture-tuned matched filter, target-constrained interference-minimized filter, and mixture-tuned target-constrained interference minimized filter are tested. The long-wave infrared (LWIR) has not yet been exploited for classification purposes. The LWIR data contain emissivity and temperature information about an object. A highest overall accuracy of 90.99% was obtained using the SAM algorithm for the combination of thermal data with a colored digital photograph. Similarly, an object-oriented approach is applied to thermal data. The image is segmented into meaningful objects based on properties such as geometry, length, etc., which are grouped into pixels using a watershed algorithm and an applied supervised classification algorithm, i.e., support vector machine (SVM). The best algorithm in the pixel-based category is the SAM technique. SVM is useful for thermal data, providing a high accuracy of 80.00% at a scale value of 83 and a merge value of 90, whereas for the combination of thermal data with a colored digital photograph, SVM gives the highest accuracy of 85.71% at a scale value of 82 and a merge value of 90.

A Continuous Square Root in Formation Filter-Swoother with Discrete Data Update

NASA Technical Reports Server (NTRS)

Miller, J. K.

1994-01-01

A differential equation for the square root information matrix is derived and adapted to the problems of filtering and smoothing. The resulting continuous square root information filter (SRIF) performs the mapping of state and process noise by numerical integration of the SRIF matrix and admits data via a discrete least square update.

MatSeis and the GNEM R&E regional seismic anaylsis tools.

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

Chael, Eric Paul; Hart, Darren M.; Young, Christopher John

2003-08-01

To improve the nuclear event monitoring capability of the U.S., the NNSA Ground-based Nuclear Explosion Monitoring Research & Engineering (GNEM R&E) program has been developing a collection of products known as the Knowledge Base (KB). Though much of the focus for the KB has been on the development of calibration data, we have also developed numerous software tools for various purposes. The Matlab-based MatSeis package and the associated suite of regional seismic analysis tools were developed to aid in the testing and evaluation of some Knowledge Base products for which existing applications were either not available or ill-suited. This presentationmore » will provide brief overviews of MatSeis and each of the tools, emphasizing features added in the last year. MatSeis was begun in 1996 and is now a fairly mature product. It is a highly flexible seismic analysis package that provides interfaces to read data from either flatfiles or an Oracle database. All of the standard seismic analysis tasks are supported (e.g. filtering, 3 component rotation, phase picking, event location, magnitude calculation), as well as a variety of array processing algorithms (beaming, FK, coherency analysis, vespagrams). The simplicity of Matlab coding and the tremendous number of available functions make MatSeis/Matlab an ideal environment for developing new monitoring research tools (see the regional seismic analysis tools below). New MatSeis features include: addition of evid information to events in MatSeis, options to screen picks by author, input and output of origerr information, improved performance in reading flatfiles, improved speed in FK calculations, and significant improvements to Measure Tool (filtering, multiple phase display), Free Plot (filtering, phase display and alignment), Mag Tool (maximum likelihood options), and Infra Tool (improved calculation speed, display of an F statistic stream). Work on the regional seismic analysis tools (CodaMag, EventID, PhaseMatch, and Dendro) began in 1999 and the tools vary in their level of maturity. All rely on MatSeis to provide necessary data (waveforms, arrivals, origins, and travel time curves). CodaMag Tool implements magnitude calculation by scaling to fit the envelope shape of the coda for a selected phase type (Mayeda, 1993; Mayeda and Walter, 1996). New tool features include: calculation of a yield estimate based on the source spectrum, display of a filtered version of the seismogram based on the selected band, and the output of codamag data records for processed events. EventID Tool implements event discrimination using phase ratios of regional arrivals (Hartse et al., 1997; Walter et al., 1999). New features include: bandpass filtering of displayed waveforms, screening of reference events based on SNR, multivariate discriminants, use of libcgi to access correction surfaces, and the output of discrim{_}data records for processed events. PhaseMatch Tool implements match filtering to isolate surface waves (Herrin and Goforth, 1977). New features include: display of the signal's observed dispersion and an option to use a station-based dispersion surface. Dendro Tool implements agglomerative hierarchical clustering using dendrograms to identify similar events based on waveform correlation (Everitt, 1993). New features include: modifications to include arrival information within the tool, and the capability to automatically add/re-pick arrivals based on the picked arrivals for similar events.« less

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

PubMed

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

2014-04-01

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

Figures of merit for self-beating filtered microwave photonic systems.

PubMed

Pérez, Daniel; Gasulla, Ivana; Capmany, José; Fandiño, Javier S; Muñoz, Pascual; Alavi, Hossein

2016-05-02

We present a model to compute the figures of merit of self-beating Microwave Photonic systems, a novel class of systems that work on a self-homodyne fashion by sharing the same laser source for information bearing and local oscillator tasks. General and simplified expressions are given and, as an example, we have considered their application to the design of a tunable RF MWP BS/UE front end for band selection, based on a Chebyshev Type-II optical filter. The applicability and usefulness of the model are also discussed.

A Real-Time Monitoring System of Industry Carbon Monoxide Based on Wireless Sensor Networks.

PubMed

Yang, Jiachen; Zhou, Jianxiong; Lv, Zhihan; Wei, Wei; Song, Houbing

2015-11-20

Carbon monoxide (CO) burns or explodes at over-standard concentration. Hence, in this paper, a Wifi-based, real-time monitoring of a CO system is proposed for application in the construction industry, in which a sensor measuring node is designed by low-frequency modulation method to acquire CO concentration reliably, and a digital filtering method is adopted for noise filtering. According to the triangulation, the Wifi network is constructed to transmit information and determine the position of nodes. The measured data are displayed on a computer or smart phone by a graphical interface. The experiment shows that the monitoring system obtains excellent accuracy and stability in long-term continuous monitoring.

Cancer classification through filtering progressive transductive support vector machine based on gene expression data

NASA Astrophysics Data System (ADS)

Lu, Xinguo; Chen, Dan

2017-08-01

Traditional supervised classifiers neglect a large amount of data which not have sufficient follow-up information, only work with labeled data. Consequently, the small sample size limits the advancement of design appropriate classifier. In this paper, a transductive learning method which combined with the filtering strategy in transductive framework and progressive labeling strategy is addressed. The progressive labeling strategy does not need to consider the distribution of labeled samples to evaluate the distribution of unlabeled samples, can effective solve the problem of evaluate the proportion of positive and negative samples in work set. Our experiment result demonstrate that the proposed technique have great potential in cancer prediction based on gene expression.

Human attention filters for single colors

PubMed Central

Sun, Peng; Chubb, Charles; Wright, Charles E.; Sperling, George

2016-01-01

The visual images in the eyes contain much more information than the brain can process. An important selection mechanism is feature-based attention (FBA). FBA is best described by attention filters that specify precisely the extent to which items containing attended features are selectively processed and the extent to which items that do not contain the attended features are attenuated. The centroid-judgment paradigm enables quick, precise measurements of such human perceptual attention filters, analogous to transmission measurements of photographic color filters. Subjects use a mouse to locate the centroid—the center of gravity—of a briefly displayed cloud of dots and receive precise feedback. A subset of dots is distinguished by some characteristic, such as a different color, and subjects judge the centroid of only the distinguished subset (e.g., dots of a particular color). The analysis efficiently determines the precise weight in the judged centroid of dots of every color in the display (i.e., the attention filter for the particular attended color in that context). We report 32 attention filters for single colors. Attention filters that discriminate one saturated hue from among seven other equiluminant distractor hues are extraordinarily selective, achieving attended/unattended weight ratios >20:1. Attention filters for selecting a color that differs in saturation or lightness from distractors are much less selective than attention filters for hue (given equal discriminability of the colors), and their filter selectivities are proportional to the discriminability distance of neighboring colors, whereas in the same range hue attention-filter selectivity is virtually independent of discriminabilty. PMID:27791040

ECG Denoising Using Marginalized Particle Extended Kalman Filter With an Automatic Particle Weighting Strategy.

PubMed

Hesar, Hamed Danandeh; Mohebbi, Maryam

2017-05-01

In this paper, a model-based Bayesian filtering framework called the "marginalized particle-extended Kalman filter (MP-EKF) algorithm" is proposed for electrocardiogram (ECG) denoising. This algorithm does not have the extended Kalman filter (EKF) shortcoming in handling non-Gaussian nonstationary situations because of its nonlinear framework. In addition, it has less computational complexity compared with particle filter. This filter improves ECG denoising performance by implementing marginalized particle filter framework while reducing its computational complexity using EKF framework. An automatic particle weighting strategy is also proposed here that controls the reliance of our framework to the acquired measurements. We evaluated the proposed filter on several normal ECGs selected from MIT-BIH normal sinus rhythm database. To do so, artificial white Gaussian and colored noises as well as nonstationary real muscle artifact (MA) noise over a range of low SNRs from 10 to -5 dB were added to these normal ECG segments. The benchmark methods were the EKF and extended Kalman smoother (EKS) algorithms which are the first model-based Bayesian algorithms introduced in the field of ECG denoising. From SNR viewpoint, the experiments showed that in the presence of Gaussian white noise, the proposed framework outperforms the EKF and EKS algorithms in lower input SNRs where the measurements and state model are not reliable. Owing to its nonlinear framework and particle weighting strategy, the proposed algorithm attained better results at all input SNRs in non-Gaussian nonstationary situations (such as presence of pink noise, brown noise, and real MA). In addition, the impact of the proposed filtering method on the distortion of diagnostic features of the ECG was investigated and compared with EKF/EKS methods using an ECG diagnostic distortion measure called the "Multi-Scale Entropy Based Weighted Distortion Measure" or MSEWPRD. The results revealed that our proposed algorithm had the lowest MSEPWRD for all noise types at low input SNRs. Therefore, the morphology and diagnostic information of ECG signals were much better conserved compared with EKF/EKS frameworks, especially in non-Gaussian nonstationary situations.

Uncertainty quantification of seabed parameters for large data volumes along survey tracks with a tempered particle filter

NASA Astrophysics Data System (ADS)

Dettmer, J.; Quijano, J. E.; Dosso, S. E.; Holland, C. W.; Mandolesi, E.

2016-12-01

Geophysical seabed properties are important for the detection and classification of unexploded ordnance. However, current surveying methods such as vertical seismic profiling, coring, or inversion are of limited use when surveying large areas with high spatial sampling density. We consider surveys based on a source and receiver array towed by an autonomous vehicle which produce large volumes of seabed reflectivity data that contain unprecedented and detailed seabed information. The data are analyzed with a particle filter, which requires efficient reflection-coefficient computation, efficient inversion algorithms and efficient use of computer resources. The filter quantifies information content of multiple sequential data sets by considering results from previous data along the survey track to inform the importance sampling at the current point. Challenges arise from environmental changes along the track where the number of sediment layers and their properties change. This is addressed by a trans-dimensional model in the filter which allows layering complexity to change along a track. Efficiency is improved by likelihood tempering of various particle subsets and including exchange moves (parallel tempering). The filter is implemented on a hybrid computer that combines central processing units (CPUs) and graphics processing units (GPUs) to exploit three levels of parallelism: (1) fine-grained parallel computation of spherical reflection coefficients with a GPU implementation of Levin integration; (2) updating particles by concurrent CPU processes which exchange information using automatic load balancing (coarse grained parallelism); (3) overlapping CPU-GPU communication (a major bottleneck) with GPU computation by staggering CPU access to the multiple GPUs. The algorithm is applied to spherical reflection coefficients for data sets along a 14-km track on the Malta Plateau, Mediterranean Sea. We demonstrate substantial efficiency gains over previous methods. [This research was supported in part by the U.S. Dept of Defense, thought the Strategic Environmental Research and Development Program (SERDP).

Comparison of the filtering models for airborne LiDAR data by three classifiers with exploration on model transfer

NASA Astrophysics Data System (ADS)

Ma, Hongchao; Cai, Zhan; Zhang, Liang

2018-01-01

This paper discusses airborne light detection and ranging (LiDAR) point cloud filtering (a binary classification problem) from the machine learning point of view. We compared three supervised classifiers for point cloud filtering, namely, Adaptive Boosting, support vector machine, and random forest (RF). Nineteen features were generated from raw LiDAR point cloud based on height and other geometric information within a given neighborhood. The test datasets issued by the International Society for Photogrammetry and Remote Sensing (ISPRS) were used to evaluate the performance of the three filtering algorithms; RF showed the best results with an average total error of 5.50%. The paper also makes tentative exploration in the application of transfer learning theory to point cloud filtering, which has not been introduced into the LiDAR field to the authors' knowledge. We performed filtering of three datasets from real projects carried out in China with RF models constructed by learning from the 15 ISPRS datasets and then transferred with little to no change of the parameters. Reliable results were achieved, especially in rural area (overall accuracy achieved 95.64%), indicating the feasibility of model transfer in the context of point cloud filtering for both easy automation and acceptable accuracy.

SU-E-J-261: The Importance of Appropriate Image Preprocessing to Augment the Information of Radiomics Image Features

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

Zhang, L; Fried, D; Fave, X

Purpose: To investigate how different image preprocessing techniques, their parameters, and the different boundary handling techniques can augment the information of features and improve feature’s differentiating capability. Methods: Twenty-seven NSCLC patients with a solid tumor volume and no visually obvious necrotic regions in the simulation CT images were identified. Fourteen of these patients had a necrotic region visible in their pre-treatment PET images (necrosis group), and thirteen had no visible necrotic region in the pre-treatment PET images (non-necrosis group). We investigated how image preprocessing can impact the ability of radiomics image features extracted from the CT to differentiate between twomore » groups. It is expected the histogram in the necrosis group is more negatively skewed, and the uniformity from the necrosis group is less. Therefore, we analyzed two first order features, skewness and uniformity, on the image inside the GTV in the intensity range [−20HU, 180HU] under the combination of several image preprocessing techniques: (1) applying the isotropic Gaussian or anisotropic diffusion smoothing filter with a range of parameter(Gaussian smoothing: size=11, sigma=0:0.1:2.3; anisotropic smoothing: iteration=4, kappa=0:10:110); (2) applying the boundaryadapted Laplacian filter; and (3) applying the adaptive upper threshold for the intensity range. A 2-tailed T-test was used to evaluate the differentiating capability of CT features on pre-treatment PT necrosis. Result: Without any preprocessing, no differences in either skewness or uniformity were observed between two groups. After applying appropriate Gaussian filters (sigma>=1.3) or anisotropic filters(kappa >=60) with the adaptive upper threshold, skewness was significantly more negative in the necrosis group(p<0.05). By applying the boundary-adapted Laplacian filtering after the appropriate Gaussian filters (0.5 <=sigma<=1.1) or anisotropic filters(20<=kappa <=50), the uniformity was significantly lower in the necrosis group (p<0.05). Conclusion: Appropriate selection of image preprocessing techniques allows radiomics features to extract more useful information and thereby improve prediction models based on these features.« less

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

PubMed

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

2018-04-01

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

GEO Optical Data Association with Concurrent Metric and Photometric Information

NASA Astrophysics Data System (ADS)

Dao, P.; Monet, D.

Data association in a congested area of the GEO belt with occasional visits by non-resident objects can be treated as a Multi-Target-Tracking (MTT) problem. For a stationary sensor surveilling the GEO belt, geosynchronous and near GEO objects are not completely motionless in the earth-fixed frame and can be observed as moving targets. In some clusters, metric or positional information is insufficiently accurate or up-to-date to associate the measurements. In the presence of measurements with uncertain origin, star tracks (residuals) and other sensor artifacts, heuristic techniques based on hard decision assignment do not perform adequately. In the MMT community, Bar-Shalom [2009 Bar-Shalom] was first in introducing the use of measurements to update the state of the target of interest in the tracking filter, e.g. Kalman filter. Following Bar-Shalom’s idea, we use the Probabilistic Data Association Filter (PDAF) but to make use of all information obtainable in the measurement of three-axis-stabilized GEO satellites, we combine photometric with metric measurements to update the filter. Therefore, our technique Concurrent Spatio- Temporal and Brightness (COSTB) has the stand-alone ability of associating a track with its identity –for resident objects. That is possible because the light curve of a stabilized GEO satellite changes minimally from night to night. We exercised COSTB on camera cadence data to associate measurements, correct mistags and detect non-residents in a simulated near real time cadence. Data on GEO clusters were used.

Wavelength-Filter Based Spectral Calibrated Wave number - Linearization in 1.3 mm Spectral Domain Optical Coherence.

PubMed

Wijeisnghe, Ruchire Eranga Henry; Cho, Nam Hyun; Park, Kibeom; Shin, Yongseung; Kim, Jeehyun

2013-12-01

In this study, we demonstrate the enhanced spectral calibration method for 1.3 μm spectral-domain optical coherence tomography (SD-OCT). The calibration method using wavelength-filter simplifies the SD-OCT system, and also the axial resolution and the entire speed of the OCT system can be dramatically improved as well. An externally connected wavelength-filter is utilized to obtain the information of the wavenumber and the pixel position. During the calibration process the wavelength-filter is placed after a broadband source by connecting through an optical circulator. The filtered spectrum with a narrow line width of 0.5 nm is detected by using a line-scan camera. The method does not require a filter or a software recalibration algorithm for imaging as it simply resamples the OCT signal from the detector array without employing rescaling or interpolation methods. One of the main drawbacks of SD-OCT is the broadened point spread functions (PSFs) with increasing imaging depth can be compensated by increasing the wavenumber-linearization order. The sensitivity of our system was measured at 99.8 dB at an imaging depth of 2.1 mm compared with the uncompensated case.

Al-Coated Conductive Fiber Filters for High-Efficiency Electrostatic Filtration: Effects of Electrical and Fiber Structural Properties.

PubMed

Choi, Dong Yun; An, Eun Jeong; Jung, Soo-Ho; Song, Dong Keun; Oh, Yong Suk; Lee, Hyung Woo; Lee, Hye Moon

2018-04-10

Through the direct decomposition of an Al precursor ink AlH 3 {O(C 4 H 9 ) 2 }, we fabricated an Al-coated conductive fiber filter for the efficient electrostatic removal of airborne particles (>99%) with a low pressure drop (~several Pascals). The effects of the electrical and structural properties of the filters were investigated in terms of collection efficiency, pressure drop, and particle deposition behavior. The collection efficiency did not show a significant correlation with the extent of electrical conductivity, as the filter is electrostatically charged by the metallic Al layers forming electrical networks throughout the fibers. Most of the charged particles were collected via surface filtration by Coulombic interactions; consequently, the filter thickness had little effect on the collection efficiency. Based on simulations of various fiber structures, we found that surface filtration can transition to depth filtration depending on the extent of interfiber distance. Therefore, the effects of structural characteristics on collection efficiency varied depending on the degree of the fiber packing density. This study will offer valuable information pertaining to the development of a conductive metal/polymer composite air filter for an energy-efficient and high-performance electrostatic filtration system.

Kullback-Leibler Divergence-Based Differential Evolution Markov Chain Filter for Global Localization of Mobile Robots.

PubMed

Martín, Fernando; Moreno, Luis; Garrido, Santiago; Blanco, Dolores

2015-09-16

One of the most important skills desired for a mobile robot is the ability to obtain its own location even in challenging environments. The information provided by the sensing system is used here to solve the global localization problem. In our previous work, we designed different algorithms founded on evolutionary strategies in order to solve the aforementioned task. The latest developments are presented in this paper. The engine of the localization module is a combination of the Markov chain Monte Carlo sampling technique and the Differential Evolution method, which results in a particle filter based on the minimization of a fitness function. The robot's pose is estimated from a set of possible locations weighted by a cost value. The measurements of the perceptive sensors are used together with the predicted ones in a known map to define a cost function to optimize. Although most localization methods rely on quadratic fitness functions, the sensed information is processed asymmetrically in this filter. The Kullback-Leibler divergence is the basis of a cost function that makes it possible to deal with different types of occlusions. The algorithm performance has been checked in a real map. The results are excellent in environments with dynamic and unmodeled obstacles, a fact that causes occlusions in the sensing area.

Kullback-Leibler Divergence-Based Differential Evolution Markov Chain Filter for Global Localization of Mobile Robots

PubMed Central

Martín, Fernando; Moreno, Luis; Garrido, Santiago; Blanco, Dolores

2015-01-01

One of the most important skills desired for a mobile robot is the ability to obtain its own location even in challenging environments. The information provided by the sensing system is used here to solve the global localization problem. In our previous work, we designed different algorithms founded on evolutionary strategies in order to solve the aforementioned task. The latest developments are presented in this paper. The engine of the localization module is a combination of the Markov chain Monte Carlo sampling technique and the Differential Evolution method, which results in a particle filter based on the minimization of a fitness function. The robot’s pose is estimated from a set of possible locations weighted by a cost value. The measurements of the perceptive sensors are used together with the predicted ones in a known map to define a cost function to optimize. Although most localization methods rely on quadratic fitness functions, the sensed information is processed asymmetrically in this filter. The Kullback-Leibler divergence is the basis of a cost function that makes it possible to deal with different types of occlusions. The algorithm performance has been checked in a real map. The results are excellent in environments with dynamic and unmodeled obstacles, a fact that causes occlusions in the sensing area. PMID:26389914

Building a good initial model for full-waveform inversion using frequency shift filter

NASA Astrophysics Data System (ADS)

Wang, Guanchao; Wang, Shangxu; Yuan, Sanyi; Lian, Shijie

2018-05-01

Accurate initial model or available low-frequency data is an important factor in the success of full waveform inversion (FWI). The low-frequency helps determine the kinematical relevant components, low-wavenumber of the velocity model, which are in turn needed to avoid FWI trap in local minima or cycle-skipping. However, in the field, acquiring data that <5 Hz is a challenging and expensive task. We attempt to find the common point of low- and high-frequency signal, then utilize the high-frequency data to obtain the low-wavenumber velocity model. It is well known that the instantaneous amplitude envelope of a wavelet is invariant under frequency shift. This means that resolution is constant for a given frequency bandwidth, and independent of the actual values of the frequencies. Based on this property, we develop a frequency shift filter (FSF) to build the relationship between low- and high-frequency information with a constant frequency bandwidth. After that, we can use the high-frequency information to get a plausible recovery of the low-wavenumber velocity model. Numerical results using synthetic data from the Marmousi and layer model demonstrate that our proposed envelope misfit function based on the frequency shift filter can build an initial model with more accurate long-wavelength components, when low-frequency signals are absent in recorded data.

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.

The attitude inversion method of geostationary satellites based on unscented particle filter

NASA Astrophysics Data System (ADS)

Du, Xiaoping; Wang, Yang; Hu, Heng; Gou, Ruixin; Liu, Hao

2018-04-01

The attitude information of geostationary satellites is difficult to be obtained since they are presented in non-resolved images on the ground observation equipment in space object surveillance. In this paper, an attitude inversion method for geostationary satellite based on Unscented Particle Filter (UPF) and ground photometric data is presented. The inversion algorithm based on UPF is proposed aiming at the strong non-linear feature in the photometric data inversion for satellite attitude, which combines the advantage of Unscented Kalman Filter (UKF) and Particle Filter (PF). This update method improves the particle selection based on the idea of UKF to redesign the importance density function. Moreover, it uses the RMS-UKF to partially correct the prediction covariance matrix, which improves the applicability of the attitude inversion method in view of UKF and the particle degradation and dilution of the attitude inversion method based on PF. This paper describes the main principles and steps of algorithm in detail, correctness, accuracy, stability and applicability of the method are verified by simulation experiment and scaling experiment in the end. The results show that the proposed method can effectively solve the problem of particle degradation and depletion in the attitude inversion method on account of PF, and the problem that UKF is not suitable for the strong non-linear attitude inversion. However, the inversion accuracy is obviously superior to UKF and PF, in addition, in the case of the inversion with large attitude error that can inverse the attitude with small particles and high precision.

A Proposed Theory Seeded Methodology for Design Based Research into Effective Use of MUVES in Vocational Education Contexts

ERIC Educational Resources Information Center

Cochrane, Todd; Davis, Niki; Morrow, Donna

2013-01-01

A methodology for design based research (DBR) into effective development and use of Multi-User Virtual Environments (MUVE) in vocational education is proposed. It blends software development with DBR with two theories selected to inform the methodology. Legitimate peripheral participation LPP (Lave & Wenger, 1991) provides a filter when…

Effects of Spatial and Non-Spatial Multi-Modal Cues on Orienting of Visual-Spatial Attention in an Augmented Environment

DTIC Science & Technology

2007-11-01

information into awareness. Broadbent’s (1958) " Filter " model of attention (see Figure 1) maps the flow of information from the senses through a number of...benefits of an attentional cueing paradigm can be explained within these models . For example, the selective filter is augmented by the information...capacity filter ’, while Wickens’ model represents this with a limited amount of ’attentional resources’ available to perception, decision making

A new edge detection algorithm based on Canny idea

NASA Astrophysics Data System (ADS)

Feng, Yingke; Zhang, Jinmin; Wang, Siming

2017-10-01

The traditional Canny algorithm has poor self-adaptability threshold, and it is more sensitive to noise. In order to overcome these drawbacks, this paper proposed a new edge detection method based on Canny algorithm. Firstly, the media filtering and filtering based on the method of Euclidean distance are adopted to process it; secondly using the Frei-chen algorithm to calculate gradient amplitude; finally, using the Otsu algorithm to calculate partial gradient amplitude operation to get images of thresholds value, then find the average of all thresholds that had been calculated, half of the average is high threshold value, and the half of the high threshold value is low threshold value. Experiment results show that this new method can effectively suppress noise disturbance, keep the edge information, and also improve the edge detection accuracy.

a Metadata Based Approach for Analyzing Uav Datasets for Photogrammetric Applications

NASA Astrophysics Data System (ADS)

Dhanda, A.; Remondino, F.; Santana Quintero, M.

2018-05-01

This paper proposes a methodology for pre-processing and analysing Unmanned Aerial Vehicle (UAV) datasets before photogrammetric processing. In cases where images are gathered without a detailed flight plan and at regular acquisition intervals the datasets can be quite large and be time consuming to process. This paper proposes a method to calculate the image overlap and filter out images to reduce large block sizes and speed up photogrammetric processing. The python-based algorithm that implements this methodology leverages the metadata in each image to determine the end and side overlap of grid-based UAV flights. Utilizing user input, the algorithm filters out images that are unneeded for photogrammetric processing. The result is an algorithm that can speed up photogrammetric processing and provide valuable information to the user about the flight path.

Ambient-aware continuous care through semantic context dissemination.

PubMed

Ongenae, Femke; Famaey, Jeroen; Verstichel, Stijn; De Zutter, Saar; Latré, Steven; Ackaert, Ann; Verhoeve, Piet; De Turck, Filip

2014-12-04

The ultimate ambient-intelligent care room contains numerous sensors and devices to monitor the patient, sense and adjust the environment and support the staff. This sensor-based approach results in a large amount of data, which can be processed by current and future applications, e.g., task management and alerting systems. Today, nurses are responsible for coordinating all these applications and supplied information, which reduces the added value and slows down the adoption rate.The aim of the presented research is the design of a pervasive and scalable framework that is able to optimize continuous care processes by intelligently reasoning on the large amount of heterogeneous care data. The developed Ontology-based Care Platform (OCarePlatform) consists of modular components that perform a specific reasoning task. Consequently, they can easily be replicated and distributed. Complex reasoning is achieved by combining the results of different components. To ensure that the components only receive information, which is of interest to them at that time, they are able to dynamically generate and register filter rules with a Semantic Communication Bus (SCB). This SCB semantically filters all the heterogeneous care data according to the registered rules by using a continuous care ontology. The SCB can be distributed and a cache can be employed to ensure scalability. A prototype implementation is presented consisting of a new-generation nurse call system supported by a localization and a home automation component. The amount of data that is filtered and the performance of the SCB are evaluated by testing the prototype in a living lab. The delay introduced by processing the filter rules is negligible when 10 or fewer rules are registered. The OCarePlatform allows disseminating relevant care data for the different applications and additionally supports composing complex applications from a set of smaller independent components. This way, the platform significantly reduces the amount of information that needs to be processed by the nurses. The delay resulting from processing the filter rules is linear in the amount of rules. Distributed deployment of the SCB and using a cache allows further improvement of these performance results.

Sequence-based heuristics for faster annotation of non-coding RNA families.

PubMed

Weinberg, Zasha; Ruzzo, Walter L

2006-01-01

Non-coding RNAs (ncRNAs) are functional RNA molecules that do not code for proteins. Covariance Models (CMs) are a useful statistical tool to find new members of an ncRNA gene family in a large genome database, using both sequence and, importantly, RNA secondary structure information. Unfortunately, CM searches are extremely slow. Previously, we created rigorous filters, which provably sacrifice none of a CM's accuracy, while making searches significantly faster for virtually all ncRNA families. However, these rigorous filters make searches slower than heuristics could be. In this paper we introduce profile HMM-based heuristic filters. We show that their accuracy is usually superior to heuristics based on BLAST. Moreover, we compared our heuristics with those used in tRNAscan-SE, whose heuristics incorporate a significant amount of work specific to tRNAs, where our heuristics are generic to any ncRNA. Performance was roughly comparable, so we expect that our heuristics provide a high-quality solution that--unlike family-specific solutions--can scale to hundreds of ncRNA families. The source code is available under GNU Public License at the supplementary web site.

Lightweight Filter Architecture for Energy Efficient Mobile Vehicle Localization Based on a Distributed Acoustic Sensor Network

PubMed Central

Kim, Keonwook

2013-01-01

The generic properties of an acoustic signal provide numerous benefits for localization by applying energy-based methods over a deployed wireless sensor network (WSN). However, the signal generated by a stationary target utilizes a significant amount of bandwidth and power in the system without providing further position information. For vehicle localization, this paper proposes a novel proximity velocity vector estimator (PVVE) node architecture in order to capture the energy from a moving vehicle and reject the signal from motionless automobiles around the WSN node. A cascade structure between analog envelope detector and digital exponential smoothing filter presents the velocity vector-sensitive output with low analog circuit and digital computation complexity. The optimal parameters in the exponential smoothing filter are obtained by analytical and mathematical methods for maximum variation over the vehicle speed. For stationary targets, the derived simulation based on the acoustic field parameters demonstrates that the system significantly reduces the communication requirements with low complexity and can be expected to extend the operation time considerably. PMID:23979482

Infrared dim-small target tracking via singular value decomposition and improved Kernelized correlation filter

NASA Astrophysics Data System (ADS)

Qian, Kun; Zhou, Huixin; Rong, Shenghui; Wang, Bingjian; Cheng, Kuanhong

2017-05-01

Infrared small target tracking plays an important role in applications including military reconnaissance, early warning and terminal guidance. In this paper, an effective algorithm based on the Singular Value Decomposition (SVD) and the improved Kernelized Correlation Filter (KCF) is presented for infrared small target tracking. Firstly, the super performance of the SVD-based algorithm is that it takes advantage of the target's global information and obtains a background estimation of an infrared image. A dim target is enhanced by subtracting the corresponding estimated background with update from the original image. Secondly, the KCF algorithm is combined with Gaussian Curvature Filter (GCF) to eliminate the excursion problem. The GCF technology is adopted to preserve the edge and eliminate the noise of the base sample in the KCF algorithm, helping to calculate the classifier parameter for a small target. At last, the target position is estimated with a response map, which is obtained via the kernelized classifier. Experimental results demonstrate that the presented algorithm performs favorably in terms of efficiency and accuracy, compared with several state-of-the-art algorithms.

Model-based adaptive 3D sonar reconstruction in reverberating environments.

PubMed

Saucan, Augustin-Alexandru; Sintes, Christophe; Chonavel, Thierry; Caillec, Jean-Marc Le

2015-10-01

In this paper, we propose a novel model-based approach for 3D underwater scene reconstruction, i.e., bathymetry, for side scan sonar arrays in complex and highly reverberating environments like shallow water areas. The presence of multipath echoes and volume reverberation generates false depth estimates. To improve the resulting bathymetry, this paper proposes and develops an adaptive filter, based on several original geometrical models. This multimodel approach makes it possible to track and separate the direction of arrival trajectories of multiple echoes impinging the array. Echo tracking is perceived as a model-based processing stage, incorporating prior information on the temporal evolution of echoes in order to reject cluttered observations generated by interfering echoes. The results of the proposed filter on simulated and real sonar data showcase the clutter-free and regularized bathymetric reconstruction. Model validation is carried out with goodness of fit tests, and demonstrates the importance of model-based processing for bathymetry reconstruction.

Improvement of the fringe analysis algorithm for wavelength scanning interferometry based on filter parameter optimization.

PubMed

Zhang, Tao; Gao, Feng; Muhamedsalih, Hussam; Lou, Shan; Martin, Haydn; Jiang, Xiangqian

2018-03-20

The phase slope method which estimates height through fringe pattern frequency and the algorithm which estimates height through the fringe phase are the fringe analysis algorithms widely used in interferometry. Generally they both extract the phase information by filtering the signal in frequency domain after Fourier transform. Among the numerous papers in the literature about these algorithms, it is found that the design of the filter, which plays an important role, has never been discussed in detail. This paper focuses on the filter design in these algorithms for wavelength scanning interferometry (WSI), trying to optimize the parameters to acquire the optimal results. The spectral characteristics of the interference signal are analyzed first. The effective signal is found to be narrow-band (near single frequency), and the central frequency is calculated theoretically. Therefore, the position of the filter pass-band is determined. The width of the filter window is optimized with the simulation to balance the elimination of the noise and the ringing of the filter. Experimental validation of the approach is provided, and the results agree very well with the simulation. The experiment shows that accuracy can be improved by optimizing the filter design, especially when the signal quality, i.e., the signal noise ratio (SNR), is low. The proposed method also shows the potential of improving the immunity to the environmental noise by adapting the signal to acquire the optimal results through designing an adaptive filter once the signal SNR can be estimated accurately.

Simultaneous learning and filtering without delusions: a Bayes-optimal combination of Predictive Inference and Adaptive Filtering.

PubMed

Kneissler, Jan; Drugowitsch, Jan; Friston, Karl; Butz, Martin V

2015-01-01

Predictive coding appears to be one of the fundamental working principles of brain processing. Amongst other aspects, brains often predict the sensory consequences of their own actions. Predictive coding resembles Kalman filtering, where incoming sensory information is filtered to produce prediction errors for subsequent adaptation and learning. However, to generate prediction errors given motor commands, a suitable temporal forward model is required to generate predictions. While in engineering applications, it is usually assumed that this forward model is known, the brain has to learn it. When filtering sensory input and learning from the residual signal in parallel, a fundamental problem arises: the system can enter a delusional loop when filtering the sensory information using an overly trusted forward model. In this case, learning stalls before accurate convergence because uncertainty about the forward model is not properly accommodated. We present a Bayes-optimal solution to this generic and pernicious problem for the case of linear forward models, which we call Predictive Inference and Adaptive Filtering (PIAF). PIAF filters incoming sensory information and learns the forward model simultaneously. We show that PIAF is formally related to Kalman filtering and to the Recursive Least Squares linear approximation method, but combines these procedures in a Bayes optimal fashion. Numerical evaluations confirm that the delusional loop is precluded and that the learning of the forward model is more than 10-times faster when compared to a naive combination of Kalman filtering and Recursive Least Squares.

The best bits in an iris code.

PubMed

Hollingsworth, Karen P; Bowyer, Kevin W; Flynn, Patrick J

2009-06-01

Iris biometric systems apply filters to iris images to extract information about iris texture. Daugman's approach maps the filter output to a binary iris code. The fractional Hamming distance between two iris codes is computed and decisions about the identity of a person are based on the computed distance. The fractional Hamming distance weights all bits in an iris code equally. However, not all the bits in an iris code are equally useful. Our research is the first to present experiments documenting that some bits are more consistent than others. Different regions of the iris are compared to evaluate their relative consistency, and contrary to some previous research, we find that the middle bands of the iris are more consistent than the inner bands. The inconsistent-bit phenomenon is evident across genders and different filter types. Possible causes of inconsistencies, such as segmentation, alignment issues, and different filters are investigated. The inconsistencies are largely due to the coarse quantization of the phase response. Masking iris code bits corresponding to complex filter responses near the axes of the complex plane improves the separation between the match and nonmatch Hamming distance distributions.

Measuring User Similarity Using Electric Circuit Analysis: Application to Collaborative Filtering

PubMed Central

Yang, Joonhyuk; Kim, Jinwook; Kim, Wonjoon; Kim, Young Hwan

2012-01-01

We propose a new technique of measuring user similarity in collaborative filtering using electric circuit analysis. Electric circuit analysis is used to measure the potential differences between nodes on an electric circuit. In this paper, by applying this method to transaction networks comprising users and items, i.e., user–item matrix, and by using the full information about the relationship structure of users in the perspective of item adoption, we overcome the limitations of one-to-one similarity calculation approach, such as the Pearson correlation, Tanimoto coefficient, and Hamming distance, in collaborative filtering. We found that electric circuit analysis can be successfully incorporated into recommender systems and has the potential to significantly enhance predictability, especially when combined with user-based collaborative filtering. We also propose four types of hybrid algorithms that combine the Pearson correlation method and electric circuit analysis. One of the algorithms exceeds the performance of the traditional collaborative filtering by 37.5% at most. This work opens new opportunities for interdisciplinary research between physics and computer science and the development of new recommendation systems PMID:23145095

Measuring user similarity using electric circuit analysis: application to collaborative filtering.

PubMed

Yang, Joonhyuk; Kim, Jinwook; Kim, Wonjoon; Kim, Young Hwan

2012-01-01

We propose a new technique of measuring user similarity in collaborative filtering using electric circuit analysis. Electric circuit analysis is used to measure the potential differences between nodes on an electric circuit. In this paper, by applying this method to transaction networks comprising users and items, i.e., user-item matrix, and by using the full information about the relationship structure of users in the perspective of item adoption, we overcome the limitations of one-to-one similarity calculation approach, such as the Pearson correlation, Tanimoto coefficient, and Hamming distance, in collaborative filtering. We found that electric circuit analysis can be successfully incorporated into recommender systems and has the potential to significantly enhance predictability, especially when combined with user-based collaborative filtering. We also propose four types of hybrid algorithms that combine the Pearson correlation method and electric circuit analysis. One of the algorithms exceeds the performance of the traditional collaborative filtering by 37.5% at most. This work opens new opportunities for interdisciplinary research between physics and computer science and the development of new recommendation systems.

Efficiency analysis for 3D filtering of multichannel images

NASA Astrophysics Data System (ADS)

Kozhemiakin, Ruslan A.; Rubel, Oleksii; Abramov, Sergey K.; Lukin, Vladimir V.; Vozel, Benoit; Chehdi, Kacem

2016-10-01

Modern remote sensing systems basically acquire images that are multichannel (dual- or multi-polarization, multi- and hyperspectral) where noise, usually with different characteristics, is present in all components. If noise is intensive, it is desirable to remove (suppress) it before applying methods of image classification, interpreting, and information extraction. This can be done using one of two approaches - by component-wise or by vectorial (3D) filtering. The second approach has shown itself to have higher efficiency if there is essential correlation between multichannel image components as this often happens for multichannel remote sensing data of different origin. Within the class of 3D filtering techniques, there are many possibilities and variations. In this paper, we consider filtering based on discrete cosine transform (DCT) and pay attention to two aspects of processing. First, we study in detail what changes in DCT coefficient statistics take place for 3D denoising compared to component-wise processing. Second, we analyze how selection of component images united into 3D data array influences efficiency of filtering and can the observed tendencies be exploited in processing of images with rather large number of channels.

Multiband tissue classification for ultrasonic transmission tomography using spectral profile detection

NASA Astrophysics Data System (ADS)

Jeong, Jeong-Won; Kim, Tae-Seong; Shin, Dae-Chul; Do, Synho; Marmarelis, Vasilis Z.

2004-04-01

Recently it was shown that soft tissue can be differentiated with spectral unmixing and detection methods that utilize multi-band information obtained from a High-Resolution Ultrasonic Transmission Tomography (HUTT) system. In this study, we focus on tissue differentiation using the spectral target detection method based on Constrained Energy Minimization (CEM). We have developed a new tissue differentiation method called "CEM filter bank". Statistical inference on the output of each CEM filter of a filter bank is used to make a decision based on the maximum statistical significance rather than the magnitude of each CEM filter output. We validate this method through 3-D inter/intra-phantom soft tissue classification where target profiles obtained from an arbitrary single slice are used for differentiation in multiple tomographic slices. Also spectral coherence between target and object profiles of an identical tissue at different slices and phantoms is evaluated by conventional cross-correlation analysis. The performance of the proposed classifier is assessed using Receiver Operating Characteristic (ROC) analysis. Finally we apply our method to classify tiny structures inside a beef kidney such as Styrofoam balls (~1mm), chicken tissue (~5mm), and vessel-duct structures.

Constrained State Estimation for Individual Localization in Wireless Body Sensor Networks

PubMed Central

Feng, Xiaoxue; Snoussi, Hichem; Liang, Yan; Jiao, Lianmeng

2014-01-01

Wireless body sensor networks based on ultra-wideband radio have recently received much research attention due to its wide applications in health-care, security, sports and entertainment. Accurate localization is a fundamental problem to realize the development of effective location-aware applications above. In this paper the problem of constrained state estimation for individual localization in wireless body sensor networks is addressed. Priori knowledge about geometry among the on-body nodes as additional constraint is incorporated into the traditional filtering system. The analytical expression of state estimation with linear constraint to exploit the additional information is derived. Furthermore, for nonlinear constraint, first-order and second-order linearizations via Taylor series expansion are proposed to transform the nonlinear constraint to the linear case. Examples between the first-order and second-order nonlinear constrained filters based on interacting multiple model extended kalman filter (IMM-EKF) show that the second-order solution for higher order nonlinearity as present in this paper outperforms the first-order solution, and constrained IMM-EKF obtains superior estimation than IMM-EKF without constraint. Another brownian motion individual localization example also illustrates the effectiveness of constrained nonlinear iterative least square (NILS), which gets better filtering performance than NILS without constraint. PMID:25390408

Colloidal Bandpass and Bandgap Filters

NASA Astrophysics Data System (ADS)

Yellen, Benjamin; Tahir, Mukarram; Ouyang, Yuyu; Nori, Franco

2013-03-01

Thermally or deterministically-driven transport of objects through asymmetric potential energy landscapes (ratchet-based motion) is of considerable interest as models for biological transport and as methods for controlling the flow of information, material, and energy. Here, we provide a general framework for implementing a colloidal bandpass filter, in which particles of a specific size range can be selectively transported through a periodic lattice, whereas larger or smaller particles are dynamically trapped in closed-orbits. Our approach is based on quasi-static (adiabatic) transition in a tunable potential energy landscape composed of a multi-frequency magnetic field input signal with the static field of a spatially-periodic magnetization. By tuning the phase shifts between the input signal and the relative forcing coefficients, large-sized particles may experience no local energy barriers, medium-sized particles experience only one local energy barrier, and small-sized particles experience two local energy barriers. The odd symmetry present in this system can be used to nudge the medium-sized particles along an open pathway, whereas the large or small beads remain trapped in a closed-orbit, leading to a bandpass filter, and vice versa for a bandgap filter. NSF CMMI - 0800173, Youth 100 Scholars Fund

Automatic arrival time detection for earthquakes based on Modified Laplacian of Gaussian filter

NASA Astrophysics Data System (ADS)

Saad, Omar M.; Shalaby, Ahmed; Samy, Lotfy; Sayed, Mohammed S.

2018-04-01

Precise identification of onset time for an earthquake is imperative in the right figuring of earthquake's location and different parameters that are utilized for building seismic catalogues. P-wave arrival detection of weak events or micro-earthquakes cannot be precisely determined due to background noise. In this paper, we propose a novel approach based on Modified Laplacian of Gaussian (MLoG) filter to detect the onset time even in the presence of very weak signal-to-noise ratios (SNRs). The proposed algorithm utilizes a denoising-filter algorithm to smooth the background noise. In the proposed algorithm, we employ the MLoG mask to filter the seismic data. Afterward, we apply a Dual-threshold comparator to detect the onset time of the event. The results show that the proposed algorithm can detect the onset time for micro-earthquakes accurately, with SNR of -12 dB. The proposed algorithm achieves an onset time picking accuracy of 93% with a standard deviation error of 0.10 s for 407 field seismic waveforms. Also, we compare the results with short and long time average algorithm (STA/LTA) and the Akaike Information Criterion (AIC), and the proposed algorithm outperforms them.

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

A comparison of evaluation metrics for biomedical journals, articles, and websites in terms of sensitivity to topic.

PubMed

Fu, Lawrence D; Aphinyanaphongs, Yindalon; Wang, Lily; Aliferis, Constantin F

2011-08-01

Evaluating the biomedical literature and health-related websites for quality are challenging information retrieval tasks. Current commonly used methods include impact factor for journals, PubMed's clinical query filters and machine learning-based filter models for articles, and PageRank for websites. Previous work has focused on the average performance of these methods without considering the topic, and it is unknown how performance varies for specific topics or focused searches. Clinicians, researchers, and users should be aware when expected performance is not achieved for specific topics. The present work analyzes the behavior of these methods for a variety of topics. Impact factor, clinical query filters, and PageRank vary widely across different topics while a topic-specific impact factor and machine learning-based filter models are more stable. The results demonstrate that a method may perform excellently on average but struggle when used on a number of narrower topics. Topic-adjusted metrics and other topic robust methods have an advantage in such situations. Users of traditional topic-sensitive metrics should be aware of their limitations. Copyright © 2011 Elsevier Inc. All rights reserved.

Effectiveness of adverse effects search filters: drugs versus medical devices.

PubMed

Farrah, Kelly; Mierzwinski-Urban, Monika; Cimon, Karen

2016-07-01

The study tested the performance of adverse effects search filters when searching for safety information on medical devices, procedures, and diagnostic tests in MEDLINE and Embase. The sensitivity of 3 filters was determined using a sample of 631 references from 131 rapid reviews related to the safety of health technologies. The references were divided into 2 sets by type of intervention: drugs and nondrug health technologies. Keyword and indexing analysis were performed on references from the nondrug testing set that 1 or more of the filters did not retrieve. For all 3 filters, sensitivity was lower for nondrug health technologies (ranging from 53%-87%) than for drugs (88%-93%) in both databases. When tested on the nondrug health technologies set, sensitivity was lower in Embase (ranging from 53%-81%) than in MEDLINE (67%-87%) for all filters. Of the nondrug records that 1 or more of the filters missed, 39% of the missed MEDLINE records and 18% of the missed Embase records did not contain any indexing terms related to adverse events. Analyzing the titles and abstracts of nondrug records that were missed by any 1 filter, the most commonly used keywords related to adverse effects were: risk, complications, mortality, contamination, hemorrhage, and failure. In this study, adverse effects filters were less effective at finding information about the safety of medical devices, procedures, and tests compared to information about the safety of drugs.

Emergency sacrificial sealing method in filters, equipment, or systems

DOEpatents

Brown, Erik P

2014-09-30

A system seals a filter or equipment component to a base and will continue to seal the filter or equipment component to the base in the event of hot air or fire. The system includes a first sealing material between the filter or equipment component and the base; and a second sealing material between the filter or equipment component and the base and proximate the first sealing material. The first sealing material and the second seal material are positioned relative to each other and relative to the filter or equipment component and the base to seal the filter or equipment component to the base and upon the event of fire the second sealing material will be activated and expand to continue to seal the filter or equipment component to the base in the event of hot air or fire.

A microprocessor based anti-aliasing filter for a PCM system

NASA Technical Reports Server (NTRS)

Morrow, D. C.; Sandlin, D. R.

1984-01-01

Described is the design and evaluation of a microprocessor based digital filter. The filter was made to investigate the feasibility of a digital replacement for the analog pre-sampling filters used in telemetry systems at the NASA Ames-Dryden Flight Research Facility (DFRF). The digital filter will utilize an Intel 2920 Analog Signal Processor (ASP) chip. Testing includes measurements of: (1) the filter frequency response and, (2) the filter signal resolution. The evaluation of the digital filter was made on the basis of circuit size, projected environmental stability and filter resolution. The 2920 based digital filter was found to meet or exceed the pre-sampling filter specifications for limited signal resolution applications.

Efficient Data Assimilation Algorithms for Bathymetry Applications

NASA Astrophysics Data System (ADS)

Ghorbanidehno, H.; Kokkinaki, A.; Lee, J. H.; Farthing, M.; Hesser, T.; Kitanidis, P. K.; Darve, E. F.

2016-12-01

Information on the evolving state of the nearshore zone bathymetry is crucial to shoreline management, recreational safety, and naval operations. The high cost and complex logistics of using ship-based surveys for bathymetry estimation have encouraged the use of remote sensing monitoring. Data assimilation methods combine monitoring data and models of nearshore dynamics to estimate the unknown bathymetry and the corresponding uncertainties. Existing applications have been limited to the basic Kalman Filter (KF) and the Ensemble Kalman Filter (EnKF). The former can only be applied to low-dimensional problems due to its computational cost; the latter often suffers from ensemble collapse and uncertainty underestimation. This work explores the use of different variants of the Kalman Filter for bathymetry applications. In particular, we compare the performance of the EnKF to the Unscented Kalman Filter and the Hierarchical Kalman Filter, both of which are KF variants for non-linear problems. The objective is to identify which method can better handle the nonlinearities of nearshore physics, while also having a reasonable computational cost. We present two applications; first, the bathymetry of a synthetic one-dimensional cross section normal to the shore is estimated from wave speed measurements. Second, real remote measurements with unknown error statistics are used and compared to in situ bathymetric survey data collected at the USACE Field Research Facility in Duck, NC. We evaluate the information content of different data sets and explore the impact of measurement error and nonlinearities.

Integrated strategy for identifying minor components in complex samples combining mass defect, diagnostic ions and neutral loss information based on ultra-performance liquid chromatography-high resolution mass spectrometry platform: Folium Artemisiae Argyi as a case study.

PubMed

Ren, Dabing; Ran, Lu; Yang, Chong; Xu, Meilin; Yi, Lunzhao

2018-05-18

Ultra-performance liquid chromatography coupled to high-resolution mass spectrometry (UPLC-HRMS) has been used as a powerful tool to profile chemicals in traditional Chinese medicines. However, identification of potentially bioactive compounds is still a challenging work because of the large amount of information contained in the raw UPLC-HRMS data. Especially the ubiquitous matrix interference makes it more difficult to characterize the minor components. Therefore, rapid recognition and efficient extraction of the corresponding parent ions is critically important for identifying the attractive compounds in complex samples. Herein, we propose an integrated filtering strategy to remove un-related or interference MS 1 ions from the raw UPLC-HRMS data, which helps to retain the MS features of the target components and expose the compounds of interest as effective as possible. The proposed strategy is based on the use of a combination of different filtering methods, including nitrogen rule, mass defect, and neutral loss/diagnostic fragment ions filtering. The strategy was validated by rapid screening and identification of 16 methoxylated flavonoids and 55 chlorogenic acids analogues from the raw UPLC-HRMS dataset of Folium Artemisiae Argyi. Particularly, successful detection of several minor components indicated that the integrated strategy has obvious advantages over individual filtering methods, and it can be used as a promising method for screening and identifying compounds from complex samples, such as herbal medicines. Copyright © 2018 Elsevier B.V. All rights reserved.

Single photon laser altimeter data processing, analysis and experimental validation

NASA Astrophysics Data System (ADS)

Vacek, Michael; Peca, Marek; Michalek, Vojtech; Prochazka, Ivan

2015-10-01

Spaceborne laser altimeters are common instruments on-board the rendezvous spacecraft. This manuscript deals with the altimeters using a single photon approach, which belongs to the family of time-of-flight range measurements. Moreover, the single photon receiver part of the altimeter may be utilized as an Earth-to-spacecraft link enabling one-way ranging, time transfer and data transfer. The single photon altimeters evaluate actual altitude through the repetitive detections of single photons of the reflected laser pulses. We propose the single photon altimeter signal processing and data mining algorithm based on the Poisson statistic filter (histogram method) and the modified Kalman filter, providing all common altimetry products (altitude, slope, background photon flux and albedo). The Kalman filter is extended for the background noise filtering, the varying slope adaptation and the non-causal extension for an abrupt slope change. Moreover, the algorithm partially removes the major drawback of a single photon altitude reading, namely that the photon detection measurement statistics must be gathered. The developed algorithm deduces the actual altitude on the basis of a single photon detection; thus, being optimal in the sense that each detected signal photon carrying altitude information is tracked and no altitude information is lost. The algorithm was tested on the simulated datasets and partially cross-probed with the experimental data collected using the developed single photon altimeter breadboard based on the microchip laser with the pulse energy on the order of microjoule and the repetition rate of several kilohertz. We demonstrated that such an altimeter configuration may be utilized for landing or hovering a small body (asteroid, comet).

An exponential filter model predicts lightness illusions

PubMed Central

Zeman, Astrid; Brooks, Kevin R.; Ghebreab, Sennay

2015-01-01

Lightness, or perceived reflectance of a surface, is influenced by surrounding context. This is demonstrated by the Simultaneous Contrast Illusion (SCI), where a gray patch is perceived lighter against a black background and vice versa. Conversely, assimilation is where the lightness of the target patch moves toward that of the bounding areas and can be demonstrated in White's effect. Blakeslee and McCourt (1999) introduced an oriented difference-of-Gaussian (ODOG) model that is able to account for both contrast and assimilation in a number of lightness illusions and that has been subsequently improved using localized normalization techniques. We introduce a model inspired by image statistics that is based on a family of exponential filters, with kernels spanning across multiple sizes and shapes. We include an optional second stage of normalization based on contrast gain control. Our model was tested on a well-known set of lightness illusions that have previously been used to evaluate ODOG and its variants, and model lightness values were compared with typical human data. We investigate whether predictive success depends on filters of a particular size or shape and whether pooling information across filters can improve performance. The best single filter correctly predicted the direction of lightness effects for 21 out of 27 illusions. Combining two filters together increased the best performance to 23, with asymptotic performance at 24 for an arbitrarily large combination of filter outputs. While normalization improved prediction magnitudes, it only slightly improved overall scores in direction predictions. The prediction performance of 24 out of 27 illusions equals that of the best performing ODOG variant, with greater parsimony. Our model shows that V1-style orientation-selectivity is not necessary to account for lightness illusions and that a low-level model based on image statistics is able to account for a wide range of both contrast and assimilation effects. PMID:26157381

Privacy-Preserving Distributed Information Sharing

DTIC Science & Technology

2006-07-01

80 B.2.4 Analysis for Bloom Filters . . . . . . . . . . . . . . . . . . . . . . . . . . . 81 B.3 Details of One...be chosen by slightly adjusting the analysis given in the proof of Theorem 26. 59 Using Bloom Filters. Bloom filters provide a compact probabilistic...representation of set membership [6]. Instead of using T filters, we can use a combined Bloom filter. This achieves the same asymptotic communication

The Development of Mobile Application to Introduce Historical Monuments in Manado

NASA Astrophysics Data System (ADS)

Rupilu, Moshe Markhasi; Suyoto; Santoso, Albertus Joko

2018-02-01

Learning the historical value of a monument is important because it preserves cultural and historical values, as well as expanding our personal insight. In Indonesia, particularly in Manado, North Sulawesi, there are many monuments. The monuments are erected for history, religion, culture and past war, however these aren't written in detail in the monuments. To get information on specific monument, manual search was required, i.e. asking related people or sources. Based on the problem, the development of an application which can utilize LBS (Location Based Service) method and some algorithmic methods specifically designed for mobile devices such as Smartphone, was required so that information on every monument in Manado can be displayed in detail using GPS coordinate. The application was developed by KNN method with K-means algorithm and collaborative filtering to recommend monument information to tourist. Tourists will get recommended options filtered by distance. Then, this method was also used to look for the closest monument from user. KNN algorithm determines the closest location by making comparisons according to calculation of longitude and latitude of several monuments tourist wants to visit. With this application, tourists who want to know and find information on monuments in Manado can do them easily and quickly because monument information is recommended directly to user without having to make selection. Moreover, tourist can see recommended monument information and search several monuments in Manado in real time.

Signal digitizing system and method based on amplitude-to-time optical mapping

DOEpatents

Chou, Jason; Bennett, Corey V; Hernandez, Vince

2015-01-13

A signal digitizing system and method based on analog-to-time optical mapping, optically maps amplitude information of an analog signal of interest first into wavelength information using an amplitude tunable filter (ATF) to impress spectral changes induced by the amplitude of the analog signal onto a carrier signal, i.e. a train of optical pulses, and next from wavelength information to temporal information using a dispersive element so that temporal information representing the amplitude information is encoded in the time domain in the carrier signal. Optical-to-electrical conversion of the optical pulses into voltage waveforms and subsequently digitizing the voltage waveforms into a digital image enables the temporal information to be resolved and quantized in the time domain. The digital image may them be digital signal processed to digitally reconstruct the analog signal based on the temporal information with high fidelity.

Binocular contrast-gain control for natural scenes: Image structure and phase alignment.

PubMed

Huang, Pi-Chun; Dai, Yu-Ming

2018-05-01

In the context of natural scenes, we applied the pattern-masking paradigm to investigate how image structure and phase alignment affect contrast-gain control in binocular vision. We measured the discrimination thresholds of bandpass-filtered natural-scene images (targets) under various types of pedestals. Our first experiment had four pedestal types: bandpass-filtered pedestals, unfiltered pedestals, notch-filtered pedestals (which enabled removal of the spatial frequency), and misaligned pedestals (which involved rotation of unfiltered pedestals). Our second experiment featured six types of pedestals: bandpass-filtered, unfiltered, and notch-filtered pedestals, and the corresponding phase-scrambled pedestals. The thresholds were compared for monocular, binocular, and dichoptic viewing configurations. The bandpass-filtered pedestal and unfiltered pedestals showed classic dipper shapes; the dipper shapes of the notch-filtered, misaligned, and phase-scrambled pedestals were weak. We adopted a two-stage binocular contrast-gain control model to describe our results. We deduced that the phase-alignment information influenced the contrast-gain control mechanism before the binocular summation stage and that the phase-alignment information and structural misalignment information caused relatively strong divisive inhibition in the monocular and interocular suppression stages. When the pedestals were phase-scrambled, the elimination of the interocular suppression processing was the most convincing explanation of the results. Thus, our results indicated that both phase-alignment information and similar image structures cause strong interocular suppression. Copyright © 2018 Elsevier Ltd. All rights reserved.

Introducing passive acoustic filter in acoustic based condition monitoring: Motor bike piston-bore fault identification

NASA Astrophysics Data System (ADS)

Jena, D. P.; Panigrahi, S. N.

2016-03-01

Requirement of designing a sophisticated digital band-pass filter in acoustic based condition monitoring has been eliminated by introducing a passive acoustic filter in the present work. So far, no one has attempted to explore the possibility of implementing passive acoustic filters in acoustic based condition monitoring as a pre-conditioner. In order to enhance the acoustic based condition monitoring, a passive acoustic band-pass filter has been designed and deployed. Towards achieving an efficient band-pass acoustic filter, a generalized design methodology has been proposed to design and optimize the desired acoustic filter using multiple filter components in series. An appropriate objective function has been identified for genetic algorithm (GA) based optimization technique with multiple design constraints. In addition, the sturdiness of the proposed method has been demonstrated in designing a band-pass filter by using an n-branch Quincke tube, a high pass filter and multiple Helmholtz resonators. The performance of the designed acoustic band-pass filter has been shown by investigating the piston-bore defect of a motor-bike using engine noise signature. On the introducing a passive acoustic filter in acoustic based condition monitoring reveals the enhancement in machine learning based fault identification practice significantly. This is also a first attempt of its own kind.

A Kalman-Filter-Based Common Algorithm Approach for Object Detection in Surgery Scene to Assist Surgeon's Situation Awareness in Robot-Assisted Laparoscopic Surgery

PubMed Central

2018-01-01

Although the use of the surgical robot is rapidly expanding for various medical treatments, there still exist safety issues and concerns about robot-assisted surgeries due to limited vision through a laparoscope, which may cause compromised situation awareness and surgical errors requiring rapid emergency conversion to open surgery. To assist surgeon's situation awareness and preventive emergency response, this study proposes situation information guidance through a vision-based common algorithm architecture for automatic detection and tracking of intraoperative hemorrhage and surgical instruments. The proposed common architecture comprises the location of the object of interest using feature texture, morphological information, and the tracking of the object based on Kalman filter for robustness with reduced error. The average recall and precision of the instrument detection in four prostate surgery videos were 96% and 86%, and the accuracy of the hemorrhage detection in two prostate surgery videos was 98%. Results demonstrate the robustness of the automatic intraoperative object detection and tracking which can be used to enhance the surgeon's preventive state recognition during robot-assisted surgery. PMID:29854366

An adaptive Kalman filter technique for context-aware heart rate monitoring.

PubMed

Xu, Min; Goldfain, Albert; Dellostritto, Jim; Iyengar, Satish

2012-01-01

Traditional physiological monitoring systems convert a person's vital sign waveforms, such as heart rate, respiration rate and blood pressure, into meaningful information by comparing the instant reading with a preset threshold or a baseline without considering the contextual information of the person. It would be beneficial to incorporate the contextual data such as activity status of the person to the physiological data in order to obtain a more accurate representation of a person's physiological status. In this paper, we proposed an algorithm based on adaptive Kalman filter that describes the heart rate response with respect to different activity levels. It is towards our final goal of intelligent detection of any abnormality in the person's vital signs. Experimental results are provided to demonstrate the feasibility of the algorithm.

Optical processing for landmark identification

NASA Technical Reports Server (NTRS)

Casasent, D.; Luu, T. K.

1981-01-01

A study of optical pattern recognition techniques, available components and airborne optical systems for use in landmark identification was conducted. A data base of imagery exhibiting multisensor, seasonal, snow and fog cover, exposure, and other differences was assembled. These were successfully processed in a scaling optical correlator using weighted matched spatial filter synthesis. Distinctive data classes were defined and a description of the data (with considerable input information and content information) emerged from this study. It has considerable merit with regard to the preprocessing needed and the image difference categories advanced. A optical pattern recognition airborne applications was developed, assembled and demontrated. It employed a laser diode light source and holographic optical elements in a new lensless matched spatial filter architecture with greatly reduced size and weight, as well as component positioning toleranced.

Parameter estimation for stiff deterministic dynamical systems via ensemble Kalman filter

NASA Astrophysics Data System (ADS)

Arnold, Andrea; Calvetti, Daniela; Somersalo, Erkki

2014-10-01

A commonly encountered problem in numerous areas of applications is to estimate the unknown coefficients of a dynamical system from direct or indirect observations at discrete times of some of the components of the state vector. A related problem is to estimate unobserved components of the state. An egregious example of such a problem is provided by metabolic models, in which the numerous model parameters and the concentrations of the metabolites in tissue are to be estimated from concentration data in the blood. A popular method for addressing similar questions in stochastic and turbulent dynamics is the ensemble Kalman filter (EnKF), a particle-based filtering method that generalizes classical Kalman filtering. In this work, we adapt the EnKF algorithm for deterministic systems in which the numerical approximation error is interpreted as a stochastic drift with variance based on classical error estimates of numerical integrators. This approach, which is particularly suitable for stiff systems where the stiffness may depend on the parameters, allows us to effectively exploit the parallel nature of particle methods. Moreover, we demonstrate how spatial prior information about the state vector, which helps the stability of the computed solution, can be incorporated into the filter. The viability of the approach is shown by computed examples, including a metabolic system modeling an ischemic episode in skeletal muscle, with a high number of unknown parameters.

Microscopy with spatial filtering for sorting particles and monitoring subcellular morphology

NASA Astrophysics Data System (ADS)

Zheng, Jing-Yi; Qian, Zhen; Pasternack, Robert M.; Boustany, Nada N.

2009-02-01

Optical scatter imaging (OSI) was developed to non-invasively track real-time changes in particle morphology with submicron sensitivity in situ without exogenous labeling, cell fixing, or organelle isolation. For spherical particles, the intensity ratio of wide-to-narrow angle scatter (OSIR, Optical Scatter Image Ratio) was shown to decrease monotonically with diameter and agree with Mie theory. In living cells, we recently reported this technique is able to detect mitochondrial morphological alterations, which were mediated by the Bcl-xL transmembrane domain, and could not be observed by fluorescence or differential interference contrast images. Here we further extend the ability of morphology assessment by adopting a digital micromirror device (DMD) for Fourier filtering. When placed in the Fourier plane the DMD can be used to select scattering intensities at desired combination of scattering angles. We designed an optical filter bank consisting of Gabor-like filters with various scales and rotations based on Gabor filters, which have been widely used for localization of spatial and frequency information in digital images and texture analysis. Using a model system consisting of mixtures of polystyrene spheres and bacteria, we show how this system can be used to sort particles on a microscopic slide based on their size, orientation and aspect ratio. We are currently applying this technique to characterize the morphology of subcellular organelles to help understand fundamental biological processes.

Absolute brightness modeling for improved measurement of electron temperature from soft x-rays on MST

NASA Astrophysics Data System (ADS)

Reusch, L. M.; Franz, P.; Goetz, J. A.; den Hartog, D. J.; Nornberg, M. D.; van Meter, P.

2017-10-01

The two-color soft x-ray tomography (SXT) diagnostic on MST is now capable of Te measurement down to 500 eV. The previous lower limit was 1 keV, due to the presence of SXR emission lines from Al sputtered from the MST wall. The two-color technique uses two filters of different thickness to form a coarse spectrometer to estimate the slope of the continuum x-ray spectrum, which depends on Te. The 1.6 - 2.0 keV Al emission lines were previously filtered out by using thick Be filters (400 µm and 800 µm), thus restricting the range of the SXT diagnostic to Te >= 1 keV. Absolute brightness modeling explicitly includes several sources of radiation in the analysis model, enabling the use of thinner filters and measurement of much lower Te. Models based on the atomic database and analysis structure (ADAS) agree very well with our experimental SXR measurements. We used ADAS to assess the effect of bremsstrahlung, recombination, dielectronic recombination, and line emission on the inferred Te. This assessment informed the choice of the optimum filter pair to extend the Te range of the SXT diagnostic. This material is based upon work supported by the U.S. Department of Energy Office of Science, Office of Fusion Energy Sciences program under Award Numbers DE-FC02-05ER54814 and DE-SC0015474.

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

ERIC Educational Resources Information Center

Orenstein, David I.

2009-01-01

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

Optical security system for the protection of personal identification information.

PubMed

Doh, Yang-Hoi; Yoon, Jong-Soo; Choi, Kyung-Hyun; Alam, Mohammad S

2005-02-10

A new optical security system for the protection of personal identification information is proposed. First, authentication of the encrypted personal information is carried out by primary recognition of a personal identification number (PIN) with the proposed multiplexed minimum average correlation energy phase-encrypted (MMACE_p) filter. The MMACE_p filter, synthesized with phase-encrypted training images, can increase the discrimination capability and prevent the leak of personal identification information. After the PIN is recognized, speedy authentication of personal information can be achieved through one-to-one optical correlation by means of the optical wavelet filter. The possibility of information counterfeiting can be significantly decreased with the double-identification process. Simulation results demonstrate the effectiveness of the proposed technique.

Colorization-Based RGB-White Color Interpolation using Color Filter Array with Randomly Sampled Pattern

PubMed Central

Oh, Paul; Lee, Sukho; Kang, Moon Gi

2017-01-01

Recently, several RGB-White (RGBW) color filter arrays (CFAs) have been proposed, which have extra white (W) pixels in the filter array that are highly sensitive. Due to the high sensitivity, the W pixels have better SNR (Signal to Noise Ratio) characteristics than other color pixels in the filter array, especially, in low light conditions. However, most of the RGBW CFAs are designed so that the acquired RGBW pattern image can be converted into the conventional Bayer pattern image, which is then again converted into the final color image by using conventional demosaicing methods, i.e., color interpolation techniques. In this paper, we propose a new RGBW color filter array based on a totally different color interpolation technique, the colorization algorithm. The colorization algorithm was initially proposed for colorizing a gray image into a color image using a small number of color seeds. Here, we adopt this algorithm as a color interpolation technique, so that the RGBW color filter array can be designed with a very large number of W pixels to make the most of the highly sensitive characteristics of the W channel. The resulting RGBW color filter array has a pattern with a large proportion of W pixels, while the small-numbered RGB pixels are randomly distributed over the array. The colorization algorithm makes it possible to reconstruct the colors from such a small number of RGB values. Due to the large proportion of W pixels, the reconstructed color image has a high SNR value, especially higher than those of conventional CFAs in low light condition. Experimental results show that many important information which are not perceived in color images reconstructed with conventional CFAs are perceived in the images reconstructed with the proposed method. PMID:28657602

Colorization-Based RGB-White Color Interpolation using Color Filter Array with Randomly Sampled Pattern.

PubMed

Oh, Paul; Lee, Sukho; Kang, Moon Gi

2017-06-28

Recently, several RGB-White (RGBW) color filter arrays (CFAs) have been proposed, which have extra white (W) pixels in the filter array that are highly sensitive. Due to the high sensitivity, the W pixels have better SNR (Signal to Noise Ratio) characteristics than other color pixels in the filter array, especially, in low light conditions. However, most of the RGBW CFAs are designed so that the acquired RGBW pattern image can be converted into the conventional Bayer pattern image, which is then again converted into the final color image by using conventional demosaicing methods, i.e., color interpolation techniques. In this paper, we propose a new RGBW color filter array based on a totally different color interpolation technique, the colorization algorithm. The colorization algorithm was initially proposed for colorizing a gray image into a color image using a small number of color seeds. Here, we adopt this algorithm as a color interpolation technique, so that the RGBW color filter array can be designed with a very large number of W pixels to make the most of the highly sensitive characteristics of the W channel. The resulting RGBW color filter array has a pattern with a large proportion of W pixels, while the small-numbered RGB pixels are randomly distributed over the array. The colorization algorithm makes it possible to reconstruct the colors from such a small number of RGB values. Due to the large proportion of W pixels, the reconstructed color image has a high SNR value, especially higher than those of conventional CFAs in low light condition. Experimental results show that many important information which are not perceived in color images reconstructed with conventional CFAs are perceived in the images reconstructed with the proposed method.

Split-spectrum intensity-based optical fiber sensors for measurement of microdisplacement, strain, and pressure

NASA Astrophysics Data System (ADS)

Wang, Anbo; Miller, Mark S.; Plante, Angela J.; Gunther, Michael F.; Murphy, Kent A.; Claus, Richard O.

1996-05-01

A self-referencing technique compensating for fiber losses and source fluctuations in reflective air-gap intensity-based optical fiber sensors is described. A dielectric multilayer short-wave-pass filter is fabricated onto or attached to the output end face of the lead-in-lead-out multimode fiber. The incoming broadband light from a white light or a light-emitting diode is partially reflected at the filter. The transmitted light through the filter projects onto a mirror. The light returning from the reflecting mirror is recoupled into the lead-in-lead-out fiber. These two reflections from the filter and the reflecting mirror are spectrally separated at the detector end. The power ratio of these two reflections is insensitive to source fluctuations and fiber-bending loss. However, because the second optical signal depends on the air-gap separation between the end face of the lead-in-lead-out fiber and the reflecting mirror, the ratio provides the information on the air-gap length. A resolution of 0.13 mu m has been obtained over a microdisplacement measurement range of 0-254 mu m. The sensor is shown to be insensitive to both fiber-bending losses and variations in source power. Based on this approach, a fiber-strain sensor was fabricated with a multilayer interference filter directly fabricated on the end face of the fiber. A resolution of 13.4 microstrain was obtained over a measurement range of 0-20,000 microstrain with a gauge length of 10 mm. The split-spectrum method is also incorporated into a diaphragm displacement-based pressure sensor with a demonstrated resolution of 450 Pa over a measurement range of 0-0.8 MPa.

Error Modelling for Multi-Sensor Measurements in Infrastructure-Free Indoor Navigation

PubMed Central

Ruotsalainen, Laura; Kirkko-Jaakkola, Martti; Rantanen, Jesperi; Mäkelä, Maija

2018-01-01

The long-term objective of our research is to develop a method for infrastructure-free simultaneous localization and mapping (SLAM) and context recognition for tactical situational awareness. Localization will be realized by propagating motion measurements obtained using a monocular camera, a foot-mounted Inertial Measurement Unit (IMU), sonar, and a barometer. Due to the size and weight requirements set by tactical applications, Micro-Electro-Mechanical (MEMS) sensors will be used. However, MEMS sensors suffer from biases and drift errors that may substantially decrease the position accuracy. Therefore, sophisticated error modelling and implementation of integration algorithms are key for providing a viable result. Algorithms used for multi-sensor fusion have traditionally been different versions of Kalman filters. However, Kalman filters are based on the assumptions that the state propagation and measurement models are linear with additive Gaussian noise. Neither of the assumptions is correct for tactical applications, especially for dismounted soldiers, or rescue personnel. Therefore, error modelling and implementation of advanced fusion algorithms are essential for providing a viable result. Our approach is to use particle filtering (PF), which is a sophisticated option for integrating measurements emerging from pedestrian motion having non-Gaussian error characteristics. This paper discusses the statistical modelling of the measurement errors from inertial sensors and vision based heading and translation measurements to include the correct error probability density functions (pdf) in the particle filter implementation. Then, model fitting is used to verify the pdfs of the measurement errors. Based on the deduced error models of the measurements, particle filtering method is developed to fuse all this information, where the weights of each particle are computed based on the specific models derived. The performance of the developed method is tested via two experiments, one at a university’s premises and another in realistic tactical conditions. The results show significant improvement on the horizontal localization when the measurement errors are carefully modelled and their inclusion into the particle filtering implementation correctly realized. PMID:29443918

Mobile Agents Applications.

ERIC Educational Resources Information Center

Martins, Rosane Maria; Chaves, Magali Ribeiro; Pirmez, Luci; Rust da Costa Carmo, Luiz Fernando

2001-01-01

Discussion of the need to filter and retrieval relevant information from the Internet focuses on the use of mobile agents, specific software components which are based on distributed artificial intelligence and integrated systems. Surveys agent technology and discusses the agent building package used to develop two applications using IBM's Aglet…

Ship detection from high-resolution imagery based on land masking and cloud filtering

NASA Astrophysics Data System (ADS)

Jin, Tianming; Zhang, Junping

2015-12-01

High resolution satellite images play an important role in target detection application presently. This article focuses on the ship target detection from the high resolution panchromatic images. Taking advantage of geographic information such as the coastline vector data provided by NOAA Medium Resolution Coastline program, the land region is masked which is a main noise source in ship detection process. After that, the algorithm tries to deal with the cloud noise which appears frequently in the ocean satellite images, which is another reason for false alarm. Based on the analysis of cloud noise's feature in frequency domain, we introduce a windowed noise filter to get rid of the cloud noise. With the help of morphological processing algorithms adapted to target detection, we are able to acquire ship targets in fine shapes. In addition, we display the extracted information such as length and width of ship targets in a user-friendly way i.e. a KML file interpreted by Google Earth.

Adaptive filtering with the self-organizing map: a performance comparison.

PubMed

Barreto, Guilherme A; Souza, Luís Gustavo M

2006-01-01

In this paper we provide an in-depth evaluation of the SOM as a feasible tool for nonlinear adaptive filtering. A comprehensive survey of existing SOM-based and related architectures for learning input-output mappings is carried out and the application of these architectures to nonlinear adaptive filtering is formulated. Then, we introduce two simple procedures for building RBF-based nonlinear filters using the Vector-Quantized Temporal Associative Memory (VQTAM), a recently proposed method for learning dynamical input-output mappings using the SOM. The aforementioned SOM-based adaptive filters are compared with standard FIR/LMS and FIR/LMS-Newton linear transversal filters, as well as with powerful MLP-based filters in nonlinear channel equalization and inverse modeling tasks. The obtained results in both tasks indicate that SOM-based filters can consistently outperform powerful MLP-based ones.

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.

Infrared and visible image fusion based on visual saliency map and weighted least square optimization

NASA Astrophysics Data System (ADS)

Ma, Jinlei; Zhou, Zhiqiang; Wang, Bo; Zong, Hua

2017-05-01

The goal of infrared (IR) and visible image fusion is to produce a more informative image for human observation or some other computer vision tasks. In this paper, we propose a novel multi-scale fusion method based on visual saliency map (VSM) and weighted least square (WLS) optimization, aiming to overcome some common deficiencies of conventional methods. Firstly, we introduce a multi-scale decomposition (MSD) using the rolling guidance filter (RGF) and Gaussian filter to decompose input images into base and detail layers. Compared with conventional MSDs, this MSD can achieve the unique property of preserving the information of specific scales and reducing halos near edges. Secondly, we argue that the base layers obtained by most MSDs would contain a certain amount of residual low-frequency information, which is important for controlling the contrast and overall visual appearance of the fused image, and the conventional "averaging" fusion scheme is unable to achieve desired effects. To address this problem, an improved VSM-based technique is proposed to fuse the base layers. Lastly, a novel WLS optimization scheme is proposed to fuse the detail layers. This optimization aims to transfer more visual details and less irrelevant IR details or noise into the fused image. As a result, the fused image details would appear more naturally and be suitable for human visual perception. Experimental results demonstrate that our method can achieve a superior performance compared with other fusion methods in both subjective and objective assessments.

Experimental investigation of the effect of inlet particle properties on the capture efficiency in an exhaust particulate filter

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

Viswanathan, Sandeep; Rothamer, David; Zelenyuk, Alla

The impact of inlet particle properties on the filtration performance of clean and particulate matter (PM) laden cordierite filter samples was evaluated using PM generated by a spark-ignition direct-injection (SIDI) engine fuelled with tier II EEE certification gasoline. Prior to the filtration experiments, a scanning mobility particle spectrometer (SMPS) was used to measure the electrical-mobility based particle size distribution (PSD) in the SIDI exhaust from distinct engine operating conditions. An advanced aerosol characterization system that comprised of a centrifugal particle mass analyser (CPMA), a differential mobility analyser (DMA), and a single particle mass spectrometer (SPLAT II) was used to obtainmore » additional information on the SIDI particulate, including particle composition, mass, and dynamic shape factors (DSFs) in the transition () and free-molecular () flow regimes. During the filtration experiments, real-time measurements of PSDs upstream and downstream of the filter sample were used to estimate the filtration performance and the total trapped mass within the filter using an integrated particle size distribution method. The filter loading process was paused multiple times to evaluate the filtration performance in the partially loaded state. The change in vacuum aerodynamic diameter () distribution of mass-selected particles was examined for flow through the filter to identify whether preferential capture of particles of certain shapes occurred in the filter. The filter was also probed using different inlet PSDs to understand their impact on particle capture within the filter sample. Results from the filtration experiment suggest that pausing the filter loading process and subsequently performing the filter probing experiments did not impact the overall evolution of filtration performance. Within the present distribution of particle sizes, filter efficiency was independent of particle shape potentially due to the diffusion-dominant filtration process. Particle mobility diameter and trapped mass within the filter appeared to be the dominant parameters that impacted filter performance.« less

Spectral analysis and filtering techniques in digital spatial data processing

USGS Publications Warehouse

Pan, Jeng-Jong

1989-01-01

A filter toolbox has been developed at the EROS Data Center, US Geological Survey, for retrieving or removing specified frequency information from two-dimensional digital spatial data. This filter toolbox provides capabilities to compute the power spectrum of a given data and to design various filters in the frequency domain. Three types of filters are available in the toolbox: point filter, line filter, and area filter. Both the point and line filters employ Gaussian-type notch filters, and the area filter includes the capabilities to perform high-pass, band-pass, low-pass, and wedge filtering techniques. These filters are applied for analyzing satellite multispectral scanner data, airborne visible and infrared imaging spectrometer (AVIRIS) data, gravity data, and the digital elevation models (DEM) data. -from Author

A Comparison of FPGA and GPGPU Designs for Bayesian Occupancy Filters.

PubMed

Medina, Luis; Diez-Ochoa, Miguel; Correal, Raul; Cuenca-Asensi, Sergio; Serrano, Alejandro; Godoy, Jorge; Martínez-Álvarez, Antonio; Villagra, Jorge

2017-11-11

Grid-based perception techniques in the automotive sector based on fusing information from different sensors and their robust perceptions of the environment are proliferating in the industry. However, one of the main drawbacks of these techniques is the traditionally prohibitive, high computing performance that is required for embedded automotive systems. In this work, the capabilities of new computing architectures that embed these algorithms are assessed in a real car. The paper compares two ad hoc optimized designs of the Bayesian Occupancy Filter; one for General Purpose Graphics Processing Unit (GPGPU) and the other for Field-Programmable Gate Array (FPGA). The resulting implementations are compared in terms of development effort, accuracy and performance, using datasets from a realistic simulator and from a real automated vehicle.

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.

Online Sensor Fault Detection Based on an Improved Strong Tracking Filter

PubMed Central

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

2015-01-01

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

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

Adaptive noise Wiener filter for scanning electron microscope imaging system.

PubMed

Sim, K S; Teh, V; Nia, M E

2016-01-01

Noise on scanning electron microscope (SEM) images is studied. Gaussian noise is the most common type of noise in SEM image. We developed a new noise reduction filter based on the Wiener filter. We compared the performance of this new filter namely adaptive noise Wiener (ANW) filter, with four common existing filters as well as average filter, median filter, Gaussian smoothing filter and the Wiener filter. Based on the experiments results the proposed new filter has better performance on different noise variance comparing to the other existing noise removal filters in the experiments. © Wiley Periodicals, Inc.

Particulate Hot Gas Stream Cleanup Technical Issues: Quarterly report, July 1-September 30, 1996

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

Pontius, D.H.

1996-12-09

This is the eighth in a series of quarterly reports describing the activities performed under Contract No. DE-AC21-94MC31160. Analyses of Hot Gas Stream Cleanup (HGCU) ashes and descriptions of filter performance address aspects of filter operation that are apparently linked to the characteristics of the collected ash or the performance of the ceramic barrier filter elements. Task 1 is designed to generate a data base of the key characteristics of ashes collected from operating advanced particle filters (APFS) and to relate these ash properties to the operation and performance of these filters. Task 2 concerns testing and failure analysis ofmore » ceramic filter elements. Under Task I during the past quarter, additional analyses were performed on ashes from the Ahlstrom 10 MWt Pressurized Fluidized Circulating Fluid Bed (PCFB) facility located at Karhula, Finland. Work continued on the HGCU data base being constructed in Microsoft Access. A variety of information has been entered into the data base, including numerical values, short or long text entries, and photographs. Detailed design of a bench top device for high temperature measurement of ash permeability has also begun. In addition to these activities, a paper was prepared and a poster was presented summarizing recent work performed under this contract at the 1996 DOE/METC Contractor`s Conference. A presentation was also given corresponding to the manuscript entitled Particle Characteristics and High-Temperature Filtration that was prepared for publication in the Proceedings of the Thirteenth Annual International Pittsburgh Coal Conference held this September in Pittsburgh, PA. Arrangements have been made to be present at the DOE/METC Modular Gas Cleanup Rig (MGCR) at the conclusion of the next run of the DOE/METC air blown Fluid Bed Gasifier (FBG). This visit will include on-site sampling to collect and characterize the filter cakes collected during FGB operation. Task 2 efforts during the past quarter focused on hoop tensile testing of Schumacher FT20 and Refractron 326 candle filter elements removed from the Karhula APF after 540 hours of service.« less

Research on Palmprint Identification Method Based on Quantum Algorithms

PubMed Central

Zhang, Zhanzhan

2014-01-01

Quantum image recognition is a technology by using quantum algorithm to process the image information. It can obtain better effect than classical algorithm. In this paper, four different quantum algorithms are used in the three stages of palmprint recognition. First, quantum adaptive median filtering algorithm is presented in palmprint filtering processing. Quantum filtering algorithm can get a better filtering result than classical algorithm through the comparison. Next, quantum Fourier transform (QFT) is used to extract pattern features by only one operation due to quantum parallelism. The proposed algorithm exhibits an exponential speed-up compared with discrete Fourier transform in the feature extraction. Finally, quantum set operations and Grover algorithm are used in palmprint matching. According to the experimental results, quantum algorithm only needs to apply square of N operations to find out the target palmprint, but the traditional method needs N times of calculation. At the same time, the matching accuracy of quantum algorithm is almost 100%. PMID:25105165

Distributed multi-sensor particle filter for bearings-only tracking

NASA Astrophysics Data System (ADS)

Zhang, Jungen; Ji, Hongbing

2012-02-01

In this article, the classical bearings-only tracking (BOT) problem for a single target is addressed, which belongs to the general class of non-linear filtering problems. Due to the fact that the radial distance observability of the target is poor, the algorithm-based sequential Monte-Carlo (particle filtering, PF) methods generally show instability and filter divergence. A new stable distributed multi-sensor PF method is proposed for BOT. The sensors process their measurements at their sites using a hierarchical PF approach, which transforms the BOT problem from Cartesian coordinate to the logarithmic polar coordinate and separates the observable components from the unobservable components of the target. In the fusion centre, the target state can be estimated by utilising the multi-sensor optimal information fusion rule. Furthermore, the computation of a theoretical Cramer-Rao lower bound is given for the multi-sensor BOT problem. Simulation results illustrate that the proposed tracking method can provide better performances than the traditional PF method.

SPECIAL ISSUE ON OPTICAL PROCESSING OF INFORMATION: Realisation of video-frequency filters on the basis of a new mode of operation of an acousto-optical correlator with spatial integration

NASA Astrophysics Data System (ADS)

Ushakov, V. N.

1995-10-01

A video-frequency acousto-optical correlator with spatial integration, which widens the functional capabilities of correlation-type acousto-optical processors, is described. The correlator is based on a two-dimensional reference transparency and it can filter arbitrary video signals of spectral width limited by the pass band of an acousto-optical modulator. The calculated pulse characteristic is governed by the structure of the reference transparency. A procedure for the synthesis of this transparency is considered and experimental results are reported.

Fiber-optic temperature sensors based on differential spectral transmittance/reflectivity and multiplexed sensing systems.

PubMed

Wang, A; Wang, G Z; Murphy, K A; Claus, R O

1995-05-01

A concept for optical temperature sensing based on the differential spectral reflectivity/transmittance from a multilayer dielectric edge filter is described and demonstrated. Two wavelengths, λ(1) and λ(2), from the spectrum of a broadband light source are selected so that they are located on the sloped and flat regions of the reflection or transmission spectrum of the filter, respectively. As temperature variations shift the reflection or transmission spectrum of the filter, they change the output power of the light at λ(1), but the output power of the light at λ(2) is insensitive to the shift and therefore to the temperature variation. The temperature information can be extracted from the ratio of the light powers at λ(1) to the light at λ(2). This ratio is immune to changes in the output power of the light source, fiber losses induced by microbending, and hence modal-power distribution fluctuations. The best resolution of 0.2 °C has been obtained over a range of 30-120 °C. Based on such a basic temperature-sensing concept, a wavelength-division-multiplexed, temperature-sensing system is constructed by cascading three sensing-edge filters that have different cutoff wavelengths along a multimode fiber. The signals from the three sensors are resolved by detecting the correspondent outputs at different wavelengths.

The Ensemble Kalman Filter for Groundwater Plume Characterization: A Case Study.

PubMed

Ross, James L; Andersen, Peter F

2018-04-17

The Kalman filter is an efficient data assimilation tool to refine an estimate of a state variable using measured data and the variable's correlations in space and/or time. The ensemble Kalman filter (EnKF) (Evensen 2004, 2009) is a Kalman filter variant that employs Monte Carlo analysis to define the correlations that help to refine the updated state. While use of EnKF in hydrology is somewhat limited, it has been successfully applied in other fields of engineering (e.g., oil reservoir modeling, weather forecasting). Here, EnKF is used to refine a simulated groundwater tetrachloroethylene (TCE) plume that underlies the Tooele Army Depot-North (TEAD-N) in Utah, based on observations of TCE in the aquifer. The resulting EnKF-based assimilated plume is simulated forward in time to predict future plume migration. The correlations that underpin EnKF updating implicitly contain information about how the plume developed over time under the influence of complex site hydrology and variable source history, as they are predicated on multiple realizations of a well-calibrated numerical groundwater flow and transport model. The EnKF methodology is compared to an ordinary kriging-based assimilation method with respect to the accurate representation of plume concentrations in order to determine the relative efficacy of EnKF for water quality data assimilation. © 2018, National Ground Water Association.

Three dimensional indoor positioning based on visible light with Gaussian mixture sigma-point particle filter technique

NASA Astrophysics Data System (ADS)

Gu, Wenjun; Zhang, Weizhi; Wang, Jin; Amini Kashani, M. R.; Kavehrad, Mohsen

2015-01-01

Over the past decade, location based services (LBS) have found their wide applications in indoor environments, such as large shopping malls, hospitals, warehouses, airports, etc. Current technologies provide wide choices of available solutions, which include Radio-frequency identification (RFID), Ultra wideband (UWB), wireless local area network (WLAN) and Bluetooth. With the rapid development of light-emitting-diodes (LED) technology, visible light communications (VLC) also bring a practical approach to LBS. As visible light has a better immunity against multipath effect than radio waves, higher positioning accuracy is achieved. LEDs are utilized both for illumination and positioning purpose to realize relatively lower infrastructure cost. In this paper, an indoor positioning system using VLC is proposed, with LEDs as transmitters and photo diodes as receivers. The algorithm for estimation is based on received-signalstrength (RSS) information collected from photo diodes and trilateration technique. By appropriately making use of the characteristics of receiver movements and the property of trilateration, estimation on three-dimensional (3-D) coordinates is attained. Filtering technique is applied to enable tracking capability of the algorithm, and a higher accuracy is reached compare to raw estimates. Gaussian mixture Sigma-point particle filter (GM-SPPF) is proposed for this 3-D system, which introduces the notion of Gaussian Mixture Model (GMM). The number of particles in the filter is reduced by approximating the probability distribution with Gaussian components.

The discrete prolate spheroidal filter as a digital signal processing tool

NASA Technical Reports Server (NTRS)

Mathews, J. D.; Breakall, J. K.; Karawas, G. K.

1983-01-01

The discrete prolate spheriodall (DPS) filter is one of the glass of nonrecursive finite impulse response (FIR) filters. The DPS filter is superior to other filters in this class in that it has maximum energy concentration in the frequency passband and minimum ringing in the time domain. A mathematical development of the DPS filter properties is given, along with information required to construct the filter. The properties of this filter were compared with those of the more commonly used filters of the same class. Use of the DPS filter allows for particularly meaningful statements of data time/frequency resolution cell values. The filter forms an especially useful tool for digital signal processing.

Emergency sacrificial sealing method in filters, equipment, or systems

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

Brown, Erik P.

A system seals a filter or equipment component to abase and will continue to seal the filter or equipment component to the base in the event of hot air or fire. The system includes a first sealing material between the filter or equipment component and the base; and a second sealing material between the filter or equipment component and the base and proximate the first sealing material. The first sealing material and the second seal material are positioned relative to each other and relative to the filter or equipment component and the base to seal the filter or equipment componentmore » to the base and upon the event of fire the second sealing material will be activated and expand to continue to seal the filter or equipment component to the base in the event of hot air or fire.« less

An innovative approach to capability-based emergency operations planning

PubMed Central

Keim, Mark E

2013-01-01

This paper describes the innovative use information technology for assisting disaster planners with an easily-accessible method for writing and improving evidence-based emergency operations plans. This process is used to identify all key objectives of the emergency response according to capabilities of the institution, community or society. The approach then uses a standardized, objective-based format, along with a consensus-based method for drafting capability-based operational-level plans. This information is then integrated within a relational database to allow for ease of access and enhanced functionality to search, sort and filter and emergency operations plan according to user need and technological capacity. This integrated approach is offered as an effective option for integrating best practices of planning with the efficiency, scalability and flexibility of modern information and communication technology. PMID:28228987

An innovative approach to capability-based emergency operations planning.

PubMed

Keim, Mark E

2013-01-01

This paper describes the innovative use information technology for assisting disaster planners with an easily-accessible method for writing and improving evidence-based emergency operations plans. This process is used to identify all key objectives of the emergency response according to capabilities of the institution, community or society. The approach then uses a standardized, objective-based format, along with a consensus-based method for drafting capability-based operational-level plans. This information is then integrated within a relational database to allow for ease of access and enhanced functionality to search, sort and filter and emergency operations plan according to user need and technological capacity. This integrated approach is offered as an effective option for integrating best practices of planning with the efficiency, scalability and flexibility of modern information and communication technology.

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.

Medical image registration based on normalized multidimensional mutual information

NASA Astrophysics Data System (ADS)

Li, Qi; Ji, Hongbing; Tong, Ming

2009-10-01

Registration of medical images is an essential research topic in medical image processing and applications, and especially a preliminary and key step for multimodality image fusion. This paper offers a solution to medical image registration based on normalized multi-dimensional mutual information. Firstly, affine transformation with translational and rotational parameters is applied to the floating image. Then ordinal features are extracted by ordinal filters with different orientations to represent spatial information in medical images. Integrating ordinal features with pixel intensities, the normalized multi-dimensional mutual information is defined as similarity criterion to register multimodality images. Finally the immune algorithm is used to search registration parameters. The experimental results demonstrate the effectiveness of the proposed registration scheme.

Fast ground filtering for TLS data via Scanline Density Analysis

NASA Astrophysics Data System (ADS)

Che, Erzhuo; Olsen, Michael J.

2017-07-01

Terrestrial Laser Scanning (TLS) efficiently collects 3D information based on lidar (light detection and ranging) technology. TLS has been widely used in topographic mapping, engineering surveying, forestry, industrial facilities, cultural heritage, and so on. Ground filtering is a common procedure in lidar data processing, which separates the point cloud data into ground points and non-ground points. Effective ground filtering is helpful for subsequent procedures such as segmentation, classification, and modeling. Numerous ground filtering algorithms have been developed for Airborne Laser Scanning (ALS) data. However, many of these are error prone in application to TLS data because of its different angle of view and highly variable resolution. Further, many ground filtering techniques are limited in application within challenging topography and experience difficulty coping with some objects such as short vegetation, steep slopes, and so forth. Lastly, due to the large size of point cloud data, operations such as data traversing, multiple iterations, and neighbor searching significantly affect the computation efficiency. In order to overcome these challenges, we present an efficient ground filtering method for TLS data via a Scanline Density Analysis, which is very fast because it exploits the grid structure storing TLS data. The process first separates the ground candidates, density features, and unidentified points based on an analysis of point density within each scanline. Second, a region growth using the scan pattern is performed to cluster the ground candidates and further refine the ground points (clusters). In the experiment, the effectiveness, parameter robustness, and efficiency of the proposed method is demonstrated with datasets collected from an urban scene and a natural scene, respectively.

Effects of degree and configuration of hearing loss on the contribution of high- and low-frequency speech information to bilateral speech understanding

PubMed Central

Hornsby, Benjamin W. Y.; Johnson, Earl E.; Picou, Erin

2011-01-01

Objectives The purpose of this study was to examine the effects of degree and configuration of hearing loss on the use of, and benefit from, information in amplified high- and low-frequency speech presented in background noise. Design Sixty-two adults with a wide range of high- and low-frequency sensorineural hearing loss (5–115+ dB HL) participated. To examine the contribution of speech information in different frequency regions, speech understanding in noise was assessed in multiple low- and high-pass filter conditions, as well as a band-pass (713–3534 Hz) and wideband (143–8976 Hz) condition. To increase audibility over a wide frequency range, speech and noise were amplified based on each individual’s hearing loss. A stepwise multiple linear regression approach was used to examine the contribution of several factors to 1) absolute performance in each filter condition and 2) the change in performance with the addition of amplified high- and low-frequency speech components. Results Results from the regression analysis showed that degree of hearing loss was the strongest predictor of absolute performance for low- and high-pass filtered speech materials. In addition, configuration of hearing loss affected both absolute performance for severely low-pass filtered speech and benefit from extending high-frequency (3534–8976 Hz) bandwidth. Specifically, individuals with steeply sloping high-frequency losses made better use of low-pass filtered speech information than individuals with similar low-frequency thresholds but less high-frequency loss. In contrast, given similar high-frequency thresholds, individuals with flat hearing losses received more benefit from extending high-frequency bandwidth than individuals with more sloping losses. Conclusions Consistent with previous work, benefit from speech information in a given frequency region generally decreases as degree of hearing loss in that frequency region increases. However, given a similar degree of loss, the configuration of hearing loss also affects the ability to use speech information in different frequency regions. Except for individuals with steeply sloping high-frequency losses, providing high-frequency amplification (3534–8976 Hz) had either a beneficial effect on, or did not significantly degrade, speech understanding. These findings highlight the importance of extended high-frequency amplification for listeners with a wide range of high-frequency hearing losses, when seeking to maximize intelligibility. PMID:21336138

Image segmentation for biomedical applications based on alternating sequential filtering and watershed transformation

NASA Astrophysics Data System (ADS)

Gorpas, D.; Yova, D.

2009-07-01

One of the major challenges in biomedical imaging is the extraction of quantified information from the acquired images. Light and tissue interaction leads to the acquisition of images that present inconsistent intensity profiles and thus the accurate identification of the regions of interest is a rather complicated process. On the other hand, the complex geometries and the tangent objects that very often are present in the acquired images, lead to either false detections or to the merging, shrinkage or expansion of the regions of interest. In this paper an algorithm, which is based on alternating sequential filtering and watershed transformation, is proposed for the segmentation of biomedical images. This algorithm has been tested over two applications, each one based on different acquisition system, and the results illustrate its accuracy in segmenting the regions of interest.

Spatially pooled depth-dependent reservoir storage, elevation, and water-quality data for selected reservoirs in Texas, January 1965-January 2010

USGS Publications Warehouse

Burley, Thomas E.; Asquith, William H.; Brooks, Donald L.

2011-01-01

The U.S. Geological Survey (USGS), in cooperation with Texas Tech University, constructed a dataset of selected reservoir storage (daily and instantaneous values), reservoir elevation (daily and instantaneous values), and water-quality data from 59 reservoirs throughout Texas. The period of record for the data is as large as January 1965-January 2010. Data were acquired from existing databases, spreadsheets, delimited text files, and hard-copy reports. The goal was to obtain as much data as possible; therefore, no data acquisition restrictions specifying a particular time window were used. Primary data sources include the USGS National Water Information System, the Texas Commission on Environmental Quality Surface Water-Quality Management Information System, and the Texas Water Development Board monthly Texas Water Condition Reports. Additional water-quality data for six reservoirs were obtained from USGS Texas Annual Water Data Reports. Data were combined from the multiple sources to create as complete a set of properties and constituents as the disparate databases allowed. By devising a unique per-reservoir short name to represent all sites on a reservoir regardless of their source, all sampling sites at a reservoir were spatially pooled by reservoir and temporally combined by date. Reservoir selection was based on various criteria including the availability of water-quality properties and constituents that might affect the trophic status of the reservoir and could also be important for understanding possible effects of climate change in the future. Other considerations in the selection of reservoirs included the general reservoir-specific period of record, the availability of concurrent reservoir storage or elevation data to match with water-quality data, and the availability of sample depth measurements. Additional separate selection criteria included historic information pertaining to blooms of golden algae. Physical properties and constituents were water temperature, reservoir storage, reservoir elevation, specific conductance, dissolved oxygen, pH, unfiltered salinity, unfiltered total nitrogen, filtered total nitrogen, unfiltered nitrate plus nitrite, unfiltered phosphorus, filtered phosphorus, unfiltered carbon, carbon in suspended sediment, total hardness, unfiltered noncarbonate hardness, filtered noncarbonate hardness, unfiltered calcium, filtered calcium, unfiltered magnesium, filtered magnesium, unfiltered sodium, filtered sodium, unfiltered potassium, filtered potassium, filtered chloride, filtered sulfate, unfiltered fluoride, and filtered fluoride. When possible, USGS and Texas Commission on Environmental Quality water-quality properties and constituents were matched using the database parameter codes for individual physical properties and constituents, descriptions of each physical property or constituent, and their reporting units. This report presents a collection of delimited text files of source-aggregated, spatially pooled, depth-dependent, instantaneous water-quality data as well as instantaneous, daily, and monthly storage and elevation reservoir data.

Colonoscopy video quality assessment using hidden Markov random fields

NASA Astrophysics Data System (ADS)

Park, Sun Young; Sargent, Dusty; Spofford, Inbar; Vosburgh, Kirby

2011-03-01

With colonoscopy becoming a common procedure for individuals aged 50 or more who are at risk of developing colorectal cancer (CRC), colon video data is being accumulated at an ever increasing rate. However, the clinically valuable information contained in these videos is not being maximally exploited to improve patient care and accelerate the development of new screening methods. One of the well-known difficulties in colonoscopy video analysis is the abundance of frames with no diagnostic information. Approximately 40% - 50% of the frames in a colonoscopy video are contaminated by noise, acquisition errors, glare, blur, and uneven illumination. Therefore, filtering out low quality frames containing no diagnostic information can significantly improve the efficiency of colonoscopy video analysis. To address this challenge, we present a quality assessment algorithm to detect and remove low quality, uninformative frames. The goal of our algorithm is to discard low quality frames while retaining all diagnostically relevant information. Our algorithm is based on a hidden Markov model (HMM) in combination with two measures of data quality to filter out uninformative frames. Furthermore, we present a two-level framework based on an embedded hidden Markov model (EHHM) to incorporate the proposed quality assessment algorithm into a complete, automated diagnostic image analysis system for colonoscopy video.

Tyre-road grip coefficient assessment - Part II: online estimation using instrumented vehicle, extended Kalman filter, and neural network

NASA Astrophysics Data System (ADS)

Luque, Pablo; Mántaras, Daniel A.; Fidalgo, Eloy; Álvarez, Javier; Riva, Paolo; Girón, Pablo; Compadre, Diego; Ferran, Jordi

2013-12-01

The main objective of this work is to determine the limit of safe driving conditions by identifying the maximal friction coefficient in a real vehicle. The study will focus on finding a method to determine this limit before reaching the skid, which is valuable information in the context of traffic safety. Since it is not possible to measure the friction coefficient directly, it will be estimated using the appropriate tools in order to get the most accurate information. A real vehicle is instrumented to collect information of general kinematics and steering tie-rod forces. A real-time algorithm is developed to estimate forces and aligning torque in the tyres using an extended Kalman filter and neural networks techniques. The methodology is based on determining the aligning torque; this variable allows evaluation of the behaviour of the tyre. It transmits interesting information from the tyre-road contact and can be used to predict the maximal tyre grip and safety margin. The maximal grip coefficient is estimated according to a knowledge base, extracted from computer simulation of a high detailed three-dimensional model, using Adams® software. The proposed methodology is validated and applied to real driving conditions, in which maximal grip and safety margin are properly estimated.

Supervised filters for EEG signal in naturally occurring epilepsy forecasting.

PubMed

Muñoz-Almaraz, Francisco Javier; Zamora-Martínez, Francisco; Botella-Rocamora, Paloma; Pardo, Juan

2017-01-01

Nearly 1% of the global population has Epilepsy. Forecasting epileptic seizures with an acceptable confidence level, could improve the disease treatment and thus the lifestyle of the people who suffer it. To do that the electroencephalogram (EEG) signal is usually studied through spectral power band filtering, but this paper proposes an alternative novel method of preprocessing the EEG signal based on supervised filters. Such filters have been employed in a machine learning algorithm, such as the K-Nearest Neighbor (KNN), to improve the prediction of seizures. The proposed solution extends with this novel approach an algorithm that was submitted to win the third prize of an international Data Science challenge promoted by Kaggle contest platform and the American Epilepsy Society, the Epilepsy Foundation, National Institutes of Health (NIH) and Mayo Clinic. A formal description of these preprocessing methods is presented and a detailed analysis in terms of Receiver Operating Characteristics (ROC) curve and Area Under ROC curve is performed. The obtained results show statistical significant improvements when compared with the spectral power band filtering (PBF) typical baseline. A trend between performance and the dataset size is observed, suggesting that the supervised filters bring better information, compared to the conventional PBF filters, as the dataset grows in terms of monitored variables (sensors) and time length. The paper demonstrates a better accuracy in forecasting when new filters are employed and its main contribution is in the field of machine learning algorithms to develop more accurate predictive systems.

Supervised filters for EEG signal in naturally occurring epilepsy forecasting

PubMed Central

2017-01-01

Nearly 1% of the global population has Epilepsy. Forecasting epileptic seizures with an acceptable confidence level, could improve the disease treatment and thus the lifestyle of the people who suffer it. To do that the electroencephalogram (EEG) signal is usually studied through spectral power band filtering, but this paper proposes an alternative novel method of preprocessing the EEG signal based on supervised filters. Such filters have been employed in a machine learning algorithm, such as the K-Nearest Neighbor (KNN), to improve the prediction of seizures. The proposed solution extends with this novel approach an algorithm that was submitted to win the third prize of an international Data Science challenge promoted by Kaggle contest platform and the American Epilepsy Society, the Epilepsy Foundation, National Institutes of Health (NIH) and Mayo Clinic. A formal description of these preprocessing methods is presented and a detailed analysis in terms of Receiver Operating Characteristics (ROC) curve and Area Under ROC curve is performed. The obtained results show statistical significant improvements when compared with the spectral power band filtering (PBF) typical baseline. A trend between performance and the dataset size is observed, suggesting that the supervised filters bring better information, compared to the conventional PBF filters, as the dataset grows in terms of monitored variables (sensors) and time length. The paper demonstrates a better accuracy in forecasting when new filters are employed and its main contribution is in the field of machine learning algorithms to develop more accurate predictive systems. PMID:28632737

A Pole-Zero Filter Cascade Provides Good Fits to Human Masking Data and to Basilar Membrane and Neural Data

NASA Astrophysics Data System (ADS)

Lyon, Richard F.

2011-11-01

A cascade of two-pole-two-zero filters with level-dependent pole and zero dampings, with few parameters, can provide a good match to human psychophysical and physiological data. The model has been fitted to data on detection threshold for tones in notched-noise masking, including bandwidth and filter shape changes over a wide range of levels, and has been shown to provide better fits with fewer parameters compared to other auditory filter models such as gammachirps. Originally motivated as an efficient machine implementation of auditory filtering related to the WKB analysis method of cochlear wave propagation, such filter cascades also provide good fits to mechanical basilar membrane data, and to auditory nerve data, including linear low-frequency tail response, level-dependent peak gain, sharp tuning curves, nonlinear compression curves, level-independent zero-crossing times in the impulse response, realistic instantaneous frequency glides, and appropriate level-dependent group delay even with minimum-phase response. As part of exploring different level-dependent parameterizations of such filter cascades, we have identified a simple sufficient condition for stable zero-crossing times, based on the shifting property of the Laplace transform: simply move all the s-domain poles and zeros by equal amounts in the real-s direction. Such pole-zero filter cascades are efficient front ends for machine hearing applications, such as music information retrieval, content identification, speech recognition, and sound indexing.

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.

Based on user interest level of modeling scenarios and browse content

NASA Astrophysics Data System (ADS)

Zhao, Yang

2017-08-01

User interest modeling is the core of personalized service, taking into account the impact of situational information on user preferences, the user behavior days of financial information. This paper proposes a method of user interest modeling based on scenario information, which is obtained by calculating the similarity of the situation. The user's current scene of the approximate scenario set; on the "user - interest items - scenarios" three-dimensional model using the situation pre-filtering method of dimension reduction processing. View the content of the user interested in the theme, the analysis of the page content to get each topic of interest keywords, based on the level of vector space model user interest. The experimental results show that the user interest model based on the scenario information is within 9% of the user's interest prediction, which is effective.

Reprint of “Non-causal spike filtering improves decoding of movement intention for intracortical BCIs”☆

PubMed Central

Masse, Nicolas Y.; Jarosiewicz, Beata; Simeral, John D.; Bacher, Daniel; Stavisky, Sergey D.; Cash, Sydney S.; Oakley, Erin M.; Berhanu, Etsub; Eskandar, Emad; Friehs, Gerhard; Hochberg, Leigh R.; Donoghue, John P.

2015-01-01

Background Multiple types of neural signals are available for controlling assistive devices through brain–computer interfaces (BCIs). Intracortically recorded spiking neural signals are attractive for BCIs because they can in principle provide greater fidelity of encoded information compared to electrocorticographic (ECoG) signals and electroencephalograms (EEGs). Recent reports show that the information content of these spiking neural signals can be reliably extracted simply by causally band-pass filtering the recorded extracellular voltage signals and then applying a spike detection threshold, without relying on “sorting” action potentials. New method We show that replacing the causal filter with an equivalent non-causal filter increases the information content extracted from the extracellular spiking signal and improves decoding of intended movement direction. This method can be used for real-time BCI applications by using a 4 ms lag between recording and filtering neural signals. Results Across 18 sessions from two people with tetraplegia enrolled in the BrainGate2 pilot clinical trial, we found that threshold crossing events extracted using this non-causal filtering method were significantly more informative of each participant’s intended cursor kinematics compared to threshold crossing events derived from causally filtered signals. This new method decreased the mean angular error between the intended and decoded cursor direction by 9.7° for participant S3, who was implanted 5.4 years prior to this study, and by 3.5° for participant T2, who was implanted 3 months prior to this study. PMID:25681017

Adaptive fault feature extraction from wayside acoustic signals from train bearings

NASA Astrophysics Data System (ADS)

Zhang, Dingcheng; Entezami, Mani; Stewart, Edward; Roberts, Clive; Yu, Dejie

2018-07-01

Wayside acoustic detection of train bearing faults plays a significant role in maintaining safety in the railway transport system. However, the bearing fault information is normally masked by strong background noises and harmonic interferences generated by other components (e.g. axles and gears). In order to extract the bearing fault feature information effectively, a novel method called improved singular value decomposition (ISVD) with resonance-based signal sparse decomposition (RSSD), namely the ISVD-RSSD method, is proposed in this paper. A Savitzky-Golay (S-G) smoothing filter is used to filter singular vectors (SVs) in the ISVD method as an extension of the singular value decomposition (SVD) theorem. Hilbert spectrum entropy and a stepwise optimisation strategy are used to optimize the S-G filter's parameters. The RSSD method is able to nonlinearly decompose the wayside acoustic signal of a faulty train bearing into high and low resonance components, the latter of which contains bearing fault information. However, the high level of noise usually results in poor decomposition results from the RSSD method. Hence, the collected wayside acoustic signal must first be de-noised using the ISVD component of the ISVD-RSSD method. Next, the de-noised signal is decomposed by using the RSSD method. The obtained low resonance component is then demodulated with a Hilbert transform such that the bearing fault can be detected by observing Hilbert envelope spectra. The effectiveness of the ISVD-RSSD method is verified through both laboratory field-based experiments as described in the paper. The results indicate that the proposed method is superior to conventional spectrum analysis and ensemble empirical mode decomposition methods.

Selection of optimal spectral sensitivity functions for color filter arrays.

PubMed

Parmar, Manu; Reeves, Stanley J

2010-12-01

A color image meant for human consumption can be appropriately displayed only if at least three distinct color channels are present. Typical digital cameras acquire three-color images with only one sensor. A color filter array (CFA) is placed on the sensor such that only one color is sampled at a particular spatial location. This sparsely sampled signal is then reconstructed to form a color image with information about all three colors at each location. In this paper, we show that the wavelength sensitivity functions of the CFA color filters affect both the color reproduction ability and the spatial reconstruction quality of recovered images. We present a method to select perceptually optimal color filter sensitivity functions based upon a unified spatial-chromatic sampling framework. A cost function independent of particular scenes is defined that expresses the error between a scene viewed by the human visual system and the reconstructed image that represents the scene. A constrained minimization of the cost function is used to obtain optimal values of color-filter sensitivity functions for several periodic CFAs. The sensitivity functions are shown to perform better than typical RGB and CMY color filters in terms of both the s-CIELAB ∆E error metric and a qualitative assessment.

A system for classifying disease comorbidity status from medical discharge summaries using automated hotspot and negated concept detection.

PubMed

Ambert, Kyle H; Cohen, Aaron M

2009-01-01

OBJECTIVE Free-text clinical reports serve as an important part of patient care management and clinical documentation of patient disease and treatment status. Free-text notes are commonplace in medical practice, but remain an under-used source of information for clinical and epidemiological research, as well as personalized medicine. The authors explore the challenges associated with automatically extracting information from clinical reports using their submission to the Integrating Informatics with Biology and the Bedside (i2b2) 2008 Natural Language Processing Obesity Challenge Task. DESIGN A text mining system for classifying patient comorbidity status, based on the information contained in clinical reports. The approach of the authors incorporates a variety of automated techniques, including hot-spot filtering, negated concept identification, zero-vector filtering, weighting by inverse class-frequency, and error-correcting of output codes with linear support vector machines. MEASUREMENTS Performance was evaluated in terms of the macroaveraged F1 measure. RESULTS The automated system performed well against manual expert rule-based systems, finishing fifth in the Challenge's intuitive task, and 13(th) in the textual task. CONCLUSIONS The system demonstrates that effective comorbidity status classification by an automated system is possible.

Empirical and numerical investigation of mass movements - data fusion and analysis

NASA Astrophysics Data System (ADS)

Schmalz, Thilo; Eichhorn, Andreas; Buhl, Volker; Tinkhof, Kurt Mair Am; Preh, Alexander; Tentschert, Ewald-Hans; Zangerl, Christian

2010-05-01

Increasing settlement activities of people in mountanious regions and the appearance of extreme climatic conditions motivate the investigation of landslides. Within the last few years a significant rising of disastrous slides could be registered which generated a broad public interest and the request for security measures. The FWF (Austrian Science Fund) funded project ‘KASIP' (Knowledge-based Alarm System with Identified Deformation Predictor) deals with the development of a new type of alarm system based on calibrated numerical slope models for the realistic calculation of failure scenarios. In KASIP, calibration is the optimal adaptation of a numerical model to available monitoring data by least-squares techniques (e.g. adaptive Kalman-filtering). Adaptation means the determination of a priori uncertain physical parameters like the strength of the geological structure. The object of our studies in KASIP is the landslide ‘Steinlehnen' near Innsbruck (Northern Tyrol, Austria). The first part of the presentation is focussed on the determination of geometrical surface-information. This also includes the description of the monitoring system for the collection of the displacement data and filter approaches for the estimation of the slopes kinematic behaviour. The necessity of continous monitoring and the effect of data gaps for reliable filter results and the prediction of the future state is discussed. The second part of the presentation is more focussed on the numerical modelling of the slope by FD- (Finite Difference-) methods and the development of the adaptive Kalman-filter. The realisation of the numerical slope model is developed by FLAC3D (software company HCItasca Ltd.). The model contains different geomechanical approaches (like Mohr-Coulomb) and enables the calculation of great deformations and the failure of the slope. Stability parameters (like the factor-of-safety FS) allow the evaluation of the current state of the slope. Until now, the adaptation of relevant material parameters is often performed by trial and error methods. This common method shall be improved by adaptive Kalman-filtering methods. In contrast to trial and error, Kalman-filtering also considers stochastical information of the input data. Especially the estimation of strength parameters (cohesion c, angle of internal friction phi) in a dynamic consideration of the slope is discussed. Problems with conditioning and numerical stability of the filter matrices, memory overflow and computing time are outlined. It is shown that the Kalman-filter is in principle suitable for an semi-automated adaptation process and obtains realistic values for the unknown material parameters.

Water Filters

NASA Technical Reports Server (NTRS)

1987-01-01

A compact, lightweight electrolytic water filter generates silver ions in concentrations of 50 to 100 parts per billion in the water flow system. Silver ions serve as effective bactericide/deodorizers. Ray Ward requested and received from NASA a technical information package on the Shuttle filter, and used it as basis for his own initial development, a home use filter.

The role of low-spatial frequencies in lexical decision and masked priming.

PubMed

Boden, C; Giaschi, D

2009-04-01

Spatial frequency filtering was used to test the hypotheses that low-spatial frequency information in printed text can: (1) lead to a rapid lexical decision or (2) facilitate word recognition. Adult proficient readers made lexical decisions in unprimed and masked repetition priming experiments with unfiltered, low-pass, high-pass and notch filtered letter strings. In the unprimed experiments, a filtered target was presented for 105 or 400 ms followed by a pattern mask. Sensitivity (d') was lowest for the low-pass filtered targets at both durations with a bias towards a 'non-word' response. Sensitivity was higher in the high-pass and notch filter conditions. In the priming experiments, a forward mask was followed by a filtered prime then an unfiltered target. Primed words, but not non-words, were identified faster than unprimed words in both the low-pass and high-pass filtered conditions. These results do not support a unique role for low-spatial frequency information in either facilitating or making rapid lexical decisions.

Developmental changes in sensitivity to vocal paralanguage

PubMed Central

Friend, Margaret

2017-01-01

Developmental changes in children’s sensitivity to the role of acoustic variation in the speech stream in conveying speaker affect (vocal paralanguage) were examined. Four-, 7- and 10-year-olds heard utterances in three formats: low-pass filtered, reiterant, and normal speech. The availability of lexical and paralinguistic information varied across these three formats in a way that required children to base their judgments of speaker affect on different configurations of cues in each format. Across ages, the best performance was obtained when a rich array of acoustic cues was present and when there was no competing lexical information. Four-year-olds performed at chance when judgments had to be based solely on speech prosody in the filtered format and they were unable to selectively attend to paralanguage when discrepant lexical cues were present in normal speech. Seven-year-olds were significantly more sensitive to the paralinguistic role of speech prosody in filtered speech than were 4-year-olds and there was a trend toward greater attention to paralanguage when lexical and paralinguistic cues were inconsistent in normal speech. An integration of the ability to utilize prosodic cues to speaker affect with attention to paralanguage in cases of lexical/paralinguistic discrepancy was observed for 10-year-olds. The results are discussed in terms of the development of a perceptual bias emerging out of selective attention to language. PMID:28713218

Improving liquid chromatography-tandem mass spectrometry determinations by modifying noise frequency spectrum between two consecutive wavelet-based low-pass filtering procedures.

PubMed

Chen, Hsiao-Ping; Liao, Hui-Ju; Huang, Chih-Min; Wang, Shau-Chun; Yu, Sung-Nien

2010-04-23

This paper employs one chemometric technique to modify the noise spectrum of liquid chromatography-tandem mass spectrometry (LC-MS/MS) chromatogram between two consecutive wavelet-based low-pass filter procedures to improve the peak signal-to-noise (S/N) ratio enhancement. Although similar techniques of using other sets of low-pass procedures such as matched filters have been published, the procedures developed in this work are able to avoid peak broadening disadvantages inherent in matched filters. In addition, unlike Fourier transform-based low-pass filters, wavelet-based filters efficiently reject noises in the chromatograms directly in the time domain without distorting the original signals. In this work, the low-pass filtering procedures sequentially convolve the original chromatograms against each set of low pass filters to result in approximation coefficients, representing the low-frequency wavelets, of the first five resolution levels. The tedious trials of setting threshold values to properly shrink each wavelet are therefore no longer required. This noise modification technique is to multiply one wavelet-based low-pass filtered LC-MS/MS chromatogram with another artificial chromatogram added with thermal noises prior to the other wavelet-based low-pass filter. Because low-pass filter cannot eliminate frequency components below its cut-off frequency, more efficient peak S/N ratio improvement cannot be accomplished using consecutive low-pass filter procedures to process LC-MS/MS chromatograms. In contrast, when the low-pass filtered LC-MS/MS chromatogram is conditioned with the multiplication alteration prior to the other low-pass filter, much better ratio improvement is achieved. The noise frequency spectrum of low-pass filtered chromatogram, which originally contains frequency components below the filter cut-off frequency, is altered to span a broader range with multiplication operation. When the frequency range of this modified noise spectrum shifts toward the high frequency regimes, the other low-pass filter is able to provide better filtering efficiency to obtain higher peak S/N ratios. Real LC-MS/MS chromatograms, of which typically less than 6-fold peak S/N ratio improvement achieved with two consecutive wavelet-based low-pass filters remains the same S/N ratio improvement using one-step wavelet-based low-pass filter, are improved to accomplish much better ratio enhancement 25-folds to 40-folds typically when the noise frequency spectrum is modified between two low-pass filters. The linear standard curves using the filtered LC-MS/MS signals are validated. The filtered LC-MS/MS signals are also reproducible. The more accurate determinations of very low concentration samples (S/N ratio about 7-9) are obtained using the filtered signals than the determinations using the original signals. Copyright 2010 Elsevier B.V. All rights reserved.

Headphone localization of speech stimuli

NASA Technical Reports Server (NTRS)

Begault, Durand R.; Wenzel, Elizabeth M.

1991-01-01

Recently, three dimensional acoustic display systems have been developed that synthesize virtual sound sources over headphones based on filtering by Head-Related Transfer Functions (HRTFs), the direction-dependent spectral changes caused primarily by the outer ears. Here, 11 inexperienced subjects judged the apparent spatial location of headphone-presented speech stimuli filtered with non-individualized HRTFs. About half of the subjects 'pulled' their judgements toward either the median or the lateral-vertical planes, and estimates were almost always elevated. Individual differences were pronounced for the distance judgements; 15 to 46 percent of stimuli were heard inside the head with the shortest estimates near the median plane. The results infer that most listeners can obtain useful azimuth information from speech stimuli filtered by nonindividualized RTFs. Measurements of localization error and reversal rates are comparable with a previous study that used broadband noise stimuli.

Headphone localization of speech

NASA Technical Reports Server (NTRS)

Begault, Durand R.; Wenzel, Elizabeth M.

1993-01-01

Three-dimensional acoustic display systems have recently been developed that synthesize virtual sound sources over headphones based on filtering by head-related transfer functions (HRTFs), the direction-dependent spectral changes caused primarily by the pinnae. In this study, 11 inexperienced subjects judged the apparent spatial location of headphone-presented speech stimuli filtered with nonindividualized HRTFs. About half of the subjects 'pulled' their judgments toward either the median or the lateral-vertical planes, and estimates were almost always elevated. Individual differences were pronounced for the distance judgments; 15 to 46 percent of stimuli were heard inside the head, with the shortest estimates near the median plane. The results suggest that most listeners can obtain useful azimuth information from speech stimuli filtered by nonindividualized HRTFs. Measurements of localization error and reversal rates are comparable with a previous study that used broadband noise stimuli.

Silicon cross-connect filters using microring resonator coupled multimode-interference-based waveguide crossings.

PubMed

Xu, Fang; Poon, Andrew W

2008-06-09

We report silicon cross-connect filters using microring resonator coupled multimode-interference (MMI) based waveguide crossings. Our experiments reveal that the MMI-based cross-connect filters impose lower crosstalk at the crossing than the conventional cross-connect filters using plain crossings, while offering a nearly symmetric resonance line shape in the drop-port transmission. As a proof-of-concept for cross-connection applications, we demonstrate on a silicon-on-insulator substrate (i) a 4-channel 1 x 4 linear-cascaded MMI-based cross-connect filter, and (ii) a 2-channel 2 x 2 array-cascaded MMI-based cross-connect filter.

Non-causal spike filtering improves decoding of movement intention for intracortical BCIs

PubMed Central

Masse, Nicolas Y.; Jarosiewicz, Beata; Simeral, John D.; Bacher, Daniel; Stavisky, Sergey D.; Cash, Sydney S.; Oakley, Erin M.; Berhanu, Etsub; Eskandar, Emad; Friehs, Gerhard; Hochberg, Leigh R.; Donoghue, John P.

2014-01-01

Background Multiple types of neural signals are available for controlling assistive devices through brain-computer interfaces (BCIs). Intracortically-recorded spiking neural signals are attractive for BCIs because they can in principle provide greater fidelity of encoded information compared to electrocorticographic (ECoG) signals and electroencephalograms (EEGs). Recent reports show that the information content of these spiking neural signals can be reliably extracted simply by causally band-pass filtering the recorded extracellular voltage signals and then applying a spike detection threshold, without relying on “sorting” action potentials. New method We show that replacing the causal filter with an equivalent non-causal filter increases the information content extracted from the extracellular spiking signal and improves decoding of intended movement direction. This method can be used for real-time BCI applications by using a 4 ms lag between recording and filtering neural signals. Results Across 18 sessions from two people with tetraplegia enrolled in the BrainGate2 pilot clinical trial, we found that threshold crossing events extracted using this non-causal filtering method were significantly more informative of each participant’s intended cursor kinematics compared to threshold crossing events derived from causally filtered signals. This new method decreased the mean angular error between the intended and decoded cursor direction by 9.7° for participant S3, who was implanted 5.4 years prior to this study, and by 3.5° for participant T2, who was implanted 3 months prior to this study. Conclusions Non-causally filtering neural signals prior to extracting threshold crossing events may be a simple yet effective way to condition intracortically recorded neural activity for direct control of external devices through BCIs. PMID:25128256

Asymptotic Cramer-Rao bounds for Morlet wavelet filter bank transforms of FM signals

NASA Astrophysics Data System (ADS)

Scheper, Richard

2002-03-01

Wavelet filter banks are potentially useful tools for analyzing and extracting information from frequency modulated (FM) signals in noise. Chief among the advantages of such filter banks is the tendency of wavelet transforms to concentrate signal energy while simultaneously dispersing noise energy over the time-frequency plane, thus raising the effective signal to noise ratio of filtered signals. Over the past decade, much effort has gone into devising new algorithms to extract the relevant information from transformed signals while identifying and discarding the transformed noise. Therefore, estimates of the ultimate performance bounds on such algorithms would serve as valuable benchmarks in the process of choosing optimal algorithms for given signal classes. Discussed here is the specific case of FM signals analyzed by Morlet wavelet filter banks. By making use of the stationary phase approximation of the Morlet transform, and assuming that the measured signals are well resolved digitally, the asymptotic form of the Fisher Information Matrix is derived. From this, Cramer-Rao bounds are analytically derived for simple cases.

A decentralized square root information filter/smoother

NASA Technical Reports Server (NTRS)

Bierman, G. J.; Belzer, M. R.

1985-01-01

A number of developments has recently led to a considerable interest in the decentralization of linear least squares estimators. The developments are partly related to the impending emergence of VLSI technology, the realization of parallel processing, and the need for algorithmic ways to speed the solution of dynamically decoupled, high dimensional estimation problems. A new method is presented for combining Square Root Information Filters (SRIF) estimates obtained from independent data sets. The new method involves an orthogonal transformation, and an information matrix filter 'homework' problem discussed by Schweppe (1973) is generalized. The employed SRIF orthogonal transformation methodology has been described by Bierman (1977).

[The interpreter in an intercultural clinical milieu].

PubMed

Vissandjée, B; Ntetu, A L; Courville, F; Breton, E R; Bourdeau, M

1998-05-01

The public's diversified language profile means that nursing practice must adjust to provide the same quality of care to all clients, no matter what language they speak. To improve quality and quantity of information exchanged in the nurse-client-interpreter triangle, the authors have investigated the type of information likely to be filtered and studied the various factors underlying the interpreter's choice to filter information. The authors also analyzed the values interpreters assign to information and the factors that form the background for filtering, including mistrust. The authors suggest adequately preparing interpreters; using interpreters' expertise; and developing an appropriate training program for intercultural interpreters to enable them to better function within health care institutions.

Downdating a time-varying square root information filter

NASA Technical Reports Server (NTRS)

Muellerschoen, Ronald J.

1990-01-01

A new method to efficiently downdate an estimate and covariance generated by a discrete time Square Root Information Filter (SRIF) is presented. The method combines the QR factor downdating algorithm of Gill and the decentralized SRIF algorithm of Bierman. Efficient removal of either measurements or a priori information is possible without loss of numerical integrity. Moreover, the method includes features for detecting potential numerical degradation. Performance on a 300 parameter system with 5800 data points shows that the method can be used in real time and hence is a promising tool for interactive data analysis. Additionally, updating a time-varying SRIF filter with either additional measurements or a priori information proceeds analogously.

A generalized model via random walks for information filtering

NASA Astrophysics Data System (ADS)

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

2016-08-01

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

Improving the Response of Accelerometers for Automotive Applications by Using LMS Adaptive Filters

PubMed Central

Hernandez, Wilmar; de Vicente, Jesús; Sergiyenko, Oleg; Fernández, Eduardo

2010-01-01

In this paper, the least-mean-squares (LMS) algorithm was used to eliminate noise corrupting the important information coming from a piezoresisitive accelerometer for automotive applications. This kind of accelerometer is designed to be easily mounted in hard to reach places on vehicles under test, and they usually feature ranges from 50 to 2,000 g (where is the gravitational acceleration, 9.81 m/s2) and frequency responses to 3,000 Hz or higher, with DC response, durable cables, reliable performance and relatively low cost. However, here we show that the response of the sensor under test had a lot of noise and we carried out the signal processing stage by using both conventional and optimal adaptive filtering. Usually, designers have to build their specific analog and digital signal processing circuits, and this fact increases considerably the cost of the entire sensor system and the results are not always satisfactory, because the relevant signal is sometimes buried in a broad-band noise background where the unwanted information and the relevant signal sometimes share a very similar frequency band. Thus, in order to deal with this problem, here we used the LMS adaptive filtering algorithm and compare it with others based on the kind of filters that are typically used for automotive applications. The experimental results are satisfactory. PMID:22315542

Theatre Ballistic Missile Defense-Multisensor Fusion, Targeting and Tracking Techniques

DTIC Science & Technology

1998-03-01

Washington, D.C., 1994. 8. Brown , R., and Hwang , P., Introduction to Random Signals and Applied Kaiman Filtering, Third Edition, John Wiley and Sons...C. ADDING MEASUREMENT NOISE 15 III. EXTENDED KALMAN FILTER 19 A. DISCRETE TIME KALMAN FILTER 19 B. EXTENDED KALMAN FILTER 21 C. EKF IN TARGET...tracking algorithms. 17 18 in. EXTENDED KALMAN FILTER This chapter provides background information on the development of a tracking algorithm

Fourier transform profilometry (FTP) using an innovative band-pass filter for accurate 3-D surface reconstruction

NASA Astrophysics Data System (ADS)

Chen, Liang-Chia; Ho, Hsuan-Wei; Nguyen, Xuan-Loc

2010-02-01

This article presents a novel band-pass filter for Fourier transform profilometry (FTP) for accurate 3-D surface reconstruction. FTP can be employed to obtain 3-D surface profiles by one-shot images to achieve high-speed measurement. However, its measurement accuracy has been significantly influenced by the spectrum filtering process required to extract the phase information representing various surface heights. Using the commonly applied 2-D Hanning filter, the measurement errors could be up to 5-10% of the overall measuring height and it is unacceptable to various industrial application. To resolve this issue, the article proposes an elliptical band-pass filter for extracting the spectral region possessing essential phase information for reconstructing accurate 3-D surface profiles. The elliptical band-pass filter was developed and optimized to reconstruct 3-D surface models with improved measurement accuracy. Some experimental results verify that the accuracy can be effectively enhanced by using the elliptical filter. The accuracy improvement of 44.1% and 30.4% can be achieved in 3-D and sphericity measurement, respectively, when the elliptical filter replaces the traditional filter as the band-pass filtering method. Employing the developed method, the maximum measured error can be kept within 3.3% of the overall measuring range.

Conditions for successful data assimilation

NASA Astrophysics Data System (ADS)

Morzfeld, M.; Chorin, A. J.

2013-12-01

Many applications in science and engineering require that the predictions of uncertain models be updated by information from a stream of noisy data. The model and the data jointly define a conditional probability density function (pdf), which contains all the information one has about the process of interest and various numerical methods can be used to study and approximate this pdf, e.g. the Kalman filter, variational methods or particle filters. Given a model and data, each of these algorithms will produce a result. We are interested in the conditions under which this result is reasonable, i.e. consistent with the real-life situation one is modeling. In particular, we show, using idealized models, that numerical data assimilation is feasible in principle only if a suitably defined effective dimension of the problem is not excessive. This effective dimension depends on the noise in the model and the data, and in physically reasonable problems it can be moderate even when the number of variables is huge. In particular, we find that the effective dimension being moderate induces a balance condition between the noises in the model and the data; this balance condition is often satisfied in realistic applications or else the noise levels are excessive and drown the underlying signal. We also study the effects of the effective dimension on particle filters in two instances, one in which the importance function is based on the model alone, and one in which it is based on both the model and the data. We have three main conclusions: (1) the stability (i.e., non-collapse of weights) in particle filtering depends on the effective dimension of the problem. Particle filters can work well if the effective dimension is moderate even if the true dimension is large (which we expect to happen often in practice). (2) A suitable choice of importance function is essential, or else particle filtering fails even when data assimilation is feasible in principle with a sequential algorithm. (3) There is a parameter range in which the model noise and the observation noise are roughly comparable, and in which even the optimal particle filter collapses, even under ideal circumstances. We further study the role of the effective dimension in variational data assimilation and particle smoothing, for both the weak and strong constraint problem. It was found that these methods too require a moderate effective dimension or else no accurate predictions can be expected. Moreover, variational data assimilation or particle smoothing may be applicable in the parameter range where particle filtering fails, because the use of more than one consecutive data set helps reduce the variance which is responsible for the collapse of the filters.

Core Noise Diagnostics of Turbofan Engine Noise Using Correlation and Coherence Functions

NASA Technical Reports Server (NTRS)

Miles, Jeffrey H.

2009-01-01

Cross-correlation and coherence functions are used to look for periodic acoustic components in turbofan engine combustor time histories, to investigate direct and indirect combustion noise source separation based on signal propagation time delays, and to provide information on combustor acoustics. Using the cross-correlation function, time delays were identified in all cases, clearly indicating the combustor is the source of the noise. In addition, unfiltered and low-pass filtered at 400 Hz signals had a cross-correlation time delay near 90 ms, while the low-pass filtered at less than 400 Hz signals had a cross-correlation time delay longer than 90 ms. Low-pass filtering at frequencies less than 400 Hz partially removes the direct combustion noise signals. The remainder includes the indirect combustion noise signal, which travels more slowly because of the dependence on the entropy convection velocity in the combustor. Source separation of direct and indirect combustion noise is demonstrated by proper use of low-pass filters with the cross-correlation function for a range of operating conditions. The results may lead to a better idea about the acoustics in the combustor and may help develop and validate improved reduced-order physics-based methods for predicting direct and indirect combustion noise.

A Highly Reliable and Cost-Efficient Multi-Sensor System for Land Vehicle Positioning.

PubMed

Li, Xu; Xu, Qimin; Li, Bin; Song, Xianghui

2016-05-25

In this paper, we propose a novel positioning solution for land vehicles which is highly reliable and cost-efficient. The proposed positioning system fuses information from the MEMS-based reduced inertial sensor system (RISS) which consists of one vertical gyroscope and two horizontal accelerometers, low-cost GPS, and supplementary sensors and sources. First, pitch and roll angle are accurately estimated based on a vehicle kinematic model. Meanwhile, the negative effect of the uncertain nonlinear drift of MEMS inertial sensors is eliminated by an H∞ filter. Further, a distributed-dual-H∞ filtering (DDHF) mechanism is adopted to address the uncertain nonlinear drift of the MEMS-RISS and make full use of the supplementary sensors and sources. The DDHF is composed of a main H∞ filter (MHF) and an auxiliary H∞ filter (AHF). Finally, a generalized regression neural network (GRNN) module with good approximation capability is specially designed for the MEMS-RISS. A hybrid methodology which combines the GRNN module and the AHF is utilized to compensate for RISS position errors during GPS outages. To verify the effectiveness of the proposed solution, road-test experiments with various scenarios were performed. The experimental results illustrate that the proposed system can achieve accurate and reliable positioning for land vehicles.

A Hybrid Positioning Strategy for Vehicles in a Tunnel Based on RFID and In-Vehicle Sensors

PubMed Central

Song, Xiang; Li, Xu; Tang, Wencheng; Zhang, Weigong; Li, Bin

2014-01-01

Many intelligent transportation system applications require accurate, reliable, and continuous vehicle positioning. How to achieve such positioning performance in extended GPS-denied environments such as tunnels is the main challenge for land vehicles. This paper proposes a hybrid multi-sensor fusion strategy for vehicle positioning in tunnels. First, the preliminary positioning algorithm is developed. The Radio Frequency Identification (RFID) technology is introduced to achieve preliminary positioning in the tunnel. The received signal strength (RSS) is used as an indicator to calculate the distances between the RFID tags and reader, and then a Least Mean Square (LMS) federated filter is designed to provide the preliminary position information for subsequent global fusion. Further, to improve the positioning performance in the tunnel, an interactive multiple model (IMM)-based global fusion algorithm is developed to fuse the data from preliminary positioning results and low-cost in-vehicle sensors, such as electronic compasses and wheel speed sensors. In the actual implementation of IMM, the strong tracking extended Kalman filter (STEKF) algorithm is designed to replace the conventional extended Kalman filter (EKF) to achieve model individual filtering. Finally, the proposed strategy is evaluated through experiments. The results validate the feasibility and effectiveness of the proposed strategy. PMID:25490581

A hybrid positioning strategy for vehicles in a tunnel based on RFID and in-vehicle sensors.

PubMed

Song, Xiang; Li, Xu; Tang, Wencheng; Zhang, Weigong; Li, Bin

2014-12-05

Many intelligent transportation system applications require accurate, reliable, and continuous vehicle positioning. How to achieve such positioning performance in extended GPS-denied environments such as tunnels is the main challenge for land vehicles. This paper proposes a hybrid multi-sensor fusion strategy for vehicle positioning in tunnels. First, the preliminary positioning algorithm is developed. The Radio Frequency Identification (RFID) technology is introduced to achieve preliminary positioning in the tunnel. The received signal strength (RSS) is used as an indicator to calculate the distances between the RFID tags and reader, and then a Least Mean Square (LMS) federated filter is designed to provide the preliminary position information for subsequent global fusion. Further, to improve the positioning performance in the tunnel, an interactive multiple model (IMM)-based global fusion algorithm is developed to fuse the data from preliminary positioning results and low-cost in-vehicle sensors, such as electronic compasses and wheel speed sensors. In the actual implementation of IMM, the strong tracking extended Kalman filter (STEKF) algorithm is designed to replace the conventional extended Kalman filter (EKF) to achieve model individual filtering. Finally, the proposed strategy is evaluated through experiments. The results validate the feasibility and effectiveness of the proposed strategy.

A Highly Reliable and Cost-Efficient Multi-Sensor System for Land Vehicle Positioning

PubMed Central

Li, Xu; Xu, Qimin; Li, Bin; Song, Xianghui

2016-01-01

In this paper, we propose a novel positioning solution for land vehicles which is highly reliable and cost-efficient. The proposed positioning system fuses information from the MEMS-based reduced inertial sensor system (RISS) which consists of one vertical gyroscope and two horizontal accelerometers, low-cost GPS, and supplementary sensors and sources. First, pitch and roll angle are accurately estimated based on a vehicle kinematic model. Meanwhile, the negative effect of the uncertain nonlinear drift of MEMS inertial sensors is eliminated by an H∞ filter. Further, a distributed-dual-H∞ filtering (DDHF) mechanism is adopted to address the uncertain nonlinear drift of the MEMS-RISS and make full use of the supplementary sensors and sources. The DDHF is composed of a main H∞ filter (MHF) and an auxiliary H∞ filter (AHF). Finally, a generalized regression neural network (GRNN) module with good approximation capability is specially designed for the MEMS-RISS. A hybrid methodology which combines the GRNN module and the AHF is utilized to compensate for RISS position errors during GPS outages. To verify the effectiveness of the proposed solution, road-test experiments with various scenarios were performed. The experimental results illustrate that the proposed system can achieve accurate and reliable positioning for land vehicles. PMID:27231917

Filter Bank Regularized Common Spatial Pattern Ensemble for Small Sample Motor Imagery Classification.

PubMed

Park, Sang-Hoon; Lee, David; Lee, Sang-Goog

2018-02-01

For the last few years, many feature extraction methods have been proposed based on biological signals. Among these, the brain signals have the advantage that they can be obtained, even by people with peripheral nervous system damage. Motor imagery electroencephalograms (EEG) are inexpensive to measure, offer a high temporal resolution, and are intuitive. Therefore, these have received a significant amount of attention in various fields, including signal processing, cognitive science, and medicine. The common spatial pattern (CSP) algorithm is a useful method for feature extraction from motor imagery EEG. However, performance degradation occurs in a small-sample setting (SSS), because the CSP depends on sample-based covariance. Since the active frequency range is different for each subject, it is also inconvenient to set the frequency range to be different every time. In this paper, we propose the feature extraction method based on a filter bank to solve these problems. The proposed method consists of five steps. First, motor imagery EEG is divided by a using filter bank. Second, the regularized CSP (R-CSP) is applied to the divided EEG. Third, we select the features according to mutual information based on the individual feature algorithm. Fourth, parameter sets are selected for the ensemble. Finally, we classify using ensemble based on features. The brain-computer interface competition III data set IVa is used to evaluate the performance of the proposed method. The proposed method improves the mean classification accuracy by 12.34%, 11.57%, 9%, 4.95%, and 4.47% compared with CSP, SR-CSP, R-CSP, filter bank CSP (FBCSP), and SR-FBCSP. Compared with the filter bank R-CSP ( , ), which is a parameter selection version of the proposed method, the classification accuracy is improved by 3.49%. In particular, the proposed method shows a large improvement in performance in the SSS.

Surface smoothing, decimation, and their effects on 3D biological specimens.

PubMed

Veneziano, Alessio; Landi, Federica; Profico, Antonio

2018-06-01

Smoothing and decimation filters are commonly used to restore the realistic appearance of virtual biological specimens, but they can cause a loss of topological information of unknown extent. In this study, we analyzed the effect of smoothing and decimation on a 3D mesh to highlight the consequences of an inappropriate use of these filters. Topological noise was simulated on four anatomical regions of the virtual reconstruction of an orangutan cranium. Sequential levels of smoothing and decimation were applied, and their effects were analyzed on the overall topology of the 3D mesh and on linear and volumetric measurements. Different smoothing algorithms affected mesh topology and measurements differently, although the influence on the latter was generally low. Decimation always produced detrimental effects on both topology and measurements. The application of smoothing and decimation, both separate and combined, is capable of recovering topological information. Based on the results, objective guidelines are provided to minimize information loss when using smoothing and decimation on 3D meshes. © 2018 Wiley Periodicals, Inc.

Design of a composite filter realizable on practical spatial light modulators

NASA Technical Reports Server (NTRS)

Rajan, P. K.; Ramakrishnan, Ramachandran

1994-01-01

Hybrid optical correlator systems use two spatial light modulators (SLM's), one at the input plane and the other at the filter plane. Currently available SLM's such as the deformable mirror device (DMD) and liquid crystal television (LCTV) SLM's exhibit arbitrarily constrained operating characteristics. The pattern recognition filters designed with the assumption that the SLM's have ideal operating characteristic may not behave as expected when implemented on the DMD or LCTV SLM's. Therefore it is necessary to incorporate the SLM constraints in the design of the filters. In this report, an iterative method is developed for the design of an unconstrained minimum average correlation energy (MACE) filter. Then using this algorithm a new approach for the design of a SLM constrained distortion invariant filter in the presence of input SLM is developed. Two different optimization algorithms are used to maximize the objective function during filter synthesis, one based on the simplex method and the other based on the Hooke and Jeeves method. Also, the simulated annealing based filter design algorithm proposed by Khan and Rajan is refined and improved. The performance of the filter is evaluated in terms of its recognition/discrimination capabilities using computer simulations and the results are compared with a simulated annealing optimization based MACE filter. The filters are designed for different LCTV SLM's operating characteristics and the correlation responses are compared. The distortion tolerance and the false class image discrimination qualities of the filter are comparable to those of the simulated annealing based filter but the new filter design takes about 1/6 of the computer time taken by the simulated annealing filter design.

A robust approach for a filter-based monocular simultaneous localization and mapping (SLAM) system.

PubMed

Munguía, Rodrigo; Castillo-Toledo, Bernardino; Grau, Antoni

2013-07-03

Simultaneous localization and mapping (SLAM) is an important problem to solve in robotics theory in order to build truly autonomous mobile robots. This work presents a novel method for implementing a SLAM system based on a single camera sensor. The SLAM with a single camera, or monocular SLAM, is probably one of the most complex SLAM variants. In this case, a single camera, which is freely moving through its environment, represents the sole sensor input to the system. The sensors have a large impact on the algorithm used for SLAM. Cameras are used more frequently, because they provide a lot of information and are well adapted for embedded systems: they are light, cheap and power-saving. Nevertheless, and unlike range sensors, which provide range and angular information, a camera is a projective sensor providing only angular measurements of image features. Therefore, depth information (range) cannot be obtained in a single step. In this case, special techniques for feature system-initialization are needed in order to enable the use of angular sensors (as cameras) in SLAM systems. The main contribution of this work is to present a novel and robust scheme for incorporating and measuring visual features in filtering-based monocular SLAM systems. The proposed method is based in a two-step technique, which is intended to exploit all the information available in angular measurements. Unlike previous schemes, the values of parameters used by the initialization technique are derived directly from the sensor characteristics, thus simplifying the tuning of the system. The experimental results show that the proposed method surpasses the performance of previous schemes.

Optimal beamforming in ultrasound using the ideal observer.

PubMed

Abbey, Craig K; Nguyen, Nghia Q; Insana, Michael F

2010-08-01

Beamforming of received pulse-echo data generally involves the compression of signals from multiple channels within an aperture. This compression is irreversible, and therefore allows the possibility that information relevant for performing a diagnostic task is irretrievably lost. The purpose of this study was to evaluate information transfer in beamforming using a previously developed ideal observer model to quantify diagnostic information relevant to performing a task. We describe an elaborated statistical model of image formation for fixed-focus transmission and single-channel reception within a moving aperture, and we use this model on a panel of tasks related to breast sonography to evaluate receive-beamforming approaches that optimize the transfer of information. Under the assumption that acquisition noise is well described as an additive wide-band Gaussian white-noise process, we show that signal compression across receive-aperture channels after a 2-D matched-filtering operation results in no loss of diagnostic information. Across tasks, the matched-filter beamformer results in more information than standard delay-and-sum beamforming in the subsequent radio-frequency signal by a factor of two. We also show that for this matched filter, 68% of the information gain can be attributed to the phase of the matched-filter and 21% can be attributed to the amplitude. A 1-D matched filtering along axial lines shows no advantage over delay-andsum, suggesting an important role for incorporating correlations across different aperture windows in beamforming. We also show that a post-compression processing before the computation of an envelope is necessary to pass the diagnostic information in the beamformed radio-frequency signal to the final envelope image.

SMARTS 0.9-m Telescope | CTIO

Science.gov Websites

Travel Information Questionnaire Vistor Support Questionaire Telescope Schedules Astronomer's Tools Contact Acknowledgments TS4 History ISPI ISPI Exposure Time Calculator OSIRIS Spartan Optical Imagers Filter list for more information on the available filters. Time Synchronization For projects that require

Surface Fitting Filtering of LIDAR Point Cloud with Waveform Information

NASA Astrophysics Data System (ADS)

Xing, S.; Li, P.; Xu, Q.; Wang, D.; Li, P.

2017-09-01

Full-waveform LiDAR is an active technology of photogrammetry and remote sensing. It provides more detailed information about objects along the path of a laser pulse than discrete-return topographic LiDAR. The point cloud and waveform information with high quality can be obtained by waveform decomposition, which could make contributions to accurate filtering. The surface fitting filtering method with waveform information is proposed to present such advantage. Firstly, discrete point cloud and waveform parameters are resolved by global convergent Levenberg Marquardt decomposition. Secondly, the ground seed points are selected, of which the abnormal ones are detected by waveform parameters and robust estimation. Thirdly, the terrain surface is fitted and the height difference threshold is determined in consideration of window size and mean square error. Finally, the points are classified gradually with the rising of window size. The filtering process is finished until window size is larger than threshold. The waveform data in urban, farmland and mountain areas from "WATER (Watershed Allied Telemetry Experimental Research)" are selected for experiments. Results prove that compared with traditional method, the accuracy of point cloud filtering is further improved and the proposed method has highly practical value.

Photon counting spectral breast CT: effect of adaptive filtration on CT numbers, noise, and contrast to noise ratio.

PubMed

Silkwood, Justin D; Matthews, Kenneth L; Shikhaliev, Polad M

2013-05-01

Photon counting spectral (PCS) computed tomography (CT) shows promise for breast imaging. An issue with current photon-counting detectors is low count rate capabilities, artifacts resulting from nonuniform count rate across the field of view, and suboptimal spectral information. These issues are addressed in part by using tissue-equivalent adaptive filtration of the x-ray beam. The purpose of the study was to investigate the effect of adaptive filtration on different aspects of PCS breast CT. The theoretical formulation for the filter shape was derived for different filter materials and evaluated by simulation and an experimental prototype of the filter was fabricated from a tissue-like material (acrylic). The PCS CT images of a glandular breast phantom with adipose and iodine contrast elements were simulated at 40, 60, 90, and 120 kVp tube voltages, with and without adaptive filter. The CT numbers, CT noise, and contrast-to-noise ratio (CNR) were compared for spectral CT images acquired with and without adaptive filters. Similar comparison was made for material-decomposed PCS CT images. The adaptive filter improved the uniformity of CT numbers, CT noise, and CNR in both ordinary and material decomposed PCS CT images. At the same tube output the average CT noise with adaptive filter, although uniform, was higher than the average noise without adaptive filter due to x-ray absorption by the filter. Increasing tube output, so that average skin exposure with the adaptive filter was same as without filter, made the noise with adaptive filter comparable to or lower than that without adaptive filter. Similar effects were observed when energy weighting was applied, and when material decompositions were performed using energy selective CT data. An adaptive filter decreases count rate requirements to the photon counting detectors which enables PCS breast CT based on commercially available detector technologies. Adaptive filter also improves image quality in PCS breast CT by decreasing beam hardening artifacts and by eliminating spatial nonuniformities of CT numbers, noise, and CNR.

Optical filter for highlighting spectral features part I: design and development of the filter for discrimination of human skin with and without an application of cosmetic foundation.

PubMed

Nishino, Ken; Nakamura, Mutsuko; Matsumoto, Masayuki; Tanno, Osamu; Nakauchi, Shigeki

2011-03-28

Light reflected from an object's surface contains much information about its physical and chemical properties. Changes in the physical properties of an object are barely detectable in spectra. Conventional trichromatic systems, on the other hand, cannot detect most spectral features because spectral information is compressively represented as trichromatic signals forming a three-dimensional subspace. We propose a method for designing a filter that optically modulates a camera's spectral sensitivity to find an alternative subspace highlighting an object's spectral features more effectively than the original trichromatic space. We designed and developed a filter that detects cosmetic foundations on human face. Results confirmed that the filter can visualize and nondestructively inspect the foundation distribution.

Statistical coding and decoding of heartbeat intervals.

PubMed

Lucena, Fausto; Barros, Allan Kardec; Príncipe, José C; Ohnishi, Noboru

2011-01-01

The heart integrates neuroregulatory messages into specific bands of frequency, such that the overall amplitude spectrum of the cardiac output reflects the variations of the autonomic nervous system. This modulatory mechanism seems to be well adjusted to the unpredictability of the cardiac demand, maintaining a proper cardiac regulation. A longstanding theory holds that biological organisms facing an ever-changing environment are likely to evolve adaptive mechanisms to extract essential features in order to adjust their behavior. The key question, however, has been to understand how the neural circuitry self-organizes these feature detectors to select behaviorally relevant information. Previous studies in computational perception suggest that a neural population enhances information that is important for survival by minimizing the statistical redundancy of the stimuli. Herein we investigate whether the cardiac system makes use of a redundancy reduction strategy to regulate the cardiac rhythm. Based on a network of neural filters optimized to code heartbeat intervals, we learn a population code that maximizes the information across the neural ensemble. The emerging population code displays filter tuning proprieties whose characteristics explain diverse aspects of the autonomic cardiac regulation, such as the compromise between fast and slow cardiac responses. We show that the filters yield responses that are quantitatively similar to observed heart rate responses during direct sympathetic or parasympathetic nerve stimulation. Our findings suggest that the heart decodes autonomic stimuli according to information theory principles analogous to how perceptual cues are encoded by sensory systems.

A Comparison of FPGA and GPGPU Designs for Bayesian Occupancy Filters

PubMed Central

Medina, Luis; Diez-Ochoa, Miguel; Correal, Raul; Cuenca-Asensi, Sergio; Godoy, Jorge; Martínez-Álvarez, Antonio

2017-01-01

Grid-based perception techniques in the automotive sector based on fusing information from different sensors and their robust perceptions of the environment are proliferating in the industry. However, one of the main drawbacks of these techniques is the traditionally prohibitive, high computing performance that is required for embedded automotive systems. In this work, the capabilities of new computing architectures that embed these algorithms are assessed in a real car. The paper compares two ad hoc optimized designs of the Bayesian Occupancy Filter; one for General Purpose Graphics Processing Unit (GPGPU) and the other for Field-Programmable Gate Array (FPGA). The resulting implementations are compared in terms of development effort, accuracy and performance, using datasets from a realistic simulator and from a real automated vehicle. PMID:29137137

A Phonocardiographic-Based Fiber-Optic Sensor and Adaptive Filtering System for Noninvasive Continuous Fetal Heart Rate Monitoring.

PubMed

Martinek, Radek; Nedoma, Jan; Fajkus, Marcel; Kahankova, Radana; Konecny, Jaromir; Janku, Petr; Kepak, Stanislav; Bilik, Petr; Nazeran, Homer

2017-04-18

This paper focuses on the design, realization, and verification of a novel phonocardiographic- based fiber-optic sensor and adaptive signal processing system for noninvasive continuous fetal heart rate (fHR) monitoring. Our proposed system utilizes two Mach-Zehnder interferometeric sensors. Based on the analysis of real measurement data, we developed a simplified dynamic model for the generation and distribution of heart sounds throughout the human body. Building on this signal model, we then designed, implemented, and verified our adaptive signal processing system by implementing two stochastic gradient-based algorithms: the Least Mean Square Algorithm (LMS), and the Normalized Least Mean Square (NLMS) Algorithm. With this system we were able to extract the fHR information from high quality fetal phonocardiograms (fPCGs), filtered from abdominal maternal phonocardiograms (mPCGs) by performing fPCG signal peak detection. Common signal processing methods such as linear filtering, signal subtraction, and others could not be used for this purpose as fPCG and mPCG signals share overlapping frequency spectra. The performance of the adaptive system was evaluated by using both qualitative (gynecological studies) and quantitative measures such as: Signal-to-Noise Ratio-SNR, Root Mean Square Error-RMSE, Sensitivity-S+, and Positive Predictive Value-PPV.

A Phonocardiographic-Based Fiber-Optic Sensor and Adaptive Filtering System for Noninvasive Continuous Fetal Heart Rate Monitoring

PubMed Central

Martinek, Radek; Nedoma, Jan; Fajkus, Marcel; Kahankova, Radana; Konecny, Jaromir; Janku, Petr; Kepak, Stanislav; Bilik, Petr; Nazeran, Homer

2017-01-01

This paper focuses on the design, realization, and verification of a novel phonocardiographic- based fiber-optic sensor and adaptive signal processing system for noninvasive continuous fetal heart rate (fHR) monitoring. Our proposed system utilizes two Mach-Zehnder interferometeric sensors. Based on the analysis of real measurement data, we developed a simplified dynamic model for the generation and distribution of heart sounds throughout the human body. Building on this signal model, we then designed, implemented, and verified our adaptive signal processing system by implementing two stochastic gradient-based algorithms: the Least Mean Square Algorithm (LMS), and the Normalized Least Mean Square (NLMS) Algorithm. With this system we were able to extract the fHR information from high quality fetal phonocardiograms (fPCGs), filtered from abdominal maternal phonocardiograms (mPCGs) by performing fPCG signal peak detection. Common signal processing methods such as linear filtering, signal subtraction, and others could not be used for this purpose as fPCG and mPCG signals share overlapping frequency spectra. The performance of the adaptive system was evaluated by using both qualitative (gynecological studies) and quantitative measures such as: Signal-to-Noise Ratio—SNR, Root Mean Square Error—RMSE, Sensitivity—S+, and Positive Predictive Value—PPV. PMID:28420215

Evidence-based tailoring of behavior-change campaigns: increasing fluoride-free water consumption in rural Ethiopia with persuasion.

PubMed

Huber, Alexandra C; Tobias, Robert; Mosler, Hans-Joachim

2014-03-01

Two hundred million people worldwide are at risk of developing dental and skeletal fluorosis due to excessive fluoride uptake from their water. Since medical treatment of the disease is difficult and mostly ineffective, preventing fluoride uptake is crucial. In the Ethiopian Rift Valley, a fluoride-removal community filter was installed. Despite having access to a fluoride filter, the community used the filter sparingly. During a baseline assessment, 173 face-to-face interviews were conducted to identify psychological factors that influence fluoride-free water consumption. Based on the results, two behavior-change campaigns were implemented: a traditional information intervention targeting perceived vulnerability, and an evidence-based persuasion intervention regarding perceived costs. The interventions were tailored to household characteristics. The campaigns were evaluated with a survey and analyzed in terms of their effectiveness in changing behavior and targeted psychological factors. While the intervention targeting perceived vulnerability showed no desirable effects, cost persuasion decreased the perceived costs and increased the consumption of fluoride-free water. This showed that altering subjective perceptions can change behavior even without changing objective circumstances. Moreover, interventions are more effective if they are based on evidence and tailored to specific households. © 2013 The International Association of Applied Psychology.

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.

Tunable Microwave Filter Design Using Thin-Film Ferroelectric Varactors

NASA Astrophysics Data System (ADS)

Haridasan, Vrinda

Military, space, and consumer-based communication markets alike are moving towards multi-functional, multi-mode, and portable transceiver units. Ferroelectric-based tunable filter designs in RF front-ends are a relatively new area of research that provides a potential solution to support wideband and compact transceiver units. This work presents design methodologies developed to optimize a tunable filter design for system-level integration, and to improve the performance of a ferroelectric-based tunable bandpass filter. An investigative approach to find the origins of high insertion loss exhibited by these filters is also undertaken. A system-aware design guideline and figure of merit for ferroelectric-based tunable band- pass filters is developed. The guideline does not constrain the filter bandwidth as long as it falls within the range of the analog bandwidth of a system's analog to digital converter. A figure of merit (FOM) that optimizes filter design for a specific application is presented. It considers the worst-case filter performance parameters and a tuning sensitivity term that captures the relation between frequency tunability and the underlying material tunability. A non-tunable parasitic fringe capacitance associated with ferroelectric-based planar capacitors is confirmed by simulated and measured results. The fringe capacitance is an appreciable proportion of the tunable capacitance at frequencies of X-band and higher. As ferroelectric-based tunable capac- itors form tunable resonators in the filter design, a proportionally higher fringe capacitance reduces the capacitance tunability which in turn reduces the frequency tunability of the filter. Methods to reduce the fringe capacitance can thus increase frequency tunability or indirectly reduce the filter insertion-loss by trading off the increased tunability achieved to lower loss. A new two-pole tunable filter topology with high frequency tunability (> 30%), steep filter skirts, wide stopband rejection, and constant bandwidth is designed, simulated, fabricated and measured. The filters are fabricated using barium strontium titanate (BST) varactors. Electromagnetic simulations and measured results of the tunable two-pole ferroelectric filter are analyzed to explore the origins of high insertion loss in ferroelectric filters. The results indicate that the high-permittivity of the BST (a ferroelectric) not only makes the filters tunable and compact, but also increases the conductive loss of the ferroelectric-based tunable resonators which translates into high insertion loss in ferroelectric filters.

Model-Based Fatigue Prognosis of Fiber-Reinforced Laminates Exhibiting Concurrent Damage Mechanisms

NASA Technical Reports Server (NTRS)

Corbetta, M.; Sbarufatti, C.; Saxena, A.; Giglio, M.; Goebel, K.

2016-01-01

Prognostics of large composite structures is a topic of increasing interest in the field of structural health monitoring for aerospace, civil, and mechanical systems. Along with recent advancements in real-time structural health data acquisition and processing for damage detection and characterization, model-based stochastic methods for life prediction are showing promising results in the literature. Among various model-based approaches, particle-filtering algorithms are particularly capable in coping with uncertainties associated with the process. These include uncertainties about information on the damage extent and the inherent uncertainties of the damage propagation process. Some efforts have shown successful applications of particle filtering-based frameworks for predicting the matrix crack evolution and structural stiffness degradation caused by repetitive fatigue loads. Effects of other damage modes such as delamination, however, are not incorporated in these works. It is well established that delamination and matrix cracks not only co-exist in most laminate structures during the fatigue degradation process but also affect each other's progression. Furthermore, delamination significantly alters the stress-state in the laminates and accelerates the material degradation leading to catastrophic failure. Therefore, the work presented herein proposes a particle filtering-based framework for predicting a structure's remaining useful life with consideration of multiple co-existing damage-mechanisms. The framework uses an energy-based model from the composite modeling literature. The multiple damage-mode model has been shown to suitably estimate the energy release rate of cross-ply laminates as affected by matrix cracks and delamination modes. The model is also able to estimate the reduction in stiffness of the damaged laminate. This information is then used in the algorithms for life prediction capabilities. First, a brief summary of the energy-based damage model is provided. Then, the paper describes how the model is embedded within the prognostic framework and how the prognostics performance is assessed using observations from run-to-failure experiments

A feasibility study of automatic lung nodule detection in chest digital tomosynthesis with machine learning based on support vector machine

NASA Astrophysics Data System (ADS)

Lee, Donghoon; Kim, Ye-seul; Choi, Sunghoon; Lee, Haenghwa; Jo, Byungdu; Choi, Seungyeon; Shin, Jungwook; Kim, Hee-Joung

2017-03-01

The chest digital tomosynthesis(CDT) is recently developed medical device that has several advantage for diagnosing lung disease. For example, CDT provides depth information with relatively low radiation dose compared to computed tomography (CT). However, a major problem with CDT is the image artifacts associated with data incompleteness resulting from limited angle data acquisition in CDT geometry. For this reason, the sensitivity of lung disease was not clear compared to CT. In this study, to improve sensitivity of lung disease detection in CDT, we developed computer aided diagnosis (CAD) systems based on machine learning. For design CAD systems, we used 100 cases of lung nodules cropped images and 100 cases of normal lesion cropped images acquired by lung man phantoms and proto type CDT. We used machine learning techniques based on support vector machine and Gabor filter. The Gabor filter was used for extracting characteristics of lung nodules and we compared performance of feature extraction of Gabor filter with various scale and orientation parameters. We used 3, 4, 5 scales and 4, 6, 8 orientations. After extracting features, support vector machine (SVM) was used for classifying feature of lesions. The linear, polynomial and Gaussian kernels of SVM were compared to decide the best SVM conditions for CDT reconstruction images. The results of CAD system with machine learning showed the capability of automatically lung lesion detection. Furthermore detection performance was the best when Gabor filter with 5 scale and 8 orientation and SVM with Gaussian kernel were used. In conclusion, our suggested CAD system showed improving sensitivity of lung lesion detection in CDT and decide Gabor filter and SVM conditions to achieve higher detection performance of our developed CAD system for CDT.

Anisotropic scene geometry resampling with occlusion filling for 3DTV applications

NASA Astrophysics Data System (ADS)

Kim, Jangheon; Sikora, Thomas

2006-02-01

Image and video-based rendering technologies are receiving growing attention due to their photo-realistic rendering capability in free-viewpoint. However, two major limitations are ghosting and blurring due to their sampling-based mechanism. The scene geometry which supports to select accurate sampling positions is proposed using global method (i.e. approximate depth plane) and local method (i.e. disparity estimation). This paper focuses on the local method since it can yield more accurate rendering quality without large number of cameras. The local scene geometry has two difficulties which are the geometrical density and the uncovered area including hidden information. They are the serious drawback to reconstruct an arbitrary viewpoint without aliasing artifacts. To solve the problems, we propose anisotropic diffusive resampling method based on tensor theory. Isotropic low-pass filtering accomplishes anti-aliasing in scene geometry and anisotropic diffusion prevents filtering from blurring the visual structures. Apertures in coarse samples are estimated following diffusion on the pre-filtered space, the nonlinear weighting of gradient directions suppresses the amount of diffusion. Aliasing artifacts from low density are efficiently removed by isotropic filtering and the edge blurring can be solved by the anisotropic method at one process. Due to difference size of sampling gap, the resampling condition is defined considering causality between filter-scale and edge. Using partial differential equation (PDE) employing Gaussian scale-space, we iteratively achieve the coarse-to-fine resampling. In a large scale, apertures and uncovered holes can be overcoming because only strong and meaningful boundaries are selected on the resolution. The coarse-level resampling with a large scale is iteratively refined to get detail scene structure. Simulation results show the marked improvements of rendering quality.

Comparison of PubMed and Google Scholar literature searches.

PubMed

Anders, Michael E; Evans, Dennis P

2010-05-01

Literature searches are essential to evidence-based respiratory care. To conduct literature searches, respiratory therapists rely on search engines to retrieve information, but there is a dearth of literature on the comparative efficiencies of search engines for researching clinical questions in respiratory care. To compare PubMed and Google Scholar search results for clinical topics in respiratory care to that of a benchmark. We performed literature searches with PubMed and Google Scholar, on 3 clinical topics. In PubMed we used the Clinical Queries search filter. In Google Scholar we used the search filters in the Advanced Scholar Search option. We used the reference list of a related Cochrane Collaboration evidence-based systematic review as the benchmark for each of the search results. We calculated recall (sensitivity) and precision (positive predictive value) with 2 x 2 contingency tables. We compared the results with the chi-square test of independence and Fisher's exact test. PubMed and Google Scholar had similar recall for both overall search results (71% vs 69%) and full-text results (43% vs 51%). PubMed had better precision than Google Scholar for both overall search results (13% vs 0.07%, P < .001) and full-text results (8% vs 0.05%, P < .001). Our results suggest that PubMed searches with the Clinical Queries filter are more precise than with the Advanced Scholar Search in Google Scholar for respiratory care topics. PubMed appears to be more practical to conduct efficient, valid searches for informing evidence-based patient-care protocols, for guiding the care of individual patients, and for educational purposes.

Online Variational Bayesian Filtering-Based Mobile Target Tracking in Wireless Sensor Networks

PubMed Central

Zhou, Bingpeng; Chen, Qingchun; Li, Tiffany Jing; Xiao, Pei

2014-01-01

The received signal strength (RSS)-based online tracking for a mobile node in wireless sensor networks (WSNs) is investigated in this paper. Firstly, a multi-layer dynamic Bayesian network (MDBN) is introduced to characterize the target mobility with either directional or undirected movement. In particular, it is proposed to employ the Wishart distribution to approximate the time-varying RSS measurement precision's randomness due to the target movement. It is shown that the proposed MDBN offers a more general analysis model via incorporating the underlying statistical information of both the target movement and observations, which can be utilized to improve the online tracking capability by exploiting the Bayesian statistics. Secondly, based on the MDBN model, a mean-field variational Bayesian filtering (VBF) algorithm is developed to realize the online tracking of a mobile target in the presence of nonlinear observations and time-varying RSS precision, wherein the traditional Bayesian filtering scheme cannot be directly employed. Thirdly, a joint optimization between the real-time velocity and its prior expectation is proposed to enable online velocity tracking in the proposed online tacking scheme. Finally, the associated Bayesian Cramer–Rao Lower Bound (BCRLB) analysis and numerical simulations are conducted. Our analysis unveils that, by exploiting the potential state information via the general MDBN model, the proposed VBF algorithm provides a promising solution to the online tracking of a mobile node in WSNs. In addition, it is shown that the final tracking accuracy linearly scales with its expectation when the RSS measurement precision is time-varying. PMID:25393784

Finger-vein and fingerprint recognition based on a feature-level fusion method

NASA Astrophysics Data System (ADS)

Yang, Jinfeng; Hong, Bofeng

2013-07-01

Multimodal biometrics based on the finger identification is a hot topic in recent years. In this paper, a novel fingerprint-vein based biometric method is proposed to improve the reliability and accuracy of the finger recognition system. First, the second order steerable filters are used here to enhance and extract the minutiae features of the fingerprint (FP) and finger-vein (FV). Second, the texture features of fingerprint and finger-vein are extracted by a bank of Gabor filter. Third, a new triangle-region fusion method is proposed to integrate all the fingerprint and finger-vein features in feature-level. Thus, the fusion features contain both the finger texture-information and the minutiae triangular geometry structure. Finally, experimental results performed on the self-constructed finger-vein and fingerprint databases are shown that the proposed method is reliable and precise in personal identification.

Truncated feature representation for automatic target detection using transformed data-based decomposition

NASA Astrophysics Data System (ADS)

Riasati, Vahid R.

2016-05-01

In this work, the data covariance matrix is diagonalized to provide an orthogonal bases set using the eigen vectors of the data. The eigen-vector decomposition of the data is transformed and filtered in the transform domain to truncate the data for robust features related to a specified set of targets. These truncated eigen features are then combined and reconstructed to utilize in a composite filter and consequently utilized for the automatic target detection of the same class of targets. The results associated with the testing of the current technique are evaluated using the peak-correlation and peak-correlation energy metrics and are presented in this work. The inverse transformed eigen-bases of the current technique may be thought of as an injected sparsity to minimize data in representing the skeletal data structure information associated with the set of targets under consideration.

Small-target leak detection for a closed vessel via infrared image sequences

NASA Astrophysics Data System (ADS)

Zhao, Ling; Yang, Hongjiu

2017-03-01

This paper focus on a leak diagnosis and localization method based on infrared image sequences. Some problems on high probability of false warning and negative affect for marginal information are solved by leak detection. An experimental model is established for leak diagnosis and localization on infrared image sequences. The differential background prediction is presented to eliminate the negative affect of marginal information on test vessel based on a kernel regression method. A pipeline filter based on layering voting is designed to reduce probability of leak point false warning. A synthesize leak diagnosis and localization algorithm is proposed based on infrared image sequences. The effectiveness and potential are shown for developed techniques through experimental results.

Thermal tracking in mobile robots for leak inspection activities.

PubMed

Ibarguren, Aitor; Molina, Jorge; Susperregi, Loreto; Maurtua, Iñaki

2013-10-09

Maintenance tasks are crucial for all kind of industries, especially in extensive industrial plants, like solar thermal power plants. The incorporation of robots is a key issue for automating inspection activities, as it will allow a constant and regular control over the whole plant. This paper presents an autonomous robotic system to perform pipeline inspection for early detection and prevention of leakages in thermal power plants, based on the work developed within the MAINBOT (http://www.mainbot.eu) European project. Based on the information provided by a thermographic camera, the system is able to detect leakages in the collectors and pipelines. Beside the leakage detection algorithms, the system includes a particle filter-based tracking algorithm to keep the target in the field of view of the camera and to avoid the irregularities of the terrain while the robot patrols the plant. The information provided by the particle filter is further used to command a robot arm, which handles the camera and ensures that the target is always within the image. The obtained results show the suitability of the proposed approach, adding a tracking algorithm to improve the performance of the leakage detection system.

Thermal Tracking in Mobile Robots for Leak Inspection Activities

PubMed Central

Ibarguren, Aitor; Molina, Jorge; Susperregi, Loreto; Maurtua, Iñaki

2013-01-01

Maintenance tasks are crucial for all kind of industries, especially in extensive industrial plants, like solar thermal power plants. The incorporation of robots is a key issue for automating inspection activities, as it will allow a constant and regular control over the whole plant. This paper presents an autonomous robotic system to perform pipeline inspection for early detection and prevention of leakages in thermal power plants, based on the work developed within the MAINBOT (http://www.mainbot.eu) European project. Based on the information provided by a thermographic camera, the system is able to detect leakages in the collectors and pipelines. Beside the leakage detection algorithms, the system includes a particle filter-based tracking algorithm to keep the target in the field of view of the camera and to avoid the irregularities of the terrain while the robot patrols the plant. The information provided by the particle filter is further used to command a robot arm, which handles the camera and ensures that the target is always within the image. The obtained results show the suitability of the proposed approach, adding a tracking algorithm to improve the performance of the leakage detection system. PMID:24113684

High-speed tunable microwave photonic notch filter based on phase modulator incorporated Lyot filter.

PubMed

Ge, Jia; Feng, Hanlin; Scott, Guy; Fok, Mable P

2015-01-01

A high-speed tunable microwave photonic notch filter with ultrahigh rejection ratio is presented, which is achieved by semiconductor optical amplifier (SOA)-based single-sideband modulation and optical spectral filtering with a phase modulator-incorporated Lyot (PM-Lyot) filter. By varying the birefringence of the phase modulator through electro-optic effect, electrically tuning of the microwave photonic notch filter is experimentally achieved at tens of gigahertz speed. The use of SOA-polarizer based single-sideband modulation scheme provides good sideband suppression over a wide frequency range, resulting in an ultrahigh rejection ratio of the microwave photonic notch filter. Stable filter spectrum with bandstop rejection ratio over 60 dB is observed over a frequency tuning range from 1.8 to 10 GHz. Compare with standard interferometric notch filter, narrower bandwidth and sharper notch profile are achieved with the unique PM-Lyot filter, resulting in better filter selectivity. Moreover, bandwidth tuning is also achieved through polarization adjustment inside the PM-Lyot filter, that the 10-dB filter bandwidth is tuned from 0.81 to 1.85 GHz.

Entropy-guided switching trimmed mean deviation-boosted anisotropic diffusion filter

NASA Astrophysics Data System (ADS)

Nnolim, Uche A.

2016-07-01

An effective anisotropic diffusion (AD) mean filter variant is proposed for filtering of salt-and-pepper impulse noise. The implemented filter is robust to impulse noise ranging from low to high density levels. The algorithm involves a switching scheme in addition to utilizing the unsymmetric trimmed mean/median deviation to filter image noise while greatly preserving image edges, regardless of impulse noise density (ND). It operates with threshold parameters selected manually or adaptively estimated from the image statistics. It is further combined with the partial differential equations (PDE)-based AD for edge preservation at high NDs to enhance the properties of the trimmed mean filter. Based on experimental results, the proposed filter easily and consistently outperforms the median filter and its other variants ranging from simple to complex filter structures, especially the known PDE-based variants. In addition, the switching scheme and threshold calculation enables the filter to avoid smoothing an uncorrupted image, and filtering is activated only when impulse noise is present. Ultimately, the particular properties of the filter make its combination with the AD algorithm a unique and powerful edge-preservation smoothing filter at high-impulse NDs.

cSRM 2035: a rare-earth oxide glass for the wavelength calibration of near-infrared dispersive and Fourier transform spectrometers

NASA Astrophysics Data System (ADS)

Choquette, Steven J.; Travis, John C.; Duewer, David L.

1998-10-01

The National Institute of Standards and Technology is developing an optical filter standard for calibration of the wavelength axis of near infrared (NIR) transmission spectrometers. A design goal for the initial candidate Standard Reference Material (cSRM) filter was to provide absorbance peaks evenly covering the spectral region between 800 nm to 1600 mm (12,000 cm-1 to 6,500 cm-1). The reproducibility of the peak location, for batch-certified filters, was to be better than 0.02 nm (approximately 0.1 cm-1). Glasses with 1 to 3 mole % Yb2O3, Sm2O3, and Nd2O3, incorporated into a commercial lanthanum oxide glass were evaluated for this proposed optical standard. An initial batch of cSRM 2035 filters was prepared based on studies of glasses made and evaluated in our laboratory. An interlaboratory comparison study was initiated in February 1997 to evaluate the utility of these filters for the chemical, pharmaceutical, instrumentation, and regulatory communities. Information concerning peak-picking algorithms, wavelength coverage, geometry preferences, and other parameters was solicited from the users. Based upon input from the participants of this interlaboratory study, we are making several changes to make SRM 2035 more useful to our customers. Two of these changes are: (1) incorporating Ho2O3 into the glass to introduce an absorbance peak at approximately 2000 nm (approximately 5000 cm-1) and (2) providing users with a standard center of gravity (COG) peak-picking algorithm to locate the absorbance peaks of the SRM filter precisely. Recent results have demonstrated that the COG method provides a 10 fold improvement in the precision of locating peaks compared with traditional peak-picking methods.

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.

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

NASA Astrophysics Data System (ADS)

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

2018-06-01

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

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

PubMed

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

2016-07-01

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

Image sharpening for mixed spatial and spectral resolution satellite systems

NASA Technical Reports Server (NTRS)

Hallada, W. A.; Cox, S.

1983-01-01

Two methods of image sharpening (reconstruction) are compared. The first, a spatial filtering technique, extrapolates edge information from a high spatial resolution panchromatic band at 10 meters and adds it to the low spatial resolution narrow spectral bands. The second method, a color normalizing technique, is based on the ability to separate image hue and brightness components in spectral data. Using both techniques, multispectral images are sharpened from 30, 50, 70, and 90 meter resolutions. Error rates are calculated for the two methods and all sharpened resolutions. The results indicate that the color normalizing method is superior to the spatial filtering technique.

Information Theoretically Secure, Enhanced Johnson Noise Based Key Distribution over the Smart Grid with Switched Filters

PubMed Central

2013-01-01

We introduce a protocol with a reconfigurable filter system to create non-overlapping single loops in the smart power grid for the realization of the Kirchhoff-Law-Johnson-(like)-Noise secure key distribution system. The protocol is valid for one-dimensional radial networks (chain-like power line) which are typical of the electricity distribution network between the utility and the customer. The speed of the protocol (the number of steps needed) versus grid size is analyzed. When properly generalized, such a system has the potential to achieve unconditionally secure key distribution over the smart power grid of arbitrary geometrical dimensions. PMID:23936164

Information theoretically secure, enhanced Johnson noise based key distribution over the smart grid with switched filters.

PubMed

Gonzalez, Elias; Kish, Laszlo B; Balog, Robert S; Enjeti, Prasad

2013-01-01

We introduce a protocol with a reconfigurable filter system to create non-overlapping single loops in the smart power grid for the realization of the Kirchhoff-Law-Johnson-(like)-Noise secure key distribution system. The protocol is valid for one-dimensional radial networks (chain-like power line) which are typical of the electricity distribution network between the utility and the customer. The speed of the protocol (the number of steps needed) versus grid size is analyzed. When properly generalized, such a system has the potential to achieve unconditionally secure key distribution over the smart power grid of arbitrary geometrical dimensions.

Gradient-based multiresolution image fusion.

PubMed

Petrović, Valdimir S; Xydeas, Costas S

2004-02-01

A novel approach to multiresolution signal-level image fusion is presented for accurately transferring visual information from any number of input image signals, into a single fused image without loss of information or the introduction of distortion. The proposed system uses a "fuse-then-decompose" technique realized through a novel, fusion/decomposition system architecture. In particular, information fusion is performed on a multiresolution gradient map representation domain of image signal information. At each resolution, input images are represented as gradient maps and combined to produce new, fused gradient maps. Fused gradient map signals are processed, using gradient filters derived from high-pass quadrature mirror filters to yield a fused multiresolution pyramid representation. The fused output image is obtained by applying, on the fused pyramid, a reconstruction process that is analogous to that of conventional discrete wavelet transform. This new gradient fusion significantly reduces the amount of distortion artefacts and the loss of contrast information usually observed in fused images obtained from conventional multiresolution fusion schemes. This is because fusion in the gradient map domain significantly improves the reliability of the feature selection and information fusion processes. Fusion performance is evaluated through informal visual inspection and subjective psychometric preference tests, as well as objective fusion performance measurements. Results clearly demonstrate the superiority of this new approach when compared to conventional fusion systems.

Processing Complex Sounds Passing through the Rostral Brainstem: The New Early Filter Model

PubMed Central

Marsh, John E.; Campbell, Tom A.

2016-01-01

The rostral brainstem receives both “bottom-up” input from the ascending auditory system and “top-down” descending corticofugal connections. Speech information passing through the inferior colliculus of elderly listeners reflects the periodicity envelope of a speech syllable. This information arguably also reflects a composite of temporal-fine-structure (TFS) information from the higher frequency vowel harmonics of that repeated syllable. The amplitude of those higher frequency harmonics, bearing even higher frequency TFS information, correlates positively with the word recognition ability of elderly listeners under reverberatory conditions. Also relevant is that working memory capacity (WMC), which is subject to age-related decline, constrains the processing of sounds at the level of the brainstem. Turning to the effects of a visually presented sensory or memory load on auditory processes, there is a load-dependent reduction of that processing, as manifest in the auditory brainstem responses (ABR) evoked by to-be-ignored clicks. Wave V decreases in amplitude with increases in the visually presented memory load. A visually presented sensory load also produces a load-dependent reduction of a slightly different sort: The sensory load of visually presented information limits the disruptive effects of background sound upon working memory performance. A new early filter model is thus advanced whereby systems within the frontal lobe (affected by sensory or memory load) cholinergically influence top-down corticofugal connections. Those corticofugal connections constrain the processing of complex sounds such as speech at the level of the brainstem. Selective attention thereby limits the distracting effects of background sound entering the higher auditory system via the inferior colliculus. Processing TFS in the brainstem relates to perception of speech under adverse conditions. Attentional selectivity is crucial when the signal heard is degraded or masked: e.g., speech in noise, speech in reverberatory environments. The assumptions of a new early filter model are consistent with these findings: A subcortical early filter, with a predictive selectivity based on acoustical (linguistic) context and foreknowledge, is under cholinergic top-down control. A prefrontal capacity limitation constrains this top-down control as is guided by the cholinergic processing of contextual information in working memory. PMID:27242396

Processing Complex Sounds Passing through the Rostral Brainstem: The New Early Filter Model.

PubMed

Marsh, John E; Campbell, Tom A

2016-01-01

The rostral brainstem receives both "bottom-up" input from the ascending auditory system and "top-down" descending corticofugal connections. Speech information passing through the inferior colliculus of elderly listeners reflects the periodicity envelope of a speech syllable. This information arguably also reflects a composite of temporal-fine-structure (TFS) information from the higher frequency vowel harmonics of that repeated syllable. The amplitude of those higher frequency harmonics, bearing even higher frequency TFS information, correlates positively with the word recognition ability of elderly listeners under reverberatory conditions. Also relevant is that working memory capacity (WMC), which is subject to age-related decline, constrains the processing of sounds at the level of the brainstem. Turning to the effects of a visually presented sensory or memory load on auditory processes, there is a load-dependent reduction of that processing, as manifest in the auditory brainstem responses (ABR) evoked by to-be-ignored clicks. Wave V decreases in amplitude with increases in the visually presented memory load. A visually presented sensory load also produces a load-dependent reduction of a slightly different sort: The sensory load of visually presented information limits the disruptive effects of background sound upon working memory performance. A new early filter model is thus advanced whereby systems within the frontal lobe (affected by sensory or memory load) cholinergically influence top-down corticofugal connections. Those corticofugal connections constrain the processing of complex sounds such as speech at the level of the brainstem. Selective attention thereby limits the distracting effects of background sound entering the higher auditory system via the inferior colliculus. Processing TFS in the brainstem relates to perception of speech under adverse conditions. Attentional selectivity is crucial when the signal heard is degraded or masked: e.g., speech in noise, speech in reverberatory environments. The assumptions of a new early filter model are consistent with these findings: A subcortical early filter, with a predictive selectivity based on acoustical (linguistic) context and foreknowledge, is under cholinergic top-down control. A prefrontal capacity limitation constrains this top-down control as is guided by the cholinergic processing of contextual information in working memory.

Traveller Information System for Heterogeneous Traffic Condition: A Case Study in Thiruvananthapuram City, India

NASA Astrophysics Data System (ADS)

Satyakumar, M.; Anil, R.; Sreeja, G. S.

2017-12-01

Traffic in Kerala has been growing at a rate of 10-11% every year, resulting severe congestion especially in urban areas. Because of the limitation of spaces it is not always possible to construct new roads. Road users rely on travel time information for journey planning and route choice decisions, while road system managers are increasingly viewing travel time as an important network performance indicator. More recently Advanced Traveler Information Systems (ATIS) are being developed to provide real-time information to roadway users. For ATIS various methodologies have been developed for dynamic travel time prediction. For this work the Kalman Filter Algorithm was selected for dynamic travel time prediction of different modes. The travel time data collected using handheld GPS device were used for prediction. Congestion Index were calculated and Range of CI values were determined according to the percentage speed drop. After prediction using Kalman Filter, the predicted values along with the GPS data was integrated to GIS and using Network Analysis of ArcGIS the offline route navigation guide was prepared. Using this database a program for route navigation based on travel time was developed. This system will help the travelers with pre-trip information.

Epidemiological monitoring for emerging infectious diseases

NASA Astrophysics Data System (ADS)

Greene, Marjorie

2010-04-01

The Homeland Security News Wire has been reporting on new ways to fight epidemics using digital tools such as iPhone, social networks, Wikipedia, and other Internet sites. Instant two-way communication now gives consumers the ability to complement official reports on emerging infectious diseases from health authorities. However, there is increasing concern that these communications networks could open the door to mass panic from unreliable or false reports. There is thus an urgent need to ensure that epidemiological monitoring for emerging infectious diseases gives health authorities the capability to identify, analyze, and report disease outbreaks in as timely and efficient a manner as possible. One of the dilemmas in the global dissemination of information on infectious diseases is the possibility that information overload will create inefficiencies as the volume of Internet-based surveillance information increases. What is needed is a filtering mechanism that will retrieve relevant information for further analysis by epidemiologists, laboratories, and other health organizations so they are not overwhelmed with irrelevant information and will be able to respond quickly. This paper introduces a self-organizing ontology that could be used as a filtering mechanism to increase relevance and allow rapid analysis of disease outbreaks as they evolve in real time.

Simultaneous Mean and Covariance Correction Filter for Orbit Estimation.

PubMed

Wang, Xiaoxu; Pan, Quan; Ding, Zhengtao; Ma, Zhengya

2018-05-05

This paper proposes a novel filtering design, from a viewpoint of identification instead of the conventional nonlinear estimation schemes (NESs), to improve the performance of orbit state estimation for a space target. First, a nonlinear perturbation is viewed or modeled as an unknown input (UI) coupled with the orbit state, to avoid the intractable nonlinear perturbation integral (INPI) required by NESs. Then, a simultaneous mean and covariance correction filter (SMCCF), based on a two-stage expectation maximization (EM) framework, is proposed to simply and analytically fit or identify the first two moments (FTM) of the perturbation (viewed as UI), instead of directly computing such the INPI in NESs. Orbit estimation performance is greatly improved by utilizing the fit UI-FTM to simultaneously correct the state estimation and its covariance. Third, depending on whether enough information is mined, SMCCF should outperform existing NESs or the standard identification algorithms (which view the UI as a constant independent of the state and only utilize the identified UI-mean to correct the state estimation, regardless of its covariance), since it further incorporates the useful covariance information in addition to the mean of the UI. Finally, our simulations demonstrate the superior performance of SMCCF via an orbit estimation example.

Periodic component analysis as a spatial filter for SSVEP-based brain-computer interface.

PubMed

Kiran Kumar, G R; Reddy, M Ramasubba

2018-06-08

Traditional Spatial filters used for steady-state visual evoked potential (SSVEP) extraction such as minimum energy combination (MEC) require the estimation of the background electroencephalogram (EEG) noise components. Even though this leads to improved performance in low signal to noise ratio (SNR) conditions, it makes such algorithms slow compared to the standard detection methods like canonical correlation analysis (CCA) due to the additional computational cost. In this paper, Periodic component analysis (πCA) is presented as an alternative spatial filtering approach to extract the SSVEP component effectively without involving extensive modelling of the noise. The πCA can separate out components corresponding to a given frequency of interest from the background electroencephalogram (EEG) by capturing the temporal information and does not generalize SSVEP based on rigid templates. Data from ten test subjects were used to evaluate the proposed method and the results demonstrate that the periodic component analysis acts as a reliable spatial filter for SSVEP extraction. Statistical tests were performed to validate the results. The experimental results show that πCA provides significant improvement in accuracy compared to standard CCA and MEC in low SNR conditions. The results demonstrate that πCA provides better detection accuracy compared to CCA and on par with that of MEC at a lower computational cost. Hence πCA is a reliable and efficient alternative detection algorithm for SSVEP based brain-computer interface (BCI). Copyright © 2018. Published by Elsevier B.V.

Coarse Alignment Technology on Moving base for SINS Based on the Improved Quaternion Filter Algorithm.

PubMed

Zhang, Tao; Zhu, Yongyun; Zhou, Feng; Yan, Yaxiong; Tong, Jinwu

2017-06-17

Initial alignment of the strapdown inertial navigation system (SINS) is intended to determine the initial attitude matrix in a short time with certain accuracy. The alignment accuracy of the quaternion filter algorithm is remarkable, but the convergence rate is slow. To solve this problem, this paper proposes an improved quaternion filter algorithm for faster initial alignment based on the error model of the quaternion filter algorithm. The improved quaternion filter algorithm constructs the K matrix based on the principle of optimal quaternion algorithm, and rebuilds the measurement model by containing acceleration and velocity errors to make the convergence rate faster. A doppler velocity log (DVL) provides the reference velocity for the improved quaternion filter alignment algorithm. In order to demonstrate the performance of the improved quaternion filter algorithm in the field, a turntable experiment and a vehicle test are carried out. The results of the experiments show that the convergence rate of the proposed improved quaternion filter is faster than that of the tradition quaternion filter algorithm. In addition, the improved quaternion filter algorithm also demonstrates advantages in terms of correctness, effectiveness, and practicability.

Transistor-based filter for inhibiting load noise from entering a power supply

DOEpatents

Taubman, Matthew S

2013-07-02

A transistor-based filter for inhibiting load noise from entering a power supply is disclosed. The filter includes a first transistor having an emitter coupled to a power supply, a collector coupled to a load, and a base. The filter also includes a first capacitor coupled between the base of the first transistor and a ground terminal. The filter further includes an impedance coupled between the base and a node between the collector and the load, or a second transistor and second capacitor. The impedance can be a resistor or an inductor.

Transistor-based filter for inhibiting load noise from entering a power supply

DOEpatents

Taubman, Matthew S

2015-02-24

A transistor-based filter for inhibiting load noise from entering a power supply is disclosed. The filter includes a first transistor having an emitter coupled to a power supply, a collector coupled to a load, and a base. The filter also includes a first capacitor coupled between the base of the first transistor and a ground terminal The filter further includes an impedance coupled between the base and a node between the collector and the load, or a second transistor and second capacitor. The impedance can be a resistor or an inductor.

Measuring Learner's Performance in E-Learning Recommender Systems

ERIC Educational Resources Information Center

Ghauth, Khairil Imran; Abdullah, Nor Aniza

2010-01-01

A recommender system is a piece of software that helps users to identify the most interesting and relevant learning items from a large number of items. Recommender systems may be based on collaborative filtering (by user ratings), content-based filtering (by keywords), and hybrid filtering (by both collaborative and content-based filtering).…

Computational Model of Primary Visual Cortex Combining Visual Attention for Action Recognition

PubMed Central

Shu, Na; Gao, Zhiyong; Chen, Xiangan; Liu, Haihua

2015-01-01

Humans can easily understand other people’s actions through visual systems, while computers cannot. Therefore, a new bio-inspired computational model is proposed in this paper aiming for automatic action recognition. The model focuses on dynamic properties of neurons and neural networks in the primary visual cortex (V1), and simulates the procedure of information processing in V1, which consists of visual perception, visual attention and representation of human action. In our model, a family of the three-dimensional spatial-temporal correlative Gabor filters is used to model the dynamic properties of the classical receptive field of V1 simple cell tuned to different speeds and orientations in time for detection of spatiotemporal information from video sequences. Based on the inhibitory effect of stimuli outside the classical receptive field caused by lateral connections of spiking neuron networks in V1, we propose surround suppressive operator to further process spatiotemporal information. Visual attention model based on perceptual grouping is integrated into our model to filter and group different regions. Moreover, in order to represent the human action, we consider the characteristic of the neural code: mean motion map based on analysis of spike trains generated by spiking neurons. The experimental evaluation on some publicly available action datasets and comparison with the state-of-the-art approaches demonstrate the superior performance of the proposed model. PMID:26132270

Personalized professional content recommendation

DOEpatents

Xu, Songhua

2015-10-27

A personalized content recommendation system includes a client interface configured to automatically monitor a user's information data stream transmitted on the Internet. A hybrid contextual behavioral and collaborative personal interest inference engine resident to a non-transient media generates automatic predictions about the interests of individual users of the system. A database server retains the user's personal interest profile based on a plurality of monitored information. The system also includes a server programmed to filter items in an incoming information stream with the personal interest profile and is further programmed to identify only those items of the incoming information stream that substantially match the personal interest profile.

Dialysis search filters for PubMed, Ovid MEDLINE, and Embase databases.

PubMed

Iansavichus, Arthur V; Haynes, R Brian; Lee, Christopher W C; Wilczynski, Nancy L; McKibbon, Ann; Shariff, Salimah Z; Blake, Peter G; Lindsay, Robert M; Garg, Amit X

2012-10-01

Physicians frequently search bibliographic databases, such as MEDLINE via PubMed, for best evidence for patient care. The objective of this study was to develop and test search filters to help physicians efficiently retrieve literature related to dialysis (hemodialysis or peritoneal dialysis) from all other articles indexed in PubMed, Ovid MEDLINE, and Embase. A diagnostic test assessment framework was used to develop and test robust dialysis filters. The reference standard was a manual review of the full texts of 22,992 articles from 39 journals to determine whether each article contained dialysis information. Next, 1,623,728 unique search filters were developed, and their ability to retrieve relevant articles was evaluated. The high-performance dialysis filters consisted of up to 65 search terms in combination. These terms included the words "dialy" (truncated), "uremic," "catheters," and "renal transplant wait list." These filters reached peak sensitivities of 98.6% and specificities of 98.5%. The filters' performance remained robust in an independent validation subset of articles. These empirically derived and validated high-performance search filters should enable physicians to effectively retrieve dialysis information from PubMed, Ovid MEDLINE, and Embase.

Correlation of Spatially Filtered Dynamic Speckles in Distance Measurement Application

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

Semenov, Dmitry V.; Nippolainen, Ervin; Kamshilin, Alexei A.

2008-04-15

In this paper statistical properties of spatially filtered dynamic speckles are considered. This phenomenon was not sufficiently studied yet while spatial filtering is an important instrument for speckles velocity measurements. In case of spatial filtering speckle velocity information is derived from the modulation frequency of filtered light power which is measured by photodetector. Typical photodetector output is represented by a narrow-band random noise signal which includes non-informative intervals. Therefore more or less precious frequency measurement requires averaging. In its turn averaging implies uncorrelated samples. However, conducting research we found that correlation is typical property not only of dynamic speckle patternsmore » but also of spatially filtered speckles. Using spatial filtering the correlation is observed as a response of measurements provided to the same part of the object surface or in case of simultaneously using several adjacent photodetectors. Found correlations can not be explained using just properties of unfiltered dynamic speckles. As we demonstrate the subject of this paper is important not only from pure theoretical point but also from the point of applied speckle metrology. E.g. using single spatial filter and an array of photodetector can greatly improve accuracy of speckle velocity measurements.« less

Duplicate document detection in DocBrowse

NASA Astrophysics Data System (ADS)

Chalana, Vikram; Bruce, Andrew G.; Nguyen, Thien

1998-04-01

Duplicate documents are frequently found in large databases of digital documents, such as those found in digital libraries or in the government declassification effort. Efficient duplicate document detection is important not only to allow querying for similar documents, but also to filter out redundant information in large document databases. We have designed three different algorithm to identify duplicate documents. The first algorithm is based on features extracted from the textual content of a document, the second algorithm is based on wavelet features extracted from the document image itself, and the third algorithm is a combination of the first two. These algorithms are integrated within the DocBrowse system for information retrieval from document images which is currently under development at MathSoft. DocBrowse supports duplicate document detection by allowing (1) automatic filtering to hide duplicate documents, and (2) ad hoc querying for similar or duplicate documents. We have tested the duplicate document detection algorithms on 171 documents and found that text-based method has an average 11-point precision of 97.7 percent while the image-based method has an average 11- point precision of 98.9 percent. However, in general, the text-based method performs better when the document contains enough high-quality machine printed text while the image- based method performs better when the document contains little or no quality machine readable text.

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.